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AndrewAnnex/log_asap_notebook_pipeline_make_dem.ipynb

Created February 8, 2023 18:45
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log_asap_notebook_pipeline_make_dem.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "d38ef116",
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"pycharm": {
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"parameters"
]
},
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"source": [
"left = None\n",
"right = None\n",
"config1 = None\n",
"config2 = None\n",
"dem_gsd = 24.0\n",
"img_gsd = 6.0\n",
"output_path = None\n",
"max_disp = None\n",
"downsample = None\n",
"refdem = None\n",
"step_kwargs = {}\n",
"# todo: add reference_dem and use to conditional pedr things"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "cb1af8b8",
"metadata": {
"execution": {
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"tags": [
"injected-parameters"
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"source": [
"# Parameters\n",
"left = \"B03_010644_1889_XN_08N001W\"\n",
"right = \"P02_001902_1889_XI_08N001W\"\n",
"peder_list = None\n",
"config1 = \"./stereo.nonmap\"\n",
"config2 = \"./stereo.map\"\n",
"output_path = \"./\"\n",
"max_disp = None\n",
"dem_gsd = 50\n",
"img_gsd = 25\n",
"downsample = None\n",
"step_kwargs = {\n",
" \"step_13\": \"--highest_accuracy False --max-num-reference-points 100000 --max-num-source-points 10000\"\n",
"}\n"
]
},
{
"cell_type": "markdown",
"id": "da11c35f",
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"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"# Stereo Config file contents:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "9a46d454",
"metadata": {
"execution": {
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"pycharm": {
"name": "#%%\n"
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},
"outputs": [],
"source": [
"if config2 == None:\n",
" config2 = config1"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "bc4e2d9c",
"metadata": {
"execution": {
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{
"name": "stdout",
"output_type": "stream",
"text": [
"# -*- mode: sh -*-\r\n",
"\r\n",
"# Pre-Processing / stereo_pprc\r\n",
"################################################################\r\n",
"\r\n",
"# Pre-alignment options\r\n",
"#\r\n",
"# Available choices are (however not all are supported by all sessions):\r\n",
"# NONE (Recommended for anything map projected)\r\n",
"# EPIPOLAR (Recommended for Pinhole Sessions)\r\n",
"# HOMOGRAPHY (Recommended for ISIS wide-angle shots)\r\n",
"# AFFINEEPIPOLAR (Recommended for ISIS narrow-angle and DG sessions)\r\n",
"alignment-method affineepipolar\r\n",
"\r\n",
"# Intensity Normalization\r\n",
"force-use-entire-range # Use entire input range\r\n",
"\r\n",
"# Select a preprocessing filter:\r\n",
"#\r\n",
"# 0 - None\r\n",
"# 1 - Subtracted Mean\r\n",
"# 2 - Laplacian of Gaussian (recommended)\r\n",
"prefilter-mode 2\r\n",
"\r\n",
"# Kernel size (1-sigma) for pre-processing\r\n",
"#\r\n",
"# Recommend 1.4 px for Laplacian of Gaussian\r\n",
"# Recommend 25 px for Subtracted Mean\r\n",
"prefilter-kernel-width 1.4\r\n",
"\r\n",
"\r\n",
"# Integer Correlation / stereo_corr\r\n",
"################################################################\r\n",
"\r\n",
"# Select a cost function to use for initialization:\r\n",
"#\r\n",
"# 0 - absolute difference (fast)\r\n",
"# 1 - squared difference (faster .. but usually bad)\r\n",
"# 2 - normalized cross correlation (recommended)\r\n",
"cost-mode 0\r\n",
"\r\n",
"# Initialization step: correlation kernel size\r\n",
"corr-kernel 25 25\r\n",
"\r\n",
"# Initialization step: correlation search range\r\n",
"#\r\n",
"# Uncomment the following to use explicit search ranges. Otherwise, a\r\n",
"# value will be choosen for you.\r\n",
"# corr-search -100 -100 100 100\r\n",
"\r\n",
"# Subpixel Refinement / stereo_rfne\r\n",
"################################################################\r\n",
"\r\n",
"# Subpixel step: subpixel modes\r\n",
"#\r\n",
"# 0 - disable subpixel correlation (fastest)\r\n",
"# 1 - parabola fitting (draft mode - not as accurate)\r\n",
"# 2 - affine adaptive window, bayes EM weighting (slower, but much more accurate)\r\n",
"subpixel-mode 1\r\n",
"\r\n",
"# Subpixel step: correlation kernel size\r\n",
"subpixel-kernel 25 25\r\n",
"\r\n",
"# Post Filtering / stereo_fltr\r\n",
"################################################################\r\n",
"\r\n",
"# Automatic \"erode\" low confidence pixels\r\n",
"rm-half-kernel 3 3\r\n",
"rm-min-matches 60\r\n",
"rm-threshold 3\r\n",
"rm-cleanup-passes 1\r\n",
"\r\n",
"# Triangulation / stereo_tri\r\n",
"################################################################\r\n",
"\r\n",
"# Size max of the universe in meters and altitude off the ground.\r\n",
"# Setting both values to zero turns this post-processing step off.\r\n",
"near-universe-radius 0.0\r\n",
"far-universe-radius 0.0\r\n"
]
}
],
"source": [
"!cat {config1}"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "9a85f064",
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-08T18:20:18.548535Z",
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"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"# -*- mode: sh -*-\r\n",
"\r\n",
"# Pre-Processing / stereo_pprc\r\n",
"################################################################\r\n",
"\r\n",
"# Pre-alignment options\r\n",
"#\r\n",
"# Available choices are (however not all are supported by all sessions):\r\n",
"# NONE (Recommended for anything map projected)\r\n",
"# EPIPOLAR (Recommended for Pinhole Sessions)\r\n",
"# HOMOGRAPHY (Recommended for ISIS wide-angle shots)\r\n",
"# AFFINEEPIPOLAR (Recommended for ISIS narrow-angle and DG sessions)\r\n",
"alignment-method none\r\n",
"\r\n",
"# Intensity Normalization\r\n",
"force-use-entire-range # Use entire input range\r\n",
"\r\n",
"# Select a preprocessing filter:\r\n",
"#\r\n",
"# 0 - None\r\n",
"# 1 - Subtracted Mean\r\n",
"# 2 - Laplacian of Gaussian (recommended)\r\n",
"prefilter-mode 2\r\n",
"\r\n",
"# Kernel size (1-sigma) for pre-processing\r\n",
"#\r\n",
"# Recommend 1.4 px for Laplacian of Gaussian\r\n",
"# Recommend 25 px for Subtracted Mean\r\n",
"prefilter-kernel-width 1.4\r\n",
"\r\n",
"\r\n",
"# Integer Correlation / stereo_corr\r\n",
"################################################################\r\n",
"\r\n",
"# Select a cost function to use for initialization:\r\n",
"#\r\n",
"# 0 - absolute difference (fast)\r\n",
"# 1 - squared difference (faster .. but usually bad)\r\n",
"# 2 - normalized cross correlation (recommended)\r\n",
"cost-mode 0\r\n",
"\r\n",
"# Initialization step: correlation kernel size\r\n",
"corr-kernel 25 25\r\n",
"\r\n",
"# Initialization step: correlation search range\r\n",
"#\r\n",
"# Uncomment the following to use explicit search ranges. Otherwise, a\r\n",
"# value will be choosen for you.\r\n",
"# corr-search -100 -100 100 100\r\n",
"\r\n",
"# Subpixel Refinement / stereo_rfne\r\n",
"################################################################\r\n",
"\r\n",
"# Subpixel step: subpixel modes\r\n",
"#\r\n",
"# 0 - disable subpixel correlation (fastest)\r\n",
"# 1 - parabola fitting (draft mode - not as accurate)\r\n",
"# 2 - affine adaptive window, bayes EM weighting (slower, but much more accurate)\r\n",
"subpixel-mode 1\r\n",
"\r\n",
"# Subpixel step: correlation kernel size\r\n",
"subpixel-kernel 25 25\r\n",
"\r\n",
"# Post Filtering / stereo_fltr\r\n",
"################################################################\r\n",
"\r\n",
"# Automatic \"erode\" low confidence pixels\r\n",
"rm-half-kernel 3 3\r\n",
"rm-min-matches 60\r\n",
"rm-threshold 3\r\n",
"rm-cleanup-passes 1\r\n",
"\r\n",
"# Triangulation / stereo_tri\r\n",
"################################################################\r\n",
"\r\n",
"# Size max of the universe in meters and altitude off the ground.\r\n",
"# Setting both values to zero turns this post-processing step off.\r\n",
"near-universe-radius 0.0\r\n",
"far-universe-radius 0.0\r\n"
]
}
],
"source": [
"!cat {config2}"
]
},
{
"cell_type": "markdown",
"id": "3b21fdd8",
"metadata": {
"papermill": {
"duration": 0.008864,
"end_time": "2023-02-08T18:20:18.722830",
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"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"# Setup Steps"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "4b86f616",
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-08T18:20:18.742311Z",
"iopub.status.busy": "2023-02-08T18:20:18.741729Z",
"iopub.status.idle": "2023-02-08T18:20:18.967792Z",
"shell.execute_reply": "2023-02-08T18:20:18.967003Z"
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"end_time": "2023-02-08T18:20:18.970345",
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"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [],
"source": [
"from IPython.display import Image\n",
"from pathlib import Path\n",
"from asap_stereo import asap\n",
"import math"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "24636b99",
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-08T18:20:18.989623Z",
"iopub.status.busy": "2023-02-08T18:20:18.988937Z",
"iopub.status.idle": "2023-02-08T18:20:20.247592Z",
"shell.execute_reply": "2023-02-08T18:20:20.246640Z"
},
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"duration": 1.270919,
"end_time": "2023-02-08T18:20:20.250075",
"exception": false,
"start_time": "2023-02-08T18:20:18.979156",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [],
"source": [
"default_output_dir = '~/auto_asap/ctx/'\n",
"left, right = asap.CTX().get_ctx_order(left, right)\n",
"if output_path == None:\n",
" output_path = default_output_dir + f'a_{left}_{right}'"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "45c7218a",
"metadata": {
"execution": {
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"iopub.status.busy": "2023-02-08T18:20:20.269304Z",
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"shell.execute_reply": "2023-02-08T18:20:20.421447Z"
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"duration": 0.165776,
"end_time": "2023-02-08T18:20:20.424889",
"exception": false,
"start_time": "2023-02-08T18:20:20.259113",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [],
"source": [
"!mkdir -p {output_path}"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "0863692a",
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-08T18:20:20.446320Z",
"iopub.status.busy": "2023-02-08T18:20:20.445722Z",
"iopub.status.idle": "2023-02-08T18:20:20.451293Z",
"shell.execute_reply": "2023-02-08T18:20:20.450670Z"
},
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"duration": 0.018661,
"end_time": "2023-02-08T18:20:20.452670",
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"start_time": "2023-02-08T18:20:20.434009",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/runner/work/asap_stereo/asap_stereo\n"
]
}
],
"source": [
"%cd {output_path}"
]
},
{
"cell_type": "markdown",
"id": "11b51e32",
"metadata": {
"papermill": {
"duration": 0.009222,
"end_time": "2023-02-08T18:20:20.470580",
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"start_time": "2023-02-08T18:20:20.461358",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"# Download images (step 1)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "a31b321f",
"metadata": {
"execution": {
"iopub.execute_input": "2023-02-08T18:20:20.490163Z",
"iopub.status.busy": "2023-02-08T18:20:20.489433Z",
"iopub.status.idle": "2023-02-08T18:20:42.727647Z",
"shell.execute_reply": "2023-02-08T18:20:42.726531Z"
},
"papermill": {
"duration": 22.250759,
"end_time": "2023-02-08T18:20:42.730029",
"exception": false,
"start_time": "2023-02-08T18:20:20.479270",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"# Started: step_1 (Download CTX EDRs from the PDS), at: 2023-02-08 18:20:20.977744\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading B03_010644_1889_XN_08N001W.IMG: 0it [00:00, ?it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading B03_010644_1889_XN_08N001W.IMG: 1it [00:00, 1.97it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading B03_010644_1889_XN_08N001W.IMG: 2it [00:00, 3.00it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading B03_010644_1889_XN_08N001W.IMG: 3it [00:00, 3.58it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading B03_010644_1889_XN_08N001W.IMG: 4it [00:01, 3.94it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading B03_010644_1889_XN_08N001W.IMG: 5it [00:01, 4.19it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading B03_010644_1889_XN_08N001W.IMG: 6it [00:01, 4.35it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading B03_010644_1889_XN_08N001W.IMG: 7it [00:01, 4.46it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading B03_010644_1889_XN_08N001W.IMG: 8it [00:02, 4.43it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading B03_010644_1889_XN_08N001W.IMG: 9it [00:02, 4.41it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading B03_010644_1889_XN_08N001W.IMG: 10it [00:02, 4.49it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading B03_010644_1889_XN_08N001W.IMG: 11it [00:02, 4.53it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading B03_010644_1889_XN_08N001W.IMG: 12it [00:02, 4.56it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading B03_010644_1889_XN_08N001W.IMG: 13it [00:03, 4.44it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading B03_010644_1889_XN_08N001W.IMG: 14it [00:03, 4.50it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading B03_010644_1889_XN_08N001W.IMG: 15it [00:03, 4.56it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading B03_010644_1889_XN_08N001W.IMG: 16it [00:03, 4.61it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading B03_010644_1889_XN_08N001W.IMG: 17it [00:03, 4.61it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading B03_010644_1889_XN_08N001W.IMG: 18it [00:04, 4.62it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading B03_010644_1889_XN_08N001W.IMG: 19it [00:04, 4.63it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading B03_010644_1889_XN_08N001W.IMG: 20it [00:04, 4.65it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading B03_010644_1889_XN_08N001W.IMG: 21it [00:04, 4.66it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading B03_010644_1889_XN_08N001W.IMG: 22it [00:05, 4.67it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading B03_010644_1889_XN_08N001W.IMG: 23it [00:05, 4.68it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading B03_010644_1889_XN_08N001W.IMG: 24it [00:05, 4.67it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading B03_010644_1889_XN_08N001W.IMG: 25it [00:05, 4.68it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading B03_010644_1889_XN_08N001W.IMG: 26it [00:05, 4.64it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading B03_010644_1889_XN_08N001W.IMG: 27it [00:06, 4.57it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading B03_010644_1889_XN_08N001W.IMG: 28it [00:06, 4.60it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading B03_010644_1889_XN_08N001W.IMG: 29it [00:06, 4.62it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading B03_010644_1889_XN_08N001W.IMG: 30it [00:06, 5.21it/s]\r",
"Downloading B03_010644_1889_XN_08N001W.IMG: 30it [00:06, 4.47it/s]\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 0it [00:00, ?it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 1it [00:00, 4.13it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 2it [00:00, 4.40it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 3it [00:00, 4.52it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 4it [00:00, 4.58it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 5it [00:01, 4.62it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 6it [00:01, 4.60it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 7it [00:01, 4.57it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 8it [00:01, 4.59it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 9it [00:01, 4.59it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 10it [00:02, 4.63it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 11it [00:02, 4.63it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 12it [00:02, 4.62it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 13it [00:02, 4.55it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 14it [00:03, 4.59it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 15it [00:03, 4.56it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 16it [00:03, 4.60it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 17it [00:03, 4.62it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 18it [00:03, 4.63it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 19it [00:04, 4.45it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 20it [00:04, 4.51it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 21it [00:04, 4.56it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 22it [00:04, 4.57it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 23it [00:05, 4.59it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 24it [00:05, 4.60it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 25it [00:05, 4.59it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 26it [00:05, 4.60it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 27it [00:05, 4.61it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 28it [00:06, 4.62it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 29it [00:06, 4.64it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 30it [00:06, 4.65it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 31it [00:06, 4.65it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 32it [00:06, 4.65it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 33it [00:07, 4.66it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 34it [00:07, 4.51it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 35it [00:07, 4.55it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 36it [00:07, 4.57it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 37it [00:08, 4.61it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 38it [00:08, 4.54it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 39it [00:08, 4.48it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 40it [00:08, 4.52it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 41it [00:08, 4.53it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 42it [00:09, 4.56it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 43it [00:09, 4.59it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 44it [00:09, 4.61it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 45it [00:09, 4.53it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 46it [00:10, 4.56it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 47it [00:10, 4.59it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 48it [00:10, 4.61it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 49it [00:10, 4.51it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"Downloading P02_001902_1889_XI_08N001W.IMG: 50it [00:10, 4.63it/s]\r\n",
"cd /home/runner/work/asap_stereo/asap_stereo\r\n",
"# Finished: step_1 (Download CTX EDRs from the PDS), at: 2023-02-08 18:20:42.565918, duration: 0:00:21.588174\r\n"
]
}
],
"source": [
"!asap ctx step_1 {left} {right} 2>&1 | tee -i -a ./1_download.log ./full_log.log"
]
},
{
"cell_type": "markdown",
"id": "cf1b5b18",
"metadata": {
"heading_collapsed": true,
"papermill": {
"duration": 0.011621,
"end_time": "2023-02-08T18:20:42.755948",
"exception": false,
"start_time": "2023-02-08T18:20:42.744327",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"# First Step of CTX processing lev1eo (Step 2)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "192d3e46",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2023-02-08T18:20:42.783940Z",
"iopub.status.busy": "2023-02-08T18:20:42.782959Z",
"iopub.status.idle": "2023-02-08T18:20:55.773811Z",
"shell.execute_reply": "2023-02-08T18:20:55.772564Z"
},
"papermill": {
"duration": 13.007096,
"end_time": "2023-02-08T18:20:55.775450",
"exception": false,
"start_time": "2023-02-08T18:20:42.768354",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"# Started: step_2 (ISIS3 CTX preprocessing, replaces ctxedr2lev1eo.sh), at: 2023-02-08 18:20:43.251519\r\n",
"INFO:sh.command:/usr/share/miniconda3/envs/isis_for_asp/bin/mroctx2isis from=B03_010644_1889_XN_08N001W.IMG to=B03_010644_1889_XN_08N001W.cub: process started\r\n",
"INFO:sh.command:/usr/share/miniconda3/envs/isis_for_asp/bin/mroctx2isis from=P02_001902_1889_XI_08N001W.IMG to=P02_001902_1889_XI_08N001W.cub: process started\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:sh.command:/usr/share/miniconda3/envs/isis_for_asp/bin/spiceinit from=B03_010644_1889_XN_08N001W.cub: process started\r\n",
"INFO:sh.command:/usr/share/miniconda3/envs/isis_for_asp/bin/spiceinit from=P02_001902_1889_XI_08N001W.cub: process started\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Group = Kernels\r\n",
" NaifFrameCode = -74021\r\n",
" LeapSecond = $base/kernels/lsk/naif0012.tls\r\n",
" TargetAttitudeShape = $mro/kernels/pck/pck00009.tpc\r\n",
" TargetPosition = ($base/kernels/spk/de430.bsp,\r\n",
" $base/kernels/spk/mar097.bsp)\r\n",
" InstrumentPointing = ($mro/kernels/ck/mro_sc_psp_081028_081103.bc,\r\n",
" $mro/kernels/fk/mro_v15.tf)\r\n",
" Instrument = Null\r\n",
" SpacecraftClock = $mro/kernels/sclk/MRO_SCLKSCET.00090.65536.tsc\r\n",
" InstrumentPosition = $mro/kernels/spk/mro_psp9_ssd_mro110c.bsp\r\n",
" InstrumentAddendum = $mro/kernels/iak/mroctxAddendum005.ti\r\n",
" ShapeModel = $base/dems/molaMarsPlanetaryRadius0005.cub\r\n",
" InstrumentPositionQuality = Reconstructed\r\n",
" InstrumentPointingQuality = Reconstructed\r\n",
" CameraVersion = 1\r\n",
" Source = ale\r\n",
"End_Group\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Group = Kernels\r\n",
" NaifFrameCode = -74021\r\n",
" LeapSecond = $base/kernels/lsk/naif0012.tls\r\n",
" TargetAttitudeShape = $mro/kernels/pck/pck00009.tpc\r\n",
" TargetPosition = ($base/kernels/spk/de430.bsp,\r\n",
" $base/kernels/spk/mar097.bsp)\r\n",
" InstrumentPointing = ($mro/kernels/ck/mro_sc_psp_061219_061225.bc,\r\n",
" $mro/kernels/fk/mro_v15.tf)\r\n",
" Instrument = Null\r\n",
" SpacecraftClock = $mro/kernels/sclk/MRO_SCLKSCET.00090.65536.tsc\r\n",
" InstrumentPosition = $mro/kernels/spk/mro_psp1_ssd_mro110c.bsp\r\n",
" InstrumentAddendum = $mro/kernels/iak/mroctxAddendum005.ti\r\n",
" ShapeModel = $base/dems/molaMarsPlanetaryRadius0005.cub\r\n",
" InstrumentPositionQuality = Reconstructed\r\n",
" InstrumentPointingQuality = Reconstructed\r\n",
" CameraVersion = 1\r\n",
" Source = ale\r\n",
"End_Group\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:sh.command:/usr/share/miniconda3/envs/isis_for_asp/bin/spicefit from=B03_010644_1889_XN_08N001W.cub: process started\r\n",
"INFO:sh.command:/usr/share/miniconda3/envs/isis_for_asp/bin/spicefit from=P02_001902_1889_XI_08N001W.cub: process started\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:sh.command:/usr/share/miniconda3/envs/isis_for_asp/bin/ctxcal from=B03_010644_1889_XN_08N001W.cub to=B03_010644_1889_XN_08N001W.lev1.cub: process started\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:sh.command:/usr/share/miniconda3/envs/isis_for_asp/bin/ctxcal from=P02_001902_1889_XI_08N001W.cub to=P02_001902_1889_XI_08N001W.lev1.cub: process started\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:sh.command:/usr/share/miniconda3/envs/isis_for_asp/bin/ctxevenodd from=B03_010644_1889_XN_08N001W.lev1.cub to=B03_010644_1889_XN_08N001W.lev1eo.cub: process started\r\n",
"INFO:sh.command:/usr/share/miniconda3/envs/isis_for_asp/bin/ctxevenodd from=P02_001902_1889_XI_08N001W.lev1.cub to=P02_001902_1889_XI_08N001W.lev1eo.cub: process started\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"# Finished: step_2 (ISIS3 CTX preprocessing, replaces ctxedr2lev1eo.sh), at: 2023-02-08 18:20:55.603465, duration: 0:00:12.351946\r\n"
]
}
],
"source": [
"!asap ctx step_2 {asap.kwarg_parse(step_kwargs, 'step_2')} 2>&1 | tee -i -a ./2_ctxedr2lev1eo.log ./full_log.log"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "b901b372",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2023-02-08T18:20:56.305528Z",
"iopub.status.busy": "2023-02-08T18:20:56.304618Z",
"iopub.status.idle": "2023-02-08T18:20:56.981436Z",
"shell.execute_reply": "2023-02-08T18:20:56.980322Z"
},
"papermill": {
"duration": 1.180478,
"end_time": "2023-02-08T18:20:56.983289",
"exception": false,
"start_time": "2023-02-08T18:20:55.802811",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"# Started: step_3 (Create various processing files for future steps), at: 2023-02-08 18:20:56.772376\r\n",
"INFO:sh.command:<Command '/usr/bin/cat ./pair.lis', pid 2503>: process started\r\n",
"INFO:sh.command:<Command '/usr/bin/cat ./stereopairs.lis', pid 2508>: process started\r\n",
"INFO:sh.command:<Command '/usr/bin/cat ./stereodirs.lis', pid 2513>: process started\r\n",
"INFO:sh.command:<Command '/usr/bin/cat ./stereopairs.lis', pid 2518>: process started\r\n",
"INFO:sh.command:<Command '/usr/bin/cat ./stereopairs.lis', pid 2523>: process started\r\n",
"INFO:sh.command:<Command '/usr/bin/mv -n ./B03_010644_1889_XN_08N001W.lev1eo.cub ./P02_001902_1889_XI_08N001W.lev1eo.cub ./B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W/', pid 2528>: process started\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"# Ran Command: /usr/bin/mv -n ./B03_010644_1889_XN_08N001W.lev1eo.cub ./P02_001902_1889_XI_08N001W.lev1eo.cub ./B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W/\r\n",
"# Finished: step_3 (Create various processing files for future steps), at: 2023-02-08 18:20:56.814522, duration: 0:00:00.042146\r\n"
]
}
],
"source": [
"!asap ctx step_3"
]
},
{
"cell_type": "markdown",
"id": "c61fc8a2",
"metadata": {
"collapsed": false,
"papermill": {
"duration": 0.013836,
"end_time": "2023-02-08T18:20:57.011049",
"exception": false,
"start_time": "2023-02-08T18:20:56.997213",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"# Stereo Quality Report"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "ed4e7ecc",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2023-02-08T18:20:57.042887Z",
"iopub.status.busy": "2023-02-08T18:20:57.041536Z",
"iopub.status.idle": "2023-02-08T18:21:14.389400Z",
"shell.execute_reply": "2023-02-08T18:21:14.388346Z"
},
"papermill": {
"duration": 17.365054,
"end_time": "2023-02-08T18:21:14.390743",
"exception": false,
"start_time": "2023-02-08T18:20:57.025689",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:sh.command:<Command '/usr/share/miniconda3/envs/isis_for_asp/bin/caminfo from=B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W/B03_010644_1889_XN_08N001W.lev1eo.cub to=B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W/B03_010644_1889_XN_08N001W.lev1eo.caminfo polygon=True', pid 2533>: process started\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:sh.command:<Command '/usr/share/miniconda3/envs/isis_for_asp/bin/caminfo from=B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W/P02_001902_1889_XI_08N001W.lev1eo.cub to=B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W/P02_001902_1889_XI_08N001W.lev1eo.caminfo polygon=True', pid 2541>: process started\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" Stereo Pair Quality Report:\n",
"\n",
" Image 1: B03_010644_1889_XN_08N001W.lev1eo.cub\n",
" Image 2: P02_001902_1889_XI_08N001W.lev1eo.cub\n",
" Image 1 Image 2\n",
" Incidence Angle: 54.09 52.05\n",
" Quality: 0.73 0.86\n",
" Emission Angle: 2.21 12.66\n",
" Quality: 0.10 0.56\n",
" Phase Angle: 56.20 64.39\n",
" Quality: 0.93 0.93\n",
"\n",
" Subspacecraft Azimuth: 78.10 82.09\n",
" Subsolar Azimuth: 276.53 276.53\n",
" Quality: 1.00\n",
" Ground Sample Distance: 5.48 5.73\n",
" Quality: 0.97\n",
"\n",
" Stereo Overlap Fraction: 0.58\n",
" Overlap Quality: 1.00\n",
"\n",
" Parallax Angle: 10.46\n",
" Parallax/Height Ratio (dp): 0.19\n",
" Stereo Strength Quality: 0.29\n",
"\n",
" Stereo Tip Distance (dsh): 0.11\n",
" Illumination Quality: 0.96\n",
" \n"
]
}
],
"source": [
"qual_report = asap.CommonSteps().get_stereo_quality_report(f'{left}_{right}/{left}.lev1eo.cub', f'{left}_{right}/{right}.lev1eo.cub')\n",
"print(qual_report)"
]
},
{
"cell_type": "markdown",
"id": "8a043fee",
"metadata": {
"collapsed": false,
"papermill": {
"duration": 0.013217,
"end_time": "2023-02-08T18:21:14.417130",
"exception": false,
"start_time": "2023-02-08T18:21:14.403913",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"# Downsample images if requested"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "072c7e88",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2023-02-08T18:21:14.445974Z",
"iopub.status.busy": "2023-02-08T18:21:14.445276Z",
"iopub.status.idle": "2023-02-08T18:21:14.452051Z",
"shell.execute_reply": "2023-02-08T18:21:14.451287Z"
},
"papermill": {
"duration": 0.02328,
"end_time": "2023-02-08T18:21:14.453455",
"exception": false,
"start_time": "2023-02-08T18:21:14.430175",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [],
"source": [
"if downsample:\n",
" true_img_gsd_left = asap.CommonSteps().get_image_gsd(f'{left}_{right}/{left}.lev1eo.cub')\n",
" true_img_gsd_right = asap.CommonSteps().get_image_gsd(f'{left}_{right}/{right}.lev1eo.cub')\n",
" # take conservative approach, pick worst image GSD\n",
" res_gsd = max(true_img_gsd_left, true_img_gsd_right)\n",
" # this is because rescale in ISIS does not update GSD in metadata\n",
" asap.CommonSteps().rescale_and_overwrite(factor=downsample)\n",
" img_gsd = math.ceil(res_gsd)*downsample\n",
" dem_gsd = 4*img_gsd\n",
" print('new img gsd', img_gsd)\n",
" print('new dem gsd', dem_gsd)"
]
},
{
"cell_type": "markdown",
"id": "6c06dd6a",
"metadata": {
"collapsed": false,
"papermill": {
"duration": 0.013791,
"end_time": "2023-02-08T18:21:14.481206",
"exception": false,
"start_time": "2023-02-08T18:21:14.467415",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"# Calculate BA and low-res DEM (Step 3)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "0888d76a",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2023-02-08T18:21:14.510975Z",
"iopub.status.busy": "2023-02-08T18:21:14.510172Z",
"iopub.status.idle": "2023-02-08T18:21:42.220447Z",
"shell.execute_reply": "2023-02-08T18:21:42.218979Z"
},
"papermill": {
"duration": 27.728577,
"end_time": "2023-02-08T18:21:42.223349",
"exception": false,
"start_time": "2023-02-08T18:21:14.494772",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"# Started: step_4 (Bundle Adjust CTX), at: 2023-02-08 18:21:14.975810\r\n",
"# Started: bundle_adjust (Bundle adjustment wrapper), at: 2023-02-08 18:21:14.975863\r\n",
"INFO:sh.command:<Command '/usr/bin/cat ./stereopairs.lis', pid 2556>: process started\r\n",
"cd /home/runner/work/asap_stereo/asap_stereo/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W\r\n",
"INFO:sh.command:/usr/share/miniconda3/envs/asap/bin/isd_generate -v --max_workers 2 B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub: process started\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Reading: P02_001902_1889_XI_08N001W.lev1eo.cub\r\n",
"Reading: B03_010644_1889_XN_08N001W.lev1eo.cub\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Writing: B03_010644_1889_XN_08N001W.lev1eo.json\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Writing: P02_001902_1889_XI_08N001W.lev1eo.json\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:sh.command:/tmp/sp/bin/cam_test --image B03_010644_1889_XN_08N001W.lev1eo.cub --cam1 B03_010644_1889_XN_08N001W.lev1eo.cub --cam2 B03_010644_1889_XN_08N001W.lev1eo.json --sample-rate 1000 --subpixel-offset 0.25: process started\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:sh.command:/tmp/sp/bin/cam_test --image P02_001902_1889_XI_08N001W.lev1eo.cub --cam1 P02_001902_1889_XI_08N001W.lev1eo.cub --cam2 P02_001902_1889_XI_08N001W.lev1eo.json --sample-rate 1000 --subpixel-offset 0.25: process started\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t--> Setting number of processing threads to: 4\r\n",
"Using session: isis\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.cub\r\n",
"Datum: Geodetic Datum --> Name: D_Mars Spheroid: Mars Semi-major axis: 3396190 Semi-minor axis: 3396190 Meridian: Reference Meridian at 0 Proj4 Str: +a=3396190 +b=3396190\r\n",
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Image dimensions: 5000 6144\r\n",
"Number of samples used: 35\r\n",
"\r\n",
"cam1 to cam2 camera direction diff norm\r\n",
"Min: 9.16994e-09\r\n",
"Median: 2.91387e-08\r\n",
"Max: 4.8953e-08\r\n",
"\r\n",
"cam1 to cam2 camera center diff (meters)\r\n",
"Min: 1.44416e-05\r\n",
"Median: 7.97314e-05\r\n",
"Max: 0.0919213\r\n",
"\r\n",
"cam1 to cam2 pixel diff\r\n",
"Min: 0.00013785\r\n",
"Median: 0.0014762\r\n",
"Max: 0.00254472\r\n",
"\r\n",
"cam2 to cam1 pixel diff\r\n",
"Min: 1.34234e-05\r\n",
"Median: 4.83155e-05\r\n",
"Max: 0.00199851\r\n",
"\r\n",
"Elapsed time per sample: 133.086 milliseconds.\r\n",
"It is suggested to adjust the sample rate to produce more samples if desired to evaluate more accurately the elapsed time per sample.\r\n",
"\r\n",
"\t--> Setting number of processing threads to: 4\r\n",
"Using session: isis\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub\r\n",
"Datum: Geodetic Datum --> Name: D_Mars Spheroid: Mars Semi-major axis: 3396190 Semi-minor axis: 3396190 Meridian: Reference Meridian at 0 Proj4 Str: +a=3396190 +b=3396190\r\n",
"Using session: csm\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Image dimensions: 5000 10240\r\n",
"Number of samples used: 55\r\n",
"\r\n",
"cam1 to cam2 camera direction diff norm\r\n",
"Min: 7.17465e-09\r\n",
"Median: 2.79381e-08\r\n",
"Max: 4.8781e-08\r\n",
"\r\n",
"cam1 to cam2 camera center diff (meters)\r\n",
"Min: 3.67019e-06\r\n",
"Median: 4.44149e-05\r\n",
"Max: 0.255089\r\n",
"\r\n",
"cam1 to cam2 pixel diff\r\n",
"Min: 0.000406557\r\n",
"Median: 0.00143725\r\n",
"Max: 0.0106217\r\n",
"\r\n",
"cam2 to cam1 pixel diff\r\n",
"Min: 1.6582e-05\r\n",
"Median: 0.000174974\r\n",
"Max: 0.0129581\r\n",
"\r\n",
"Elapsed time per sample: 133.818 milliseconds.\r\n",
"It is suggested to adjust the sample rate to produce more samples if desired to evaluate more accurately the elapsed time per sample.\r\n",
"\r\n",
"cd /home/runner/work/asap_stereo/asap_stereo\r\n",
"INFO:sh.command:<Command '/usr/bin/cat ./stereopairs.lis', pid 2641>: process started\r\n",
"cd /home/runner/work/asap_stereo/asap_stereo/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:sh.command:/tmp/sp/bin/parallel_bundle_adjust --threads 2 B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json -o adjust/ba --save-cnet-as-csv --datum D_MARS --max-iterations 100: process started\r\n",
