P8_deep_segmentation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "187afe0e-18e4-4f5c-b548-7005ece6ad93",
   "metadata": {},
   "source": [
    "### test GPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2e5a9e00-569a-4afd-8a1d-39eca2420856",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num GPUs Available:  1\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "print(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "52681ca9-0ec1-4d06-a9a5-ff447dbfedde",
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras.models import *\n",
    "from keras.layers import Conv2D, MaxPooling2D, ZeroPadding2D, UpSampling2D, BatchNormalization,Concatenate, concatenate, Reshape, Activation,Lambda, AveragePooling2D\n",
    "import keras.backend as K\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8d395ff1-4e77-4822-ae30-75812b495ff1",
   "metadata": {},
   "outputs": [],
   "source": [
    "IMAGE_ORDERING_CHANNELS_LAST = \"channels_last\"\n",
    "IMAGE_ORDERING = IMAGE_ORDERING_CHANNELS_LAST\n",
    "\n",
    "if IMAGE_ORDERING == 'channels_last':\n",
    "    pretrained_url = \"https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\"\n",
    "    MERGE_AXIS = -1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cafa1bd7-02c0-4349-aabd-17ab13425f79",
   "metadata": {},
   "source": [
    "### fonction qui récupère le VGG encodeur pré entrainé imagenet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "71ea2357-a0a0-43c2-ad4c-5554ea540ede",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_vgg_encoder(input_height=224,  input_width=224, pretrained='imagenet', channels=3):\n",
    "\n",
    "    #doit etre un multiple de 32 car image divisée par 32\n",
    "    assert input_height % 32 == 0\n",
    "    assert input_width % 32 == 0\n",
    "\n",
    "    if IMAGE_ORDERING == 'channels_first':\n",
    "        img_input = Input(shape=(channels, input_height, input_width))\n",
    "    elif IMAGE_ORDERING == 'channels_last':\n",
    "        img_input = Input(shape=(input_height, input_width, channels))\n",
    "\n",
    "    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1', data_format=IMAGE_ORDERING)(img_input)\n",
    "    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2', data_format=IMAGE_ORDERING)(x)\n",
    "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool', data_format=IMAGE_ORDERING)(x)\n",
    "    f1 = x\n",
    "    \n",
    "    # Block 2\n",
    "    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1', data_format=IMAGE_ORDERING)(x)\n",
    "    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2', data_format=IMAGE_ORDERING)(x)\n",
    "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool', data_format=IMAGE_ORDERING)(x)\n",
    "    f2 = x\n",
    "\n",
    "    # Block 3\n",
    "    x = Conv2D(256, (3, 3), activation='relu', padding='same',name='block3_conv1', data_format=IMAGE_ORDERING)(x)\n",
    "    x = Conv2D(256, (3, 3), activation='relu', padding='same',name='block3_conv2', data_format=IMAGE_ORDERING)(x)\n",
    "    x = Conv2D(256, (3, 3), activation='relu', padding='same',name='block3_conv3', data_format=IMAGE_ORDERING)(x)\n",
    "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool',data_format=IMAGE_ORDERING)(x)\n",
    "    f3 = x\n",
    "\n",
    "    # Block 4\n",
    "    x = Conv2D(512, (3, 3), activation='relu', padding='same',  name='block4_conv1', data_format=IMAGE_ORDERING)(x)\n",
    "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2', data_format=IMAGE_ORDERING)(x)\n",
    "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3', data_format=IMAGE_ORDERING)(x)\n",
    "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool', data_format=IMAGE_ORDERING)(x)\n",
    "    f4 = x\n",
    "\n",
    "    # Block 5\n",
    "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1', data_format=IMAGE_ORDERING)(x)\n",
    "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2', data_format=IMAGE_ORDERING)(x)\n",
    "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3', data_format=IMAGE_ORDERING)(x)\n",
    "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool', data_format=IMAGE_ORDERING)(x)\n",
    "    f5 = x\n",
    "\n",
    "    if pretrained == 'imagenet':\n",
    "        #telechargement du fichier de poids \n",
    "        #astuce pour extraire le nom en dernier de l'url avec pretrained_url.split(\"/\")[-1]\n",
    "        VGG_Weights_path = keras.utils.get_file(pretrained_url.split(\"/\")[-1], pretrained_url)\n",
    "        #chargement : https://keras.io/api/models/model_saving_apis/#loadweights-method\n",
    "            #by_name=True: to load weights by name => necessite d avoir les memes noms de couches\n",
    "        Model(img_input, x).load_weights(VGG_Weights_path, by_name=True, skip_mismatch=True)\n",
    "\n",
    "    return img_input, [f1, f2, f3, f4, f5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf79ec88-08f7-4328-82be-3c214b1b5428",
   "metadata": {},
   "source": [
    "### fonction VGG unet et PSP net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d7fe3e03-6e74-4f2c-ab17-75516b567b26",
   "metadata": {},
   "outputs": [],
   "source": [
    "def vgg_unet(n_classes, input_height=416, input_width=608, encoder_level=3, channels=3):\n",
    "\n",
    "    model = _unet(n_classes, get_vgg_encoder,input_height=input_height, input_width=input_width, channels=channels)\n",
    "    model.model_name = \"vgg_unet\"\n",
    "    return model\n",
    "\n",
    "def vgg_pspnet(n_classes,  input_height=384, input_width=576, channels=3):\n",
    "\n",
    "    model = _pspnet(n_classes, get_vgg_encoder,input_height=input_height, input_width=input_width, channels=channels)\n",
    "    model.model_name = \"vgg_pspnet\"\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "43164c35-6169-4eea-8b6a-aa4ef74bb1d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def _unet(n_classes, encoder, l1_skip_conn=True, input_height=416, input_width=608, channels=3):\n",
    "\n",
    "    img_input, levels = encoder(input_height=input_height, input_width=input_width, channels=channels)\n",
    "    [f1, f2, f3, f4, f5] = levels\n",
    "\n",
    "    o = f4\n",
    "\n",
    "    #rajoute du padding de 0 autour \n",
    "    o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)\n",
    "    o = (Conv2D(512, (3, 3), padding='valid' , activation='relu' , data_format=IMAGE_ORDERING))(o)\n",
    "    #normalisation de la sortie\n",
    "    o = (BatchNormalization())(o)\n",
    "\n",
    "    o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)\n",
    "    o = (concatenate([o, f3], axis=MERGE_AXIS))\n",
    "    o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)\n",
    "    o = (Conv2D(256, (3, 3), padding='valid', activation='relu' , data_format=IMAGE_ORDERING))(o)\n",
    "    o = (BatchNormalization())(o)\n",
    "\n",
    "    o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)\n",
    "    o = (concatenate([o, f2], axis=MERGE_AXIS))\n",
    "    o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)\n",
    "    o = (Conv2D(128, (3, 3), padding='valid' , activation='relu' , data_format=IMAGE_ORDERING))(o)\n",
    "    o = (BatchNormalization())(o)\n",
    "\n",
    "    o = (UpSampling2D((2, 2), data_format=IMAGE_ORDERING))(o)\n",
    "\n",
    "    if l1_skip_conn:\n",
    "        o = (concatenate([o, f1], axis=MERGE_AXIS))\n",
    "\n",
    "    o = (ZeroPadding2D((1, 1), data_format=IMAGE_ORDERING))(o)\n",
    "    o = (Conv2D(64, (3, 3), padding='valid', activation='relu', data_format=IMAGE_ORDERING, name=\"seg_feats\"))(o)\n",
    "    o = (BatchNormalization())(o)\n",
    "\n",
    "    o = Conv2D(n_classes, (3, 3), padding='same',data_format=IMAGE_ORDERING)(o)\n",
    "    ########################\n",
    "    \n",
    "    oh = Model(img_input, o).output_shape[1]\n",
    "    ow = Model(img_input, o).output_shape[2]\n",
    "    nc = Model(img_input, o).output_shape[3]\n",
    "    ih = Model(img_input, o).input_shape[1]\n",
    "    iw = Model(img_input, o).input_shape[2]\n",
    "    \n",
    "    #segmentation\n",
    "    o = (Reshape((Model(img_input, o).output_shape[1]*Model(img_input, o).output_shape[2], -1)))(o)\n",
    "    o = (Activation('softmax'))(o)\n",
    "    model = Model(img_input, o)\n",
    "    \n",
    "    model.output_height = oh\n",
    "    model.output_width = ow\n",
    "    model.n_classes = nc\n",
    "    model.input_height = ih\n",
    "    model.input_width = iw\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9ffdaf40-d9bc-4d92-b393-1a10cea39b4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def resize_image(inp,  s, data_format):\n",
    "\n",
    "    try:\n",
    "\n",
    "        return Lambda(lambda x: K.resize_images(x,\n",
    "                                                height_factor=s[0],\n",
    "                                                width_factor=s[1],\n",
    "                                                data_format=data_format,\n",
    "                                                interpolation='bilinear'))(inp)\n",
    "\n",
    "    except Exception as e:\n",
    "        # if keras is old, then rely on the tf function\n",
    "        # Sorry theano/cntk users!!!\n",
    "        assert data_format == 'channels_last'\n",
    "        assert IMAGE_ORDERING == 'channels_last'\n",
    "\n",
    "        import tensorflow as tf\n",
    "\n",
    "        return Lambda(\n",
    "            lambda x: tf.image.resize_images(\n",
    "                x, (K.int_shape(x)[1]*s[0], K.int_shape(x)[2]*s[1]))\n",
    "        )(inp)\n",
    "    \n",
    "def pool_block(feats, pool_factor):\n",
    "\n",
    "    if IMAGE_ORDERING == 'channels_first':\n",
    "        h = K.int_shape(feats)[2]\n",
    "        w = K.int_shape(feats)[3]\n",
    "    elif IMAGE_ORDERING == 'channels_last':\n",
    "        h = K.int_shape(feats)[1]\n",
    "        w = K.int_shape(feats)[2]\n",
    "\n",
    "    pool_size = strides = [\n",
    "        int(np.round(float(h) / pool_factor)),\n",
    "        int(np.round(float(w) / pool_factor))]\n",
    "\n",
    "    x = AveragePooling2D(pool_size, data_format=IMAGE_ORDERING,\n",
    "                         strides=strides, padding='same')(feats)\n",
    "    x = Conv2D(512, (1, 1), data_format=IMAGE_ORDERING,\n",
    "               padding='same', use_bias=False)(x)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = Activation('relu')(x)\n",
    "\n",
    "    x = resize_image(x, strides, data_format=IMAGE_ORDERING)\n",
    "\n",
    "    return x\n",
    "\n",
    "def _pspnet(n_classes, encoder,  input_height=384, input_width=576, channels=3):\n",
    "\n",
    "    assert input_height % 192 == 0\n",
    "    assert input_width % 192 == 0\n",
    "\n",
    "    img_input, levels = encoder(input_height=input_height,  input_width=input_width, channels=channels)\n",
    "    [f1, f2, f3, f4, f5] = levels\n",
    "\n",
    "    o = f5\n",
    "\n",
    "    pool_factors = [1, 2, 3, 6]\n",
    "    pool_outs = [o]\n",
    "\n",
    "    for p in pool_factors:\n",
    "        pooled = pool_block(o, p)\n",
    "        pool_outs.append(pooled)\n",
    "\n",
    "        \n",
    "    o = Concatenate(axis=MERGE_AXIS)(pool_outs)\n",
    "\n",
    "    o = Conv2D(512, (1, 1), data_format=IMAGE_ORDERING, use_bias=False , name=\"seg_feats\" )(o)\n",
    "    o = BatchNormalization()(o)\n",
    "    o = Activation('relu')(o)\n",
    "\n",
    "    o = Conv2D(n_classes, (3, 3), data_format=IMAGE_ORDERING,padding='same')(o)\n",
    "    \n",
    "    o = resize_image(o, (8, 8), data_format=IMAGE_ORDERING)\n",
    "\n",
    "    ######################\n",
    "    oh = Model(img_input, o).output_shape[1]\n",
    "    ow = Model(img_input, o).output_shape[2]\n",
    "    nc = Model(img_input, o).output_shape[3]\n",
    "    ih = Model(img_input, o).input_shape[1]\n",
    "    iw = Model(img_input, o).input_shape[2]\n",
    "    \n",
    "    #segmentation\n",
    "    o = (Reshape((Model(img_input, o).output_shape[1]*Model(img_input, o).output_shape[2], -1)))(o)\n",
    "    o = (Activation('softmax'))(o)\n",
    "    model = Model(img_input, o)\n",
    "    \n",
    "    model.output_height = oh\n",
    "    model.output_width = ow\n",
    "    model.n_classes = nc\n",
    "    model.input_height = ih\n",
    "    model.input_width = iw\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ae6ae59-cc74-4d5f-9d2e-48669432dba2",
   "metadata": {},
   "source": [
    "### creation des modèles de segmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9dd8ef48-097d-48e2-8837-dbc6eaf397b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#model = vgg_unet(n_classes=8 ,  input_height=224, input_width=224)\n",
    "model = vgg_unet(n_classes=8 ,  input_height=256, input_width=512)\n",
    "#model = vgg_pspnet(n_classes=8,  input_height=384, input_width=576)\n",
    "\n",
    "#par defaut on prend 416x608\n",
    "#on charge l'encodeur VGG16 416x608 (defaut 224x224) qui renvoie l'ing_input et les levels/ blocs [f1, f2, f3, f4, f5]\n",
    "    # les tailles doivent etres des multiples de 32 (assert % 32==0 dans le code) =>416 et 608 sont ok\n",
    "        #pourquoi 32? car la partie CNN du VGG divise par 32 la taille apres 5 maxpooling consecutifs\n",
    "    # les channels couleurs sont à la fin\n",
    "    # archi: \n",
    "        #2 CL de 64 filtres /maxpooling(2,2) -> f1\n",
    "        #2 CL de 128 filtres /maxpooling(2,2) -> f2\n",
    "        #3 CL de 256 filtres /maxpooling(2,2) -> f3\n",
    "        #3 CL de 512 filtres /maxpooling(2,2) -> f4\n",
    "        #3 CL de 512 filtres /maxpooling(2,2) -> f5\n",
    "    # chargement des poids VGG16 préentrainés pour imagenet , necessite d'avoir les memes noms que dan sle github de fchollet pour le VGG16\n",
    "    #renvoie le format de l'input et les blocks préentrainés\n",
    "    \n",
    "    #on plug le unet en oubliant le 5eme block du vgg\n",
    "        #1 CL de 512 filtres avec normalisation\n",
    "        #1 upsampling x2 avec concatenation f3 selon l'axe des channels de filtres puis 1CL de 256 filtres avec normalisation\n",
    "        #1 upsampling x2 avec concatenation f2 selon l'axe des channels de filtre puis 1CL de 128 filtres avec normalisation\n",
    "        #1 upsampling x2 avec concatenation f1 hypothetique selon l'axe des channels de filtre puis 1CL de 64 filtres avec normalisation\n",
    "        #1 CL à n classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9424d3e8-49b3-419b-89db-293ba936446b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_8\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 256, 512, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv1 (Conv2D)           (None, 256, 512, 64) 1792        input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv2 (Conv2D)           (None, 256, 512, 64) 36928       block1_conv1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block1_pool (MaxPooling2D)      (None, 128, 256, 64) 0           block1_conv2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block2_conv1 (Conv2D)           (None, 128, 256, 128 73856       block1_pool[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block2_conv2 (Conv2D)           (None, 128, 256, 128 147584      block2_conv1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block2_pool (MaxPooling2D)      (None, 64, 128, 128) 0           block2_conv2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block3_conv1 (Conv2D)           (None, 64, 128, 256) 295168      block2_pool[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block3_conv2 (Conv2D)           (None, 64, 128, 256) 590080      block3_conv1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block3_conv3 (Conv2D)           (None, 64, 128, 256) 590080      block3_conv2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block3_pool (MaxPooling2D)      (None, 32, 64, 256)  0           block3_conv3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block4_conv1 (Conv2D)           (None, 32, 64, 512)  1180160     block3_pool[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block4_conv2 (Conv2D)           (None, 32, 64, 512)  2359808     block4_conv1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block4_conv3 (Conv2D)           (None, 32, 64, 512)  2359808     block4_conv2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block4_pool (MaxPooling2D)      (None, 16, 32, 512)  0           block4_conv3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d (ZeroPadding2D)  (None, 18, 34, 512)  0           block4_pool[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv2d (Conv2D)                 (None, 16, 32, 512)  2359808     zero_padding2d[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 16, 32, 512)  2048        conv2d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d (UpSampling2D)    (None, 32, 64, 512)  0           batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, 32, 64, 768)  0           up_sampling2d[0][0]              \n",
