UNEToptic (3).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "UNEToptic.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "nZEYj5bo8D74",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "10749d41-e64b-4b9f-c8ca-1ab2c0bffbce"
      },
      "source": [
        "import tensorflow as tf\n",
        "import os\n",
        "import random\n",
        "import numpy as np\n",
        "import cv2\n",
        "\n",
        "from tqdm import tqdm \n",
        "\n",
        "from skimage.io import imread, imshow\n",
        "from skimage.transform import resize\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "seed = 42\n",
        "np.random.seed = seed\n",
        "\n",
        "IMG_WIDTH = 128\n",
        "IMG_HEIGHT = 128\n",
        "IMG_CHANNELS = 3\n",
        "\n",
        "directory = '/content/drive/My Drive/retpy'\n",
        "\n",
        "dir_train_path = directory + '/train_data'\n",
        "dir_test_path = directory + '/test_data'\n",
        "dir_train_labels_path = directory + '/train_labels' \n",
        "#dir_test_labels_path = directory + '/test_labels'\n",
        "\n",
        "#train_ids = next(os.walk(TRAIN_PATH))[1]\n",
        "#test_ids = next(os.walk(TEST_PATH))[1]\n",
        "\n",
        "X_train = np.zeros((443, IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS), dtype=np.uint8)\n",
        "Y_train = np.zeros((443, IMG_HEIGHT, IMG_WIDTH, 1), dtype=np.bool)\n",
        "\n",
        "X_test = np.zeros((77, IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS), dtype=np.uint8)\n",
        "\n",
        "#X_train_list = []\n",
        "#Y_train_list = []\n",
        "#X_test_list = []\n",
        "\n",
        "def imgtoXtrainarr(image_path, image_name, n1):\n",
        "\n",
        "  img1 = cv2.imread(image_path)[:,:,:IMG_CHANNELS]\n",
        "  #img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)\n",
        "  #img = cv2.resize(img, (IMG_HEIGHT, IMG_WIDTH), interpolation = cv2.INTER_CUBIC)\n",
        "  img1 = resize(img1, (IMG_HEIGHT, IMG_WIDTH), mode='constant', preserve_range=True)\n",
        "  #X_train_list.append(img)\n",
        "  X_train[n1] = img1\n",
        "\n",
        "def imgtoYtrainarr(image_path, image_name, n2):\n",
        "\n",
        "  img2 = cv2.imread(image_path)[:,:,:1]\n",
        "  #img2[:,:,1]=0\n",
        "  #img2[:,:,0]=0\n",
        "  #img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)\n",
        "  #img = cv2.resize(img, (IMG_HEIGHT, IMG_WIDTH), interpolation = cv2.INTER_CUBIC)\n",
        "  img2 = resize(img2, (IMG_HEIGHT, IMG_WIDTH), mode='constant', preserve_range=True)\n",
        "  #Y_train_list.append(img)\n",
        "  Y_train[n2] = img2\n",
        "\n",
        "def imgtoXtestarr(image_path, image_name, n3):\n",
        "\n",
        "  img3 = cv2.imread(image_path)[:,:,:IMG_CHANNELS]\n",
        "  #img3 = cv2.cvtColor(img3, cv2.COLOR_BGR2GRAY)\n",
        "  #img = cv2.resize(img, (IMG_HEIGHT, IMG_WIDTH), interpolation = cv2.INTER_CUBIC)\n",
        "  img3 = resize(img3, (IMG_HEIGHT, IMG_WIDTH), mode='constant', preserve_range=True)\n",
        "  #X_test_list.append(img)\n",
        "  X_test[n3] = img3\n",
        "\n",
        "nm = 0\n",
        "n1 = 0\n",
        "n2 = 0\n",
        "n3 = 0\n",
        "\n",
        "for files1 in sorted(os.listdir(dir_train_path)):\n",
        "\n",
        "  imgtoXtrainarr(dir_train_path+'/'+files1, files1, n1)\n",
        "  n1 = n1 + 1\n",
        "  nm = nm + 1\n",
        "\n",
        "for files2 in sorted(os.listdir(dir_train_labels_path)):\n",
        "\n",
        "  imgtoYtrainarr(dir_train_labels_path+'/'+files2, files2, n2)\n",
        "  n2 = n2 + 1\n",
        "\n",
        "for files3 in sorted(os.listdir(dir_test_path)):\n",
        "\n",
        "  imgtoXtestarr(dir_test_path+'/'+files3, files3, n3)\n",
        "  n3 = n3 + 1\n",
        "\n",
        "\n",
        "#X_train = np.array(X_train_list, dtype=np.uint8)\n",
        "#Y_train = np.array(Y_train_list, dtype=np.bool)\n",
        "#X_test = np.array(X_test_list, dtype=np.uint8)\n",