"/tmp/sp/bin/parallel_bundle_adjust: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"/tmp/sp/bin/bundle_adjust: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n",
"/tmp/sp/bin/bundle_adjust: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t--> Setting number of processing threads to: 2\r\n",
"Found 0 GCP files on the command line.\r\n",
"Will use the datum:\r\n",
"Geodetic Datum --> Name: D_MARS Spheroid: MARS Semi-major axis: 3396190 Semi-minor axis: 3396190 Meridian: Reference Meridian at 0 Proj4 Str: +a=3396190 +b=3396190\r\n",
"Writing log info to: adjust/ba-log-bundle_adjust-02-08-1821-2774.txt\r\n",
"\t--> Setting number of processing threads to: 2\r\n",
"Found 0 GCP files on the command line.\r\n",
"Will use the datum:\r\n",
"Geodetic Datum --> Name: D_MARS Spheroid: MARS Semi-major axis: 3396190 Semi-minor axis: 3396190 Meridian: Reference Meridian at 0 Proj4 Str: +a=3396190 +b=3396190\r\n",
"Writing log info to: adjust/ba-log-bundle_adjust-02-08-1821-2752.txt\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using session: csm\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using session: csm\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Computing statistics for P02_001902_1889_XI_08N001W.lev1eo.cub\r\n",
"Using downsample scale: 8\r\n",
"Computing statistics for B03_010644_1889_XN_08N001W.lev1eo.cub\r\n",
"Using downsample scale: 6\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t Writing stats file: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo-stats.tif\r\n",
"\t B03_010644_1889_XN_08N001W.lev1eo.cub: [ lo: 0.0594155 hi: 0.141163 mean: 0.102397 std_dev: 0.0150335 ]\r\n",
"Quitting after statistics computation.\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t Writing stats file: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo-stats.tif\r\n",
"\t P02_001902_1889_XI_08N001W.lev1eo.cub: [ lo: 0.0604056 hi: 0.146844 mean: 0.107769 std_dev: 0.0145469 ]\r\n",
"Quitting after statistics computation.\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"/tmp/sp/bin/bundle_adjust: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n",
"/tmp/sp/bin/bundle_adjust: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t--> Setting number of processing threads to: 2\r\n",
"Found 0 GCP files on the command line.\r\n",
"Will use the datum:\r\n",
"Geodetic Datum --> Name: D_MARS Spheroid: MARS Semi-major axis: 3396190 Semi-minor axis: 3396190 Meridian: Reference Meridian at 0 Proj4 Str: +a=3396190 +b=3396190\r\n",
"Writing log info to: adjust/ba-log-bundle_adjust-02-08-1821-2896.txt\r\n",
"\t--> Setting number of processing threads to: 2\r\n",
"Found 0 GCP files on the command line.\r\n",
"Will use the datum:\r\n",
"Geodetic Datum --> Name: D_MARS Spheroid: MARS Semi-major axis: 3396190 Semi-minor axis: 3396190 Meridian: Reference Meridian at 0 Proj4 Str: +a=3396190 +b=3396190\r\n",
"Writing log info to: adjust/ba-log-bundle_adjust-02-08-1821-2897.txt\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using session: csm\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Computing statistics for B03_010644_1889_XN_08N001W.lev1eo.cub\r\n",
"\t--> Reading statistics from file adjust/ba-B03_010644_1889_XN_08N001W.lev1eo-stats.tif\r\n",
"\t B03_010644_1889_XN_08N001W.lev1eo.cub: [ lo: 0.0594155 hi: 0.141163 mean: 0.102397 std_dev: 0.0150335 ]\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using session: csm\r\n",
"Computing statistics for B03_010644_1889_XN_08N001W.lev1eo.cub\r\n",
"\t--> Reading statistics from file adjust/ba-B03_010644_1889_XN_08N001W.lev1eo-stats.tif\r\n",
"\t B03_010644_1889_XN_08N001W.lev1eo.cub: [ lo: 0.0594155 hi: 0.141163 mean: 0.102397 std_dev: 0.0150335 ]\r\n",
"Computing statistics for P02_001902_1889_XI_08N001W.lev1eo.cub\r\n",
"\t--> Reading statistics from file adjust/ba-P02_001902_1889_XI_08N001W.lev1eo-stats.tif\r\n",
"\t P02_001902_1889_XI_08N001W.lev1eo.cub: [ lo: 0.0604056 hi: 0.146844 mean: 0.107769 std_dev: 0.0145469 ]\r\n",
"\t--> Matching interest points in StereoSession.\r\n",
"\t Using epipolar threshold = 528.094\r\n",
"\t IP uniqueness threshold = 0.8\r\n",
"\t Datum: Geodetic Datum --> Name: D_MARS Spheroid: MARS Semi-major axis: 3396190 Semi-minor axis: 3396190 Meridian: Reference Meridian at 0 Proj4 Str: +a=3396190 +b=3396190\r\n",
"\t Skipping rough homography.\r\n",
"\t Looking for IP in left image...\r\n",
"Using session: csm\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"\t Using 170 interest points per tile (1024^2 px).\r\n",
"\t Detecting IP\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Computing statistics for P02_001902_1889_XI_08N001W.lev1eo.cub\r\n",
"\t--> Reading statistics from file adjust/ba-P02_001902_1889_XI_08N001W.lev1eo-stats.tif\r\n",
"\t P02_001902_1889_XI_08N001W.lev1eo.cub: [ lo: 0.0604056 hi: 0.146844 mean: 0.107769 std_dev: 0.0145469 ]\r\n",
"Quitting after matches computation.\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t Removing IP near nodata with radius 4\r\n",
"\t Building descriptors\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t Found interest points: 4986\r\n",
"\t Looking for IP in right image...\r\n",
"\t Using 102 interest points per tile (1024^2 px).\r\n",
"\t Detecting IP\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t Removing IP near nodata with radius 4\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t Building descriptors\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t Found interest points: 4990\r\n",
"\t--> Matching interest points using the epipolar line.\r\n",
"\t Uniqueness threshold: 0.8\r\n",
"\t Epipolar threshold: 528.094\r\n",
"\t Matching forward\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t ---> Obtained 4986 matches.\r\n",
"\t Matching backward\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t ---> Obtained 4990 matches.\r\n",
"\t Matched 827 points.\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t Removed 13 points in stddev filtering.\r\n",
"\t Reduced matches to 814\r\n",
"\t * Writing match file: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo__P02_001902_1889_XI_08N001W.lev1eo.match\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"IP coverage fraction = 0.625\r\n",
"Quitting after matches computation.\r\n",
"/tmp/sp/bin/bundle_adjust: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t--> Setting number of processing threads to: 2\r\n",
"Found 0 GCP files on the command line.\r\n",
"Will use the datum:\r\n",
"Geodetic Datum --> Name: D_MARS Spheroid: MARS Semi-major axis: 3396190 Semi-minor axis: 3396190 Meridian: Reference Meridian at 0 Proj4 Str: +a=3396190 +b=3396190\r\n",
"Writing log info to: adjust/ba-log-bundle_adjust-02-08-1821-3093.txt\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using session: csm\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t--> Using cached match file: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo__P02_001902_1889_XI_08N001W.lev1eo.match\r\n",
"Match file adjust/ba-B03_010644_1889_XN_08N001W.lev1eo__P02_001902_1889_XI_08N001W.lev1eo.match has 814 matches.\r\n",
"Loaded 814 matches.\r\n",
"Building the control network took 0.007222 seconds.\r\n",
"\r",
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"Triangulating: [*****************************************************] Complete!\r\n",
"Loading GCP files...\r\n",
"--> Bundle adjust pass: 0\r\n",
"Writing: adjust/ba-cnet.csv\r\n",
"Writing initial condition files.\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Writing: adjust/ba-initial_residuals_stats.txt\r\n",
"Writing: adjust/ba-initial_residuals_raw_pixels.txt\r\n",
"Writing: adjust/ba-initial_residuals_raw_gcp.txt\r\n",
"Writing: adjust/ba-initial_residuals_raw_cameras.txt\r\n",
"Writing: adjust/ba-initial_residuals_pointmap.csv\r\n",
"Writing: adjust/ba-initial_points.kml\r\n",
"Starting the Ceres optimizer.\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"iter cost cost_change |gradient| |step| tr_ratio tr_radius ls_iter iter_time total_time\r\n",
" 0 5.211626e+02 0.00e+00 2.42e+06 0.00e+00 0.00e+00 1.00e+04 0 6.84e-02 7.26e-02\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 1 4.186996e+01 4.79e+02 3.41e+05 2.78e+02 2.69e+00 3.00e+04 1 7.31e-02 1.47e-01\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 2 4.149784e+01 3.72e-01 4.17e+04 1.29e+02 1.13e+00 9.00e+04 1 7.26e-02 2.19e-01\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 3 4.147999e+01 1.78e-02 5.56e+03 1.30e+02 1.24e+00 2.70e+05 1 7.20e-02 2.91e-01\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 4 4.145878e+01 2.12e-02 2.49e+03 3.77e+02 1.18e+00 8.10e+05 1 7.22e-02 3.64e-01\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 5 4.141884e+01 3.99e-02 1.46e+04 1.09e+03 1.08e+00 2.43e+06 1 7.32e-02 4.37e-01\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 6 4.137192e+01 4.69e-02 7.57e+04 3.10e+03 8.42e-01 3.57e+06 1 7.14e-02 5.08e-01\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 7 4.133737e+01 3.46e-02 9.21e+04 4.28e+03 7.17e-01 3.89e+06 1 7.15e-02 5.80e-01\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 8 4.130948e+01 2.79e-02 7.02e+04 4.37e+03 7.92e-01 4.86e+06 1 7.11e-02 6.51e-01\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 9 4.129418e+01 1.53e-02 7.37e+04 5.00e+03 6.79e-01 5.09e+06 1 6.92e-02 7.20e-01\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 10 4.127975e+01 1.44e-02 5.89e+04 4.76e+03 7.80e-01 6.18e+06 1 7.18e-02 7.92e-01\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 11 4.126838e+01 1.14e-02 6.28e+04 5.14e+03 7.67e-01 7.28e+06 1 7.06e-02 8.62e-01\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 12 4.125127e+01 1.71e-02 6.24e+04 5.29e+03 1.06e+00 2.18e+07 1 6.87e-02 9.31e-01\r\n",
" 13 4.126954e+01 -1.83e-02 0.00e+00 1.08e+04 -6.40e-01 1.09e+07 1 4.72e-03 9.36e-01\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 14 4.121374e+01 3.75e-02 8.97e+04 6.39e+03 1.55e+00 3.28e+07 1 7.06e-02 1.01e+00\r\n",
" 15 4.114558e+01 6.82e-02 2.69e+05 1.18e+04 1.13e+00 9.83e+07 1 4.29e-02 1.05e+00\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 16 4.070826e+01 4.37e-01 3.01e+05 1.53e+04 2.07e+00 2.95e+08 1 3.96e-02 1.09e+00\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 17 3.972569e+01 9.83e-01 5.07e+05 2.05e+04 2.45e+00 8.84e+08 1 3.96e-02 1.13e+00\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 18 3.968634e+01 3.93e-02 1.39e+06 2.97e+04 4.38e-02 5.03e+08 1 3.96e-02 1.17e+00\r\n",
" 19 3.438390e+01 5.30e+00 2.15e+05 1.16e+04 1.74e+00 1.51e+09 1 3.88e-02 1.21e+00\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 20 3.192530e+01 2.46e+00 3.22e+05 1.31e+04 3.99e+00 4.52e+09 1 4.08e-02 1.25e+00\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 21 3.035836e+01 1.57e+00 1.21e+05 8.90e+03 3.18e+00 1.36e+10 1 4.10e-02 1.29e+00\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 22 2.919774e+01 1.16e+00 1.07e+04 3.00e+03 4.62e+00 4.07e+10 1 4.14e-02 1.33e+00\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 23 2.912759e+01 7.01e-02 4.16e+02 8.00e+02 1.28e+00 1.22e+11 1 4.01e-02 1.37e+00\r\n",
" 24 2.913669e+01 -9.10e-03 0.00e+00 1.77e+03 -1.36e+00 6.11e+10 1 3.85e-03 1.37e+00\r\n",
" 25 2.913669e+01 -9.10e-03 0.00e+00 1.77e+03 -1.36e+00 1.53e+10 1 3.97e-03 1.38e+00\r\n",
" 26 2.913669e+01 -9.10e-03 0.00e+00 1.72e+03 -1.36e+00 1.91e+09 1 3.75e-03 1.38e+00\r\n",
" 27 2.913669e+01 -9.10e-03 0.00e+00 1.45e+03 -1.36e+00 1.19e+08 1 3.94e-03 1.39e+00\r\n",
" 28 2.913666e+01 -9.07e-03 0.00e+00 8.40e+02 -1.36e+00 3.73e+06 1 3.76e-03 1.39e+00\r\n",
" 29 2.913626e+01 -8.67e-03 0.00e+00 3.28e+02 -1.30e+00 5.82e+04 1 3.73e-03 1.39e+00\r\n",
" 30 2.912859e+01 -9.98e-04 0.00e+00 1.27e+02 -2.06e-01 4.55e+02 1 3.68e-03 1.40e+00\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 31 2.912630e+01 1.29e-03 1.24e+02 1.72e+00 1.89e+00 1.36e+03 1 3.95e-02 1.44e+00\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 32 2.912463e+01 1.67e-03 3.28e+02 1.43e+00 2.01e+00 4.09e+03 1 4.10e-02 1.48e+00\r\n",
" 33 2.912269e+01 1.94e-03 4.65e+02 1.10e+00 2.04e+00 1.23e+04 1 3.84e-02 1.52e+00\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 34 2.912085e+01 1.84e-03 4.69e+02 1.13e+01 1.74e+00 3.69e+04 1 4.04e-02 1.56e+00\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 35 2.911942e+01 1.43e-03 3.81e+02 3.15e+00 1.77e+00 1.11e+05 1 4.08e-02 1.60e+00\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 36 2.911878e+01 6.45e-04 4.15e+02 8.01e+00 6.99e-01 1.18e+05 1 4.16e-02 1.64e+00\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 37 2.911809e+01 6.88e-04 1.90e+02 1.28e+01 1.41e+00 3.54e+05 1 4.17e-02 1.68e+00\r\n",
" 38 2.911981e+01 -1.73e-03 0.00e+00 7.91e+01 -1.05e+00 1.77e+05 1 3.81e-03 1.68e+00\r\n",
" 39 2.911959e+01 -1.50e-03 0.00e+00 5.99e+01 -9.40e-01 4.43e+04 1 3.89e-03 1.69e+00\r\n",
" 40 2.911907e+01 -9.82e-04 0.00e+00 2.59e+01 -7.12e-01 5.53e+03 1 3.95e-03 1.69e+00\r\n",
" 41 2.911832e+01 -2.35e-04 0.00e+00 8.15e+00 -2.71e-01 3.46e+02 1 3.90e-03 1.70e+00\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 42 2.911793e+01 1.57e-04 1.36e+02 1.28e+00 6.92e-01 3.67e+02 1 4.11e-02 1.74e+00\r\n",
"\r\n",
"Solver Summary (v 1.14.0-eigen-(3.4.0)-lapack-suitesparse-(5.10.1)-cxsparse-(3.2.0)-eigensparse-openmp-no_tbb)\r\n",
"\r\n",
" Original Reduced\r\n",
"Parameter blocks 816 816\r\n",
"Parameters 2454 2454\r\n",
"Residual blocks 1630 1630\r\n",
"Residuals 3268 3268\r\n",
"\r\n",
"Minimizer TRUST_REGION\r\n",
"\r\n",
"Dense linear algebra library EIGEN\r\n",
"Trust region strategy LEVENBERG_MARQUARDT\r\n",
"\r\n",
" Given Used\r\n",
"Linear solver DENSE_SCHUR DENSE_SCHUR\r\n",
"Threads 2 2\r\n",
"Linear solver ordering AUTOMATIC 814,2\r\n",
"Schur structure 2,3,6 2,3,6\r\n",
"\r\n",
"Cost:\r\n",
"Initial 5.211626e+02\r\n",
"Final 2.911793e+01\r\n",
"Change 4.920447e+02\r\n",
"\r\n",
"Minimizer iterations 43\r\n",
"Successful steps 31\r\n",
"Unsuccessful steps 12\r\n",
"\r\n",
"Time (in seconds):\r\n",
"Preprocessor 0.004180\r\n",
"\r\n",
" Residual only evaluation 0.111218 (43)\r\n",
" Jacobian & residual evaluation 1.544580 (31)\r\n",
" Linear solver 0.070903 (43)\r\n",
"Minimizer 1.737305\r\n",
"\r\n",
"Postprocessor 0.000045\r\n",
"Total 1.741530\r\n",
"\r\n",
"Termination: CONVERGENCE (Parameter tolerance reached. Relative step_norm: 3.239445e-09 <= 1.000000e-08.)\r\n",
"\r\n",
"Writing final condition log files.\r\n",
"Writing: adjust/ba-final_residuals_stats.txt\r\n",
"Writing: adjust/ba-final_residuals_raw_pixels.txt\r\n",
"Writing: adjust/ba-final_residuals_raw_gcp.txt\r\n",
"Writing: adjust/ba-final_residuals_raw_cameras.txt\r\n",
"Writing: adjust/ba-final_residuals_pointmap.csv\r\n",
"Writing: adjust/ba-final_points.kml\r\n",
"Removing pixel outliers in preparation for another solver attempt.\r\n",
"Outlier statistics: b = -0.15896, e = 0.256997.\r\n",
"Removing as outliers points with mean reprojection error > 2.\r\n",
"Removed 11 outliers out of 1628 by reprojection error. Ratio: 0.00675676.\r\n",
"Removed 0 outlier(s) based on spatial distribution of triangulated points.\r\n",
"IP coverage fraction after cleaning = 0.525\r\n",
"Saving 801 filtered interest points.\r\n",
"Writing: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo__P02_001902_1889_XI_08N001W.lev1eo-clean.match\r\n",
"Writing: adjust/ba-convergence_angles.txt\r\n",
"--> Bundle adjust pass: 1\r\n",
"Starting the Ceres optimizer.\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"iter cost cost_change |gradient| |step| tr_ratio tr_radius ls_iter iter_time total_time\r\n",
" 0 1.354732e+01 0.00e+00 4.86e+02 0.00e+00 0.00e+00 1.00e+04 0 3.60e-02 3.77e-02\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 1 1.354728e+01 3.93e-05 8.22e+01 4.13e+00 6.00e-01 1.01e+04 1 4.03e-02 7.81e-02\r\n",
" 2 1.354728e+01 -8.86e-07 0.00e+00 1.42e+01 -2.20e-03 5.04e+03 1 3.81e-03 8.19e-02\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 3 1.354725e+01 2.95e-05 4.27e+01 7.23e+00 1.19e-01 3.50e+03 1 4.04e-02 1.22e-01\r\n",
" 4 1.354812e+01 -8.74e-04 0.00e+00 2.04e+01 -3.46e-01 1.75e+03 1 3.77e-03 1.26e-01\r\n",
" 5 1.354762e+01 -3.67e-04 0.00e+00 1.30e+01 -2.09e-01 4.37e+02 1 3.67e-03 1.30e-01\r\n",
" 6 1.354729e+01 -3.99e-05 0.00e+00 4.08e+00 -6.52e-02 5.46e+01 1 3.82e-03 1.34e-01\r\n",
"\r\n",
"Solver Summary (v 1.14.0-eigen-(3.4.0)-lapack-suitesparse-(5.10.1)-cxsparse-(3.2.0)-eigensparse-openmp-no_tbb)\r\n",
"\r\n",
" Original Reduced\r\n",
"Parameter blocks 805 805\r\n",
"Parameters 2421 2421\r\n",
"Residual blocks 1608 1608\r\n",
"Residuals 3224 3224\r\n",
"\r\n",
"Minimizer TRUST_REGION\r\n",
"\r\n",
"Dense linear algebra library EIGEN\r\n",
"Trust region strategy LEVENBERG_MARQUARDT\r\n",
"\r\n",
" Given Used\r\n",
"Linear solver DENSE_SCHUR DENSE_SCHUR\r\n",
"Threads 2 2\r\n",
"Linear solver ordering AUTOMATIC 803,2\r\n",
"Schur structure 2,3,6 2,3,6\r\n",
"\r\n",
"Cost:\r\n",
"Initial 1.354732e+01\r\n",
"Final 1.354725e+01\r\n",
"Change 6.879982e-05\r\n",
"\r\n",
"Minimizer iterations 7\r\n",
"Successful steps 3\r\n",
"Unsuccessful steps 4\r\n",
"\r\n",
"Time (in seconds):\r\n",
"Preprocessor 0.001752\r\n",
"\r\n",
" Residual only evaluation 0.013382 (7)\r\n",
" Jacobian & residual evaluation 0.108134 (3)\r\n",
" Linear solver 0.012803 (7)\r\n",
"Minimizer 0.135596\r\n",
"\r\n",
"Postprocessor 0.000042\r\n",
"Total 0.137390\r\n",
"\r\n",
"Termination: CONVERGENCE (Parameter tolerance reached. Relative step_norm: 5.645268e-09 <= 1.000000e-08.)\r\n",
"\r\n",
"Writing final condition log files.\r\n",
"Writing: adjust/ba-final_residuals_stats.txt\r\n",
"Writing: adjust/ba-final_residuals_raw_pixels.txt\r\n",
"Writing: adjust/ba-final_residuals_raw_gcp.txt\r\n",
"Writing: adjust/ba-final_residuals_raw_cameras.txt\r\n",
"Writing: adjust/ba-final_residuals_pointmap.csv\r\n",
"Writing: adjust/ba-final_points.kml\r\n",
"Removing pixel outliers in preparation for another solver attempt.\r\n",
"Outlier statistics: b = -0.150734, e = 0.245385.\r\n",
"Removing as outliers points with mean reprojection error > 2.\r\n",
"Removed 0 outliers out of 1628 by reprojection error. Ratio: 0.\r\n",
"Removed 0 outlier(s) based on spatial distribution of triangulated points.\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"IP coverage fraction after cleaning = 0.525\r\n",
"Saving 801 filtered interest points.\r\n",
"Writing: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo__P02_001902_1889_XI_08N001W.lev1eo-clean.match\r\n",
"Writing: adjust/ba-convergence_angles.txt\r\n",
"Writing: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Writing adjusted model state: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjusted_state.json\r\n",
"Writing: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Writing adjusted model state: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjusted_state.json\r\n",
"cd /home/runner/work/asap_stereo/asap_stereo\r\n",
"# Ran Command: /tmp/sp/bin/parallel_bundle_adjust --threads 2 B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json -o adjust/ba --save-cnet-as-csv --datum D_MARS --max-iterations 100\r\n",
"# Finished: bundle_adjust (Bundle adjustment wrapper), at: 2023-02-08 18:21:42.040602, duration: 0:00:27.064739\r\n",
"# Finished: step_4 (Bundle Adjust CTX), at: 2023-02-08 18:21:42.040641, duration: 0:00:27.064831\r\n"
]
}
],
"source": [
"!asap ctx step_4 {asap.kwarg_parse(step_kwargs, 'step_4')} 2>&1 | tee -i -a ./2_bundle_adjust.log ./full_log.log"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "f06cddd9",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2023-02-08T18:21:42.262386Z",
"iopub.status.busy": "2023-02-08T18:21:42.261764Z",
"iopub.status.idle": "2023-02-08T18:27:30.479051Z",
"shell.execute_reply": "2023-02-08T18:27:30.477637Z"
},
"papermill": {
"duration": 348.239704,
"end_time": "2023-02-08T18:27:30.481838",
"exception": false,
"start_time": "2023-02-08T18:21:42.242134",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"# Started: step_5 (Parallel Stereo Part 1), at: 2023-02-08 18:21:42.751826\r\n",
"# Started: stereo_asap (parallel stereo common step), at: 2023-02-08 18:21:42.751887\r\n",
"INFO:sh.command:<Command '/usr/bin/cat ./stereopairs.lis', pid 3149>: process started\r\n",
"cd /home/runner/work/asap_stereo/asap_stereo/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:sh.command:/tmp/sp/bin/parallel_stereo --processes 1 --threads-singleprocess 2 --threads-multiprocess 1 --stop-point 5 --bundle-adjust-prefix adjust/ba --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba: process started\r\n",
"/tmp/sp/bin/parallel_stereo: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"/tmp/sp/bin/stereo_pprc: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r\n",
"[ 2023-Feb-08 18:21:44 ] : Stage 0 --> PREPROCESSING \r\n",
"\t--> Setting number of processing threads to: 2\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap.\r\n",
"Writing log info to: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-log-stereo_pprc-02-08-1821-3197.txt\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"Using image files: B03_010644_1889_XN_08N001W.lev1eo.cub, P02_001902_1889_XI_08N001W.lev1eo.cub\r\n",
"Using camera files: B03_010644_1889_XN_08N001W.lev1eo.json, P02_001902_1889_XI_08N001W.lev1eo.json\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Computing statistics for left\r\n",
"Using downsample scale: 6\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t Writing stats file: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-B03_010644_1889_XN_08N001W.lev1eo-stats.tif\r\n",
"\t left: [ lo: 0.0594155 hi: 0.141163 mean: 0.102397 std_dev: 0.0150335 ]\r\n",
"Computing statistics for right\r\n",
"Using downsample scale: 8\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t Writing stats file: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-P02_001902_1889_XI_08N001W.lev1eo-stats.tif\r\n",
"\t right: [ lo: 0.0604056 hi: 0.146844 mean: 0.107769 std_dev: 0.0145469 ]\r\n",
"\t--> Applying alignment method: affineepipolar\r\n",
"\t--> Matching interest points in StereoSession.\r\n",
"\t Using epipolar threshold = 528.094\r\n",
"\t IP uniqueness threshold = 0.8\r\n",
"\t Datum: Geodetic Datum --> Name: D_MARS Spheroid: MARS Semi-major axis: 3396190 Semi-minor axis: 3396190 Meridian: Reference Meridian at 0 Proj4 Str: +a=3396190 +b=3396190\r\n",
"\t Using rough homography.\r\n",
"Performing IP matching with alignment.\r\n",
"\r",
" Rough homography--> [.............................................] 0%\r",
" Rough homography--> [.............................................] 1%\r",
" Rough homography--> [.............................................] 2%\r",
" Rough homography--> [*............................................] 3%\r",
" Rough homography--> [*............................................] 4%\r",
" Rough homography--> [**...........................................] 5%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" Rough homography--> [**...........................................] 6%\r",
" Rough homography--> [***..........................................] 7%\r",
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" Rough homography--> [*****........................................] 12%\r",
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" Rough homography--> [******.......................................] 14%\r",
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" Rough homography--> [********************************.............] 72%\r",
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" Rough homography--> [***************************************......] 87%\r",
" Rough homography--> [***************************************......] 88%\r",
" Rough homography--> [***************************************......] 89%\r",
" Rough homography--> [****************************************.....] 90%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" Rough homography--> [****************************************.....] 91%\r",
" Rough homography--> [*****************************************....] 92%\r",
" Rough homography--> [*****************************************....] 93%\r",
" Rough homography--> [******************************************...] 94%\r",
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" Rough homography--> [********************************************.] 99%\r",
" Rough homography--> [********************************************.] 100%\r",
" Rough homography--> [****************************************] Complete!\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Projected 15529 rays for rough homography.\r\n",
"Number of inliers: 15529.\r\n",
"Aligning right to left for IP capture using rough homography: Matrix3x3((1.0547,-0.00123741,0)(0.00590389,1.00496,0)(1.32105e-06,1.43113e-07,1))\r\n",
"\t Looking for IP in left image...\r\n",
"\t Using 170 interest points per tile (1024^2 px).\r\n",
"\t Detecting IP\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t Removing IP near nodata with radius 4\r\n",
"\t Building descriptors\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t Found interest points: 4986\r\n",
"\t Recording interest points to file: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-B03_010644_1889_XN_08N001W.lev1eo.vwip\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t Looking for IP in right image...\r\n",
"\t Using 97 interest points per tile (1024^2 px).\r\n",
"\t Detecting IP\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t Removing IP near nodata with radius 4\r\n",
"\t Building descriptors\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t Found interest points: 4848\r\n",
"\t--> Matching interest points using the epipolar line.\r\n",
"\t Uniqueness threshold: 0.8\r\n",
"\t Epipolar threshold: 528.094\r\n",
"\t Matching forward\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t ---> Obtained 4986 matches.\r\n",
"\t Matching backward\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t ---> Obtained 4848 matches.\r\n",
"\t Matched 765 points.\r\n",
"\t Inlier cluster:\r\n",
"\t Triangulation error: 3.02517 +- 2.20584 meters\r\n",
"\t Removed 20 points in triangulation filtering.\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t Removed 8 points in stddev filtering.\r\n",
"\t Reduced matches to 737\r\n",
"\t * Writing match file: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-B03_010644_1889_XN_08N001W.lev1eo__P02_001902_1889_XI_08N001W.lev1eo.match\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Computing the epipolar rectification using RANSAC with 1000 iterations and inlier threshold 10.\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 737 / 737 inliers.\r\n",
"Maximum absolute difference of y components of aligned inlier interest points is 1.86423 pixels.\r\n",
"\t--> Aligning left and right images using affine matrices:\r\n",
"\t Matrix2x3((0.999989,-0.00463412,28)(0.0050463,0.999987,0))\r\n",
"\t Matrix2x3((1.04907,-0.00813696,-142.563)(0.00736287,0.999923,-1725.41))\r\n",
"\t--> Normalizing globally to: [0.0594155 0.146844]\r\n",
"\t--> Writing pre-aligned images.\r\n",
"\t--> Writing: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-L.tif.\r\n",
"\r",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
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]
},
{
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"\r",
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},
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},
{
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"\r",
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]
},
{
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"text": [
"\r",
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]
},
{
"name": "stdout",
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"text": [
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{
"name": "stdout",
"output_type": "stream",
"text": [
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{
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{
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{
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"text": [
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{
"name": "stdout",
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"text": [
"\r",
" L: [******************************************************] Complete!\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t--> Writing: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-R.tif.\r\n",
"\r",
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{
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{
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{
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{
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{
"name": "stdout",
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"text": [
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{
"name": "stdout",
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"text": [
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" R: [******************************************************] Complete!\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t--> Generating image masks... \r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Writing masks: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-lMask.tif results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-rMask.tif.\r\n",
"\r",
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]
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{
"name": "stdout",
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"text": [
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},
{
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"text": [
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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},
{
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},
{
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"text": [
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" Mask L: [*******************************************************.] 100%\r",
" Mask L: [***************************************************] Complete!\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
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{
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"text": [
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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{
"name": "stdout",
"output_type": "stream",
"text": [
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},
{
"name": "stdout",
"output_type": "stream",
"text": [
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},
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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" Mask R: [***************************************************] Complete!\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t--> Creating previews. Subsampling by 0.269358 by using a tile of size 256 and 2 threads.\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
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]
},
{
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"output_type": "stream",
"text": [
"\r",
" Sub L: [*************************************************] Complete!\r\n"
]
},
{
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"text": [
"\r",
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" Sub R: [*************************************************] Complete!\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
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" Sub L Mask: [********************************************] Complete!\r\n",
"\r",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" Sub R Mask: [********************************************] Complete!\r\n",
"Convergence angle percentiles (in degrees) based on interest point matches:\r\n",
"\t25% 10.5248, 50% 10.5928, 75% 10.6337.\r\n",
"\r\n",
"[ 2023-Feb-08 18:22:32 ] : PREPROCESSING FINISHED \r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"/tmp/sp/bin/stereo_corr: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t--> Setting number of processing threads to: 4\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap.\r\n",
"Writing log info to: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-log-stereo_corr-02-08-1822-3447.txt\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"\t--> Using LOG pre-processing filter with 1.4 sigma blur.\r\n",
"\r\n",
"[ 2023-Feb-08 18:22:33 ] : Stage 1 --> CORRELATION\r\n",
"\r\n",
"[ 2023-Feb-08 18:22:33 ] : Stage 1 --> LOW-RESOLUTION CORRELATION\r\n",
"Cached IP match file found: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-B03_010644_1889_XN_08N001W.lev1eo__P02_001902_1889_XI_08N001W.lev1eo.match\r\n",
"\t--> Using interest points to determine search window.\r\n",
"\t * Loading match file: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-B03_010644_1889_XN_08N001W.lev1eo__P02_001902_1889_XI_08N001W.lev1eo.match\r\n",
"Removed 0 outliers based on percentiles of differences of interest points with --outlier-removal-params.\r\n",
"D_sub search range: (Origin: (-16.5, -1.25) width: 25 height: 2.5) px\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Writing: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-D_sub.tif\r\n",
"\r",
" --> Low-resolution disparity:[....................................] 0%\r",
" --> Low-resolution disparity:[*********...........................] 25%\r",
" --> Low-resolution disparity:[******************..................] 50%\r",
" --> Low-resolution disparity:[***************************.........] 75%\r",
" --> Low-resolution disparity:[************************************] 100%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Low-resolution disparity:[*******************************] Complete!\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Filtering outliers in D_sub based on --outlier-removal-params.\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Inlier range based on x coordinate of disparity: -42 35.\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Inlier range based on y coordinate of disparity: 0 0.\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number (and fraction) of removed outliers by disparity values in x and y: 24041 (0.0106832).\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Height above datum inlier range: -1215.72 6198.31.\r\n",
"Number (and fraction) of removed outliers by the height check: 0 (0).\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Triangulation error inlier range: -8.01858 9.60444.\r\n",
"Number (and fraction) of removed outliers by the triangulation error check: 0 (0).\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Writing filtered D_sub: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-D_sub.tif\r\n",
"\r",
" D_sub: [.........................................................] 0%\r",