      "                                                                 block3_pool[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_1 (ZeroPadding2D (None, 34, 66, 768)  0           concatenate[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 32, 64, 256)  1769728     zero_padding2d_1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 32, 64, 256)  1024        conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_1 (UpSampling2D)  (None, 64, 128, 256) 0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 64, 128, 384) 0           up_sampling2d_1[0][0]            \n",
      "                                                                 block2_pool[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_2 (ZeroPadding2D (None, 66, 130, 384) 0           concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 64, 128, 128) 442496      zero_padding2d_2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 64, 128, 128) 512         conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_2 (UpSampling2D)  (None, 128, 256, 128 0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)     (None, 128, 256, 192 0           up_sampling2d_2[0][0]            \n",
      "                                                                 block1_pool[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "zero_padding2d_3 (ZeroPadding2D (None, 130, 258, 192 0           concatenate_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "seg_feats (Conv2D)              (None, 128, 256, 64) 110656      zero_padding2d_3[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 128, 256, 64) 256         seg_feats[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 128, 256, 8)  4616        batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "reshape (Reshape)               (None, 32768, 8)     0           conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation (Activation)         (None, 32768, 8)     0           reshape[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 12,326,408\n",
      "Trainable params: 12,324,488\n",
      "Non-trainable params: 1,920\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc305f58-cbd5-41bc-b79a-f3a37262a26a",
   "metadata": {},
   "source": [
    "### création des métriques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "40f31744-0fe2-4c5f-9922-62c41b30aebc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.python.keras import backend as K\n",
    "\n",
    "def flat_dice(y_true, y_pred, smooth = 0.001):\n",
    "    \"\"\"union et intersection calculée sur 1 seule grosse classe \n",
    "        de dimension height*width*nclasses (voir *batch_size)\"\"\"\n",
    "    \n",
    "    #type F1 score\n",
    "    #reshape en 1D / attention voir si ok avec le keras.backend pas si sur en focntion des version\n",
    "    #y_true_f = K.flatten(y_true)\n",
    "    #y_pred_f = K.flatten(y_pred)\n",
    "    #on fait l intersection des 0 et des 1\n",
    "    intersection = K.sum(K.flatten(y_true) * K.flatten(y_pred)) #intersection totale\n",
    "    union = K.sum(K.flatten(y_true)) + K.sum(K.flatten(y_pred)) #union totale sans retirer l'intersection\n",
    "    flat_dice = (2. * intersection + smooth) / (union + smooth) # quand l'intersection est max, 2 intersections = union sans retirer l'intersection = cercles concentriques\n",
    "    \n",
    "    #intersection = K.sum(y_true * y_pred, axis=[1,2,3])\n",
    "    #union=K.sum(y_true, axis=[1,2,3])+K.sum(y_pred, axis=[1,2,3])\n",
    "    #dice = K.mean((2. * intersection + smooth) / (union + smooth),axis=0]\n",
    "    return flat_dice\n",
    "\n",
    "def flat_iou(y_true, y_pred,smooth = 0.001):\n",
    "    #reshape en 1D / attention voir si ok avec le keras.b&ackend pas si sur en focntion des version\n",
    "    #y_true_f = K.flatten(y_true)\n",
    "    #y_pred_f = K.flatten(y_pred)\n",
    "    #on fait l intersection des 0 et des 1\n",
    "    \n",
    "    #attention ne fait pas la moyenne du resultat pour chaque classe\n",
    "    intersection = K.sum(K.flatten(y_true) * K.flatten(y_pred)) \n",
    "    union = K.sum(K.flatten(y_true)) + K.sum(K.flatten(y_pred)) - intersection #union totale sans retirer l'intersection\n",
    "    flat_iou = (intersection + smooth) / (union + smooth) #\n",
    "    \n",
    "    #intersection = K.sum(y_true * y_pred, axis=[1,2,3])\n",
    "    #union=K.sum(y_true, axis=[1,2,3])+K.sum(y_pred, axis=[1,2,3])\n",
    "    #dice = K.mean((2. * intersection + smooth) / (union + smooth),axis=0]\n",
    "    return flat_iou\n",
    "\n",
    "def mean_dice(y_true, y_pred, smooth = 0.001):\n",
    "    \"\"\"union et intersection calculée par classe puis moyennée\"\"\"\n",
    "    \n",
    "    #type F1 score\n",
    "    #reshape en 1D / attention voir si ok avec le keras.backend pas si sur en focntion des version\n",
    "    #y_true_f = K.flatten(y_true)\n",
    "    #y_pred_f = K.flatten(y_pred)\n",
    "    #on fait l intersection des 0 et des 1\n",
    "\n",
    "    intersection = K.sum(y_true * y_pred, axis=[0,1])\n",
    "    union=K.sum(y_true, axis=[0,1])+K.sum(y_pred, axis=[0,1])\n",
    "    mean_dice = K.mean((2. * intersection + smooth) / (union + smooth))\n",
    "    return mean_dice\n",
    "\n",
    "def mean_iou(y_true, y_pred, smooth = 0.001):\n",
    "    \"\"\"union et intersection calculée par classe puis moyennée\"\"\"\n",
    "    \n",
    "    #type F1 score\n",
    "    #reshape en 1D / attention voir si ok avec le keras.backend pas si sur en focntion des version\n",
    "    #y_true_f = K.flatten(y_true)\n",
    "    #y_pred_f = K.flatten(y_pred)\n",
    "    #on fait l intersection des 0 et des 1\n",
    "\n",
    "    intersection = K.sum(y_true * y_pred, axis=[0,1])\n",
    "    union=K.sum(y_true, axis=[0,1])+K.sum(y_pred, axis=[0,1])-intersection\n",
    "    mean_iou = K.mean((intersection + smooth) / (union + smooth))\n",
    "    return mean_iou\n",
    "\n",
    "def shape0(y_true, y_pred,smooth = 1):\n",
    "    return K.flatten(y_true).shape\n",
    "def shape1(y_true, y_pred,smooth = 1):\n",
    "    return y_true.shape[1]\n",
    "def shape2(y_true, y_pred,smooth = 1):\n",
    "    return y_true.shape[2]\n",
    "def shape3(y_true, y_pred,smooth = 1):\n",
    "    return y_true.shape[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6b39f79c-8084-412f-be1a-696e7e136d07",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=[mean_dice, mean_iou, flat_dice, flat_iou,'accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5331a10-b002-43de-986d-324040cacf4f",
   "metadata": {},
   "source": [
    "### stockage du config file du modèle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "175a4058-d145-42ce-8b8b-a299be12693b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, json\n",
    "config_file = \"vgg_unet_256_512/_config.json\"\n",
    "#config_file = \"vgg_pspnet_384_576/_config.json\"\n",
    "dir_name = os.path.dirname(config_file)\n",
    "\n",
    "if ( not os.path.exists(dir_name) )  and len( dir_name ) > 0 :\n",
    "    os.makedirs(dir_name)\n",
    "\n",
    "with open(config_file, \"w\") as f:\n",
    "    json.dump({\n",
    "                \"model_class\": model.model_name,\n",
    "                \"n_classes\": model.n_classes,\n",
    "                \"input_height\": model.input_height,\n",
    "                \"input_width\": model.input_width,\n",
    "                \"output_height\": model.output_height,\n",
    "                \"output_width\": model.output_width\n",
    "            }, f)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd23e353-f70b-45aa-acbc-bbf6e10f55ea",
   "metadata": {},
   "source": [
    "### fonction d'augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "72cfde53-88d0-4b5c-b0c1-a8609c4d8d56",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import imgaug as ia\n",
    "from imgaug import augmenters as iaa\n",
    "\n",
    "#pipeline /augmenteur sequentiel\n",
    "def _load_augmentation_aug_all(other=0):\n",
    "    \"\"\" Load image augmentation model\n",
    "        other va de 0 à 5 et représente le nombre de transfo couteuses max:\n",
    "            0 veut dire aucune transfo couteuse\n",
    "            5 veut dire un chiffre entre 0 et 5 transfo couteuses\"\"\"\n",
    "\n",
    "    def sometimes(aug):\n",
    "        return iaa.Sometimes(0.5, aug)\n",
    "\n",
    "    return iaa.Sequential(\n",
    "        [\n",
    "            #MIRROIR\n",
    "            # imgaug flip/mirroir\n",
    "            iaa.Fliplr(0.5),  # mirroir horizontal avec une proba de realisation de 50%\n",
    "            iaa.Flipud(0.2),  # mirroir vertical avec une proba de 20%\n",
    "            \n",
    "            #CROP\n",
    "            # crop & pad avec une proba de realisation de 50%, l'image finale reste à la meme dimension\n",
    "            sometimes(iaa.CropAndPad(\n",
    "                percent=(-0.05, 0.1), # crop height by -5% (top & bottom) / pad width by 10% \n",
    "                pad_mode='constant', #le padding se fait en donnant une valeur constante  aux nouveaux pixels\n",
    "                pad_cval=(50, 255) #cette valeur constante est comprise entre 0 et 255\n",
    "            )),\n",
    "            \n",
    "            #TRANSFO AFFINE / SCALING\n",
    "            #scaling avec avec une proba de realisation de 50%\n",
    "            sometimes(iaa.Affine(\n",
    "                scale={\"x\": (0.8, 1.2), \"y\": (0.8, 1.2)}, #zoom in/it entre 80 et 120%\n",
    "                translate_percent={\"x\": (-0.2, 0.2), \"y\": (-0.2, 0.2)}, #translation de -20 à +20%\n",
    "                rotate=(-45, 45),  # rotation -45 to +45 degrees\n",
    "                shear=(-16, 16),  # cisaillement -16 to +16 degrees\n",
    "                order=[0, 1], #fast interpolation\n",
    "                cval=(50, 255), #si mode constant pixel remplacé par une valeur entre 0 et 255 / on choisit un nombre superieures aux instance du masque\n",
    "                mode='constant'\n",
    "            )),\n",
    "            #AUTRES TRANSFO A UTILISER AVEC PARCIMONIE\n",
    "            iaa.SomeOf((0, other), #DE 0 0 5 DES TRANSFO SUIVANTES EXECUTEES\n",
    "                       [\n",
    "                # convert images into their superpixel representation\n",
    "                sometimes(iaa.Superpixels(p_replace=(0, 1.0), n_segments=(20, 200))), #ASSEZ LENT  de 20 à 200 superpixels remplacés par leur couleur moyenne \n",
    "                #BLUR / troubler\n",
    "                iaa.OneOf([\n",
    "                    iaa.GaussianBlur((0, 3.0)), #gaussian blur\n",
    "                    iaa.AverageBlur(k=(2, 7)), #average blur\n",
    "                    iaa.MedianBlur(k=(3, 11)), #median blur\n",
    "                ]),\n",
    "                #FINESSE           \n",
    "                iaa.Sharpen(alpha=(0, 1.0), lightness=(0.75, 1.5)),  # sharpen images / finess\n",
    "                iaa.Emboss(alpha=(0, 1.0), strength=(0, 2.0)),  # emboss images / clair-sombre\n",
    "                #DETECTION DE BORDS PAR ALPHABLENDING\n",
    "                iaa.BlendAlphaSimplexNoise(iaa.OneOf([\n",
    "                    iaa.EdgeDetect(alpha=(0.5, 1.0)),\n",
    "                    iaa.DirectedEdgeDetect(alpha=(0.5, 1.0), direction=(0.0, 1.0)),\n",
    "                ])),\n",
    "                #BRUIT GAUSSIEN\n",
    "                iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.05*255), per_channel=0.5),\n",
    "                #SUPPRESION DE PIXELS\n",
    "                iaa.OneOf([\n",
    "                    iaa.Dropout((0.01, 0.1), per_channel=0.5),\n",
    "                    iaa.CoarseDropout((0.03, 0.15), size_percent=(0.02, 0.05), per_channel=0.2),\n",
    "                ]),\n",
    "                iaa.Invert(0.05, per_channel=True), #inverison de couleur\n",
    "                iaa.Add((-10, 10), per_channel=0.5), #luminosité\n",
    "                iaa.AddToHueAndSaturation((-20, 20)), #teinte et saturation\n",
    "\n",
    "                iaa.OneOf([\n",
    "                    iaa.Multiply((0.5, 1.5), per_channel=0.5),\n",
    "                    iaa.BlendAlphaFrequencyNoise(\n",
    "                        exponent=(-4, 0),\n",
    "                        foreground=iaa.Multiply((0.5, 1.5), per_channel=True),\n",
    "                        background=iaa.contrast.LinearContrast((0.5, 2.0))\n",
    "                    )\n",
    "                ]),\n",
    "                iaa.contrast.LinearContrast((0.5, 2.0), per_channel=0.5), #contraste\n",
    "                iaa.Grayscale(alpha=(0.0, 1.0)),\n",
    "                sometimes(iaa.ElasticTransformation(alpha=(0.5, 3.5), sigma=0.25)),  # move pixels locally around\n",
    "                sometimes(iaa.PiecewiseAffine(scale=(0.01, 0.05))),  # sometimes move parts of the image around\n",
    "                sometimes(iaa.PerspectiveTransform(scale=(0.01, 0.1)))\n",
    "            ],\n",
    "                random_order=True\n",
    "            )            \n",
    "\n",
    "        ],\n",
    "        random_order=True\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f631a0c2-b636-4f27-9c92-fc3495c1ccbd",
   "metadata": {},
   "source": [
    "### creation de la liste des paires de chemins images/GT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "32c2682a-40c5-4fa1-b452-e6f730dc7017",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from tensorflow.python.keras.preprocessing import image\n",
    "import time\n",
    "\n",
    "def im_seg_pairs():\n",
    "    \"\"\"renvoie une liste de listes de pairs de chemins images/masks\n",
    "        en [0] : la paire train 69%\n",
    "        en [1] : la paire val 14%\n",
    "        en [2] : la paire test 17% \"\"\"\n",
    "    \n",
    "    ACCEPTABLE_IMAGE_FORMATS = [\".jpg\", \".jpeg\", \".png\", \".bmp\"]\n",
    "    ACCEPTABLE_SEGMENTATION_FORMATS = [\".png\", \".bmp\"]\n",
    "\n",
    "    #images_path=\"input/dataset1/images_prepped_train_light/\"\n",
    "    #segs_path = \"input/dataset1/annotations_prepped_train_light/\"\n",
    "    \n",
    "    images_path_main = \"input/P8_Cityscapes_leftImg8bit_trainvaltest/leftImg8bit/\"\n",
    "    segs_path_main = \"input/P8_Cityscapes_gtFine_trainvaltest/gtFine/\"\n",
    "    \n",
    "    return_pairs = []\n",
    "\n",
    "    for dir_type in [\"train\",\"val\",\"test\"]:\n",
    "        images_path=os.path.join(images_path_main, dir_type)\n",
    "        segs_path=os.path.join(segs_path_main, dir_type)\n",
    "\n",
    "        #liste\n",
    "        image_files = []\n",
    "        return_type_value = []\n",
    "        #dict\n",
    "        segmentation_files = {}\n",
    "\n",
    "        #retourne la liste des repertoires et/ou fichiers dans le repertoire d'images\n",
    "        for dir_city in os.listdir(images_path):\n",
    "            for dir_entry in os.listdir(os.path.join(images_path, dir_city)):\n",
    "                #si le fichier existe, est un format image et n'est pas completement noire \n",
    "                #with image.load_img(os.path.join(images_path, dir_city, dir_entry)) as imgg: #fonction lente\n",
    "                    if os.path.isfile(os.path.join(images_path, dir_city, dir_entry))\\\n",
    "                    and os.path.splitext(dir_entry)[1] in ACCEPTABLE_IMAGE_FORMATS:\n",
    "                    #and np.max(imgg)>0: #fonction lente\n",
    "                        #on separe en nom/extension\n",
    "                        file_name, file_extension = os.path.splitext(dir_entry)\n",
    "                        #on ajoute à la liste des images le triplet(nom sans extension et sans suffixe, extension, chemin)\n",
    "                        image_files.append((file_name.rstrip(\"_leftImg8bit\"), file_extension,os.path.join(images_path, dir_city, dir_entry)))\n",
    "\n",
    "        #retourne la liste des chemins des masks sous forme de dictionnaire pour etre facilement atteignable     \n",
    "        for dir_city in os.listdir(segs_path):\n",
    "            for dir_entry in os.listdir(os.path.join(segs_path, dir_city)):\n",
    "                #si le fichier existe , est un labelids,  est un format image et n'est pas completement noire\n",
    "                #with image.load_img(os.path.join(segs_path, dir_city, dir_entry)) as imgg: #fonction lente\n",
    "                    if os.path.isfile(os.path.join(segs_path, dir_city, dir_entry)) \\\n",
