U_net.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.image as mpimg\n",
    "\n",
    "from my_site.image.Algorithms.U_net_algorithm.model import *\n",
    "from my_site.image.Algorithms.U_net_algorithm.data import *\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from tensorflow.python.framework.ops import disable_eager_execution\n",
    "\n",
    "disable_eager_execution()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## TRAINING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Asus\\AppData\\Local\\Temp\\ipykernel_1644\\3247938464.py:11: UserWarning: `model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
      "  model.fit_generator(myGene,steps_per_epoch=155,epochs=20,callbacks=[model_checkpoint])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1231 images belonging to 1 classes.\n",
      "Found 1231 images belonging to 1 classes.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception ignored in sys.unraisablehook: <built-in function unraisablehook>\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\Asus\\Desktop\\PCD\\venv\\lib\\site-packages\\ipykernel\\iostream.py\", line 535, in write\n",
      "KeyboardInterrupt: \n",
      "Exception ignored in: <function BaseSession._Callable.__del__ at 0x0000016D304D4670>\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\Asus\\Desktop\\PCD\\venv\\lib\\site-packages\\tensorflow\\python\\client\\session.py\", line 1497, in __del__\n",
      "    tf_session.TF_SessionReleaseCallable(self._session._session,\n",
      "KeyboardInterrupt: \n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mKeyboardInterrupt\u001B[0m                         Traceback (most recent call last)",
      "Input \u001B[1;32mIn [8]\u001B[0m, in \u001B[0;36m<cell line: 11>\u001B[1;34m()\u001B[0m\n\u001B[0;32m      9\u001B[0m model \u001B[38;5;241m=\u001B[39m unet()\n\u001B[0;32m     10\u001B[0m model_checkpoint \u001B[38;5;241m=\u001B[39m ModelCheckpoint(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124munet_membrane.hdf5\u001B[39m\u001B[38;5;124m'\u001B[39m, monitor\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mloss\u001B[39m\u001B[38;5;124m'\u001B[39m,verbose\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m1\u001B[39m, save_best_only\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n\u001B[1;32m---> 11\u001B[0m \u001B[43mmodel\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfit_generator\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmyGene\u001B[49m\u001B[43m,\u001B[49m\u001B[43msteps_per_epoch\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m155\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43mepochs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m20\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43mcallbacks\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m[\u001B[49m\u001B[43mmodel_checkpoint\u001B[49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\Desktop\\PCD\\venv\\lib\\site-packages\\keras\\engine\\training_v1.py:1233\u001B[0m, in \u001B[0;36mModel.fit_generator\u001B[1;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001B[0m\n\u001B[0;32m   1222\u001B[0m \u001B[38;5;124;03m\"\"\"Fits the model on data yielded batch-by-batch by a Python generator.\u001B[39;00m\n\u001B[0;32m   1223\u001B[0m \n\u001B[0;32m   1224\u001B[0m \u001B[38;5;124;03mDEPRECATED:\u001B[39;00m\n\u001B[0;32m   1225\u001B[0m \u001B[38;5;124;03m  `Model.fit` now supports generators, so there is no longer any need to use\u001B[39;00m\n\u001B[0;32m   1226\u001B[0m \u001B[38;5;124;03m  this endpoint.\u001B[39;00m\n\u001B[0;32m   1227\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m   1228\u001B[0m warnings\u001B[38;5;241m.\u001B[39mwarn(\n\u001B[0;32m   1229\u001B[0m     \u001B[38;5;124m'\u001B[39m\u001B[38;5;124m`model.fit_generator` is deprecated and \u001B[39m\u001B[38;5;124m'\u001B[39m\n\u001B[0;32m   1230\u001B[0m     \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mwill be removed in a future version. \u001B[39m\u001B[38;5;124m'\u001B[39m\n\u001B[0;32m   1231\u001B[0m     \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mPlease use `Model.fit`, which supports generators.\u001B[39m\u001B[38;5;124m'\u001B[39m,\n\u001B[0;32m   1232\u001B[0m     stacklevel\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m2\u001B[39m)\n\u001B[1;32m-> 1233\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfit\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m   1234\u001B[0m \u001B[43m    \u001B[49m\u001B[43mgenerator\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1235\u001B[0m \u001B[43m    \u001B[49m\u001B[43msteps_per_epoch\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43msteps_per_epoch\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1236\u001B[0m \u001B[43m    \u001B[49m\u001B[43mepochs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mepochs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1237\u001B[0m \u001B[43m    \u001B[49m\u001B[43mverbose\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mverbose\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1238\u001B[0m \u001B[43m    \u001B[49m\u001B[43mcallbacks\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcallbacks\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1239\u001B[0m \u001B[43m    \u001B[49m\u001B[43mvalidation_data\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mvalidation_data\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1240\u001B[0m \u001B[43m    \u001B[49m\u001B[43mvalidation_steps\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mvalidation_steps\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1241\u001B[0m \u001B[43m    \u001B[49m\u001B[43mvalidation_freq\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mvalidation_freq\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1242\u001B[0m \u001B[43m    \u001B[49m\u001B[43mclass_weight\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mclass_weight\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1243\u001B[0m \u001B[43m    \u001B[49m\u001B[43mmax_queue_size\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mmax_queue_size\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1244\u001B[0m \u001B[43m    \u001B[49m\u001B[43mworkers\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mworkers\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1245\u001B[0m \u001B[43m    \u001B[49m\u001B[43muse_multiprocessing\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43muse_multiprocessing\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1246\u001B[0m \u001B[43m    \u001B[49m\u001B[43mshuffle\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mshuffle\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1247\u001B[0m \u001B[43m    \u001B[49m\u001B[43minitial_epoch\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43minitial_epoch\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\Desktop\\PCD\\venv\\lib\\site-packages\\keras\\engine\\training_v1.py:777\u001B[0m, in \u001B[0;36mModel.fit\u001B[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001B[0m\n\u001B[0;32m    774\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_check_call_args(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mfit\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m    776\u001B[0m func \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_select_training_loop(x)\n\u001B[1;32m--> 777\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfit\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m    778\u001B[0m \u001B[43m    \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[0;32m    779\u001B[0m \u001B[43m    \u001B[49m\u001B[43mx\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mx\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    780\u001B[0m \u001B[43m    \u001B[49m\u001B[43my\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43my\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    