" D_sub: [**************...........................................] 25%\r",
" D_sub: [****************************.............................] 50%\r",
" D_sub: [******************************************...............] 75%\r",
" D_sub: [*********************************************************] 100%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" D_sub: [****************************************************] Complete!\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Writing triangulated point cloud based on D_sub: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-PC_sub.tif\r\n",
"\r",
" PC_sub: [........................................................] 0%\r",
" PC_sub: [**************..........................................] 25%\r",
" PC_sub: [****************************............................] 50%\r",
" PC_sub: [******************************************..............] 75%\r",
" PC_sub: [********************************************************] 100%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" PC_sub: [***************************************************] Complete!\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t--> Full-res search range based on D_sub: (Origin: (-45, 0) width: 0 height: 0)\r\n",
"\r\n",
"[ 2023-Feb-08 18:22:49 ] : LOW-RESOLUTION CORRELATION FINISHED\r\n",
"\r\n",
"[ 2023-Feb-08 18:22:49 ] : CORRELATION FINISHED\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using tiles (before collar addition) of 2048 x 2048 pixels.\r\n",
"Using a collar (padding) for each tile of 0 pixels.\r\n",
"/usr/bin/time -f \"stereo_corr: elapsed=%E ([hours:]minutes:seconds), memory=%M (kb)\" /tmp/sp/bin/stereo_corr --bundle-adjust-prefix adjust/ba B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_0_2048_2048/0_0_2048_2048 --skip-low-res-disparity-comp --corr-seed-mode 1 --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap --sgm-collar-size 0 --threads 1 --trans-crop-win 0 0 2048 2048\r\n",
"--> Setting number of processing threads to: 1\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap.\r\n",
"Writing log info to: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_0_2048_2048/0_0_2048_2048-log-stereo_corr-02-08-1822-3558.txt\r\n",
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"--> Using LOG pre-processing filter with 1.4 sigma blur.\r\n",
"\r\n",
"[ 2023-Feb-08 18:22:51 ] : Stage 1 --> CORRELATION\r\n",
"--> Full-res search range based on D_sub: (Origin: (-45, 0) width: 0 height: 0)\r\n",
"--------------------------------------------------\r\n",
"Kernel size: Vector2(25,25)\r\n",
"Search range: (Origin: (-45, 0) width: 0 height: 0)\r\n",
"Cost mode: 0\r\n",
"--------------------------------------------------\r\n",
"Writing: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_0_2048_2048/0_0_2048_2048-D.tif\r\n",
"\r\n",
"--> Correlation :[................................................] 0%\r\n",
"--> Correlation :[************....................................] 25%\r\n",
"--> Correlation :[************************........................] 50%\r\n",
"--> Correlation :[************************************............] 75%\r\n",
"--> Correlation :[************************************************] 100%\r\n",
"--> Correlation :[*******************************************] Complete!\r\n",
"\r\n",
"[ 2023-Feb-08 18:23:07 ] : CORRELATION FINISHED\r\n",
"/tmp/sp/bin/stereo_corr: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n",
"stereo_corr: elapsed=0:16.27 ([hours:]minutes:seconds), memory=486612 (kb)\r\n",
"\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using tiles (before collar addition) of 2048 x 2048 pixels.\r\n",
"Using a collar (padding) for each tile of 0 pixels.\r\n",
"/usr/bin/time -f \"stereo_corr: elapsed=%E ([hours:]minutes:seconds), memory=%M (kb)\" /tmp/sp/bin/stereo_corr --bundle-adjust-prefix adjust/ba B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-2048_0_2048_2048/2048_0_2048_2048 --skip-low-res-disparity-comp --corr-seed-mode 1 --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap --sgm-collar-size 0 --threads 1 --trans-crop-win 2048 0 2048 2048\r\n",
"--> Setting number of processing threads to: 1\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap.\r\n",
"Writing log info to: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-2048_0_2048_2048/2048_0_2048_2048-log-stereo_corr-02-08-1823-3641.txt\r\n",
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"--> Using LOG pre-processing filter with 1.4 sigma blur.\r\n",
"\r\n",
"[ 2023-Feb-08 18:23:09 ] : Stage 1 --> CORRELATION\r\n",
"--> Full-res search range based on D_sub: (Origin: (-45, 0) width: 0 height: 0)\r\n",
"--------------------------------------------------\r\n",
"Kernel size: Vector2(25,25)\r\n",
"Search range: (Origin: (-45, 0) width: 0 height: 0)\r\n",
"Cost mode: 0\r\n",
"--------------------------------------------------\r\n",
"Writing: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-2048_0_2048_2048/2048_0_2048_2048-D.tif\r\n",
"\r\n",
"--> Correlation :[................................................] 0%\r\n",
"--> Correlation :[************....................................] 25%\r\n",
"--> Correlation :[************************........................] 50%\r\n",
"--> Correlation :[************************************............] 75%\r\n",
"--> Correlation :[************************************************] 100%\r\n",
"--> Correlation :[*******************************************] Complete!\r\n",
"\r\n",
"[ 2023-Feb-08 18:23:25 ] : CORRELATION FINISHED\r\n",
"/tmp/sp/bin/stereo_corr: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n",
"stereo_corr: elapsed=0:16.52 ([hours:]minutes:seconds), memory=519832 (kb)\r\n",
"\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using tiles (before collar addition) of 2048 x 2048 pixels.\r\n",
"Using a collar (padding) for each tile of 0 pixels.\r\n",
"/usr/bin/time -f \"stereo_corr: elapsed=%E ([hours:]minutes:seconds), memory=%M (kb)\" /tmp/sp/bin/stereo_corr --bundle-adjust-prefix adjust/ba B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-4096_0_931_2048/4096_0_931_2048 --skip-low-res-disparity-comp --corr-seed-mode 1 --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap --sgm-collar-size 0 --threads 1 --trans-crop-win 4096 0 931 2048\r\n",
"--> Setting number of processing threads to: 1\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap.\r\n",
"Writing log info to: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-4096_0_931_2048/4096_0_931_2048-log-stereo_corr-02-08-1823-3732.txt\r\n",
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"--> Using LOG pre-processing filter with 1.4 sigma blur.\r\n",
"\r\n",
"[ 2023-Feb-08 18:23:27 ] : Stage 1 --> CORRELATION\r\n",
"--> Full-res search range based on D_sub: (Origin: (-45, 0) width: 0 height: 0)\r\n",
"--------------------------------------------------\r\n",
"Kernel size: Vector2(25,25)\r\n",
"Search range: (Origin: (-45, 0) width: 0 height: 0)\r\n",
"Cost mode: 0\r\n",
"--------------------------------------------------\r\n",
"Writing: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-4096_0_931_2048/4096_0_931_2048-D.tif\r\n",
"\r\n",
"--> Correlation :[................................................] 0%\r\n",
"--> Correlation :[************************........................] 50%\r\n",
"--> Correlation :[************************************************] 100%\r\n",
"--> Correlation :[*******************************************] Complete!\r\n",
"\r\n",
"[ 2023-Feb-08 18:23:37 ] : CORRELATION FINISHED\r\n",
"/tmp/sp/bin/stereo_corr: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n",
"stereo_corr: elapsed=0:10.56 ([hours:]minutes:seconds), memory=396976 (kb)\r\n",
"\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using tiles (before collar addition) of 2048 x 2048 pixels.\r\n",
"Using a collar (padding) for each tile of 0 pixels.\r\n",
"/usr/bin/time -f \"stereo_corr: elapsed=%E ([hours:]minutes:seconds), memory=%M (kb)\" /tmp/sp/bin/stereo_corr --bundle-adjust-prefix adjust/ba B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_2048_2048_2048/0_2048_2048_2048 --skip-low-res-disparity-comp --corr-seed-mode 1 --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap --sgm-collar-size 0 --threads 1 --trans-crop-win 0 2048 2048 2048\r\n",
"--> Setting number of processing threads to: 1\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap.\r\n",
"Writing log info to: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_2048_2048_2048/0_2048_2048_2048-log-stereo_corr-02-08-1823-3811.txt\r\n",
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"--> Using LOG pre-processing filter with 1.4 sigma blur.\r\n",
"\r\n",
"[ 2023-Feb-08 18:23:40 ] : Stage 1 --> CORRELATION\r\n",
"--> Full-res search range based on D_sub: (Origin: (-45, 0) width: 0 height: 0)\r\n",
"--------------------------------------------------\r\n",
"Kernel size: Vector2(25,25)\r\n",
"Search range: (Origin: (-45, 0) width: 0 height: 0)\r\n",
"Cost mode: 0\r\n",
"--------------------------------------------------\r\n",
"Writing: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_2048_2048_2048/0_2048_2048_2048-D.tif\r\n",
"\r\n",
"--> Correlation :[................................................] 0%\r\n",
"--> Correlation :[************....................................] 25%\r\n",
"--> Correlation :[************************........................] 50%\r\n",
"--> Correlation :[************************************............] 75%\r\n",
"--> Correlation :[************************************************] 100%\r\n",
"--> Correlation :[*******************************************] Complete!\r\n",
"\r\n",
"[ 2023-Feb-08 18:23:54 ] : CORRELATION FINISHED\r\n",
"/tmp/sp/bin/stereo_corr: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n",
"stereo_corr: elapsed=0:14.41 ([hours:]minutes:seconds), memory=516364 (kb)\r\n",
"\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using tiles (before collar addition) of 2048 x 2048 pixels.\r\n",
"Using a collar (padding) for each tile of 0 pixels.\r\n",
"/usr/bin/time -f \"stereo_corr: elapsed=%E ([hours:]minutes:seconds), memory=%M (kb)\" /tmp/sp/bin/stereo_corr --bundle-adjust-prefix adjust/ba B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-2048_2048_2048_2048/2048_2048_2048_2048 --skip-low-res-disparity-comp --corr-seed-mode 1 --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap --sgm-collar-size 0 --threads 1 --trans-crop-win 2048 2048 2048 2048\r\n",
"--> Setting number of processing threads to: 1\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap.\r\n",
"Writing log info to: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-2048_2048_2048_2048/2048_2048_2048_2048-log-stereo_corr-02-08-1823-3896.txt\r\n",
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"--> Using LOG pre-processing filter with 1.4 sigma blur.\r\n",
"\r\n",
"[ 2023-Feb-08 18:23:56 ] : Stage 1 --> CORRELATION\r\n",
"--> Full-res search range based on D_sub: (Origin: (-45, 0) width: 0 height: 0)\r\n",
"--------------------------------------------------\r\n",
"Kernel size: Vector2(25,25)\r\n",
"Search range: (Origin: (-45, 0) width: 0 height: 0)\r\n",
"Cost mode: 0\r\n",
"--------------------------------------------------\r\n",
"Writing: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-2048_2048_2048_2048/2048_2048_2048_2048-D.tif\r\n",
"\r\n",
"--> Correlation :[................................................] 0%\r\n",
"--> Correlation :[************....................................] 25%\r\n",
"--> Correlation :[************************........................] 50%\r\n",
"--> Correlation :[************************************............] 75%\r\n",
"--> Correlation :[************************************************] 100%\r\n",
"--> Correlation :[*******************************************] Complete!\r\n",
"\r\n",
"[ 2023-Feb-08 18:24:08 ] : CORRELATION FINISHED\r\n",
"/tmp/sp/bin/stereo_corr: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n",
"stereo_corr: elapsed=0:12.97 ([hours:]minutes:seconds), memory=551916 (kb)\r\n",
"\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using tiles (before collar addition) of 2048 x 2048 pixels.\r\n",
"Using a collar (padding) for each tile of 0 pixels.\r\n",
"/usr/bin/time -f \"stereo_corr: elapsed=%E ([hours:]minutes:seconds), memory=%M (kb)\" /tmp/sp/bin/stereo_corr --bundle-adjust-prefix adjust/ba B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-4096_2048_931_2048/4096_2048_931_2048 --skip-low-res-disparity-comp --corr-seed-mode 1 --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap --sgm-collar-size 0 --threads 1 --trans-crop-win 4096 2048 931 2048\r\n",
"--> Setting number of processing threads to: 1\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap.\r\n",
"Writing log info to: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-4096_2048_931_2048/4096_2048_931_2048-log-stereo_corr-02-08-1824-3979.txt\r\n",
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"--> Using LOG pre-processing filter with 1.4 sigma blur.\r\n",
"\r\n",
"[ 2023-Feb-08 18:24:10 ] : Stage 1 --> CORRELATION\r\n",
"--> Full-res search range based on D_sub: (Origin: (-45, 0) width: 0 height: 0)\r\n",
"--------------------------------------------------\r\n",
"Kernel size: Vector2(25,25)\r\n",
"Search range: (Origin: (-45, 0) width: 0 height: 0)\r\n",
"Cost mode: 0\r\n",
"--------------------------------------------------\r\n",
"Writing: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-4096_2048_931_2048/4096_2048_931_2048-D.tif\r\n",
"\r\n",
"--> Correlation :[................................................] 0%\r\n",
"--> Correlation :[************************........................] 50%\r\n",
"--> Correlation :[************************************************] 100%\r\n",
"--> Correlation :[*******************************************] Complete!\r\n",
"\r\n",
"[ 2023-Feb-08 18:24:21 ] : CORRELATION FINISHED\r\n",
"/tmp/sp/bin/stereo_corr: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n",
"stereo_corr: elapsed=0:11.24 ([hours:]minutes:seconds), memory=415100 (kb)\r\n",
"\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using tiles (before collar addition) of 2048 x 2048 pixels.\r\n",
"Using a collar (padding) for each tile of 0 pixels.\r\n",
"/usr/bin/time -f \"stereo_corr: elapsed=%E ([hours:]minutes:seconds), memory=%M (kb)\" /tmp/sp/bin/stereo_corr --bundle-adjust-prefix adjust/ba B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_4096_2048_2048/0_4096_2048_2048 --skip-low-res-disparity-comp --corr-seed-mode 1 --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap --sgm-collar-size 0 --threads 1 --trans-crop-win 0 4096 2048 2048\r\n",
"--> Setting number of processing threads to: 1\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap.\r\n",
"Writing log info to: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_4096_2048_2048/0_4096_2048_2048-log-stereo_corr-02-08-1824-4063.txt\r\n",
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"--> Using LOG pre-processing filter with 1.4 sigma blur.\r\n",
"\r\n",
"[ 2023-Feb-08 18:24:23 ] : Stage 1 --> CORRELATION\r\n",
"--> Full-res search range based on D_sub: (Origin: (-45, 0) width: 0 height: 0)\r\n",
"--------------------------------------------------\r\n",
"Kernel size: Vector2(25,25)\r\n",
"Search range: (Origin: (-45, 0) width: 0 height: 0)\r\n",
"Cost mode: 0\r\n",
"--------------------------------------------------\r\n",
"Writing: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_4096_2048_2048/0_4096_2048_2048-D.tif\r\n",
"\r\n",
"--> Correlation :[................................................] 0%\r\n",
"--> Correlation :[************....................................] 25%\r\n",
"--> Correlation :[************************........................] 50%\r\n",
"--> Correlation :[************************************............] 75%\r\n",
"--> Correlation :[************************************************] 100%\r\n",
"--> Correlation :[*******************************************] Complete!\r\n",
"\r\n",
"[ 2023-Feb-08 18:24:38 ] : CORRELATION FINISHED\r\n",
"/tmp/sp/bin/stereo_corr: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n",
"stereo_corr: elapsed=0:14.72 ([hours:]minutes:seconds), memory=457756 (kb)\r\n",
"\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using tiles (before collar addition) of 2048 x 2048 pixels.\r\n",
"Using a collar (padding) for each tile of 0 pixels.\r\n",
"/usr/bin/time -f \"stereo_corr: elapsed=%E ([hours:]minutes:seconds), memory=%M (kb)\" /tmp/sp/bin/stereo_corr --bundle-adjust-prefix adjust/ba B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-2048_4096_2048_2048/2048_4096_2048_2048 --skip-low-res-disparity-comp --corr-seed-mode 1 --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap --sgm-collar-size 0 --threads 1 --trans-crop-win 2048 4096 2048 2048\r\n",
"--> Setting number of processing threads to: 1\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap.\r\n",
"Writing log info to: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-2048_4096_2048_2048/2048_4096_2048_2048-log-stereo_corr-02-08-1824-4146.txt\r\n",
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"--> Using LOG pre-processing filter with 1.4 sigma blur.\r\n",
"\r\n",
"[ 2023-Feb-08 18:24:40 ] : Stage 1 --> CORRELATION\r\n",
"--> Full-res search range based on D_sub: (Origin: (-45, 0) width: 0 height: 0)\r\n",
"--------------------------------------------------\r\n",
"Kernel size: Vector2(25,25)\r\n",
"Search range: (Origin: (-45, 0) width: 0 height: 0)\r\n",
"Cost mode: 0\r\n",
"--------------------------------------------------\r\n",
"Writing: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-2048_4096_2048_2048/2048_4096_2048_2048-D.tif\r\n",
"\r\n",
"--> Correlation :[................................................] 0%\r\n",
"--> Correlation :[************....................................] 25%\r\n",
"--> Correlation :[************************........................] 50%\r\n",
"--> Correlation :[************************************............] 75%\r\n",
"--> Correlation :[************************************************] 100%\r\n",
"--> Correlation :[*******************************************] Complete!\r\n",
"\r\n",
"[ 2023-Feb-08 18:24:54 ] : CORRELATION FINISHED\r\n",
"/tmp/sp/bin/stereo_corr: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n",
"stereo_corr: elapsed=0:14.47 ([hours:]minutes:seconds), memory=519496 (kb)\r\n",
"\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using tiles (before collar addition) of 2048 x 2048 pixels.\r\n",
"Using a collar (padding) for each tile of 0 pixels.\r\n",
"/usr/bin/time -f \"stereo_corr: elapsed=%E ([hours:]minutes:seconds), memory=%M (kb)\" /tmp/sp/bin/stereo_corr --bundle-adjust-prefix adjust/ba B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-4096_4096_931_2048/4096_4096_931_2048 --skip-low-res-disparity-comp --corr-seed-mode 1 --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap --sgm-collar-size 0 --threads 1 --trans-crop-win 4096 4096 931 2048\r\n",
"--> Setting number of processing threads to: 1\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap.\r\n",
"Writing log info to: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-4096_4096_931_2048/4096_4096_931_2048-log-stereo_corr-02-08-1824-4234.txt\r\n",
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"--> Using LOG pre-processing filter with 1.4 sigma blur.\r\n",
"\r\n",
"[ 2023-Feb-08 18:24:56 ] : Stage 1 --> CORRELATION\r\n",
"--> Full-res search range based on D_sub: (Origin: (-45, 0) width: 0 height: 0)\r\n",
"--------------------------------------------------\r\n",
"Kernel size: Vector2(25,25)\r\n",
"Search range: (Origin: (-45, 0) width: 0 height: 0)\r\n",
"Cost mode: 0\r\n",
"--------------------------------------------------\r\n",
"Writing: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-4096_4096_931_2048/4096_4096_931_2048-D.tif\r\n",
"\r\n",
"--> Correlation :[................................................] 0%\r\n",
"--> Correlation :[************************........................] 50%\r\n",
"--> Correlation :[************************************************] 100%\r\n",
"--> Correlation :[*******************************************] Complete!\r\n",
"\r\n",
"[ 2023-Feb-08 18:25:05 ] : CORRELATION FINISHED\r\n",
"/tmp/sp/bin/stereo_corr: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n",
"stereo_corr: elapsed=0:09.57 ([hours:]minutes:seconds), memory=385860 (kb)\r\n",
"\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using tiles (before collar addition) of 2048 x 2048 pixels.\r\n",
"Using a collar (padding) for each tile of 0 pixels.\r\n",
"/usr/bin/time -f \"stereo_corr: elapsed=%E ([hours:]minutes:seconds), memory=%M (kb)\" /tmp/sp/bin/stereo_corr --bundle-adjust-prefix adjust/ba B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_6144_2048_25/0_6144_2048_25 --skip-low-res-disparity-comp --corr-seed-mode 1 --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap --sgm-collar-size 0 --threads 1 --trans-crop-win 0 6144 2048 25\r\n",
"--> Setting number of processing threads to: 1\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap.\r\n",
"Writing log info to: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_6144_2048_25/0_6144_2048_25-log-stereo_corr-02-08-1825-4317.txt\r\n",
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"--> Using LOG pre-processing filter with 1.4 sigma blur.\r\n",
"\r\n",
"[ 2023-Feb-08 18:25:08 ] : Stage 1 --> CORRELATION\r\n",
"--> Full-res search range based on D_sub: (Origin: (-45, 0) width: 0 height: 0)\r\n",
"--------------------------------------------------\r\n",
"Kernel size: Vector2(25,25)\r\n",
"Search range: (Origin: (-45, 0) width: 0 height: 0)\r\n",
"Cost mode: 0\r\n",
"--------------------------------------------------\r\n",
"Writing: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_6144_2048_25/0_6144_2048_25-D.tif\r\n",
"\r\n",
"--> Correlation :[................................................] 0%\r\n",
"--> Correlation :[************************........................] 50%\r\n",
"--> Correlation :[************************************************] 100%\r\n",
"--> Correlation :[*******************************************] Complete!\r\n",
"\r\n",
"[ 2023-Feb-08 18:25:13 ] : CORRELATION FINISHED\r\n",
"/tmp/sp/bin/stereo_corr: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n",
"stereo_corr: elapsed=0:05.93 ([hours:]minutes:seconds), memory=269924 (kb)\r\n",
"\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using tiles (before collar addition) of 2048 x 2048 pixels.\r\n",
"Using a collar (padding) for each tile of 0 pixels.\r\n",
"/usr/bin/time -f \"stereo_corr: elapsed=%E ([hours:]minutes:seconds), memory=%M (kb)\" /tmp/sp/bin/stereo_corr --bundle-adjust-prefix adjust/ba B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-2048_6144_2048_25/2048_6144_2048_25 --skip-low-res-disparity-comp --corr-seed-mode 1 --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap --sgm-collar-size 0 --threads 1 --trans-crop-win 2048 6144 2048 25\r\n",
"--> Setting number of processing threads to: 1\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap.\r\n",
"Writing log info to: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-2048_6144_2048_25/2048_6144_2048_25-log-stereo_corr-02-08-1825-4396.txt\r\n",
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"--> Using LOG pre-processing filter with 1.4 sigma blur.\r\n",
"\r\n",
"[ 2023-Feb-08 18:25:15 ] : Stage 1 --> CORRELATION\r\n",
"--> Full-res search range based on D_sub: (Origin: (-45, 0) width: 0 height: 0)\r\n",
"--------------------------------------------------\r\n",
"Kernel size: Vector2(25,25)\r\n",
"Search range: (Origin: (-45, 0) width: 0 height: 0)\r\n",
"Cost mode: 0\r\n",
"--------------------------------------------------\r\n",
"Writing: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-2048_6144_2048_25/2048_6144_2048_25-D.tif\r\n",
"\r\n",
"--> Correlation :[................................................] 0%\r\n",
"--> Correlation :[************************........................] 50%\r\n",
"--> Correlation :[************************************************] 100%\r\n",
"--> Correlation :[*******************************************] Complete!\r\n",
"\r\n",
"[ 2023-Feb-08 18:25:21 ] : CORRELATION FINISHED\r\n",
"/tmp/sp/bin/stereo_corr: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n",
"stereo_corr: elapsed=0:05.67 ([hours:]minutes:seconds), memory=286560 (kb)\r\n",
"\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using tiles (before collar addition) of 2048 x 2048 pixels.\r\n",
"Using a collar (padding) for each tile of 0 pixels.\r\n",
"/usr/bin/time -f \"stereo_corr: elapsed=%E ([hours:]minutes:seconds), memory=%M (kb)\" /tmp/sp/bin/stereo_corr --bundle-adjust-prefix adjust/ba B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-4096_6144_931_25/4096_6144_931_25 --skip-low-res-disparity-comp --corr-seed-mode 1 --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap --sgm-collar-size 0 --threads 1 --trans-crop-win 4096 6144 931 25\r\n",
"--> Setting number of processing threads to: 1\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap.\r\n",
"Writing log info to: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-4096_6144_931_25/4096_6144_931_25-log-stereo_corr-02-08-1825-4480.txt\r\n",
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"--> Using LOG pre-processing filter with 1.4 sigma blur.\r\n",
"\r\n",
"[ 2023-Feb-08 18:25:23 ] : Stage 1 --> CORRELATION\r\n",
"--> Full-res search range based on D_sub: (Origin: (-45, 0) width: 0 height: 0)\r\n",
"--------------------------------------------------\r\n",
"Kernel size: Vector2(25,25)\r\n",
"Search range: (Origin: (-45, 0) width: 0 height: 0)\r\n",
"Cost mode: 0\r\n",
"--------------------------------------------------\r\n",
"Writing: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-4096_6144_931_25/4096_6144_931_25-D.tif\r\n",
"\r\n",
"--> Correlation :[................................................] 0%\r\n",
"--> Correlation :[*******************************************] Complete!\r\n",
"\r\n",
"[ 2023-Feb-08 18:25:28 ] : CORRELATION FINISHED\r\n",
"/tmp/sp/bin/stereo_corr: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n",
"stereo_corr: elapsed=0:05.81 ([hours:]minutes:seconds), memory=204704 (kb)\r\n",
"\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using tiles (before collar addition) of 2048 x 2048 pixels.\r\n",
"Using a collar (padding) for each tile of 0 pixels.\r\n",
"/usr/bin/time -f \"stereo_rfne: elapsed=%E ([hours:]minutes:seconds), memory=%M (kb)\" /tmp/sp/bin/stereo_rfne --bundle-adjust-prefix adjust/ba B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_0_2048_2048/0_0_2048_2048 --corr-seed-mode 1 --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap --sgm-collar-size 0 --threads 1 --trans-crop-win 0 0 2048 2048\r\n",
"\r\n",
"[ 2023-Feb-08 18:25:30 ] : Stage 2 --> REFINEMENT\r\n",
"--> Setting number of processing threads to: 1\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap.\r\n",
"Writing log info to: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_0_2048_2048/0_0_2048_2048-log-stereo_rfne-02-08-1825-4570.txt\r\n",
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"Left image nodata: -32768\r\n",
"Right image nodata: -32768\r\n",
"--> Using LOG pre-processing filter with 1.4 sigma blur.\r\n",
"--> Using parabola subpixel mode.\r\n",
"Writing: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_0_2048_2048/0_0_2048_2048-RD.tif\r\n",
"\r\n",
"--> Refinement :[.................................................] 0%\r\n",
"--> Refinement :[.................................................] 2%\r\n",
"--> Refinement :[*................................................] 3%\r\n",
"--> Refinement :[**...............................................] 5%\r\n",
"--> Refinement :[***..............................................] 6%\r\n",
"--> Refinement :[***..............................................] 8%\r\n",
"--> Refinement :[****.............................................] 9%\r\n",
"--> Refinement :[*****............................................] 11%\r\n",
"--> Refinement :[******...........................................] 12%\r\n",
"--> Refinement :[******...........................................] 14%\r\n",
"--> Refinement :[*******..........................................] 16%\r\n",
"--> Refinement :[********.........................................] 17%\r\n",
"--> Refinement :[*********........................................] 19%\r\n",
"--> Refinement :[*********........................................] 20%\r\n",
"--> Refinement :[**********.......................................] 22%\r\n",
"--> Refinement :[***********......................................] 23%\r\n",
"--> Refinement :[************.....................................] 25%\r\n",
"--> Refinement :[*************....................................] 27%\r\n",
"--> Refinement :[*************....................................] 28%\r\n",
"--> Refinement :[**************...................................] 30%\r\n",
"--> Refinement :[***************..................................] 31%\r\n",
"--> Refinement :[****************.................................] 33%\r\n",
"--> Refinement :[****************.................................] 34%\r\n",
"--> Refinement :[*****************................................] 36%\r\n",
"--> Refinement :[******************...............................] 38%\r\n",
"--> Refinement :[*******************..............................] 39%\r\n",
"--> Refinement :[*******************..............................] 41%\r\n",
"--> Refinement :[********************.............................] 42%\r\n",
"--> Refinement :[*********************............................] 44%\r\n",
"--> Refinement :[**********************...........................] 45%\r\n",
"--> Refinement :[**********************...........................] 47%\r\n",
"--> Refinement :[***********************..........................] 48%\r\n",
"--> Refinement :[************************.........................] 50%\r\n",
"--> Refinement :[*************************........................] 52%\r\n",
"--> Refinement :[**************************.......................] 53%\r\n",
"--> Refinement :[**************************.......................] 55%\r\n",
"--> Refinement :[***************************......................] 56%\r\n",
"--> Refinement :[****************************.....................] 58%\r\n",
"--> Refinement :[*****************************....................] 59%\r\n",
"--> Refinement :[*****************************....................] 61%\r\n",
"--> Refinement :[******************************...................] 62%\r\n",
"--> Refinement :[*******************************..................] 64%\r\n",
"--> Refinement :[********************************.................] 66%\r\n",
"--> Refinement :[********************************.................] 67%\r\n",
"--> Refinement :[*********************************................] 69%\r\n",
"--> Refinement :[**********************************...............] 70%\r\n",
"--> Refinement :[***********************************..............] 72%\r\n",
"--> Refinement :[***********************************..............] 73%\r\n",
"--> Refinement :[************************************.............] 75%\r\n",
"--> Refinement :[*************************************............] 77%\r\n",
"--> Refinement :[**************************************...........] 78%\r\n",
"--> Refinement :[***************************************..........] 80%\r\n",
"--> Refinement :[***************************************..........] 81%\r\n",
"--> Refinement :[****************************************.........] 83%\r\n",
"--> Refinement :[*****************************************........] 84%\r\n",
"--> Refinement :[******************************************.......] 86%\r\n",
"--> Refinement :[******************************************.......] 88%\r\n",
"--> Refinement :[*******************************************......] 89%\r\n",
"--> Refinement :[********************************************.....] 91%\r\n",
"--> Refinement :[*********************************************....] 92%\r\n",
"--> Refinement :[*********************************************....] 94%\r\n",
"--> Refinement :[**********************************************...] 95%\r\n",
"--> Refinement :[***********************************************..] 97%\r\n",
"--> Refinement :[************************************************.] 98%\r\n",
"--> Refinement :[*************************************************] 100%\r\n",
"--> Refinement :[********************************************] Complete!\r\n",
"\r\n",
"[ 2023-Feb-08 18:25:34 ] : REFINEMENT FINISHED\r\n",
"/tmp/sp/bin/stereo_rfne: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n",
"stereo_rfne: elapsed=0:04.59 ([hours:]minutes:seconds), memory=384428 (kb)\r\n",
"\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using tiles (before collar addition) of 2048 x 2048 pixels.\r\n",
"Using a collar (padding) for each tile of 0 pixels.\r\n",
"/usr/bin/time -f \"stereo_rfne: elapsed=%E ([hours:]minutes:seconds), memory=%M (kb)\" /tmp/sp/bin/stereo_rfne --bundle-adjust-prefix adjust/ba B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-2048_0_2048_2048/2048_0_2048_2048 --corr-seed-mode 1 --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap --sgm-collar-size 0 --threads 1 --trans-crop-win 2048 0 2048 2048\r\n",
"\r\n",
"[ 2023-Feb-08 18:25:36 ] : Stage 2 --> REFINEMENT\r\n",
"--> Setting number of processing threads to: 1\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap.\r\n",
"Writing log info to: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-2048_0_2048_2048/2048_0_2048_2048-log-stereo_rfne-02-08-1825-4709.txt\r\n",
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"Left image nodata: -32768\r\n",
"Right image nodata: -32768\r\n",
"--> Using LOG pre-processing filter with 1.4 sigma blur.\r\n",
"--> Using parabola subpixel mode.\r\n",
"Writing: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-2048_0_2048_2048/2048_0_2048_2048-RD.tif\r\n",
"\r\n",
"--> Refinement :[.................................................] 0%\r\n",
"--> Refinement :[..........................."
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"......................] 2%\r\n",
"--> Refinement :[*................................................] 3%\r\n",
"--> Refinement :[**...............................................] 5%\r\n",
"--> Refinement :[***..............................................] 6%\r\n",
"--> Refinement :[***..............................................] 8%\r\n",