        "\n",
        "#X_train = np.zeros((len(train_ids), IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS), dtype=np.uint8)\n",
        "#Y_train = np.zeros((len(train_ids), IMG_HEIGHT, IMG_WIDTH, 1), dtype=np.bool)\n",
        "\n",
        "#print('Resizing training images and masks')\n",
        "#for n, id_ in tqdm(enumerate(train_ids), total=len(train_ids)):   \n",
        "    #path = TRAIN_PATH + id_\n",
        "    #img = imread(path + '/images/' + id_ + '.png')[:,:,:IMG_CHANNELS]  \n",
        "    #img = resize(img, (IMG_HEIGHT, IMG_WIDTH), mode='constant', preserve_range=True)\n",
        "    #X_train[n] = img  #Fill empty X_train with values from img\n",
        "    #mask = np.zeros((IMG_HEIGHT, IMG_WIDTH, 1), dtype=np.bool)\n",
        "    #for mask_file in next(os.walk(path + '/masks/'))[2]:\n",
        "        #mask_ = imread(path + '/masks/' + mask_file)\n",
        "        #mask_ = np.expand_dims(resize(mask_, (IMG_HEIGHT, IMG_WIDTH), mode='constant',  preserve_range=True), axis=-1)\n",
        "        #mask = np.maximum(mask, mask_)  \n",
        "            \n",
        "    #Y_train[n] = mask   \n",
        "\n",
        "# test images\n",
        "#X_test = np.zeros((len(test_ids), IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS), dtype=np.uint8)\n",
        "#sizes_test = []\n",
        "#print('Resizing test images') \n",
        "#for n, id_ in tqdm(enumerate(test_ids), total=len(test_ids)):\n",
        "    #path = TEST_PATH + id_\n",
        "    #img = imread(path + '/images/' + id_ + '.png')[:,:,:IMG_CHANNELS]\n",
        "    #sizes_test.append([img.shape[0], img.shape[1]])\n",
        "    #img = resize(img, (IMG_HEIGHT, IMG_WIDTH), mode='constant', preserve_range=True)\n",
        "    #X_test[n] = img\n",
        "\n",
        "#print('Done!')\n",
        "\n",
        "image_x = random.randint(0, nm)\n",
        "imshow(X_train[image_x])\n",
        "plt.show()\n",
        "imshow(np.squeeze(Y_train[image_x]))\n",
        "plt.show()\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "#Build the model\n",
        "inputs = tf.keras.layers.Input((IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS))\n",
        "s = tf.keras.layers.Lambda(lambda x: x / 255)(inputs)\n",
        "\n",
        "#Contraction path\n",
        "c1 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(s)\n",
        "c1 = tf.keras.layers.Dropout(0.1)(c1)\n",
        "c1 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c1)\n",
        "p1 = tf.keras.layers.MaxPooling2D((2, 2))(c1)\n",
        "\n",
        "c2 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p1)\n",
        "c2 = tf.keras.layers.Dropout(0.1)(c2)\n",
        "c2 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c2)\n",
        "p2 = tf.keras.layers.MaxPooling2D((2, 2))(c2)\n",
        " \n",
        "c3 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p2)\n",
        "c3 = tf.keras.layers.Dropout(0.2)(c3)\n",
        "c3 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c3)\n",
        "p3 = tf.keras.layers.MaxPooling2D((2, 2))(c3)\n",
        " \n",
        "c4 = tf.keras.layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p3)\n",
        "c4 = tf.keras.layers.Dropout(0.2)(c4)\n",
        "c4 = tf.keras.layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c4)\n",
        "p4 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(c4)\n",
        " \n",
        "c5 = tf.keras.layers.Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(p4)\n",
        "c5 = tf.keras.layers.Dropout(0.3)(c5)\n",
        "c5 = tf.keras.layers.Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c5)\n",
        "\n",
        "#Expansive path \n",
        "u6 = tf.keras.layers.Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(c5)\n",
        "u6 = tf.keras.layers.concatenate([u6, c4])\n",
        "c6 = tf.keras.layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u6)\n",
        "c6 = tf.keras.layers.Dropout(0.2)(c6)\n",
        "c6 = tf.keras.layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c6)\n",