781\u001B[0m \u001B[43m    \u001B[49m\u001B[43mbatch_size\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mbatch_size\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    782\u001B[0m \u001B[43m    \u001B[49m\u001B[43mepochs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mepochs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    783\u001B[0m \u001B[43m    \u001B[49m\u001B[43mverbose\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mverbose\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    784\u001B[0m \u001B[43m    \u001B[49m\u001B[43mcallbacks\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcallbacks\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    785\u001B[0m \u001B[43m    \u001B[49m\u001B[43mvalidation_split\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mvalidation_split\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    786\u001B[0m \u001B[43m    \u001B[49m\u001B[43mvalidation_data\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mvalidation_data\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    787\u001B[0m \u001B[43m    \u001B[49m\u001B[43mshuffle\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mshuffle\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    788\u001B[0m \u001B[43m    \u001B[49m\u001B[43mclass_weight\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mclass_weight\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    789\u001B[0m \u001B[43m    \u001B[49m\u001B[43msample_weight\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43msample_weight\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    790\u001B[0m \u001B[43m    \u001B[49m\u001B[43minitial_epoch\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43minitial_epoch\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    791\u001B[0m \u001B[43m    \u001B[49m\u001B[43msteps_per_epoch\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43msteps_per_epoch\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    792\u001B[0m \u001B[43m    \u001B[49m\u001B[43mvalidation_steps\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mvalidation_steps\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    793\u001B[0m \u001B[43m    \u001B[49m\u001B[43mvalidation_freq\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mvalidation_freq\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    794\u001B[0m \u001B[43m    \u001B[49m\u001B[43mmax_queue_size\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mmax_queue_size\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    795\u001B[0m \u001B[43m    \u001B[49m\u001B[43mworkers\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mworkers\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    796\u001B[0m \u001B[43m    \u001B[49m\u001B[43muse_multiprocessing\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43muse_multiprocessing\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\Desktop\\PCD\\venv\\lib\\site-packages\\keras\\engine\\training_generator_v1.py:570\u001B[0m, in \u001B[0;36mGeneratorOrSequenceTrainingLoop.fit\u001B[1;34m(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing)\u001B[0m\n\u001B[0;32m    567\u001B[0m model\u001B[38;5;241m.\u001B[39m_validate_or_infer_batch_size(batch_size, steps_per_epoch, x)\n\u001B[0;32m    568\u001B[0m training_utils_v1\u001B[38;5;241m.\u001B[39mcheck_generator_arguments(\n\u001B[0;32m    569\u001B[0m     y, sample_weight, validation_split\u001B[38;5;241m=\u001B[39mvalidation_split)\n\u001B[1;32m--> 570\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfit_generator\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m    571\u001B[0m \u001B[43m    \u001B[49m\u001B[43mmodel\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    572\u001B[0m \u001B[43m    \u001B[49m\u001B[43mx\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    573\u001B[0m \u001B[43m    \u001B[49m\u001B[43msteps_per_epoch\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43msteps_per_epoch\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    574\u001B[0m \u001B[43m    \u001B[49m\u001B[43mepochs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mepochs\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    575\u001B[0m \u001B[43m    \u001B[49m\u001B[43mverbose\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mverbose\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    576\u001B[0m \u001B[43m    \u001B[49m\u001B[43mcallbacks\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcallbacks\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    577\u001B[0m \u001B[43m    \u001B[49m\u001B[43mvalidation_data\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mvalidation_data\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    578\u001B[0m \u001B[43m    \u001B[49m\u001B[43mvalidation_steps\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mvalidation_steps\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    579\u001B[0m \u001B[43m    \u001B[49m\u001B[43mvalidation_freq\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mvalidation_freq\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    580\u001B[0m \u001B[43m    \u001B[49m\u001B[43mclass_weight\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mclass_weight\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    581\u001B[0m \u001B[43m    \u001B[49m\u001B[43mmax_queue_size\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mmax_queue_size\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    582\u001B[0m \u001B[43m    \u001B[49m\u001B[43mworkers\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mworkers\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    583\u001B[0m \u001B[43m    \u001B[49m\u001B[43muse_multiprocessing\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43muse_multiprocessing\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    584\u001B[0m \u001B[43m    \u001B[49m\u001B[43mshuffle\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mshuffle\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    585\u001B[0m \u001B[43m    \u001B[49m\u001B[43minitial_epoch\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43minitial_epoch\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    586\u001B[0m \u001B[43m    \u001B[49m\u001B[43msteps_name\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43msteps_per_epoch\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\Desktop\\PCD\\venv\\lib\\site-packages\\keras\\engine\\training_generator_v1.py:198\u001B[0m, in \u001B[0;36mmodel_iteration\u001B[1;34m(model, data, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch, mode, batch_size, steps_name, **kwargs)\u001B[0m\n\u001B[0;32m    195\u001B[0m   \u001B[38;5;28;01mbreak\u001B[39;00m\n\u001B[0;32m    197\u001B[0m \u001B[38;5;66;03m# Setup work for each epoch.\u001B[39;00m\n\u001B[1;32m--> 198\u001B[0m \u001B[43mmodel\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mreset_metrics\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    199\u001B[0m epoch_logs \u001B[38;5;241m=\u001B[39m {}\n\u001B[0;32m    200\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m mode \u001B[38;5;241m==\u001B[39m ModeKeys\u001B[38;5;241m.\u001B[39mTRAIN:\n",
      "File \u001B[1;32m~\\Desktop\\PCD\\venv\\lib\\site-packages\\keras\\engine\\training_v1.py:985\u001B[0m, in \u001B[0;36mModel.reset_metrics\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m    983\u001B[0m metrics \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_get_training_eval_metrics()\n\u001B[0;32m    984\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m m \u001B[38;5;129;01min\u001B[39;00m metrics:\n\u001B[1;32m--> 985\u001B[0m   \u001B[43mm\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mreset_state\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    987\u001B[0m \u001B[38;5;66;03m# Reset metrics on all the distributed (cloned) models.\u001B[39;00m\n\u001B[0;32m    988\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_distribution_strategy:\n",
