Boundary_Detection.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Boundary_Detection.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "FzmLMZ3pxtLC"
      },
      "source": [
        "# Install the PyDrive wrapper & import libraries.\n",
        "# This only needs to be done once per notebook.\n",
        "!pip install -U -q PyDrive\n",
        "\n",
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "\n",
        "# Authenticate and create the PyDrive client.\n",
        "# This only needs to be done once per notebook.\n",
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oqOolg1tzric",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cd346a58-e9da-491b-eb81-794b251d6cd3"
      },
      "source": [
        "#for download the dataset\n",
        "#Fill the ... with the id of dataset.zip in your google drive\n",
        "\n",
        "download = drive.CreateFile({'id': '1FlNvqpmzP9G_qqv1boaCHmtAmwn8KCvm'})\n",
        "download.GetContentFile('rotated_3100.zip')\n",
        "\n",
        "# unzip your file\n",
        "! unzip rotated_3100.zip"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XVT8Qvg00r0z"
      },
      "source": [
        "# Run it to obtain reproducible results across machines (from keras.io)\n",
        "from keras import backend as K\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import os, glob\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import keras\n",
        "from tensorflow.keras.preprocessing import image as kImage\n",
        "from tensorflow.keras.models import Model, load_model\n",
        "from tensorflow.keras.layers import Input\n",
        "from keras.applications.vgg16 import preprocess_input\n",
        "from keras.preprocessing.image import load_img, img_to_array\n",
        "\n",
        "#session_conf = tf.compat.v1.ConfigProto(intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)\n",
        "\n",
        "#sess = tf.compat.v1.Session(graph=tf.compat.v1.get_default_graph(), config=session_conf)\n",
        "#tf.compat.v1.keras.backend.set_session(sess)\n",
        "#K.set_session(sess)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ynm7vlRPqStR"
      },
      "source": [
        "\"\"\"\r\n",
        "Just for uploading a trained model - When there is no need to train a new model.\r\n",
        "Get the shareable id same as the way of dataset.zip.\r\n",
        "\"\"\"\r\n",
        "download = drive.CreateFile({'id': '...'})\r\n",
        "download.GetContentFile('trained_model.h5')\r\n",
        "\r\n",
        "model = load_model('trained_model.h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u7NjO0Ne0zNM"
      },
      "source": [
        "# load data function\n",
        "\n",
        "def getData(dataset_dir):\n",
        "    X_list= sorted(glob.glob(os.path.join(dataset_dir, 'x','*.jpg')))\n",
        "    Y_list = sorted(glob.glob(os.path.join(dataset_dir, 'y' ,'*.png')))\n",
        "    \n",
        "    X= []\n",
        "    Y= []\n",
        "    for i in range(len(X_list)):\n",
        "        # Load input image\n",
        "        x = kImage.load_img(X_list[i])\n",
        "        x = kImage.img_to_array(x)\n",
        "        x /= 255.0\n",
        "        X.append(x)\n",
        "        \n",
        "        # Load ground-truth label and encode it to label 0 and 1\n",
        "        y = kImage.load_img(Y_list[i], grayscale = True)\n",
        "        y = kImage.img_to_array(y)\n",
        "        y /= 255.0\n",
        "        y = np.floor(y)\n",
        "        Y.append(y)\n",
        "    X = np.asarray(X)\n",
        "    Y = np.asarray(Y)\n",
        "    \n",
        "    # Shuffle the training data\n",
        "    idx = list(range(X.shape[0]))\n",
        "    np.random.shuffle(idx)\n",
        "    X = X[idx]\n",
        "    Y = Y[idx]\n",
        "    return X, Y"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YuZclwog036c"
      },
      "source": [
        "\"\"\"\n",
        "Structure of the enocder-decoder network\n",
        "This network is used for supervised image segmentation tasks.\n",
        "Here, we used 17 layers of VGG16 as an encoder for feature extraction.\n",
        "\"\"\"\n",
        "\n",
        "def initModel():\n",
        "    ### Encoder\n",
        "    net_input = Input(shape=(240,320,3))\n",
        "    vgg16 = tf.compat.v1.keras.applications.vgg16.VGG16(include_top=False, weights='imagenet', input_tensor=net_input)\n",
        "\n",
        "    for layer in vgg16.layers[:17]:\n",
        "        layer.trainable = False\n",
        "\n",
        "    x = vgg16.layers[-2].output # output of the ENCODER: 2nd layer from the last, < block5_conv3 >\n",
        "    \n",
        "    ### Decoder\n",
