TrueOP.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f2c6b433",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Segmentation Models: using `keras` framework.\n",
      "Number GPUs Available:  1\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import tifffile\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "\n",
    "from segmentation_models import Unet, get_preprocessing\n",
    "from segmentation_models.losses import bce_dice_loss\n",
    "from segmentation_models.metrics import f1_score\n",
    "\n",
    "print(\"Number GPUs Available: \", len(tf.config.list_physical_devices('GPU')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "156f663a",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Model Parameters ###\n",
    "\n",
    "MODEL = Unet\n",
    "BACKBONE = \"seresnet50\"\n",
    "EPOCH = 100\n",
    "\n",
    "OPTIMIZER = \"Adam\"\n",
    "METRICS = [f1_score]\n",
    "LOSS = bce_dice_loss\n",
    "MONITOR = \"val_f1-score\"\n",
    "\n",
    "BATCH_SIZE = 16\n",
    "IMG_WIDTH = 256\n",
    "IMG_HEIGHT = 256\n",
    "IMG_CHANNELS = 3\n",
    "\n",
    "MODEL_NAME = f\"{MODEL.__name__}_{BACKBONE}_{EPOCH}epoch_{BATCH_SIZE}batch_TrueOP\"\n",
    "os.makedirs(f'{MODEL_NAME}\\\\Results')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee9b3fcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# #Generating List of True Ortophotos for training\n",
    "# trueop_train = open(\"train_tiles_trueop_manual.txt\", \"w\")\n",
    "# trueop_train.write(\"id\\n\")\n",
    "# for path, subdirs, _ in os.walk(r'new_test_data\\\\trueop\\\\train'):\n",
    "#     for dir in _:\n",
    "#         if dir.endswith(\".tif\"):\n",
    "#             fpath = os.path.join(path, dir)\n",
    "#             trueop_train.write(fpath + \"\\n\")\n",
    "# trueop_train.close()\n",
    "\n",
    "# # Generating List of nDSM for training\n",
    "# ndsm_train = open(\"train_tiles_ndsm_manual.txt\", \"w\")\n",
    "# ndsm_train.write(\"id\\n\")\n",
    "# for path, subdirs, _ in os.walk(r'new_test_data\\\\ndsm\\\\train'):\n",
    "#     for dir in _:\n",
    "#         if dir.endswith(\".tif\"):\n",
    "#             fpath = os.path.join(path, dir)\n",
    "#             ndsm_train.write(fpath + \"\\n\")\n",
    "# ndsm_train.close()\n",
    "    \n",
    "# # Generating List of masks for training\n",
    "# mask_train = open(\"train_tiles_mask_manual.txt\", \"w\")\n",
    "# mask_train.write(\"id\\n\")\n",
    "# for path, subdirs, _ in os.walk(r'new_test_data\\\\mask\\\\train'):\n",
    "#     for dir in _:\n",
    "#         if dir.endswith(\".tif\"):\n",
    "#             fpath = os.path.join(path, dir)\n",
    "#             mask_train.write(fpath + \"\\n\")\n",
    "# mask_train.close()\n",
    "\n",
    "# # TEST ###################33\n",
    "# trueop_test = open(\"test_tiles_trueop_manual.txt\", \"w\")\n",
    "# trueop_test.write(\"id\\n\")\n",
    "# for path, subdirs, _ in os.walk(r'new_test_data\\\\trueop\\\\test'):\n",
    "#     for dir in _:\n",
    "#         if dir.endswith(\".tif\"):\n",
    "#             fpath = os.path.join(path, dir)\n",
    "#             trueop_test.write(fpath + \"\\n\")\n",
    "# trueop_test.close()\n",
    "\n",
    "# # Generating List of nDSM for training\n",
    "# ndsm_test = open(\"test_tiles_ndsm_manual.txt\", \"w\")\n",
    "# ndsm_test.write(\"id\\n\")\n",
    "# for path, subdirs, _ in os.walk(r'new_test_data\\\\ndsm\\\\test'):\n",
    "#     for dir in _:\n",
    "#         if dir.endswith(\".tif\"):\n",
    "#             fpath = os.path.join(path, dir)\n",
    "#             ndsm_test.write(fpath + \"\\n\")\n",
    "# ndsm_test.close()\n",
    "    \n",
    "# # Generating List of masks for training\n",
    "# mask_test = open(\"test_tiles_mask_manual.txt\", \"w\")\n",
    "# mask_test.write(\"id\\n\")\n",
    "# for path, subdirs, _ in os.walk(r'new_test_data\\\\mask\\\\test'):\n",
    "#     for dir in _:\n",
    "#         if dir.endswith(\".tif\"):\n",
    "#             fpath = os.path.join(path, dir)\n",
    "#             mask_test.write(fpath + \"\\n\")\n",
    "# mask_test.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7d60e2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Generating List of True Ortophotos for training\n",
    "trueop_train = open(\"new_train_tiles_trueop_manual.txt\", \"w\")\n",
    "trueop_train.write(\"id\\n\")\n",
    "for path, subdirs, _ in os.walk(r'new_test_data\\\\trueop\\\\train'):\n",
    "    for dir in _:\n",
    "        if dir.endswith(\".tif\"):\n",
    "            fpath = os.path.join(path, dir)\n",
    "            trueop_train.write(fpath + \"\\n\")\n",
    "trueop_train.close()\n",
    "\n",
    "# # Generating List of nDSM for training\n",
    "# ndsm_train = open(\"new_train_tiles_ndsm_manual.txt\", \"w\")\n",
    "# ndsm_train.write(\"id\\n\")\n",
    "# for path, subdirs, _ in os.walk(r'new_test_data\\\\ndsm\\\\train'):\n",