      "File \u001B[1;32m~\\Desktop\\PCD\\venv\\lib\\site-packages\\keras\\metrics.py:267\u001B[0m, in \u001B[0;36mMetric.reset_state\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m    265\u001B[0m   \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mreset_states()\n\u001B[0;32m    266\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m--> 267\u001B[0m   \u001B[43mbackend\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mbatch_set_value\u001B[49m\u001B[43m(\u001B[49m\u001B[43m[\u001B[49m\u001B[43m(\u001B[49m\u001B[43mv\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m0\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mfor\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mv\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;129;43;01min\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mvariables\u001B[49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\Desktop\\PCD\\venv\\lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py:150\u001B[0m, in \u001B[0;36mfilter_traceback.<locals>.error_handler\u001B[1;34m(*args, **kwargs)\u001B[0m\n\u001B[0;32m    148\u001B[0m filtered_tb \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[0;32m    149\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 150\u001B[0m   \u001B[38;5;28;01mreturn\u001B[39;00m fn(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m    151\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mException\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[0;32m    152\u001B[0m   filtered_tb \u001B[38;5;241m=\u001B[39m _process_traceback_frames(e\u001B[38;5;241m.\u001B[39m__traceback__)\n",
      "File \u001B[1;32m~\\Desktop\\PCD\\venv\\lib\\site-packages\\tensorflow\\python\\util\\dispatch.py:1082\u001B[0m, in \u001B[0;36madd_dispatch_support.<locals>.decorator.<locals>.op_dispatch_handler\u001B[1;34m(*args, **kwargs)\u001B[0m\n\u001B[0;32m   1080\u001B[0m \u001B[38;5;66;03m# Fallback dispatch system (dispatch v1):\u001B[39;00m\n\u001B[0;32m   1081\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m-> 1082\u001B[0m   \u001B[38;5;28;01mreturn\u001B[39;00m dispatch_target(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m   1083\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m (\u001B[38;5;167;01mTypeError\u001B[39;00m, \u001B[38;5;167;01mValueError\u001B[39;00m):\n\u001B[0;32m   1084\u001B[0m   \u001B[38;5;66;03m# Note: convert_to_eager_tensor currently raises a ValueError, not a\u001B[39;00m\n\u001B[0;32m   1085\u001B[0m   \u001B[38;5;66;03m# TypeError, when given unexpected types.  So we need to catch both.\u001B[39;00m\n\u001B[0;32m   1086\u001B[0m   result \u001B[38;5;241m=\u001B[39m dispatch(op_dispatch_handler, args, kwargs)\n",
      "File \u001B[1;32m~\\Desktop\\PCD\\venv\\lib\\site-packages\\keras\\backend.py:4044\u001B[0m, in \u001B[0;36mbatch_set_value\u001B[1;34m(tuples)\u001B[0m\n\u001B[0;32m   4042\u001B[0m   assign_ops\u001B[38;5;241m.\u001B[39mappend(assign_op)\n\u001B[0;32m   4043\u001B[0m   feed_dict[assign_placeholder] \u001B[38;5;241m=\u001B[39m value\n\u001B[1;32m-> 4044\u001B[0m \u001B[43mget_session\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241m.\u001B[39mrun(assign_ops, feed_dict\u001B[38;5;241m=\u001B[39mfeed_dict)\n",
      "File \u001B[1;32m~\\Desktop\\PCD\\venv\\lib\\site-packages\\keras\\backend.py:763\u001B[0m, in \u001B[0;36mget_session\u001B[1;34m(op_input_list)\u001B[0m\n\u001B[0;32m    761\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m _MANUAL_VAR_INIT:\n\u001B[0;32m    762\u001B[0m   \u001B[38;5;28;01mwith\u001B[39;00m session\u001B[38;5;241m.\u001B[39mgraph\u001B[38;5;241m.\u001B[39mas_default():\n\u001B[1;32m--> 763\u001B[0m     \u001B[43m_initialize_variables\u001B[49m\u001B[43m(\u001B[49m\u001B[43msession\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    764\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m session\n",
      "File \u001B[1;32m~\\Desktop\\PCD\\venv\\lib\\site-packages\\keras\\backend.py:1226\u001B[0m, in \u001B[0;36m_initialize_variables\u001B[1;34m(session)\u001B[0m\n\u001B[0;32m   1224\u001B[0m   v\u001B[38;5;241m.\u001B[39m_keras_initialized \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mTrue\u001B[39;00m\n\u001B[0;32m   1225\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m uninitialized_vars:\n\u001B[1;32m-> 1226\u001B[0m   \u001B[43msession\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrun\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtf\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcompat\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mv1\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mvariables_initializer\u001B[49m\u001B[43m(\u001B[49m\u001B[43muninitialized_vars\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\Desktop\\PCD\\venv\\lib\\site-packages\\tensorflow\\python\\client\\session.py:967\u001B[0m, in \u001B[0;36mBaseSession.run\u001B[1;34m(self, fetches, feed_dict, options, run_metadata)\u001B[0m\n\u001B[0;32m    964\u001B[0m run_metadata_ptr \u001B[38;5;241m=\u001B[39m tf_session\u001B[38;5;241m.\u001B[39mTF_NewBuffer() \u001B[38;5;28;01mif\u001B[39;00m run_metadata \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[0;32m    966\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 967\u001B[0m   result \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_run\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfetches\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfeed_dict\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43moptions_ptr\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    968\u001B[0m \u001B[43m                     \u001B[49m\u001B[43mrun_metadata_ptr\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    969\u001B[0m   \u001B[38;5;28;01mif\u001B[39;00m run_metadata:\n\u001B[0;32m    970\u001B[0m     proto_data \u001B[38;5;241m=\u001B[39m tf_session\u001B[38;5;241m.\u001B[39mTF_GetBuffer(run_metadata_ptr)\n",
      "File \u001B[1;32m~\\Desktop\\PCD\\venv\\lib\\site-packages\\tensorflow\\python\\client\\session.py:1190\u001B[0m, in \u001B[0;36mBaseSession._run\u001B[1;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001B[0m\n\u001B[0;32m   1187\u001B[0m \u001B[38;5;66;03m# We only want to really perform the run if fetches or targets are provided,\u001B[39;00m\n\u001B[0;32m   1188\u001B[0m \u001B[38;5;66;03m# or if the call is a partial run that specifies feeds.\u001B[39;00m\n\u001B[0;32m   1189\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m final_fetches \u001B[38;5;129;01mor\u001B[39;00m final_targets \u001B[38;5;129;01mor\u001B[39;00m (handle \u001B[38;5;129;01mand\u001B[39;00m feed_dict_tensor):\n\u001B[1;32m-> 1190\u001B[0m   results \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_do_run\u001B[49m\u001B[43m(\u001B[49m\u001B[43mhandle\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfinal_targets\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfinal_fetches\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1191\u001B[0m \u001B[43m                         \u001B[49m\u001B[43mfeed_dict_tensor\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43moptions\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mrun_metadata\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   1192\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m   1193\u001B[0m   results \u001B[38;5;241m=\u001B[39m []\n",