        " \n",
        "u7 = tf.keras.layers.Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(c6)\n",
        "u7 = tf.keras.layers.concatenate([u7, c3])\n",
        "c7 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u7)\n",
        "c7 = tf.keras.layers.Dropout(0.2)(c7)\n",
        "c7 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c7)\n",
        " \n",
        "u8 = tf.keras.layers.Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(c7)\n",
        "u8 = tf.keras.layers.concatenate([u8, c2])\n",
        "c8 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u8)\n",
        "c8 = tf.keras.layers.Dropout(0.1)(c8)\n",
        "c8 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c8)\n",
        " \n",
        "u9 = tf.keras.layers.Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same')(c8)\n",
        "u9 = tf.keras.layers.concatenate([u9, c1], axis=3)\n",
        "c9 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u9)\n",
        "c9 = tf.keras.layers.Dropout(0.1)(c9)\n",
        "c9 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c9)\n",
        " \n",
        "outputs = tf.keras.layers.Conv2D(1, (1, 1), activation='sigmoid')(c9)\n",
        " \n",
        "model = tf.keras.Model(inputs=[inputs], outputs=[outputs])\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "model.summary()\n",
        "\n",
        "################################\n",
        "#Modelcheckpoint\n",
        "checkpointer = tf.keras.callbacks.ModelCheckpoint('model_for_opticdisc.h5', verbose=1, save_best_only=True)\n",
        "\n",
        "callbacks = [tf.keras.callbacks.EarlyStopping(patience=2, monitor='val_loss'),tf.keras.callbacks.TensorBoard(log_dir='logs')]\n",
        "\n",
        "results = model.fit(X_train, Y_train, validation_split=0.1, batch_size=16, epochs=25)#, callbacks=callbacks)\n",
        "\n",
        "####################################\n",
        "\n",
        "idx = random.randint(0, len(X_train))\n",
        "\n",
        "\n",
        "preds_train = model.predict(X_train[:int(X_train.shape[0]*0.9)], verbose=1)\n",
        "preds_val = model.predict(X_train[int(X_train.shape[0]*0.9):], verbose=1)\n",
        "preds_test = model.predict(X_test, verbose=1)\n",
        "\n",
        " \n",
        "preds_train_t = (preds_train > 0.5).astype(np.uint8)\n",
        "preds_val_t = (preds_val > 0.5).astype(np.uint8)\n",
        "preds_test_t = (preds_test > 0.5).astype(np.uint8)\n",
        "\n",
        "\n",
        "# Perform a sanity check on some random training samples\n",
        "ix = random.randint(0, len(preds_train_t))\n",
        "imshow(X_train[ix])\n",
        "plt.show()\n",
        "imshow(np.squeeze(Y_train[ix]))\n",
        "plt.show()\n",
        "imshow(np.squeeze(preds_train_t[ix]))\n",
        "plt.show()\n",
        "\n",
        "# Perform a sanity check on some random validation samples\n",
        "ix = random.randint(0, len(preds_val_t))\n",
        "imshow(X_train[int(X_train.shape[0]*0.9):][ix])\n",
        "plt.show()\n",
        "imshow(np.squeeze(Y_train[int(Y_train.shape[0]*0.9):][ix]))\n",
        "plt.show()\n",
        "imshow(np.squeeze(preds_val_t[ix]))\n",
        "plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xmmKdneT5w+/1VF1T77MFluqj/cB7h3e3zgYeWzAla7HqF+3sWlhbZmHrbYxWzr8PfGSKdbyB0XD3DuDbw+1tjNZTDgD3Al8DtkyxxjcCNw33Xzb80o8Afw88d0o1vRY4NPTbPwKbZ6HPgI8C3wXuBP6O0UUop9JnwDWM1poeZzRKvGSpPmL0xsFnhtfDdxi9M9dd2xFGazmnXgd/vWD/jwy13QNcMM45/eSypHazMNWStM4YPJLaGTyS2hk8ktoZPJLaGTyS2hk8ktoZPJLa/T8ainNRfmxD7gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model: \"functional_5\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_3 (InputLayer)            [(None, 128, 128, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "lambda_2 (Lambda)               (None, 128, 128, 3)  0           input_3[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_38 (Conv2D)              (None, 128, 128, 16) 448         lambda_2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "dropout_18 (Dropout)            (None, 128, 128, 16) 0           