    "                    and \"_labelIds\" in os.path.splitext(dir_entry)[0] \\\n",
    "                    and os.path.splitext(dir_entry)[1] in ACCEPTABLE_SEGMENTATION_FORMATS:\n",
    "                    #and np.max(imgg)>0: #fonction lente\n",
    "                        #on separe en nom/extension\n",
    "                        file_name, file_extension = os.path.splitext(dir_entry)\n",
    "                        #on popûle le dictionnaire de mask avec en valeur un duo extension/chemin\n",
    "                        segmentation_files[file_name.rstrip(\"_gtFine_labelIds\")] = (file_extension, os.path.join(segs_path, dir_city, dir_entry))\n",
    "    \n",
    "        #boucle sur la liste du triplet image\n",
    "        for image_file, _ , image_full_path in image_files:\n",
    "            #si nom de l'image dispo dans le dictionnaire de mask\n",
    "            #ATTENTION SUPPOSE LE MEME NOM DANS LES IMAGES ET LES MASKS +> DONC CODE A MODIFIER\n",
    "            if image_file in segmentation_files:\n",
    "                #on renvoie une liste de duo: chemin de l'image / chemin du mask\n",
    "                return_type_value.append((image_full_path,segmentation_files[image_file][1]))\n",
    "\n",
    "        return_pairs.append(return_type_value)\n",
    "        \n",
    "    return return_pairs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1394b9f5-7585-46fe-a839-ef5bebad27a5",
   "metadata": {},
   "source": [
    "### generateur"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b5253fdf-e78b-4aab-8881-68db09f0e98a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "    \n",
    "def im_seg_generator(img_seg_pairs=im_seg_pairs()[0], batch_size=2, nClasses=8, with_aug=True, other_aug=0):\n",
    "    \"\"\"renvoie un generateur X/Y d'images/masks\n",
    "        prend en entrée:\n",
    "            des pairs de chemins images/mask\n",
    "                si on utilise la fonction im_seg_pairs():\n",
    "                en [0], c est le train\n",
    "                en [1], le val\n",
    "                en [2], le test\n",
    "            le batch_size\n",
    "            nombre de classes qui doit erte egal à 8 dans notre cas cityscape\n",
    "            à cause des labels codés en durs plus bas dans la fonction\n",
    "            \n",
    "            attention également à l'augmentation et à la creation de pixels hors label/instances\n",
    "            comportement a voir\n",
    "            \n",
    "            other: nombre d'augmention couteuses max: voir _load_augmentation_aug_all\"\"\"\n",
    "    \n",
    "    #mélange aléatoire\n",
    "    random.shuffle(img_seg_pairs)\n",
    "\n",
    "    #iterateur infini\n",
    "    zipped = itertools.cycle(img_seg_pairs)\n",
    "\n",
    "    #on sort d'un while true avec un break, mais ici pas besoin car le but est d avoir un generateur infini\n",
    "    while True:\n",
    "        X = []\n",
    "        Y = []\n",
    "        #boucle sur le batch size: nombre d'image\n",
    "        for _ in range(batch_size):\n",
    "            #on extrait le duo chemin image/chemin mask\n",
    "            im, seg = next(zipped)\n",
    "            #lecture de l'image en couleur\n",
    "            im = cv2.imread(im, 1)\n",
    "            #plt.imshow(im)\n",
    "            #plt.show()\n",
    "            #lecture du mask en grayscale 0/couleur 1\n",
    "            seg = cv2.imread(seg, 0)\n",
    "            #plt.imshow(seg)\n",
    "            #plt.show()\n",
    "        \n",
    "            #avec augmentation\n",
    "            if with_aug==True:\n",
    "                AUGMENTEUR_STO = _load_augmentation_aug_all(other=other_aug)\n",
    "                #on rend l'augmenteut deterministe pour pouvoir repliquer le meme traitement\n",
    "                AUGMENTEUR_DET = AUGMENTEUR_STO.to_deterministic()\n",
    "            \n",
    "                # Augmente une image : pour un batch utiliser: augment_images\n",
    "                im_aug = AUGMENTEUR_DET.augment_image(im)\n",
    "        \n",
    "                #plt.imshow(im_aug)\n",
    "                #plt.show()\n",
    "        \n",
    "                #instanciation de la segmentation map à partir du mask associé à une image\n",
    "                SEGMAP = ia.SegmentationMapsOnImage(seg, shape=im.shape)\n",
    "                # Augmente la segmentation MAP\n",
    "                segmap_aug = AUGMENTEUR_DET.augment_segmentation_maps(SEGMAP)\n",
    "                #Retourne le seg.map array, with original dtype and shape ndim\n",
    "                segmap_aug = segmap_aug.get_arr()\n",
    "        \n",
    "                #plt.imshow(segmap_aug)\n",
    "                #plt.show()\n",
    "                \n",
    "            else:\n",
    "                im_aug=im\n",
    "                segmap_aug=seg\n",
    "            ################################################################################\n",
    "            \n",
    "            \n",
    "            #get_image_array\n",
    "            imgNorm = \"sub_mean\"\n",
    "            if imgNorm == \"sub_mean\":\n",
    "                #print(im_aug.shape)\n",
    "                #resize à la taille de l input du modele\n",
    "                img = cv2.resize(im_aug, (model.input_width, model.input_height))\n",
    "                #on passe de uint8 à float32\n",
    "                img = img.astype(np.float32)\n",
    "                #array de 3d au moins , c est deja le cas donc ca ne change rien\n",
    "                img = np.atleast_3d(img)\n",
    "\n",
    "            \n",
    "                means = [103.939, 116.779, 123.68] #moyenne imagenet\n",
    "                for i in range(min(img.shape[2], len(means))):\n",
    "                    #soustraction de la moyenne pour chaque channel de facon a etre autour de 0\n",
    "                    img[:, :, i] -= means[i]\n",
    "                #ca flippe la couleur de rgb à bgr ou le contraire si ca vient d'opencv\n",
    "                #https://stackoverflow.com/questions/53716324/what-does-1-mean-in-python\n",
    "                img = img[:, :, ::-1]\n",
    "        \n",
    "            X.append(img)\n",
    "            \n",
    "            #dictionnaire de main categories\n",
    "            cats = {'void': [0, 1, 2, 3, 4, 5, 6],\n",
    "                    'flat': [7, 8, 9, 10],\n",
    "                    'construction': [11, 12, 13, 14, 15, 16],\n",
    "                     'object': [17, 18, 19, 20],\n",
    "                     'nature': [21, 22],\n",
    "                     'sky': [23],\n",
    "                     'human': [24, 25],\n",
    "                     'vehicle': [26, 27, 28, 29, 30, 31, 32, 33, -1]}\n",
    "        \n",
    "            #get_segmentation_array\n",
    "            seg_labels = np.zeros((model.output_height, model.output_width, nClasses))\n",
    "            #resizing du mask avec une interpolation qui ne moyenne pas de facon à ne pas modifier les valeurs des labels\n",
    "            segimg = cv2.resize(segmap_aug, (model.output_width, model.output_height), interpolation=cv2.INTER_NEAREST)\n",
    "            #on ne garde que le premier channel\n",
    "            #segimg = segimg[:, :, 0]\n",
    "            #normalement seg_labels reste en chiffre et pas en boolean du fait de son initialisation\n",
    "            for i in range(-1, 34):\n",
    "                if i in cats['void']:\n",
    "                    seg_labels[:,:,0] = np.logical_or(seg_labels[:,:,0],(segimg==i))\n",
    "                elif i in cats['flat']:\n",
    "                    seg_labels[:,:,1] = np.logical_or(seg_labels[:,:,1],(segimg==i))\n",
    "                elif i in cats['construction']:\n",
    "                    seg_labels[:,:,2] = np.logical_or(seg_labels[:,:,2],(segimg==i))\n",
    "                elif i in cats['object']:\n",
    "                    seg_labels[:,:,3] = np.logical_or(seg_labels[:,:,3],(segimg==i))\n",
    "                elif i in cats['nature']:\n",
    "                    seg_labels[:,:,4] = np.logical_or(seg_labels[:,:,4],(segimg==i))\n",
    "                elif i in cats['sky']:\n",
    "                    seg_labels[:,:,5] = np.logical_or(seg_labels[:,:,5],(segimg==i))\n",
    "                elif i in cats['human']:\n",
    "                    seg_labels[:,:,6] = np.logical_or(seg_labels[:,:,6],(segimg==i))\n",
    "                elif i in cats['vehicle']:\n",
    "                    seg_labels[:,:,7] = np.logical_or(seg_labels[:,:,7],(segimg==i))\n",
    "                \n",
    "            seg_labels = np.reshape(seg_labels, (model.output_width*model.output_height, nClasses))\n",
    "            Y.append(seg_labels)\n",
    "        #yield fonctionne comme return mais permet de retourner un generateur dans une fonction\n",
    "        yield np.array(X), np.array(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "988460b5-6f24-4aac-b917-a83a77b7d9b4",
   "metadata": {},
   "source": [
    "### generateur\n",
    "* https://deeplylearning.fr/cours-pratiques-deep-learning/realiser-son-propre-generateur-de-donnees/\n",
    "* il est préférable d’utiliser un générateur pour charger l’ensemble de ses données à son réseau de neurones via des mini-lots de données, \n",
    "    par rapport à envoyer le dataset dans sa globalité via un fichier\n",
    "    Cette solution nous permet donc de nous adapter face à de large dataset plus facilement, \n",
    "    et évite tout saturation de RAM ou celle du GPU. On profitera en plus du calcul parallèles via les threads de votre CPU.\n",
    "    \n",
    "    sequelette de base:\n",
    "    * import keras\n",
    "    * class DatasetLoader(keras.utils.Sequence):\n",
    "        * def __init__(self, data, xLabel, yLabel, batchSize, shuffle):\n",
    "            * self.xData = data[xLabel]\n",
    "            * self.yData = data[yLabel]\n",
    "            * self.batchSize = batchSize \n",
    "                * **taille d’un mini lot de données**\n",
    "            * self.shuffle = shuffle \n",
    "                * **booléen si on souhaite envoyer des données de façon aléatoire, ou dans l’ordre de l’index du dataframe**\n",
    "            * self.targetSize = targetSize #resize nos images\n",
    "            * self.on_epoch_end()\n",
    "        * def __len__(self):\n",
    "            * return int(np.floor(len(self.xData) / self.batchSize))\n",
    "                * **obligatoire: Définie le nombre de batch durant une époch\n",
    "        * def on_epoch_end(self):\n",
    "            * self.indexes = np.arange(len(self.xData))\n",
    "            * if self.shuffle == True: np.random.shuffle(self.indexes)\n",
    "                * **pas obligatoire: Appelé à chaque fin d’epoch. On va s’en servir ici afin de rendre aléatoire l’ordre de la liste des ID des items à envoyer pour constituer le batch de données courant.\n",
    "        * def __getitem__(self, index):\n",
    "            *\n",
    "                * **obligatoire: Concrètement je récupère une liste d’ID qui correspond à des items spécifiques (X et Y) contenu dans le dataframe de données, de taille batchSize\n",
    "\n",
    "    le classe doit hériter de la classe Keras.utils.Sequence :\n",
    "    Cette classe parente vous assure dans le cas ou vous souhaitez utiliser du calcul parallèle avec vos threads, \n",
    "    de garantir de parcourir une seule et unique fois vos données au cours d’une époch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "41080258-9ab9-42d0-b14c-b5726bc15802",
   "metadata": {},
   "outputs": [],
   "source": [
    "#classe generateur de sequences:\n",
    "from tensorflow.keras.utils import Sequence\n",
    "\n",
    "class seg_gen(Sequence):\n",
    "    #premiere fonction appelée aprés l'instanciation\n",
    "    #sorte de constructeur mais l'object existe deja avant son appel\n",
    "    #par definition , le premier arguement appelé est l'objet self qui fait reference au nouvel objet crée, il faut le definir mais pas besoin de le specifier à l'instanciation\n",
    "    def __init__(self, pair_set, batch_size):\n",
    "        #liste de paires de chemins image/GT\n",
    "        self.pair = pair_set\n",
    "        #taille des lots\n",
    "        self.batch_size = batch_size\n",
    "\n",
    "    #ceil=plafond entier\n",
    "    #nombre de lots\n",
    "    def __len__(self):\n",
    "        return int(np.ceil(len(self.pair) // float(self.batch_size)))\n",
    "\n",
    "    \n",
    "    def __getitem__(self, idx,with_aug=False,other_aug=0):\n",
    "        #normalement lors de l'appel du generateur les model.input/output_height/width sont connus en variables globales\n",
    "        #mais on peut les entrees en paramètres, c'est à modifier\n",
    "        nClasses=8\n",
    "        #array alétoire de taille batch_size donnant des indices entre 0 et samples \n",
    "        samples=len(self.pair)\n",
    "        idx = np.random.randint(0, samples, self.batch_size)\n",
    "        batch_x, batch_y = [], []\n",
    "        drawn = 0\n",
    "        \n",
    "        for i in idx:\n",
    "            im = cv2.imread(self.pair[i][0], 1)\n",
    "            seg = cv2.imread(self.pair[i][1], 0)#lecture en grayscale\n",
    "            #avec augmentation\n",
    "            if with_aug==True:\n",
    "                AUGMENTEUR_STO = _load_augmentation_aug_all(other=other_aug)\n",
    "                #on rend l'augmenteut deterministe pour pouvoir repliquer le meme traitement\n",
    "                AUGMENTEUR_DET = AUGMENTEUR_STO.to_deterministic()\n",
    "            \n",
    "                # Augmente une image : pour un batch utiliser: augment_images\n",
    "                im_aug = AUGMENTEUR_DET.augment_image(im)\n",
    "        \n",
    "                #plt.imshow(im_aug)\n",
    "                #plt.show()\n",
    "        \n",
    "                #instanciation de la segmentation map à partir du mask associé à une image\n",
    "                SEGMAP = ia.SegmentationMapsOnImage(seg, shape=im.shape)\n",
    "                # Augmente la segmentation MAP\n",
    "                segmap_aug = AUGMENTEUR_DET.augment_segmentation_maps(SEGMAP)\n",
    "                #Retourne le seg.map array, with original dtype and shape ndim\n",
    "                segmap_aug = segmap_aug.get_arr()\n",
    "        \n",
    "                #plt.imshow(segmap_aug)\n",
    "                #plt.show()\n",
    "                \n",
    "            else:\n",
    "                im_aug=im\n",
    "                segmap_aug=seg\n",
    "            ################################################################################\n",
    "            \n",
    "            \n",
    "            #get_image_array\n",
    "            imgNorm = \"sub_mean\"\n",
    "            if imgNorm == \"sub_mean\":\n",
    "                #print(im_aug.shape)\n",
    "                #resize à la taille de l input du modele\n",
    "                img = cv2.resize(im_aug, (model.input_width, model.input_height))\n",
    "                #on passe de uint8 à float32\n",
    "                img = img.astype(np.float32)\n",
    "                #array de 3d au moins , c est deja le cas donc ca ne change rien\n",
    "                img = np.atleast_3d(img)\n",
    "\n",
    "            \n",
    "                means = [103.939, 116.779, 123.68] #moyenne imagenet\n",
    "                for j in range(min(img.shape[2], len(means))):\n",
    "                    #soustraction de la moyenne pour chaque channel de facon a etre autour de 0\n",
    "                    img[:, :, j] -= means[j]\n",
    "                #ca flippe la couleur de rgb à bgr ou le contraire si ca vient d'opencv\n",
    "                #https://stackoverflow.com/questions/53716324/what-does-1-mean-in-python\n",
    "                img = img[:, :, ::-1]\n",
    "        \n",
    "            #on ajoute l'image np array au batch courant X\n",
    "            batch_x.append(img)\n",
    "            \n",
    "            #dictionnaire de main categories\n",
    "            cats = {'void': [0, 1, 2, 3, 4, 5, 6],\n",
    "                    'flat': [7, 8, 9, 10],\n",
    "                    'construction': [11, 12, 13, 14, 15, 16],\n",
    "                     'object': [17, 18, 19, 20],\n",
    "                     'nature': [21, 22],\n",
    "                     'sky': [23],\n",
    "                     'human': [24, 25],\n",
    "                     'vehicle': [26, 27, 28, 29, 30, 31, 32, 33, -1]}\n",
    "        \n",
    "            #get_segmentation_array\n",