      "File \u001B[1;32m~\\Desktop\\PCD\\venv\\lib\\site-packages\\tensorflow\\python\\client\\session.py:1370\u001B[0m, in \u001B[0;36mBaseSession._do_run\u001B[1;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001B[0m\n\u001B[0;32m   1367\u001B[0m   \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_call_tf_sessionprun(handle, feed_dict, fetch_list)\n\u001B[0;32m   1369\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m handle \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m-> 1370\u001B[0m   \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_do_call\u001B[49m\u001B[43m(\u001B[49m\u001B[43m_run_fn\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfeeds\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfetches\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtargets\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43moptions\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1371\u001B[0m \u001B[43m                       \u001B[49m\u001B[43mrun_metadata\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   1372\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m   1373\u001B[0m   \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_do_call(_prun_fn, handle, feeds, fetches)\n",
      "File \u001B[1;32m~\\Desktop\\PCD\\venv\\lib\\site-packages\\tensorflow\\python\\client\\session.py:1377\u001B[0m, in \u001B[0;36mBaseSession._do_call\u001B[1;34m(self, fn, *args)\u001B[0m\n\u001B[0;32m   1375\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m_do_call\u001B[39m(\u001B[38;5;28mself\u001B[39m, fn, \u001B[38;5;241m*\u001B[39margs):\n\u001B[0;32m   1376\u001B[0m   \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m-> 1377\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfn\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   1378\u001B[0m   \u001B[38;5;28;01mexcept\u001B[39;00m errors\u001B[38;5;241m.\u001B[39mOpError \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[0;32m   1379\u001B[0m     message \u001B[38;5;241m=\u001B[39m compat\u001B[38;5;241m.\u001B[39mas_text(e\u001B[38;5;241m.\u001B[39mmessage)\n",
      "File \u001B[1;32m~\\Desktop\\PCD\\venv\\lib\\site-packages\\tensorflow\\python\\client\\session.py:1360\u001B[0m, in \u001B[0;36mBaseSession._do_run.<locals>._run_fn\u001B[1;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001B[0m\n\u001B[0;32m   1357\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m_run_fn\u001B[39m(feed_dict, fetch_list, target_list, options, run_metadata):\n\u001B[0;32m   1358\u001B[0m   \u001B[38;5;66;03m# Ensure any changes to the graph are reflected in the runtime.\u001B[39;00m\n\u001B[0;32m   1359\u001B[0m   \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_extend_graph()\n\u001B[1;32m-> 1360\u001B[0m   \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_call_tf_sessionrun\u001B[49m\u001B[43m(\u001B[49m\u001B[43moptions\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfeed_dict\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfetch_list\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1361\u001B[0m \u001B[43m                                  \u001B[49m\u001B[43mtarget_list\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mrun_metadata\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\Desktop\\PCD\\venv\\lib\\site-packages\\tensorflow\\python\\client\\session.py:1453\u001B[0m, in \u001B[0;36mBaseSession._call_tf_sessionrun\u001B[1;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001B[0m\n\u001B[0;32m   1451\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m_call_tf_sessionrun\u001B[39m(\u001B[38;5;28mself\u001B[39m, options, feed_dict, fetch_list, target_list,\n\u001B[0;32m   1452\u001B[0m                         run_metadata):\n\u001B[1;32m-> 1453\u001B[0m   \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mtf_session\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mTF_SessionRun_wrapper\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_session\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43moptions\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfeed_dict\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1454\u001B[0m \u001B[43m                                          \u001B[49m\u001B[43mfetch_list\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mtarget_list\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   1455\u001B[0m \u001B[43m                                          \u001B[49m\u001B[43mrun_metadata\u001B[49m\u001B[43m)\u001B[49m\n",
      "\u001B[1;31mKeyboardInterrupt\u001B[0m: "
     ]
    }
   ],
   "source": [
    "data_gen_args = dict(rotation_range=0.2,\n",
    "                     width_shift_range=0.05,\n",
    "                     height_shift_range=0.05,\n",
    "                     shear_range=0.05,\n",
    "                     zoom_range=0.05,\n",
    "                     horizontal_flip=True,\n",
    "                     fill_mode='nearest')\n",
    "myGene = trainGenerator(8, r'C:\\Users\\Asus\\Desktop\\PCD\\Organized dataset\\train_test_data\\train', 'image', 'label',\n",
    "                        data_gen_args, save_to_dir=None)\n",
    "model = unet()\n",
    "model_checkpoint = ModelCheckpoint('unet_membrane.hdf5', monitor='loss', verbose=1, save_best_only=True)\n",
    "model.fit_generator(myGene, steps_per_epoch=155, epochs=20, callbacks=[model_checkpoint])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n",
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "testGene = testGenerator_many(r\"C:\\Users\\Asus\\Desktop\\PCD\\Organized dataset\\train_test_data\\test\")\n",
    "model = unet()\n",
    "model.load_weights(r\"C:\\Users\\Asus\\PycharmProjects\\pythonProject1\\unet_membrane.hdf5\")\n",
    "results = model.predict_generator(testGene, 30, verbose=1)  # model\n",
    "saveResult(r\"C:\\Users\\Asus\\Desktop\\PCD\\Organized dataset\\train_test_data\\predicted\", results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape: (256, 256)\n",
      "Dimensions: 2\n",
      "Type: <class 'numpy.ndarray'>\n",
      "Data type: uint8\n",
      "min value, max value: (0, 95)\n",
      "---\n",
      "Shape: (256, 256)\n",
      "Dimensions: 2\n",
      "Type: <class 'numpy.ndarray'>\n",
      "Data type: uint8\n",
      "min value, max value: (0, 35)\n",
      "---\n",
      "Shape: (256, 256)\n",
      "Dimensions: 2\n",
      "Type: <class 'numpy.ndarray'>\n",
      "Data type: uint8\n",
      "min value, max value: (0, 26)\n",
      "---\n",
      "Shape: (256, 256)\n",
      "Dimensions: 2\n",
      "Type: <class 'numpy.ndarray'>\n",
      "Data type: uint8\n",
      "min value, max value: (0, 25)\n",
      "---\n",
      "Shape: (256, 256)\n",
      "Dimensions: 2\n",
      "Type: <class 'numpy.ndarray'>\n",
      "Data type: uint8\n",
      "min value, max value: (0, 126)\n",
      "---\n",
      "Shape: (256, 256)\n",
      "Dimensions: 2\n",
      "Type: <class 'numpy.ndarray'>\n",
      "Data type: uint8\n",
      "min value, max value: (0, 126)\n",
      "---\n",
      "Shape: (256, 256)\n",
      "Dimensions: 2\n",
      "Type: <class 'numpy.ndarray'>\n",
      "Data type: uint8\n",
      "min value, max value: (0, 126)\n",
      "---\n",
      "Shape: (256, 256)\n",
      "Dimensions: 2\n",
      "Type: <class 'numpy.ndarray'>\n",
      "Data type: uint8\n",
      "min value, max value: (0, 126)\n",
      "---\n",
      "Shape: (256, 256)\n",
      "Dimensions: 2\n",
      "Type: <class 'numpy.ndarray'>\n",
      "Data type: uint8\n",
      "min value, max value: (0, 126)\n",
      "---\n",
      "Shape: (256, 256)\n",
      "Dimensions: 2\n",
      "Type: <class 'numpy.ndarray'>\n",
      "Data type: uint8\n",
      "min value, max value: (0, 126)\n",
      "---\n",
      "Shape: (256, 256)\n",
      "Dimensions: 2\n",
      "Type: <class 'numpy.ndarray'>\n",
      "Data type: uint8\n",
      "min value, max value: (0, 43)\n",
      "---\n",
      "Shape: (256, 256)\n",
      "Dimensions: 2\n",
      "Type: <class 'numpy.ndarray'>\n",
      "Data type: uint8\n",
      "min value, max value: (0, 20)\n",
      "---\n",
      "Shape: (256, 256)\n",
      "Dimensions: 2\n",
      "Type: <class 'numpy.ndarray'>\n",
      "Data type: uint8\n",
      "min value, max value: (0, 126)\n",
      "---\n",
      "Shape: (256, 256)\n",
      "Dimensions: 2\n",
      "Type: <class 'numpy.ndarray'>\n",
      "Data type: uint8\n",
      "min value, max value: (0, 126)\n",
      "---\n",