conv2d_38[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_39 (Conv2D)              (None, 128, 128, 16) 2320        dropout_18[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_8 (MaxPooling2D)  (None, 64, 64, 16)   0           conv2d_39[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_40 (Conv2D)              (None, 64, 64, 32)   4640        max_pooling2d_8[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "dropout_19 (Dropout)            (None, 64, 64, 32)   0           conv2d_40[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_41 (Conv2D)              (None, 64, 64, 32)   9248        dropout_19[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_9 (MaxPooling2D)  (None, 32, 32, 32)   0           conv2d_41[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_42 (Conv2D)              (None, 32, 32, 64)   18496       max_pooling2d_9[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "dropout_20 (Dropout)            (None, 32, 32, 64)   0           conv2d_42[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_43 (Conv2D)              (None, 32, 32, 64)   36928       dropout_20[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_10 (MaxPooling2D) (None, 16, 16, 64)   0           conv2d_43[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_44 (Conv2D)              (None, 16, 16, 128)  73856       max_pooling2d_10[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "dropout_21 (Dropout)            (None, 16, 16, 128)  0           conv2d_44[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_45 (Conv2D)              (None, 16, 16, 128)  147584      dropout_21[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_11 (MaxPooling2D) (None, 8, 8, 128)    0           conv2d_45[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_46 (Conv2D)              (None, 8, 8, 256)    295168      max_pooling2d_11[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "dropout_22 (Dropout)            (None, 8, 8, 256)    0           conv2d_46[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_47 (Conv2D)              (None, 8, 8, 256)    590080      dropout_22[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_transpose_8 (Conv2DTrans (None, 16, 16, 128)  131200      conv2d_47[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_8 (Concatenate)     (None, 16, 16, 256)  0           conv2d_transpose_8[0][0]         \n",
            "                                                                 conv2d_45[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_48 (Conv2D)              (None, 16, 16, 128)  295040      concatenate_8[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_23 (Dropout)            (None, 16, 16, 128)  0           conv2d_48[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_49 (Conv2D)              (None, 16, 16, 128)  147584      dropout_23[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_transpose_9 (Conv2DTrans (None, 32, 32, 64)   32832       conv2d_49[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_9 (Concatenate)     (None, 32, 32, 128)  0           conv2d_transpose_9[0][0]         \n",
            "                                                                 conv2d_43[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_50 (Conv2D)              (None, 32, 32, 64)   73792       concatenate_9[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_24 (Dropout)            (None, 32, 32, 64)   0           conv2d_50[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_51 (Conv2D)              (None, 32, 32, 64)   36928       dropout_24[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_transpose_10 (Conv2DTran (None, 64, 64, 32)   8224        conv2d_51[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_10 (Concatenate)    (None, 64, 64, 64)   0           conv2d_transpose_10[0][0]        \n",