    "            seg_labels = np.zeros((model.output_height, model.output_width, nClasses))\n",
    "            #resizing du mask avec une interpolation qui ne moyenne pas de facon à ne pas modifier les valeurs des labels\n",
    "            segimg = cv2.resize(segmap_aug, (model.output_width, model.output_height), interpolation=cv2.INTER_NEAREST)\n",
    "            #on ne garde que le premier channel\n",
    "            #segimg = segimg[:, :, 0]\n",
    "            #normalement seg_labels reste en chiffre et pas en boolean du fait de son initialisation\n",
    "            for i in range(-1, 34):\n",
    "                if i in cats['void']:\n",
    "                    seg_labels[:,:,0] = np.logical_or(seg_labels[:,:,0],(segimg==i))\n",
    "                elif i in cats['flat']:\n",
    "                    seg_labels[:,:,1] = np.logical_or(seg_labels[:,:,1],(segimg==i))\n",
    "                elif i in cats['construction']:\n",
    "                    seg_labels[:,:,2] = np.logical_or(seg_labels[:,:,2],(segimg==i))\n",
    "                elif i in cats['object']:\n",
    "                    seg_labels[:,:,3] = np.logical_or(seg_labels[:,:,3],(segimg==i))\n",
    "                elif i in cats['nature']:\n",
    "                    seg_labels[:,:,4] = np.logical_or(seg_labels[:,:,4],(segimg==i))\n",
    "                elif i in cats['sky']:\n",
    "                    seg_labels[:,:,5] = np.logical_or(seg_labels[:,:,5],(segimg==i))\n",
    "                elif i in cats['human']:\n",
    "                    seg_labels[:,:,6] = np.logical_or(seg_labels[:,:,6],(segimg==i))\n",
    "                elif i in cats['vehicle']:\n",
    "                    seg_labels[:,:,7] = np.logical_or(seg_labels[:,:,7],(segimg==i))\n",
    "                \n",
    "            seg_labels = np.reshape(seg_labels, (model.output_width*model.output_height, nClasses))\n",
    "            #on ajoute le mask calculé au batch courant Y\n",
    "            batch_y.append(seg_labels)\n",
    "            \n",
    "            drawn += 1\n",
    "        return np.array(batch_x), np.array(batch_y)\n",
    "    #test\n",
    "    #x,y=seg_gen(im_seg_pairs()[0], 2).__getitem__(0,with_aug=True)\n",
    "    #x.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b886221e-c0ea-4e8b-9d62-8b7e5560276d",
   "metadata": {},
   "source": [
    "### paramétrage du keras callback"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "caa0b72d-b763-484f-89f7-1e8b643d4490",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.python.keras.callbacks import TensorBoard, ModelCheckpoint, EarlyStopping\n",
    "checkpoints_path=\"vgg_unet_256_512/vgg_unet_10\"#\"vgg_pspnet_384_576/vgg_pspnet_0\" #tmp/\n",
    "\n",
    "tb = TensorBoard(log_dir='logs', write_graph=True)\n",
    "mc = ModelCheckpoint(mode='max', filepath=checkpoints_path + \".{epoch:05d}\", monitor='val_mean_iou', save_best_only='True', save_weights_only='True', verbose=1)\n",
    "es = EarlyStopping(mode='max', monitor='val_mean_iou', patience=5, verbose=1)\n",
    "\n",
    "#vis = visualize()\n",
    "callbacks = [tb, mc, es]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9931bc0a-a331-4940-8b36-0f4fff4d2a23",
   "metadata": {},
   "source": [
    "### fonction de prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2088dbc7-3ab2-48d2-9987-4876f16c4331",
   "metadata": {},
   "outputs": [],
   "source": [
    "def seg_prediction(model,adress,gt_adress):\n",
    "    #lecture en couleur\n",
    "    read_image_type=1\n",
    "    #lecture en BGR\n",
    "    im2p = cv2.imread(adress, read_image_type)\n",
    "\n",
    "    #soft denoising\n",
    "    #im2p = cv2.fastNlMeansDenoising(im2p, h=3)\n",
    "    #equalization\n",
    "    #im2p[:,:,0] = cv2.equalizeHist(im2p[:,:,0])\n",
    "    #im2p[:,:,1] = cv2.equalizeHist(im2p[:,:,1])\n",
    "    #im2p[:,:,2] = cv2.equalizeHist(im2p[:,:,2])\n",
    "    #pour affichier plus tard en RGB\n",
    "    im2show=im2p[:, :, ::-1]\n",
    "    #resize width et height dans cet ordre pour etre coherent avec ce qu'on a fait avant\n",
    "    im2p = cv2.resize(im2p, (model.input_width, model.input_height)).astype(np.float32)\n",
    "\n",
    "\n",
    "    means = [103.939, 116.779, 123.68]\n",
    "\n",
    "    for i in range(3):\n",
    "        im2p[:, :, i] -= means[i]\n",
    "    #passage en RGB\n",
    "    im2p = im2p[:, :, ::-1]\n",
    "    #on rajoute la dimension batch en first: on est oblige de mettre np.array pour faire le 4D sinon ca nous fait une liste de 3D\n",
    "    im2p = np.array([im2p])\n",
    "    #prediction en 3D (batch=1,height*weight,classes)\n",
    "    im_p = model.predict(im2p)\n",
    "    #reshaping avec prediction de la classe la plus probable avec argmax selon les classes => on passe en 2D\n",
    "    im_p = im_p.reshape((model.output_height,  model.output_width, model.n_classes)).argmax(axis=2)\n",
    "    \n",
    "    #VISUALISATION DE LA PREDICTION\n",
    "    #squelette RGB\n",
    "    seg_img = np.zeros((model.output_height, model.output_width, 3))\n",
    "    #colorisation des classes en RGB : les couleurs tirés aleatoirement seront les memes a cause du seed à 0\n",
    "    random.seed(24)\n",
    "    colors = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)) for _ in range(model.n_classes)]\n",
    "    random.seed(24)\n",
    "    colors2 = [(random.randint(0, 255)/255, random.randint(0, 255)/255, random.randint(0, 255)/255) for _ in range(model.n_classes)]\n",
    "    for c in range(model.n_classes):\n",
    "            seg_c = im_p[:, :] == c\n",
    "            seg_img[:, :, 0] += ((seg_c)*(colors[c][0]))\n",
    "            seg_img[:, :, 1] += ((seg_c)*(colors[c][1]))\n",
    "            seg_img[:, :, 2] += ((seg_c)*(colors[c][2]))\n",
    "    #resizing au dimension de l'image brute de depart\n",
    "    original_h = im2show.shape[0]\n",
    "    original_w = im2show.shape[1]\n",
    "    seg_img = cv2.resize(seg_img, (original_w, original_h), interpolation=cv2.INTER_NEAREST)\n",
    "\n",
    "    \n",
    "    #COMPARAISON AVEC LE GROUND TRUTH\n",
    "    #lecture en BGR 2D\n",
    "    gt = cv2.imread(gt_adress, 0)\n",
    "    #squelette \n",
    "    gt2c = np.zeros((gt.shape[0], gt.shape[1]))\n",
    "\n",
    "    #dictionnaire de main categories\n",
    "    cats = {'void': [0, 1, 2, 3, 4, 5, 6],\n",
    "        'flat': [7, 8, 9, 10],\n",
    "        'construction': [11, 12, 13, 14, 15, 16],\n",
    "        'object': [17, 18, 19, 20],\n",
    "        'nature': [21, 22],\n",
    "        'sky': [23],\n",
    "        'human': [24, 25],\n",
    "        'vehicle': [26, 27, 28, 29, 30, 31, 32, 33, -1]}\n",
    "\n",
    "    for i in range(-1, 34):\n",
    "        if i in cats['void']:\n",
    "            gt2c[gt==i]=0\n",
    "        elif i in cats['flat']:\n",
    "            gt2c[gt==i]=1\n",
    "        elif i in cats['construction']:\n",
    "            gt2c[gt==i]=2\n",
    "        elif i in cats['object']:\n",
    "            gt2c[gt==i]=3\n",
    "        elif i in cats['nature']:\n",
    "            gt2c[gt==i]=4\n",
    "        elif i in cats['sky']:\n",
    "            gt2c[gt==i]=5\n",
    "        elif i in cats['human']:\n",
    "            gt2c[gt==i]=6\n",
    "        elif i in cats['vehicle']:\n",
    "            gt2c[gt==i]=7\n",
    "\n",
    "    #squelette RGB\n",
    "    gt2c_color = np.zeros((gt.shape[0], gt.shape[1], 3))\n",
    "    for c in range(model.n_classes):\n",
    "        seg_c = gt2c[:, :] == c\n",
    "        gt2c_color[:, :, 0] += ((seg_c)*(colors[c][0]))\n",
    "        gt2c_color[:, :, 1] += ((seg_c)*(colors[c][1]))\n",
    "        gt2c_color[:, :, 2] += ((seg_c)*(colors[c][2]))\n",
    "        \n",
    "    #LEGENDE\n",
    "    #squelette de legende blanc(+255)\n",
    "    legend = np.zeros((gt.shape[0], gt.shape[1]//4, 3),dtype=\"uint8\") + 255\n",
    "    #enumerate = compteur et zip 2 listes comme une fermeture eclair\n",
    "    for (i, (class_name, color)) in enumerate(zip(list(cats.keys()),colors)):\n",
    "        color = [int(c) for c in color]\n",
    "        cv2.putText(legend, class_name, (20, (i * gt.shape[0]//8) + (gt.shape[0]//8)*2//3), cv2.FONT_HERSHEY_COMPLEX, 2, (0, 0, 0), 4)\n",
    "        cv2.rectangle(legend, ((gt.shape[1]//4)-50, (i * gt.shape[0]//8)), ((gt.shape[1]//4), (i * gt.shape[0]//8) + gt.shape[0]//8),tuple(color), -1)\n",
    "        \n",
    "    #AFFICHAGE\n",
    "    f, (a0, a1, a2) = plt.subplots(1, 3,figsize=(20,5), gridspec_kw={'width_ratios': [4, 4, 4]})\n",
    "\n",
    "    a0.title.set_text('image brute')\n",
    "    a0.imshow(im2show)\n",
    "\n",
    "    #a1.title.set_text(\"Légende\")\n",
    "    #a1.imshow(legend.astype('uint8'))\n",
    "\n",
    "    a1.title.set_text(\"segmentation prediction\")\n",
    "    a1.imshow(seg_img.astype('uint8'))\n",
    "\n",
    "    a2.title.set_text(\"ground thruth\")\n",
    "    a2.imshow(gt2c_color.astype('uint8'))\n",
    "    plt.show()\n",
    "    \n",
    "    #EVALUATION DE LA PREDICTION:\n",
    "    tp = np.zeros(model.n_classes)\n",
    "    fp = np.zeros(model.n_classes)\n",
    "    fn = np.zeros(model.n_classes)\n",
    "    tn = np.zeros(model.n_classes)\n",
    "    n_pixels = np.zeros(model.n_classes)\n",
    "    \n",
    "        #on size sur la sortie de modèle \n",
    "    gt2c_rs = cv2.resize(gt2c, (model.output_width, model.output_height), interpolation=cv2.INTER_NEAREST)\n",
    "    prf = im_p.flatten()\n",
    "    gtf = gt2c_rs.flatten()\n",
    "    \n",
    "    #boucles sur les 8 classes\n",
    "    for cl_i in range(model.n_classes):\n",
    "        #calcul des TP par classe\n",
    "        tp[cl_i] += np.sum((prf == cl_i) * (gtf == cl_i))\n",
    "        #calcul des FP par classe\n",
    "        fp[cl_i] += np.sum((prf == cl_i) * (gtf != cl_i))\n",
    "        #calcul des FN par classe\n",
    "        fn[cl_i] += np.sum((prf != cl_i) * (gtf == cl_i))\n",
    "        #calcul des TN par classe\n",
    "        tn[cl_i] += np.sum((prf != cl_i) * (gtf != cl_i))\n",
    "        #nombre de pixels par classe\n",
    "        n_pixels[cl_i] += np.sum(gtf == cl_i)\n",
    "\n",
    "    cl_IOU1 = (tp+0.000000000001) / (tp + fp + fn + 0.000000000001)\n",
    "    cl_pct_pixel1 = n_pixels / np.sum(n_pixels)\n",
    "    flat_IOU1 = np.sum(cl_IOU1*cl_pct_pixel1)\n",
    "    mean_IOU1 = np.mean(cl_IOU1)\n",
    "    \n",
    "    #print(\"flat IOU sur le format \",model.output_width,'X',model.output_height,\" :\",\"{:.2f}\".format(flat_IOU1))\n",
    "    #print(\"mean IOU sur le format \",model.output_width,'X',model.output_height,\" :\",\"{:.2f}\".format(mean_IOU1))\n",
    "    \n",
    "    #############\n",
    "    \n",
    "    tp = np.zeros(model.n_classes)\n",
    "    fp = np.zeros(model.n_classes)\n",
    "    fn = np.zeros(model.n_classes)\n",
    "    tn = np.zeros(model.n_classes)\n",
    "    n_pixels = np.zeros(model.n_classes)\n",
    "    \n",
    "        #on size sur la sortie de modèle \n",
    "    im_p_rs = cv2.resize(im_p, (gt2c.shape[1], gt2c.shape[0]), interpolation=cv2.INTER_NEAREST)\n",
    "    prf = im_p_rs.flatten()\n",
    "    gtf = gt2c.flatten()\n",
    "\n",
    "    \n",
    "    \n",
    "    #boucles sur les 8 classes\n",
    "    for cl_i in range(model.n_classes):\n",
    "        #calcul des TP par classe\n",
    "        tp[cl_i] += np.sum((prf == cl_i) * (gtf == cl_i))\n",
    "        #calcul des FP par classe\n",
    "        fp[cl_i] += np.sum((prf == cl_i) * (gtf != cl_i))\n",
    "        #calcul des FN par classe\n",
    "        fn[cl_i] += np.sum((prf != cl_i) * (gtf == cl_i))\n",
    "        #calcul des TN par classe\n",
    "        tn[cl_i] += np.sum((prf != cl_i) * (gtf != cl_i))\n",
    "        #nombre de pixels par classe\n",
    "        n_pixels[cl_i] += np.sum(gtf == cl_i)\n",
    "\n",
    "    cl_IOU2 = (tp+0.000000000001) / (tp + fp + fn + 0.000000000001)\n",
    "    cl_pct_pixel2 = n_pixels / np.sum(n_pixels)\n",
    "    flat_IOU2 = np.sum(cl_IOU2*cl_pct_pixel2)\n",
    "    mean_IOU2 = np.mean(cl_IOU2)\n",
    "    \n",
    "    #print(\"flat IOU sur le format original :\",\"{:.2f}\".format(flat_IOU2))\n",
    "    #print(\"mean IOU sur le format original :\",\"{:.2f}\".format(mean_IOU2))\n",
    "    \n",
    "    #AFFICHAGE\n",
    "    f, (b0, b1, b2) = plt.subplots(1, 3,figsize=(20,3), gridspec_kw={'width_ratios': [4, 4, 4]})\n",
    "\n",
    "    b0.set_title('GT : Pourcentage de pixels par classe')\n",
    "    b0.set_xticks(range(len(cats.keys())),cats.keys(),  rotation=45)\n",
    "    b0.set_yticks(np.arange(0, max(cl_pct_pixel1)*100+10, 10))\n",
    "    b0.bar(cats.keys(), cl_pct_pixel1*100, color=colors2)\n",
    "\n",
    "    b1.set_title(('Prediction Mean IoU :{:.2f}'.format(mean_IOU1)+' format modèle {}'.format(model.output_width) +'X{}'.format(model.output_height)))\n",
    "    b1.set_xticks(range(len(cats.keys())),cats.keys(),  rotation=45)\n",
    "    b1.set_yticks(np.arange(0, 1, 0.1))\n",
    "    b1.bar(cats.keys(), cl_IOU1, color=colors2)\n",
    "    \n",
    "    b2.set_title(('Prediction Mean IoU :{:.2f}'.format(mean_IOU2)+' format upsizé {}'.format(gt2c.shape[1]) +'X{}'.format(gt2c.shape[0])))\n",
    "    b2.set_xticks(range(len(cats.keys())),cats.keys(),  rotation=45)\n",
    "    b2.set_yticks(np.arange(0, 1, 0.1))\n",
    "    b2.bar(cats.keys(), cl_IOU2, color=colors2)\n",
    "\n",
    "    plt.show()\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbed1f1f-b9a7-4c04-8218-91be7cfea9c3",
   "metadata": {},
   "source": [
    "### fonction de rechargement de modèle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "59e9c862-85cc-407a-998a-5112ddad2980",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def model_from_checkpoint_path(config_path=\"vgg_unet_256_512/\",checkpoints_path=\"vgg_unet_256_512/vgg_unet_4.00037\"):\n",
    "\n",
    "\n",
    "\n",
    "    model_config = json.loads(open(config_path+\"_config.json\", \"r\").read())\n",
    "    \n",
    "\n",
    "    model2load = vgg_unet(n_classes=model_config['n_classes'] ,  \n",
    "                          input_height=model_config['input_height'], \n",
    "                          input_width=model_config['input_width'])\n",
    "    \n",
    "    #print(\"loaded weights \", latest_weights)\n",
    "    status = model2load.load_weights(checkpoints_path)\n",
    "\n",
    "    if status is not None:\n",
    "        status.expect_partial()\n",
    "\n",
    "    return model2load\n",
    "#mod=model_from_checkpoint_path(config_path=\"vgg_unet_256_512/\",checkpoints_path=\"vgg_unet_256_512/vgg_unet_4.00037\")\n",
    "#mod=model_from_checkpoint_path(config_path=\"vgg_unet/\",checkpoints_path=\"tmp/vgg_unet_3.00015\")\n",
    "#mod.compile(loss='categorical_crossentropy',optimizer='adam',metrics=[mean_dice, mean_iou, flat_dice, flat_iou,'accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "054d618d-ae04-4d10-88d5-c47a06f2b820",
   "metadata": {},
   "source": [
    "### fit de modèle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a510d126-a1d4-4903-b755-07ba37ffdef6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "153/153 [==============================] - 502s 3s/step - loss: 1.1411 - mean_dice: 0.3463 - mean_iou: 0.2424 - flat_dice: 0.5143 - flat_iou: 0.3596 - accuracy: 0.6492 - val_loss: 1.0301 - val_mean_dice: 0.3568 - val_mean_iou: 0.2442 - val_flat_dice: 0.5258 - val_flat_iou: 0.3569 - val_accuracy: 0.6090\n",
      "\n",
      "Epoch 00001: val_mean_iou improved from -inf to 0.24419, saving model to vgg_unet_256_512\\vgg_unet_10.00001\n",
      "Epoch 2/50\n",
      "153/153 [==============================] - 464s 3s/step - loss: 0.5332 - mean_dice: 0.5417 - mean_iou: 0.4274 - flat_dice: 0.7510 - flat_iou: 0.6020 - accuracy: 0.8302 - val_loss: 0.6855 - val_mean_dice: 0.4542 - val_mean_iou: 0.3326 - val_flat_dice: 0.6606 - val_flat_iou: 0.4934 - val_accuracy: 0.7714\n",
      "\n",
      "Epoch 00002: val_mean_iou improved from 0.24419 to 0.33258, saving model to vgg_unet_256_512\\vgg_unet_10.00002\n",
      "Epoch 3/50\n",
      "153/153 [==============================] - 462s 3s/step - loss: 0.4407 - mean_dice: 0.5941 - mean_iou: 0.4824 - flat_dice: 0.7920 - flat_iou: 0.6560 - accuracy: 0.8577 - val_loss: 0.5545 - val_mean_dice: 0.5409 - val_mean_iou: 0.4154 - val_flat_dice: 0.7324 - val_flat_iou: 0.5781 - val_accuracy: 0.8169\n",
      "\n",
      "Epoch 00003: val_mean_iou improved from 0.33258 to 0.41544, saving model to vgg_unet_256_512\\vgg_unet_10.00003\n",
      "Epoch 4/50\n",
      "153/153 [==============================] - 462s 3s/step - loss: 0.4040 - mean_dice: 0.6228 - mean_iou: 0.5108 - flat_dice: 0.8092 - flat_iou: 0.6800 - accuracy: 0.8693 - val_loss: 0.4360 - val_mean_dice: 0.6062 - val_mean_iou: 0.4803 - val_flat_dice: 0.7746 - val_flat_iou: 0.6325 - val_accuracy: 0.8559\n",