"--> Refinement :[****.............................................] 9%\r\n",
"--> Refinement :[*****............................................] 11%\r\n",
"--> Refinement :[******...........................................] 12%\r\n",
"--> Refinement :[******...........................................] 14%\r\n",
"--> Refinement :[*******..........................................] 16%\r\n",
"--> Refinement :[********.........................................] 17%\r\n",
"--> Refinement :[*********........................................] 19%\r\n",
"--> Refinement :[*********........................................] 20%\r\n",
"--> Refinement :[**********.......................................] 22%\r\n",
"--> Refinement :[***********......................................] 23%\r\n",
"--> Refinement :[************.....................................] 25%\r\n",
"--> Refinement :[*************....................................] 27%\r\n",
"--> Refinement :[*************....................................] 28%\r\n",
"--> Refinement :[**************...................................] 30%\r\n",
"--> Refinement :[***************..................................] 31%\r\n",
"--> Refinement :[****************.................................] 33%\r\n",
"--> Refinement :[****************.................................] 34%\r\n",
"--> Refinement :[*****************................................] 36%\r\n",
"--> Refinement :[******************...............................] 38%\r\n",
"--> Refinement :[*******************..............................] 39%\r\n",
"--> Refinement :[*******************..............................] 41%\r\n",
"--> Refinement :[********************.............................] 42%\r\n",
"--> Refinement :[*********************............................] 44%\r\n",
"--> Refinement :[**********************...........................] 45%\r\n",
"--> Refinement :[**********************...........................] 47%\r\n",
"--> Refinement :[***********************..........................] 48%\r\n",
"--> Refinement :[************************.........................] 50%\r\n",
"--> Refinement :[*************************........................] 52%\r\n",
"--> Refinement :[**************************.......................] 53%\r\n",
"--> Refinement :[**************************.......................] 55%\r\n",
"--> Refinement :[***************************......................] 56%\r\n",
"--> Refinement :[****************************.....................] 58%\r\n",
"--> Refinement :[*****************************....................] 59%\r\n",
"--> Refinement :[*****************************....................] 61%\r\n",
"--> Refinement :[******************************...................] 62%\r\n",
"--> Refinement :[*******************************..................] 64%\r\n",
"--> Refinement :[********************************.................] 66%\r\n",
"--> Refinement :[********************************.................] 67%\r\n",
"--> Refinement :[*********************************................] 69%\r\n",
"--> Refinement :[**********************************...............] 70%\r\n",
"--> Refinement :[***********************************..............] 72%\r\n",
"--> Refinement :[***********************************..............] 73%\r\n",
"--> Refinement :[************************************.............] 75%\r\n",
"--> Refinement :[*************************************............] 77%\r\n",
"--> Refinement :[**************************************...........] 78%\r\n",
"--> Refinement :[***************************************..........] 80%\r\n",
"--> Refinement :[***************************************..........] 81%\r\n",
"--> Refinement :[****************************************.........] 83%\r\n",
"--> Refinement :[*****************************************........] 84%\r\n",
"--> Refinement :[******************************************.......] 86%\r\n",
"--> Refinement :[******************************************.......] 88%\r\n",
"--> Refinement :[*******************************************......] 89%\r\n",
"--> Refinement :[********************************************.....] 91%\r\n",
"--> Refinement :[*********************************************....] 92%\r\n",
"--> Refinement :[*********************************************....] 94%\r\n",
"--> Refinement :[**********************************************...] 95%\r\n",
"--> Refinement :[***********************************************..] 97%\r\n",
"--> Refinement :[************************************************.] 98%\r\n",
"--> Refinement :[*************************************************] 100%\r\n",
"--> Refinement :[********************************************] Complete!\r\n",
"\r\n",
"[ 2023-Feb-08 18:25:40 ] : REFINEMENT FINISHED\r\n",
"/tmp/sp/bin/stereo_rfne: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n",
"stereo_rfne: elapsed=0:04.61 ([hours:]minutes:seconds), memory=393688 (kb)\r\n",
"\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using tiles (before collar addition) of 2048 x 2048 pixels.\r\n",
"Using a collar (padding) for each tile of 0 pixels.\r\n",
"/usr/bin/time -f \"stereo_rfne: elapsed=%E ([hours:]minutes:seconds), memory=%M (kb)\" /tmp/sp/bin/stereo_rfne --bundle-adjust-prefix adjust/ba B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-4096_0_931_2048/4096_0_931_2048 --corr-seed-mode 1 --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap --sgm-collar-size 0 --threads 1 --trans-crop-win 4096 0 931 2048\r\n",
"\r\n",
"[ 2023-Feb-08 18:25:43 ] : Stage 2 --> REFINEMENT\r\n",
"--> Setting number of processing threads to: 1\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap.\r\n",
"Writing log info to: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-4096_0_931_2048/4096_0_931_2048-log-stereo_rfne-02-08-1825-4852.txt\r\n",
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"Left image nodata: -32768\r\n",
"Right image nodata: -32768\r\n",
"--> Using LOG pre-processing filter with 1.4 sigma blur.\r\n",
"--> Using parabola subpixel mode.\r\n",
"Writing: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-4096_0_931_2048/4096_0_931_2048-RD.tif\r\n",
"\r\n",
"--> Refinement :[.................................................] 0%\r\n",
"--> Refinement :[*................................................] 3%\r\n",
"--> Refinement :[***..............................................] 6%\r\n",
"--> Refinement :[****.............................................] 9%\r\n",
"--> Refinement :[******...........................................] 12%\r\n",
"--> Refinement :[*******..........................................] 16%\r\n",
"--> Refinement :[*********........................................] 19%\r\n",
"--> Refinement :[**********.......................................] 22%\r\n",
"--> Refinement :[************.....................................] 25%\r\n",
"--> Refinement :[*************....................................] 28%\r\n",
"--> Refinement :[***************..................................] 31%\r\n",
"--> Refinement :[****************.................................] 34%\r\n",
"--> Refinement :[******************...............................] 38%\r\n",
"--> Refinement :[*******************..............................] 41%\r\n",
"--> Refinement :[*********************............................] 44%\r\n",
"--> Refinement :[**********************...........................] 47%\r\n",
"--> Refinement :[************************.........................] 50%\r\n",
"--> Refinement :[**************************.......................] 53%\r\n",
"--> Refinement :[***************************......................] 56%\r\n",
"--> Refinement :[*****************************....................] 59%\r\n",
"--> Refinement :[******************************...................] 62%\r\n",
"--> Refinement :[********************************.................] 66%\r\n",
"--> Refinement :[*********************************................] 69%\r\n",
"--> Refinement :[***********************************..............] 72%\r\n",
"--> Refinement :[************************************.............] 75%\r\n",
"--> Refinement :[**************************************...........] 78%\r\n",
"--> Refinement :[***************************************..........] 81%\r\n",
"--> Refinement :[*****************************************........] 84%\r\n",
"--> Refinement :[******************************************.......] 88%\r\n",
"--> Refinement :[********************************************.....] 91%\r\n",
"--> Refinement :[*********************************************....] 94%\r\n",
"--> Refinement :[***********************************************..] 97%\r\n",
"--> Refinement :[*************************************************] 100%\r\n",
"--> Refinement :[********************************************] Complete!\r\n",
"\r\n",
"[ 2023-Feb-08 18:25:45 ] : REFINEMENT FINISHED\r\n",
"/tmp/sp/bin/stereo_rfne: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n",
"stereo_rfne: elapsed=0:02.43 ([hours:]minutes:seconds), memory=265416 (kb)\r\n",
"\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using tiles (before collar addition) of 2048 x 2048 pixels.\r\n",
"Using a collar (padding) for each tile of 0 pixels.\r\n",
"/usr/bin/time -f \"stereo_rfne: elapsed=%E ([hours:]minutes:seconds), memory=%M (kb)\" /tmp/sp/bin/stereo_rfne --bundle-adjust-prefix adjust/ba B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_2048_2048_2048/0_2048_2048_2048 --corr-seed-mode 1 --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap --sgm-collar-size 0 --threads 1 --trans-crop-win 0 2048 2048 2048\r\n",
"\r\n",
"[ 2023-Feb-08 18:25:47 ] : Stage 2 --> REFINEMENT\r\n",
"--> Setting number of processing threads to: 1\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap.\r\n",
"Writing log info to: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_2048_2048_2048/0_2048_2048_2048-log-stereo_rfne-02-08-1825-4961.txt\r\n",
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"Left image nodata: -32768\r\n",
"Right image nodata: -32768\r\n",
"--> Using LOG pre-processing filter with 1.4 sigma blur.\r\n",
"--> Using parabola subpixel mode.\r\n",
"Writing: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_2048_2048_2048/0_2048_2048_2048-RD.tif\r\n",
"\r\n",
"--> Refinement :[.................................................] 0%\r\n",
"--> Refinement :[.................................................] 2%\r\n",
"--> Refinement :[*................................................] 3%\r\n",
"--> Refinement :[**...............................................] 5%\r\n",
"--> Refinement :[***..............................................] 6%\r\n",
"--> Refinement :[***..............................................] 8%\r\n",
"--> Refinement :[****.............................................] 9%\r\n",
"--> Refinement :[*****............................................] 11%\r\n",
"--> Refinement :[******...........................................] 12%\r\n",
"--> Refinement :[******...........................................] 14%\r\n",
"--> Refinement :[*******..........................................] 16%\r\n",
"--> Refinement :[********.........................................] 17%\r\n",
"--> Refinement :[*********........................................] 19%\r\n",
"--> Refinement :[*********........................................] 20%\r\n",
"--> Refinement :[**********.......................................] 22%\r\n",
"--> Refinement :[***********......................................] 23%\r\n",
"--> Refinement :[************.....................................] 25%\r\n",
"--> Refinement :[*************....................................] 27%\r\n",
"--> Refinement :[*************....................................] 28%\r\n",
"--> Refinement :[**************...................................] 30%\r\n",
"--> Refinement :[***************..................................] 31%\r\n",
"--> Refinement :[****************.................................] 33%\r\n",
"--> Refinement :[****************.................................] 34%\r\n",
"--> Refinement :[*****************................................] 36%\r\n",
"--> Refinement :[******************...............................] 38%\r\n",
"--> Refinement :[*******************..............................] 39%\r\n",
"--> Refinement :[*******************..............................] 41%\r\n",
"--> Refinement :[********************.............................] 42%\r\n",
"--> Refinement :[*********************............................] 44%\r\n",
"--> Refinement :[**********************...........................] 45%\r\n",
"--> Refinement :[**********************...........................] 47%\r\n",
"--> Refinement :[***********************..........................] 48%\r\n",
"--> Refinement :[************************.........................] 50%\r\n",
"--> Refinement :[*************************........................] 52%\r\n",
"--> Refinement :[**************************.......................] 53%\r\n",
"--> Refinement :[**************************.......................] 55%\r\n",
"--> Refinement :[***************************......................] 56%\r\n",
"--> Refinement :[****************************.....................] 58%\r\n",
"--> Refinement :[*****************************....................] 59%\r\n",
"--> Refinement :[*****************************....................] 61%\r\n",
"--> Refinement :[******************************...................] 62%\r\n",
"--> Refinement :[*******************************..................] 64%\r\n",
"--> Refinement :[********************************.................] 66%\r\n",
"--> Refinement :[********************************.................] 67%\r\n",
"--> Refinement :[*********************************................] 69%\r\n",
"--> Refinement :[**********************************...............] 70%\r\n",
"--> Refinement :[***********************************..............] 72%\r\n",
"--> Refinement :[***********************************..............] 73%\r\n",
"--> Refinement :[************************************.............] 75%\r\n",
"--> Refinement :[*************************************............] 77%\r\n",
"--> Refinement :[**************************************...........] 78%\r\n",
"--> Refinement :[***************************************..........] 80%\r\n",
"--> Refinement :[***************************************..........] 81%\r\n",
"--> Refinement :[****************************************.........] 83%\r\n",
"--> Refinement :[*****************************************........] 84%\r\n",
"--> Refinement :[******************************************.......] 86%\r\n",
"--> Refinement :[******************************************.......] 88%\r\n",
"--> Refinement :[*******************************************......] 89%\r\n",
"--> Refinement :[********************************************.....] 91%\r\n",
"--> Refinement :[*********************************************....] 92%\r\n",
"--> Refinement :[*********************************************....] 94%\r\n",
"--> Refinement :[**********************************************...] 95%\r\n",
"--> Refinement :[***********************************************..] 97%\r\n",
"--> Refinement :[************************************************.] 98%\r\n",
"--> Refinement :[*************************************************] 100%\r\n",
"--> Refinement :[********************************************] Complete!\r\n",
"\r\n",
"[ 2023-Feb-08 18:25:51 ] : REFINEMENT FINISHED\r\n",
"/tmp/sp/bin/stereo_rfne: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n",
"stereo_rfne: elapsed=0:04.06 ([hours:]minutes:seconds), memory=393068 (kb)\r\n",
"\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using tiles (before collar addition) of 2048 x 2048 pixels.\r\n",
"Using a collar (padding) for each tile of 0 pixels.\r\n",
"/usr/bin/time -f \"stereo_rfne: elapsed=%E ([hours:]minutes:seconds), memory=%M (kb)\" /tmp/sp/bin/stereo_rfne --bundle-adjust-prefix adjust/ba B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-2048_2048_2048_2048/2048_2048_2048_2048 --corr-seed-mode 1 --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap --sgm-collar-size 0 --threads 1 --trans-crop-win 2048 2048 2048 2048\r\n",
"\r\n",
"[ 2023-Feb-08 18:25:53 ] : Stage 2 --> REFINEMENT\r\n",
"--> Setting number of processing threads to: 1\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap.\r\n",
"Writing log info to: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-2048_2048_2048_2048/2048_2048_2048_2048-log-stereo_rfne-02-08-1825-5103.txt\r\n",
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"Left image nodata: -32768\r\n",
"Right image nodata: -32768\r\n",
"--> Using LOG pre-processing filter with 1.4 sigma blur.\r\n",
"--> Using parabola subpixel mode.\r\n",
"Writing: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-2048_2048_2048_2048/2048_2048_2048_2048-RD.tif\r\n",
"\r\n",
"--> Refinement :[.................................................] 0%\r\n",
"--> Refinement :[.................................................] 2%\r\n",
"--> Refinement :[*................................................] 3%\r\n",
"--> Refinement :[**...............................................] 5%\r\n",
"--> Refinement :[***..............................................] 6%\r\n",
"--> Refinement :[***..............................................] 8%\r\n",
"--> Refinement :[****.............................................] 9%\r\n",
"--> Refinement :[*****............................................] 11%\r\n",
"--> Refinement :[******...........................................] 12%\r\n",
"--> Refinement :[******...........................................] 14%\r\n",
"--> Refinement :[*******..........................................] 16%\r\n",
"--> Refinement :[********.........................................] 17%\r\n",
"--> Refinement :[*********........................................] 19%\r\n",
"--> Refinement :[*********........................................] 20%\r\n",
"--> Refinement :[**********.......................................] 22%\r\n",
"--> Refinement :[***********......................................] 23%\r\n",
"--> Refinement :[************.....................................] 25%\r\n",
"--> Refinement :[*************....................................] 27%\r\n",
"--> Refinement :[*************....................................] 28%\r\n",
"--> Refinement :[**************...................................] 30%\r\n",
"--> Refinement :[***************..................................] 31%\r\n",
"--> Refinement :[****************.................................] 33%\r\n",
"--> Refinement :[****************.................................] 34%\r\n",
"--> Refinement :[*****************................................] 36%\r\n",
"--> Refinement :[******************...............................] 38%\r\n",
"--> Refinement :[*******************..............................] 39%\r\n",
"--> Refinement :[*******************..............................] 41%\r\n",
"--> Refinement :[********************.............................] 42%\r\n",
"--> Refinement :[*********************............................] 44%\r\n",
"--> Refinement :[**********************...........................] 45%\r\n",
"--> Refinement :[**********************...........................] 47%\r\n",
"--> Refinement :[***********************..........................] 48%\r\n",
"--> Refinement :[************************.........................] 50%\r\n",
"--> Refinement :[*************************........................] 52%\r\n",
"--> Refinement :[**************************.......................] 53%\r\n",
"--> Refinement :[**************************.......................] 55%\r\n",
"--> Refinement :[***************************......................] 56%\r\n",
"--> Refinement :[****************************.....................] 58%\r\n",
"--> Refinement :[*****************************....................] 59%\r\n",
"--> Refinement :[*****************************....................] 61%\r\n",
"--> Refinement :[******************************...................] 62%\r\n",
"--> Refinement :[*******************************..................] 64%\r\n",
"--> Refinement :[********************************.................] 66%\r\n",
"--> Refinement :[********************************.................] 67%\r\n",
"--> Refinement :[*********************************................] 69%\r\n",
"--> Refinement :[**********************************...............] 70%\r\n",
"--> Refinement :[***********************************..............] 72%\r\n",
"--> Refinement :[***********************************..............] 73%\r\n",
"--> Refinement :[************************************.............] 75%\r\n",
"--> Refinement :[*************************************............] 77%\r\n",
"--> Refinement :[**************************************...........] 78%\r\n",
"--> Refinement :[***************************************..........] 80%\r\n",
"--> Refinement :[***************************************..........] 81%\r\n",
"--> Refinement :[****************************************.........] 83%\r\n",
"--> Refinement :[*****************************************........] 84%\r\n",
"--> Refinement :[******************************************.......] 86%\r\n",
"--> Refinement :[******************************************.......] 88%\r\n",
"--> Refinement :[*******************************************......] 89%\r\n",
"--> Refinement :[********************************************.....] 91%\r\n",
"--> Refinement :[*********************************************....] 92%\r\n",
"--> Refinement :[*********************************************....] 94%\r\n",
"--> Refinement :[**********************************************...] 95%\r\n",
"--> Refinement :[***********************************************..] 97%\r\n",
"--> Refinement :[************************************************.] 98%\r\n",
"--> Refinement :[*************************************************] 100%\r\n",
"--> Refinement :[********************************************] Complete!\r\n",
"\r\n",
"[ 2023-Feb-08 18:25:56 ] : REFINEMENT FINISHED\r\n",
"/tmp/sp/bin/stereo_rfne: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n",
"stereo_rfne: elapsed=0:03.68 ([hours:]minutes:seconds), memory=401888 (kb)\r\n",
"\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using tiles (before collar addition) of 2048 x 2048 pixels.\r\n",
"Using a collar (padding) for each tile of 0 pixels.\r\n",
"/usr/bin/time -f \"stereo_rfne: elapsed=%E ([hours:]minutes:seconds), memory=%M (kb)\" /tmp/sp/bin/stereo_rfne --bundle-adjust-prefix adjust/ba B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-4096_2048_931_2048/4096_2048_931_2048 --corr-seed-mode 1 --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap --sgm-collar-size 0 --threads 1 --trans-crop-win 4096 2048 931 2048\r\n",
"\r\n",
"[ 2023-Feb-08 18:25:58 ] : Stage 2 --> REFINEMENT\r\n",
"--> Setting number of processing threads to: 1\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap.\r\n",
"Writing log info to: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-4096_2048_931_2048/4096_2048_931_2048-log-stereo_rfne-02-08-1825-5243.txt\r\n",
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"Left image nodata: -32768\r\n",
"Right image nodata: -32768\r\n",
"--> Using LOG pre-processing filter with 1.4 sigma blur.\r\n",
"--> Using parabola subpixel mode.\r\n",
"Writing: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-4096_2048_931_2048/4096_2048_931_2048-RD.tif\r\n",
"\r\n",
"--> Refinement :[.................................................] 0%\r\n",
"--> Refinement :[*................................................] 3%\r\n",
"--> Refinement :[***..............................................] 6%\r\n",
"--> Refinement :[****.............................................] 9%\r\n",
"--> Refinement :[******...........................................] 12%\r\n",
"--> Refinement :[*******..........................................] 16%\r\n",
"--> Refinement :[*********........................................] 19%\r\n",
"--> Refinement :[**********.......................................] 22%\r\n",
"--> Refinement :[************.....................................] 25%\r\n",
"--> Refinement :[*************....................................] 28%\r\n",
"--> Refinement :[***************..................................] 31%\r\n",
"--> Refinement :[****************.................................] 34%\r\n",
"--> Refinement :[******************...............................] 38%\r\n",
"--> Refinement :[*******************..............................] 41%\r\n",
"--> Refinement :[*********************............................] 44%\r\n",
"--> Refinement :[**********************...........................] 47%\r\n",
"--> Refinement :[************************.........................] 50%\r\n",
"--> Refinement :[**************************.......................] 53%\r\n",
"--> Refinement :[***************************......................] 56%\r\n",
"--> Refinement :[*****************************....................] 59%\r\n",
"--> Refinement :[******************************...................] 62%\r\n",
"--> Refinement :[********************************.................] 66%\r\n",
"--> Refinement :[*********************************................] 69%\r\n",
"--> Refinement :[***********************************..............] 72%\r\n",
"--> Refinement :[************************************.............] 75%\r\n",
"--> Refinement :[**************************************...........] 78%\r\n",
"--> Refinement :[***************************************..........] 81%\r\n",
"--> Refinement :[*****************************************........] 84%\r\n",
"--> Refinement :[******************************************.......] 88%\r\n",
"--> Refinement :[********************************************.....] 91%\r\n",
"--> Refinement :[*********************************************....] 94%\r\n",
"--> Refinement :[***********************************************..] 97%\r\n",
"--> Refinement :[*************************************************] 100%\r\n",
"--> Refinement :[********************************************] Complete!\r\n",
"\r\n",
"[ 2023-Feb-08 18:26:00 ] : REFINEMENT FINISHED\r\n",
"/tmp/sp/bin/stereo_rfne: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n",
"stereo_rfne: elapsed=0:02.36 ([hours:]minutes:seconds), memory=269872 (kb)\r\n",
"\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using tiles (before collar addition) of 2048 x 2048 pixels.\r\n",
"Using a collar (padding) for each tile of 0 pixels.\r\n",
"/usr/bin/time -f \"stereo_rfne: elapsed=%E ([hours:]minutes:seconds), memory=%M (kb)\" /tmp/sp/bin/stereo_rfne --bundle-adjust-prefix adjust/ba B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_4096_2048_2048/0_4096_2048_2048 --corr-seed-mode 1 --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap --sgm-collar-size 0 --threads 1 --trans-crop-win 0 4096 2048 2048\r\n",
"\r\n",
"[ 2023-Feb-08 18:26:02 ] : Stage 2 --> REFINEMENT\r\n",
"--> Setting number of processing threads to: 1\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap.\r\n",
"Writing log info to: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_4096_2048_2048/0_4096_2048_2048-log-stereo_rfne-02-08-1826-5350.txt\r\n",
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"Left image nodata: -32768\r\n",
"Right image nodata: -32768\r\n",
"--> Using LOG pre-processing filter with 1.4 sigma blur.\r\n",
"--> Using parabola subpixel mode.\r\n",
"Writing: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_4096_2048_2048/0_4096_2048_2048-RD.tif\r\n",
"\r\n",
"--> Refinement :[.................................................] 0%\r\n",
"--> Refinement :[.................................................] 2%\r\n",
"--> Refinement :[*................................................] 3%\r\n",
"--> Refinement :[**...............................................] 5%\r\n",
"--> Refinement :[***..............................................] 6%\r\n",
"--> Refinement :[***..............................................] 8%\r\n",
"--> Refinement :[****.............................................] 9%\r\n",
"--> Refinement :[*****............................................] 11%\r\n",
"--> Refinement :[******...........................................] 12%\r\n",
"--> Refinement :[******...........................................] 14%\r\n",
"--> Refinement :[*******..........................................] 16%\r\n",
"--> Refinement :[********.........................................] 17%\r\n",
"--> Refinement :[*********........................................] 19%\r\n",
"--> Refinement :[*********........................................] 20%\r\n",
"--> Refinement :[**********.......................................] 22%\r\n",
"--> Refinement :[***********......................................] 23%\r\n",
"--> Refinement :[************.....................................] 25%\r\n",
"--> Refinement :[*************....................................] 27%\r\n",
"--> Refinement :[*************....................................] 28%\r\n",
"--> Refinement :[**************...................................] 30%\r\n",
"--> Refinement :[***************..................................] 31%\r\n",
"--> Refinement :[****************.................................] 33%\r\n",
"--> Refinement :[****************.................................] 34%\r\n",
"--> Refinement :[*****************................................] 36%\r\n",
"--> Refinement :[******************...............................] 38%\r\n",
"--> Refinement :[*******************..............................] 39%\r\n",
"--> Refinement :[*******************..............................] 41%\r\n",
"--> Refinement :[********************.............................] 42%\r\n",
"--> Refinement :[*********************............................] 44%\r\n",
"--> Refinement :[**********************...........................] 45%\r\n",
"--> Refinement :[**********************...........................] 47%\r\n",
"--> Refinement :[***********************..........................] 48%\r\n",
"--> Refinement :[************************.........................] 50%\r\n",
"--> Refinement :[*************************........................] 52%\r\n",
"--> Refinement :[**************************.......................] 53%\r\n",
"--> Refinement :[**************************.......................] 55%\r\n",
"--> Refinement :[***************************......................] 56%\r\n",
"--> Refinement :[****************************.....................] 58%\r\n",
"--> Refinement :[*****************************....................] 59%\r\n",
"--> Refinement :[*****************************....................] 61%\r\n",
"--> Refinement :[******************************...................] 62%\r\n",
"--> Refinement :[*******************************..................] 64%\r\n",
"--> Refinement :[********************************.................] 66%\r\n",
"--> Refinement :[********************************.................] 67%\r\n",
"--> Refinement :[*********************************................] 69%\r\n",
"--> Refinement :[**********************************...............] 70%\r\n",
"--> Refinement :[***********************************..............] 72%\r\n",
"--> Refinement :[***********************************..............] 73%\r\n",
"--> Refinement :[************************************.............] 75%\r\n",
"--> Refinement :[*************************************............] 77%\r\n",
"--> Refinement :[**************************************...........] 78%\r\n",
"--> Refinement :[***************************************..........] 80%\r\n",
"--> Refinement :[***************************************..........] 81%\r\n",
"--> Refinement :[****************************************.........] 83%\r\n",
"--> Refinement :[*****************************************........] 84%\r\n",
"--> Refinement :[******************************************.......] 86%\r\n",
"--> Refinement :[******************************************.......] 88%\r\n",
"--> Refinement :[*******************************************......] 89%\r\n",
"--> Refinement :[********************************************.....] 91%\r\n",
"--> Refinement :[*********************************************....] 92%\r\n",
"--> Refinement :[*********************************************....] 94%\r\n",
"--> Refinement :[**********************************************...] 95%\r\n",
"--> Refinement :[***********************************************..] 97%\r\n",
"--> Refinement :[************************************************.] 98%\r\n",
"--> Refinement :[*************************************************] 100%\r\n",
"--> Refinement :[********************************************] Complete!\r\n",
"\r\n",
"[ 2023-Feb-08 18:26:06 ] : REFINEMENT FINISHED\r\n",
"/tmp/sp/bin/stereo_rfne: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n",
"stereo_rfne: elapsed=0:04.01 ([hours:]minutes:seconds), memory=386884 (kb)\r\n",
"\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using tiles (before collar addition) of 2048 x 2048 pixels.\r\n",
"Using a collar (padding) for each tile of 0 pixels.\r\n",
"/usr/bin/time -f \"stereo_rfne: elapsed=%E ([hours:]minutes:seconds), memory=%M (kb)\" /tmp/sp/bin/stereo_rfne --bundle-adjust-prefix adjust/ba B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-2048_4096_2048_2048/2048_4096_2048_2048 --corr-seed-mode 1 --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap --sgm-collar-size 0 --threads 1 --trans-crop-win 2048 4096 2048 2048\r\n",
"\r\n",
"[ 2023-Feb-08 18:26:08 ] : Stage 2 --> REFINEMENT\r\n",
"--> Setting number of processing threads to: 1\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap.\r\n",
"Writing log info to: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-2048_4096_2048_2048/2048_4096_2048_2048-log-stereo_rfne-02-08-1826-5489.txt\r\n",
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"Left image nodata: -32768\r\n",
"Right image nodata: -32768\r\n",
"--> Using LOG pre-processing filter with 1.4 sigma blur.\r\n",
"--> Using parabola subpixel mode.\r\n",
"Writing: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-2048_4096_2048_2048/2048_4096_2048_2048-RD.tif\r\n",
"\r\n",
"--> Refinement :[.................................................] 0%\r\n",
"--> Refinement :[.................................................] 2%\r\n",
"--> Refinement :[*................................................] 3%\r\n",
"--> Refinement :[**...............................................] 5%\r\n",