            "                                                                 conv2d_41[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_52 (Conv2D)              (None, 64, 64, 32)   18464       concatenate_10[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "dropout_25 (Dropout)            (None, 64, 64, 32)   0           conv2d_52[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_53 (Conv2D)              (None, 64, 64, 32)   9248        dropout_25[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_transpose_11 (Conv2DTran (None, 128, 128, 16) 2064        conv2d_53[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_11 (Concatenate)    (None, 128, 128, 32) 0           conv2d_transpose_11[0][0]        \n",
            "                                                                 conv2d_39[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_54 (Conv2D)              (None, 128, 128, 16) 4624        concatenate_11[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "dropout_26 (Dropout)            (None, 128, 128, 16) 0           conv2d_54[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_55 (Conv2D)              (None, 128, 128, 16) 2320        dropout_26[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_56 (Conv2D)              (None, 128, 128, 1)  17          conv2d_55[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 1,941,105\n",
            "Trainable params: 1,941,105\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n",
            "Epoch 1/25\n",
            " 2/25 [=>............................] - ETA: 1s - loss: 0.3498 - accuracy: 0.9117WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0249s vs `on_train_batch_end` time: 0.0386s). Check your callbacks.\n",
            "25/25 [==============================] - 1s 54ms/step - loss: 0.1489 - accuracy: 0.9808 - val_loss: 0.0896 - val_accuracy: 0.9864\n",
            "Epoch 2/25\n",
            "25/25 [==============================] - 1s 42ms/step - loss: 0.0656 - accuracy: 0.9871 - val_loss: 0.0623 - val_accuracy: 0.9864\n",
            "Epoch 3/25\n",
            "25/25 [==============================] - 1s 41ms/step - loss: 0.0546 - accuracy: 0.9871 - val_loss: 0.0858 - val_accuracy: 0.9864\n",
            "Epoch 4/25\n",
            "25/25 [==============================] - 1s 42ms/step - loss: 0.0434 - accuracy: 0.9871 - val_loss: 0.0619 - val_accuracy: 0.9864\n",
            "Epoch 5/25\n",
            "25/25 [==============================] - 1s 42ms/step - loss: 0.0384 - accuracy: 0.9871 - val_loss: 0.0522 - val_accuracy: 0.9864\n",
            "Epoch 6/25\n",
            "25/25 [==============================] - 1s 42ms/step - loss: 0.0339 - accuracy: 0.9871 - val_loss: 0.0486 - val_accuracy: 0.9864\n",
            "Epoch 7/25\n",
            "25/25 [==============================] - 1s 42ms/step - loss: 0.0255 - accuracy: 0.9872 - val_loss: 0.0377 - val_accuracy: 0.9866\n",
            "Epoch 8/25\n",
            "25/25 [==============================] - 1s 41ms/step - loss: 0.0201 - accuracy: 0.9918 - val_loss: 0.0225 - val_accuracy: 0.9918\n",
            "Epoch 9/25\n",
            "25/25 [==============================] - 1s 42ms/step - loss: 0.0151 - accuracy: 0.9944 - val_loss: 0.0298 - val_accuracy: 0.9871\n",
            "Epoch 10/25\n",
            "25/25 [==============================] - 1s 42ms/step - loss: 0.0127 - accuracy: 0.9951 - val_loss: 0.0276 - val_accuracy: 0.9893\n",
            "Epoch 11/25\n",
            "25/25 [==============================] - 1s 42ms/step - loss: 0.0122 - accuracy: 0.9953 - val_loss: 0.0207 - val_accuracy: 0.9913\n",
            "Epoch 12/25\n",
            "25/25 [==============================] - 1s 42ms/step - loss: 0.0106 - accuracy: 0.9959 - val_loss: 0.0317 - val_accuracy: 0.9863\n",
            "Epoch 13/25\n",
            "25/25 [==============================] - 1s 42ms/step - loss: 0.0099 - accuracy: 0.9961 - val_loss: 0.0260 - val_accuracy: 0.9884\n",
            "Epoch 14/25\n",
            "25/25 [==============================] - 1s 42ms/step - loss: 0.0095 - accuracy: 0.9963 - val_loss: 0.0173 - val_accuracy: 0.9928\n",
            "Epoch 15/25\n",
            "25/25 [==============================] - 1s 42ms/step - loss: 0.0093 - accuracy: 0.9964 - val_loss: 0.0292 - val_accuracy: 0.9877\n",
            "Epoch 16/25\n",
