test_matcher.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "45cff753",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "os.environ['ISISROOT'] = '/Users/krodriguez/anaconda3/envs/isis6' \n",
    "os.environ['ISISDATA'] = '/Volumes/ISISDATA/isisdata/' \n",
    "\n",
    "import pandas as pd\n",
    "from plio.io.io_controlnetwork import to_isis\n",
    "from plio.io.isis_serial_number import generate_serial_number\n",
    "from plio.io.io_gdal import GeoDataset\n",
    "import pvl\n",
    "\n",
    "from skimage import transform\n",
    "from skimage.util import img_as_float32\n",
    "\n",
    "\n",
    "\n",
    "# DEFINE REFERENCE AND MOVING IMAGE PATHS\n",
    "\n",
    "# I got these images with: \n",
    "# scp prog28:/work/users/jlaura/autocnet_553/* ../tests/test_subpixel_match/\n",
    "reference_image = GeoDataset('../tests/test_subpixel_match/B08_012650_1780_XN_02S046W.l1.cal.destriped.cub')\n",
    "moving_image = GeoDataset('../tests/test_subpixel_match/J04_046447_1777_XI_02S046W.l1.cal.destriped.cub')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f51a17eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ProjectiveTransform(matrix=\n",
       "    [[ 0.97415888, -0.01091757,  1.12114015],\n",
       "     [-0.00121801,  0.99902349,  0.06693281],\n",
       "     [ 0.        ,  0.        ,  1.        ]]) at 0x7f85e0c5d210>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# match \n",
    "from autocnet.transformation.affine import *\n",
    "from autocnet.matcher import subpixel as sp \n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# offset the point to see if it walks back\n",
    "errorx = 10\n",
    "errory = 10\n",
    "\n",
    "ref_center = (3811.7802039064, 1120.4993337409)\n",
    "moving_center = (3393.966940328594+errorx, 26525.699814573134+errory)\n",
    "\n",
    "ref_roi = Roi(reference_image, *ref_center, 60, 60)\n",
    "moving_roi = Roi(moving_image, moving_center[0], moving_center[1], 30, 30)\n",
    "\n",
    "affine = estimate_local_affine(reference_image, moving_image, *ref_center, 30, 30)\n",
    "\n",
    "affine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e678a095",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ProjectiveTransform(matrix=\n",
       "    [[ 0.97415888, -0.01091757,  1.12114015],\n",
       "     [-0.00121801,  0.99902349,  0.06693281],\n",
       "     [ 0.        ,  0.        ,  1.        ]]) at 0x7f85e0c5d210>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "affine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ef9fc533",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AffineTransform(matrix=\n",
       "    [[ 1.        , -0.        ,  9.80196914],\n",
       "     [ 0.        ,  1.        ,  9.82960478],\n",
       "     [ 0.        ,  0.        ,  1.        ]]) at 0x7f8600e4e510>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_affine, metric, coormap = sp.subpixel_template(ref_roi, moving_roi, affine)\n",
    "new_affine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6a27784e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3394.16497119, 26525.87020979])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# apply affine to moving image center\n",
    "new_moving_center = new_affine.inverse(moving_center)[0]\n",
    "new_moving_center"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "06eeb768",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = ['id', 'pointType', 'aprioiri', 'adjusted', 'pointIgnore', 'referenceIndex', 'identifier',\n",
    "           'serialnumber', 'sample', 'line', 'measureType', 'imageid', 'measureIgnore',\n",
    "           'measureJigsawRejected', 'aprioriline', 'apriorisample']\n",
    "\n",
    "# REFERENCE POINT; GET SAMPLE/LINE FROM TESTED POSITION\n",
    "reference_measure = [0, 2, None, None, False, 0, 'manual', \n",
    "                     generate_serial_number(pvl.load(reference_image.file_name)), \n",
    "                     *ref_center, 2, 1, \n",
    "                     False, False, *ref_center]\n",
    "\n",
    "# OTHER MEASURE(S); GET SAMPLE/LINE FROM RESULT AND A PRIORI SAMPLE LINE FROM AFFINE CENTER\n",
    "measure = [0, 2, None, None, False, 0, 'manual',\n",
    "           generate_serial_number(pvl.load(moving_image.file_name)),\n",
    "           *new_moving_center, 2, 1,\n",
    "          False, False, *moving_center]\n",
    "df = pd.DataFrame([reference_measure, measure],columns=columns)\n",
    "\n",
    "# ONLY REALLY NEEDED FOR GROUND; BEING VERY EXPLICIT HERE\n",
    "df['aprioriX'] = 0\n",
    "df['aprioriY'] = 0\n",
    "df['aprioriZ'] = 0\n",
    "df['adjustedX'] = 0\n",
    "df['adjustedY'] = 0\n",
    "df['adjustedZ'] = 0\n",
    "df['aprioriCovar'] = [[] for _ in range(len(df))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9333deef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# SET THE OUTPUT PATHS\n",
    "to_isis(df, 'test.cnet', targetname='Mars')\n",
    "with open('test.lis', 'w') as stream:\n",
    "    stream.write(reference_image.file_name + '\\n')\n",
    "    stream.write(moving_image.file_name + '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee96d4c5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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