    "#     for dir in _:\n",
    "#         if dir.endswith(\".tif\"):\n",
    "#             fpath = os.path.join(path, dir)\n",
    "#             ndsm_train.write(fpath + \"\\n\")\n",
    "# ndsm_train.close()\n",
    "    \n",
    "# Generating List of masks for training\n",
    "mask_train = open(\"new_train_tiles_mask_manual.txt\", \"w\")\n",
    "mask_train.write(\"id\\n\")\n",
    "for path, subdirs, _ in os.walk(r'new_test_data\\\\mask\\\\train'):\n",
    "    for dir in _:\n",
    "        if dir.endswith(\".tif\"):\n",
    "            fpath = os.path.join(path, dir)\n",
    "            mask_train.write(fpath + \"\\n\")\n",
    "mask_train.close()\n",
    "\n",
    "\n",
    "# TEST ###################33\n",
    "trueop_test = open(\"new_test_tiles_trueop_manual.txt\", \"w\")\n",
    "trueop_test.write(\"id\\n\")\n",
    "for path, subdirs, _ in os.walk(r'new_test_data\\\\trueop\\\\test'):\n",
    "    for dir in _:\n",
    "        if dir.endswith(\".tif\"):\n",
    "            fpath = os.path.join(path, dir)\n",
    "            trueop_test.write(fpath + \"\\n\")\n",
    "trueop_test.close()\n",
    "\n",
    "# # Generating List of nDSM for training\n",
    "# ndsm_test = open(\"new_test_tiles_ndsm_manual.txt\", \"w\")\n",
    "# ndsm_test.write(\"id\\n\")\n",
    "# for path, subdirs, _ in os.walk(r'new_test_data\\\\ndsm\\\\test'):\n",
    "#     for dir in _:\n",
    "#         if dir.endswith(\".tif\"):\n",
    "#             fpath = os.path.join(path, dir)\n",
    "#             ndsm_test.write(fpath + \"\\n\")\n",
    "# ndsm_test.close()\n",
    "    \n",
    "# Generating List of masks for training\n",
    "mask_test = open(\"new_test_tiles_mask_manual.txt\", \"w\")\n",
    "mask_test.write(\"id\\n\")\n",
    "for path, subdirs, _ in os.walk(r'new_test_data\\\\mask\\\\test'):\n",
    "    for dir in _:\n",
    "        if dir.endswith(\".tif\"):\n",
    "            fpath = os.path.join(path, dir)\n",
    "            mask_test.write(fpath + \"\\n\")\n",
    "mask_test.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce121ae9",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_trueop = pd.read_csv(\"new_train_tiles_trueop_manual.txt\")\n",
    "train_data_mask = pd.read_csv(\"new_train_tiles_mask_manual.txt\")\n",
    "\n",
    "test_data_trueop = pd.read_csv(\"new_test_tiles_trueop_manual.txt\")\n",
    "test_data_mask = pd.read_csv(\"new_test_tiles_mask_manual.txt\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2612b1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = np.zeros((len(train_data_trueop), IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS))\n",
    "Y_train = np.zeros((len(train_data_trueop), IMG_HEIGHT, IMG_WIDTH, 1))\n",
    "\n",
    "for i in range(len(train_data_trueop)):\n",
    "    cache = np.zeros((IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS))\n",
    "    truop_img= tifffile.imread(train_data_trueop[\"id\"][i])\n",
    "    mask_img = tifffile.imread(train_data_mask[\"id\"][i])\n",
    "    #ndsm_img = tifffile.imread(train_data_nDSM[\"id\"][i])\n",
    "    #ndsm_img[np.where(ndsm_img==-32767)] = 0 \n",
    "    cache[:,:, :] = truop_img\n",
    "    #cache[:,:, 3] = ndsm_img    \n",
    "    X_train[i] = cache\n",
    "    mask = mask_img[:,:,0]\n",
    "    mask = np.expand_dims(mask, axis=-1)\n",
    "    Y_train[i] = mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2680a83f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = np.zeros((len(test_data_trueop), IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS))\n",
    "Y_test= np.zeros((len(test_data_trueop), IMG_HEIGHT, IMG_WIDTH, 1))\n",
    "\n",
    "for i in range(len(test_data_trueop)):\n",
    "    cache = np.zeros((IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS))\n",
    "    truop_img= tifffile.imread(test_data_trueop[\"id\"][i])\n",
    "    mask_img = tifffile.imread(test_data_mask[\"id\"][i])\n",
    "    #ndsm_img = tifffile.imread(test_data_nDSM[\"id\"][i])\n",
    "    #ndsm_img[np.where(ndsm_img==-32767)] = 0 \n",
    "    cache[:,:, :] = truop_img\n",
    "    #cache[:,:, 3] = ndsm_img    \n",
    "    X_test[i] = cache\n",
    "    mask = mask_img[:,:,0]\n",
    "    mask = np.expand_dims(mask, axis=-1)\n",
    "    Y_test[i] = mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5aea2fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(\"new_X_train_manual\", X_train)\n",
    "np.save(\"new_Y_train_manual\", Y_train)\n",
    "np.save(\"new_X_test_manual\", X_test)\n",
    "np.save(\"new_Y_test_manual\", Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "50cc0c76",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = np.load(\"new_X_train_manual.npy\")\n",
    "Y_train = np.load(\"new_Y_train_manual.npy\")\n",
    "X_test= np.load(\"new_X_test_manual.npy\")\n",
    "Y_test = np.load(\"new_Y_test_manual.npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3eadd994",