"--> Refinement :[***..............................................] 6%\r\n",
"--> Refinement :[***..............................................] 8%\r\n",
"--> Refinement :[****.............................................] 9%\r\n",
"--> Refinement :[*****............................................] 11%\r\n",
"--> Refinement :[******...........................................] 12%\r\n",
"--> Refinement :[******...........................................] 14%\r\n",
"--> Refinement :[*******..........................................] 16%\r\n",
"--> Refinement :[********.........................................] 17%\r\n",
"--> Refinement :[*********........................................] 19%\r\n",
"--> Refinement :[*********........................................] 20%\r\n",
"--> Refinement :[**********.......................................] 22%\r\n",
"--> Refinement :[***********......................................] 23%\r\n",
"--> Refinement :[************.....................................] 25%\r\n",
"--> Refinement :[*************....................................] 27%\r\n",
"--> Refinement :[*************....................................] 28%\r\n",
"--> Refinement :[**************...................................] 30%\r\n",
"--> Refinement :[***************..................................] 31%\r\n",
"--> Refinement :[****************.................................] 33%\r\n",
"--> Refinement :[****************.................................] 34%\r\n",
"--> Refinement :[*****************................................] 36%\r\n",
"--> Refinement :[******************...............................] 38%\r\n",
"--> Refinement :[*******************..............................] 39%\r\n",
"--> Refinement :[*******************..............................] 41%\r\n",
"--> Refinement :[********************.............................] 42%\r\n",
"--> Refinement :[*********************............................] 44%\r\n",
"--> Refinement :[**********************...........................] 45%\r\n",
"--> Refinement :[**********************...........................] 47%\r\n",
"--> Refinement :[***********************..........................] 48%\r\n",
"--> Refinement :[************************.........................] 50%\r\n",
"--> Refinement :[*************************........................] 52%\r\n",
"--> Refinement :[**************************.......................] 53%\r\n",
"--> Refinement :[**************************.......................] 55%\r\n",
"--> Refinement :[***************************......................] 56%\r\n",
"--> Refinement :[****************************.....................] 58%\r\n",
"--> Refinement :[*****************************....................] 59%\r\n",
"--> Refinement :[*****************************....................] 61%\r\n",
"--> Refinement :[******************************...................] 62%\r\n",
"--> Refinement :[*******************************..................] 64%\r\n",
"--> Refinement :[********************************.................] 66%\r\n",
"--> Refinement :[********************************.................] 67%\r\n",
"--> Refinement :[*********************************................] 69%\r\n",
"--> Refinement :[**********************************...............] 70%\r\n",
"--> Refinement :[***********************************..............] 72%\r\n",
"--> Refinement :[***********************************..............] 73%\r\n",
"--> Refinement :[************************************.............] 75%\r\n",
"--> Refinement :[*************************************............] 77%\r\n",
"--> Refinement :[**************************************...........] 78%\r\n",
"--> Refinement :[***************************************..........] 80%\r\n",
"--> Refinement :[***************************************..........] 81%\r\n",
"--> Refinement :[****************************************.........] 83%\r\n",
"--> Refinement :[*****************************************........] 84%\r\n",
"--> Refinement :[******************************************.......] 86%\r\n",
"--> Refinement :[******************************************.......] 88%\r\n",
"--> Refinement :[*******************************************......] 89%\r\n",
"--> Refinement :[********************************************.....] 91%\r\n",
"--> Refinement :[*********************************************....] 92%\r\n",
"--> Refinement :[*********************************************....] 94%\r\n",
"--> Refinement :[**********************************************...] 95%\r\n",
"--> Refinement :[***********************************************..] 97%\r\n",
"--> Refinement :[************************************************.] 98%\r\n",
"--> Refinement :[*************************************************] 100%\r\n",
"--> Refinement :[********************************************] Complete!\r\n",
"\r\n",
"[ 2023-Feb-08 18:26:12 ] : REFINEMENT FINISHED\r\n",
"/tmp/sp/bin/stereo_rfne: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n",
"stereo_rfne: elapsed=0:04.16 ([hours:]minutes:seconds), memory=397056 (kb)\r\n",
"\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using tiles (before collar addition) of 2048 x 2048 pixels.\r\n",
"Using a collar (padding) for each tile of 0 pixels.\r\n",
"/usr/bin/time -f \"stereo_rfne: elapsed=%E ([hours:]minutes:seconds), memory=%M (kb)\" /tmp/sp/bin/stereo_rfne --bundle-adjust-prefix adjust/ba B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-4096_4096_931_2048/4096_4096_931_2048 --corr-seed-mode 1 --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap --sgm-collar-size 0 --threads 1 --trans-crop-win 4096 4096 931 2048\r\n",
"\r\n",
"[ 2023-Feb-08 18:26:14 ] : Stage 2 --> REFINEMENT\r\n",
"--> Setting number of processing threads to: 1\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap.\r\n",
"Writing log info to: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-4096_4096_931_2048/4096_4096_931_2048-log-stereo_rfne-02-08-1826-5629.txt\r\n",
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"Left image nodata: -32768\r\n",
"Right image nodata: -32768\r\n",
"--> Using LOG pre-processing filter with 1.4 sigma blur.\r\n",
"--> Using parabola subpixel mode.\r\n",
"Writing: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-4096_4096_931_2048/4096_4096_931_2048-RD.tif\r\n",
"\r\n",
"--> Refinement :[.................................................] 0%\r\n",
"--> Refinement :[*................................................] 3%\r\n",
"--> Refinement :[***..............................................] 6%\r\n",
"--> Refinement :[****.............................................] 9%\r\n",
"--> Refinement :[******...........................................] 12%\r\n",
"--> Refinement :[*******..........................................] 16%\r\n",
"--> Refinement :[*********........................................] 19%\r\n",
"--> Refinement :[**********.......................................] 22%\r\n",
"--> Refinement :[************.....................................] 25%\r\n",
"--> Refinement :[*************....................................] 28%\r\n",
"--> Refinement :[***************..................................] 31%\r\n",
"--> Refinement :[****************.................................] 34%\r\n",
"--> Refinement :[******************...............................] 38%\r\n",
"--> Refinement :[*******************..............................] 41%\r\n",
"--> Refinement :[*********************............................] 44%\r\n",
"--> Refinement :[**********************...........................] 47%\r\n",
"--> Refinement :[************************.........................] 50%\r\n",
"--> Refinement :[**************************.......................] 53%\r\n",
"--> Refinement :[***************************......................] 56%\r\n",
"--> Refinement :[*****************************....................] 59%\r\n",
"--> Refinement :[******************************...................] 62%\r\n",
"--> Refinement :[********************************.................] 66%\r\n",
"--> Refinement :[*********************************................] 69%\r\n",
"--> Refinement :[***********************************..............] 72%\r\n",
"--> Refinement :[************************************.............] 75%\r\n",
"--> Refinement :[**************************************...........] 78%\r\n",
"--> Refinement :[***************************************..........] 81%\r\n",
"--> Refinement :[*****************************************........] 84%\r\n",
"--> Refinement :[******************************************.......] 88%\r\n",
"--> Refinement :[********************************************.....] 91%\r\n",
"--> Refinement :[*********************************************....] 94%\r\n",
"--> Refinement :[***********************************************..] 97%\r\n",
"--> Refinement :[*************************************************] 100%\r\n",
"--> Refinement :[********************************************] Complete!\r\n",
"\r\n",
"[ 2023-Feb-08 18:26:16 ] : REFINEMENT FINISHED\r\n",
"/tmp/sp/bin/stereo_rfne: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n",
"stereo_rfne: elapsed=0:02.27 ([hours:]minutes:seconds), memory=268312 (kb)\r\n",
"\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using tiles (before collar addition) of 2048 x 2048 pixels.\r\n",
"Using a collar (padding) for each tile of 0 pixels.\r\n",
"/usr/bin/time -f \"stereo_rfne: elapsed=%E ([hours:]minutes:seconds), memory=%M (kb)\" /tmp/sp/bin/stereo_rfne --bundle-adjust-prefix adjust/ba B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_6144_2048_25/0_6144_2048_25 --corr-seed-mode 1 --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap --sgm-collar-size 0 --threads 1 --trans-crop-win 0 6144 2048 25\r\n",
"\r\n",
"[ 2023-Feb-08 18:26:18 ] : Stage 2 --> REFINEMENT\r\n",
"--> Setting number of processing threads to: 1\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap.\r\n",
"Writing log info to: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_6144_2048_25/0_6144_2048_25-log-stereo_rfne-02-08-1826-5737.txt\r\n",
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"Left image nodata: -32768\r\n",
"Right image nodata: -32768\r\n",
"--> Using LOG pre-processing filter with 1.4 sigma blur.\r\n",
"--> Using parabola subpixel mode.\r\n",
"Writing: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_6144_2048_25/0_6144_2048_25-RD.tif\r\n",
"\r\n",
"--> Refinement :[.................................................] 0%\r\n",
"--> Refinement :[******...........................................] 12%\r\n",
"--> Refinement :[************.....................................] 25%\r\n",
"--> Refinement :[******************...............................] 38%\r\n",
"--> Refinement :[************************.........................] 50%\r\n",
"--> Refinement :[******************************...................] 62%\r\n",
"--> Refinement :[************************************.............] 75%\r\n",
"--> Refinement :[******************************************.......] 88%\r\n",
"--> Refinement :[*************************************************] 100%\r\n",
"--> Refinement :[********************************************] Complete!\r\n",
"\r\n",
"[ 2023-Feb-08 18:26:18 ] : REFINEMENT FINISHED\r\n",
"/tmp/sp/bin/stereo_rfne: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n",
"stereo_rfne: elapsed=0:00.65 ([hours:]minutes:seconds), memory=177252 (kb)\r\n",
"\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using tiles (before collar addition) of 2048 x 2048 pixels.\r\n",
"Using a collar (padding) for each tile of 0 pixels.\r\n",
"/usr/bin/time -f \"stereo_rfne: elapsed=%E ([hours:]minutes:seconds), memory=%M (kb)\" /tmp/sp/bin/stereo_rfne --bundle-adjust-prefix adjust/ba B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-2048_6144_2048_25/2048_6144_2048_25 --corr-seed-mode 1 --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap --sgm-collar-size 0 --threads 1 --trans-crop-win 2048 6144 2048 25\r\n",
"\r\n",
"[ 2023-Feb-08 18:26:20 ] : Stage 2 --> REFINEMENT\r\n",
"--> Setting number of processing threads to: 1\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap.\r\n",
"Writing log info to: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-2048_6144_2048_25/2048_6144_2048_25-log-stereo_rfne-02-08-1826-5820.txt\r\n",
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"Left image nodata: -32768\r\n",
"Right image nodata: -32768\r\n",
"--> Using LOG pre-processing filter with 1.4 sigma blur.\r\n",
"--> Using parabola subpixel mode.\r\n",
"Writing: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-2048_6144_2048_25/2048_6144_2048_25-RD.tif\r\n",
"\r\n",
"--> Refinement :[.................................................] 0%\r\n",
"--> Refinement :[******...........................................] 12%\r\n",
"--> Refinement :[************.....................................] 25%\r\n",
"--> Refinement :[******************...............................] 38%\r\n",
"--> Refinement :[************************.........................] 50%\r\n",
"--> Refinement :[******************************...................] 62%\r\n",
"--> Refinement :[************************************.............] 75%\r\n",
"--> Refinement :[******************************************.......] 88%\r\n",
"--> Refinement :[*************************************************] 100%\r\n",
"--> Refinement :[********************************************] Complete!\r\n",
"\r\n",
"[ 2023-Feb-08 18:26:20 ] : REFINEMENT FINISHED\r\n",
"/tmp/sp/bin/stereo_rfne: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n",
"stereo_rfne: elapsed=0:00.66 ([hours:]minutes:seconds), memory=179524 (kb)\r\n",
"\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using tiles (before collar addition) of 2048 x 2048 pixels.\r\n",
"Using a collar (padding) for each tile of 0 pixels.\r\n",
"/usr/bin/time -f \"stereo_rfne: elapsed=%E ([hours:]minutes:seconds), memory=%M (kb)\" /tmp/sp/bin/stereo_rfne --bundle-adjust-prefix adjust/ba B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-4096_6144_931_25/4096_6144_931_25 --corr-seed-mode 1 --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap --sgm-collar-size 0 --threads 1 --trans-crop-win 4096 6144 931 25\r\n",
"\r\n",
"[ 2023-Feb-08 18:26:22 ] : Stage 2 --> REFINEMENT\r\n",
"--> Setting number of processing threads to: 1\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap.\r\n",
"Writing log info to: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-4096_6144_931_25/4096_6144_931_25-log-stereo_rfne-02-08-1826-5902.txt\r\n",
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"Left image nodata: -32768\r\n",
"Right image nodata: -32768\r\n",
"--> Using LOG pre-processing filter with 1.4 sigma blur.\r\n",
"--> Using parabola subpixel mode.\r\n",
"Writing: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-4096_6144_931_25/4096_6144_931_25-RD.tif\r\n",
"\r\n",
"--> Refinement :[.................................................] 0%\r\n",
"--> Refinement :[************.....................................] 25%\r\n",
"--> Refinement :[************************.........................] 50%\r\n",
"--> Refinement :[************************************.............] 75%\r\n",
"--> Refinement :[*************************************************] 100%\r\n",
"--> Refinement :[********************************************] Complete!\r\n",
"\r\n",
"[ 2023-Feb-08 18:26:22 ] : REFINEMENT FINISHED\r\n",
"/tmp/sp/bin/stereo_rfne: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n",
"stereo_rfne: elapsed=0:00.53 ([hours:]minutes:seconds), memory=155352 (kb)\r\n",
"\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"/tmp/sp/bin/stereo_fltr: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r\n",
"[ 2023-Feb-08 18:26:23 ] : Stage 3 --> FILTERING \r\n",
"Warning: Hole-filling is disabled by default in stereo_fltr. It is suggested to use instead point2dem's analogous functionality. It can be re-enabled using --enable-fill-holes.\r\n",
"\t--> Setting number of processing threads to: 2\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap.\r\n",
"Writing log info to: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-log-stereo_fltr-02-08-1826-5962.txt\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t--> Cleaning up disparity map prior to filtering processes (1 pass).\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Writing: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-GoodPixelMap.tif\r\n",
"\r",
" --> Good pixel map: [.............................................] 0%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [.............................................] 2%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [*............................................] 3%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [**...........................................] 5%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [**...........................................] 6%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [***..........................................] 8%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [****.........................................] 9%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [****.........................................] 11%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [*****........................................] 12%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [******.......................................] 14%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [******.......................................] 15%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [*******......................................] 17%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [********.....................................] 18%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [*********....................................] 20%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [*********....................................] 22%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [**********...................................] 23%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [***********..................................] 25%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [***********..................................] 26%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [************.................................] 28%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [*************................................] 29%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [*************................................] 31%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [**************...............................] 32%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [***************..............................] 34%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [***************..............................] 35%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [****************.............................] 37%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [*****************............................] 38%"
]
},
{
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"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [*****************............................] 40%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [******************...........................] 42%"
]
},
{
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"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [*******************..........................] 43%"
]
},
{
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"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [********************.........................] 45%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [********************.........................] 46%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [*********************........................] 48%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [**********************.......................] 49%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [**********************.......................] 51%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [***********************......................] 52%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [************************.....................] 54%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [************************.....................] 55%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [*************************....................] 57%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [**************************...................] 58%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [**************************...................] 60%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [***************************..................] 62%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [****************************.................] 63%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [*****************************................] 65%"
]
},
{
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"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [*****************************................] 66%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [******************************...............] 68%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [*******************************..............] 69%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [*******************************..............] 71%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [********************************.............] 72%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [*********************************............] 74%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [*********************************............] 75%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [**********************************...........] 77%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [***********************************..........] 78%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [***********************************..........] 80%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [************************************.........] 82%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [*************************************........] 83%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [**************************************.......] 85%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [**************************************.......] 86%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [***************************************......] 88%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [****************************************.....] 89%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [****************************************.....] 91%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [*****************************************....] 92%\r",
" --> Good pixel map: [******************************************...] 94%\r",
" --> Good pixel map: [******************************************...] 95%\r",
" --> Good pixel map: [*******************************************..] 97%\r",
" --> Good pixel map: [********************************************.] 98%\r",
" --> Good pixel map: [********************************************.] 100%\r",
" --> Good pixel map: [****************************************] Complete!\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Writing: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-F.tif\r\n",
"\r",
" --> Filtering: [..................................................] 0%"
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},
{
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"\r",
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"\r",
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{
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},
{
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"\r",
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},
{
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"\r",
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{
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{
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{
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{
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},
{
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"text": [
"\r",
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},
{
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},
{
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"output_type": "stream",
"text": [
"\r",
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{
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"\r",
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},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r\n",
"[ 2023-Feb-08 18:27:30 ] : FILTERING FINISHED \r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using tiles (before collar addition) of 2048 x 2048 pixels.\r\n",
"Using a collar (padding) for each tile of 0 pixels.\r\n",
"Writing: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-dirList.txt\r\n",
"Writing: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-dirList.txt\r\n",
"Writing: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-D.tif\r\n",
"Writing: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-dirList.txt\r\n",
"Writing: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-dirList.txt\r\n",
"Writing: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-RD.tif\r\n",
"Writing: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-dirList.txt\r\n",
"cd /home/runner/work/asap_stereo/asap_stereo\r\n",
"# Ran Command: /tmp/sp/bin/parallel_stereo --processes 1 --threads-singleprocess 2 --threads-multiprocess 1 --stop-point 5 --bundle-adjust-prefix adjust/ba --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba\r\n",
"# Finished: stereo_asap (parallel stereo common step), at: 2023-02-08 18:27:30.299405, duration: 0:05:47.547518\r\n",
"# Finished: step_5 (Parallel Stereo Part 1), at: 2023-02-08 18:27:30.299445, duration: 0:05:47.547619\r\n"
]
}
],
"source": [
"!asap ctx step_5 {config1} {asap.kwarg_parse(step_kwargs, 'step_5')} 2>&1 | tee -i -a ./3_lev1eo2dem.log ./full_log.log"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "f9128de1",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2023-02-08T18:27:30.559289Z",
"iopub.status.busy": "2023-02-08T18:27:30.558467Z",
"iopub.status.idle": "2023-02-08T18:28:27.359722Z",
"shell.execute_reply": "2023-02-08T18:28:27.357839Z"
},
"papermill": {
"duration": 56.84013,
"end_time": "2023-02-08T18:28:27.361523",
"exception": false,
"start_time": "2023-02-08T18:27:30.521393",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"# Started: step_6 (Parallel Stereo Part 2), at: 2023-02-08 18:27:31.071648\r\n",
"# Started: stereo_asap (parallel stereo common step), at: 2023-02-08 18:27:31.071710\r\n",
"INFO:sh.command:<Command '/usr/bin/cat ./stereopairs.lis', pid 6663>: process started\r\n",
"cd /home/runner/work/asap_stereo/asap_stereo/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W\r\n",
"INFO:sh.command:/tmp/sp/bin/parallel_stereo --processes 2 --threads-singleprocess 2 --threads-multiprocess 1 --entry-point 5 --bundle-adjust-prefix adjust/ba --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba: process started\r\n",
"/tmp/sp/bin/parallel_stereo: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"/tmp/sp/bin/stereo_tri: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r\n",
"[ 2023-Feb-08 18:27:32 ] : Stage 4 --> TRIANGULATION \r\n",
"\t--> Setting number of processing threads to: 2\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap.\r\n",
"Writing log info to: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-log-stereo_tri-02-08-1827-6711.txt\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"\t--> Generating a 3D point cloud.\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Writing point cloud center: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-PC-center.txt\r\n",
"Computed the point cloud center. Will stop here.\r\n",
"\r\n",
"[ 2023-Feb-08 18:27:33 ] : TRIANGULATION FINISHED \r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using tiles (before collar addition) of 2048 x 2048 pixels.\r\n",
"Using a collar (padding) for each tile of 0 pixels.\r\n",
"/usr/bin/time -f \"stereo_tri: elapsed=%E ([hours:]minutes:seconds), memory=%M (kb)\" /tmp/sp/bin/stereo_tri --bundle-adjust-prefix adjust/ba B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-2048_0_2048_2048/2048_0_2048_2048 --skip-point-cloud-center-comp --corr-seed-mode 1 --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap --sgm-collar-size 0 --threads 1 --trans-crop-win 2048 0 2048 2048\r\n",
"\r\n",
"[ 2023-Feb-08 18:27:34 ] : Stage 4 --> TRIANGULATION\r\n",
"--> Setting number of processing threads to: 1\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap.\r\n",
"Writing log info to: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-2048_0_2048_2048/2048_0_2048_2048-log-stereo_tri-02-08-1827-6813.txt\r\n",
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"--> Generating a 3D point cloud.\r\n",
"Reading existing point cloud center: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-2048_0_2048_2048/2048_0_2048_2048-PC-center.txt\r\n",
"Writing point cloud: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-2048_0_2048_2048/2048_0_2048_2048-PC.tif\r\n",
"\r\n",
"--> Triangulating: [..............................................] 0%\r\n",
"--> Triangulating: [..............................................] 2%\r\n",
"--> Triangulating: [*.............................................] 3%\r\n",
"--> Triangulating: [**............................................] 5%\r\n",
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"--> Triangulating: [******........................................] 14%\r\n",
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"--> Triangulating: [****************************************......] 89%\r\n",
"--> Triangulating: [*****************************************.....] 91%\r\n",
"--> Triangulating: [******************************************....] 92%\r\n",
"--> Triangulating: [*******************************************...] 94%\r\n",
"--> Triangulating: [*******************************************...] 95%\r\n",
"--> Triangulating: [********************************************..] 97%\r\n",
"--> Triangulating: [*********************************************.] 98%\r\n",
"--> Triangulating: [**********************************************] 100%\r\n",
"--> Triangulating: [*****************************************] Complete!\r\n",
"--> Universe Radius Limits: [ 0, Inf ]\r\n",
"Rejected 0/4194304 vertices (0%).\r\n",
"\r\n",
"[ 2023-Feb-08 18:27:45 ] : TRIANGULATION FINISHED\r\n",
"/tmp/sp/bin/stereo_tri: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n",
"stereo_tri: elapsed=0:11.06 ([hours:]minutes:seconds), memory=294656 (kb)\r\n",
"\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using tiles (before collar addition) of 2048 x 2048 pixels.\r\n",
"Using a collar (padding) for each tile of 0 pixels.\r\n",
"/usr/bin/time -f \"stereo_tri: elapsed=%E ([hours:]minutes:seconds), memory=%M (kb)\" /tmp/sp/bin/stereo_tri --bundle-adjust-prefix adjust/ba B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_0_2048_2048/0_0_2048_2048 --skip-point-cloud-center-comp --corr-seed-mode 1 --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap --sgm-collar-size 0 --threads 1 --trans-crop-win 0 0 2048 2048\r\n",
"\r\n",
"[ 2023-Feb-08 18:27:35 ] : Stage 4 --> TRIANGULATION\r\n",
"--> Setting number of processing threads to: 1\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap.\r\n",
"Writing log info to: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_0_2048_2048/0_0_2048_2048-log-stereo_tri-02-08-1827-6843.txt\r\n",
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"--> Generating a 3D point cloud.\r\n",
"Reading existing point cloud center: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_0_2048_2048/0_0_2048_2048-PC-center.txt\r\n",
"Writing point cloud: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_0_2048_2048/0_0_2048_2048-PC.tif\r\n",
"\r\n",
"--> Triangulating: [..............................................] 0%\r\n",
"--> Triangulating: [..............................................] 2%\r\n",
"--> Triangulating: [*.............................................] 3%\r\n",
"--> Triangulating: [**............................................] 5%\r\n",
"--> Triangulating: [**............................................] 6%\r\n",
"--> Triangulating: [***...........................................] 8%\r\n",
"--> Triangulating: [****..........................................] 9%\r\n",
"--> Triangulating: [*****.........................................] 11%\r\n",
"--> Triangulating: [*****.........................................] 12%\r\n",
"--> Triangulating: [******........................................] 14%\r\n",
"--> Triangulating: [*******.......................................] 16%\r\n",
"--> Triangulating: [*******.......................................] 17%\r\n",
"--> Triangulating: [********......................................] 19%\r\n",
"--> Triangulating: [*********.....................................] 20%\r\n",
"--> Triangulating: [**********....................................] 22%\r\n",
"--> Triangulating: [**********....................................] 23%\r\n",
"--> Triangulating: [***********...................................] 25%\r\n",
"--> Triangulating: [************..................................] 27%\r\n",
"--> Triangulating: [************..................................] 28%\r\n",
"--> Triangulating: [*************.................................] 30%\r\n",
"--> Triangulating: [**************................................] 31%\r\n",
"--> Triangulating: [***************...............................] 33%\r\n",
"--> Triangulating: [***************...............................] 34%\r\n",
"--> Triangulating: [****************..............................] 36%\r\n",
"--> Triangulating: [*****************.............................] 38%\r\n",
"--> Triangulating: [*****************.............................] 39%\r\n",
"--> Triangulating: [******************............................] 41%\r\n",
"--> Triangulating: [*******************...........................] 42%\r\n",
"--> Triangulating: [********************..........................] 44%\r\n",
"--> Triangulating: [********************..........................] 45%\r\n",