            "25/25 [==============================] - 1s 42ms/step - loss: 0.0092 - accuracy: 0.9964 - val_loss: 0.0181 - val_accuracy: 0.9922\n",
            "Epoch 17/25\n",
            "25/25 [==============================] - 1s 42ms/step - loss: 0.0090 - accuracy: 0.9965 - val_loss: 0.0245 - val_accuracy: 0.9890\n",
            "Epoch 18/25\n",
            "25/25 [==============================] - 1s 42ms/step - loss: 0.0083 - accuracy: 0.9967 - val_loss: 0.0326 - val_accuracy: 0.9855\n",
            "Epoch 19/25\n",
            "25/25 [==============================] - 1s 42ms/step - loss: 0.0083 - accuracy: 0.9966 - val_loss: 0.0263 - val_accuracy: 0.9885\n",
            "Epoch 20/25\n",
            "25/25 [==============================] - 1s 42ms/step - loss: 0.0077 - accuracy: 0.9969 - val_loss: 0.0194 - val_accuracy: 0.9926\n",
            "Epoch 21/25\n",
            "25/25 [==============================] - 1s 42ms/step - loss: 0.0074 - accuracy: 0.9971 - val_loss: 0.0193 - val_accuracy: 0.9914\n",
            "Epoch 22/25\n",
            "25/25 [==============================] - 1s 42ms/step - loss: 0.0077 - accuracy: 0.9969 - val_loss: 0.0162 - val_accuracy: 0.9926\n",
            "Epoch 23/25\n",
            "25/25 [==============================] - 1s 42ms/step - loss: 0.0075 - accuracy: 0.9970 - val_loss: 0.0144 - val_accuracy: 0.9934\n",
            "Epoch 24/25\n",
            "25/25 [==============================] - 1s 42ms/step - loss: 0.0073 - accuracy: 0.9970 - val_loss: 0.0141 - val_accuracy: 0.9937\n",
            "Epoch 25/25\n",
            "25/25 [==============================] - 1s 42ms/step - loss: 0.0077 - accuracy: 0.9969 - val_loss: 0.0132 - val_accuracy: 0.9940\n",
            " 1/13 [=>............................] - ETA: 0sWARNING:tensorflow:Callbacks method `on_predict_batch_end` is slow compared to the batch time (batch time: 0.0029s vs `on_predict_batch_end` time: 0.0111s). Check your callbacks.\n",
            "13/13 [==============================] - 0s 17ms/step\n",
            "1/2 [==============>...............] - ETA: 0sWARNING:tensorflow:Callbacks method `on_predict_batch_end` is slow compared to the batch time (batch time: 0.0025s vs `on_predict_batch_end` time: 0.0050s). Check your callbacks.\n",
            "2/2 [==============================] - 0s 10ms/step\n",
            "1/3 [=========>....................] - ETA: 0sWARNING:tensorflow:Callbacks method `on_predict_batch_end` is slow compared to the batch time (batch time: 0.0025s vs `on_predict_batch_end` time: 0.0120s). Check your callbacks.\n",
            "3/3 [==============================] - 0s 12ms/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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d/U301n6aqaorq2pdVR1Pr3++W1WXAg8AF02qrq62F4Dnk3y4azqL3nXVJtpn9KZYpyY5uvu9Hqhr4n3WZ7E+2gp8tju7dSrwWt+UrImxX7Sz1cLaEgtb59JbOf8RcNUE6/gEveHuo8APutu59NZTtgHPAN8BVk2wxjOAe7v7H+p+6buAvweOnFBNHwN2dP32j8DKaegz4CvAU8DjwN/RuwjlRPoMuI3eWtPr9EaJly3WR/ROHFzXvR8eo3dmrnVtu+it5Rx4H/x13/Ov6mp7GjhnkGP6yWVJzU3DVEvSYcbgkdScwSOpOYNHUnMGj6TmDB5JzRk8kpozeCQ19/8xCZpHNrpTFwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/skimage/io/_plugins/matplotlib_plugin.py:150: UserWarning: Low image data range; displaying image with stretched contrast.\n",
            "  lo, hi, cmap = _get_display_range(image)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XzPtsiZX6aB/w3uHTrbOAx5ZMyVpM/aadXQtrqyxsvY3Ryvn3gY/MsI43MBru3gF8e3i8jdF6ys3AvcDXgM0zrPGNwI3D85cN/+iHgL8Hnjujml4LHBj67R+BTfPQZ8BHge8CdwJ/x+gmlDPpM+BqRmtNjzMaJV68Uh8x+uDgM8P74TuMPpnrru0Qo7Wc4++Dv15y/EeG2u4Bzh/nmn5zWVK7eZhqSTrJGDyS2hk8ktoZPJLaGTyS2hk8ktoZPJLaGTyS2v0/UTBzVzSTnA4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hymxHERdAu-E",
        "outputId": "96ccc23c-1260-4805-cd40-9224b5c0af75"
      },
      "source": [
        "import tensorflow as tf\n",
        "import os\n",
        "import random\n",
        "import numpy as np\n",
        "import cv2\n",
        "\n",
        "from tqdm import tqdm \n",
        "\n",
        "from skimage.io import imread, imshow\n",