        "    x = tf.keras.layers.Conv2DTranspose(256, (3,3), strides=(2,2), activation='relu', padding='same')(x)\n",
        "    x = tf.keras.layers.Conv2DTranspose(128, (3,3), strides=(2,2), activation='relu', padding='same')(x)\n",
        "    x = tf.keras.layers.Conv2DTranspose(64, (3,3), strides=(2,2), activation='relu', padding='same')(x)\n",
        "    x = tf.keras.layers.Conv2DTranspose(32, (3,3), strides=(2,2), activation='relu', padding='same')(x)\n",
        "    x = tf.keras.layers.Conv2DTranspose(1, (1,1), activation='sigmoid', padding='same')(x)\n",
        "    \n",
        "    model = Model(inputs = vgg16.input, outputs = x)\n",
        "    model.compile(loss = tf.keras.losses.binary_crossentropy, optimizer = tf.keras.optimizers.RMSprop(lr=5e-4), metrics=['accuracy'])\n",
        "    model.summary()\n",
        "\n",
        "    return model"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bkct-iqx7ycw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eccb237a-d04a-4662-c2d5-925c6a811cf6"
      },
      "source": [
        "model = initModel()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_6\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_7 (InputLayer)         [(None, 240, 320, 3)]     0         \n",
            "_________________________________________________________________\n",
            "block1_conv1 (Conv2D)        (None, 240, 320, 64)      1792      \n",
            "_________________________________________________________________\n",
            "block1_conv2 (Conv2D)        (None, 240, 320, 64)      36928     \n",
            "_________________________________________________________________\n",
            "block1_pool (MaxPooling2D)   (None, 120, 160, 64)      0         \n",
            "_________________________________________________________________\n",
            "block2_conv1 (Conv2D)        (None, 120, 160, 128)     73856     \n",
            "_________________________________________________________________\n",
            "block2_conv2 (Conv2D)        (None, 120, 160, 128)     147584    \n",
            "_________________________________________________________________\n",
            "block2_pool (MaxPooling2D)   (None, 60, 80, 128)       0         \n",
            "_________________________________________________________________\n",
            "block3_conv1 (Conv2D)        (None, 60, 80, 256)       295168    \n",
            "_________________________________________________________________\n",
            "block3_conv2 (Conv2D)        (None, 60, 80, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv3 (Conv2D)        (None, 60, 80, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_pool (MaxPooling2D)   (None, 30, 40, 256)       0         \n",
            "_________________________________________________________________\n",
            "block4_conv1 (Conv2D)        (None, 30, 40, 512)       1180160   \n",
            "_________________________________________________________________\n",
            "block4_conv2 (Conv2D)        (None, 30, 40, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv3 (Conv2D)        (None, 30, 40, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_pool (MaxPooling2D)   (None, 15, 20, 512)       0         \n",
            "_________________________________________________________________\n",
            "block5_conv1 (Conv2D)        (None, 15, 20, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv2 (Conv2D)        (None, 15, 20, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv3 (Conv2D)        (None, 15, 20, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "conv2d_transpose_30 (Conv2DT (None, 30, 40, 256)       1179904   \n",
            "_________________________________________________________________\n",
            "conv2d_transpose_31 (Conv2DT (None, 60, 80, 128)       295040    \n",
            "_________________________________________________________________\n",
            "conv2d_transpose_32 (Conv2DT (None, 120, 160, 64)      73792     \n",
            "_________________________________________________________________\n",
            "conv2d_transpose_33 (Conv2DT (None, 240, 320, 32)      18464     \n",
            "_________________________________________________________________\n",
            "conv2d_transpose_34 (Conv2DT (None, 240, 320, 1)       33        \n",
            "=================================================================\n",
            "Total params: 16,281,921\n",
            "Trainable params: 3,927,041\n",
            "Non-trainable params: 12,354,880\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eVq-zKEw1Fg5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "05b99bb9-6103-405d-f82f-5736b5eb18c0"
      },
      "source": [
