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tempdeltavalue/video_feature_inversion.ipynb

Created January 25, 2022 04:16
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video_feature_inversion.ipynb
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video_feature_inversion.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "video_feature_inversion.ipynb",
"provenance": [],
"collapsed_sections": [],
"authorship_tag": "ABX9TyPZoL48RuaRDAW4pj5UBU3R",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/tempdeltavalue/d7a7c57bc522c1099492c80691f17abf/video_feature_inversion.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "03A34ijp2HHF",
"outputId": "9e25751c-b547-498f-dcd1-10e2e360b735"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Drive already mounted at /content/drive/; to attempt to forcibly remount, call drive.mount(\"/content/drive/\", force_remount=True).\n"
]
}
],
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive/')"
]
},
{
"cell_type": "code",
"source": [
"%cd /content/drive/MyDrive/video_inversion\n",
"%ls"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "WW8reRvS2q2a",
"outputId": "03d57759-26db-40a7-b6c4-30a84f8ec784"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"/content/drive/MyDrive/video_inversion\n",
"grapefruit-slice-332-332.jpg IMG_2515.MOV \u001b[0m\u001b[01;34mtemp_imgs\u001b[0m/\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!pip install --quiet git+https://github.com/greentfrapp/lucent.git"
],
"metadata": {
"id": "E79rQ0Tz39oR"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from PIL import Image\n",
"import numpy as np\n",
"import scipy.ndimage as nd\n",
"import torch\n",
"\n",
"from google.colab import files\n",
"\n",
"from lucent.optvis import render, param, transform, objectives\n",
"from lucent.modelzoo import inceptionv1\n",
"from lucent.misc.io import show"
],
"metadata": {
"id": "SEns7Bhc3enQ"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
"model = inceptionv1(pretrained=True)\n",
"_ = model.to(device).eval()"
],
"metadata": {
"id": "YqLNGSbz32oZ"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"@objectives.wrap_objective()\n",
"def dot_compare(layer, batch=1, cossim_pow=0):\n",
" def inner(T):\n",
" dot = (T(layer)[batch] * T(layer)[0]).sum()\n",
" mag = torch.sqrt(torch.sum(T(layer)[0]**2))\n",
" cossim = dot/(1e-6 + mag)\n",
" return -dot * cossim ** cossim_pow\n",
" return inner"
],
"metadata": {
"id": "rC2nyexB4H2I"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def feature_inversion(img, layer=None, n_steps=512, cossim_pow=0.0):\n",
" # Convert image to torch.tensor and scale image\n",
" img = torch.tensor(np.transpose(img, [2, 0, 1])).to(device)\n",
" # upsample = torch.nn.Upsample(224)\n",
" # img = upsample(img)\n",
" \n",
" obj = objectives.Objective.sum([\n",
" 1.0 * dot_compare(layer, cossim_pow=cossim_pow),\n",
" objectives.blur_input_each_step(),\n",
" ])\n",
"\n",
" # Initialize parameterized input and stack with target image\n",
" # to be accessed in the objective function\n",
" params, image_f = param.image(1024)\n",
" def stacked_param_f():\n",
" return params, lambda: torch.stack([image_f()[0], img])\n",
"\n",
" transforms = [\n",
" transform.pad(8, mode='constant', constant_value=.5),\n",
" transform.jitter(8),\n",
" transform.random_scale([0.9, 0.95, 1.05, 1.1] + [1]*4),\n",
" transform.random_rotate(list(range(-5, 5)) + [0]*5),\n",
" transform.jitter(2),\n",
" ]\n",
"\n",
" _ = render.render_vis(model, obj, stacked_param_f, transforms=transforms, thresholds=(n_steps,), show_image=False, progress=True)\n",
"\n",
" # show(_[0][0])\n",
" return _[0][0]"
],
"metadata": {
"id": "5sx8XB3k4SHx"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# layers = ['conv2d%d' % i for i in range(0, 3)] + \\\n",