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocess_input = get_preprocessing(BACKBONE)\n",
    "from sklearn.model_selection import train_test_split\n",
    "# load your data\n",
    "x_train, x_val, y_train, y_val = train_test_split(X_train, Y_train, test_size=0.111, random_state=42)\n",
    "\n",
    "# preprocess input\n",
    "x_train = preprocess_input(x_train)\n",
    "x_val = preprocess_input(x_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0692f2d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpointer = tf.keras.callbacks.ModelCheckpoint(filepath=f'{MODEL_NAME}\\\\{MODEL_NAME}.h5', verbose=1, save_best_only=True, monitor=MONITOR, mode='max')\n",
    "csv_logger = tf.keras.callbacks.CSVLogger(f'{MODEL_NAME}\\\\{MODEL_NAME}_log.csv', separator=',', append=False)\n",
    "early_stopping = tf.keras.callbacks.EarlyStopping(patience=10, restore_best_weights=True, verbose=1, monitor=MONITOR, mode='max')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5d82beca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "286/286 [==============================] - 223s 635ms/step - loss: 0.6476 - f1-score: 0.6451 - val_loss: 1.2227 - val_f1-score: 0.7716\n",
      "\n",
      "Epoch 00001: val_f1-score improved from -inf to 0.77165, saving model to Unet_seresnet50_100epoch_16batch_nDSM\\Unet_seresnet50_100epoch_16batch_nDSM.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Mehmet\\anaconda3\\envs\\tf25\\lib\\site-packages\\keras\\utils\\generic_utils.py:494: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
      "  warnings.warn('Custom mask layers require a config and must override '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/100\n",
      "286/286 [==============================] - 172s 602ms/step - loss: 0.3975 - f1-score: 0.7959 - val_loss: 0.3461 - val_f1-score: 0.8519\n",
      "\n",
      "Epoch 00002: val_f1-score improved from 0.77165 to 0.85188, saving model to Unet_seresnet50_100epoch_16batch_nDSM\\Unet_seresnet50_100epoch_16batch_nDSM.h5\n",
      "Epoch 3/100\n",
      "286/286 [==============================] - 171s 598ms/step - loss: 0.3304 - f1-score: 0.8311 - val_loss: 0.4385 - val_f1-score: 0.7961\n",
      "\n",
      "Epoch 00003: val_f1-score did not improve from 0.85188\n",
      "Epoch 4/100\n",
      "286/286 [==============================] - 169s 591ms/step - loss: 0.3078 - f1-score: 0.8409 - val_loss: 0.2680 - val_f1-score: 0.8734\n",
      "\n",
      "Epoch 00004: val_f1-score improved from 0.85188 to 0.87341, saving model to Unet_seresnet50_100epoch_16batch_nDSM\\Unet_seresnet50_100epoch_16batch_nDSM.h5\n",
      "Epoch 5/100\n",
      "286/286 [==============================] - 169s 590ms/step - loss: 0.2670 - f1-score: 0.8622 - val_loss: 0.2751 - val_f1-score: 0.8637\n",
      "\n",
      "Epoch 00005: val_f1-score did not improve from 0.87341\n",
      "Epoch 6/100\n",
      "286/286 [==============================] - 169s 591ms/step - loss: 0.2420 - f1-score: 0.8775 - val_loss: 1.1559 - val_f1-score: 0.6745\n",
      "\n",
      "Epoch 00006: val_f1-score did not improve from 0.87341\n",
      "Epoch 7/100\n",
      "286/286 [==============================] - 170s 593ms/step - loss: 0.2460 - f1-score: 0.8740 - val_loss: 0.2239 - val_f1-score: 0.8967\n",
      "\n",
      "Epoch 00007: val_f1-score improved from 0.87341 to 0.89669, saving model to Unet_seresnet50_100epoch_16batch_nDSM\\Unet_seresnet50_100epoch_16batch_nDSM.h5\n",
      "Epoch 8/100\n",
      "286/286 [==============================] - 170s 593ms/step - loss: 0.2534 - f1-score: 0.8708 - val_loss: 0.2217 - val_f1-score: 0.8956\n",
      "\n",
      "Epoch 00008: val_f1-score did not improve from 0.89669\n",
      "Epoch 9/100\n",
      "286/286 [==============================] - 169s 590ms/step - loss: 0.2136 - f1-score: 0.8914 - val_loss: 0.2083 - val_f1-score: 0.8996\n",
      "\n",
      "Epoch 00009: val_f1-score improved from 0.89669 to 0.89958, saving model to Unet_seresnet50_100epoch_16batch_nDSM\\Unet_seresnet50_100epoch_16batch_nDSM.h5\n",
      "Epoch 10/100\n",
      "286/286 [==============================] - 168s 588ms/step - loss: 0.1900 - f1-score: 0.9035 - val_loss: 0.3440 - val_f1-score: 0.8485\n",
      "\n",
      "Epoch 00010: val_f1-score did not improve from 0.89958\n",
      "Epoch 11/100\n",
      "286/286 [==============================] - 169s 589ms/step - loss: 0.1967 - f1-score: 0.9003 - val_loss: 0.2212 - val_f1-score: 0.9080\n",
      "\n",
      "Epoch 00011: val_f1-score improved from 0.89958 to 0.90805, saving model to Unet_seresnet50_100epoch_16batch_nDSM\\Unet_seresnet50_100epoch_16batch_nDSM.h5\n",
      "Epoch 12/100\n",
      "286/286 [==============================] - 169s 590ms/step - loss: 0.1772 - f1-score: 0.9081 - val_loss: 0.2447 - val_f1-score: 0.8849\n",
      "\n",
      "Epoch 00012: val_f1-score did not improve from 0.90805\n",