      "Shape: (256, 256)\n",
      "Dimensions: 2\n",
      "Type: <class 'numpy.ndarray'>\n",
      "Data type: uint8\n",
      "min value, max value: (0, 126)\n",
      "---\n",
      "Shape: (256, 256)\n",
      "Dimensions: 2\n",
      "Type: <class 'numpy.ndarray'>\n",
      "Data type: uint8\n",
      "min value, max value: (0, 126)\n",
      "---\n",
      "Shape: (256, 256)\n",
      "Dimensions: 2\n",
      "Type: <class 'numpy.ndarray'>\n",
      "Data type: uint8\n",
      "min value, max value: (0, 126)\n",
      "---\n",
      "Shape: (256, 256)\n",
      "Dimensions: 2\n",
      "Type: <class 'numpy.ndarray'>\n",
      "Data type: uint8\n",
      "min value, max value: (0, 126)\n",
      "---\n",
      "Shape: (256, 256)\n",
      "Dimensions: 2\n",
      "Type: <class 'numpy.ndarray'>\n",
      "Data type: uint8\n",
      "min value, max value: (0, 31)\n",
      "---\n",
      "Shape: (256, 256)\n",
      "Dimensions: 2\n",
      "Type: <class 'numpy.ndarray'>\n",
      "Data type: uint8\n",
      "min value, max value: (0, 56)\n",
      "---\n",
      "Shape: (256, 256)\n",
      "Dimensions: 2\n",
      "Type: <class 'numpy.ndarray'>\n",
      "Data type: uint8\n",
      "min value, max value: (0, 66)\n",
      "---\n",
      "Shape: (256, 256)\n",
      "Dimensions: 2\n",
      "Type: <class 'numpy.ndarray'>\n",
      "Data type: uint8\n",
      "min value, max value: (0, 126)\n",
      "---\n",
      "Shape: (256, 256)\n",
      "Dimensions: 2\n",
      "Type: <class 'numpy.ndarray'>\n",
      "Data type: uint8\n",
      "min value, max value: (0, 126)\n",
      "---\n",
      "Shape: (256, 256)\n",
      "Dimensions: 2\n",
      "Type: <class 'numpy.ndarray'>\n",
      "Data type: uint8\n",
      "min value, max value: (0, 30)\n",
      "---\n",
      "Shape: (256, 256)\n",
      "Dimensions: 2\n",
      "Type: <class 'numpy.ndarray'>\n",
      "Data type: uint8\n",
      "min value, max value: (0, 55)\n",
      "---\n",
      "Shape: (256, 256)\n",
      "Dimensions: 2\n",
      "Type: <class 'numpy.ndarray'>\n",
      "Data type: uint8\n",
      "min value, max value: (0, 72)\n",
      "---\n",
      "Shape: (256, 256)\n",
      "Dimensions: 2\n",
      "Type: <class 'numpy.ndarray'>\n",
      "Data type: uint8\n",
      "min value, max value: (0, 126)\n",
      "---\n",
      "Shape: (256, 256)\n",
      "Dimensions: 2\n",
      "Type: <class 'numpy.ndarray'>\n",
      "Data type: uint8\n",
      "min value, max value: (0, 126)\n",
      "---\n",
      "Shape: (256, 256)\n",
      "Dimensions: 2\n",
      "Type: <class 'numpy.ndarray'>\n",
      "Data type: uint8\n",
      "min value, max value: (0, 126)\n",
      "---\n",
      "Shape: (256, 256)\n",
      "Dimensions: 2\n",
      "Type: <class 'numpy.ndarray'>\n",
      "Data type: uint8\n",
      "min value, max value: (0, 126)\n",
      "---\n"
     ]
    }
   ],
   "source": [
    "from skimage import img_as_ubyte\n",
    "import cv2\n",
    "\n",
    "\n",
    "def save(save_path, npyfile):\n",
    "    for i, item in enumerate(npyfile):\n",
    "        img = item[:, :, 0]\n",
    "        img = img_as_ubyte(img)\n",
    "        (thresh, blackAndWhiteImage) = cv2.threshold(img, 30, 255, cv2.THRESH_BINARY)\n",
    "        cv2.imwrite(os.path.join(save_path, \"%d_predict_cv2.jpg\" % i), blackAndWhiteImage)\n",
    "\n",
    "\n",
    "save(r\"C:\\Users\\Asus\\Desktop\\PCD\\Organized dataset\\train_test_data\\predicted\", results)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "def testGenerator_one(test_path, target_size=(256, 256), flag_multi_class=False, as_gray=True):\n",
    "    img = io.imread(os.path.join(test_path), as_gray=as_gray)\n",
    "    img = img / 255\n",
    "    img = trans.resize(img, target_size)\n",
    "    img = np.reshape(img, img.shape + (1,)) if (not flag_multi_class) else img\n",
    "    img = np.reshape(img, (1,) + img.shape)\n",
    "\n",
    "    yield img"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Asus\\AppData\\Local\\Temp\\ipykernel_8512\\1298040292.py:28: UserWarning: `Model.predict_generator` is deprecated and will be removed in a future version. Please use `Model.predict`, which supports generators.\n",
      "  results = model.predict_generator(testGene, 1, verbose=1)\n",
      "C:\\Users\\Asus\\Desktop\\PCD\\venv\\lib\\site-packages\\keras\\engine\\training_v1.py:2079: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
      "  updates=self.state_updates,\n"
     ]
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd\n",
    "from my_site.image.utilities.Path_Join import p_join\n",
    "from my_site.image.Traitement_image.Crop_From_Center import crop_image_from_center\n",
    "from my_site.image.utilities.IOU import iou\n",
    "\n",
    "df = pd.read_excel(r\"C:\\Users\\Asus\\Desktop\\PCD\\Organized dataset\\metadata\\metadata_good2.0.xlsx\")\n",
    "rslt_df_test = df[df['description'] == 'Mass-Test']\n",
    "rslt_df_test = rslt_df_test.drop_duplicates('file path')\n",
    "list_test = list(rslt_df_test['image full mammo path'])\n",
    "\n",
    "filename = \"4\"\n",
    "mammo_path = list_test[eval(filename)]\n",
    "index_df = df[df['image full mammo path'] == mammo_path].index.values[0]\n",
    "number = index_df\n",
    "df = pd.read_excel(r\"C:\\Users\\Asus\\Desktop\\PCD\\Organized dataset\\metadata\\metadata_good2.0.xlsx\")\n",
    "parent_dir = r'C:\\Users\\Asus\\Desktop\\PCD\\Organized dataset\\preprocessed_dataset'\n",
    "image_path = p_join(parent_dir, df['image full mammo path'][number])\n",
    "center = eval(df['center of roi preprocessed'][number])\n",
    "shape = eval(df['Shape of cropped image'][number])\n",
    "g_path = os.path.join(df['file path'][number], r\"GT_mask.jpg\")\n",
    "gt = mpimg.imread(p_join(parent_dir, g_path))\n",
    "image = mpimg.imread(image_path)\n",
    "# segmentation\n",
    "testGene = testGenerator_one(image_path)\n",
    "model = unet()\n",
    "model.load_weights(r\"C:\\Users\\Asus\\PycharmProjects\\pythonProject1\\my_site\\image\\unet_membrane.hdf5\")\n",
    "results = model.predict_generator(testGene, 1, verbose=1)\n",
    "img = results[0]\n",
    "#iou coefficient\n",
    "resized_gt = cv2.resize(gt, (256, 256))\n",
    "img = img_as_ubyte(img)\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.image.AxesImage at 0x1ab3a1cb760>"
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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+IRhfo2BYojFMOOZVL4YTsXyBVvh9wjxG6HXM44UQsmiiEIa88CH0HsKQIiydtidKhcVEhl/OzZ6MHT63clZnLEqM+mO8eIQ2+rbwe1AISExEIQzA/bDBd5rQxbaY3iZV+apIu4P7ikLgfnLPFxyFHkLYUT0+OejFyp/X9jMb7Tx5xUzhi5AYiUIY8jyGvKScueZ+SM8LQ5j5D9d1CMOUvDUY8q5ZNGvS2s0ms6dIDEJvIk8kKBYkBqIQBgAT8X1Rh/XFRf5OXlRMFHKUOD7Pa/B2+bJpL1rTvo+15f3NUQmySqIQhqK4Pa/T+ESkbS9j8tG084YC5nMZRSHRPNelGJBVE4UwANlS4VlJwGl/55FXWJTHtKHG0NuwYUy/CEs41Boe468dipx9RmEgqyYaYZg3IVd2vyKXPE8YZtUfhOcIw4m80Yk8O9nxSexEIQxHydLPOqZMuBF+7v/OGyaddq28/cqIEj0FEhNRCMOyCV14T94oRigS4XnmFYm8a5aBCUiyKs6EMACT8XweeV7DYTrkYWZGhkva0Xsgq+bMCEMRYe1DOEMyLGA67HBnWVtCYaBIkFVwJpcqnhY6hCx6Neei800bNeGK0uS4ieZ/3HEuhlqUK5jVAaclKRdFkQ3hCtOELJMoQom8EuZVUJSH8Gsu5NUd+OOnMW/SMe+8eSMmZa5NyDxEIQzAZHwNLOc/el7HzyuWyrtrzypKKjqf/3weYZiVLA1tZi6CLJJohMGzrP/kviP71Zf8dW2/WbUGeWJQ1JHLdPBp37fs8RQHsiiiFAZgujiEuYF5Yv1QHELXvKjzG+EK0OF+ZYYrp5V0zxrpCOdleLvzzk3IYYhGGOadM1AUX89zd512fNmRilAIZq0BedjP8mDdA1kW0QgDUC6EKOo8hxWHecOA0MZqtZoRqbz1G8rYXqZzz5N7IOSwRCMMZYt7ZrnMYecrGv6b1sHCYqfQpvC4ovBh1t38MFWSeeJCb4EsmmiEwci7i0+bhDSrU0xbudkSkOF5RbKPrQ+v6/EPywkpEodFPVSGD6chyyA6YQgpO226jEDMqjo0Qch7XkW4f971whWbzK5F5QC8t1C0hD0hiyBKYQgf02YdNO/uOI/3kBc+2Lm9INizKM2WPPtCl97qH/yiLYdh2jMv8/bz1ydkUUQjDKFnkPcsy7w2f7zdmct2krwwwpafN7Hwn4V1D3lLxYcPp/HM03lDESvzTEuKA1kUUQiDvzuGbndeoi8UgHD/eYf8TARMFMxjyMsz+OtWq9XUJn/t0IZ5koXT5mt4YSryoAhZBFEIA5C9Gxfd/X37IsQhL4zIez7FtA6ZdzcvGrIMbZ2XcBIXvQSyDKIShrKED78N11csUwUYdjARmRAFH1KEQjTLrTfbiq6/qA5NYSDLIgphsE7p/y5K/oWiMBqN0nagfOWkDxHyEpJ5f4deSt51PdOEgaEAiZkohAHAhOvunztZVH9gD8G1Y+x5lEA5cfC5AO/yh5Or7N0/KSsv6ejFalalo//