jessemapel/MDIS_Stereo.ipynb

## MDIS_Stereo.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ctypes\n",
    "from ctypes.util import find_library\n",
    "import csmapi as csm\n",
    "import os\n",
    "import pyproj\n",
    "import matplotlib.pyplot as plt\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<CDLL '/Users/jmapel/miniconda3/envs/csmapi/bin/../lib/libusgscsm.dylib', handle 7fa989e13db0 at 0x120ec4e48>\n"
     ]
    }
   ],
   "source": [
    "lib = ctypes.CDLL(find_library('usgscsm'))\n",
    "print(lib)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_csm(image):\n",
    "    \"\"\"\n",
    "    Given an image file create a Community Sensor Model.\n",
    "    Parameters\n",
    "    ----------\n",
    "    image : str\n",
    "            The image filename to create a CSM for\n",
    "    Returns\n",
    "    -------\n",
    "    model : object\n",
    "            A CSM sensor model (or None if no associated model is available.)\n",
    "    \"\"\"\n",
    "    isd = csm.Isd(image)\n",
    "    plugins = csm.Plugin.getList()\n",
    "    for plugin in plugins:\n",
    "        num_models = plugin.getNumModels()\n",
    "        for model_index in range(num_models):\n",
    "            model_name = plugin.getModelName(model_index)\n",
    "            if plugin.canModelBeConstructedFromISD(isd, model_name):\n",
    "                return plugin.constructModelFromISD(isd, model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_dir = '/usgs/shareall/jmapel/mdis_stereo'\n",
    "EN0213023991M = os.path.join(image_dir, 'EN0213023991M.IMG')\n",
    "EN0213110924M = os.path.join(image_dir, 'EN0213110924M.IMG')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "EN0213023991M_cam = create_csm(EN0213023991M)\n",
    "EN0213110924M_cam = create_csm(EN0213110924M)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EN0213023991M\n",
      "[(-2080718.7744600587, -185633.32068822882, 1259691.2367162416), (-2077113.7592865254, -114103.48446807172, 1274068.7523893046), (-2046408.9888007683, -124803.68495125533, 1321857.2732250646), (-2049404.6902888957, -196546.54934161785, 1308427.3878835537)]\n",
      "EN0213110924M\n",
      "[(-2088600.7706305275, -207764.23888551746, 1243082.1380592226), (-2083034.1833333941, -119808.38933187095, 1263838.162467917), (-2050977.2860463343, -133146.5700676071, 1313939.3148091917), (-2055633.2119064806, -221488.0492170276, 1294599.3597098233)]\n"
     ]
    }
   ],
   "source": [
    "image_corners = [(0,0), (0,512), (512,512), (512,0)]\n",
    "EN0213023991M_pts = []\n",
    "EN0213110924M_pts = []\n",
    "\n",
    "for line, sample in image_corners:\n",
    "    EN0213023991M_gp = EN0213023991M_cam.imageToGround(csm.ImageCoord(line, sample), 0.0)\n",
    "    EN0213023991M_pts.append((EN0213023991M_gp.x, EN0213023991M_gp.y, EN0213023991M_gp.z))\n",
    "    EN0213110924M_gp = EN0213110924M_cam.imageToGround(csm.ImageCoord(line, sample), 0.0)\n",
    "    EN0213110924M_pts.append((EN0213110924M_gp.x, EN0213110924M_gp.y, EN0213110924M_gp.z))\n",
    "print('EN0213023991M')\n",
    "print(EN0213023991M_pts)\n",
    "print('EN0213110924M')\n",
    "print(EN0213110924M_pts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EN0213023991M\n",
      "[(-174.90179985131846, 31.090669614640582, 4.656612873077393e-10), (-176.85569280840633, 31.485835143582847, -9.313225746154785e-10), (-176.5100433108931, 32.811576414470004, -4.656612873077393e-10), (-174.52184752899575, 32.43704760597564, 4.656612873077393e-10)]\n",
      "EN0213110924M\n",
      "[(-174.31917247157054, 30.636204105122445, 4.656612873077393e-10), (-176.70818628452528, 31.204478043599497, -4.656612873077393e-10), (-176.28565041633854, 32.59057317268081, 9.313225746154785e-10), (-173.8502838777742, 32.05303423258575, 4.656612873077393e-10)]\n"
     ]
    }
   ],
   "source": [
    "semi_major = 2439.4 * 1000\n",
    "semi_minor = 2439.4 * 1000\n",
    "ecef = pyproj.Proj(proj='geocent', a=semi_major, b=semi_minor)\n",
    "lla = pyproj.Proj(proj='latlon', a=semi_major, b=semi_minor)\n",
    "\n",
    "EN0213023991M_lla = []\n",
    "EN0213110924M_lla = []\n",
    "for x, y, z in EN0213023991M_pts:\n",
    "    EN0213023991M_lla.append(pyproj.transform(ecef, lla, x, y, z))\n",
    "for x, y, z in EN0213110924M_pts:\n",
    "    EN0213110924M_lla.append(pyproj.transform(ecef, lla, x, y, z))\n",
    "print('EN0213023991M')\n",
    "print(EN0213023991M_lla)\n",
    "print('EN0213110924M')\n",
    "print(EN0213110924M_lla)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "EN0213023991M_lon = [lla[0] for lla in EN0213023991M_lla]\n",
    "EN0213023991M_lon.append(EN0213023991M_lon[0])\n",
    "EN0213023991M_lat = [lla[1] for lla in EN0213023991M_lla]\n",
    "EN0213023991M_lat.append(EN0213023991M_lat[0])\n",
    "EN0213110924M_lon = [lla[0] for lla in EN0213110924M_lla]\n",
    "EN0213110924M_lon.append(EN0213110924M_lon[0])\n",
    "EN0213110924M_lat = [lla[1] for lla in EN0213110924M_lla]\n",
    "EN0213110924M_lat.append(EN0213110924M_lat[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1213cde80>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(EN0213023991M_lon, EN0213023991M_lat, 'b')\n",
    "plt.plot(EN0213110924M_lon, EN0213110924M_lat, 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}