      "Epoch 13/100\n",
      "286/286 [==============================] - 169s 591ms/step - loss: 0.1749 - f1-score: 0.9105 - val_loss: 0.2699 - val_f1-score: 0.8810\n",
      "\n",
      "Epoch 00013: val_f1-score did not improve from 0.90805\n",
      "Epoch 14/100\n",
      "286/286 [==============================] - 169s 590ms/step - loss: 0.1836 - f1-score: 0.9055 - val_loss: 0.1796 - val_f1-score: 0.9160\n",
      "\n",
      "Epoch 00014: val_f1-score improved from 0.90805 to 0.91603, saving model to Unet_seresnet50_100epoch_16batch_nDSM\\Unet_seresnet50_100epoch_16batch_nDSM.h5\n",
      "Epoch 15/100\n",
      "286/286 [==============================] - 169s 593ms/step - loss: 0.1526 - f1-score: 0.9227 - val_loss: 0.2484 - val_f1-score: 0.8821\n",
      "\n",
      "Epoch 00015: val_f1-score did not improve from 0.91603\n",
      "Epoch 16/100\n",
      "286/286 [==============================] - 168s 588ms/step - loss: 0.1561 - f1-score: 0.9192 - val_loss: 0.2193 - val_f1-score: 0.9038\n",
      "\n",
      "Epoch 00016: val_f1-score did not improve from 0.91603\n",
      "Epoch 17/100\n",
      "286/286 [==============================] - 170s 595ms/step - loss: 0.1611 - f1-score: 0.9170 - val_loss: 0.2057 - val_f1-score: 0.8995\n",
      "\n",
      "Epoch 00017: val_f1-score did not improve from 0.91603\n",
      "Epoch 18/100\n",
      "286/286 [==============================] - 178s 623ms/step - loss: 0.1499 - f1-score: 0.9228 - val_loss: 0.1658 - val_f1-score: 0.9237\n",
      "\n",
      "Epoch 00018: val_f1-score improved from 0.91603 to 0.92367, saving model to Unet_seresnet50_100epoch_16batch_nDSM\\Unet_seresnet50_100epoch_16batch_nDSM.h5\n",
      "Epoch 19/100\n",
      "286/286 [==============================] - 178s 624ms/step - loss: 0.1392 - f1-score: 0.9291 - val_loss: 0.1981 - val_f1-score: 0.9118\n",
      "\n",
      "Epoch 00019: val_f1-score did not improve from 0.92367\n",
      "Epoch 20/100\n",
      "286/286 [==============================] - 172s 600ms/step - loss: 0.1370 - f1-score: 0.9292 - val_loss: 0.3260 - val_f1-score: 0.8480\n",
      "\n",
      "Epoch 00020: val_f1-score did not improve from 0.92367\n",
      "Epoch 21/100\n",
      "286/286 [==============================] - 173s 605ms/step - loss: 0.1676 - f1-score: 0.9159 - val_loss: 0.1714 - val_f1-score: 0.9201\n",
      "\n",
      "Epoch 00021: val_f1-score did not improve from 0.92367\n",
      "Epoch 22/100\n",
      "286/286 [==============================] - 174s 608ms/step - loss: 0.1348 - f1-score: 0.9307 - val_loss: 0.2202 - val_f1-score: 0.9049\n",
      "\n",
      "Epoch 00022: val_f1-score did not improve from 0.92367\n",
      "Epoch 23/100\n",
      "286/286 [==============================] - 169s 589ms/step - loss: 0.1454 - f1-score: 0.9248 - val_loss: 0.1867 - val_f1-score: 0.9159\n",
      "\n",
      "Epoch 00023: val_f1-score did not improve from 0.92367\n",
      "Epoch 24/100\n",
      "286/286 [==============================] - 169s 591ms/step - loss: 0.1301 - f1-score: 0.9343 - val_loss: 0.1711 - val_f1-score: 0.9244\n",
      "\n",
      "Epoch 00024: val_f1-score improved from 0.92367 to 0.92437, saving model to Unet_seresnet50_100epoch_16batch_nDSM\\Unet_seresnet50_100epoch_16batch_nDSM.h5\n",
      "Epoch 25/100\n",
      "286/286 [==============================] - 170s 593ms/step - loss: 0.1096 - f1-score: 0.9446 - val_loss: 0.1543 - val_f1-score: 0.9308\n",
      "\n",
      "Epoch 00025: val_f1-score improved from 0.92437 to 0.93078, saving model to Unet_seresnet50_100epoch_16batch_nDSM\\Unet_seresnet50_100epoch_16batch_nDSM.h5\n",
      "Epoch 26/100\n",
      "286/286 [==============================] - 171s 598ms/step - loss: 0.1253 - f1-score: 0.9356 - val_loss: 0.1862 - val_f1-score: 0.9199\n",
      "\n",
      "Epoch 00026: val_f1-score did not improve from 0.93078\n",
      "Epoch 27/100\n",
      "286/286 [==============================] - 169s 590ms/step - loss: 0.1143 - f1-score: 0.9421 - val_loss: 0.1548 - val_f1-score: 0.9344\n",
      "\n",
      "Epoch 00027: val_f1-score improved from 0.93078 to 0.93437, saving model to Unet_seresnet50_100epoch_16batch_nDSM\\Unet_seresnet50_100epoch_16batch_nDSM.h5\n",
      "Epoch 28/100\n",
      "286/286 [==============================] - 168s 589ms/step - loss: 0.1224 - f1-score: 0.9358 - val_loss: 0.1412 - val_f1-score: 0.9348\n",
      "\n",
      "Epoch 00028: val_f1-score improved from 0.93437 to 0.93479, saving model to Unet_seresnet50_100epoch_16batch_nDSM\\Unet_seresnet50_100epoch_16batch_nDSM.h5\n",
      "Epoch 29/100\n",
      "286/286 [==============================] - 169s 592ms/step - loss: 0.1010 - f1-score: 0.9482 - val_loss: 0.1599 - val_f1-score: 0.9303\n",
      "\n",
      "Epoch 00029: val_f1-score did not improve from 0.93479\n",
      "Epoch 30/100\n",
      "286/286 [==============================] - 167s 583ms/step - loss: 0.0915 - f1-score: 0.9536 - val_loss: 0.1650 - val_f1-score: 0.9304\n",