"--> Triangulating: [*********************.........................] 47%\r\n",
"--> Triangulating: [**********************........................] 48%\r\n",
"--> Triangulating: [***********************.......................] 50%\r\n",
"--> Triangulating: [***********************.......................] 52%\r\n",
"--> Triangulating: [************************......................] 53%\r\n",
"--> Triangulating: [*************************.....................] 55%\r\n",
"--> Triangulating: [*************************.....................] 56%\r\n",
"--> Triangulating: [**************************....................] 58%\r\n",
"--> Triangulating: [***************************...................] 59%\r\n",
"--> Triangulating: [****************************..................] 61%\r\n",
"--> Triangulating: [****************************..................] 62%\r\n",
"--> Triangulating: [*****************************.................] 64%\r\n",
"--> Triangulating: [******************************................] 66%\r\n",
"--> Triangulating: [******************************................] 67%\r\n",
"--> Triangulating: [*******************************...............] 69%\r\n",
"--> Triangulating: [********************************..............] 70%\r\n",
"--> Triangulating: [*********************************.............] 72%\r\n",
"--> Triangulating: [*********************************.............] 73%\r\n",
"--> Triangulating: [**********************************............] 75%\r\n",
"--> Triangulating: [***********************************...........] 77%\r\n",
"--> Triangulating: [***********************************...........] 78%\r\n",
"--> Triangulating: [************************************..........] 80%\r\n",
"--> Triangulating: [*************************************.........] 81%\r\n",
"--> Triangulating: [**************************************........] 83%\r\n",
"--> Triangulating: [**************************************........] 84%\r\n",
"--> Triangulating: [***************************************.......] 86%\r\n",
"--> Triangulating: [****************************************......] 88%\r\n",
"--> Triangulating: [****************************************......] 89%\r\n",
"--> Triangulating: [*****************************************.....] 91%\r\n",
"--> Triangulating: [******************************************....] 92%\r\n",
"--> Triangulating: [*******************************************...] 94%\r\n",
"--> Triangulating: [*******************************************...] 95%\r\n",
"--> Triangulating: [********************************************..] 97%\r\n",
"--> Triangulating: [*********************************************.] 98%\r\n",
"--> Triangulating: [**********************************************] 100%\r\n",
"--> Triangulating: [*****************************************] Complete!\r\n",
"--> Universe Radius Limits: [ 0, Inf ]\r\n",
"Rejected 0/4194304 vertices (0%).\r\n",
"\r\n",
"[ 2023-Feb-08 18:27:46 ] : TRIANGULATION FINISHED\r\n",
"/tmp/sp/bin/stereo_tri: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n",
"stereo_tri: elapsed=0:11.56 ([hours:]minutes:seconds), memory=294868 (kb)\r\n",
"\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using tiles (before collar addition) of 2048 x 2048 pixels.\r\n",
"Using a collar (padding) for each tile of 0 pixels.\r\n",
"/usr/bin/time -f \"stereo_tri: elapsed=%E ([hours:]minutes:seconds), memory=%M (kb)\" /tmp/sp/bin/stereo_tri --bundle-adjust-prefix adjust/ba B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-4096_0_931_2048/4096_0_931_2048 --skip-point-cloud-center-comp --corr-seed-mode 1 --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap --sgm-collar-size 0 --threads 1 --trans-crop-win 4096 0 931 2048\r\n",
"\r\n",
"[ 2023-Feb-08 18:27:47 ] : Stage 4 --> TRIANGULATION\r\n",
"--> Setting number of processing threads to: 1\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap.\r\n",
"Writing log info to: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-4096_0_931_2048/4096_0_931_2048-log-stereo_tri-02-08-1827-7092.txt\r\n",
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"--> Generating a 3D point cloud.\r\n",
"Reading existing point cloud center: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-4096_0_931_2048/4096_0_931_2048-PC-center.txt\r\n",
"Writing point cloud: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-4096_0_931_2048/4096_0_931_2048-PC.tif\r\n",
"\r\n",
"--> Triangulating: [..............................................] 0%\r\n",
"--> Triangulating: [*.............................................] 3%\r\n",
"--> Triangulating: [**............................................] 6%\r\n",
"--> Triangulating: [****..........................................] 9%\r\n",
"--> Triangulating: [*****.........................................] 12%\r\n",
"--> Triangulating: [*******.......................................] 16%\r\n",
"--> Triangulating: [********......................................] 19%\r\n",
"--> Triangulating: [**********....................................] 22%\r\n",
"--> Triangulating: [***********...................................] 25%\r\n",
"--> Triangulating: [************..................................] 28%\r\n",
"--> Triangulating: [**************................................] 31%\r\n",
"--> Triangulating: [***************...............................] 34%\r\n",
"--> Triangulating: [*****************.............................] 38%\r\n",
"--> Triangulating: [******************............................] 41%\r\n",
"--> Triangulating: [********************..........................] 44%\r\n",
"--> Triangulating: [*********************.........................] 47%\r\n",
"--> Triangulating: [***********************.......................] 50%\r\n",
"--> Triangulating: [************************......................] 53%\r\n",
"--> Triangulating: [*************************.....................] 56%\r\n",
"--> Triangulating: [***************************...................] 59%\r\n",
"--> Triangulating: [****************************..................] 62%\r\n",
"--> Triangulating: [******************************................] 66%\r\n",
"--> Triangulating: [*******************************...............] 69%\r\n",
"--> Triangulating: [*********************************.............] 72%\r\n",
"--> Triangulating: [**********************************............] 75%\r\n",
"--> Triangulating: [***********************************...........] 78%\r\n",
"--> Triangulating: [*************************************.........] 81%\r\n",
"--> Triangulating: [**************************************........] 84%\r\n",
"--> Triangulating: [****************************************......] 88%\r\n",
"--> Triangulating: [*****************************************.....] 91%\r\n",
"--> Triangulating: [*******************************************...] 94%\r\n",
"--> Triangulating: [********************************************..] 97%\r\n",
"--> Triangulating: [**********************************************] 100%\r\n",
"--> Triangulating: [*****************************************] Complete!\r\n",
"--> Universe Radius Limits: [ 0, Inf ]\r\n",
"Rejected 0/1906688 vertices (0%).\r\n",
"\r\n",
"[ 2023-Feb-08 18:27:52 ] : TRIANGULATION FINISHED\r\n",
"/tmp/sp/bin/stereo_tri: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n",
"stereo_tri: elapsed=0:05.47 ([hours:]minutes:seconds), memory=216652 (kb)\r\n",
"\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using tiles (before collar addition) of 2048 x 2048 pixels.\r\n",
"Using a collar (padding) for each tile of 0 pixels.\r\n",
"/usr/bin/time -f \"stereo_tri: elapsed=%E ([hours:]minutes:seconds), memory=%M (kb)\" /tmp/sp/bin/stereo_tri --bundle-adjust-prefix adjust/ba B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_2048_2048_2048/0_2048_2048_2048 --skip-point-cloud-center-comp --corr-seed-mode 1 --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap --sgm-collar-size 0 --threads 1 --trans-crop-win 0 2048 2048 2048\r\n",
"\r\n",
"[ 2023-Feb-08 18:27:48 ] : Stage 4 --> TRIANGULATION\r\n",
"--> Setting number of processing threads to: 1\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap.\r\n",
"Writing log info to: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_2048_2048_2048/0_2048_2048_2048-log-stereo_tri-02-08-1827-7150.txt\r\n",
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"--> Generating a 3D point cloud.\r\n",
"Reading existing point cloud center: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_2048_2048_2048/0_2048_2048_2048-PC-center.txt\r\n",
"Writing point cloud: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_2048_2048_2048/0_2048_2048_2048-PC.tif\r\n",
"\r\n",
"--> Triangulating: [..............................................] 0%\r\n",
"--> Triangulating: [..............................................] 2%\r\n",
"--> Triangulating: [*.............................................] 3%\r\n",
"--> Triangulating: [**............................................] 5%\r\n",
"--> Triangulating: [**............................................] 6%\r\n",
"--> Triangulating: [***...........................................] 8%\r\n",
"--> Triangulating: [****..........................................] 9%\r\n",
"--> Triangulating: [*****.........................................] 11%\r\n",
"--> Triangulating: [*****.........................................] 12%\r\n",
"--> Triangulating: [******........................................] 14%\r\n",
"--> Triangulating: [*******.......................................] 16%\r\n",
"--> Triangulating: [*******.......................................] 17%\r\n",
"--> Triangulating: [********......................................] 19%\r\n",
"--> Triangulating: [*********.....................................] 20%\r\n",
"--> Triangulating: [**********....................................] 22%\r\n",
"--> Triangulating: [**********....................................] 23%\r\n",
"--> Triangulating: [***********...................................] 25%\r\n",
"--> Triangulating: [************..................................] 27%\r\n",
"--> Triangulating: [************..................................] 28%\r\n",
"--> Triangulating: [*************.................................] 30%\r\n",
"--> Triangulating: [**************................................] 31%\r\n",
"--> Triangulating: [***************...............................] 33%\r\n",
"--> Triangulating: [***************...............................] 34%\r\n",
"--> Triangulating: [****************..............................] 36%\r\n",
"--> Triangulating: [*****************.............................] 38%\r\n",
"--> Triangulating: [*****************.............................] 39%\r\n",
"--> Triangulating: [******************............................] 41%\r\n",
"--> Triangulating: [*******************...........................] 42%\r\n",
"--> Triangulating: [********************..........................] 44%\r\n",
"--> Triangulating: [********************..........................] 45%\r\n",
"--> Triangulating: [*********************.........................] 47%\r\n",
"--> Triangulating: [**********************........................] 48%\r\n",
"--> Triangulating: [***********************.......................] 50%\r\n",
"--> Triangulating: [***********************.......................] 52%\r\n",
"--> Triangulating: [************************......................] 53%\r\n",
"--> Triangulating: [*************************.....................] 55%\r\n",
"--> Triangulating: [*************************.....................] 56%\r\n",
"--> Triangulating: [**************************....................] 58%\r\n",
"--> Triangulating: [***************************...................] 59%\r\n",
"--> Triangulating: [****************************..................] 61%\r\n",
"--> Triangulating: [****************************..................] 62%\r\n",
"--> Triangulating: [*****************************.................] 64%\r\n",
"--> Triangulating: [******************************................] 66%\r\n",
"--> Triangulating: [******************************................] 67%\r\n",
"--> Triangulating: [*******************************...............] 69%\r\n",
"--> Triangulating: [********************************..............] 70%\r\n",
"--> Triangulating: [*********************************.............] 72%\r\n",
"--> Triangulating: [*********************************.............] 73%\r\n",
"--> Triangulating: [**********************************............] 75%\r\n",
"--> Triangulating: [***********************************...........] 77%\r\n",
"--> Triangulating: [***********************************...........] 78%\r\n",
"--> Triangulating: [************************************..........] 80%\r\n",
"--> Triangulating: [*************************************.........] 81%\r\n",
"--> Triangulating: [**************************************........] 83%\r\n",
"--> Triangulating: [**************************************........] 84%\r\n",
"--> Triangulating: [***************************************.......] 86%\r\n",
"--> Triangulating: [****************************************......] 88%\r\n",
"--> Triangulating: [****************************************......] 89%\r\n",
"--> Triangulating: [*****************************************.....] 91%\r\n",
"--> Triangulating: [******************************************....] 92%\r\n",
"--> Triangulating: [*******************************************...] 94%\r\n",
"--> Triangulating: [*******************************************...] 95%\r\n",
"--> Triangulating: [********************************************..] 97%\r\n",
"--> Triangulating: [*********************************************.] 98%\r\n",
"--> Triangulating: [**********************************************] 100%\r\n",
"--> Triangulating: [*****************************************] Complete!\r\n",
"--> Universe Radius Limits: [ 0, Inf ]\r\n",
"Rejected 0/4194304 vertices (0%).\r\n",
"\r\n",
"[ 2023-Feb-08 18:27:59 ] : TRIANGULATION FINISHED\r\n",
"/tmp/sp/bin/stereo_tri: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n",
"stereo_tri: elapsed=0:12.15 ([hours:]minutes:seconds), memory=293948 (kb)\r\n",
"\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using tiles (before collar addition) of 2048 x 2048 pixels.\r\n",
"Using a collar (padding) for each tile of 0 pixels.\r\n",
"/usr/bin/time -f \"stereo_tri: elapsed=%E ([hours:]minutes:seconds), memory=%M (kb)\" /tmp/sp/bin/stereo_tri --bundle-adjust-prefix adjust/ba B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-2048_2048_2048_2048/2048_2048_2048_2048 --skip-point-cloud-center-comp --corr-seed-mode 1 --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap --sgm-collar-size 0 --threads 1 --trans-crop-win 2048 2048 2048 2048\r\n",
"\r\n",
"[ 2023-Feb-08 18:27:54 ] : Stage 4 --> TRIANGULATION\r\n",
"--> Setting number of processing threads to: 1\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap.\r\n",
"Writing log info to: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-2048_2048_2048_2048/2048_2048_2048_2048-log-stereo_tri-02-08-1827-7298.txt\r\n",
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"--> Generating a 3D point cloud.\r\n",
"Reading existing point cloud center: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-2048_2048_2048_2048/2048_2048_2048_2048-PC-center.txt\r\n",
"Writing point cloud: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-2048_2048_2048_2048/2048_2048_2048_2048-PC.tif\r\n",
"\r\n",
"--> Triangulating: [..............................................] 0%\r\n",
"--> Triangulating: [..............................................] 2%\r\n",
"--> Triangulating: [*.............................................] 3%\r\n",
"--> Triangulating: [**............................................] 5%\r\n",
"--> Triangulating: [**............................................] 6%\r\n",
"--> Triangulating: [***...........................................] 8%\r\n",
"--> Triangulating: [****..........................................] 9%\r\n",
"--> Triangulating: [*****.........................................] 11%\r\n",
"--> Triangulating: [*****.........................................] 12%\r\n",
"--> Triangulating: [******........................................] 14%\r\n",
"--> Triangulating: [*******.......................................] 16%\r\n",
"--> Triangulating: [*******.......................................] 17%\r\n",
"--> Triangulating: [********......................................] 19%\r\n",
"--> Triangulating: [*********.....................................] 20%\r\n",
"--> Triangulating: [**********....................................] 22%\r\n",
"--> Triangulating: [**********....................................] 23%\r\n",
"--> Triangulating: [***********...................................] 25%\r\n",
"--> Triangulating: [************..................................] 27%\r\n",
"--> Triangulating: [************..................................] 28%\r\n",
"--> Triangulating: [*************.................................] 30%\r\n",
"--> Triangulating: [**************................................] 31%\r\n",
"--> Triangulating: [***************...............................] 33%\r\n",
"--> Triangulating: [***************...............................] 34%\r\n",
"--> Triangulating: [****************..............................] 36%\r\n",
"--> Triangulating: [*****************.............................] 38%\r\n",
"--> Triangulating: [*****************.............................] 39%\r\n",
"--> Triangulating: [******************............................] 41%\r\n",
"--> Triangulating: [*******************...........................] 42%\r\n",
"--> Triangulating: [********************..........................] 44%\r\n",
"--> Triangulating: [********************..........................] 45%\r\n",
"--> Triangulating: [*********************.........................] 47%\r\n",
"--> Triangulating: [**********************........................] 48%\r\n",
"--> Triangulating: [***********************.......................] 50%\r\n",
"--> Triangulating: [***********************.......................] 52%\r\n",
"--> Triangulating: [************************......................] 53%\r\n",
"--> Triangulating: [*************************.....................] 55%\r\n",
"--> Triangulating: [*************************.....................] 56%\r\n",
"--> Triangulating: [**************************....................] 58%\r\n",
"--> Triangulating: [***************************...................] 59%\r\n",
"--> Triangulating: [****************************..................] 61%\r\n",
"--> Triangulating: [****************************..................] 62%\r\n",
"--> Triangulating: [*****************************.................] 64%\r\n",
"--> Triangulating: [******************************................] 66%\r\n",
"--> Triangulating: [******************************................] 67%\r\n",
"--> Triangulating: [*******************************...............] 69%\r\n",
"--> Triangulating: [********************************..............] 70%\r\n",
"--> Triangulating: [*********************************.............] 72%\r\n",
"--> Triangulating: [*********************************.............] 73%\r\n",
"--> Triangulating: [**********************************............] 75%\r\n",
"--> Triangulating: [***********************************...........] 77%\r\n",
"--> Triangulating: [***********************************...........] 78%\r\n",
"--> Triangulating: [************************************..........] 80%\r\n",
"--> Triangulating: [*************************************.........] 81%\r\n",
"--> Triangulating: [**************************************........] 83%\r\n",
"--> Triangulating: [**************************************........] 84%\r\n",
"--> Triangulating: [***************************************.......] 86%\r\n",
"--> Triangulating: [****************************************......] 88%\r\n",
"--> Triangulating: [****************************************......] 89%\r\n",
"--> Triangulating: [*****************************************.....] 91%\r\n",
"--> Triangulating: [******************************************....] 92%\r\n",
"--> Triangulating: [*******************************************...] 94%\r\n",
"--> Triangulating: [*******************************************...] 95%\r\n",
"--> Triangulating: [********************************************..] 97%\r\n",
"--> Triangulating: [*********************************************.] 98%\r\n",
"--> Triangulating: [**********************************************] 100%\r\n",
"--> Triangulating: [*****************************************] Complete!\r\n",
"--> Universe Radius Limits: [ 0, Inf ]\r\n",
"Rejected 0/4194304 vertices (0%).\r\n",
"\r\n",
"[ 2023-Feb-08 18:28:06 ] : TRIANGULATION FINISHED\r\n",
"/tmp/sp/bin/stereo_tri: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n",
"stereo_tri: elapsed=0:12.42 ([hours:]minutes:seconds), memory=293948 (kb)\r\n",
"\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using tiles (before collar addition) of 2048 x 2048 pixels.\r\n",
"Using a collar (padding) for each tile of 0 pixels.\r\n",
"/usr/bin/time -f \"stereo_tri: elapsed=%E ([hours:]minutes:seconds), memory=%M (kb)\" /tmp/sp/bin/stereo_tri --bundle-adjust-prefix adjust/ba B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-4096_2048_931_2048/4096_2048_931_2048 --skip-point-cloud-center-comp --corr-seed-mode 1 --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap --sgm-collar-size 0 --threads 1 --trans-crop-win 4096 2048 931 2048\r\n",
"\r\n",
"[ 2023-Feb-08 18:28:01 ] : Stage 4 --> TRIANGULATION\r\n",
"--> Setting number of processing threads to: 1\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap.\r\n",
"Writing log info to: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-4096_2048_931_2048/4096_2048_931_2048-log-stereo_tri-02-08-1828-7446.txt\r\n",
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"--> Generating a 3D point cloud.\r\n",
"Reading existing point cloud center: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-4096_2048_931_2048/4096_2048_931_2048-PC-center.txt\r\n",
"Writing point cloud: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-4096_2048_931_2048/4096_2048_931_2048-PC.tif\r\n",
"\r\n",
"--> Triangulating: [..............................................] 0%\r\n",
"--> Triangulating: [*.............................................] 3%\r\n",
"--> Triangulating: [**............................................] 6%\r\n",
"--> Triangulating: [****..........................................] 9%\r\n",
"--> Triangulating: [*****.........................................] 12%\r\n",
"--> Triangulating: [*******.......................................] 16%\r\n",
"--> Triangulating: [********......................................] 19%\r\n",
"--> Triangulating: [**********....................................] 22%\r\n",
"--> Triangulating: [***********...................................] 25%\r\n",
"--> Triangulating: [************..................................] 28%\r\n",
"--> Triangulating: [**************................................] 31%\r\n",
"--> Triangulating: [***************...............................] 34%\r\n",
"--> Triangulating: [*****************.............................] 38%\r\n",
"--> Triangulating: [******************............................] 41%\r\n",
"--> Triangulating: [********************..........................] 44%\r\n",
"--> Triangulating: [*********************.........................] 47%\r\n",
"--> Triangulating: [***********************.......................] 50%\r\n",
"--> Triangulating: [************************......................] 53%\r\n",
"--> Triangulating: [*************************.....................] 56%\r\n",
"--> Triangulating: [***************************...................] 59%\r\n",
"--> Triangulating: [****************************..................] 62%\r\n",
"--> Triangulating: [******************************................] 66%\r\n",
"--> Triangulating: [*******************************...............] 69%\r\n",
"--> Triangulating: [*********************************.............] 72%\r\n",
"--> Triangulating: [**********************************............] 75%\r\n",
"--> Triangulating: [***********************************...........] 78%\r\n",
"--> Triangulating: [*************************************.........] 81%\r\n",
"--> Triangulating: [**************************************........] 84%\r\n",
"--> Triangulating: [****************************************......] 88%\r\n",
"--> Triangulating: [*****************************************.....] 91%\r\n",
"--> Triangulating: [*******************************************...] 94%\r\n",
"--> Triangulating: [********************************************..] 97%\r\n",
"--> Triangulating: [**********************************************] 100%\r\n",
"--> Triangulating: [*****************************************] Complete!\r\n",
"--> Universe Radius Limits: [ 0, Inf ]\r\n",
"Rejected 0/1906688 vertices (0%).\r\n",
"\r\n",
"[ 2023-Feb-08 18:28:07 ] : TRIANGULATION FINISHED\r\n",
"/tmp/sp/bin/stereo_tri: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n",
"stereo_tri: elapsed=0:05.70 ([hours:]minutes:seconds), memory=216392 (kb)\r\n",
"\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using tiles (before collar addition) of 2048 x 2048 pixels.\r\n",
"Using a collar (padding) for each tile of 0 pixels.\r\n",
"/usr/bin/time -f \"stereo_tri: elapsed=%E ([hours:]minutes:seconds), memory=%M (kb)\" /tmp/sp/bin/stereo_tri --bundle-adjust-prefix adjust/ba B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_4096_2048_2048/0_4096_2048_2048 --skip-point-cloud-center-comp --corr-seed-mode 1 --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap --sgm-collar-size 0 --threads 1 --trans-crop-win 0 4096 2048 2048\r\n",
"\r\n",
"[ 2023-Feb-08 18:28:08 ] : Stage 4 --> TRIANGULATION\r\n",
"--> Setting number of processing threads to: 1\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap.\r\n",
"Writing log info to: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_4096_2048_2048/0_4096_2048_2048-log-stereo_tri-02-08-1828-7591.txt\r\n",
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"--> Generating a 3D point cloud.\r\n",
"Reading existing point cloud center: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_4096_2048_2048/0_4096_2048_2048-PC-center.txt\r\n",
"Writing point cloud: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_4096_2048_2048/0_4096_2048_2048-PC.tif\r\n",
"\r\n",
"--> Triangulating: [..............................................] 0%\r\n",
"--> Triangulating: [..............................................] 2%\r\n",
"--> Triangulating: [*.............................................] 3%\r\n",
"--> Triangulating: [**............................................] 5%\r\n",
"--> Triangulating: [**............................................] 6%\r\n",
"--> Triangulating: [***...........................................] 8%\r\n",
"--> Triangulating: [****..........................................] 9%\r\n",
"--> Triangulating: [*****.........................................] 11%\r\n",
"--> Triangulating: [*****.........................................] 12%\r\n",
"--> Triangulating: [******........................................] 14%\r\n",
"--> Triangulating: [*******.......................................] 16%\r\n",
"--> Triangulating: [*******.......................................] 17%\r\n",
"--> Triangulating: [********......................................] 19%\r\n",
"--> Triangulating: [*********.....................................] 20%\r\n",
"--> Triangulating: [**********....................................] 22%\r\n",
"--> Triangulating: [**********....................................] 23%\r\n",
"--> Triangulating: [***********...................................] 25%\r\n",
"--> Triangulating: [************..................................] 27%\r\n",
"--> Triangulating: [************..................................] 28%\r\n",
"--> Triangulating: [*************.................................] 30%\r\n",
"--> Triangulating: [**************................................] 31%\r\n",
"--> Triangulating: [***************...............................] 33%\r\n",
"--> Triangulating: [***************...............................] 34%\r\n",
"--> Triangulating: [****************..............................] 36%\r\n",
"--> Triangulating: [*****************.............................] 38%\r\n",
"--> Triangulating: [*****************.............................] 39%\r\n",
"--> Triangulating: [******************............................] 41%\r\n",
"--> Triangulating: [*******************...........................] 42%\r\n",
"--> Triangulating: [********************..........................] 44%\r\n",
"--> Triangulating: [********************..........................] 45%\r\n",
"--> Triangulating: [*********************.........................] 47%\r\n",
"--> Triangulating: [**********************........................] 48%\r\n",
"--> Triangulating: [***********************.......................] 50%\r\n",
"--> Triangulating: [***********************.......................] 52%\r\n",
"--> Triangulating: [************************......................] 53%\r\n",
"--> Triangulating: [*************************.....................] 55%\r\n",
"--> Triangulating: [*************************.....................] 56%\r\n",
"--> Triangulating: [**************************....................] 58%\r\n",
"--> Triangulating: [***************************...................] 59%\r\n",
"--> Triangulating: [****************************..................] 61%\r\n",
"--> Triangulating: [****************************..................] 62%\r\n",
"--> Triangulating: [*****************************.................] 64%\r\n",
"--> Triangulating: [******************************................] 66%\r\n",
"--> Triangulating: [******************************................] 67%\r\n",
"--> Triangulating: [*******************************...............] 69%\r\n",
"--> Triangulating: [********************************..............] 70%\r\n",
"--> Triangulating: [*********************************.............] 72%\r\n",
"--> Triangulating: [*********************************.............] 73%\r\n",
"--> Triangulating: [**********************************............] 75%\r\n",
"--> Triangulating: [***********************************...........] 77%\r\n",
"--> Triangulating: [***********************************...........] 78%\r\n",
"--> Triangulating: [************************************..........] 80%\r\n",
"--> Triangulating: [*************************************.........] 81%\r\n",
"--> Triangulating: [**************************************........] 83%\r\n",
"--> Triangulating: [**************************************........] 84%\r\n",
"--> Triangulating: [***************************************.......] 86%\r\n",
"--> Triangulating: [****************************************......] 88%\r\n",
"--> Triangulating: [****************************************......] 89%\r\n",
"--> Triangulating: [*****************************************.....] 91%\r\n",
"--> Triangulating: [******************************************....] 92%\r\n",
"--> Triangulating: [*******************************************...] 94%\r\n",
"--> Triangulating: [*******************************************...] 95%\r\n",
"--> Triangulating: [********************************************..] 97%\r\n",
"--> Triangulating: [*********************************************.] 98%\r\n",
"--> Triangulating: [**********************************************] 100%\r\n",
"--> Triangulating: [*****************************************] Complete!\r\n",
"--> Universe Radius Limits: [ 0, Inf ]\r\n",
"Rejected 0/4194304 vertices (0%).\r\n",
"\r\n",
"[ 2023-Feb-08 18:28:19 ] : TRIANGULATION FINISHED\r\n",
"/tmp/sp/bin/stereo_tri: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n",
"stereo_tri: elapsed=0:11.55 ([hours:]minutes:seconds), memory=298032 (kb)\r\n",
"\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using tiles (before collar addition) of 2048 x 2048 pixels.\r\n",
"Using a collar (padding) for each tile of 0 pixels.\r\n",
"/usr/bin/time -f \"stereo_tri: elapsed=%E ([hours:]minutes:seconds), memory=%M (kb)\" /tmp/sp/bin/stereo_tri --bundle-adjust-prefix adjust/ba B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-2048_4096_2048_2048/2048_4096_2048_2048 --skip-point-cloud-center-comp --corr-seed-mode 1 --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap --sgm-collar-size 0 --threads 1 --trans-crop-win 2048 4096 2048 2048\r\n",
"\r\n",
"[ 2023-Feb-08 18:28:09 ] : Stage 4 --> TRIANGULATION\r\n",
"--> Setting number of processing threads to: 1\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap.\r\n",
"Writing log info to: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-2048_4096_2048_2048/2048_4096_2048_2048-log-stereo_tri-02-08-1828-7651.txt\r\n",
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"--> Generating a 3D point cloud.\r\n",
"Reading existing point cloud center: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-2048_4096_2048_2048/2048_4096_2048_2048-PC-center.txt\r\n",
"Writing point cloud: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-2048_4096_2048_2048/2048_4096_2048_2048-PC.tif\r\n",
"\r\n",
"--> Triangulating: [..............................................] 0%\r\n",
"--> Triangulating: [..............................................] 2%\r\n",
"--> Triangulating: [*.............................................] 3%\r\n",
"--> Triangulating: [**............................................] 5%\r\n",
"--> Triangulating: [**............................................] 6%\r\n",
"--> Triangulating: [***...........................................] 8%\r\n",
"--> Triangulating: [****..........................................] 9%\r\n",
"--> Triangulating: [*****.........................................] 11%\r\n",
"--> Triangulating: [*****.........................................] 12%\r\n",
"--> Triangulating: [******........................................] 14%\r\n",
"--> Triangulating: [*******.......................................] 16%\r\n",
"--> Triangulating: [*******.......................................] 17%\r\n",
"--> Triangulating: [********......................................] 19%\r\n",
"--> Triangulating: [*********.....................................] 20%\r\n",
"--> Triangulating: [**********....................................] 22%\r\n",
"--> Triangulating: [**********....................................] 23%\r\n",