      "\n",
      "Epoch 00004: val_mean_iou improved from 0.41544 to 0.48034, saving model to vgg_unet_256_512\\vgg_unet_10.00004\n",
      "Epoch 5/50\n",
      "153/153 [==============================] - 462s 3s/step - loss: 0.3744 - mean_dice: 0.6482 - mean_iou: 0.5368 - flat_dice: 0.8227 - flat_iou: 0.6994 - accuracy: 0.8798 - val_loss: 0.4202 - val_mean_dice: 0.6242 - val_mean_iou: 0.5013 - val_flat_dice: 0.7873 - val_flat_iou: 0.6494 - val_accuracy: 0.8628\n",
      "\n",
      "Epoch 00005: val_mean_iou improved from 0.48034 to 0.50135, saving model to vgg_unet_256_512\\vgg_unet_10.00005\n",
      "Epoch 6/50\n",
      "153/153 [==============================] - 462s 3s/step - loss: 0.3457 - mean_dice: 0.6705 - mean_iou: 0.5583 - flat_dice: 0.8361 - flat_iou: 0.7187 - accuracy: 0.8884 - val_loss: 0.4179 - val_mean_dice: 0.6463 - val_mean_iou: 0.5199 - val_flat_dice: 0.7956 - val_flat_iou: 0.6610 - val_accuracy: 0.8639\n",
      "\n",
      "Epoch 00006: val_mean_iou improved from 0.50135 to 0.51993, saving model to vgg_unet_256_512\\vgg_unet_10.00006\n",
      "Epoch 7/50\n",
      "153/153 [==============================] - 462s 3s/step - loss: 0.3332 - mean_dice: 0.6848 - mean_iou: 0.5717 - flat_dice: 0.8395 - flat_iou: 0.7238 - accuracy: 0.8918 - val_loss: 0.4037 - val_mean_dice: 0.6579 - val_mean_iou: 0.5334 - val_flat_dice: 0.8054 - val_flat_iou: 0.6747 - val_accuracy: 0.8711\n",
      "\n",
      "Epoch 00007: val_mean_iou improved from 0.51993 to 0.53340, saving model to vgg_unet_256_512\\vgg_unet_10.00007\n",
      "Epoch 8/50\n",
      "153/153 [==============================] - 462s 3s/step - loss: 0.3232 - mean_dice: 0.6985 - mean_iou: 0.5859 - flat_dice: 0.8456 - flat_iou: 0.7329 - accuracy: 0.8946 - val_loss: 0.3553 - val_mean_dice: 0.6840 - val_mean_iou: 0.5625 - val_flat_dice: 0.8239 - val_flat_iou: 0.7009 - val_accuracy: 0.8838\n",
      "\n",
      "Epoch 00008: val_mean_iou improved from 0.53340 to 0.56254, saving model to vgg_unet_256_512\\vgg_unet_10.00008\n",
      "Epoch 9/50\n",
      "153/153 [==============================] - 462s 3s/step - loss: 0.3048 - mean_dice: 0.7129 - mean_iou: 0.6007 - flat_dice: 0.8533 - flat_iou: 0.7445 - accuracy: 0.9006 - val_loss: 0.3508 - val_mean_dice: 0.6856 - val_mean_iou: 0.5599 - val_flat_dice: 0.8196 - val_flat_iou: 0.6945 - val_accuracy: 0.8825\n",
      "\n",
      "Epoch 00009: val_mean_iou did not improve from 0.56254\n",
      "Epoch 10/50\n",
      "153/153 [==============================] - 462s 3s/step - loss: 0.2989 - mean_dice: 0.7182 - mean_iou: 0.6053 - flat_dice: 0.8559 - flat_iou: 0.7485 - accuracy: 0.9026 - val_loss: 0.3543 - val_mean_dice: 0.6968 - val_mean_iou: 0.5729 - val_flat_dice: 0.8305 - val_flat_iou: 0.7106 - val_accuracy: 0.8828\n",
      "\n",
      "Epoch 00010: val_mean_iou improved from 0.56254 to 0.57288, saving model to vgg_unet_256_512\\vgg_unet_10.00010\n",
      "Epoch 11/50\n",
      "153/153 [==============================] - 462s 3s/step - loss: 0.2978 - mean_dice: 0.7239 - mean_iou: 0.6108 - flat_dice: 0.8562 - flat_iou: 0.7489 - accuracy: 0.9020 - val_loss: 0.3550 - val_mean_dice: 0.7051 - val_mean_iou: 0.5828 - val_flat_dice: 0.8317 - val_flat_iou: 0.7122 - val_accuracy: 0.8836\n",
      "\n",
      "Epoch 00011: val_mean_iou improved from 0.57288 to 0.58278, saving model to vgg_unet_256_512\\vgg_unet_10.00011\n",
      "Epoch 12/50\n",
      "153/153 [==============================] - 462s 3s/step - loss: 0.2869 - mean_dice: 0.7298 - mean_iou: 0.6177 - flat_dice: 0.8613 - flat_iou: 0.7568 - accuracy: 0.9058 - val_loss: 0.3381 - val_mean_dice: 0.7165 - val_mean_iou: 0.5975 - val_flat_dice: 0.8452 - val_flat_iou: 0.7322 - val_accuracy: 0.8902\n",
      "\n",
      "Epoch 00012: val_mean_iou improved from 0.58278 to 0.59754, saving model to vgg_unet_256_512\\vgg_unet_10.00012\n",
      "Epoch 13/50\n",
      "153/153 [==============================] - 462s 3s/step - loss: 0.2742 - mean_dice: 0.7448 - mean_iou: 0.6343 - flat_dice: 0.8667 - flat_iou: 0.7652 - accuracy: 0.9091 - val_loss: 0.3394 - val_mean_dice: 0.7282 - val_mean_iou: 0.6102 - val_flat_dice: 0.8518 - val_flat_iou: 0.7424 - val_accuracy: 0.8903\n",
      "\n",
      "Epoch 00013: val_mean_iou improved from 0.59754 to 0.61023, saving model to vgg_unet_256_512\\vgg_unet_10.00013\n",
      "Epoch 14/50\n",
      "153/153 [==============================] - 462s 3s/step - loss: 0.2714 - mean_dice: 0.7463 - mean_iou: 0.6359 - flat_dice: 0.8690 - flat_iou: 0.7687 - accuracy: 0.9107 - val_loss: 0.3555 - val_mean_dice: 0.7335 - val_mean_iou: 0.6137 - val_flat_dice: 0.8540 - val_flat_iou: 0.7457 - val_accuracy: 0.8871\n",
      "\n",
      "Epoch 00014: val_mean_iou improved from 0.61023 to 0.61365, saving model to vgg_unet_256_512\\vgg_unet_10.00014\n",
      "Epoch 15/50\n",
      "153/153 [==============================] - 462s 3s/step - loss: 0.2698 - mean_dice: 0.7492 - mean_iou: 0.6388 - flat_dice: 0.8693 - flat_iou: 0.7692 - accuracy: 0.9105 - val_loss: 0.3388 - val_mean_dice: 0.7350 - val_mean_iou: 0.6200 - val_flat_dice: 0.8585 - val_flat_iou: 0.7523 - val_accuracy: 0.8907\n",
      "\n",
      "Epoch 00015: val_mean_iou improved from 0.61365 to 0.62001, saving model to vgg_unet_256_512\\vgg_unet_10.00015\n",
      "Epoch 16/50\n",
      "153/153 [==============================] - 463s 3s/step - loss: 0.2739 - mean_dice: 0.7463 - mean_iou: 0.6353 - flat_dice: 0.8671 - flat_iou: 0.7657 - accuracy: 0.9090 - val_loss: 0.3532 - val_mean_dice: 0.7399 - val_mean_iou: 0.6217 - val_flat_dice: 0.8527 - val_flat_iou: 0.7436 - val_accuracy: 0.8871\n",
      "\n",
      "Epoch 00016: val_mean_iou improved from 0.62001 to 0.62168, saving model to vgg_unet_256_512\\vgg_unet_10.00016\n",
      "Epoch 17/50\n",
      "153/153 [==============================] - 473s 3s/step - loss: 0.2638 - mean_dice: 0.7536 - mean_iou: 0.6437 - flat_dice: 0.8714 - flat_iou: 0.7725 - accuracy: 0.9118 - val_loss: 0.3426 - val_mean_dice: 0.7275 - val_mean_iou: 0.6115 - val_flat_dice: 0.8533 - val_flat_iou: 0.7446 - val_accuracy: 0.8885\n",
      "\n",
      "Epoch 00017: val_mean_iou did not improve from 0.62168\n",
      "Epoch 18/50\n",
      "153/153 [==============================] - 470s 3s/step - loss: 0.2586 - mean_dice: 0.7586 - mean_iou: 0.6493 - flat_dice: 0.8731 - flat_iou: 0.7752 - accuracy: 0.9139 - val_loss: 0.3833 - val_mean_dice: 0.7363 - val_mean_iou: 0.6133 - val_flat_dice: 0.8502 - val_flat_iou: 0.7399 - val_accuracy: 0.8823\n",
      "\n",
      "Epoch 00018: val_mean_iou did not improve from 0.62168\n",
      "Epoch 19/50\n",
      "153/153 [==============================] - 465s 3s/step - loss: 0.2635 - mean_dice: 0.7593 - mean_iou: 0.6479 - flat_dice: 0.8718 - flat_iou: 0.7731 - accuracy: 0.9128 - val_loss: 0.3230 - val_mean_dice: 0.7512 - val_mean_iou: 0.6323 - val_flat_dice: 0.8530 - val_flat_iou: 0.7440 - val_accuracy: 0.8946\n",
      "\n",
      "Epoch 00019: val_mean_iou improved from 0.62168 to 0.63226, saving model to vgg_unet_256_512\\vgg_unet_10.00019\n",
      "Epoch 20/50\n",
      "153/153 [==============================] - 468s 3s/step - loss: 0.2516 - mean_dice: 0.7608 - mean_iou: 0.6529 - flat_dice: 0.8761 - flat_iou: 0.7799 - accuracy: 0.9162 - val_loss: 0.3438 - val_mean_dice: 0.7430 - val_mean_iou: 0.6231 - val_flat_dice: 0.8537 - val_flat_iou: 0.7453 - val_accuracy: 0.8910\n",
      "\n",
      "Epoch 00020: val_mean_iou did not improve from 0.63226\n",
      "Epoch 21/50\n",
      "153/153 [==============================] - 467s 3s/step - loss: 0.2474 - mean_dice: 0.7712 - mean_iou: 0.6640 - flat_dice: 0.8788 - flat_iou: 0.7841 - accuracy: 0.9170 - val_loss: 0.3381 - val_mean_dice: 0.7489 - val_mean_iou: 0.6292 - val_flat_dice: 0.8508 - val_flat_iou: 0.7407 - val_accuracy: 0.8896\n",
      "\n",
      "Epoch 00021: val_mean_iou did not improve from 0.63226\n",
      "Epoch 22/50\n",
      "153/153 [==============================] - 470s 3s/step - loss: 0.2413 - mean_dice: 0.7741 - mean_iou: 0.6677 - flat_dice: 0.8816 - flat_iou: 0.7887 - accuracy: 0.9189 - val_loss: 0.3279 - val_mean_dice: 0.7563 - val_mean_iou: 0.6408 - val_flat_dice: 0.8632 - val_flat_iou: 0.7597 - val_accuracy: 0.8980\n",
      "\n",
      "Epoch 00022: val_mean_iou improved from 0.63226 to 0.64079, saving model to vgg_unet_256_512\\vgg_unet_10.00022\n",
      "Epoch 23/50\n",
      "153/153 [==============================] - 467s 3s/step - loss: 0.2444 - mean_dice: 0.7727 - mean_iou: 0.6656 - flat_dice: 0.8797 - flat_iou: 0.7856 - accuracy: 0.9179 - val_loss: 0.3461 - val_mean_dice: 0.7529 - val_mean_iou: 0.6344 - val_flat_dice: 0.8553 - val_flat_iou: 0.7475 - val_accuracy: 0.8885\n",
      "\n",
      "Epoch 00023: val_mean_iou did not improve from 0.64079\n",
      "Epoch 24/50\n",
      "153/153 [==============================] - 466s 3s/step - loss: 0.2386 - mean_dice: 0.7745 - mean_iou: 0.6682 - flat_dice: 0.8821 - flat_iou: 0.7895 - accuracy: 0.9191 - val_loss: 0.3197 - val_mean_dice: 0.7602 - val_mean_iou: 0.6462 - val_flat_dice: 0.8692 - val_flat_iou: 0.7690 - val_accuracy: 0.8993\n",
      "\n",
      "Epoch 00024: val_mean_iou improved from 0.64079 to 0.64624, saving model to vgg_unet_256_512\\vgg_unet_10.00024\n",
      "Epoch 25/50\n",
      "153/153 [==============================] - 467s 3s/step - loss: 0.2443 - mean_dice: 0.7725 - mean_iou: 0.6651 - flat_dice: 0.8802 - flat_iou: 0.7864 - accuracy: 0.9182 - val_loss: 0.3466 - val_mean_dice: 0.7390 - val_mean_iou: 0.6176 - val_flat_dice: 0.8495 - val_flat_iou: 0.7387 - val_accuracy: 0.8894\n",
      "\n",
      "Epoch 00025: val_mean_iou did not improve from 0.64624\n",
      "Epoch 26/50\n",
      "153/153 [==============================] - 465s 3s/step - loss: 0.2474 - mean_dice: 0.7679 - mean_iou: 0.6581 - flat_dice: 0.8775 - flat_iou: 0.7820 - accuracy: 0.9164 - val_loss: 0.3193 - val_mean_dice: 0.7533 - val_mean_iou: 0.6347 - val_flat_dice: 0.8545 - val_flat_iou: 0.7462 - val_accuracy: 0.8992\n",
      "\n",
      "Epoch 00026: val_mean_iou did not improve from 0.64624\n",
      "Epoch 27/50\n",
      "153/153 [==============================] - 466s 3s/step - loss: 0.2326 - mean_dice: 0.7788 - mean_iou: 0.6729 - flat_dice: 0.8847 - flat_iou: 0.7935 - accuracy: 0.9215 - val_loss: 0.3210 - val_mean_dice: 0.7543 - val_mean_iou: 0.6399 - val_flat_dice: 0.8631 - val_flat_iou: 0.7597 - val_accuracy: 0.9046\n",
      "\n",
      "Epoch 00027: val_mean_iou did not improve from 0.64624\n",
      "Epoch 28/50\n",
      "153/153 [==============================] - 466s 3s/step - loss: 0.2296 - mean_dice: 0.7840 - mean_iou: 0.6790 - flat_dice: 0.8859 - flat_iou: 0.7955 - accuracy: 0.9223 - val_loss: 0.3262 - val_mean_dice: 0.7702 - val_mean_iou: 0.6556 - val_flat_dice: 0.8696 - val_flat_iou: 0.7697 - val_accuracy: 0.9006\n",
      "\n",
      "Epoch 00028: val_mean_iou improved from 0.64624 to 0.65556, saving model to vgg_unet_256_512\\vgg_unet_10.00028\n",
      "Epoch 29/50\n",
      "153/153 [==============================] - 466s 3s/step - loss: 0.2295 - mean_dice: 0.7823 - mean_iou: 0.6762 - flat_dice: 0.8865 - flat_iou: 0.7964 - accuracy: 0.9228 - val_loss: 0.3186 - val_mean_dice: 0.7587 - val_mean_iou: 0.6436 - val_flat_dice: 0.8663 - val_flat_iou: 0.7646 - val_accuracy: 0.9033\n",
      "\n",
      "Epoch 00029: val_mean_iou did not improve from 0.65556\n",
      "Epoch 30/50\n",
      "153/153 [==============================] - 466s 3s/step - loss: 0.2253 - mean_dice: 0.7848 - mean_iou: 0.6796 - flat_dice: 0.8886 - flat_iou: 0.7999 - accuracy: 0.9241 - val_loss: 0.3050 - val_mean_dice: 0.7671 - val_mean_iou: 0.6534 - val_flat_dice: 0.8697 - val_flat_iou: 0.7698 - val_accuracy: 0.9052\n",
      "\n",
      "Epoch 00030: val_mean_iou did not improve from 0.65556\n",
      "Epoch 31/50\n",
      "153/153 [==============================] - 467s 3s/step - loss: 0.2237 - mean_dice: 0.7881 - mean_iou: 0.6839 - flat_dice: 0.8888 - flat_iou: 0.8001 - accuracy: 0.9243 - val_loss: 0.3070 - val_mean_dice: 0.7666 - val_mean_iou: 0.6508 - val_flat_dice: 0.8648 - val_flat_iou: 0.7623 - val_accuracy: 0.9041\n",
      "\n",
      "Epoch 00031: val_mean_iou did not improve from 0.65556\n",
      "Epoch 32/50\n",
      "153/153 [==============================] - 468s 3s/step - loss: 0.2204 - mean_dice: 0.7901 - mean_iou: 0.6866 - flat_dice: 0.8898 - flat_iou: 0.8017 - accuracy: 0.9253 - val_loss: 0.3065 - val_mean_dice: 0.7656 - val_mean_iou: 0.6494 - val_flat_dice: 0.8636 - val_flat_iou: 0.7603 - val_accuracy: 0.9036\n",
      "\n",
      "Epoch 00032: val_mean_iou did not improve from 0.65556\n",
      "Epoch 33/50\n",
      "153/153 [==============================] - 466s 3s/step - loss: 0.2234 - mean_dice: 0.7880 - mean_iou: 0.6838 - flat_dice: 0.8889 - flat_iou: 0.8003 - accuracy: 0.9243 - val_loss: 0.3005 - val_mean_dice: 0.7787 - val_mean_iou: 0.6649 - val_flat_dice: 0.8728 - val_flat_iou: 0.7746 - val_accuracy: 0.9059\n",
      "\n",
      "Epoch 00033: val_mean_iou improved from 0.65556 to 0.66494, saving model to vgg_unet_256_512\\vgg_unet_10.00033\n",
      "Epoch 34/50\n",
      "153/153 [==============================] - 466s 3s/step - loss: 0.2204 - mean_dice: 0.7879 - mean_iou: 0.6839 - flat_dice: 0.8897 - flat_iou: 0.8015 - accuracy: 0.9249 - val_loss: 0.3257 - val_mean_dice: 0.7532 - val_mean_iou: 0.6339 - val_flat_dice: 0.8601 - val_flat_iou: 0.7549 - val_accuracy: 0.9005\n",
      "\n",
      "Epoch 00034: val_mean_iou did not improve from 0.66494\n",
      "Epoch 35/50\n",
      "153/153 [==============================] - 461s 3s/step - loss: 0.2176 - mean_dice: 0.7906 - mean_iou: 0.6869 - flat_dice: 0.8913 - flat_iou: 0.8041 - accuracy: 0.9261 - val_loss: 0.3247 - val_mean_dice: 0.7587 - val_mean_iou: 0.6386 - val_flat_dice: 0.8565 - val_flat_iou: 0.7494 - val_accuracy: 0.8964\n",
      "\n",
      "Epoch 00035: val_mean_iou did not improve from 0.66494\n",
      "Epoch 36/50\n",
      "153/153 [==============================] - 461s 3s/step - loss: 0.2167 - mean_dice: 0.7957 - mean_iou: 0.6928 - flat_dice: 0.8915 - flat_iou: 0.8045 - accuracy: 0.9266 - val_loss: 0.3020 - val_mean_dice: 0.7726 - val_mean_iou: 0.6578 - val_flat_dice: 0.8626 - val_flat_iou: 0.7588 - val_accuracy: 0.9030\n",
      "\n",
      "Epoch 00036: val_mean_iou did not improve from 0.66494\n",
      "Epoch 37/50\n",
      "153/153 [==============================] - 461s 3s/step - loss: 0.2179 - mean_dice: 0.7935 - mean_iou: 0.6906 - flat_dice: 0.8908 - flat_iou: 0.8033 - accuracy: 0.9262 - val_loss: 0.2871 - val_mean_dice: 0.7738 - val_mean_iou: 0.6596 - val_flat_dice: 0.8672 - val_flat_iou: 0.7658 - val_accuracy: 0.9091\n",
      "\n",
      "Epoch 00037: val_mean_iou did not improve from 0.66494\n",
      "Epoch 38/50\n",
      "153/153 [==============================] - 461s 3s/step - loss: 0.2100 - mean_dice: 0.7991 - mean_iou: 0.6976 - flat_dice: 0.8948 - flat_iou: 0.8098 - accuracy: 0.9283 - val_loss: 0.2858 - val_mean_dice: 0.7823 - val_mean_iou: 0.6691 - val_flat_dice: 0.8700 - val_flat_iou: 0.7703 - val_accuracy: 0.9110\n",
      "\n",
      "Epoch 00038: val_mean_iou improved from 0.66494 to 0.66906, saving model to vgg_unet_256_512\\vgg_unet_10.00038\n",
      "Epoch 39/50\n",
      "153/153 [==============================] - 461s 3s/step - loss: 0.2086 - mean_dice: 0.7983 - mean_iou: 0.6975 - flat_dice: 0.8960 - flat_iou: 0.8118 - accuracy: 0.9295 - val_loss: 0.3101 - val_mean_dice: 0.7734 - val_mean_iou: 0.6591 - val_flat_dice: 0.8655 - val_flat_iou: 0.7632 - val_accuracy: 0.9019\n",
      "\n",
      "Epoch 00039: val_mean_iou did not improve from 0.66906\n",
      "Epoch 40/50\n",
      "153/153 [==============================] - 461s 3s/step - loss: 0.2144 - mean_dice: 0.7967 - mean_iou: 0.6939 - flat_dice: 0.8925 - flat_iou: 0.8062 - accuracy: 0.9268 - val_loss: 0.3148 - val_mean_dice: 0.7758 - val_mean_iou: 0.6605 - val_flat_dice: 0.8629 - val_flat_iou: 0.7592 - val_accuracy: 0.9001\n",
      "\n",
      "Epoch 00040: val_mean_iou did not improve from 0.66906\n",
      "Epoch 41/50\n",
      "153/153 [==============================] - 461s 3s/step - loss: 0.2152 - mean_dice: 0.7957 - mean_iou: 0.6927 - flat_dice: 0.8929 - flat_iou: 0.8068 - accuracy: 0.9269 - val_loss: 0.3245 - val_mean_dice: 0.7700 - val_mean_iou: 0.6529 - val_flat_dice: 0.8641 - val_flat_iou: 0.7613 - val_accuracy: 0.9012\n",
      "\n",
      "Epoch 00041: val_mean_iou did not improve from 0.66906\n",
      "Epoch 42/50\n",