      "\n",
      "Epoch 00030: val_f1-score did not improve from 0.93479\n",
      "Epoch 31/100\n",
      "286/286 [==============================] - 166s 579ms/step - loss: 0.0936 - f1-score: 0.9526 - val_loss: 0.1570 - val_f1-score: 0.9354\n",
      "\n",
      "Epoch 00031: val_f1-score improved from 0.93479 to 0.93544, saving model to Unet_seresnet50_100epoch_16batch_nDSM\\Unet_seresnet50_100epoch_16batch_nDSM.h5\n",
      "Epoch 32/100\n",
      "286/286 [==============================] - 165s 578ms/step - loss: 0.1083 - f1-score: 0.9450 - val_loss: 0.1561 - val_f1-score: 0.9301\n",
      "\n",
      "Epoch 00032: val_f1-score did not improve from 0.93544\n",
      "Epoch 33/100\n",
      "286/286 [==============================] - 166s 580ms/step - loss: 0.1207 - f1-score: 0.9385 - val_loss: 0.1961 - val_f1-score: 0.9154\n",
      "\n",
      "Epoch 00033: val_f1-score did not improve from 0.93544\n",
      "Epoch 34/100\n",
      "286/286 [==============================] - 166s 579ms/step - loss: 0.1201 - f1-score: 0.9385 - val_loss: 0.2729 - val_f1-score: 0.8931\n",
      "\n",
      "Epoch 00034: val_f1-score did not improve from 0.93544\n",
      "Epoch 35/100\n",
      "286/286 [==============================] - 166s 579ms/step - loss: 0.0999 - f1-score: 0.9492 - val_loss: 0.1594 - val_f1-score: 0.9339\n",
      "\n",
      "Epoch 00035: val_f1-score did not improve from 0.93544\n",
      "Epoch 36/100\n",
      "286/286 [==============================] - 167s 583ms/step - loss: 0.1002 - f1-score: 0.9484 - val_loss: 0.1616 - val_f1-score: 0.9361\n",
      "\n",
      "Epoch 00036: val_f1-score improved from 0.93544 to 0.93611, saving model to Unet_seresnet50_100epoch_16batch_nDSM\\Unet_seresnet50_100epoch_16batch_nDSM.h5\n",
      "Epoch 37/100\n",
      "286/286 [==============================] - 168s 586ms/step - loss: 0.0838 - f1-score: 0.9573 - val_loss: 0.1959 - val_f1-score: 0.9269\n",
      "\n",
      "Epoch 00037: val_f1-score did not improve from 0.93611\n",
      "Epoch 38/100\n",
      "286/286 [==============================] - 167s 584ms/step - loss: 0.0811 - f1-score: 0.9590 - val_loss: 0.1442 - val_f1-score: 0.9354\n",
      "\n",
      "Epoch 00038: val_f1-score did not improve from 0.93611\n",
      "Epoch 39/100\n",
      "286/286 [==============================] - 165s 578ms/step - loss: 0.0844 - f1-score: 0.9568 - val_loss: 0.1349 - val_f1-score: 0.9451\n",
      "\n",
      "Epoch 00039: val_f1-score improved from 0.93611 to 0.94507, saving model to Unet_seresnet50_100epoch_16batch_nDSM\\Unet_seresnet50_100epoch_16batch_nDSM.h5\n",
      "Epoch 40/100\n",
      "286/286 [==============================] - 166s 579ms/step - loss: 0.0723 - f1-score: 0.9633 - val_loss: 0.1450 - val_f1-score: 0.9381\n",
      "\n",
      "Epoch 00040: val_f1-score did not improve from 0.94507\n",
      "Epoch 41/100\n",
      "286/286 [==============================] - 166s 581ms/step - loss: 0.1168 - f1-score: 0.9407 - val_loss: 0.1659 - val_f1-score: 0.9304\n",
      "\n",
      "Epoch 00041: val_f1-score did not improve from 0.94507\n",
      "Epoch 42/100\n",
      "286/286 [==============================] - 166s 581ms/step - loss: 0.0753 - f1-score: 0.9618 - val_loss: 0.1481 - val_f1-score: 0.9393\n",
      "\n",
      "Epoch 00042: val_f1-score did not improve from 0.94507\n",
      "Epoch 43/100\n",
      "286/286 [==============================] - 165s 579ms/step - loss: 0.1026 - f1-score: 0.9473 - val_loss: 0.1630 - val_f1-score: 0.9341\n",
      "\n",
      "Epoch 00043: val_f1-score did not improve from 0.94507\n",
      "Epoch 44/100\n",
      "286/286 [==============================] - 165s 578ms/step - loss: 0.0797 - f1-score: 0.9597 - val_loss: 0.3592 - val_f1-score: 0.8840\n",
      "\n",
      "Epoch 00044: val_f1-score did not improve from 0.94507\n",
      "Epoch 45/100\n",
      "286/286 [==============================] - 166s 579ms/step - loss: 0.0908 - f1-score: 0.9540 - val_loss: 0.1506 - val_f1-score: 0.9383\n",
      "\n",
      "Epoch 00045: val_f1-score did not improve from 0.94507\n",
      "Epoch 46/100\n",
      "286/286 [==============================] - 167s 583ms/step - loss: 0.0694 - f1-score: 0.9646 - val_loss: 0.1740 - val_f1-score: 0.9344\n",
      "\n",
      "Epoch 00046: val_f1-score did not improve from 0.94507\n",
      "Epoch 47/100\n",
      "286/286 [==============================] - 170s 593ms/step - loss: 0.0617 - f1-score: 0.9684 - val_loss: 0.1402 - val_f1-score: 0.9466\n",
      "\n",
      "Epoch 00047: val_f1-score improved from 0.94507 to 0.94661, saving model to Unet_seresnet50_100epoch_16batch_nDSM\\Unet_seresnet50_100epoch_16batch_nDSM.h5\n",
      "Epoch 48/100\n",
      "286/286 [==============================] - 177s 619ms/step - loss: 0.0600 - f1-score: 0.9695 - val_loss: 0.1442 - val_f1-score: 0.9448\n",