"--> Triangulating: [***********...................................] 25%\r\n",
"--> Triangulating: [************..................................] 27%\r\n",
"--> Triangulating: [************..................................] 28%\r\n",
"--> Triangulating: [*************.................................] 30%\r\n",
"--> Triangulating: [**************................................] 31%\r\n",
"--> Triangulating: [***************...............................] 33%\r\n",
"--> Triangulating: [***************...............................] 34%\r\n",
"--> Triangulating: [****************..............................] 36%\r\n",
"--> Triangulating: [*****************.............................] 38%\r\n",
"--> Triangulating: [*****************.............................] 39%\r\n",
"--> Triangulating: [******************............................] 41%\r\n",
"--> Triangulating: [*******************...........................] 42%\r\n",
"--> Triangulating: [********************..........................] 44%\r\n",
"--> Triangulating: [********************..........................] 45%\r\n",
"--> Triangulating: [*********************.........................] 47%\r\n",
"--> Triangulating: [**********************........................] 48%\r\n",
"--> Triangulating: [***********************.......................] 50%\r\n",
"--> Triangulating: [***********************.......................] 52%\r\n",
"--> Triangulating: [************************......................] 53%\r\n",
"--> Triangulating: [*************************.....................] 55%\r\n",
"--> Triangulating: [*************************.....................] 56%\r\n",
"--> Triangulating: [**************************....................] 58%\r\n",
"--> Triangulating: [***************************...................] 59%\r\n",
"--> Triangulating: [****************************..................] 61%\r\n",
"--> Triangulating: [****************************..................] 62%\r\n",
"--> Triangulating: [*****************************.................] 64%\r\n",
"--> Triangulating: [******************************................] 66%\r\n",
"--> Triangulating: [******************************................] 67%\r\n",
"--> Triangulating: [*******************************...............] 69%\r\n",
"--> Triangulating: [********************************..............] 70%\r\n",
"--> Triangulating: [*********************************.............] 72%\r\n",
"--> Triangulating: [*********************************.............] 73%\r\n",
"--> Triangulating: [**********************************............] 75%\r\n",
"--> Triangulating: [***********************************...........] 77%\r\n",
"--> Triangulating: [***********************************...........] 78%\r\n",
"--> Triangulating: [************************************..........] 80%\r\n",
"--> Triangulating: [*************************************.........] 81%\r\n",
"--> Triangulating: [**************************************........] 83%\r\n",
"--> Triangulating: [**************************************........] 84%\r\n",
"--> Triangulating: [***************************************.......] 86%\r\n",
"--> Triangulating: [****************************************......] 88%\r\n",
"--> Triangulating: [****************************************......] 89%\r\n",
"--> Triangulating: [*****************************************.....] 91%\r\n",
"--> Triangulating: [******************************************....] 92%\r\n",
"--> Triangulating: [*******************************************...] 94%\r\n",
"--> Triangulating: [*******************************************...] 95%\r\n",
"--> Triangulating: [********************************************..] 97%\r\n",
"--> Triangulating: [*********************************************.] 98%\r\n",
"--> Triangulating: [**********************************************] 100%\r\n",
"--> Triangulating: [*****************************************] Complete!\r\n",
"--> Universe Radius Limits: [ 0, Inf ]\r\n",
"Rejected 0/4194304 vertices (0%).\r\n",
"\r\n",
"[ 2023-Feb-08 18:28:20 ] : TRIANGULATION FINISHED\r\n",
"/tmp/sp/bin/stereo_tri: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n",
"stereo_tri: elapsed=0:12.27 ([hours:]minutes:seconds), memory=298104 (kb)\r\n",
"\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using tiles (before collar addition) of 2048 x 2048 pixels.\r\n",
"Using a collar (padding) for each tile of 0 pixels.\r\n",
"/usr/bin/time -f \"stereo_tri: elapsed=%E ([hours:]minutes:seconds), memory=%M (kb)\" /tmp/sp/bin/stereo_tri --bundle-adjust-prefix adjust/ba B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_6144_2048_25/0_6144_2048_25 --skip-point-cloud-center-comp --corr-seed-mode 1 --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap --sgm-collar-size 0 --threads 1 --trans-crop-win 0 6144 2048 25\r\n",
"\r\n",
"[ 2023-Feb-08 18:28:22 ] : Stage 4 --> TRIANGULATION\r\n",
"--> Setting number of processing threads to: 1\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap.\r\n",
"Writing log info to: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_6144_2048_25/0_6144_2048_25-log-stereo_tri-02-08-1828-7939.txt\r\n",
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"--> Generating a 3D point cloud.\r\n",
"Reading existing point cloud center: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_6144_2048_25/0_6144_2048_25-PC-center.txt\r\n",
"Writing point cloud: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_6144_2048_25/0_6144_2048_25-PC.tif\r\n",
"\r\n",
"--> Triangulating: [..............................................] 0%\r\n",
"--> Triangulating: [*****.........................................] 12%\r\n",
"--> Triangulating: [***********...................................] 25%\r\n",
"--> Triangulating: [*****************.............................] 38%\r\n",
"--> Triangulating: [***********************.......................] 50%\r\n",
"--> Triangulating: [****************************..................] 62%\r\n",
"--> Triangulating: [**********************************............] 75%\r\n",
"--> Triangulating: [****************************************......] 88%\r\n",
"--> Triangulating: [**********************************************] 100%\r\n",
"--> Triangulating: [*****************************************] Complete!\r\n",
"--> Universe Radius Limits: [ 0, Inf ]\r\n",
"Rejected 0/51200 vertices (0%).\r\n",
"\r\n",
"[ 2023-Feb-08 18:28:23 ] : TRIANGULATION FINISHED\r\n",
"/tmp/sp/bin/stereo_tri: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n",
"stereo_tri: elapsed=0:00.69 ([hours:]minutes:seconds), memory=135412 (kb)\r\n",
"\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using tiles (before collar addition) of 2048 x 2048 pixels.\r\n",
"Using a collar (padding) for each tile of 0 pixels.\r\n",
"/usr/bin/time -f \"stereo_tri: elapsed=%E ([hours:]minutes:seconds), memory=%M (kb)\" /tmp/sp/bin/stereo_tri --bundle-adjust-prefix adjust/ba B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-2048_6144_2048_25/2048_6144_2048_25 --skip-point-cloud-center-comp --corr-seed-mode 1 --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap --sgm-collar-size 0 --threads 1 --trans-crop-win 2048 6144 2048 25\r\n",
"\r\n",
"[ 2023-Feb-08 18:28:24 ] : Stage 4 --> TRIANGULATION\r\n",
"--> Setting number of processing threads to: 1\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap.\r\n",
"Writing log info to: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-2048_6144_2048_25/2048_6144_2048_25-log-stereo_tri-02-08-1828-8034.txt\r\n",
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"--> Generating a 3D point cloud.\r\n",
"Reading existing point cloud center: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-2048_6144_2048_25/2048_6144_2048_25-PC-center.txt\r\n",
"Writing point cloud: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-2048_6144_2048_25/2048_6144_2048_25-PC.tif\r\n",
"\r\n",
"--> Triangulating: [..............................................] 0%\r\n",
"--> Triangulating: [*****.........................................] 12%\r\n",
"--> Triangulating: [***********...................................] 25%\r\n",
"--> Triangulating: [*****************.............................] 38%\r\n",
"--> Triangulating: [***********************.......................] 50%\r\n",
"--> Triangulating: [****************************..................] 62%\r\n",
"--> Triangulating: [**********************************............] 75%\r\n",
"--> Triangulating: [****************************************......] 88%\r\n",
"--> Triangulating: [**********************************************] 100%\r\n",
"--> Triangulating: [*****************************************] Complete!\r\n",
"--> Universe Radius Limits: [ 0, Inf ]\r\n",
"Rejected 0/51200 vertices (0%).\r\n",
"\r\n",
"[ 2023-Feb-08 18:28:25 ] : TRIANGULATION FINISHED\r\n",
"/tmp/sp/bin/stereo_tri: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n",
"stereo_tri: elapsed=0:00.69 ([hours:]minutes:seconds), memory=135124 (kb)\r\n",
"\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using tiles (before collar addition) of 2048 x 2048 pixels.\r\n",
"Using a collar (padding) for each tile of 0 pixels.\r\n",
"/usr/bin/time -f \"stereo_tri: elapsed=%E ([hours:]minutes:seconds), memory=%M (kb)\" /tmp/sp/bin/stereo_tri --bundle-adjust-prefix adjust/ba B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-4096_4096_931_2048/4096_4096_931_2048 --skip-point-cloud-center-comp --corr-seed-mode 1 --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap --sgm-collar-size 0 --threads 1 --trans-crop-win 4096 4096 931 2048\r\n",
"\r\n",
"[ 2023-Feb-08 18:28:21 ] : Stage 4 --> TRIANGULATION\r\n",
"--> Setting number of processing threads to: 1\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap.\r\n",
"Writing log info to: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-4096_4096_931_2048/4096_4096_931_2048-log-stereo_tri-02-08-1828-7858.txt\r\n",
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"--> Generating a 3D point cloud.\r\n",
"Reading existing point cloud center: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-4096_4096_931_2048/4096_4096_931_2048-PC-center.txt\r\n",
"Writing point cloud: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-4096_4096_931_2048/4096_4096_931_2048-PC.tif\r\n",
"\r\n",
"--> Triangulating: [..............................................] 0%\r\n",
"--> Triangulating: [*.............................................] 3%\r\n",
"--> Triangulating: [**............................................] 6%\r\n",
"--> Triangulating: [****..........................................] 9%\r\n",
"--> Triangulating: [*****.........................................] 12%\r\n",
"--> Triangulating: [*******.......................................] 16%\r\n",
"--> Triangulating: [********......................................] 19%\r\n",
"--> Triangulating: [**********....................................] 22%\r\n",
"--> Triangulating: [***********...................................] 25%\r\n",
"--> Triangulating: [************..................................] 28%\r\n",
"--> Triangulating: [**************................................] 31%\r\n",
"--> Triangulating: [***************...............................] 34%\r\n",
"--> Triangulating: [*****************.............................] 38%\r\n",
"--> Triangulating: [******************............................] 41%\r\n",
"--> Triangulating: [********************..........................] 44%\r\n",
"--> Triangulating: [*********************.........................] 47%\r\n",
"--> Triangulating: [***********************.......................] 50%\r\n",
"--> Triangulating: [************************......................] 53%\r\n",
"--> Triangulating: [*************************.....................] 56%\r\n",
"--> Triangulating: [***************************...................] 59%\r\n",
"--> Triangulating: [****************************..................] 62%\r\n",
"--> Triangulating: [******************************................] 66%\r\n",
"--> Triangulating: [*******************************...............] 69%\r\n",
"--> Triangulating: [*********************************.............] 72%\r\n",
"--> Triangulating: [**********************************............] 75%\r\n",
"--> Triangulating: [***********************************...........] 78%\r\n",
"--> Triangulating: [*************************************.........] 81%\r\n",
"--> Triangulating: [**************************************........] 84%\r\n",
"--> Triangulating: [****************************************......] 88%\r\n",
"--> Triangulating: [*****************************************.....] 91%\r\n",
"--> Triangulating: [*******************************************...] 94%\r\n",
"--> Triangulating: [********************************************..] 97%\r\n",
"--> Triangulating: [**********************************************] 100%\r\n",
"--> Triangulating: [*****************************************] Complete!\r\n",
"--> Universe Radius Limits: [ 0, Inf ]\r\n",
"Rejected 0/1906688 vertices (0%).\r\n",
"\r\n",
"[ 2023-Feb-08 18:28:26 ] : TRIANGULATION FINISHED\r\n",
"/tmp/sp/bin/stereo_tri: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n",
"stereo_tri: elapsed=0:05.55 ([hours:]minutes:seconds), memory=217444 (kb)\r\n",
"\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using tiles (before collar addition) of 2048 x 2048 pixels.\r\n",
"Using a collar (padding) for each tile of 0 pixels.\r\n",
"/usr/bin/time -f \"stereo_tri: elapsed=%E ([hours:]minutes:seconds), memory=%M (kb)\" /tmp/sp/bin/stereo_tri --bundle-adjust-prefix adjust/ba B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-4096_6144_931_25/4096_6144_931_25 --skip-point-cloud-center-comp --corr-seed-mode 1 --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap --sgm-collar-size 0 --threads 1 --trans-crop-win 4096 6144 931 25\r\n",
"\r\n",
"[ 2023-Feb-08 18:28:26 ] : Stage 4 --> TRIANGULATION\r\n",
"--> Setting number of processing threads to: 1\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap.\r\n",
"Writing log info to: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-4096_6144_931_25/4096_6144_931_25-log-stereo_tri-02-08-1828-8123.txt\r\n",
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"--> Generating a 3D point cloud.\r\n",
"Reading existing point cloud center: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-4096_6144_931_25/4096_6144_931_25-PC-center.txt\r\n",
"Writing point cloud: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-4096_6144_931_25/4096_6144_931_25-PC.tif\r\n",
"\r\n",
"--> Triangulating: [..............................................] 0%\r\n",
"--> Triangulating: [***********...................................] 25%\r\n",
"--> Triangulating: [***********************.......................] 50%\r\n",
"--> Triangulating: [**********************************............] 75%\r\n",
"--> Triangulating: [**********************************************] 100%\r\n",
"--> Triangulating: [*****************************************] Complete!\r\n",
"--> Universe Radius Limits: [ 0, Inf ]\r\n",
"Rejected 0/23275 vertices (0%).\r\n",
"\r\n",
"[ 2023-Feb-08 18:28:27 ] : TRIANGULATION FINISHED\r\n",
"/tmp/sp/bin/stereo_tri: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n",
"stereo_tri: elapsed=0:00.54 ([hours:]minutes:seconds), memory=126860 (kb)\r\n",
"\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using tiles (before collar addition) of 2048 x 2048 pixels.\r\n",
"Using a collar (padding) for each tile of 0 pixels.\r\n",
"Writing: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-dirList.txt\r\n",
"Writing: results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-PC.tif\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"cd /home/runner/work/asap_stereo/asap_stereo\r\n",
"# Ran Command: /tmp/sp/bin/parallel_stereo --processes 2 --threads-singleprocess 2 --threads-multiprocess 1 --entry-point 5 --bundle-adjust-prefix adjust/ba --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.nonmap B03_010644_1889_XN_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba\r\n",
"# Finished: stereo_asap (parallel stereo common step), at: 2023-02-08 18:28:27.174761, duration: 0:00:56.103051\r\n",
"# Finished: step_6 (Parallel Stereo Part 2), at: 2023-02-08 18:28:27.174797, duration: 0:00:56.103149\r\n"
]
}
],
"source": [
"!asap ctx step_6 {config1} {asap.kwarg_parse(step_kwargs, 'step_6')} 2>&1 | tee -i -a ./3_lev1eo2dem.log ./full_log.log"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "47a0a7fd",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2023-02-08T18:28:27.477902Z",
"iopub.status.busy": "2023-02-08T18:28:27.477587Z",
"iopub.status.idle": "2023-02-08T18:30:05.889320Z",
"shell.execute_reply": "2023-02-08T18:30:05.887690Z"
},
"papermill": {
"duration": 98.492162,
"end_time": "2023-02-08T18:30:05.891990",
"exception": false,
"start_time": "2023-02-08T18:28:27.399828",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"# Started: step_7 (Produce preview DEMs/Orthos), at: 2023-02-08 18:28:27.964201\r\n",
"# Started: point_to_dem, at: 2023-02-08 18:28:27.964338\r\n",
"INFO:sh.command:<Command '/usr/bin/cat ./stereopairs.lis', pid 8188>: process started\r\n",
"INFO:sh.command:<Command '/usr/share/miniconda3/envs/asap/bin/gdalsrsinfo ./B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W/B03_010644_1889_XN_08N001W.lev1eo.cub -o proj4', pid 8193>: process started\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/runner/work/asap_stereo/asap_stereo/src/asap_stereo/asap.py:503: UserWarning: No SRS info, falling back to use ISIS caminfo.\r\n",
" exception was: \r\n",
"\r\n",
" RAN: /usr/share/miniconda3/envs/asap/bin/gdalsrsinfo ./B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W/B03_010644_1889_XN_08N001W.lev1eo.cub -o proj4\r\n",
"\r\n",
" STDOUT:\r\n",
"\r\n",
"\r\n",
" STDERR:\r\n",
"ERROR 1: ERROR - failed to load SRS definition from ./B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W/B03_010644_1889_XN_08N001W.lev1eo.cub\r\n",
"\r\n",
" warnings.warn(f'No SRS info, falling back to use ISIS caminfo.\\n exception was: {e}')\r\n",
"INFO:sh.command:/usr/share/miniconda3/envs/isis_for_asp/bin/camrange from=B03_010644_1889_XN_08N001W.lev1eo.cub: process started\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:sh.command:<Command '/usr/share/miniconda3/envs/asap/bin/gdalinfo -json ./B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W/B03_010644_1889_XN_08N001W.lev1eo.cub', pid 8206>: process started\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:sh.command:/usr/share/miniconda3/envs/isis_for_asp/bin/camrange from=B03_010644_1889_XN_08N001W.lev1eo.cub: process started\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"cd /home/runner/work/asap_stereo/asap_stereo/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W/results_ba\r\n",
"INFO:sh.command:<Command '/usr/bin/mkdir dem -p', pid 8217>: process started\r\n",
"cd dem\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:sh.command:/tmp/sp/bin/point2dem --threads 2 --reference-spheroid mars --nodata -32767 --output-prefix B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba_100_0 --dem-spacing 100 --t_srs '+proj=ortho +lon_0=-1.0481625741350247 +lat_0=8.8231877138671 +x_0=0 +y_0=0 +a=3396190.0 +b=3396190.0 +units=m +no_defs' --dem-hole-fill-len 50 /home/runner/work/asap_stereo/asap_stereo/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W/results_ba/dem/../B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-PC.tif --errorimage: process started\r\n",
"/tmp/sp/bin/point2dem: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n"
]
},
{
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"text": [
"\t--> Setting number of processing threads to: 2\r\n"
]
},
{
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"output_type": "stream",
"text": [
"Writing log info to: B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba_100_0-log-point2dem-02-08-1828-8222.txt\r\n",
"\t--> Re-referencing altitude values using datum: D_MARS.\r\n",
"\t Axes [3.39619e+06 3.39619e+06] meters.\r\n"
]
},
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"text": [
"\r",
"QuadTree: [********************************...............................] 51%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [********************************...............................] 52%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [*********************************..............................] 53%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [**********************************.............................] 54%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [**********************************.............................] 55%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [***********************************............................] 56%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [************************************...........................] 57%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [************************************...........................] 58%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [*************************************..........................] 59%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [*************************************..........................] 60%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [**************************************.........................] 61%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [***************************************........................] 62%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [***************************************........................] 63%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [****************************************.......................] 64%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [*****************************************......................] 65%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [*****************************************......................] 66%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [******************************************.....................] 67%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [******************************************.....................] 68%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [*******************************************....................] 69%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [********************************************...................] 70%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [********************************************...................] 71%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [*********************************************..................] 72%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [**********************************************.................] 73%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [**********************************************.................] 74%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [***********************************************................] 75%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [************************************************...............] 76%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [************************************************...............] 77%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [*************************************************..............] 78%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [*************************************************..............] 79%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [**************************************************.............] 80%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [***************************************************............] 81%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [***************************************************............] 82%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [****************************************************...........] 83%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [*****************************************************..........] 84%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [*****************************************************..........] 85%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [******************************************************.........] 86%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [******************************************************.........] 87%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [*******************************************************........] 88%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [********************************************************.......] 89%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [********************************************************.......] 90%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [*********************************************************......] 91%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [**********************************************************.....] 92%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [**********************************************************.....] 93%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [***********************************************************....] 94%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [***********************************************************....] 95%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"QuadTree: [**********************************************************] Complete!\r\n",
"Collected a sample of 28589248 positive triangulation errors.\r\n",
"Error percentiles: Q1 (25%): 0.379131, Q2 (50%): 0.842514, Q3 (75%): 1.79034.\r\n",
"Computing triangulation error cutoff based on --remove-outliers-params.\r\n",
"Triangulation error cutoff is 5.37103 meters.\r\n",
"\t-- Starting DEM rasterization --\r\n",
"\t--> DEM spacing: 100 pt/px\r\n",
"\t or: 0.01 px/pt\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Creating output file that is Vector2(306,394) px.\r\n",
"Writing: B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba_100_0-DEM.tif\r\n",
"\r",
"DEM: [....................................................................] 0%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"DEM: [*****************...................................................] 25%\r",
"DEM: [**********************************..................................] 50%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"DEM: [***************************************************.................] 75%\r",
"DEM: [********************************************************************] 100%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"DEM: [***************************************************************] Complete!\r\n",
"Percentage of valid pixels: 67.9067%\r\n",
"Writing: B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba_100_0-IntersectionErr.tif\r\n",
"\r",
"IntersectionErr: [........................................................] 0%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"IntersectionErr: [**************..........................................] 25%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"IntersectionErr: [****************************............................] 50%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"IntersectionErr: [******************************************..............] 75%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"IntersectionErr: [********************************************************] 100%\r",
"IntersectionErr: [***************************************************] Complete!\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"cd /home/runner/work/asap_stereo/asap_stereo/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W/results_ba\r\n",
"cd /home/runner/work/asap_stereo/asap_stereo\r\n",
"# Ran Command: /tmp/sp/bin/point2dem --threads 2 --reference-spheroid mars --nodata -32767 --output-prefix B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba_100_0 --dem-spacing 100 --t_srs +proj=ortho +lon_0=-1.0481625741350247 +lat_0=8.8231877138671 +x_0=0 +y_0=0 +a=3396190.0 +b=3396190.0 +units=m +no_defs --dem-hole-fill-len 50 /home/runner/work/asap_stereo/asap_stereo/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W/results_ba/dem/../B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-PC.tif --errorimage\r\n",
"# Finished: point_to_dem, at: 2023-02-08 18:30:05.563644, duration: 0:01:37.599306\r\n",
"# Finished: step_7 (Produce preview DEMs/Orthos), at: 2023-02-08 18:30:05.563992, duration: 0:01:37.599791\r\n"
]
}
],
"source": [
"!asap ctx step_7 --mpp 100 --just_ortho False --dem_hole_fill_len 50 {asap.kwarg_parse(step_kwargs, 'step_7')} 2>&1 | tee -i -a ./4_make_100m_dem.log ./full_log.log"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "033cb7ae",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2023-02-08T18:30:05.997181Z",
"iopub.status.busy": "2023-02-08T18:30:05.995795Z",
"iopub.status.idle": "2023-02-08T18:30:07.503812Z",
"shell.execute_reply": "2023-02-08T18:30:07.502401Z"
},
"papermill": {
"duration": 1.56487,
"end_time": "2023-02-08T18:30:07.505860",
"exception": false,
"start_time": "2023-02-08T18:30:05.940990",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"# Started: step_8 (hillshade First step in asp_ctx_step2_map2dem script), at: 2023-02-08 18:30:06.955686\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:sh.command:<Command '/usr/bin/cat ./stereopairs.lis', pid 8333>: process started\r\n",
"cd /home/runner/work/asap_stereo/asap_stereo/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W/results_ba/dem\r\n",
"INFO:sh.command:/usr/share/miniconda3/envs/asap/bin/gdaldem hillshade B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba_100_0-DEM.tif ./B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba_100_0-DEM-hillshade.tif: process started\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"0...10...20...30...40...50...60...70...80...90...100 - done.\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"cd /home/runner/work/asap_stereo/asap_stereo\r\n",
"# Finished: step_8 (hillshade First step in asp_ctx_step2_map2dem script), at: 2023-02-08 18:30:07.324497, duration: 0:00:00.368811\r\n"
]
}
],
"source": [
"!asap ctx step-8"
]
},
{
"cell_type": "markdown",
"id": "09306e8e",
"metadata": {
"collapsed": false,
"papermill": {
"duration": 0.046756,
"end_time": "2023-02-08T18:30:07.599554",
"exception": false,
"start_time": "2023-02-08T18:30:07.552798",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"## Good Pixel Map "
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "5fa4ed5e",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2023-02-08T18:30:07.702723Z",
"iopub.status.busy": "2023-02-08T18:30:07.701549Z",
"iopub.status.idle": "2023-02-08T18:30:07.707355Z",
"shell.execute_reply": "2023-02-08T18:30:07.706798Z"
},
"papermill": {
"duration": 0.055446,
"end_time": "2023-02-08T18:30:07.708804",
"exception": false,
"start_time": "2023-02-08T18:30:07.653358",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [],
"source": [
"both = f'{left}_{right}'\n",
"img = f'./{both}/results_ba/{both}_ba-GoodPixelMap.tif'\n",
"out = img.replace('.tif', '.png')"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "1c701051",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2023-02-08T18:30:07.805372Z",
"iopub.status.busy": "2023-02-08T18:30:07.804593Z",
"iopub.status.idle": "2023-02-08T18:30:08.464496Z",
"shell.execute_reply": "2023-02-08T18:30:08.462943Z"
},
"papermill": {
"duration": 0.70968,
"end_time": "2023-02-08T18:30:08.467392",
"exception": false,
"start_time": "2023-02-08T18:30:07.757712",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Input file size is 2514, 3085\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"0..."
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"10..."
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"20...30"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"...40"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"...50."
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"..60.."
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
".70..."
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"80...90"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"...100 - done.\r\n"
]
}
],
"source": [
"!gdal_translate -of PNG -co worldfile=yes {img} {out}"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "7a991fe7",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2023-02-08T18:30:08.565745Z",
"iopub.status.busy": "2023-02-08T18:30:08.564911Z",
"iopub.status.idle": "2023-02-08T18:30:08.584143Z",
"shell.execute_reply": "2023-02-08T18:30:08.583565Z"
},
"papermill": {
"duration": 0.071561,
"end_time": "2023-02-08T18:30:08.589141",
"exception": false,
"start_time": "2023-02-08T18:30:08.517580",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 22,
"metadata": {
"image/png": {
"width": 800
}
},
"output_type": "execute_result"
}
],
"source": [
"Image(filename=out, width=800)"
]
},
{
"cell_type": "markdown",
"id": "d13dee19",
"metadata": {
"collapsed": false,
"papermill": {
"duration": 0.049943,
"end_time": "2023-02-08T18:30:08.689900",
"exception": false,
"start_time": "2023-02-08T18:30:08.639957",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"## Hillshade of low res DEM"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "cc173353",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2023-02-08T18:30:08.791935Z",
"iopub.status.busy": "2023-02-08T18:30:08.790950Z",
"iopub.status.idle": "2023-02-08T18:30:08.795687Z",
"shell.execute_reply": "2023-02-08T18:30:08.795067Z"
},
"papermill": {
"duration": 0.057423,
"end_time": "2023-02-08T18:30:08.796967",
"exception": false,
"start_time": "2023-02-08T18:30:08.739544",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [],
"source": [
"both = f'{left}_{right}'\n",
"img = f'./{both}/results_ba/dem/{both}_ba_100_0-DEM-hillshade.tif'\n",
"out = img.replace('.tif', '.png')"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "446aa932",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2023-02-08T18:30:08.899529Z",
"iopub.status.busy": "2023-02-08T18:30:08.898464Z",
"iopub.status.idle": "2023-02-08T18:30:09.138729Z",
"shell.execute_reply": "2023-02-08T18:30:09.137193Z"
},
"papermill": {
"duration": 0.293787,
"end_time": "2023-02-08T18:30:09.140778",
"exception": false,
"start_time": "2023-02-08T18:30:08.846991",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Input file size is 306, 394\r\n",
"0...10...20...30...40...50..."