      "153/153 [==============================] - 461s 3s/step - loss: 0.2039 - mean_dice: 0.8024 - mean_iou: 0.7013 - flat_dice: 0.8971 - flat_iou: 0.8138 - accuracy: 0.9302 - val_loss: 0.3229 - val_mean_dice: 0.7748 - val_mean_iou: 0.6593 - val_flat_dice: 0.8643 - val_flat_iou: 0.7615 - val_accuracy: 0.9033\n",
      "\n",
      "Epoch 00042: val_mean_iou did not improve from 0.66906\n",
      "Epoch 43/50\n",
      "153/153 [==============================] - 461s 3s/step - loss: 0.2205 - mean_dice: 0.7920 - mean_iou: 0.6871 - flat_dice: 0.8904 - flat_iou: 0.8027 - accuracy: 0.9250 - val_loss: 0.3201 - val_mean_dice: 0.7753 - val_mean_iou: 0.6594 - val_flat_dice: 0.8693 - val_flat_iou: 0.7692 - val_accuracy: 0.9031\n",
      "\n",
      "Epoch 00043: val_mean_iou did not improve from 0.66906\n",
      "Epoch 00043: early stopping\n"
     ]
    }
   ],
   "source": [
    "nb_img_train=2303\n",
    "batch_size=15\n",
    "steps_per_epoch=nb_img_train//batch_size #les generaturs sont infinis , on definit un nombre fictif de pas par epoch pour definir fictivement une epoch\n",
    "nb_img_test=403\n",
    "val_steps_per_epoch=nb_img_test//batch_size\n",
    "epochs=50\n",
    "initial_epoch=0\n",
    "\n",
    "res=model.fit(x=im_seg_generator(img_seg_pairs=im_seg_pairs()[0], batch_size=batch_size, with_aug=True, other_aug=0),\n",
    "          epochs=epochs,\n",
    "          steps_per_epoch=steps_per_epoch,\n",
    "          validation_data=im_seg_generator(img_seg_pairs=im_seg_pairs()[1], batch_size=batch_size, with_aug=False, other_aug=0),\n",
    "          validation_steps=val_steps_per_epoch,  \n",
    "          callbacks=callbacks)\n",
    "          #use_multiprocessing=False, \n",
    "          #initial_epoch=initial_epoch)\n",
    "          #workers=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cd753c8d-589c-4106-86f8-a3b4790f6f43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "adress=\"input/P8_Cityscapes_leftImg8bit_trainvaltest/leftImg8bit/test/aachen/aachen_000010_000019_leftImg8bit.png\"\n",
    "gt_adress=\"input/P8_Cityscapes_gtFine_trainvaltest/gtFine/test/aachen/aachen_000010_000019_gtFine_labelIds.png\"\n",
    "seg_prediction(model=model,adress=adress,gt_adress=gt_adress)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7a17885d-8756-47c4-b7a5-62d443633b2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "153/153 [==============================] - 655s 4s/step - loss: 0.8833 - mean_dice: 0.3787 - mean_iou: 0.2681 - flat_dice: 0.5812 - flat_iou: 0.4121 - accuracy: 0.6844 - val_loss: 0.9426 - val_mean_dice: 0.3734 - val_mean_iou: 0.2656 - val_flat_dice: 0.6031 - val_flat_iou: 0.4320 - val_accuracy: 0.6907\n",
      "\n",
      "Epoch 00001: val_mean_iou improved from -inf to 0.26555, saving model to vgg_pspnet_384_576\\vgg_pspnet_0.00001\n",
      "Epoch 2/50\n",
      "153/153 [==============================] - 622s 4s/step - loss: 0.7125 - mean_dice: 0.4571 - mean_iou: 0.3399 - flat_dice: 0.6634 - flat_iou: 0.4968 - accuracy: 0.7609 - val_loss: 1.9756 - val_mean_dice: 0.2324 - val_mean_iou: 0.1554 - val_flat_dice: 0.4632 - val_flat_iou: 0.3016 - val_accuracy: 0.4857\n",
      "\n",
      "Epoch 00002: val_mean_iou did not improve from 0.26555\n",
      "Epoch 3/50\n",
      "153/153 [==============================] - 622s 4s/step - loss: 0.6608 - mean_dice: 0.4824 - mean_iou: 0.3646 - flat_dice: 0.6868 - flat_iou: 0.5235 - accuracy: 0.7816 - val_loss: 1.2164 - val_mean_dice: 0.3093 - val_mean_iou: 0.2090 - val_flat_dice: 0.5145 - val_flat_iou: 0.3466 - val_accuracy: 0.5494\n",
      "\n",
      "Epoch 00003: val_mean_iou did not improve from 0.26555\n",
      "Epoch 4/50\n",
      "153/153 [==============================] - 622s 4s/step - loss: 0.6159 - mean_dice: 0.5053 - mean_iou: 0.3865 - flat_dice: 0.7087 - flat_iou: 0.5493 - accuracy: 0.7982 - val_loss: 1.0534 - val_mean_dice: 0.3413 - val_mean_iou: 0.2339 - val_flat_dice: 0.5437 - val_flat_iou: 0.3737 - val_accuracy: 0.6210\n",
      "\n",
      "Epoch 00004: val_mean_iou did not improve from 0.26555\n",
      "Epoch 5/50\n",
      "153/153 [==============================] - 622s 4s/step - loss: 0.5767 - mean_dice: 0.5275 - mean_iou: 0.4076 - flat_dice: 0.7276 - flat_iou: 0.5723 - accuracy: 0.8111 - val_loss: 0.8390 - val_mean_dice: 0.4450 - val_mean_iou: 0.3230 - val_flat_dice: 0.6285 - val_flat_iou: 0.4587 - val_accuracy: 0.7165\n",
      "\n",
      "Epoch 00005: val_mean_iou improved from 0.26555 to 0.32296, saving model to vgg_pspnet_384_576\\vgg_pspnet_0.00005\n",
      "Epoch 6/50\n",
      "153/153 [==============================] - 622s 4s/step - loss: 0.5391 - mean_dice: 0.5505 - mean_iou: 0.4290 - flat_dice: 0.7433 - flat_iou: 0.5920 - accuracy: 0.8226 - val_loss: 0.6837 - val_mean_dice: 0.5193 - val_mean_iou: 0.3911 - val_flat_dice: 0.6873 - val_flat_iou: 0.5238 - val_accuracy: 0.7721\n",
      "\n",
      "Epoch 00006: val_mean_iou improved from 0.32296 to 0.39111, saving model to vgg_pspnet_384_576\\vgg_pspnet_0.00006\n",
      "Epoch 7/50\n",
      "153/153 [==============================] - 622s 4s/step - loss: 0.5068 - mean_dice: 0.5684 - mean_iou: 0.4460 - flat_dice: 0.7563 - flat_iou: 0.6085 - accuracy: 0.8316 - val_loss: 0.5771 - val_mean_dice: 0.5615 - val_mean_iou: 0.4350 - val_flat_dice: 0.7368 - val_flat_iou: 0.5835 - val_accuracy: 0.8072\n",
      "\n",
      "Epoch 00007: val_mean_iou improved from 0.39111 to 0.43495, saving model to vgg_pspnet_384_576\\vgg_pspnet_0.00007\n",
      "Epoch 8/50\n",
      "153/153 [==============================] - 622s 4s/step - loss: 0.4780 - mean_dice: 0.5839 - mean_iou: 0.4621 - flat_dice: 0.7675 - flat_iou: 0.6231 - accuracy: 0.8404 - val_loss: 0.5468 - val_mean_dice: 0.5807 - val_mean_iou: 0.4556 - val_flat_dice: 0.7592 - val_flat_iou: 0.6123 - val_accuracy: 0.8222\n",
      "\n",
      "Epoch 00008: val_mean_iou improved from 0.43495 to 0.45565, saving model to vgg_pspnet_384_576\\vgg_pspnet_0.00008\n",
      "Epoch 9/50\n",
      "153/153 [==============================] - 622s 4s/step - loss: 0.4613 - mean_dice: 0.5959 - mean_iou: 0.4735 - flat_dice: 0.7748 - flat_iou: 0.6327 - accuracy: 0.8450 - val_loss: 0.5306 - val_mean_dice: 0.5925 - val_mean_iou: 0.4652 - val_flat_dice: 0.7653 - val_flat_iou: 0.6201 - val_accuracy: 0.8282\n",
      "\n",
      "Epoch 00009: val_mean_iou improved from 0.45565 to 0.46522, saving model to vgg_pspnet_384_576\\vgg_pspnet_0.00009\n",
      "Epoch 10/50\n",
      "153/153 [==============================] - 622s 4s/step - loss: 0.4450 - mean_dice: 0.6057 - mean_iou: 0.4839 - flat_dice: 0.7814 - flat_iou: 0.6415 - accuracy: 0.8496 - val_loss: 0.5481 - val_mean_dice: 0.5811 - val_mean_iou: 0.4530 - val_flat_dice: 0.7561 - val_flat_iou: 0.6082 - val_accuracy: 0.8241\n",
      "\n",
      "Epoch 00010: val_mean_iou did not improve from 0.46522\n",
      "Epoch 11/50\n",
      "153/153 [==============================] - 622s 4s/step - loss: 0.4290 - mean_dice: 0.6162 - mean_iou: 0.4940 - flat_dice: 0.7878 - flat_iou: 0.6502 - accuracy: 0.8539 - val_loss: 0.5402 - val_mean_dice: 0.5961 - val_mean_iou: 0.4685 - val_flat_dice: 0.7680 - val_flat_iou: 0.6236 - val_accuracy: 0.8294\n",
      "\n",
      "Epoch 00011: val_mean_iou improved from 0.46522 to 0.46852, saving model to vgg_pspnet_384_576\\vgg_pspnet_0.00011\n",
      "Epoch 12/50\n",
      "153/153 [==============================] - 622s 4s/step - loss: 0.4172 - mean_dice: 0.6235 - mean_iou: 0.5013 - flat_dice: 0.7922 - flat_iou: 0.6561 - accuracy: 0.8570 - val_loss: 0.5713 - val_mean_dice: 0.5825 - val_mean_iou: 0.4539 - val_flat_dice: 0.7503 - val_flat_iou: 0.6007 - val_accuracy: 0.8200\n",
      "\n",
      "Epoch 00012: val_mean_iou did not improve from 0.46852\n",
      "Epoch 13/50\n",
      "153/153 [==============================] - 622s 4s/step - loss: 0.4073 - mean_dice: 0.6306 - mean_iou: 0.5083 - flat_dice: 0.7965 - flat_iou: 0.6622 - accuracy: 0.8599 - val_loss: 0.5947 - val_mean_dice: 0.5865 - val_mean_iou: 0.4578 - val_flat_dice: 0.7567 - val_flat_iou: 0.6091 - val_accuracy: 0.8112\n",
      "\n",
      "Epoch 00013: val_mean_iou did not improve from 0.46852\n",
      "Epoch 14/50\n",
      "153/153 [==============================] - 622s 4s/step - loss: 0.3959 - mean_dice: 0.6375 - mean_iou: 0.5154 - flat_dice: 0.8013 - flat_iou: 0.6687 - accuracy: 0.8631 - val_loss: 0.5556 - val_mean_dice: 0.6070 - val_mean_iou: 0.4815 - val_flat_dice: 0.7823 - val_flat_iou: 0.6428 - val_accuracy: 0.8318\n",
      "\n",
      "Epoch 00014: val_mean_iou improved from 0.46852 to 0.48152, saving model to vgg_pspnet_384_576\\vgg_pspnet_0.00014\n",
      "Epoch 15/50\n",
      "153/153 [==============================] - 622s 4s/step - loss: 0.3898 - mean_dice: 0.6420 - mean_iou: 0.5197 - flat_dice: 0.8036 - flat_iou: 0.6720 - accuracy: 0.8645 - val_loss: 0.5591 - val_mean_dice: 0.6142 - val_mean_iou: 0.4868 - val_flat_dice: 0.7792 - val_flat_iou: 0.6386 - val_accuracy: 0.8352\n",
      "\n",
      "Epoch 00015: val_mean_iou improved from 0.48152 to 0.48679, saving model to vgg_pspnet_384_576\\vgg_pspnet_0.00015\n",
      "Epoch 16/50\n",
      "153/153 [==============================] - 622s 4s/step - loss: 0.3765 - mean_dice: 0.6504 - mean_iou: 0.5284 - flat_dice: 0.8091 - flat_iou: 0.6797 - accuracy: 0.8685 - val_loss: 0.5877 - val_mean_dice: 0.5965 - val_mean_iou: 0.4700 - val_flat_dice: 0.7755 - val_flat_iou: 0.6337 - val_accuracy: 0.8273\n",
      "\n",
      "Epoch 00016: val_mean_iou did not improve from 0.48679\n",
      "Epoch 17/50\n",
      "153/153 [==============================] - 622s 4s/step - loss: 0.3698 - mean_dice: 0.6537 - mean_iou: 0.5322 - flat_dice: 0.8123 - flat_iou: 0.6842 - accuracy: 0.8706 - val_loss: 0.5732 - val_mean_dice: 0.6096 - val_mean_iou: 0.4818 - val_flat_dice: 0.7773 - val_flat_iou: 0.6359 - val_accuracy: 0.8323\n",
      "\n",
      "Epoch 00017: val_mean_iou did not improve from 0.48679\n",
      "Epoch 18/50\n",
      "153/153 [==============================] - 622s 4s/step - loss: 0.3724 - mean_dice: 0.6540 - mean_iou: 0.5314 - flat_dice: 0.8112 - flat_iou: 0.6826 - accuracy: 0.8697 - val_loss: 0.5616 - val_mean_dice: 0.6155 - val_mean_iou: 0.4903 - val_flat_dice: 0.7873 - val_flat_iou: 0.6496 - val_accuracy: 0.8390\n",
      "\n",
      "Epoch 00018: val_mean_iou improved from 0.48679 to 0.49034, saving model to vgg_pspnet_384_576\\vgg_pspnet_0.00018\n",
      "Epoch 19/50\n",
      "153/153 [==============================] - 622s 4s/step - loss: 0.3595 - mean_dice: 0.6631 - mean_iou: 0.5408 - flat_dice: 0.8163 - flat_iou: 0.6899 - accuracy: 0.8734 - val_loss: 0.5947 - val_mean_dice: 0.6055 - val_mean_iou: 0.4775 - val_flat_dice: 0.7668 - val_flat_iou: 0.6221 - val_accuracy: 0.8254\n",
      "\n",
      "Epoch 00019: val_mean_iou did not improve from 0.49034\n",
      "Epoch 20/50\n",
      "153/153 [==============================] - 622s 4s/step - loss: 0.3493 - mean_dice: 0.6685 - mean_iou: 0.5466 - flat_dice: 0.8207 - flat_iou: 0.6962 - accuracy: 0.8761 - val_loss: 0.6789 - val_mean_dice: 0.5694 - val_mean_iou: 0.4433 - val_flat_dice: 0.7433 - val_flat_iou: 0.5918 - val_accuracy: 0.8062\n",
      "\n",
      "Epoch 00020: val_mean_iou did not improve from 0.49034\n",
      "Epoch 21/50\n",
      "153/153 [==============================] - 622s 4s/step - loss: 0.3429 - mean_dice: 0.6738 - mean_iou: 0.5522 - flat_dice: 0.8237 - flat_iou: 0.7005 - accuracy: 0.8780 - val_loss: 0.6101 - val_mean_dice: 0.6004 - val_mean_iou: 0.4754 - val_flat_dice: 0.7733 - val_flat_iou: 0.6308 - val_accuracy: 0.8278\n",
      "\n",
      "Epoch 00021: val_mean_iou did not improve from 0.49034\n",
      "Epoch 22/50\n",
      "153/153 [==============================] - 622s 4s/step - loss: 0.3390 - mean_dice: 0.6756 - mean_iou: 0.5540 - flat_dice: 0.8252 - flat_iou: 0.7026 - accuracy: 0.8793 - val_loss: 0.6269 - val_mean_dice: 0.6287 - val_mean_iou: 0.4998 - val_flat_dice: 0.7826 - val_flat_iou: 0.6431 - val_accuracy: 0.8397\n",
      "\n",
      "Epoch 00022: val_mean_iou improved from 0.49034 to 0.49982, saving model to vgg_pspnet_384_576\\vgg_pspnet_0.00022\n",
      "Epoch 23/50\n",
      "153/153 [==============================] - 622s 4s/step - loss: 0.3272 - mean_dice: 0.6836 - mean_iou: 0.5626 - flat_dice: 0.8306 - flat_iou: 0.7104 - accuracy: 0.8829 - val_loss: 0.5562 - val_mean_dice: 0.6438 - val_mean_iou: 0.5156 - val_flat_dice: 0.7959 - val_flat_iou: 0.6614 - val_accuracy: 0.8500\n",
      "\n",
      "Epoch 00023: val_mean_iou improved from 0.49982 to 0.51561, saving model to vgg_pspnet_384_576\\vgg_pspnet_0.00023\n",
      "Epoch 24/50\n",
      "153/153 [==============================] - 621s 4s/step - loss: 0.3204 - mean_dice: 0.6866 - mean_iou: 0.5661 - flat_dice: 0.8333 - flat_iou: 0.7144 - accuracy: 0.8848 - val_loss: 0.5340 - val_mean_dice: 0.6487 - val_mean_iou: 0.5196 - val_flat_dice: 0.7926 - val_flat_iou: 0.6568 - val_accuracy: 0.8476\n",
      "\n",
      "Epoch 00024: val_mean_iou improved from 0.51561 to 0.51961, saving model to vgg_pspnet_384_576\\vgg_pspnet_0.00024\n",
      "Epoch 25/50\n",
      "153/153 [==============================] - 621s 4s/step - loss: 0.3144 - mean_dice: 0.6910 - mean_iou: 0.5708 - flat_dice: 0.8359 - flat_iou: 0.7182 - accuracy: 0.8868 - val_loss: 0.5528 - val_mean_dice: 0.6493 - val_mean_iou: 0.5219 - val_flat_dice: 0.7984 - val_flat_iou: 0.6647 - val_accuracy: 0.8502\n",
      "\n",
      "Epoch 00025: val_mean_iou improved from 0.51961 to 0.52187, saving model to vgg_pspnet_384_576\\vgg_pspnet_0.00025\n",
      "Epoch 26/50\n",
      "153/153 [==============================] - 621s 4s/step - loss: 0.3147 - mean_dice: 0.6900 - mean_iou: 0.5698 - flat_dice: 0.8358 - flat_iou: 0.7181 - accuracy: 0.8862 - val_loss: 0.5837 - val_mean_dice: 0.6455 - val_mean_iou: 0.5174 - val_flat_dice: 0.7975 - val_flat_iou: 0.6635 - val_accuracy: 0.8480\n",
      "\n",
      "Epoch 00026: val_mean_iou did not improve from 0.52187\n",
      "Epoch 27/50\n",
      "153/153 [==============================] - 621s 4s/step - loss: 0.3041 - mean_dice: 0.6962 - mean_iou: 0.5768 - flat_dice: 0.8403 - flat_iou: 0.7247 - accuracy: 0.8897 - val_loss: 0.5949 - val_mean_dice: 0.6369 - val_mean_iou: 0.5111 - val_flat_dice: 0.7969 - val_flat_iou: 0.6628 - val_accuracy: 0.8392\n",
      "\n",
      "Epoch 00027: val_mean_iou did not improve from 0.52187\n",
      "Epoch 28/50\n",
      "153/153 [==============================] - 621s 4s/step - loss: 0.3109 - mean_dice: 0.6929 - mean_iou: 0.5730 - flat_dice: 0.8378 - flat_iou: 0.7210 - accuracy: 0.8874 - val_loss: 0.6326 - val_mean_dice: 0.6394 - val_mean_iou: 0.5128 - val_flat_dice: 0.7998 - val_flat_iou: 0.6668 - val_accuracy: 0.8396\n",
      "\n",
      "Epoch 00028: val_mean_iou did not improve from 0.52187\n",
      "Epoch 29/50\n",
      "153/153 [==============================] - 622s 4s/step - loss: 0.3121 - mean_dice: 0.6916 - mean_iou: 0.5712 - flat_dice: 0.8366 - flat_iou: 0.7193 - accuracy: 0.8868 - val_loss: 0.5972 - val_mean_dice: 0.6480 - val_mean_iou: 0.5250 - val_flat_dice: 0.8063 - val_flat_iou: 0.6757 - val_accuracy: 0.8457\n",
      "\n",
      "Epoch 00029: val_mean_iou improved from 0.52187 to 0.52499, saving model to vgg_pspnet_384_576\\vgg_pspnet_0.00029\n",
      "Epoch 30/50\n",
      "153/153 [==============================] - 622s 4s/step - loss: 0.3003 - mean_dice: 0.6991 - mean_iou: 0.5799 - flat_dice: 0.8423 - flat_iou: 0.7277 - accuracy: 0.8908 - val_loss: 0.6094 - val_mean_dice: 0.6610 - val_mean_iou: 0.5370 - val_flat_dice: 0.8186 - val_flat_iou: 0.6931 - val_accuracy: 0.8558\n",
      "\n",
      "Epoch 00030: val_mean_iou improved from 0.52499 to 0.53704, saving model to vgg_pspnet_384_576\\vgg_pspnet_0.00030\n",
      "Epoch 31/50\n",
      "153/153 [==============================] - 622s 4s/step - loss: 0.2918 - mean_dice: 0.7039 - mean_iou: 0.5853 - flat_dice: 0.8462 - flat_iou: 0.7335 - accuracy: 0.8935 - val_loss: 0.7150 - val_mean_dice: 0.6483 - val_mean_iou: 0.5219 - val_flat_dice: 0.8065 - val_flat_iou: 0.6760 - val_accuracy: 0.8427\n",
      "\n",
      "Epoch 00031: val_mean_iou did not improve from 0.53704\n",
      "Epoch 32/50\n",
      "153/153 [==============================] - 621s 4s/step - loss: 0.2848 - mean_dice: 0.7077 - mean_iou: 0.5898 - flat_dice: 0.8492 - flat_iou: 0.7380 - accuracy: 0.8958 - val_loss: 0.6790 - val_mean_dice: 0.6565 - val_mean_iou: 0.5316 - val_flat_dice: 0.8132 - val_flat_iou: 0.6855 - val_accuracy: 0.8495\n",
      "\n",
      "Epoch 00032: val_mean_iou did not improve from 0.53704\n",
      "Epoch 33/50\n",
      "153/153 [==============================] - 623s 4s/step - loss: 0.2789 - mean_dice: 0.7119 - mean_iou: 0.5942 - flat_dice: 0.8518 - flat_iou: 0.7420 - accuracy: 0.8975 - val_loss: 0.6873 - val_mean_dice: 0.6555 - val_mean_iou: 0.5313 - val_flat_dice: 0.8117 - val_flat_iou: 0.6834 - val_accuracy: 0.8476\n",