      "\n",
      "Epoch 00048: val_f1-score did not improve from 0.94661\n",
      "Epoch 49/100\n",
      "286/286 [==============================] - 176s 614ms/step - loss: 0.0555 - f1-score: 0.9720 - val_loss: 0.1525 - val_f1-score: 0.9429\n",
      "\n",
      "Epoch 00049: val_f1-score did not improve from 0.94661\n",
      "Epoch 50/100\n",
      "286/286 [==============================] - 171s 599ms/step - loss: 0.0583 - f1-score: 0.9699 - val_loss: 0.1658 - val_f1-score: 0.9413\n",
      "\n",
      "Epoch 00050: val_f1-score did not improve from 0.94661\n",
      "Epoch 51/100\n",
      "286/286 [==============================] - 168s 589ms/step - loss: 0.0611 - f1-score: 0.9690 - val_loss: 0.1909 - val_f1-score: 0.9264\n",
      "\n",
      "Epoch 00051: val_f1-score did not improve from 0.94661\n",
      "Epoch 52/100\n",
      "286/286 [==============================] - 168s 587ms/step - loss: 0.0830 - f1-score: 0.9581 - val_loss: 0.1487 - val_f1-score: 0.9431\n",
      "\n",
      "Epoch 00052: val_f1-score did not improve from 0.94661\n",
      "Epoch 53/100\n",
      "286/286 [==============================] - 167s 585ms/step - loss: 0.0734 - f1-score: 0.9630 - val_loss: 0.1648 - val_f1-score: 0.9400\n",
      "\n",
      "Epoch 00053: val_f1-score did not improve from 0.94661\n",
      "Epoch 54/100\n",
      "286/286 [==============================] - 167s 583ms/step - loss: 0.0570 - f1-score: 0.9715 - val_loss: 0.1642 - val_f1-score: 0.9405\n",
      "\n",
      "Epoch 00054: val_f1-score did not improve from 0.94661\n",
      "Epoch 55/100\n",
      "286/286 [==============================] - 168s 586ms/step - loss: 0.0512 - f1-score: 0.9738 - val_loss: 0.1410 - val_f1-score: 0.9455\n",
      "\n",
      "Epoch 00055: val_f1-score did not improve from 0.94661\n",
      "Epoch 56/100\n",
      "286/286 [==============================] - 169s 590ms/step - loss: 0.0480 - f1-score: 0.9754 - val_loss: 0.2514 - val_f1-score: 0.9136\n",
      "\n",
      "Epoch 00056: val_f1-score did not improve from 0.94661\n",
      "Epoch 57/100\n",
      "286/286 [==============================] - 168s 586ms/step - loss: 0.0570 - f1-score: 0.9703 - val_loss: 0.1571 - val_f1-score: 0.9442\n",
      "\n",
      "Epoch 00057: val_f1-score did not improve from 0.94661\n",
      "Epoch 58/100\n",
      "286/286 [==============================] - 168s 588ms/step - loss: 0.0555 - f1-score: 0.9716 - val_loss: 0.2014 - val_f1-score: 0.9298\n",
      "\n",
      "Epoch 00058: val_f1-score did not improve from 0.94661\n",
      "Epoch 59/100\n",
      "286/286 [==============================] - 166s 579ms/step - loss: 0.0834 - f1-score: 0.9574 - val_loss: 0.2199 - val_f1-score: 0.9139\n",
      "\n",
      "Epoch 00059: val_f1-score did not improve from 0.94661\n",
      "Epoch 60/100\n",
      "286/286 [==============================] - 166s 580ms/step - loss: 0.0750 - f1-score: 0.9615 - val_loss: 0.1559 - val_f1-score: 0.9411\n",
      "\n",
      "Epoch 00060: val_f1-score did not improve from 0.94661\n",
      "Epoch 61/100\n",
      "286/286 [==============================] - 165s 579ms/step - loss: 0.0650 - f1-score: 0.9674 - val_loss: 0.1482 - val_f1-score: 0.9430\n",
      "\n",
      "Epoch 00061: val_f1-score did not improve from 0.94661\n",
      "Epoch 62/100\n",
      "286/286 [==============================] - 165s 579ms/step - loss: 0.0465 - f1-score: 0.9760 - val_loss: 0.1428 - val_f1-score: 0.9472\n",
      "\n",
      "Epoch 00062: val_f1-score improved from 0.94661 to 0.94720, saving model to Unet_seresnet50_100epoch_16batch_nDSM\\Unet_seresnet50_100epoch_16batch_nDSM.h5\n",
      "Epoch 63/100\n",
      "286/286 [==============================] - 169s 590ms/step - loss: 0.0419 - f1-score: 0.9782 - val_loss: 0.1513 - val_f1-score: 0.9470\n",
      "\n",
      "Epoch 00063: val_f1-score did not improve from 0.94720\n",
      "Epoch 64/100\n",
      "286/286 [==============================] - 166s 582ms/step - loss: 0.0398 - f1-score: 0.9790 - val_loss: 0.1631 - val_f1-score: 0.9450\n",
      "\n",
      "Epoch 00064: val_f1-score did not improve from 0.94720\n",
      "Epoch 65/100\n",
      "286/286 [==============================] - 168s 586ms/step - loss: 0.0415 - f1-score: 0.9780 - val_loss: 0.1594 - val_f1-score: 0.9444\n",
      "\n",
      "Epoch 00065: val_f1-score did not improve from 0.94720\n",
      "Epoch 66/100\n",
      "286/286 [==============================] - 167s 585ms/step - loss: 0.0393 - f1-score: 0.9797 - val_loss: 0.1555 - val_f1-score: 0.9487\n",
      "\n",
      "Epoch 00066: val_f1-score improved from 0.94720 to 0.94873, saving model to Unet_seresnet50_100epoch_16batch_nDSM\\Unet_seresnet50_100epoch_16batch_nDSM.h5\n",
      "Epoch 67/100\n",
      "286/286 [==============================] - 167s 585ms/step - loss: 0.0373 - f1-score: 0.9805 - val_loss: 0.1554 - val_f1-score: 0.9467\n",