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"60...70...80...90...100 - done.\r\n"
]
}
],
"source": [
"!gdal_translate -of PNG -co worldfile=yes {img} {out}"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "07fcdade",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2023-02-08T18:30:09.243726Z",
"iopub.status.busy": "2023-02-08T18:30:09.242837Z",
"iopub.status.idle": "2023-02-08T18:30:09.249887Z",
"shell.execute_reply": "2023-02-08T18:30:09.249240Z"
},
"papermill": {
"duration": 0.059859,
"end_time": "2023-02-08T18:30:09.251760",
"exception": false,
"start_time": "2023-02-08T18:30:09.191901",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 25,
"metadata": {
"image/png": {
"width": 800
}
},
"output_type": "execute_result"
}
],
"source": [
"Image(filename=out, width=800)"
]
},
{
"cell_type": "markdown",
"id": "36c2b527",
"metadata": {
"collapsed": false,
"papermill": {
"duration": 0.052127,
"end_time": "2023-02-08T18:30:09.354427",
"exception": false,
"start_time": "2023-02-08T18:30:09.302300",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"# Mapproject ctx against 100m DEM"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "9957e047",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2023-02-08T18:30:09.462494Z",
"iopub.status.busy": "2023-02-08T18:30:09.461706Z",
"iopub.status.idle": "2023-02-08T18:30:51.856920Z",
"shell.execute_reply": "2023-02-08T18:30:51.855501Z"
},
"papermill": {
"duration": 42.451419,
"end_time": "2023-02-08T18:30:51.859114",
"exception": false,
"start_time": "2023-02-08T18:30:09.407695",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"# Started: step_9 (Mapproject the left and right ctx images against the reference DEM), at: 2023-02-08 18:30:09.948441\r\n",
"INFO:sh.command:<Command '/usr/bin/cat ./stereopairs.lis', pid 8353>: process started\r\n",
"cd /home/runner/work/asap_stereo/asap_stereo/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W\r\n",
"INFO:sh.command:<Command '/usr/share/miniconda3/envs/asap/bin/gdalinfo -json B03_010644_1889_XN_08N001W.lev1eo.cub', pid 8358>: process started\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:sh.command:/usr/share/miniconda3/envs/isis_for_asp/bin/camrange from=B03_010644_1889_XN_08N001W.lev1eo.cub: process started\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:sh.command:<Command '/usr/share/miniconda3/envs/asap/bin/gdalinfo -json P02_001902_1889_XI_08N001W.lev1eo.cub', pid 8370>: process started\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:sh.command:/usr/share/miniconda3/envs/isis_for_asp/bin/camrange from=P02_001902_1889_XI_08N001W.lev1eo.cub: process started\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:sh.command:/tmp/sp/bin/mapproject /home/runner/work/asap_stereo/asap_stereo/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W/results_ba/dem/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba_100_0-DEM.tif B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json B03_010644_1889_XN_08N001W.ba.map.tif --mpp 25 --bundle-adjust-prefix adjust/ba: process started\r\n",
"/tmp/sp/bin/mapproject: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"mapproject_single --t_pixelwin 0 0 1221 1573 /home/runner/work/asap_stereo/asap_stereo/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W/results_ba/dem/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba_100_0-DEM.tif B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json ./B03_010644_1889_XN_08N001W_ba_map_tif_tiles/tile_0_0_1221_1573.tif --threads 8 --mpp 25 --bundle-adjust-prefix adjust/ba\r\n",
"--> Setting number of processing threads to: 8\r\n",
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Output pixel size: 25\r\n",
"Projected space bounding box: (Origin: (-14700, -19400) width: 30500 height: 39300)\r\n",
"Image box: (Origin: (0, 0) width: 1221 height: 1573)\r\n",
"Output image size:\r\n",
"(width: 1221 height: 1573)\r\n",
"Writing: ./B03_010644_1889_XN_08N001W_ba_map_tif_tiles/tile_0_0_1221_1573.tif\r\n",
"\r\n",
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},
{
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"text": [
"0...10...20...30...40...50...60...70...80...90...100 - done.\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Input file size is 1221, 1573\r\n",
"0"
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},
{
"name": "stdout",
"output_type": "stream",
"text": [
"...10...20...30...40."
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{
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".90...100 - done.\r\n",
"mapproject_single --query-projection /home/runner/work/asap_stereo/asap_stereo/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W/results_ba/dem/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba_100_0-DEM.tif B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json B03_010644_1889_XN_08N001W.ba.map.tif --mpp 25 --bundle-adjust-prefix adjust/ba\r\n",
"\t--> Setting number of processing threads to: 4\r\n",
"Using session: csm\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Output pixel size: 25\r\n",
"Projected space bounding box: (Origin: (-14700, -19400) width: 30500 height: 39300)\r\n",
"Image box: (Origin: (0, 0) width: 1221 height: 1573)\r\n",
"Output image size:\r\n",
"(width: 1221 height: 1573)\r\n",
"Query finished, exiting mapproject tool.\r\n",
"\r\n",
"Output image size is 1221 by 1573 pixels.\r\n",
"Splitting into 1 by 1 tiles.\r\n",
"parallel --will-cite --workdir /home/runner/work/asap_stereo/asap_stereo/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W -u --env PATH --env PYTHONPATH --env ISISROOT --env ASP_LIBRARY_PATH --env ISISDATA -a ./B03_010644_1889_XN_08N001W_ba_map_tif_tiles/argumentList.txt -P 1 --colsep \\t /tmp/sp/bin/python /tmp/sp/libexec/mapproject --pixelStartX {1} --pixelStartY {2} --pixelStopX {3} --pixelStopY {4} --work-dir ./B03_010644_1889_XN_08N001W_ba_map_tif_tiles/ /home/runner/work/asap_stereo/asap_stereo/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W/results_ba/dem/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba_100_0-DEM.tif B03_010644_1889_XN_08N001W.lev1eo.cub B03_010644_1889_XN_08N001W.lev1eo.json B03_010644_1889_XN_08N001W.ba.map.tif --threads 8 --mpp 25 --bundle-adjust-prefix adjust/ba\r\n",
"gdal_translate -co compress=lzw -co bigtiff=yes -co TILED=yes -co INTERLEAVE=BAND -co BLOCKXSIZE=256 -co BLOCKYSIZE=256 ./B03_010644_1889_XN_08N001W_ba_map_tif_tiles/mosaic.vrt B03_010644_1889_XN_08N001W.ba.map.tif\r\n",
"Removing: ./B03_010644_1889_XN_08N001W_ba_map_tif_tiles/\r\n",
"Wrote: B03_010644_1889_XN_08N001W.ba.map.tif\r\n",
"Finished in 10.886358261108398 seconds.\r\n",
"INFO:sh.command:/tmp/sp/bin/mapproject /home/runner/work/asap_stereo/asap_stereo/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W/results_ba/dem/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba_100_0-DEM.tif P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.ba.map.tif --mpp 25 --bundle-adjust-prefix adjust/ba: process started\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"/tmp/sp/bin/mapproject: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"mapproject_single --t_pixelwin 0 0 1221 1573 /home/runner/work/asap_stereo/asap_stereo/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W/results_ba/dem/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba_100_0-DEM.tif P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json ./P02_001902_1889_XI_08N001W_ba_map_tif_tiles/tile_0_0_1221_1573.tif --threads 8 --mpp 25 --bundle-adjust-prefix adjust/ba\r\n",
"--> Setting number of processing threads to: 8\r\n",
"Using session: csm\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Output pixel size: 25\r\n",
"Projected space bounding box: (Origin: (-14700, -19400) width: 30500 height: 39300)\r\n",
"Image box: (Origin: (0, 0) width: 1221 height: 1573)\r\n",
"Output image size:\r\n",
"(width: 1221 height: 1573)\r\n",
"Writing: ./P02_001902_1889_XI_08N001W_ba_map_tif_tiles/tile_0_0_1221_1573.tif\r\n",
"\r\n",
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},
{
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"output_type": "stream",
"text": [
"0...10...20...30...40...50...60...70...80...90...100 - done.\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Input file size is 1221, 1573\r\n",
"0"
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},
{
"name": "stdout",
"output_type": "stream",
"text": [
"...10...20...30...40."
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"text": [
"..50...60...70...80.."
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{
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"output_type": "stream",
"text": [
".90...100 - done.\r\n",
"mapproject_single --query-projection /home/runner/work/asap_stereo/asap_stereo/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W/results_ba/dem/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba_100_0-DEM.tif P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.ba.map.tif --mpp 25 --bundle-adjust-prefix adjust/ba\r\n",
"\t--> Setting number of processing threads to: 4\r\n",
"Using session: csm\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Output pixel size: 25\r\n",
"Projected space bounding box: (Origin: (-14700, -19400) width: 30500 height: 39300)\r\n",
"Image box: (Origin: (0, 0) width: 1221 height: 1573)\r\n",
"Output image size:\r\n",
"(width: 1221 height: 1573)\r\n",
"Query finished, exiting mapproject tool.\r\n",
"\r\n",
"Output image size is 1221 by 1573 pixels.\r\n",
"Splitting into 1 by 1 tiles.\r\n",
"parallel --will-cite --workdir /home/runner/work/asap_stereo/asap_stereo/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W -u --env PATH --env PYTHONPATH --env ISISROOT --env ASP_LIBRARY_PATH --env ISISDATA -a ./P02_001902_1889_XI_08N001W_ba_map_tif_tiles/argumentList.txt -P 1 --colsep \\t /tmp/sp/bin/python /tmp/sp/libexec/mapproject --pixelStartX {1} --pixelStartY {2} --pixelStopX {3} --pixelStopY {4} --work-dir ./P02_001902_1889_XI_08N001W_ba_map_tif_tiles/ /home/runner/work/asap_stereo/asap_stereo/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W/results_ba/dem/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba_100_0-DEM.tif P02_001902_1889_XI_08N001W.lev1eo.cub P02_001902_1889_XI_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.ba.map.tif --threads 8 --mpp 25 --bundle-adjust-prefix adjust/ba\r\n",
"gdal_translate -co compress=lzw -co bigtiff=yes -co TILED=yes -co INTERLEAVE=BAND -co BLOCKXSIZE=256 -co BLOCKYSIZE=256 ./P02_001902_1889_XI_08N001W_ba_map_tif_tiles/mosaic.vrt P02_001902_1889_XI_08N001W.ba.map.tif\r\n",
"Removing: ./P02_001902_1889_XI_08N001W_ba_map_tif_tiles/\r\n",
"Wrote: P02_001902_1889_XI_08N001W.ba.map.tif\r\n",
"Finished in 10.754485368728638 seconds.\r\n",
"cd /home/runner/work/asap_stereo/asap_stereo\r\n",
"# Finished: step_9 (Mapproject the left and right ctx images against the reference DEM), at: 2023-02-08 18:30:51.676327, duration: 0:00:41.727886\r\n"
]
}
],
"source": [
"!asap ctx step_9 --mpp {img_gsd} {asap.kwarg_parse(step_kwargs, 'step_9')} 2>&1 | tee -i -a ./5_mapproject_to_100m_dem.log ./full_log.log"
]
},
{
"cell_type": "markdown",
"id": "847ac319",
"metadata": {
"collapsed": false,
"papermill": {
"duration": 0.053444,
"end_time": "2023-02-08T18:30:51.966571",
"exception": false,
"start_time": "2023-02-08T18:30:51.913127",
"status": "completed"
},
"pycharm": {
"name": "#%% md\n"
},
"tags": []
},
"source": [
"# Calculate Better DEM using prior "
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "bdeae9dc",
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2023-02-08T18:30:52.076434Z",
"iopub.status.busy": "2023-02-08T18:30:52.075720Z",
"iopub.status.idle": "2023-02-08T18:31:28.624952Z",
"shell.execute_reply": "2023-02-08T18:31:28.623905Z"
},
"papermill": {
"duration": 36.607175,
"end_time": "2023-02-08T18:31:28.626854",
"exception": false,
"start_time": "2023-02-08T18:30:52.019679",
"status": "completed"
},
"pycharm": {
"name": "#%%\n"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"# Started: step_10 (Second stereo first step), at: 2023-02-08 18:30:52.570192\r\n",
"INFO:sh.command:<Command '/usr/bin/cat ./stereopairs.lis', pid 8602>: process started\r\n",
"# Started: stereo_asap (parallel stereo common step), at: 2023-02-08 18:30:52.578635\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:sh.command:<Command '/usr/bin/cat ./stereopairs.lis', pid 8607>: process started\r\n",
"cd /home/runner/work/asap_stereo/asap_stereo/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W\r\n",
"INFO:sh.command:/tmp/sp/bin/parallel_stereo --processes 1 --threads-singleprocess 2 --threads-multiprocess 1 --stop-point 5 --bundle-adjust-prefix adjust/ba --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.map B03_010644_1889_XN_08N001W.ba.map.tif P02_001902_1889_XI_08N001W.ba.map.tif B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_map_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba /home/runner/work/asap_stereo/asap_stereo/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W/results_ba/dem/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba_100_0-DEM.tif: process started\r\n",
"/tmp/sp/bin/parallel_stereo: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"/tmp/sp/bin/stereo_pprc: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r\n",
"[ 2023-Feb-08 18:30:54 ] : Stage 0 --> PREPROCESSING \r\n",
"\t--> Setting number of processing threads to: 2\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.map.\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Writing log info to: results_map_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-log-stereo_pprc-02-08-1830-8655.txt\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using session: csmmapcsm\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.ba.map.tif B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.ba.map.tif P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"Using image files: B03_010644_1889_XN_08N001W.ba.map.tif, P02_001902_1889_XI_08N001W.ba.map.tif\r\n",
"Using camera files: B03_010644_1889_XN_08N001W.lev1eo.json, P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using input DEM: /home/runner/work/asap_stereo/asap_stereo/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W/results_ba/dem/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba_100_0-DEM.tif\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Computing statistics for left\r\n",
"Using downsample scale: 2\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t Writing stats file: results_map_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-B03_010644_1889_XN_08N001W.ba.map-stats.tif\r\n",
"\t left: [ lo: 0.0595556 hi: 0.139632 mean: 0.102382 std_dev: 0.014884 ]\r\n",
"Computing statistics for right\r\n",
"Using downsample scale: 2\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t Writing stats file: results_map_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-P02_001902_1889_XI_08N001W.ba.map-stats.tif\r\n",
"\t right: [ lo: 0.0599079 hi: 0.144116 mean: 0.105446 std_dev: 0.0156837 ]\r\n",
"\t--> Applying alignment method: none\r\n",
"\t--> Normalizing globally to: [0.0595556 0.144116]\r\n",
"\t--> Writing pre-aligned images.\r\n",
"\t--> Writing: results_map_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-L.tif.\r\n",
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"\r",
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},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t--> Writing: results_map_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-R.tif.\r\n",
"\r",
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},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t--> Generating image masks... \r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Writing masks: results_map_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-lMask.tif results_map_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-rMask.tif.\r\n"
]
},
{
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},
{
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"text": [
"\t--> Creating previews. Subsampling by 0.6 by using a tile of size 256 and 2 threads.\r\n"
]
},
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"output_type": "stream",
"text": [
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"\r\n",
"[ 2023-Feb-08 18:30:55 ] : PREPROCESSING FINISHED \r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"/tmp/sp/bin/stereo_corr: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t--> Setting number of processing threads to: 4\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.map.\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Writing log info to: results_map_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-log-stereo_corr-02-08-1830-8759.txt\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using session: csmmapcsm\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.ba.map.tif B03_010644_1889_XN_08N001W.lev1eo.json\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.ba.map.tif P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"\t--> Using LOG pre-processing filter with 1.4 sigma blur.\r\n",
"\r\n",
"[ 2023-Feb-08 18:30:56 ] : Stage 1 --> CORRELATION\r\n",
"\r\n",
"[ 2023-Feb-08 18:30:56 ] : Stage 1 --> LOW-RESOLUTION CORRELATION\r\n",
"No IP file found, computing IP now.\r\n",
"\t * Detecting interest points.\r\n",
"\t--> Matching interest points using homography.\r\n",
"\t Looking for IP in left image...\r\n",
"\t Using 2729 interest points per tile (1024^2 px).\r\n",
"\t Detecting IP\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t Removing IP near nodata with radius 4\r\n",
"\t Building descriptors\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t Found interest points: 3597\r\n",
"\t Recording interest points to file: results_map_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-L.vwip\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t Looking for IP in right image...\r\n",
"\t Using 2729 interest points per tile (1024^2 px).\r\n",
"\t Detecting IP\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t Removing IP near nodata with radius 4\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t Building descriptors\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t Found interest points: 3593\r\n",
"\t Recording interest points to file: results_map_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-R.vwip\r\n",
"\t--> Uniqueness threshold: 0.8\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
},
{
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"output_type": "stream",
"text": [
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},
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r\n",
"\t Matched points: 2255\r\n",
"\t Inlier threshold: 200\r\n",
"\t RANSAC iterations: 100\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t * Writing match file: results_map_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-L__R.match\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t--> Using interest points to determine search window.\r\n",
"\t * Loading match file: results_map_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-L__R.match\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Removed 19 outliers based on percentiles of differences of interest points with --outlier-removal-params.\r\n",
"D_sub search range: (Origin: (-10.125, -5.125) width: 21.25 height: 11.25) px\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Writing: results_map_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-D_sub.tif\r\n",
"\r",
" --> Low-resolution disparity:[....................................] 0%\r",
" --> Low-resolution disparity:[*******************************] Complete!\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Filtering outliers in D_sub based on --outlier-removal-params.\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Inlier range based on x coordinate of disparity: 0 0.\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Inlier range based on y coordinate of disparity: 0 0.\r\n",
"Number (and fraction) of removed outliers by disparity values in x and y: 8695 (0.0125659).\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Height above datum inlier range: -2.98261e+06 -2.9753e+06.\r\n",
"Number (and fraction) of removed outliers by the height check: 0 (0).\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Triangulation error inlier range: -0.00206399 0.00253029.\r\n",
"Number (and fraction) of removed outliers by the triangulation error check: 0 (0).\r\n",
"Writing filtered D_sub: results_map_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-D_sub.tif\r\n",
"\r",
" D_sub: [.........................................................] 0%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" D_sub: [****************************************************] Complete!\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Writing triangulated point cloud based on D_sub: results_map_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-PC_sub.tif\r\n",
"\r",
" PC_sub: [........................................................] 0%\r",
" PC_sub: [***************************************************] Complete!\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t--> Full-res search range based on D_sub: (Origin: (0, 0) width: 0 height: 0)\r\n",
"\r\n",
"[ 2023-Feb-08 18:31:10 ] : LOW-RESOLUTION CORRELATION FINISHED\r\n",
"\r\n",
"[ 2023-Feb-08 18:31:10 ] : CORRELATION FINISHED\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using tiles (before collar addition) of 2048 x 2048 pixels.\r\n",
"Using a collar (padding) for each tile of 0 pixels.\r\n",
"/usr/bin/time -f \"stereo_corr: elapsed=%E ([hours:]minutes:seconds), memory=%M (kb)\" /tmp/sp/bin/stereo_corr --bundle-adjust-prefix adjust/ba B03_010644_1889_XN_08N001W.ba.map.tif P02_001902_1889_XI_08N001W.ba.map.tif B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_map_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_0_1221_1573/0_0_1221_1573 /home/runner/work/asap_stereo/asap_stereo/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W/results_ba/dem/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba_100_0-DEM.tif --skip-low-res-disparity-comp --corr-seed-mode 1 --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.map --sgm-collar-size 0 --threads 1 --trans-crop-win 0 0 1221 1573\r\n",
"--> Setting number of processing threads to: 1\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.map.\r\n",
"Writing log info to: results_map_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_0_1221_1573/0_0_1221_1573-log-stereo_corr-02-08-1831-8875.txt\r\n",
"Using session: csmmapcsm\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.ba.map.tif B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.ba.map.tif P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"--> Using LOG pre-processing filter with 1.4 sigma blur.\r\n",
"\r\n",
"[ 2023-Feb-08 18:31:13 ] : Stage 1 --> CORRELATION\r\n",
"--> Full-res search range based on D_sub: (Origin: (0, 0) width: 0 height: 0)\r\n",
"--------------------------------------------------\r\n",
"Kernel size: Vector2(25,25)\r\n",
"Search range: (Origin: (0, 0) width: 0 height: 0)\r\n",
"Cost mode: 0\r\n",
"--------------------------------------------------\r\n",
"Writing: results_map_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_0_1221_1573/0_0_1221_1573-D.tif\r\n",
"\r\n",
"--> Correlation :[................................................] 0%\r\n",
"--> Correlation :[************....................................] 25%\r\n",
"--> Correlation :[************************........................] 50%\r\n",
"--> Correlation :[************************************............] 75%\r\n",
"--> Correlation :[************************************************] 100%\r\n",
"--> Correlation :[*******************************************] Complete!\r\n",
"\r\n",
"[ 2023-Feb-08 18:31:20 ] : CORRELATION FINISHED\r\n",
"/tmp/sp/bin/stereo_corr: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n",
"stereo_corr: elapsed=0:07.50 ([hours:]minutes:seconds), memory=296724 (kb)\r\n",
"\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using tiles (before collar addition) of 2048 x 2048 pixels.\r\n",
"Using a collar (padding) for each tile of 0 pixels.\r\n",
"/usr/bin/time -f \"stereo_rfne: elapsed=%E ([hours:]minutes:seconds), memory=%M (kb)\" /tmp/sp/bin/stereo_rfne --bundle-adjust-prefix adjust/ba B03_010644_1889_XN_08N001W.ba.map.tif P02_001902_1889_XI_08N001W.ba.map.tif B03_010644_1889_XN_08N001W.lev1eo.json P02_001902_1889_XI_08N001W.lev1eo.json results_map_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_0_1221_1573/0_0_1221_1573 /home/runner/work/asap_stereo/asap_stereo/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W/results_ba/dem/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba_100_0-DEM.tif --corr-seed-mode 1 --stereo-file /home/runner/work/asap_stereo/asap_stereo/stereo.map --sgm-collar-size 0 --threads 1 --trans-crop-win 0 0 1221 1573\r\n",
"\r\n",
"[ 2023-Feb-08 18:31:21 ] : Stage 2 --> REFINEMENT\r\n",
"--> Setting number of processing threads to: 1\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.map.\r\n",
"Writing log info to: results_map_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_0_1221_1573/0_0_1221_1573-log-stereo_rfne-02-08-1831-8968.txt\r\n",
"Using session: csmmapcsm\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.ba.map.tif B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.ba.map.tif P02_001902_1889_XI_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"Left image nodata: -32768\r\n",
"Right image nodata: -32768\r\n",
"--> Using LOG pre-processing filter with 1.4 sigma blur.\r\n",
"--> Using parabola subpixel mode.\r\n",
"Writing: results_map_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-0_0_1221_1573/0_0_1221_1573-RD.tif\r\n",
"\r\n",
"--> Refinement :[.................................................] 0%\r\n",
"--> Refinement :[*................................................] 3%\r\n",
"--> Refinement :[**...............................................] 6%\r\n",
"--> Refinement :[****.............................................] 9%\r\n",
"--> Refinement :[*****............................................] 11%\r\n",
"--> Refinement :[*******..........................................] 14%\r\n",
"--> Refinement :[********.........................................] 17%\r\n",
"--> Refinement :[*********........................................] 20%\r\n",
"--> Refinement :[***********......................................] 23%\r\n",
"--> Refinement :[************.....................................] 26%\r\n",
"--> Refinement :[**************...................................] 29%\r\n",
"--> Refinement :[***************..................................] 31%\r\n",
"--> Refinement :[****************.................................] 34%\r\n",
"--> Refinement :[******************...............................] 37%\r\n",
"--> Refinement :[*******************..............................] 40%\r\n",
"--> Refinement :[*********************............................] 43%\r\n",
"--> Refinement :[**********************...........................] 46%\r\n",
"--> Refinement :[***********************..........................] 49%\r\n",
"--> Refinement :[*************************........................] 51%\r\n",
"--> Refinement :[**************************.......................] 54%\r\n",
"--> Refinement :[****************************.....................] 57%\r\n",
"--> Refinement :[*****************************....................] 60%\r\n",
"--> Refinement :[******************************...................] 63%\r\n",
"--> Refinement :[********************************.................] 66%\r\n",
"--> Refinement :[*********************************................] 69%\r\n",
"--> Refinement :[***********************************..............] 71%\r\n",
"--> Refinement :[************************************.............] 74%\r\n",
"--> Refinement :[*************************************............] 77%\r\n",
"--> Refinement :[***************************************..........] 80%\r\n",
"--> Refinement :[****************************************.........] 83%\r\n",
"--> Refinement :[******************************************.......] 86%\r\n",
"--> Refinement :[*******************************************......] 89%\r\n",
"--> Refinement :[********************************************.....] 91%\r\n",
"--> Refinement :[**********************************************...] 94%\r\n",
"--> Refinement :[***********************************************..] 97%\r\n",
"--> Refinement :[*************************************************] 100%\r\n",
"--> Refinement :[********************************************] Complete!\r\n",
"\r\n",
"[ 2023-Feb-08 18:31:24 ] : REFINEMENT FINISHED\r\n",
"/tmp/sp/bin/stereo_rfne: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n",
"stereo_rfne: elapsed=0:02.45 ([hours:]minutes:seconds), memory=273572 (kb)\r\n",
"\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"/tmp/sp/bin/stereo_fltr: 52: /lib/x86_64-linux-gnu/libc.so.6: Permission denied\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r\n",
"[ 2023-Feb-08 18:31:24 ] : Stage 3 --> FILTERING \r\n",
"Warning: Hole-filling is disabled by default in stereo_fltr. It is suggested to use instead point2dem's analogous functionality. It can be re-enabled using --enable-fill-holes.\r\n",
"\t--> Setting number of processing threads to: 2\r\n",
"Using stereo file /home/runner/work/asap_stereo/asap_stereo/stereo.map.\r\n",
"Writing log info to: results_map_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-log-stereo_fltr-02-08-1831-9059.txt\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using session: csmmapcsm\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Loading camera model: B03_010644_1889_XN_08N001W.ba.map.tif B03_010644_1889_XN_08N001W.lev1eo.json\r\n",
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-B03_010644_1889_XN_08N001W.lev1eo.adjust\r\n",
"Loading camera model: P02_001902_1889_XI_08N001W.ba.map.tif P02_001902_1889_XI_08N001W.lev1eo.json\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using plugin: UsgsAstroPluginCSM with model name USGS_ASTRO_LINE_SCANNER_SENSOR_MODEL\r\n",
"Using adjusted camera model: adjust/ba-P02_001902_1889_XI_08N001W.lev1eo.adjust\r\n",
"Distance between camera centers in meters: 52056.1.\r\n",
"\t--> Cleaning up disparity map prior to filtering processes (1 pass).\r\n",
"Writing: results_map_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-GoodPixelMap.tif\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [.............................................] 0%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [*............................................] 3%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [**...........................................] 6%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [***..........................................] 9%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [*****........................................] 11%\r",
" --> Good pixel map: [******.......................................] 14%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [*******......................................] 17%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [*********....................................] 20%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [**********...................................] 23%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [***********..................................] 26%\r",
" --> Good pixel map: [************.................................] 29%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [**************...............................] 31%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [***************..............................] 34%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [****************.............................] 37%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [******************...........................] 40%\r",
" --> Good pixel map: [*******************..........................] 43%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [********************.........................] 46%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [*********************........................] 49%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [***********************......................] 51%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [************************.....................] 54%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [*************************....................] 57%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [***************************..................] 60%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [****************************.................] 63%\r",
" --> Good pixel map: [*****************************................] 66%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [******************************...............] 69%\r",
" --> Good pixel map: [********************************.............] 71%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [*********************************............] 74%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [**********************************...........] 77%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [************************************.........] 80%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Good pixel map: [*************************************........] 83%\r",
" --> Good pixel map: [**************************************.......] 86%\r",
" --> Good pixel map: [***************************************......] 89%\r",
" --> Good pixel map: [*****************************************....] 91%\r",
" --> Good pixel map: [******************************************...] 94%\r",
" --> Good pixel map: [*******************************************..] 97%\r",
" --> Good pixel map: [*********************************************] 100%\r",
" --> Good pixel map: [****************************************] Complete!\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Writing: results_map_ba/B03_010644_1889_XN_08N001W_P02_001902_1889_XI_08N001W_ba-F.tif\r\n",
"\r",
" --> Filtering: [..................................................] 0%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Filtering: [*.................................................] 3%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Filtering: [**................................................] 6%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Filtering: [****..............................................] 9%\r",
" --> Filtering: [*****.............................................] 11%\r",
" --> Filtering: [*******...........................................] 14%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Filtering: [********..........................................] 17%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Filtering: [**********........................................] 20%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Filtering: [***********.......................................] 23%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Filtering: [************......................................] 26%\r",
" --> Filtering: [**************....................................] 29%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Filtering: [***************...................................] 31%\r",
" --> Filtering: [*****************.................................] 34%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Filtering: [******************................................] 37%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Filtering: [********************..............................] 40%\r",
" --> Filtering: [*********************.............................] 43%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Filtering: [**********************............................] 46%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Filtering: [************************..........................] 49%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Filtering: [*************************.........................] 51%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Filtering: [***************************.......................] 54%\r",
" --> Filtering: [****************************......................] 57%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Filtering: [******************************....................] 60%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Filtering: [*******************************...................] 63%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Filtering: [********************************..................] 66%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Filtering: [**********************************................] 69%\r",
" --> Filtering: [***********************************...............] 71%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Filtering: [*************************************.............] 74%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Filtering: [**************************************............] 77%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" --> Filtering: [****************************************..........] 80%"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
"
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