ORxEHegtkWUQhDCKCWq2WuvCW/PPtHvMURqMRqtUqBoNB2sH8iMC0RKbP+vtOLSITz7QM9ynyGsLPp3kSnmnzLmZBYSDLIhphqNfrqFQqqNfrqRiEj7M3TBSGw2H6Wdgp87yGvDoG34lt3/F4PDEyEeYY8pKe4bXnGTb1v8O0Y4o8ocNci5BpRCUMtVoNjUYjfdXr9fTdOrR5C8PhEL1eD/V6PW3P65TTEpFhWGAeiB3v8w95IUfe+UJx8N+xyA6/T5Gdhx3+JOQwzBQGEXkWwH8GcEdV/23S9jkA/xXAvyS7fVZV/zL57DcAfArAPoD/pqp/VeIaaLVaqNVqaDabWFtbS1/NZhONRiPtsKPRCL1eD4PBAM1mE7VaDaqK4XCI0Wg0ERKEHTPPW/BegmF5Bj9cmfek6jymhQOHEQd6A2QVlPEY/gTAHwD4UtD++6r6u75BRB4F8HEAPwfgpwB8TUR+VlXzSwETKpUKWq1WKgobGxvY3NzE5uYm1tfX0Ww2U8+g1+uh2+2i0+lgd3cXIoLRaIR+v5+KQ1iyPK1EOW8lJ/d9cjv6rKHKWR0/T6CKQp9ZosCRDbIMZgqDqn5dRN5b8nxXATyvqn0A/ywiNwE8AeD/TjvIhGFtbQ3r6+vY3NzE+fPn8cADD6TiUK/XMR6P0e12sbu7i2q1ivF4POE51Go1VKvVdAizLH7kwech5p3zcNx3dnoSZFkcJcfwaRH5JICXAXxGVd8F8BCAb7h93kraMojINQDXAKDZbKLVaqWv9fV1nDt3DhcvXsQDDzyAtbU11Ot1jEYjNJvNVBDq9XqaJAwrFi0MKJpsNG2EAsDEiMRRJkUtEwoDWRaHfa7EFwH8DIDHANwG8HvznkBVr6vqFVW9YqGCT0CaWJgXYbkGG7Gwu7s9hr5sJylyz8MhxrBuwb+W2SHn+R6ELItDCYOqvq2q+6o6BvBHOAgXAOAWgEfcrg8nbVPJm2LsO+VwOMRwOMRgMEC/30e/30ev10vzCoPBAKPRKK0/KENRbD4rZi9ao+GwzBpxmHYcIcviUKGEiDyoqreTP38ZwGvJ9g0AfyYin8dB8vEygL+bdT4bVbDiJhuK7Ha7qFQqGAwGUFX0+33s7Ozg3r172N3dxe7uLvb29jAYDDAYDNJqyGXe1YtE4bDzFo7rGELmocxw5ZcBfAjAe0TkLQC/BeBDIvIYAAXwJoBfBQBVfV1EXgDwBoARgKdnjUgkx2EwGKR/++nOu7u7qNVqae1Cu93Gzs4O2u022u12Kg42IhFWJIbX8deYl2mPjwPyRWPaepDThiU5TElWicTwn29zc1Pf//73o1aroV6vp0OWGxsbaDQa6eIn/X4f3W4Xe3t76HQ66HQ66d8WXlhVpJ91aRStjuTxnbxoIte84cSsIc88EZiWC4nh34ycSF5R1Stldoyi8nE8HqPX66VzIyyX0G63UavV0noCn2fo9XppoVO/358aSpTtSOFUbHufNhPT7xuWSRcxq3KRnZ+smmiEodvtpsOOg8EAvV4vnVQFIB2B2N/fx3A4nEg8Whjhk4/TioT88KQRCoC9h4vCFolDeM6ijl0m2Vj2WEKWRRTCYInFvAVTPDZPwsTBwobQUzhK5zEbQlHIE4687zEv4TGsZCQxEIUwWJgATK7BmLefFwdbl6FoAhUwf2fNEwTzWkJvoaiUObQ5b//wszLQWyDHRRTCYMOV3h33S7GHYYEJgYUOvj0872EJBSpPqMpMivK2sGOTk0IUwgAgM78hXF3JyFt34bAFQmFIcJghzLzr5nku0/af9/yELJsohCHvbm/tRRn7sPPNe0fOEwbfVpRTyJsWnfde5nvMA4WBHCdRCAOA3Ie0lC1bnlZENA9eGPyaDL49L0cQitNRBIuQGIhCGMq43sDsQqF5KXqeZSgEResy+OuHYuDPVZaifTlSQY6bKIQBKOeSh/sv4k5sHd/O5RdusW3rmEULt3pbwvY88pKk02oXCDluohKGeZJ1i+owRbkGLwqz1o0sErUyxUuzRIHCQFZBNMKQx1HqEYDyMx5Dr6HomoetZiz6jKJAYiUaYSjbYY7TDvMW8uZQlBGRWR7QIoczCVkk0QiDsUhRKFuAZJ/n7W/b4cNt8yiTJJz1/ZhoJDEQlTAsw1M4ijhYiDFtVKIsDBvISSIKYYilU4TiUNQ26/hZbb49lu9OiCcKYVg2885p8McUteUdM8/5KQgkZs6EMMxL0RAmkC8YZTs5QwZyUjj8rKETxiJWdz7KnAeKAjlJnCmPYZ6QogjvOZQ9D0cayEnjTAkDsBhx8OdicpGcRqIRhuN0tY8iDn6+BIcfyWnlzOQYQsosIT8NigI5zUTjMawCE4ewmGna/oScBc60MBhlRizy5kzMU9BEyEkimlBi0Q+LXSR5tnGkgZxm6DHMoKxgMbdAThNReAyzHuQSG/NOsSbkpBGFMABxisO0x9ERcpqJRhiMGIQhfCSdh94COQswx5DDtFmUsaw0RcgyoTA4pnkrFAVylqAwID+/MWtxV4oCOc3MzDGIyCMi8jci8oaIvC4iv5a0XxSRF0XkB8n7haRdROQLInJTRF4VkcfnMei4O1yYT7BO71+hfRQFctopk3wcAfiMqj4K4AMAnhaRRwE8A+AlVb0M4KXkbwD4CIDLyesagC+WMWSVsxFniUG4HyGnnZnCoKq3VfXbyXYbwPcAPATgKoDnkt2eA/DRZPsqgC/pAd8AcF5EHly04SH+zh++plFWEOzJ2oScBeYarhSR9wL4eQDfBHBJVW8nH/0YwKVk+yEAP3KHvZW0LYVpgjCPQEyDgkDOGqWTjyKyCeDPAfy6qu4EiToVkbl6j4hcw0GoYX/P3QHLFkUddv0FzocgZ5VSHoOI1HEgCn+qqn+RNL9tIULyfidpvwXgEXf4w0nbBKp6XVWvqOoV37HLrIVg+83rCZRdZ8FCB0LOKmVGJQTAHwP4nqp+3n10A8BTyfZTAL7q2j+ZjE58AMC2CznKGTWjA5cVjzzK1CowdCBnHZnVCUTkgwD+FsB3Adht9LM4yDO8AOBfA/ghgI+p6lYiJH8A4MMA9gD8iqq+PO0alUpF6/X61CKio+QJptUk5F2LkFPKK6p6pcyOM4XhOKhUKtpoNCY656LsmhUSUBDIGaK0MERR+SgiqFQqU4uK5mURD5gl5KwShTAAQLVanUj8lem0RUurTTuOy7oTMptohCEcmbA7vi3SWsSsiU1l8guEkEmiEAYfSpggmDiEcxjC47wHkCcCFANC5ieahVr8qIO9+2FJE495ahEoCoQcjmiE4ahJxyLPgRAyP1GEEiE+v2BMEw6/P0WBkKMTjTDYSIR18qKRibCNokDI4olCGEwQvBj4OoSimgSGD4QshyiEAQD29/cLF0wpqlfI2yaEHJ0ohMEXNZXNLxBClkcUwgAgDSUMigEhqyMKYQiTjoSQ1RKFMADMExASE9EUOAEUB0JiIRphoCgQEg/RCAMhJB4oDISQDBQGQkgGCgMhJAOFgRCSgcJACMlAYSCEZKAwEEIyUBgIIRkoDISQDBQGQkgGCgMhJAOFgRCSgcJACMlAYSCEZKAwEEIyUBgIIRkoDISQDDOFQUQeEZG/EZE3ROR1Efm1pP1zInJLRL6TvJ50x/yGiNwUke+LyH9a5hcghCyeMqtEjwB8RlW/LSLnALwiIi8mn/2+qv6u31lEHgXwcQA/B+CnAHxNRH5WVfcXaTghZHnM9BhU9baqfjvZbgP4HoCHphxyFcDzqtpX1X8GcBPAE4swlhByPMyVYxCR9wL4eQDfTJo+LSKvisizInIhaXsIwI/cYW8hR0hE5JqIvCwiL89vNiFkmZQWBhHZBPDnAH5dVXcAfBHAzwB4DMBtAL83z4VV9bqqXlHVK/McRwhZPqWEQUTqOBCFP1XVvwAAVX1bVfdVdQzgj3A/XLgF4BF3+MNJGyHkhFBmVEIA/DGA76nq5137g263XwbwWrJ9A8DHRaQpIu8DcBnA3y3OZELIsikzKvHvAPwXAN8Vke8kbZ8F8AkReQyAAngTwK8CgKq+LiIvAHgDByMaT3NEgpCThcTwaDgR+RcAHQDvrNqWErwHJ8NO4OTYSjsXT56t/0ZVf6LMwVEIAwCIyMsnIRF5UuwETo6ttHPxHNVWlkQTQjJQGAghGWIShuurNqAkJ8VO4OTYSjsXz5FsjSbHQAiJh5g8BkJIJKxcGETkw8n07Jsi8syq7QkRkTdF5LvJ1PKXk7aLIvKiiPwgeb8w6zxLsOtZEbkjIq+5tly75IAvJL/xqyLyeAS2Rjdtf8oSA1H9rseyFIKqruwFoArgHwH8NIAGgL8H8Ogqbcqx8U0A7wna/geAZ5LtZwD8zgrs+kUAjwN4bZZdAJ4E8L8BCIAPAPhmBLZ+DsB/z9n30eT/QRPA+5L/H9VjsvNBAI8n2+cA/ENiT1S/6xQ7F/abrtpjeALATVX9J1UdAHgeB9O2Y+cqgOeS7ecAfPS4DVDVrwPYCpqL7LoK4Et6wDcAnA9K2pdKga1FrGzavhYvMRDV7zrFziLm/k1XLQylpmivGAXw1yLyiohcS9ouqertZPvHAC6txrQMRXbF+jsfetr+sgmWGIj2d13kUgieVQvDSeCDqvo4gI8AeFpEftF/qAe+WnRDO7Ha5TjStP1lkrPEQEpMv+uil0LwrFoYop+iraq3kvc7AL6CAxfsbXMZk/c7q7NwgiK7ovudNdJp+3lLDCDC33XZSyGsWhi+BeCyiLxPRBo4WCvyxoptShGRjWSdS4jIBoBfwsH08hsAnkp2ewrAV1djYYYiu24A+GSSRf8AgG3nGq+EGKftFy0xgMh+1yI7F/qbHkcWdUaG9UkcZFX/EcBvrtqewLafxkE29+8BvG72AfhXAF4C8AMAXwNwcQW2fRkH7uIQBzHjp4rswkHW/A+T3/i7AK5EYOv/TGx5NfmP+6Db/zcTW78P4CPHaOcHcRAmvArgO8nrydh+1yl2Luw3ZeUjISTDqkMJQkiEUBgIIRkoDISQDBQGQkgGCgMhJAOFgRCSgcJACMlAYSCEZPj/k77CZiCxilQAAAAASUVORK5CYII=\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img,cmap=\"gray\")\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.image.AxesImage at 0x1ab48376ee0>"
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(thresh, predict_img) = cv2.threshold(img, 100, 255, cv2.THRESH_BINARY)\n",
    "plt.imshow(predict_img,cmap=\"gray\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.image.AxesImage at 0x1ab3bfce280>"
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(gt, cmap=\"gray\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [
    {
     "data": {
      "text/plain": "True"
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv2.imwrite(r'C:\\Users\\Asus\\PycharmProjects\\pythonProject1\\temp\\test2.jpg',resized_gt)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "True"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv2.imwrite(r'C:\\Users\\Asus\\PycharmProjects\\pythonProject1\\temp\\test.jpg', predict_img)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IoU is 0.38333333333333336\n"
     ]
    },
    {
     "data": {
      "text/plain": "0.38333333333333336"
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the scaling factor\n",
    "img_shape = gt.shape\n",
    "reshaped_img_shape = (256, 256)\n",
    "scale = np.flipud(np.divide(reshaped_img_shape,\n",
    "                            img_shape))  # you have to flip because the image.shape is (y,x) but your corner points are (x,y)\n",
    "\n",
    "#use this on to get new top left corner and bottom right corner coordinates\n",
    "new_center = int(np.multiply(center, scale)[0]), int(np.multiply(center, scale)[1])\n",
    "new_shape = int(np.multiply(shape, scale)[0]+20), int(np.multiply(shape, scale)[1]+20)\n",
    "\n",
    "y_true = crop_image_from_center(resized_gt, shape=new_shape, center=new_center)\n",
    "y_pred = crop_image_from_center(predict_img, shape=new_shape, center=new_center)\n",
    "\n",
    "iou(y_true, y_pred)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IoU is 0.38333333333333336\n",
      "0.38333333333333336\n"
     ]
    }
   ],
   "source": [
    "from my_site.image.utilities.IOU import iou\n",
    "\n",
    "print(iou(y_true, y_pred))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9458128078817734\n"
     ]
    }
   ],
   "source": [
    "intersection = 0\n",
    "union = new_shape[0]*new_shape[1]\n",
    "for i in range(y_true.shape[0]) :\n",
    "    for j in range(y_true.shape[1]):\n",
    "        if y_true[i][j] == y_pred[i][j]  :\n",
    "            intersection+=1\n",
    "print(intersection/union)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.image.AxesImage at 0x1ab3809cac0>"