      "\n",
      "Epoch 00067: val_f1-score did not improve from 0.94873\n",
      "Epoch 68/100\n",
      "286/286 [==============================] - 168s 588ms/step - loss: 0.0372 - f1-score: 0.9808 - val_loss: 0.1630 - val_f1-score: 0.9466\n",
      "\n",
      "Epoch 00068: val_f1-score did not improve from 0.94873\n",
      "Epoch 69/100\n",
      "286/286 [==============================] - 167s 585ms/step - loss: 0.0360 - f1-score: 0.9812 - val_loss: 0.1870 - val_f1-score: 0.9362\n",
      "\n",
      "Epoch 00069: val_f1-score did not improve from 0.94873\n",
      "Epoch 70/100\n",
      "286/286 [==============================] - 166s 582ms/step - loss: 0.0501 - f1-score: 0.9743 - val_loss: 0.1630 - val_f1-score: 0.9433\n",
      "\n",
      "Epoch 00070: val_f1-score did not improve from 0.94873\n",
      "Epoch 71/100\n",
      "286/286 [==============================] - 168s 587ms/step - loss: 0.0450 - f1-score: 0.9764 - val_loss: 0.1650 - val_f1-score: 0.9413\n",
      "\n",
      "Epoch 00071: val_f1-score did not improve from 0.94873\n",
      "Epoch 72/100\n",
      "286/286 [==============================] - 169s 590ms/step - loss: 0.0445 - f1-score: 0.9768 - val_loss: 0.1608 - val_f1-score: 0.9404\n",
      "\n",
      "Epoch 00072: val_f1-score did not improve from 0.94873\n",
      "Epoch 73/100\n",
      "286/286 [==============================] - 167s 584ms/step - loss: 0.0392 - f1-score: 0.9797 - val_loss: 0.2439 - val_f1-score: 0.9142\n",
      "\n",
      "Epoch 00073: val_f1-score did not improve from 0.94873\n",
      "Epoch 74/100\n",
      "286/286 [==============================] - 173s 605ms/step - loss: 0.0430 - f1-score: 0.9777 - val_loss: 0.1579 - val_f1-score: 0.9481\n",
      "\n",
      "Epoch 00074: val_f1-score did not improve from 0.94873\n",
      "Epoch 75/100\n",
      "286/286 [==============================] - 175s 612ms/step - loss: 0.0349 - f1-score: 0.9816 - val_loss: 0.1654 - val_f1-score: 0.9468\n",
      "\n",
      "Epoch 00075: val_f1-score did not improve from 0.94873\n",
      "Epoch 76/100\n",
      "286/286 [==============================] - 176s 615ms/step - loss: 0.0376 - f1-score: 0.9803 - val_loss: 0.1958 - val_f1-score: 0.9350\n",
      "\n",
      "Epoch 00076: val_f1-score did not improve from 0.94873\n",
      "Epoch 77/100\n",
      "286/286 [==============================] - 177s 620ms/step - loss: 0.0363 - f1-score: 0.9807 - val_loss: 0.1702 - val_f1-score: 0.9453\n",
      "\n",
      "Epoch 00077: val_f1-score did not improve from 0.94873\n",
      "Epoch 78/100\n",
      "286/286 [==============================] - 176s 615ms/step - loss: 0.0323 - f1-score: 0.9831 - val_loss: 0.1633 - val_f1-score: 0.9476\n",
      "\n",
      "Epoch 00078: val_f1-score did not improve from 0.94873\n",
      "Epoch 79/100\n",
      "286/286 [==============================] - 174s 609ms/step - loss: 0.0320 - f1-score: 0.9832 - val_loss: 0.1741 - val_f1-score: 0.9425\n",
      "\n",
      "Epoch 00079: val_f1-score did not improve from 0.94873\n",
      "Epoch 80/100\n",
      "286/286 [==============================] - 174s 610ms/step - loss: 0.0311 - f1-score: 0.9837 - val_loss: 0.1683 - val_f1-score: 0.9479\n",
      "\n",
      "Epoch 00080: val_f1-score did not improve from 0.94873\n",
      "Epoch 81/100\n",
      "286/286 [==============================] - 174s 610ms/step - loss: 0.0300 - f1-score: 0.9842 - val_loss: 0.2105 - val_f1-score: 0.9383\n",
      "\n",
      "Epoch 00081: val_f1-score did not improve from 0.94873\n",
      "Epoch 82/100\n",
      "286/286 [==============================] - 176s 614ms/step - loss: 0.0443 - f1-score: 0.9775 - val_loss: 0.2503 - val_f1-score: 0.9042\n",
      "\n",
      "Epoch 00082: val_f1-score did not improve from 0.94873\n",
      "Epoch 83/100\n",
      "286/286 [==============================] - 175s 611ms/step - loss: 0.0825 - f1-score: 0.9581 - val_loss: 0.1542 - val_f1-score: 0.9407\n",
      "\n",
      "Epoch 00083: val_f1-score did not improve from 0.94873\n",
      "Epoch 84/100\n",
      "286/286 [==============================] - 175s 611ms/step - loss: 0.0442 - f1-score: 0.9770 - val_loss: 0.1540 - val_f1-score: 0.9437\n",
      "\n",
      "Epoch 00084: val_f1-score did not improve from 0.94873\n",
      "Epoch 85/100\n",
      "286/286 [==============================] - 174s 609ms/step - loss: 0.0365 - f1-score: 0.9808 - val_loss: 0.1645 - val_f1-score: 0.9448\n",
      "\n",
      "Epoch 00085: val_f1-score did not improve from 0.94873\n",
      "Epoch 86/100\n",
      "286/286 [==============================] - 174s 608ms/step - loss: 0.0322 - f1-score: 0.9832 - val_loss: 0.1568 - val_f1-score: 0.9477\n",
      "\n",
      "Epoch 00086: val_f1-score did not improve from 0.94873\n",
      "Epoch 87/100\n",
      "286/286 [==============================] - 174s 610ms/step - loss: 0.0348 - f1-score: 0.9819 - val_loss: 0.1652 - val_f1-score: 0.9435\n",