      "\n",
      "Epoch 00033: val_mean_iou did not improve from 0.53704\n",
      "Epoch 34/50\n",
      "153/153 [==============================] - 628s 4s/step - loss: 0.2740 - mean_dice: 0.7160 - mean_iou: 0.5987 - flat_dice: 0.8541 - flat_iou: 0.7454 - accuracy: 0.8989 - val_loss: 0.6599 - val_mean_dice: 0.6598 - val_mean_iou: 0.5361 - val_flat_dice: 0.8129 - val_flat_iou: 0.6852 - val_accuracy: 0.8513\n",
      "\n",
      "Epoch 00034: val_mean_iou did not improve from 0.53704\n",
      "Epoch 35/50\n",
      "153/153 [==============================] - 627s 4s/step - loss: 0.2714 - mean_dice: 0.7170 - mean_iou: 0.5999 - flat_dice: 0.8551 - flat_iou: 0.7469 - accuracy: 0.8994 - val_loss: 0.6734 - val_mean_dice: 0.6578 - val_mean_iou: 0.5318 - val_flat_dice: 0.8101 - val_flat_iou: 0.6811 - val_accuracy: 0.8501\n",
      "\n",
      "Epoch 00035: val_mean_iou did not improve from 0.53704\n",
      "Epoch 00035: early stopping\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x27693b8af70>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "nb_img_train=2303\n",
    "batch_size=15\n",
    "steps_per_epoch=nb_img_train//batch_size #les generaturs sont infinis , on definit un nombre fictif de pas par epoch pour definir fictivement une epoch\n",
    "nb_img_test=403\n",
    "val_steps_per_epoch=nb_img_test//batch_size\n",
    "epochs=50\n",
    "initial_epoch=0\n",
    "\n",
    "model.fit(x=im_seg_generator(img_seg_pairs=im_seg_pairs()[0], batch_size=batch_size, with_aug=False, other_aug=0),\n",
    "          epochs=epochs,\n",
    "          steps_per_epoch=steps_per_epoch,\n",
    "          validation_data=im_seg_generator(img_seg_pairs=im_seg_pairs()[1], batch_size=batch_size, with_aug=False, other_aug=0),\n",
    "          validation_steps=val_steps_per_epoch,  \n",
    "          callbacks=callbacks)\n",
    "          #use_multiprocessing=False, \n",
    "          #initial_epoch=initial_epoch)\n",
    "          #workers=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3c2de237-c635-4a22-a07d-9563f17217e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "adress=\"input/P8_Cityscapes_leftImg8bit_trainvaltest/leftImg8bit/test/aachen/aachen_000010_000019_leftImg8bit.png\"\n",
    "gt_adress=\"input/P8_Cityscapes_gtFine_trainvaltest/gtFine/test/aachen/aachen_000010_000019_gtFine_labelIds.png\"\n",
    "seg_prediction(model=model,adress=adress,gt_adress=gt_adress)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a862dd76-0d62-4efc-b5cc-9bdcd6853a99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "666/666 [==============================] - 923s 1s/step - loss: 0.7527 - mean_dice: 0.4703 - mean_iou: 0.3583 - flat_dice: 0.6632 - flat_iou: 0.5096 - accuracy: 0.7633 - val_loss: 0.4655 - val_mean_dice: 0.5917 - val_mean_iou: 0.4770 - val_flat_dice: 0.7902 - val_flat_iou: 0.6537 - val_accuracy: 0.8500\n",
      "\n",
      "Epoch 00001: mean_iou improved from -inf to 0.43703, saving model to tmp\\vgg_unet_2.00001\n",
      "Epoch 2/10\n",
      "666/666 [==============================] - 880s 1s/step - loss: 0.3804 - mean_dice: 0.6396 - mean_iou: 0.5292 - flat_dice: 0.8205 - flat_iou: 0.6961 - accuracy: 0.8775 - val_loss: 0.3867 - val_mean_dice: 0.6565 - val_mean_iou: 0.5442 - val_flat_dice: 0.8296 - val_flat_iou: 0.7094 - val_accuracy: 0.8787\n",
      "\n",
      "Epoch 00002: mean_iou improved from 0.43703 to 0.53966, saving model to tmp\\vgg_unet_2.00002\n",
      "Epoch 3/10\n",
      "666/666 [==============================] - 876s 1s/step - loss: 0.3355 - mean_dice: 0.6753 - mean_iou: 0.5658 - flat_dice: 0.8403 - flat_iou: 0.7250 - accuracy: 0.8906 - val_loss: 0.3587 - val_mean_dice: 0.6568 - val_mean_iou: 0.5397 - val_flat_dice: 0.8160 - val_flat_iou: 0.6897 - val_accuracy: 0.8779\n",
      "\n",
      "Epoch 00003: mean_iou improved from 0.53966 to 0.57166, saving model to tmp\\vgg_unet_2.00003\n",
      "Epoch 4/10\n",
      "666/666 [==============================] - 879s 1s/step - loss: 0.3063 - mean_dice: 0.6994 - mean_iou: 0.5909 - flat_dice: 0.8525 - flat_iou: 0.7433 - accuracy: 0.8995 - val_loss: 0.3585 - val_mean_dice: 0.6733 - val_mean_iou: 0.5599 - val_flat_dice: 0.8299 - val_flat_iou: 0.7099 - val_accuracy: 0.8854\n",
      "\n",
      "Epoch 00004: mean_iou improved from 0.57166 to 0.59610, saving model to tmp\\vgg_unet_2.00004\n",
      "Epoch 5/10\n",
      "666/666 [==============================] - 879s 1s/step - loss: 0.2867 - mean_dice: 0.7142 - mean_iou: 0.6067 - flat_dice: 0.8613 - flat_iou: 0.7567 - accuracy: 0.9056 - val_loss: 0.3340 - val_mean_dice: 0.6898 - val_mean_iou: 0.5778 - val_flat_dice: 0.8486 - val_flat_iou: 0.7375 - val_accuracy: 0.8920\n",
      "\n",
      "Epoch 00005: mean_iou improved from 0.59610 to 0.61004, saving model to tmp\\vgg_unet_2.00005\n",
      "Epoch 6/10\n",
      "666/666 [==============================] - 876s 1s/step - loss: 0.2799 - mean_dice: 0.7231 - mean_iou: 0.6152 - flat_dice: 0.8641 - flat_iou: 0.7610 - accuracy: 0.9072 - val_loss: 0.3270 - val_mean_dice: 0.7064 - val_mean_iou: 0.5940 - val_flat_dice: 0.8475 - val_flat_iou: 0.7359 - val_accuracy: 0.8926\n",
      "\n",
      "Epoch 00006: mean_iou improved from 0.61004 to 0.61816, saving model to tmp\\vgg_unet_2.00006\n",
      "Epoch 7/10\n",
      "666/666 [==============================] - 878s 1s/step - loss: 0.2661 - mean_dice: 0.7342 - mean_iou: 0.6283 - flat_dice: 0.8704 - flat_iou: 0.7708 - accuracy: 0.9117 - val_loss: 0.3326 - val_mean_dice: 0.7108 - val_mean_iou: 0.6008 - val_flat_dice: 0.8540 - val_flat_iou: 0.7457 - val_accuracy: 0.8983\n",
      "\n",
      "Epoch 00007: mean_iou improved from 0.61816 to 0.62810, saving model to tmp\\vgg_unet_2.00007\n",
      "Epoch 8/10\n",
      "666/666 [==============================] - 879s 1s/step - loss: 0.2605 - mean_dice: 0.7377 - mean_iou: 0.6316 - flat_dice: 0.8727 - flat_iou: 0.7744 - accuracy: 0.9131 - val_loss: 0.3389 - val_mean_dice: 0.7140 - val_mean_iou: 0.6052 - val_flat_dice: 0.8606 - val_flat_iou: 0.7558 - val_accuracy: 0.8968\n",
      "\n",
      "Epoch 00008: mean_iou improved from 0.62810 to 0.63469, saving model to tmp\\vgg_unet_2.00008\n",
      "Epoch 9/10\n",
      "666/666 [==============================] - 878s 1s/step - loss: 0.2507 - mean_dice: 0.7471 - mean_iou: 0.6427 - flat_dice: 0.8771 - flat_iou: 0.7814 - accuracy: 0.9160 - val_loss: 0.3137 - val_mean_dice: 0.7256 - val_mean_iou: 0.6161 - val_flat_dice: 0.8621 - val_flat_iou: 0.7583 - val_accuracy: 0.8971\n",
      "\n",
      "Epoch 00009: mean_iou improved from 0.63469 to 0.64339, saving model to tmp\\vgg_unet_2.00009\n",
      "Epoch 10/10\n",
      "666/666 [==============================] - 879s 1s/step - loss: 0.2466 - mean_dice: 0.7495 - mean_iou: 0.6452 - flat_dice: 0.8794 - flat_iou: 0.7851 - accuracy: 0.9175 - val_loss: 0.3272 - val_mean_dice: 0.7240 - val_mean_iou: 0.6155 - val_flat_dice: 0.8619 - val_flat_iou: 0.7578 - val_accuracy: 0.8991\n",
      "\n",
      "Epoch 00010: mean_iou improved from 0.64339 to 0.64497, saving model to tmp\\vgg_unet_2.00010\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x186a32962e0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#790 sec par epoch sans aug\n",
    "#923 sec - 15 min par epoch avec aug\n",
    "nb_img_train=10000\n",
    "batch_size=15\n",
    "steps_per_epoch=nb_img_train//batch_size #les generaturs sont infinis , on definit un nombre fictif de pas par epoch pour definir fictivement une epoch\n",
    "nb_img_test=nb_img_train//5\n",
    "val_steps_per_epoch=nb_img_test//batch_size\n",
    "epochs=10\n",
    "initial_epoch=0\n",
    "\n",
    "model_info=model.fit(x=im_seg_generator(img_seg_pairs=im_seg_pairs()[0], batch_size=batch_size, with_aug=True, other_aug=0),\n",
    "          epochs=epochs,\n",
    "          steps_per_epoch=steps_per_epoch,\n",
    "          validation_data=im_seg_generator(img_seg_pairs=im_seg_pairs()[1], batch_size=batch_size, with_aug=True, other_aug=0),\n",
    "          validation_steps=val_steps_per_epoch,  \n",
    "          callbacks=callbacks)\n",
    "          #use_multiprocessing=False, \n",
    "          #initial_epoch=initial_epoch)\n",
    "          #workers=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "699095d8-d854-4413-a161-5e3b84058e71",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "66/66 [==============================] - 105s 1s/step - loss: 1.3349 - mean_dice: 0.3165 - mean_iou: 0.2144 - flat_dice: 0.4572 - flat_iou: 0.3066 - accuracy: 0.5933 - val_loss: 1.6685 - val_mean_dice: 0.4138 - val_mean_iou: 0.2947 - val_flat_dice: 0.5820 - val_flat_iou: 0.4111 - val_accuracy: 0.6174\n",
      "\n",
      "Epoch 00001: val_accuracy improved from -inf to 0.61744, saving model to tmp\\vgg_unet_1.00001\n",
      "Epoch 2/5\n",
      "66/66 [==============================] - 76s 1s/step - loss: 0.6378 - mean_dice: 0.5157 - mean_iou: 0.3963 - flat_dice: 0.7150 - flat_iou: 0.5572 - accuracy: 0.8073 - val_loss: 0.7556 - val_mean_dice: 0.4997 - val_mean_iou: 0.3729 - val_flat_dice: 0.6655 - val_flat_iou: 0.4992 - val_accuracy: 0.7627\n",
      "\n",
      "Epoch 00002: val_accuracy improved from 0.61744 to 0.76271, saving model to tmp\\vgg_unet_1.00002\n",
      "Epoch 3/5\n",
      "66/66 [==============================] - 76s 1s/step - loss: 0.5197 - mean_dice: 0.5724 - mean_iou: 0.4546 - flat_dice: 0.7620 - flat_iou: 0.6158 - accuracy: 0.8407 - val_loss: 0.7074 - val_mean_dice: 0.5161 - val_mean_iou: 0.3915 - val_flat_dice: 0.6975 - val_flat_iou: 0.5371 - val_accuracy: 0.7819\n",
      "\n",
      "Epoch 00003: val_accuracy improved from 0.76271 to 0.78191, saving model to tmp\\vgg_unet_1.00003\n",
      "Epoch 4/5\n",
      "66/66 [==============================] - 78s 1s/step - loss: 0.4644 - mean_dice: 0.5999 - mean_iou: 0.4840 - flat_dice: 0.7848 - flat_iou: 0.6462 - accuracy: 0.8564 - val_loss: 0.5810 - val_mean_dice: 0.5319 - val_mean_iou: 0.4119 - val_flat_dice: 0.7349 - val_flat_iou: 0.5811 - val_accuracy: 0.8071\n",
      "\n",
      "Epoch 00004: val_accuracy improved from 0.78191 to 0.80705, saving model to tmp\\vgg_unet_1.00004\n",
      "Epoch 5/5\n",
      "66/66 [==============================] - 77s 1s/step - loss: 0.4240 - mean_dice: 0.6265 - mean_iou: 0.5118 - flat_dice: 0.8015 - flat_iou: 0.6690 - accuracy: 0.8665 - val_loss: 0.5492 - val_mean_dice: 0.5402 - val_mean_iou: 0.4214 - val_flat_dice: 0.7257 - val_flat_iou: 0.5698 - val_accuracy: 0.8195\n",
      "\n",
      "Epoch 00005: val_accuracy improved from 0.80705 to 0.81945, saving model to tmp\\vgg_unet_1.00005\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x21cc627fa60>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb_img_train=1000\n",
    "batch_size=15\n",
    "steps_per_epoch=nb_img_train//batch_size #les generaturs sont infinis , on definit un nombre fictif de pas par epoch pour definir fictivement une epoch\n",
    "nb_img_test=nb_img_train/5\n",
    "val_steps_per_epoch=nb_img_test//batch_size\n",
    "epochs=5\n",
    "initial_epoch=0\n",
    "\n",
    "model.fit(x=im_seg_generator(img_seg_pairs=im_seg_pairs()[0], batch_size=batch_size, with_aug=False, other_aug=0),\n",
    "          epochs=epochs,\n",
    "          steps_per_epoch=steps_per_epoch,\n",
    "          validation_data=im_seg_generator(img_seg_pairs=im_seg_pairs()[1], batch_size=batch_size, with_aug=False, other_aug=0),\n",
    "          validation_steps=val_steps_per_epoch,  \n",
    "          callbacks=callbacks)\n",
    "          #use_multiprocessing=False, \n",
    "          #initial_epoch=initial_epoch)\n",
    "          #workers=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "2bb7ec06-55ca-41a6-b034-8c796e57e30a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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95lRXN95vD7y27bMW1v/rphnL1d3mzcy/UArBXYB/aMTQyzKPpBSaP4vS2eTTx4jrpsz8Q6fYImKPiDg/IlZFxC2UJqTbjLFe7bodF2uJiHtHxNkRcV3dr59oLSczr6D00XAicEOdr7XO3dazl32tuatT2djSXnZ0++6NV1Y0LQSuzjX3efeivcy+nZI3mmX2hMvS+hnXUE4STgZ+kZnt3+Uxy5yIeFGUTmlb03Zh7fLh7nga5fJEOktda13r+9aP/tWUE6F2G1B+CEzE7ZSTppYtgNvb9uFaImITytXsM7vM8ixK8/OvTzAGWHc9qcPNfdqpfL2+8f5PHYY3g3HzZS8mtDzG3m/3a65LzTnNzq23B/6ldsL5M0qr11vq/9I23wbAbxrH3ccoV7e7itIx8rspt5a0+uIgM7+Xmbdl5h2ZeSal1c9TG+t2VWaenqVfibPrOuzZ+NzHU348nUm5ut2T2m/Tv1MqwY5ujH8SpcXF3pQWJHsBp3XqgFTTypxgToDJ54TbG//f/KzbJhjHRH6bTSUndD3XnkbNcv52Sl68X2Z+jXIb46mUc/llEbFFpw+IiC0pLVvfnJmtDs4n8nuy02e9j3IcPrexP9v3N3X4tojYlFL2vqrL5z2Icuvmiyhl80Mo/Tg9rTHPhpS89FHg6NrPz0R1OhahcQxFxD0ot8RdmJnvbsz7IcrFolt6WN64rPiZvO9SrlQdNOhAKFcjtm8NRERQCu/r6qg/UmrjW9q/WO2F4bV07kjqWuDrNZG2Xptl5ssnGfdrKTX9e2TmFpQrK1CaDf4GuFctjFsWtsXy722xbJqZ7+mwnN8A29Xt0rKo7bPe1fZZm2Tmp8eIvRnLIso+WEft1PFtwL8B/xC1N/delpmZv8jMwygnw6cAn62FWSf3bJvWjO1TlPtiF2bmlpTmpO1POuiaGOl+XLQ7uX7OQ+t+fUFzOZn5qcx8HOWYzbpOY61nL/ta6qR5XI/13RuvrGi6FljUpVPGsb5HsG6ZvSnlVp7ruv7HxJxFKVfP6jCt63pHxPbAv1J+rG5dL2D8lHXLh8lYa11Z05rzespV6G2anRnWbb893X9ctbuEcjtyy8MYv8PlZ1JOYC/oMv1w4Kyxfih00L6eUNa1uU97+bx2Y+XLqX52J2Ptt9/QyIE1T2/dmPdaypXPBzVe22Rm+/F9LeU8apvGMblFZj6kW1C1Y9V/pXTc+5Nx1iFZs30uZt1tdPdwPfk+ndKp8yuBnSPiBeN8fjOuqP9/b0rfPs0fqbtRbttZkZl3ZeZFlD4Q+/7EHd3NnGBOaFonJ2TmTZT93+tntYz32wymVm6Pea49gc/+A43fhhHRqdKlWc5vRrk98tcAmfmRzHwk5dbfnSi3Ja2lVoZ/itKKc1ljUs+/JyPi7ZTbxZ6cmbc2Jl0C7Nr2Hd21jt+R0pLqmxHxW8pthfeN8pStxZRKpJ9n5nm1bL6c0k9t86mMb6G0Vno15XfTx7rF2EGnY/H6rE8Brb8Jv0BpvdreAfm+wPtqrK1K1u9GxPN7WP46rPiZpMy8mXJbyj9FxCERsXlErFev4HT7UT5TzgGeFhH7Rumh/LWUk6lWL+k/Ap4f5fGk+1OuNo3lNOAdEbFjFLtGxNaUeyR3iogXRsQG9fWoiHjwBOO8nnJPb8vmlNrrmyPiXpQKEgAy82pKR78nRnkE32MozfFaPgE8IyKeUtdr44jYOyIWdFjudylJ5VU15mdR+qtp+VfgZVFaxEREbBoRT4uIzcdYl+Mi4p4RsZBSGHymfYZaCJ1BORk8kpJA3tHrMiPiBRExv14tuLmOvmuM2N5et9njKR33tZ5KszmlFdWfI2J3yn3RvTgd+Nt6nK0XEdvFmiegNG1OqeW+pVZ83Z0MImLnKI9O3IhyL2urM7yx1rOXfS2NZ6zv3nhlRdP3Kd/p99TP2DgiWq0HrgcWxLpPlWj5NOW7tFv9LpwMfC8zr5riun2G0i/AOR2mjbXem1JOElcBRMTfUk6IpsOngWMiYod64ngy8JnMXF2vSH8POCUiNqvb4jjKld0LJ/j5ZwHH1vLofpT8d8Y4/9O1YqeWK0+ke2ugbs6l5MfnR8T6EfE8ygnxl3r8nG665suqPb9OVdf9BnwWeHpEPK4e4yex9vnkvwAnR8QOcPdj1Ne5SJaZvwG+TLkoskXNKw+IiI7nKBGxD6UJ/7Mz8/tt07aqOWLjuv3/hlI59r91ls9TLowcXvPIIcACSqsgKOdzV2XmGVlaML0U+GCseaxzRMTGlKvC1OVs1Ajhnyn9XDyjrWUJlP6EHh+1hU9EPBx4PKUySoNnTjAndMsJZwFvjnK+/yBKn0bjfVbLeL/NpqrruXY1Xk74MfCQesxtTGmJ3+6pjXL+HZRWKddG+e23R12vP1DO5zv9LnkX5Vh6ddv4nn5PRsQJlN8sT2pVmjRcQLkF7VURsVFEtFpbfo1SWbmQUvm+G/ASynbZjVL59ENgxyi/SyIiHkD53XRxXe7DKK2F/q4eGycCi+v3oRXbhnX7BbBB/d638uFZwJERsSQitqJ0an5G/b8NKLn0T5SuK9q3306UiqJW7FB+B3++0zaasJzivWJz/UVpmvZ9SquaVZQC6yhq512N+ca9N4/SEdv/jDH9Krrcr0mprb6U0pz669Se/Ou0pZRax9sozZA/TZcnEtRx8+rB+av6Pxex5ukhO1NqQ1tPLPgasFuddgZrP6Fgrc+m3Fr0G8qP+udSmkFeQCm4fk450UrW9JnwAOCbNYavUu7RP73xeXvUdf19jee/gUVdts9Syhf8Nkoi/ExbrPvX9by5xvgfwOZdPitZ81SvGyn9MMyr045gTednr6YUrK2O3O5X43z8eMtk7fuhP0Gpbb697seDu8S1N6XW+E2UTsiuoXaWXKcfQrlichul0P1H1vSZtLi57cc4Bp9JKRBvozQhfkodfwFrOnd+CKWvitsplY6vZc098LtSvi+31f32JdZ09Nx1PXvZ177m3ouxy8Z1yt5xvntdywrWLdMWUa7WtJ6q8ZE6fsN6jP4e+F0ddwZrlzkvo9w62foeLOgWc/v/tq3LiTSedtg2rf0JLmOt97ta8VI66/06XTqt77ZdO+0PSoXAWyknWatqTPdszLuwxvHbuuzzGKODR0rOvaQxHJSm3L+vr/eydn8ct1PL3Dq8HeWHXLfYT6Ct74Eu8y2mrcyk3IL6A0oe/gFr98lxAW19VHTYz2v1EUB5wtVp9f14+fIxdfxNreNwAvGuZO0OkD9BaY4/kf12OCXHdHuq17GU/kJuoxznJ3eKg9KJ9T/XWG6hfPcO7bLNz6/7rvmUmFbfDfMpx/ZtlOP7Qhr9DNV5Hk/pR+F2yoWlVi5eWpe9Q9v8/wZ8si3u5uuqOm37Otx6qlrr1Xyq5tGUnHkb5dzhtZ3W0df0vDAnmBOmISdQ+gv7OGueNnvsGHGsdSzUcWP9Nrt7m9ThxfRWRnc9167TD6KU0TcDr+sSc+v3wrWUFkN370PWfqrX7ZTbi3eo0/al/Ba4nTVPFNus8X/vbKxjx3KRMX5PdvnO3tH2OW9sTH943RZ/ojyB7+E97KPnUiqIbqvb+xTKMTqPkide3+Ezfkft9JuSl9tzQ3OfHVuPnVspOaXVefpedd4/tq3X48fYBlPu46fVMZI09CLiM5TOot824DgS2DFLfzVDIyL2piR7W8JIkiRJ6llEnEGpJHnzoGPR9PFWLw2t2uzvAbX59/6U2usvDDgsSZIkSZJmjU4dkEnD4j6Ujri2pjS/e3lm/nCwIUmSJEmSNHt4q5ckSZIkSdKI8lYvSZIkSZKkEWXFjyRJkiRJ0oiacB8/EdF6rNl1mfn0iNgBOJvS/8oPKI+NvnOsz9hmm21y8eLFUwhXkkbTD37wg99l5vxBxzFo5glJ6sw8UZgnJKmzsfJEL507vxq4DNiiDp8CfDAzz46IfwGOBP55rA9YvHgxK1as6GGRkjQ3RMTVg45hGJgnJKkz80RhnpCkzsbKExO61SsiFgBPA06rwwHsA3y2znImcPCUopQkSZIkSdK0mmgfPx8CXg/cVYe3Bm7OzNV1eCWwXad/jIijImJFRKxYtWrVVGKVJEmSJElSD8at+ImIpwM3ZOYPJrOAzFyWmUszc+n8+XP+tmRJkiRJkqS+mUgfP3sCB0bEU4GNKX38fBjYKiLWr61+FgDXzVyYkiRJkiRJ6tW4LX4y84TMXJCZi4FDga9l5t8A5wOH1NkOB744Y1FKkiRJkiSpZ7081avdG4CzI+KdwA+B06cnpNnluS953sCWfc5pnxnYsiVJEzOoPGGOkKTZwTwhaab1VPGTmRcAF9T3VwK7T39IkiRJkiRJmg4TfaqXJEmSJEmSZhkrfiRJkiRJkkaUFT+SJEmSplVE7B8Rl0fEFRFxfIfp20fEVyPi4oi4ICIWDCJOSZoLrPiRJEmSNG0iYh5wKnAAsAQ4LCKWtM32fuCszNwVOAl4d3+jlKS5YypP9ZIkSZJmlWeePbgH0X7+0CMHtuw+2x24oj4Mhog4GzgIuLQxzxLg2Pr+fOAL/QxQkuYSK34kSZIkTaftgGsbwyuBPdrm+THwLODDwDOBzSNi68y8sT8hSlJno3iBwFu9JEk9s+8GSdIUvQ7YKyJ+COwFXAf8tdOMEXFURKyIiBWrVq3qZ4ySNBKs+JEk9cS+GyRJ47gOWNgYXlDH3S0zf52Zz8rMhwNvquNu7vRhmbksM5dm5tL58+fPUMiSNLqs+JEk9eruvhsy806g1XdD0xLga/X9+R2mS5JG10XAjhGxQ0RsCBwKLG/OEBHbRETrt8gJwMf7HKMkzRlW/EiSetWp74bt2uZp9d0Ajb4bOn2YTfglabRk5mrgaOA84DLgnMy8JCJOiogD62x7A5dHxM+BewPvGkiwkjQH2LmzJGkmvA74x4g4AvgGY/TdkJnLgGUAS5cuzX4FKPXqcVs+byDL/dYtnxnIcqWpyMxzgXPbxr218f6zwGf7HZc0k8wTGlZW/EiSejWhvhuoLX4iYjPg2d36bpAkSZI0c7zVS5LUK/tukCRJkmYJK34kST2x7wZJkiRp9vBWL0lSz+y7QZIkSZodbPEjSZIkSZI0oqz4kSRJkiRJGlFW/EiSJEmSJI0oK34kSZIkSZJGlBU/kiRJkqZVROwfEZdHxBURcXyH6Ysi4vyI+GFEXBwRTx1EnJI0F1jxI0mSJGnaRMQ84FTgAGAJcFhELGmb7c3AOZn5cOBQ4J/6G6UkzR1W/EiSJEmaTrsDV2TmlZl5J3A2cFDbPAlsUd9vCfy6j/FJ0pxixY8kaVJsxi9J6mI74NrG8Mo6rulE4AURsRI4F/j7bh8WEUdFxIqIWLFq1arpjlWSRp4VP5KkntmMX5I0RYcBZ2TmAuCpwL9HRMffJpm5LDOXZubS+fPn9zVISRoFVvxIkibDZvySpG6uAxY2hhfUcU1HAucAZOZ3gY2BbfoSnSTNMVb8SJImY9qa8duEX5JGzkXAjhGxQ0RsSGn1ubxtnmuAfQEi4sGUih+TgCTNACt+JEkzZULN+G3CL0mjJTNXA0cD5wGXUW77vSQiToqIA+tsrwX+LiJ+DHwaOCIzczARS9JoW3/QAUiSZqWJNuPfH0oz/ohoNeO/oS8RSpIGJjPPpbT2bI57a+P9pcCe/Y5LkuYiW/xIkibDZvySJEnSLGDFjySpZzbjlyRJkmaHcW/1qk3zvwFsVOf/bGa+LSJ2oDzFZWvgB8AL65NdJElzgM34JUmSpOE3kRY/dwD7ZObDgN2A/SPi0cApwAcz84HATZS+HCRJkiRJkjQkxq34yeL2OrhBfSWwD/DZOv5M4OCZCFCSJEmSJEmTM6E+fiJiXkT8iPIklq8AvwRurn08AKwEtpuRCCVJkiRJkjQpE3qce2b+FdgtIrYCPg88aKILiIijgKMAFi1aNIkQJUka37df9JqBLHfPsz40kOVKkiRJE9HTU70y82bgfOAxwFYR0ao4WgBc1+V/lmXm0sxcOn/+/KnEKkmSJGkWiIj9I+LyiLgiIo7vMP2DEfGj+vp5RNw8gDAlaU4Yt+InIubXlj5ExD2A/SiP7j0fOKTOdjjwxRmKUZIkSdIsERHzgFOBA4AlwGERsaQ5T2Yek5m7ZeZuwEeBz/U9UEmaIybS4ue+wPkRcTFwEfCVzPwS8Abg2Ii4gvJI99NnLkxJkiRJs8TuwBWZeWVm3gmcDRw0xvyHAZ/uS2SSNAeN28dPZl4MPLzD+CsphbokaY6JiP2BDwPzgNMy8z1t0z8IPLEObgJsm5lb9TVISdKgbAdc2xheCezRacaI2B7YAfhaH+KSpDlpQp07S5LU0mjCvx/lZP6iiFiemZe25snMYxrz/z0dLiBIkgQcCny2PkymIx8WI0lT01PnzpIkYRN+SdLYrgMWNoa7PgiGUvEzZo7wYTGSNDVW/EiSetWpCf92nWacSBP+iDgqIlZExIpVq1ZNa6CSpIG4CNgxInaIiA0plTvL22eKiAcB9wS+2+f4JGlO8VYvSdJMGrcJf2YuA5YBLF26NPsVmDQqVq88bWDLXn/BS7pP3OUd/Quk3U/fMrhli8xcHRFHA+dR+oL7eGZeEhEnASsys1UJdChwdmZa9kvSDLLiR5LUq16b8L9yxiOSJA2VzDwXOLdt3Fvbhk/sZ0ySNFd5q5ckqVc24ZckSZJmCVv8SJJ6YhP+0fCwAz8ysGX/ePmrBrZsSZKkucaKH0lSz2zCL0mSJM0O3uolSZIkSZI0omzxI0mSJGnkfftFrxnIcvc860MDWa4ktdjiR5IkSZIkaURZ8SNJkiRJkjSivNVLkiRJkrQWn/4ojQ5b/EiSJEmaVhGxf0RcHhFXRMTxXeZ5bkRcGhGXRMSn+h2jJM0VtviRJEmSNG0iYh5wKrAfsBK4KCKWZ+aljXl2BE4A9szMmyJi28FEK0mjzxY/kqSeeSVXkjSG3YErMvPKzLwTOBs4qG2evwNOzcybADLzhj7HKElzhi1+JEk98UquJGkc2wHXNoZXAnu0zbMTQER8G5gHnJiZ/9vpwyLiKOAogEWLFk17sJIGZJd3DG7ZP33L4JY9AFb8SJJ6dfeVXICIaF3JvbQxj1dyJUljWR/YEdgbWAB8IyIempk3t8+YmcuAZQBLly7NPsYojYTVK08byHLXX/CSgSxX6/JWL0lSrzpdyd2ubZ6dgJ0i4tsRcWFE7N/twyLiqIhYERErVq1aNQPhSpL67DpgYWN4QR3XtBJYnpl/ycxfAT+nVARJkqaZFT+SpJnQvJJ7GPCvEbFVpxkzc1lmLs3MpfPnz+9fhJKkmXIRsGNE7BARGwKHAsvb5vkCJUcQEdtQLhhc2ccYJWnOsOJHktQrr+RKkrrKzNXA0cB5wGXAOZl5SUScFBEH1tnOA26MiEuB84HjMvPGwUQsSaPNPn4kSb26+0oupcLnUOD5bfN8gdLS59+8kitJc09mnguc2zburY33CRxbX5KkGWSLH0lST7ySK0mSJM0etviRJPXMK7mSJEnS7GCLH0mSJEmSpBFlxY8kSZIkSdKIsuJHkiRJkiRpRFnxI0mSJEmSNKLs3FmSJA2Nja65eWDLvmPRVgNbtiRJ0kyxxY8kSZKkaRUR+0fE5RFxRUQc32H6ERGxKiJ+VF8vGUSckjQX2OJHkiRJ0rSJiHnAqcB+wErgoohYnpmXts36mcw8uu8BStIcM26Ln4hYGBHnR8SlEXFJRLy6jr9XRHwlIn5R/95z5sOVJA0Dr+RKksawO3BFZl6ZmXcCZwMHDTgmSZqzJnKr12rgtZm5BHg08MqIWAIcD3w1M3cEvlqHJUkjrnEl9wBgCXBYzQvtPpOZu9XXaX0NUpI0SNsB1zaGV9Zx7Z4dERdHxGcjYmF/QpOkuWfcW70y8zfAb+r72yLiMkrBfRCwd53tTOAC4A0zEqXUB4/b8nkDWe63bvnMQJYrTcHdV3IBIqJ1Jbe9Cb8kSd38F/DpzLwjIl5K+T2xT6cZI+Io4CiARYsW9S9CSRoRPXXuHBGLgYcD3wPuXSuFAH4L3Ht6Q5MkDalpvZIbEUdFxIqIWLFq1arpjlWS1H/XAc1yf0Edd7fMvDEz76iDpwGP7PZhmbksM5dm5tL58+dPe7CSNOom3LlzRGwG/Cfwmsy8NSLunpaZGRHZ5f+soZfUV888+/SBLPfzhx45kOUOqQlfyc3MZcAygKVLl3bMJZKkWeUiYMeI2IFS4XMo8PzmDBFx38ZF5AOBy/oboiTNHRNq8RMRG1AqfT6ZmZ+ro6+PiPvW6fcFbuj0v9bQS9LImdYruZKk0ZKZq4GjgfMoFTrnZOYlEXFSRBxYZ3tVfXDMj4FXAUcMJlpJGn3jtviJ0rTndOCyzPxAY9Jy4HDgPfXvF2ckQknSsPFKriRpTJl5LnBu27i3Nt6fAJzQ77gkaS6ayK1eewIvBH4SET+q495IqfA5JyKOBK4GnjsjEUqShkpmro6I1pXcecDHW1dygRWZuZxyJfdAypMhf49XciVJkqSBmMhTvb4FRJfJ+05vOJKk2cAruZIkSdLs0NNTvSRJkiRJkjR7TPipXpIkSZIkDdJG19w8sGXfsWirgS1bmgpb/EiSJEmSJI0oK34kSZIkSZJGlBU/kiRJkiRJI8qKH0mSJEmSpBFlxY8kSZKkaRUR+0fE5RFxRUQcP8Z8z46IjIil/YxPkuYSK34kSZPiSb0kqZOImAecChwALAEOi4glHebbHHg18L3+RihJc4sVP5KknnlSL0kaw+7AFZl5ZWbeCZwNHNRhvncApwB/7mdwkjTXWPEjSZoMT+olSd1sB1zbGF5Zx90tIh4BLMzM/x7vwyLiqIhYERErVq1aNb2RStIcYMWPJGkypu2k3hN6SZpbImI94APAaycyf2Yuy8ylmbl0/vz5MxucJI0gK34kSdOul5N6T+glaeRcByxsDC+o41o2B3YBLoiIq4BHA8vtC06SZoYVP5KkyfCkXpLUzUXAjhGxQ0RsCBwKLG9NzMxbMnObzFycmYuBC4EDM3PFYMKVpNFmxY8kaTI8qZckdZSZq4GjgfOAy4BzMvOSiDgpIg4cbHSSNPesP+gAJEmzT2aujojWSf084OOtk3pgRWYuH/sTJEmjLDPPBc5tG/fWLvPu3Y+YJGmusuJHkjQpntRLkiRJw89bvSRJkiRJkkaUFT+SJEmSJEkjyoofSZIkSZKkEWXFjyRJkiRJ0oiy4keSJEmSJGlEWfEjSZIkSZI0oqz4kSRJkjStImL/iLg8Iq6IiOM7TH9ZRPwkIn4UEd+KiCWDiFOS5gIrfiRJk+JJvSSpk4iYB5wKHAAsAQ7rkAM+lZkPzczdgPcCH+hvlJI0d1jxI0nqmSf1kqQx7A5ckZlXZuadwNnAQc0ZMvPWxuCmQPYxPkmaU6z4kSRNhif1kqRutgOubQyvrOPWEhGvjIhfUi4OvKrbh0XEURGxIiJWrFq1atqDlaRRZ8WPJGkypvWkXpI092TmqZn5AOANwJvHmG9ZZi7NzKXz58/vX4CSNCKs+JEkzZiJnNR7JVeSRs51wMLG8II6rpuzgYNnMiBJmsus+JEkTca0ndR7JVeSRs5FwI4RsUNEbAgcCixvzhAROzYGnwb8oo/xSdKcsv6gA5AkzUp3n9RTKnwOBZ7fnCEidszM1om8J/WSNEdk5uqIOBo4D5gHfDwzL4mIk4AVmbkcODoingT8BbgJOHxwEUvSaLPiR5LUM0/qJUljycxzgXPbxr218f7VfQ9KkuaocSt+IuLjwNOBGzJzlzruXsBngMXAVcBzM/OmmQtTkjRsPKmXJEmSht9E+vg5A9i/bdzxwFczc0fgq3VYkiRJkiRJQ2Tcip/M/Abw+7bRBwFn1vdnYi/8kiRJkiRJQ2eyT/W6d2b+pr7/LXDvaYpHkiRJkiRJ02TKj3PPzASy2/SIOCoiVkTEilWrVk11cZIkSZIkSZqgyVb8XB8R9wWof2/oNmNmLsvMpZm5dP78+ZNcnCRJkiRJkno12ce5L6c8lvc99e8Xpy0iTYuHHfiRgS37x8tfNbBlS5IkSZKkNcZt8RMRnwa+C+wcESsj4khKhc9+EfEL4El1WJIkSZKIiP0j4vKIuCIi1nkCcEQcGxGXRsTFEfHViNh+EHFK0lwwboufzDysy6R9pzkWSdIsERH7Ax8G5gGnZeZ72qYfC7wEWA2sAl6cmVf3PVBJUt9FxDzgVGA/YCVwUUQsz8xLG7P9EFiamX+MiJcD7wWe1/9oJWn0TblzZ0nS3NI4oT8AWAIcFhFL2mZrndDvCnyWckIvSZobdgeuyMwrM/NO4GzgoOYMmXl+Zv6xDl4ILOhzjJI0Z1jxI0nqlSf0kqSxbAdc2xheWcd1cyTwPzMakSTNYZPt3FmSNHd1OqHfY4z5PaGXJHUUES8AlgJ7jTHPUcBRAIsWLepTZJI0OmzxI0maMY0T+veNMc9REbEiIlasWrWqf8FJkmbKdcDCxvCCOm4tEfEk4E3AgZl5R7cPy8xlmbk0M5fOnz9/2oOVpFFnxY8kqVee0EuSxnIRsGNE7BARGwKHAsubM0TEw4GPUXLEDQOIUZLmDCt+JEm98oRektRVZq4GjgbOAy4DzsnMSyLipIg4sM72PmAz4D8i4kcRsbzLx0mSpmjW9PHz7Re9ZiDL3fOsDw1kuZI0rDJzdUS0TujnAR9vndADKzJzOWuf0ANck5kHdv1QSdJIycxzgXPbxr218f5JfQ9KkuaoWVPxI0kaHp7QS5IkSbODt3pJkiRJkiSNKCt+JEmSJEmSRpQVP5IkSZIkSSPKih9JkiRJkqQRZcWPJEmSJEnSiLLiR5IkSZIkaURZ8SNJkiRJkjSirPiRJPUsIvaPiMsj4oqIOL7D9CdExP+LiNURccggYpQkDY55QpKGhxU/kqSeRMQ84FTgAGAJcFhELGmb7RrgCOBT/Y1OkjRo5glJGi7rDzoASdKssztwRWZeCRARZwMHAZe2ZsjMq+q0uwYRoCRpoMwTkjREbPEjSerVdsC1jeGVdZwkSTDNeSIijoqIFRGxYtWqVVMOTpLmGit+JEkD5Qm9JGksmbksM5dm5tL58+cPOhxJmnWs+JEk9eo6YGFjeEEdNyme0EvSyJnWPCFJmhorfiRJvboI2DEidoiIDYFDgeUDjkmSNDzME5I0RKz4kST1JDNXA0cD5wGXAedk5iURcVJEHAgQEY+KiJXAc4CPRcQlg4tYktRP5glJGi4+1Ut9t9E1Nw9kuXcs2mogy5VGUWaeC5zbNu6tjfcXUZr2S5LmIPOEJA0PW/xIkiRJkiSNKCt+JEmSJEmSRpQVP5IkSZIkSSPKih9JkiRJkqQRZcWPJEmSJEnSiLLiR5IkSZIkaURZ8SNJkiRJkjSi1p/KP0fE/sCHgXnAaZn5nmmJStLw2+Udg1v2T98yuGXrbuPlgIjYCDgLeCRwI/C8zLyq33FKkvrPHCFJw2PSLX4iYh5wKnAAsAQ4LCKWTFdgkqThNcEccCRwU2Y+EPggcEp/o5QkDYI5QpKGy1Ru9doduCIzr8zMO4GzgYOmJyxJ0pCbSA44CDizvv8ssG9ERB9jlCQNhjlCkobIVG712g64tjG8EthjauFIard65WkDW/b6C14ysGVr6E0kB9w9T2aujohbgK2B3/UlQknSoJgjJGmIRGZO7h8jDgH2z8yX1OEXAntk5tFt8x0FHFUHdwYun3y4k7YNw5lEhjUuGN7YjKs3wxoXDG9sg4pr+8ycP4DlTspEckBE/LTOs7IO/7LO87u2zzJPdGdcvRvW2IyrN8MaF5gnxjWdOaJOM090Z1y9G9bYjKs3wxoXDGGemEqLn+uAhY3hBXXcWjJzGbBsCsuZsohYkZlLBxlDJ8MaFwxvbMbVm2GNC4Y3tmGNawhNJAe05lkZEesDW1I68FyLeaI74+rdsMZmXL0Z1rhguGMbItOWI8A8MRbj6t2wxmZcvRnWuGA4Y5tKHz8XATtGxA4RsSFwKLB8esKSJA25ieSA5cDh9f0hwNdyss1MJUmziTlCkobIpFv81HtxjwbOozym8eOZecm0RSZJGlrdckBEnASsyMzlwOnAv0fEFcDvKSf+kqQRZ46QpOEylVu9yMxzgXOnKZaZNNCmoWMY1rhgeGMzrt4Ma1wwvLENa1xDp1MOyMy3Nt7/GXhOv+OapGHd78bVu2GNzbh6M6xxwXDHNjRGLEfA8O534+rdsMZmXL0Z1rhgCGObdOfOkiRJkiRJGm5T6eNHkiRJkiRJQ8yKH0mSJEmSpBE1pyp+6qMiJUnqyDwhSRqLeULSbDRnKn4i4pHAmyNi60HH0hIRGzTej8y+iIjo47I2iojN6/ttR2k7TreImFf/9m3/jBK32+gzT/SPeWI4mScmz202N5gn+sc8MXzMEVMz6O02lw7q3wF7AS+OiG0GHUxEbAk8OiI2joinAbsNOKRpERGRtcfwiNhkhpc1D1gKvCgiDgdOAu45k8ucjIi4X0Tcb4DLj4jYFfgiQA5xj+6dEu2gC8lWDI3j+sERscugY9KMME/0gXliXeaJiRvGPGGOmFPME31gnljXIPPEbMoRYJ7oZuSbKrZ2cmZeHRGfBF4DbB8Rb8zMWwcY2j2BPYDjgQcAjxpgLNOi7YB+KbBPRFwI/Gtm3j7dy8vMv0bEDcDTgEcAr8zMGyNivcy8a7qXNxkR8Tpgb+CeEfFl4L2Z+ad+xlD3ycURsToiDsnMz/Zz+b1o7beIOATYArg4M1cMap+2junGcX0McCDwh4j4C/CSzLyx33Fpepkn+sc8sS7zRG+GKU+YI+YO80T/mCfWNeg8MZtyBJgnupkLLX4iMzMijgaeDfwz8FTgdRExsNrczLwK+BPwBGrt6WzXOKCfAxwGfAo4BDgmIhZM57IaNbm/BH4BfB14aEQsGqJC+iBgv8x8OnAZJZn8uc8xbNoYPBfYuZ/Ln4x6teXdwP2Br0XEvpl5V6fa+z7YuBHXvpT9+URgBbAJ8PvG9IG3TNKkmSf6xDyxNvPE5AxRnjBHzB3miT4xT6xt0HliNuYIME90MrIVPxGxe0RsXHfwVpSatZMz81RKjenewEl1Wr9iat+ZpwPPAFYDr4qIB9T57h31HsrZJsq9z0cDH83MLwIvAnYF/jYitp+mZUTdrwcBHwaOA95F+WK9KiLmRcSCiHjcdCxvkjHeC7gWOD0i3gwsBA6pJw0P7VMMuwEXRsQrI+JJwH8CR0TEnv1Y/mRExN6UJtRPz8w3A0cBn4+IffpdWEfE/YFPRsQD66gbgc9FxLuAx9QYMyKeDMPf7FXrMk8Mhnni7hjNE5MwLHnCHDE3mCcGwzxxd4wDzROzMUeAeaKbkaz4qQXivpTmcPMy82bgZ8D9I2LTzLyG0iTy5cAL+lEo1sKlVYN9VES8g3IQXgT8B7At8OyIOAk4GdhopmOaDh2Szzzgt8DhEbE4M38JvB54HHBoTMOTEOoX5EnA24FzM/POzLyYcqXjL8AXgG9SEmDfRcTLgM9QasSPAx4JHJCZf4mIVwLvjYhNp7tWNyI2bLzfA3gK8GZKTfK7gBcAPwX2rPMM/Pvf2gYRsV49Np4BPBR4TD3ROpvyPfm/iNirz1df7gAuBd5VT6JuB/6Osj/3r/vzSOBtMcCrfZoc80T/mCfWZZ6YuCHOE+aIEWee6B/zxLoGkSdmY44A88SEZebIvoCHARdSCr1DgU8Ce9Rp+wPnANv3OabXAF+jNA/9BnA2MB/YhZI8zgd2HfS2m+C6ROP9o+rrHsCDKR2j/UNr+wLbAwumcdmvB55f32/cGL8QeB7wxAFtk72BC4At6/C/UBLxIcBrgYuBh8zAcu8JfAd4RB3eC3h3Y/rWwKuAzwHXAPccsuPnXvXv+sAbgY8AjwfWr+OfDezcp7jWa7zfFnhb3Yf3Ap4LfLd+j981U/vTV/9e5okZXxfzxLpxmScmd/wMRZ4wR8y9l3lixtfFPLFuXH3PE7MxR3Q4fswTY7yiBjQSmrXgdXgDYBmwJfAc4BXAY+vwQuA5mfmzGY7pkcC2mfk/EXEfSq3pa4G/p1xF+CmlEHtVZv42Iu6Rfe7UsVcdtvOrgJcAVwD3BY4BglLbujXwzsy8dppj+Afgvpn5/Ma4RwG/yHJFZp04Z1qtbT4eeDVwbGZ+qo4/gdJsdD7wkZk65iLijZQk9UJgO2DfzHxdh/k+DFydmR+YiTh6FRGvAA6g3Ov6XeD/gLdSEtAXgW9kZl+utrRdSXsG5UrPJpTa+SWsqaV/KOXY/mRm/rwfsWl6mCf6wzzRNSbzxCQMS54wR8wN5on+ME90jWlgeWK25ggwT0xIP2qX+vFi7dq+XYHH1Pf3AD5K2eEbAJtSHtk3bbXFY8R0MOW+zM8Cz67jtqY0U/xmjWdPSkddn6bUTsZMxzUN67Wg8X4J8C1gmzp8DLCcUmDvSvnCbTsd+5ZyL+QBlEdVbku5uvHmOu2xlETx6AFtkxcCT6jvjwNOozTha84zb4aW3arF3pJyRWAFpZb7o5STk2fX1zPrfG8B3j/o46jG8hLKlaoHAV+lFNIvpNyG+j7gvcA9BhDXKyhNM+9fh7cDTqR0MLh40NvN16T3q3mif9vaPLFujOaJycU+dHnCHDG6L/NEX7e1eWLdGAeSJ2ZzjqjxmCcm8BqK+/KmQ9YtGuVxdx8HTomIz1Kad70ZWEnpqT0zc0VmrpzJeCJia8q9hC+kFNT7RsSzsjyuLYFLM/MvlJ7Gv0Cp0V3dWo9hFaWTsfdFxGZ11G+A6ykFM5n5QeAG4Jgs98m+NzNvmMoyMzMj4kBKc72HUTpgeyrwHmD/iPgi8E/AqzPzwqksawo2Aj4dEY8BPgBcAhwcEU9rzDMj95Nm5uqIOJjSrPdESid/x1Ou/NyL0lz0GcAN9SrCPYAzZiKWiYpiK2Bz4JnAfnXSv1E68DuM0vy2L4+rjIjNG++XAEcAT8nMKwEy8zpK8r0WeHtEbDAs9zVr4swT/WGe6Mo80YNhyhPmiLnDPNEf5omuBpInZmOOAPNEzwZR2zRTL8qX+H+p92gCx1JqLbeldBL2AWBRn2LZHDgPOItyL/BRlC/RQcAWwA8pNX7XAUsGve16XLd7UAqAv6/DpwEvpTSVBHgZ8JZpWEarZn5zyv2kW1IS34XUKwJ1+n2AhfV9X69wAJs13v8NcCXlasH6wAmUpLLJDMewG/Aj4EF1eB7lMaPfpl5NYe0rWBsN6LhZZ9/U7bQA+FIdXo9yT/PHgS36FNdDKE+O2LAO79yKpxVj/bsxsFXz2PM1+17mib5tZ/PEmmWbJyYe59DlCXPE3HuZJ/q2nc0Ta5Y90DwxW3JEt31jnphgnIPaaTOwwR8FvAH4CvDIxvgvAm8bUEyvp/Te/aY6/BJKDeRj647ftVXADPuLRgdelKsKj6c82eDA+kX7XP1yfYzSSdUuU1jWvShNC7esw5vUhHcipRnfjnX8U4EH93k7NAu9J1Fqu7dvjHsxcDnwaErT23v1IaYH1zheSmkK+5W6vX5WY9mqVeAMw6sWjGcCfwvsQLn39hfATpTa+i8A8/sYz9bANpRmxg+vyeL81ve2znMU5aRvvX7F5WtG9rV5YmbXxTyR5olpindo8oQ5Ym69zBMzvi7miRy+PDHbckSN2TzR42skOneuTc5eBzwC+D6wCvheZv6idvS0SWa+fwBxbQ/sCPwj8CHKF+hQyn2lZ2Tmf/c7psmI8njK51AO5F8AewCvpNxb/CHgTZT7KR9LWd8vZ3ns4lSWuZBS27woM79Rm9weAxyamd+MiL0oX57nZWkCOuNq5347Z+ZPI2If4Grg3ZSmqe+lNP/dDPg8pQB4TGb+uQ9xbUZpTvh84P2UQvpxlHuUr8jyuNGBaevk7DHAOyjbaDElmb0FOJxy9WV94MjM/FE/4oK7m/5uTDmebqFc4dikxrWa8ojU5wFHZOZPZjouzQzzxMwyT9wdk3liEoYxT5gj5h7zxMwyT9wd09DliWHPEWCemA6zvuInIjbJzD9GxPrAfwN/AX5OqTX+DaUW96DMvHSAMT4C+AzlHtKzgWcB/5eZvxlUTL2q9yD+mNIx1YMz8/o6fh9Kp1kfy8xlU1xGUI7Ju+rwcZQa2+OA31OSxTMonb0dBrw+M780lWX2GN8ulMLkvsCemfmAWnifAdxEuTrxSEqt+Yf6vX8jYsPMvDPK0wjOpDSd/Wo/Y+gQU7OQ3o1yZefXmfmfUZ5QcTClg8STgT9Qmkje0qfY1svMuyJi08z8Q0RsQznp+BPlkYtXAS+nFOBfy8zL+hGXpp95oj/ME+aJScY0lHnCHDG3mCf6wzwx3HliGHNEjcs8MQ1mdcVPRDyRcm/oRZn5pYjYD9iF0rTqPsADgf+Zam3xdIiIhwFfo3S6duag45mIti/Z/Sid2m0PrMzMlzbm27dOexZwc07xoIqI+2Tmb+v7IyhXNd5Bufd0H8qjHVdl5nebMfZDRJxEudf7jZn5kTpuY0rt+MaUgujZmfnTfsXUiG0e5R7dfwJOzswv9juGbiLilZSrOn8EbsvMJ9bxDwdeQDnBekuWDgpnOpalwCWZ+acojw49iHIv/3LKyd07KQX2GYMuoDV15omZZZ7oGJt5YhKGJU+YI+Ye88TMMk90jG0o88Qw5wgwT0zVbK/4uT/li3ss8K+UplTPBo7LzIsGGVsntYb3z5l5xaBj6UVEHENpJnk6pRb1bOD2zHxRTZZ3AP9vss0QozRh3Tszz4yIAyiF8s+BX1GaPj6b0tHZOzPzG1NeoSmIiAXAXpQThG8C52Xm9RGxJfBnSjPgmwYY36aUx13+qt9JbIyY9qHcn35gvYrwLUph+dI6/WHAdZn5uz7EEpTmlw+mdM54BOXRp3tQTgD+ndKs9UOUprYnZ+YdMx2XZo55oj/ME2uYJyYV01DkCXPE3GSe6A/zxBrDnCeGMUeAeWI6zOqKn5aI2Ily39xGwBspTateAAz94wyHXUS8iFKz+pys93dGxHxK879tKPdQHpL1UXWTXMZjKYX/6ZQv0T8BGwJ7UjroOpzS2djTKbW4K1pN6ya9Yr3H+CLKfbZXZuYXI+LplOahX6D03L4t9RGa/YppNoiIjYC3Ub6Pf5PlfupNgf+hXOl5/gBiugflZOBg4HWZ+YWIeDCltn5bSuH9M0rSvb7f8WlmmCdmjnni7hjNE5MwbHnCHDF3mSdmjnni7hjNE5NgnpgeI1HxA3cfEK1O2c7JzJ8POKRZqVWz2/j7IeDCzDw7IjZu1cLX2s5DKZ3eTbqQbiz3scA/UGr+94tyD/D9gJOAT2Tm1yLi1cBTgGdk5l+nusweYnsN5Yv9KcqVgu/WuPai9MT/aOAVmfnjfsU0G0TEYZTOEa+jdG62FXBWZn4vSidy/0np5GzG711uv2IRERtSnhrxCOARmfnniNiRcs/1BsCJw1I7r+ljnpge5omOsb0G80TPhiVPmCPUYp6YHuaJjrG9BvNEz8wT02e9QQcwje7MzD9n5jstpCevcUAvao0CFtb3dwJExFMoTQA/PcWa+ah/H0C5D/I1wCMj4sWZeVdmrqzLfGj9l+9THtW3xWSXOYkYdwIeRimQt6CcDGxKqXX+dma+FniKhXRHDwLOo9wf/z7gt8ALI2LPzLwd2L/flT4R8ZSIeBals8ZXAv8HLI/SKdsvKFee3j9sBbWmjXliGpgn1onRPDF5A88T5gi1MU9MA/PEOjGaJybPPDFNRqbixyaY0ycilgAfqrXmpwEnRMTzgC1qreuHKE0yp6ReATiYUlP7Ecq9t/8MvCUiTo7yqL7HUXr/B7gEeH726Z7XiNiBcm/m24HdgQMp94CvoDQTfVNEzKuFjqoondORmW+j3Ct/LqV2/qPAbcCzWvP0Q6Ogfh2l6faewCconde9ntIU8/woT/T4ZWbe2K/Y1F/mieljnijME5MzTHnCHKEm88T0MU8U5onJMU9Mv5Gp+NHktWrKG/5Iqb18OXAXsB/wakph+grK/bnXTcNytwZeRrlX8/GUL83vKT36H03pMOulmXlBRKyfmbdm5q+mutwJxnY08BXgw8ATKbXN38o199x+GfhAP5uIDqvajLb1fn/g5Ii4N0BmnkK5z/U/gS2BU4B316tp/Xx6whJgl8zci9JU9FbgW5Te/48HvkG5x1xSB+aJjrGZJyZo2POEOUKaOvNEx9jMExNknph5I9PHjyanrenaszLzc/X9Isr9r/tQmiFeSens7h45Tb2lR+m5/lzKowy/HhEbUGror6AkinmZ+b3pWFaPcR1IqYE/hbL+i4F7UJqOngY8DXhSDtHj+YZBlCcUzKPc7/plyiMMr6/jl1Oe4PDE7HOHdRHxKMpjMbcH5lOa9x6YmX+JiOcC/5eZv+9nTNJsYp7oGJd5YhKGMU+YI6SpM090jMs8MQnmiZlji585rK2Qvifwioi4CCBLj/tfrbP+M/D4zPzDdBXSdRm3UGpu94qIXTLzL8DngO2AiwdUSG8H/COwfmb+EjgLuAy4to7/MvAYC+nSgV5EHFrf/z2lpvsVwAXAAcAREbGY0mndfwGHDaDSJ+ryH0upnb835WkJf4mIIyidxPXttjNptjFPrMs8MXHDnifMEdLUmSfWZZ6YOPNE/9jiR0TES4B9MvP5EfFFYOvMfFyd9hZgM+DDmfnrGVj2dpTmmY+m9G7/QuCVmXnudC+rh5ieRSmUj83y9IF5wBGUTuk+MhtqdPshIp5G2U5nUjo4ezelGe9WlJrwnYBfU65oPDUzL+1zfJtk5h8jYn3K4x5voiTcBwA3UO7PfW5mXtLPuKTZyDyxTkzmiQkY5jxhjpCml3linZjMExNgnugfK37moLaa+cOAw4DjMvPyOu6/gK0pzSYPpXzJrpnBeDYHHgM8EPhRZn5nppY1UbUQejdwci2s1wM2y8xbBxzaUImI/Sj3Tl+YmX8X5TGoh1Cas24MfAz483Re2ZlgXE8E9gYuyswv1TgfCvwvpYnmvYD/l5lX9zMuabYwT4zPPDExw5gnzBHS1JknxmeemBjzRH9MuSd1zS4RsUWrsImI+1HuNX068H7gcoDMfEZEHEu5B/fQmSyk6/JuozR5/PJMLqcXmfnfEXEXsCwiVmfmZymdeKkhM78SEW8G/jUivlqT2qeBw4Edgdsz8+YBhHY1pUb+vRGxI7Ca8hSFb2Xm1wcQjzRrmCcmxjwxMUOaJ8wR0hSYJybGPDEx5on+sMXPHFKbqR0D3A7cSGkydyRwAvAcyj2Ts6KpWr/U2t1fZuaVg45lmA3rFY2I2Al4HuWk443AfwAvAFanhZ+0DvNE78wTEzOMecIcIfXOPNE788TEmCdmlhU/c0xEbEa5J/FWYFFm3lm/VK+hHNQvzcwfDS5CzVYRcQCwDDimXtEYCrW5aACvA87JzJ8POCRpqJknNFOGMU+YI6TemSc0U8wTM8eKnzmgQ2/77wSeCXwoM9/bmO84SjPN/YC/zLZaTA3eMF7RaB7/kjozT6hfhi1PmCOkiTFPqF/MEzPDip8R11ZIvxK4OTM/GRFbAZcA/5aZb661q1cD12V5LKIkaQ4wT0iSxmKekGY/O3cecY1C+hWURwg+p46/OSKWAj+IiAcBS4BnWEhL0txinpAkjcU8Ic1+tviZA+rjDT8DnAz8BHgW8GDg88BlwLOB84elOZ0kqb/ME5KksZgnpNnNip8RFBHrZeZdbePeAuxN6YH/V8AfKfv/hP5HKEkaJPOEJGks5glptFjxM8Ii4pnA5sB3KPfb7g1ckpm/jojDgecDBwF3jEKHVZKk3pgnJEljMU9Io2G9QQegmRERRwDvAx4KXAQ8KjO/AtwYES+mPI7u2Mz8s4W0JM095glJ0ljME9LosHPnERQRewFPAPbPzCsi4lLgfyLiKZl5YURsAzw3My8bbKSSpEEwT0iSxmKekEaLFT8joPWIxYhYD5gHPBN4GPDoiLgmM/8tIgC+ExGPzMz3DjJeSVJ/mSckSWMxT0ijzT5+ZrlWIV3fb5uZN9QC+wRgW0rv+9/LzL9GxAuB72fm5QMMWZLUR+YJSdJYzBPS6LPiZ0RExCuAQ4Hrgasy87iIeDuwJeUxi99s75lfkjR3mCckSWMxT0ijy86dZ6mobS3r+wOAlwEvBY4D9oyIZZn5NkpTzScDGw0kUEnSQJgnJEljMU9Ic4d9/MxCbc0x7w/cAnyx0bnaYyPimxHxCEoTzU0y808DCleS1GfmCUnSWMwT0txii59ZqFFIvxz4MLAT8JyIuHdjtkuBe2bm7Zl5wwDClCQNiHlCkjQW84Q0t9jiZ5aKiAOBlwNPz8xrImIH4MKIOAbYHtgdOGWQMUqSBsc8IUkai3lCmjus+Jm97gecXQvpeZn5toj4DfBwYCHwgsy8crAhSpIGyDwhSRqLeUKaI7zVa/a6GnhCROycmX+t424ALsrMF2fmJQOMTZI0eOYJSdJYzBPSHOHj3GepiNiC0uP++sC3KY9ZfA3w/Mz8xQBDkyQNAfOEJGks5glp7rDiZxaLiPsCBwEHUnrif3dmXjzYqCRJw8I8IUkai3lCmhus+BkBEbEhQGbeOehYJEnDxzwhSRqLeUIabVb8SJIkSZIkjSg7d5YkSZIkSRpRVvxIkiRJkiSNKCt+JEmSJEmSRpQVP5IkSZIkSSPKih9JkiRJkqQRZcWPJEmSJEnSiLLiR5IkSZIkaUT9fz7FZE5QgdINAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "adress=\"input/P8_Cityscapes_leftImg8bit_trainvaltest/leftImg8bit/test/aachen/aachen_000010_000019_leftImg8bit.png\"\n",
    "gt_adress=\"input/P8_Cityscapes_gtFine_trainvaltest/gtFine/test/aachen/aachen_000010_000019_gtFine_labelIds.png\"\n",
    "seg_prediction(model=mod,adress=adress,gt_adress=gt_adress)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5809ba9c-f8ae-456d-81e8-53b5e210f934",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#model vgg_unet4 256x512 50 epochs de 2303/403 sans augmentation\n",
    "adress=\"input/P8_Cityscapes_leftImg8bit_trainvaltest/leftImg8bit/test/aachen/aachen_000010_000019_leftImg8bit.png\"\n",
    "gt_adress=\"input/P8_Cityscapes_gtFine_trainvaltest/gtFine/test/aachen/aachen_000010_000019_gtFine_labelIds.png\"\n",
    "seg_prediction(model=model,adress=adress,gt_adress=gt_adress)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "env",
   "language": "python",
   "name": "env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}