"# ['mixed3a', 'mixed3b', 'mixed4a',\n",
"# 'mixed4b', 'mixed4c', 'mixed4d',\n",
"# 'mixed4e', 'mixed5a', 'mixed5b']\n",
"\n",
"\n",
"layers = ['conv2d2']"
],
"metadata": {
"id": "deJph-Nu46Fg"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import cv2\n",
"from google.colab.patches import cv2_imshow\n",
"\n",
"cap = cv2.VideoCapture('/content/drive/MyDrive/video_inversion/IMG_2515.MOV')\n",
"fps = cap.get(cv2.CAP_PROP_FPS) # OpenCV2 version 2 used \"CV_CAP_PROP_FPS\"\n",
"frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
"duration = frame_count/fps\n",
"print(\"frame_count\", frame_count)\n",
"\n",
"print(\"Duration\", duration)\n",
"\n",
"count = 0\n",
"delta = 0\n",
"max_delta = 1920 - 1024 - 1\n",
"while cap.isOpened():\n",
" ret,frame = cap.read()\n",
" orig_size = frame.shape\n",
" print(orig_size)\n",
" if delta < max_delta:\n",
" delta += 1\n",
" else:\n",
" delta -=1\n",
"\n",
" frame = frame[0:1024, delta:1024+delta]\n",
"\n",
" # cv2_imshow(frame)\n",
"\n",
" for layer in layers:\n",
" # resized_img = cv2.resize(frame, (1024, 1024))\n",
" inverted_img = feature_inversion(frame, layer=layer, n_steps=100)\n",
"\n",
" aft_proc_img = inverted_img * 255\n",
" # aft_proc_img = cv2.resize(aft_proc_img, (1920, 1080))\n",
" cv2.imwrite(\"/content/drive/MyDrive/video_inversion/temp_imgs/%s.png\" % str(count), aft_proc_img)\n",
" # cv2_imshow(aft_proc_img)\n",
"\n",
"\n",
" count = count + 1\n",
"\n",
" if cv2.waitKey(10) & 0xFF == ord('q'):\n",
" break"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "VXoEy1eR4VCH",
"outputId": "a5461ccb-408b-4a60-c823-d14ebb54e72a"
},
"execution_count": null,
"outputs": [
{
"metadata": {
"tags": null
},
"name": "stdout",
"output_type": "stream",
"text": [
"frame_count 7176\n",
"Duration 119.67166666666667\n",
"(1080, 1920, 3)\n"
]
},
{
"metadata": {
"tags": null
},
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 100/100 [00:45<00:00, 2.21it/s]\n"
]
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"(1080, 1920, 3)\n"
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{
"cell_type": "code",
"source": [
"import numpy as np\n",
"\n",
"def grid_image(img, g_cell_side):\n",
" print(img.shape)\n",
" h_count = int(img.shape[0] / g_cell_side)\n",
" v_count = int(img.shape[1] / g_cell_side)\n",
" slices = []\n",
" for v_ind in range(v_count):\n",
" for h_ind in range(h_count):\n",
" h_start_ind = h_ind * g_cell_side\n",
" v_start_ind = v_ind * g_cell_side\n",
"\n",
" sliced_img = img[v_start_ind:v_start_ind+g_cell_side, \n",
" h_start_ind:h_start_ind+g_cell_side]\n",
" # cv2_imshow(sliced_img)\n",
" slices.append(sliced_img)\n",
" \n",
" return slices\n",
"\n",
"def merge_grid(shape, grid_arr):\n",
" g_cell_side = grid_arr[0].shape[0]\n",
"\n",
" result_img = np.zeros(shape=shape)\n",
"\n",
" v_ind = 0\n",
" h_ind = 0\n",
" for slice_ind, slice_item in enumerate(grid_arr):\n",
" if h_ind * g_cell_side >= shape[0]:\n",
" v_ind += 1\n",
" h_ind = 0\n",
" \n",
" h_start_ind = h_ind * g_cell_side\n",
" v_start_ind = v_ind * g_cell_side\n",
" \n",
" # print(result_img[v_start_ind:v_start_ind+g_cell_side, h_start_ind:h_start_ind+g_cell_side].shape)\n",
" # print(h_start_ind)\n",
" result_img[v_start_ind:v_start_ind+g_cell_side, \n",
" h_start_ind:h_start_ind+g_cell_side] = slice_item\n",
"\n",
" h_ind += 1\n",
"\n",
" return result_img\n",
"\n",
"\n",
"\n",
"\n",
"test_img = cv2.imread(\"/content/drive/MyDrive/video_inversion/grapefruit-slice-332-332.jpg\")\n",
"# test_img.resize(400, 600)\n",
"grid_arr = grid_image(test_img, 2)\n",
"cv2_imshow(merge_grid(test_img.shape, grid_arr))"
],
"metadata": {
"id": "sGTzM5Lz4ZrR"
},
"execution_count": null,
"outputs": []
}
]
}
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