        "from skimage.transform import resize\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "seed = 42\n",
        "np.random.seed = seed\n",
        "\n",
        "IMG_WIDTH = 128\n",
        "IMG_HEIGHT = 128\n",
        "IMG_CHANNELS = 3\n",
        "\n",
        "directory = '/content/drive/My Drive/retpy'\n",
        "\n",
        "dir_train_path = directory + '/train_data'\n",
        "dir_test_path = directory + '/test_data'\n",
        "dir_train_labels_path = directory + '/train_labels' \n",
        "#dir_test_labels_path = directory + '/test_labels'\n",
        "\n",
        "#train_ids = next(os.walk(TRAIN_PATH))[1]\n",
        "#test_ids = next(os.walk(TEST_PATH))[1]\n",
        "\n",
        "X_train = np.zeros((443, IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS), dtype=np.uint8)\n",
        "Y_train = np.zeros((443, IMG_HEIGHT, IMG_WIDTH, 1), dtype=np.bool)\n",
        "\n",
        "X_test = np.zeros((77, IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS), dtype=np.uint8)\n",
        "\n",
        "#X_train_list = []\n",
        "#Y_train_list = []\n",
        "#X_test_list = []\n",
        "\n",
        "def imgtoXtrainarr(image_path, image_name, n1):\n",
        "\n",
        "  img1 = cv2.imread(image_path)\n",
        "  #img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)\n",
        "  #img = cv2.resize(img, (IMG_HEIGHT, IMG_WIDTH), interpolation = cv2.INTER_CUBIC)\n",
        "  img1 = resize(img1, (IMG_HEIGHT, IMG_WIDTH), mode='constant', preserve_range=True)\n",
        "  #X_train_list.append(img)\n",
        "  X_train[n1] = img1\n",
        "\n",
        "def imgtoYtrainarr(image_path, image_name, n2):\n",
        "\n",
        "  img2 = cv2.imread(image_path)[:,:,:1]\n",
        "  #img2[:,:,1]=0\n",
        "  #img2[:,:,0]=0\n",
        "  #img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)\n",
        "  #img = cv2.resize(img, (IMG_HEIGHT, IMG_WIDTH), interpolation = cv2.INTER_CUBIC)\n",
        "  img2 = resize(img2, (IMG_HEIGHT, IMG_WIDTH), mode='constant', preserve_range=True)\n",
        "  #Y_train_list.append(img)\n",
        "  Y_train[n2] = img2\n",
        "\n",
        "def imgtoXtestarr(image_path, image_name, n3):\n",
        "\n",
        "  img3 = cv2.imread(image_path)\n",
        "  #img3 = cv2.cvtColor(img3, cv2.COLOR_BGR2GRAY)\n",
        "  #img = cv2.resize(img, (IMG_HEIGHT, IMG_WIDTH), interpolation = cv2.INTER_CUBIC)\n",
        "  img3 = resize(img3, (IMG_HEIGHT, IMG_WIDTH), mode='constant', preserve_range=True)\n",
        "  #X_test_list.append(img)\n",
        "  X_test[n3] = img3\n",
        "\n",
        "nm = 0\n",
        "n1 = 0\n",
        "n2 = 0\n",
        "n3 = 0\n",
        "\n",
        "for files1 in sorted(os.listdir(dir_train_path)):\n",
        "\n",
        "  print(files1)\n",
        "  #imgtoXtrainarr(dir_train_path+'/'+files1, files1, n1)\n",
        "  #n1 = n1 + 1\n",
        "  #nm = nm + 1\n",
        "\n",
        "#for files2 in os.listdir(dir_train_labels_path):\n",
        "\n",
        "  #imgtoYtrainarr(dir_train_labels_path+'/'+files2, files2, n2)\n",
        "  #n2 = n2 + 1\n",
        "\n",
        "#for files3 in os.listdir(dir_test_path):\n",
        "\n",
        "  #imgtoXtestarr(dir_test_path+'/'+files3, files3, n3)\n",
        "  #n3 = n3 + 1"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(10).jpg\n",
            "(100).jpg\n",
            "(101).jpg\n",
            "(102).jpg\n",
            "(103).jpg\n",
            "(104).jpg\n",
            "(105).jpg\n",
            "(106).jpg\n",
            "(107).jpg\n",
            "(108).jpg\n",
            "(109).jpg\n",
            "(11).jpg\n",
            "(110).jpg\n",
            "(111).jpg\n",
            "(112).jpg\n",
            "(113).jpg\n",
            "(114).jpg\n",
            "(115).jpg\n",
            "(116).jpg\n",
            "(117).jpg\n",
            "(118).jpg\n",
            "(119).jpg\n",
            "(12).jpg\n",
            "(120).jpg\n",
            "(121).jpg\n",
            "(122).jpg\n",
            "(123).jpg\n",