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPQAAAD5CAYAAAAQsRggAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAALFUlEQVR4nO3dUahkhX3H8e/P1byoyNq0y2JsTUUKEugqixQqJYUmmH1RX4I+lC0ENg8RIuQhkjzURylqyEMRNlWyKalpwQQllCZWAvahiO6y1dVtqxUlu6wuYkV9Snf334d7tly3996ZnZk7Z/bv9wPDzpwzd+bvcb8758y5906qCkk9XDb2AJIWx6ClRgxaasSgpUYMWmrEoKVGLp/ni5PcAXwf2AH8TVU9NOH+niOT5vdeVf32RitmfoVOsgP4a+ArwM3AvUlunvXxJE3t7c1WzLPLfRvwRlW9WVW/AX4C3DnH40ma0zxBXwf8et3tE8MySSOZ6xh6GkkOAAe2+3kkzRf0SeD6dbc/Nyz7hKo6CBwE3xSTtts8u9wvAjcl+XySzwD3AM8sZixJs5j5FbqqziS5D/gFa6etnqiqVxc2maSLlmX++KS73NJCHK6qvRut8DvFpEYMWmrEoKVGDFpqxKClRgxaasSgpUYMWmrEoKVGDFpqxKClRgxaasSgpUYMWmrEoKVGDFpqxKClRgxaasSgpUYMWmrEoKVGDFpqxKClRgxaasSgpUYMWmrEoKVGDFpqxKClRub5wHeSvAV8BJwFzmz2iXiSlmOuoAd/WlXvLeBxJM3JXW6pkXmDLuCXSQ4nObCIgSTNbt5d7tur6mSS3wGeTfLvVfX8+jsMoRu7tASpqsU8UPIg8HFVPbzFfRbzZNKn2+HN3oCeeZc7yZVJrj5/HfgycGzWx5M0v3l2uXcBP0ty/nH+rqr+aSFTSZrJzEFX1ZvAHy5wFklz8rSV1IhBS40YtNSIQUuNGLTUiEFLjRi01IhBS40YtNSIQUuNGLTUiEFLjRi01IhBS40YtNSIQUuNGLTUiEFLjRi01IhBS40YtNSIQUuNGLTUiEFLjRi01IhBS40YtNSIQUuNTAw6yRNJTic5tm7ZtUmeTfL68OfO7R1T0jSmeYX+IXDHBcseAJ6rqpuA54bbkkY2Meiqeh54/4LFdwKHhuuHgLsWO5akWcx6DL2rqk4N199h7cPfJY1s5g98P6+qKklttj7JAeDAvM8jabJZX6HfTbIbYPjz9GZ3rKqDVbW3qvbO+FySpjRr0M8A+4fr+4GnFzOOpHlMc9rqSeBfgT9IciLJ14CHgC8leR34s+G2pJGlatPD38U/2RbH2pKmdnizQ1i/U0xqxKClRgxaasSgpUYMWmrEoKVG5v7WT62eyy6b/d/pW2+9ddN1R44c2fJrz507N/PzajF8hZYaMWipEYOWGjFoqRGDlhoxaKkRg5Ya8Tz0JWjSeeazZ88uaZLpJRl7hE8FX6GlRgxaasSgpUYMWmrEoKVGDFpqxNNWl6BVPC2l1eArtNSIQUuNGLTUiEFLjRi01IhBS40YtNTINB8n+0SS00mOrVv2YJKTSY4Ol33bO6YudUm2vGgxpnmF/iFwxwbLv1dVe4bLPy52LEmzmBh0VT0PvL+EWSTNaZ5j6PuSvDzsku9c2ESSZjZr0I8BNwJ7gFPAI5vdMcmBJC8leWnG55I0pZmCrqp3q+psVZ0DfgDctsV9D1bV3qraO+uQkqYzU9BJdq+7eTdwbLP7SlqeiT8+meRJ4IvAZ5OcAP4S+GKSPUABbwFf374RdaHLL9/6f9uZM2eWNIlWTapqeU+WLO/JGtuxY8eW61cx6Em/eniZfw8bOLzZIazfKSY1YtBSIwYtNWLQUiMGLTVi0FIj/hrfS9CkX+O71SmgsX5U8YMPPthy/TXXXLOcQZrzFVpqxKClRgxaasSgpUYMWmrEoKVGPG11CZp06mmsU1Nb/RTYuXPnljjJp5ev0FIjBi01YtBSIwYtNWLQUiMGLTVi0FIjnoe+BE36DZnbdR76nnvu2XK955rH5yu01IhBS40YtNSIQUuNGLTUiEFLjUz8sLok1wM/Anax9mmTB6vq+0muBf4euIG1T6D8alX994TH8hPJpPnN9WF1Z4BvVdXNwB8B30hyM/AA8FxV3QQ8N9yWNKKJQVfVqao6Mlz/CDgOXAfcCRwa7nYIuGubZpQ0pYs6hk5yA3AL8AKwq6pODaveYW2XXNKIpv7WzyRXAU8B91fVh+u/vbCqarPj4yQHgAPzDippsqleoZNcwVrMP66qnw6L302ye1i/Gzi90ddW1cGq2rvZQbykxZkYdNZeih8HjlfVo+tWPQPsH67vB55e/HiSLsY0p61uB/4FeAU4/+M032HtOPofgN8F3mbttNX7Ex7L01bS/DY9bTUx6EUyaGkh5joPLekSYdBSIwYtNWLQUiMGLTVi0FIjBi01YtBSIwYtNWLQUiMGLTVi0FIjBi01YtBSIwYtNWLQUiMGLTVi0FIjBi01YtBSIwYtNWLQUiMGLTVi0FIjBi01YtBSIwYtNWLQUiMGLTUyzedDX5/kV0leS/Jqkm8Oyx9McjLJ0eGyb/vHlbSVy6e4zxngW1V1JMnVwOEkzw7rvldVD2/feJIuxsSgq+oUcGq4/lGS48B12z2YpIt3UcfQSW4AbgFeGBbdl+TlJE8k2bno4SRdnKmDTnIV8BRwf1V9CDwG3AjsYe0V/JFNvu5AkpeSvDT/uJK2kqqafKfkCuDnwC+q6tEN1t8A/LyqvjDhcSY/maRJDlfV3o1WTPMud4DHgePrY06ye93d7gaOzTulpPlM8y73HwN/DryS5Oiw7DvAvUn2AAW8BXx9G+aTdBGm2uVe2JO5yy0twuy73JIuHQYtNWLQUiMGLTVi0FIjBi01YtBSIwYtNWLQUiMGLTVi0FIjBi01YtBSIwYtNWLQUiMGLTVi0FIjBi01YtBSIwYtNWLQUiPT/BrfRXoPeHvd7c8Oy1bJKs4EqznXKs4EqznXImf6vc1WLPXX+P6/J09e2uzXkY5lFWeC1ZxrFWeC1ZxrWTO5yy01YtBSI2MHfXDk59/IKs4EqznXKs4EqznXUmYa9Rha0mKN/QotaYFGCTrJHUn+I8kbSR4YY4aNJHkryStJjo71AfVJnkhyOsmxdcuuTfJskteHP3euyFwPJjk5bK+jSfYteabrk/wqyWtJXk3yzWH5qNtri7m2fXstfZc7yQ7gP4EvASeAF4F7q+q1pQ6ygSRvAXurarRzmEn+BPgY+FFVfWFY9lfA+1X10PAP4M6q+vYKzPUg8HFVPbzMWdbNtBvYXVVHklwNHAbuAv6CEbfXFnN9lW3eXmO8Qt8GvFFVb1bVb4CfAHeOMMdKqqrngfcvWHwncGi4foi1vxxLtclco6qqU1V1ZLj+EXAcuI6Rt9cWc227MYK+Dvj1utsnWNJ/7BQK+GWSw0kOjD3MOruq6tRw/R1g15jDXOC+JC8Pu+RLPxQ4L8kNwC3AC6zQ9rpgLtjm7eWbYp90e1XdCnwF+Mawm7lSau0YaVVOTTwG3AjsAU4Bj4wxRJKrgKeA+6vqw/XrxtxeG8y17dtrjKBPAtevu/25Ydnoqurk8Odp4GesHR6sgneH47Lzx2enR54HgKp6t6rOVtU54AeMsL2SXMFaND+uqp8Oi0ffXhvNtYztNUbQLwI3Jfl8ks8A9wDPjDDHJyS5cngDgyRXAl8Gjm39VUvzDLB/uL4feHrEWf7P+WgGd7Pk7ZUkwOPA8ap6dN2qUbfXZnMtZXtV1dIvwD7W3un+L+C7Y8ywwUy/D/zbcHl1rLmAJ1nbHfsf1t5f+BrwW8BzwOvAPwPXrshcfwu8ArzMWkS7lzzT7aztTr8MHB0u+8beXlvMte3by+8UkxrxTTGpEYOWGjFoqRGDlhoxaKkRg5YaMWipEYOWGvlf5f5ZafJ5bO4AAAAASUVORK5CYII=\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from my_site.image.Traitement_image.Restore_image import restore_mask\n",
    "\n",
    "plt.imshow(y_true, cmap=\"gray\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.image.AxesImage at 0x1ab380f4b20>"
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from my_site.image.Traitement_image.Restore_image import restore_mask\n",
    "\n",
    "plt.imshow(y_pred, cmap=\"gray\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.image.AxesImage at 0x13b0e79fa00>"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img, cmap=\"gray\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.image.AxesImage at 0x13b0eab2700>"
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQYAAAD8CAYAAACVSwr3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAATuklEQVR4nO3df2zV9b3H8ee7tDTMoqVDSYVeflRuSOdyHWlQc4lAGBsyFzpzYzT+YG5ZDQMH2+4yHMlcttzs6rxuc4gGxQzNxOvcmHAnXn6IcpOxTVikgI5RUAcMQRFmY5OW0vf943zLjnxa+m3POf1+T/t6JJ+ccz7n+/2ed7+0L74/P8fcHRGRbCVJFyAi6aNgEJGAgkFEAgoGEQkoGEQkoGAQkUDBgsHM5prZfjNrNrNlhfocEck/K8R1DGY2DPgLMAc4ArwK3OLur+f9w0Qk7wq1xTANaHb3Q+7eDjwDzC/QZ4lInpUWaLljgcNZr48AV/c0sZnp8kuRwnvP3S+NM2GhgqFXZtYINCb1+SJD0NtxJyxUMBwFarJej4v6znH3VcAq0BaDSNoU6hjDq8BkM5toZsOBm4H1BfosEcmzgmwxuHuHmS0G/hcYBjzh7vsK8Vkikn8FOV3Z5yK0KyEyEHa5e32cCXXlo4gEFAwiElAwiEhAwSAiAQWDiAQUDCISUDCISEDBICIBBYOIBBQMIhJQMIhIQMEgIgEFg4gEFAwiElAwiEhAwSAiAQWDiAQUDCISUDCISEDBICIBBYOIBBQMIhJQMIhIQMEgIgEFg4gEFAwiElAwiEhAwSAiAQWDiAQUDCISUDCISEDBICIBBYOIBBQMIhIozWVmM3sLaAHOAh3uXm9mVcB/AxOAt4Cb3P1UbmWKyEDKxxbDLHe/yt3ro9fLgK3uPhnYGr0WkSJSiF2J+cCa6PkaoKEAnyEiBZRrMDiwycx2mVlj1DfG3Y9Fz98BxnQ3o5k1mtlOM9tZWVmZYxkikk+5BsN0d58KXA8sMrPrst90dycTHgF3X+Xu9e5eP2nSJH7605/mWIqI5EtOweDuR6PHE8A6YBpw3MyqAaLHE70tx8z45Cc/SUmJTpKIpEG//xLN7CIzG9n1HPgMsBdYDyyIJlsAPB9neddeey3Lly/vbzkikke5nK4cA6wzs67lPO3uL5rZq8CzZvZl4G3gpjgL+93vfscPfvCDHMoRkXzpdzC4+yHgX7rpPwnMzqUoEUlWKnbqz549yw9/+MOkyxCRSGqCYcuWLUmXISIRy5xRTNawYcO8s7Mz6TJEBrtdWVcoX1AqthgUCiLpktNNVEmqqamhvv4f4dfe3s5vf/vbBCsSGTyKLhiWL1/O8OHDufLKK7nxxhvP9be2tvLAAw8AsGHDBnbu3JlUiSLFz90Tb2Qum+61PfTQQ97R0eG92b17t69bt84rKytjLVdNbYi0nbH/JpMOhbjB8JOf/MTb2tp6DYVs+/fv95KSkqT/MdTU0tIGVzAsW7bMz54926dQ6HLw4EGvqKhI+h9ETS0NLXYwpOKsRG9KS0v7fYPVpEmT2LJlC+PGjctzVSKDV1EEQ66uvvpqHnvsMWpra5MuRaQoDIlgAJg7dy4rVqzQloNIDEMmGCATDs899xwVFRVJlyKSbkkfeIxz8PHiiy/2DRs29OvgY3eam5t1tkJtKLbBdfDxgw8+YP78+Wzfvr0rSHJSW1vLnj178lCZyOBUFMEAmfspZsyYwUsvvZSX5Y0cOZK6urq8LEtksCmaYOjyuc99jpUrV7J58+acllNTU8PatWuZNm1anioTGUSSPr4Q5xhDd622ttYbGxt9z549OR1v+P73v5/0fp+a2kC12McYiu4mqi4HDx7k4MGDbN++naqqKl555RVKS4v2xxFJlaL/S/rzn/8MQHV1NfX19axfvx7IXC0ZDVQrIn1UdMcYevLee+/x4osvUl5eTnl5Ob/85S958803OXz4cI/ztLa2curUqQGsUqQ4pGJoNzMrWBGjR4/mySef7Pa9HTt2aMh6GUpiD+026INBRM4prjEfRSRdFAwiElAwiEhAwSAiAQWDiAQUDCISUDCISEDBICIBBYOIBBQMIhLoNRjM7AkzO2Fme7P6qsxss5kdiB5HRf1mZg+ZWbOZNZnZ1EIWLyKFEWeL4efA3PP6lgFb3X0ysDV6DXA9MDlqjcAj+SlTRAZSr8Hg7tuB98/rng+siZ6vARqy+p+MBkf6PVBpZtV5qlVEBkh/jzGMcfdj0fN3gDHR87FA9gAIR6I+ESkiOY/g5O7en9umzayRzO6GiKRMf7cYjnftIkSPJ6L+o0BN1nTjor6Au69y9/q494cPBhUVFcycOVNfkyep198thvXAAuA/o8fns/oXm9kzwNXA37N2OYas2267jVGjRlFTU8O3vvUt1q1bx7Zt24DM8HKrV6/Oy+dMmTKFOXPmsG7dOo4cOZKXZcoQFWNo97XAMeAMmWMGXwY+TuZsxAFgC1AVTWvAw8BBYA9QX6jh44ulLVy40E+fPt3j8PWtra3++OOP++OPP+7XXXddTp915513urv7Cy+84JWVlYn/7Gqpa7GHj0/8OyUGczB85Stf8ffff7/HUDjfoUOH/Morr+z353UFg7v72LFjE//51VLXFAxJtxtuuMFbW1tjh0KX06dPe3V1db8+s7y83H/2s595Z2enT5gwIfF1oJa6Nri+1LbYlJWVUVNTw4gRI/o87yWXXMLbb7/N+PHj+zxvW1sbd999N9u2baO5uZlhw4b1eRkioHslCqK2tpaVK1f2e/6ysrJzByf7q6SkhNmzZ+e0DBm6FAx5Vlpayl133ZXzciorK7npppv6Pb+Z8dxzz3HrrbfmXIsMPfpeiTwbMWIEra2teVnWxo0bmTdvXuzpZ8yYwcKFC5k1axaXXXYZAO+++y4vvfTSR6Y7fvw4S5YsyUuNUlRif69E4gceB9vBxxEjRvT5gGNPWlpafOnSpd1+zuLFi72pqekj7fDhw7GW29bW5k1NTf61r30t8fWlNqBNZyWSavkMBnf39vZ2v/32272iosIvu+wyb2lp8ZaWFm9vb8/LsltaWryuri7x9aY2IC12MBT9t10PdmVlZT1+92Y+ll1WVkZJiQ41yUfpNyLPzp49y8aNG5MuQyQnCoY8a29v5xvf+EbSZYjkRMFQAEeOHOGGG25g7dq1SZci0i86XVlAo0aNoqqqCoCRI0fy1FNP0dDQEGvecePG8fLLLxeuuMiSJUt49NFHaW9vL/hnSeJ0ujKNraysrE/Tz5071z/88MOczz70pLW11RsaGhJfL2oD1nS6crC0b3/72wUJhVOnTvnChQsT//nUBrTpJqrBYvfu3Rw6dCjWtAcOHGD16tUcO3bhsXFaWlq49957eeQRDeItPUh6a0FbDL23p59++oL/+3/961/3u+++22fPnu2ANzQ0+JIlS7yzszOYtqOjw++4447Efya1RJp2JQZTmzJlijc3Nwd/5Pfcc4/PnDmzazDeoM2YMcOXLl3q7u7333+/z5w5M+dRotSKusUOBp2VKBKXXnopF198MRs2bODTn/40ACdPnqStre2C8w0fPpzRo0dz+vTpvN3cJUUr9lkJBUORKSkpobOzM+kypDjFDgYdfCwyCgUZCAoGEQkoGEQkoGAQkYCCYQjZsGEDJ0+eZNeuXUmXIimnYBhCPv/5z3Po0CEuueSSpEuRlFMwDDHXXnstW7ZsSboMSTldxyAydOg6BhHpPwWDiAQUDCISUDCISEDBICIBBYOIBBQMIhLoNRjM7AkzO2Fme7P6vmdmR83stajNy3rvHjNrNrP9ZvbZQhUuIoUTZ4vh58Dcbvp/7O5XRe0FADOrA24GPhHNs9LMhuWrWBEZGL1+qa27bzezCTGXNx94xt3bgDfNrBmYBuzof4lDT319PaWlmX+av/3tb/z1r39NuCIZanI5xrDYzJqiXY1RUd9Y4HDWNEeivoCZNZrZTjPbmUMNg8r06dNpbGxk27Zt7Nixgx07drBq1Spqa2uTLk2GmpijOE8A9ma9HgMMIxMs/wE8EfWvAG7Lmm418G8aJfrC7YorrvCHH37Y9+3bF4wE7e6+adMmHzt2bOJ1qhV9iz1KdK+7Et1x9+Ndz83sMeB/opdHgZqsScdFfXIBl19+OV/96ld7fH/OnDmMHj2ao0e1KmVg9GtXwsyqs15+Aeg6Y7EeuNnMys1sIjAZ+GNuJQ5+O3bsYNmyZV1bT+e4Ox0dHXR0dATviRRSr1sMZrYWmAmMNrMjwL3ATDO7iszmyVvAXQDuvs/MngVeBzqARe5+tiCVDyJnzpzhvvvuo6qqiltuueVc/8svv8wXv/hFQKNDy8DSeAwiQ4fGYxCR/lMwiEhAwSAiAQWDiAQUDCIS6NcFTkmZNWtWt9+JsGnTJn3Fu0g+xb1EspCNXi7lnD59un/3u9/1EydOdHvJ8IMPPuglJSVJX26qppb2FvuS6FRfx1BdXc2KFSuYMmUKdXV1Pc7v7pSXl3PmzJmC1SgyCAyO6xguuugibrzxxguGAoCZ8corrwxQVSKDX2qDoby8nKamptjTX3HFFQWsRmRoSW0wAIwYMSLpEkSGpFQHg4gkQ8EgIoHUBsOZM2f40pe+FHv6W2+9tYDViAwxSV/DcKHrGEpLS338+PH+ox/9yDs7O72zs/Mj1y909U2fPj3p88NqasXQBsd1DF1KSkooKcls3Bw4cICPfexj/OY3v2HRokUAdHR0FL5IkeIX+zqGorgkurOz89wIRhMnTky4GpHBL7XHGEQkOQoGEQkoGEQkoGAQkYCCQUQCCgYRCSgYRCSgYBCRgIJBRAIKBhEJKBhEJKBgEJGAgkFEAgoGEQkoGEQkoGAQkUCvwWBmNWa2zcxeN7N9ZrYk6q8ys81mdiB6HBX1m5k9ZGbNZtZkZlML/UOISH7F2WLoAL7p7nXANcAiM6sDlgFb3X0ysDV6DXA9MDlqjcAjea9aRAqq12Bw92Pu/qfoeQvwBjAWmA+siSZbAzREz+cDT0bjtf4eqDSz6nwXLiKF06djDGY2AfgU8AdgjLsfi956BxgTPR8LHM6a7UjUJyJFIvZgsGZWAfwKWOruH5jZuffc3Xsb6bmb5TWS2dUQkZSJtcVgZmVkQuEX7v7rqPt41y5C9Hgi6j8K1GTNPi7q+wh3X+Xu9XGHsxaRgRPnrIQBq4E33P3BrLfWAwui5wuA57P674jOTlwD/D1rl0NEikCvXzhjZtOB/wP2AJ1R93fIHGd4Fvgn4G3gJnd/PwqSFcBcoBW409139vIZyX/rjcjgF/sLZ4rim6hEJC9iB4OufBSRgIJBRAIKBhEJKBhEJKBgEJGAgkFEAgoGEQkoGEQkoGAQkYCCQUQCCgYRCSgYRCSgYBCRgIJBRAIKBhEJKBhEJKBgEJGAgkFEAgoGEQkoGEQkoGAQkYCCQUQCCgYRCSgYRCSgYBCRgIJBRAIKBhEJKBhEJKBgEJGAgkFEAgoGEQkoGEQkoGAQkYCCQUQCvQaDmdWY2TYze93M9pnZkqj/e2Z21Mxei9q8rHnuMbNmM9tvZp8t5A8gIvlXGmOaDuCb7v4nMxsJ7DKzzdF7P3b3B7InNrM64GbgE8DlwBYz+2d3P5vPwkWkcHrdYnD3Y+7+p+h5C/AGMPYCs8wHnnH3Nnd/E2gGpuWjWBEZGH06xmBmE4BPAX+IuhabWZOZPWFmo6K+scDhrNmO0E2QmFmjme00s519L1tECil2MJhZBfArYKm7fwA8AtQCVwHHgP/qywe7+yp3r3f3+r7MJyKFFysYzKyMTCj8wt1/DeDux939rLt3Ao/xj92Fo0BN1uzjoj4RKRJxzkoYsBp4w90fzOqvzprsC8De6Pl64GYzKzezicBk4I/5K1lECi3OWYl/BW4H9pjZa1Hfd4BbzOwqwIG3gLsA3H2fmT0LvE7mjMYinZEQKS7m7knXgJm9C3wIvJd0LTGMpjjqhOKpVXXmX3e1jnf3S+PMnIpgADCzncVwILJY6oTiqVV15l+uteqSaBEJKBhEJJCmYFiVdAExFUudUDy1qs78y6nW1BxjEJH0SNMWg4ikROLBYGZzo9uzm81sWdL1nM/M3jKzPdGt5Tujvioz22xmB6LHUb0tpwB1PWFmJ8xsb1Zft3VZxkPROm4ys6kpqDV1t+1fYIiBVK3XARkKwd0Ta8Aw4CAwCRgO7AbqkqypmxrfAkaf13c/sCx6vgy4L4G6rgOmAnt7qwuYB2wEDLgG+EMKav0e8O/dTFsX/R6UAxOj349hA1RnNTA1ej4S+EtUT6rW6wXqzNs6TXqLYRrQ7O6H3L0deIbMbdtpNx9YEz1fAzQMdAHuvh14/7zunuqaDzzpGb8HKs+7pL2geqi1J4ndtu89DzGQqvV6gTp70ud1mnQwxLpFO2EObDKzXWbWGPWNcfdj0fN3gDHJlBboqa60rud+37ZfaOcNMZDa9ZrPoRCyJR0MxWC6u08FrgcWmdl12W96Zlstdad20lpXlpxu2y+kboYYOCdN6zXfQyFkSzoYUn+LtrsfjR5PAOvIbIId79pkjB5PJFfhR/RUV+rWs6f0tv3uhhggheu10EMhJB0MrwKTzWyimQ0nM1bk+oRrOsfMLorGucTMLgI+Q+b28vXAgmiyBcDzyVQY6Kmu9cAd0VH0a4C/Z20aJyKNt+33NMQAKVuvPdWZ13U6EEdReznCOo/MUdWDwPKk6zmvtklkjubuBvZ11Qd8HNgKHAC2AFUJ1LaWzObiGTL7jF/uqS4yR80fjtbxHqA+BbU+FdXSFP3iVmdNvzyqdT9w/QDWOZ3MbkIT8FrU5qVtvV6gzrytU135KCKBpHclRCSFFAwiElAwiEhAwSAiAQWDiAQUDCISUDCISEDBICKB/wdnKRlBTA5YCQAAAABJRU5ErkJggg==\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = img_as_ubyte(img)\n",
    "(thresh, blackAndWhiteImage) = cv2.threshold(img, 5, 255, cv2.THRESH_BINARY)\n",
    "plt.imshow(blackAndWhiteImage, cmap=\"gray\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.image.AxesImage at 0x13b0eb065e0>"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(resized_gt, cmap=\"gray\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.image.AxesImage at 0x13b0ea52fd0>"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from my_site.image.Traitement_image.Crop_From_Center import crop_image_from_center\n",
    "\n",
    "c = crop_image_from_center(gt, shape=shape, center=center)\n",
    "\n",
    "plt.imshow(c, cmap=\"gray\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.image.AxesImage at 0x16d7d6a7730>"
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path_image = r\"C:\\Users\\Asus\\Desktop\\PCD\\Organized dataset\\train_test_data\\predicted\\0_predict_cv2.jpg\"\n",
    "why = mpimg.imread(path_image)\n",
    "plt.imshow(why)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.image.AxesImage at 0x16d7d558880>"
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "resized_image = cv2.resize(gt, why.shape)\n",
    "plt.imshow(resized_image, cmap=\"gray\")\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(176, 22)\n",
      "(24, 24)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<matplotlib.image.AxesImage at 0x16d957e6ac0>"
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the scaling factor\n",
    "img_shape = gt.shape\n",
    "reshaped_img_shape = why.shape\n",
    "scale = np.flipud(np.divide(reshaped_img_shape,\n",
    "                            img_shape))  # you have to flip because the image.shape is (y,x) but your corner points are (x,y)\n",
    "\n",
    "#use this on to get new top left corner and bottom right corner coordinates\n",
    "new_center = int(np.multiply(center, scale)[0]), int(np.multiply(center, scale)[1])\n",
    "new_shape = int(np.multiply(shape, scale)[0]), int(np.multiply(shape, scale)[1])\n",
    "print(new_center)\n",
    "print(new_shape)\n",
    "y_true = crop_image_from_center(resized_image, shape=new_shape, center=new_center)\n",
    "y_pred = crop_image_from_center(why, shape=new_shape, center=new_center)\n",
    "plt.imshow(y_true, cmap=\"gray\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.image.AxesImage at 0x16d94b9f1c0>"
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(y_pred, cmap=\"gray\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IoU is 0.5764411027568922\n",
      "0.5764411027568922\n",
      "Dice coefficient is 0.5833333333333334\n",
      "0.5833333333333334\n"
     ]
    }
   ],
   "source": [
    "from my_site.image.utilities.Dice_coef import dice_coef\n",
    "from my_site.image.utilities.IOU import iou\n",
    "\n",
    "print(iou(y_true, y_pred))\n",
    "print(dice_coef(y_true, y_pred))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Output one element"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Asus\\AppData\\Local\\Temp\\ipykernel_13096\\987203364.py:4: UserWarning: `Model.predict_generator` is deprecated and will be removed in a future version. Please use `Model.predict`, which supports generators.\n",
      "  results = model.predict_generator(testGene,1,verbose=1)\n",
      "C:\\Users\\Asus\\Desktop\\PCD\\venv\\lib\\site-packages\\keras\\engine\\training_v1.py:2079: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
      "  updates=self.state_updates,\n"
     ]
    }
   ],
   "source": [
    "testGene = testGenerator_one(r\"C:\\Users\\Asus\\Desktop\\PCD\\Organized dataset\\train_test_data\\test\\0.jpg\")\n",
    "model = unet()\n",
    "model.load_weights(\"unet_membrane.hdf5\")\n",
    "results = model.predict_generator(testGene, 1, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "(256, 256, 1)\n"
     ]
    }
   ],
   "source": [
    "print(type(results[0]))\n",
    "print(results[0].shape)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "img = results[0]\n",
    "img = img[:, :, 0]\n",
    "img = img_as_ubyte(img)\n",
    "(thresh, blackAndWhiteImage) = cv2.threshold(img, 30, 255, cv2.THRESH_BINARY)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.image.AxesImage at 0x17f6d24af10>"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.imshow(blackAndWhiteImage, cmap=\"gray\")"
   ],
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    "pycharm": {
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   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
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