      "\n",
      "Epoch 00087: val_f1-score did not improve from 0.94873\n",
      "Epoch 88/100\n",
      "286/286 [==============================] - 177s 620ms/step - loss: 0.0404 - f1-score: 0.9792 - val_loss: 0.1586 - val_f1-score: 0.9455\n",
      "\n",
      "Epoch 00088: val_f1-score did not improve from 0.94873\n",
      "Epoch 89/100\n",
      "286/286 [==============================] - 177s 619ms/step - loss: 0.0306 - f1-score: 0.9838 - val_loss: 0.1656 - val_f1-score: 0.9473\n",
      "\n",
      "Epoch 00089: val_f1-score did not improve from 0.94873\n",
      "Epoch 90/100\n",
      "286/286 [==============================] - 176s 617ms/step - loss: 0.0273 - f1-score: 0.9855 - val_loss: 0.1651 - val_f1-score: 0.9466\n",
      "\n",
      "Epoch 00090: val_f1-score did not improve from 0.94873\n",
      "Epoch 91/100\n",
      "286/286 [==============================] - 177s 618ms/step - loss: 0.0280 - f1-score: 0.9853 - val_loss: 0.1716 - val_f1-score: 0.9482\n",
      "\n",
      "Epoch 00091: val_f1-score did not improve from 0.94873\n",
      "Epoch 92/100\n",
      "286/286 [==============================] - 178s 622ms/step - loss: 0.0262 - f1-score: 0.9862 - val_loss: 0.1712 - val_f1-score: 0.9479\n",
      "\n",
      "Epoch 00092: val_f1-score did not improve from 0.94873\n",
      "Epoch 93/100\n",
      "286/286 [==============================] - 177s 618ms/step - loss: 0.0277 - f1-score: 0.9851 - val_loss: 0.1712 - val_f1-score: 0.9474\n",
      "\n",
      "Epoch 00093: val_f1-score did not improve from 0.94873\n",
      "Epoch 94/100\n",
      "286/286 [==============================] - 176s 617ms/step - loss: 0.0248 - f1-score: 0.9869 - val_loss: 0.1718 - val_f1-score: 0.9487\n",
      "\n",
      "Epoch 00094: val_f1-score did not improve from 0.94873\n",
      "Epoch 95/100\n",
      "286/286 [==============================] - 176s 617ms/step - loss: 0.0251 - f1-score: 0.9865 - val_loss: 0.1660 - val_f1-score: 0.9507\n",
      "\n",
      "Epoch 00095: val_f1-score improved from 0.94873 to 0.95066, saving model to Unet_seresnet50_100epoch_16batch_nDSM\\Unet_seresnet50_100epoch_16batch_nDSM.h5\n",
      "Epoch 96/100\n",
      "286/286 [==============================] - 175s 612ms/step - loss: 0.0268 - f1-score: 0.9857 - val_loss: 0.1689 - val_f1-score: 0.9468\n",
      "\n",
      "Epoch 00096: val_f1-score did not improve from 0.95066\n",
      "Epoch 97/100\n",
      "286/286 [==============================] - 175s 612ms/step - loss: 0.0390 - f1-score: 0.9798 - val_loss: 0.2058 - val_f1-score: 0.9303\n",
      "\n",
      "Epoch 00097: val_f1-score did not improve from 0.95066\n",
      "Epoch 98/100\n",
      "286/286 [==============================] - 169s 592ms/step - loss: 0.0346 - f1-score: 0.9817 - val_loss: 0.1602 - val_f1-score: 0.9467\n",
      "\n",
      "Epoch 00098: val_f1-score did not improve from 0.95066\n",
      "Epoch 99/100\n",
      "286/286 [==============================] - 165s 578ms/step - loss: 0.0269 - f1-score: 0.9856 - val_loss: 0.1725 - val_f1-score: 0.9473\n",
      "\n",
      "Epoch 00099: val_f1-score did not improve from 0.95066\n",
      "Epoch 100/100\n",
      "286/286 [==============================] - 166s 580ms/step - loss: 0.0247 - f1-score: 0.9867 - val_loss: 0.1663 - val_f1-score: 0.9498\n",
      "\n",
      "Epoch 00100: val_f1-score did not improve from 0.95066\n"
     ]
    }
   ],
   "source": [
    "# define model\n",
    "model = MODEL(BACKBONE, input_shape=(None, None, IMG_CHANNELS), encoder_weights=None, classes=1, activation='sigmoid', decoder_block_type='transpose')\n",
    "model.compile(OPTIMIZER, LOSS, METRICS)\n",
    "\n",
    "# fit model\n",
    "history = model.fit(\n",
    "    x=x_train,\n",
    "    y=y_train,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    epochs=EPOCH,\n",
    "    validation_data=(x_val, y_val),\n",
    "    callbacks=[checkpointer, csv_logger] #Earlystopping removed\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8740732b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18/18 [==============================] - 12s 275ms/step\n",
      "\n",
      "All figures saved successfully.\n"
     ]
    }
   ],
   "source": [
    "from skimage.io import imsave\n",
    "preds_val = model.predict(X_test, verbose=1)\n",
    "preds_val_threshold = (preds_val > 0.5).astype(np.uint8)\n",
    "\n",
    "i = -1\n",
    "for path, subdirs, _ in os.walk(r'new_test_data\\\\mask\\\\test'):\n",
    "    for dir in _:\n",
    "        if dir.endswith(\".tif\"):\n",
    "            i += 1\n",
    "            img_pred = np.reshape(preds_val_threshold[i]*255, (256,256))\n",
    "            imsave(f'{MODEL_NAME}\\\\Results\\\\{dir}', img_pred, check_contrast=False)\n",
    "print(\"\\nAll figures saved successfully.\")"
   ]
  }
 ],
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