            "(124).jpg\n",
            "(125).jpg\n",
            "(126).jpg\n",
            "(127).jpg\n",
            "(128).jpg\n",
            "(129).jpg\n",
            "(13) (copy).jpg\n",
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          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pNOPn-8IBk9R",
        "outputId": "2a0b5904-999a-46dd-df2c-405770b8e1cb"
      },
      "source": [
        "import tensorflow as tf\n",
        "import os\n",
        "import random\n",
        "import numpy as np\n",
        "import cv2\n",
        "\n",
        "from tqdm import tqdm \n",
        "\n",
        "from skimage.io import imread, imshow\n",
        "from skimage.transform import resize\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "seed = 42\n",
        "np.random.seed = seed\n",
        "\n",
        "IMG_WIDTH = 128\n",
        "IMG_HEIGHT = 128\n",
        "IMG_CHANNELS = 3\n",
        "\n",
        "directory = '/content/drive/My Drive/retpy'\n",
        "\n",
        "dir_train_path = directory + '/train_data'\n",
        "dir_test_path = directory + '/test_data'\n",
        "dir_train_labels_path = directory + '/train_labels' \n",
        "#dir_test_labels_path = directory + '/test_labels'\n",
        "\n",
        "#train_ids = next(os.walk(TRAIN_PATH))[1]\n",
        "#test_ids = next(os.walk(TEST_PATH))[1]\n",
        "\n",
        "X_train = np.zeros((443, IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS), dtype=np.uint8)\n",
        "Y_train = np.zeros((443, IMG_HEIGHT, IMG_WIDTH, 1), dtype=np.bool)\n",
        "\n",
        "X_test = np.zeros((77, IMG_HEIGHT, IMG_WIDTH, IMG_CHANNELS), dtype=np.uint8)\n",
        "\n",
        "#X_train_list = []\n",
        "#Y_train_list = []\n",
        "#X_test_list = []\n",
        "\n",
        "def imgtoXtrainarr(image_path, image_name, n1):\n",
        "\n",
        "  img1 = cv2.imread(image_path)\n",
        "  #img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)\n",
        "  #img = cv2.resize(img, (IMG_HEIGHT, IMG_WIDTH), interpolation = cv2.INTER_CUBIC)\n",
        "  img1 = resize(img1, (IMG_HEIGHT, IMG_WIDTH), mode='constant', preserve_range=True)\n",
        "  #X_train_list.append(img)\n",
        "  X_train[n1] = img1\n",
        "\n",
        "def imgtoYtrainarr(image_path, image_name, n2):\n",
        "\n",
        "  img2 = cv2.imread(image_path)[:,:,:1]\n",
        "  #img2[:,:,1]=0\n",
        "  #img2[:,:,0]=0\n",
        "  #img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)\n",
        "  #img = cv2.resize(img, (IMG_HEIGHT, IMG_WIDTH), interpolation = cv2.INTER_CUBIC)\n",
        "  img2 = resize(img2, (IMG_HEIGHT, IMG_WIDTH), mode='constant', preserve_range=True)\n",
        "  #Y_train_list.append(img)\n",
        "  Y_train[n2] = img2\n",
        "\n",
        "def imgtoXtestarr(image_path, image_name, n3):\n",
        "\n",
        "  img3 = cv2.imread(image_path)\n",
        "  #img3 = cv2.cvtColor(img3, cv2.COLOR_BGR2GRAY)\n",
        "  #img = cv2.resize(img, (IMG_HEIGHT, IMG_WIDTH), interpolation = cv2.INTER_CUBIC)\n",
        "  img3 = resize(img3, (IMG_HEIGHT, IMG_WIDTH), mode='constant', preserve_range=True)\n",
        "  #X_test_list.append(img)\n",
        "  X_test[n3] = img3\n",
        "\n",
        "nm = 0\n",
        "n1 = 0\n",
        "n2 = 0\n",
        "n3 = 0\n",
        "\n",
        "#for files1 in os.listdir(dir_train_path):\n",
        "\n",
        "  #imgtoXtrainarr(dir_train_path+'/'+files1, files1, n1)\n",
        "  #n1 = n1 + 1\n",
        "  #nm = nm + 1\n",
        "\n",
        "for files2 in sorted(os.listdir(dir_train_labels_path)):\n",
        "\n",
        "  print(files2)\n",
        "  #imgtoYtrainarr(dir_train_labels_path+'/'+files2, files2, n2)\n",
        "  #n2 = n2 + 1\n",
        "\n",
        "#for files3 in os.listdir(dir_test_path):\n",
        "\n",
        "  #imgtoXtestarr(dir_test_path+'/'+files3, files3, n3)\n",
        "  #n3 = n3 + 1"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
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