        "# load train data\n",
        "dataset_path = os.path.join('rotated_3100/train')\n",
        "X, Y = getData(dataset_path)\n",
        "\n",
        "# init the model\n",
        "model = initModel()\n",
        "\n",
        "early = tf.compat.v1.keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=1e-4, patience=10)\n",
        "reduce = tf.compat.v1.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=5)\n",
        "\n",
        "#training process\n",
        "history = model.fit(X, Y, batch_size=32, epochs=10, verbose=2, validation_split=0.2, callbacks=[reduce, early], shuffle=True)\n",
        "model.save('trained_model.h5')"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/utils.py:107: UserWarning: grayscale is deprecated. Please use color_mode = \"grayscale\"\n",
            "  warnings.warn('grayscale is deprecated. Please use '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_2 (InputLayer)         [(None, 240, 320, 3)]     0         \n",
            "_________________________________________________________________\n",
            "block1_conv1 (Conv2D)        (None, 240, 320, 64)      1792      \n",
            "_________________________________________________________________\n",
            "block1_conv2 (Conv2D)        (None, 240, 320, 64)      36928     \n",
            "_________________________________________________________________\n",
            "block1_pool (MaxPooling2D)   (None, 120, 160, 64)      0         \n",
            "_________________________________________________________________\n",
            "block2_conv1 (Conv2D)        (None, 120, 160, 128)     73856     \n",
            "_________________________________________________________________\n",
            "block2_conv2 (Conv2D)        (None, 120, 160, 128)     147584    \n",
            "_________________________________________________________________\n",
            "block2_pool (MaxPooling2D)   (None, 60, 80, 128)       0         \n",
            "_________________________________________________________________\n",
            "block3_conv1 (Conv2D)        (None, 60, 80, 256)       295168    \n",
            "_________________________________________________________________\n",
            "block3_conv2 (Conv2D)        (None, 60, 80, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv3 (Conv2D)        (None, 60, 80, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_pool (MaxPooling2D)   (None, 30, 40, 256)       0         \n",
            "_________________________________________________________________\n",
            "block4_conv1 (Conv2D)        (None, 30, 40, 512)       1180160   \n",
            "_________________________________________________________________\n",
            "block4_conv2 (Conv2D)        (None, 30, 40, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv3 (Conv2D)        (None, 30, 40, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_pool (MaxPooling2D)   (None, 15, 20, 512)       0         \n",
            "_________________________________________________________________\n",
            "block5_conv1 (Conv2D)        (None, 15, 20, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv2 (Conv2D)        (None, 15, 20, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv3 (Conv2D)        (None, 15, 20, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "conv2d_transpose_5 (Conv2DTr (None, 30, 40, 256)       1179904   \n",
            "_________________________________________________________________\n",
            "conv2d_transpose_6 (Conv2DTr (None, 60, 80, 128)       295040    \n",
            "_________________________________________________________________\n",
            "conv2d_transpose_7 (Conv2DTr (None, 120, 160, 64)      73792     \n",
            "_________________________________________________________________\n",
            "conv2d_transpose_8 (Conv2DTr (None, 240, 320, 32)      18464     \n",
            "_________________________________________________________________\n",
            "conv2d_transpose_9 (Conv2DTr (None, 240, 320, 1)       33        \n",
            "=================================================================\n",
            "Total params: 16,281,921\n",
            "Trainable params: 8,646,657\n",
            "Non-trainable params: 7,635,264\n",
            "_________________________________________________________________\n",
            "Epoch 1/10\n",
            "75/75 - 42s - loss: 0.4737 - accuracy: 0.7469 - val_loss: 0.3488 - val_accuracy: 0.8378\n",
            "Epoch 2/10\n",
            "75/75 - 30s - loss: 0.3377 - accuracy: 0.8810 - val_loss: 0.2233 - val_accuracy: 0.9172\n",
            "Epoch 3/10\n",
            "75/75 - 31s - loss: 0.1925 - accuracy: 0.9259 - val_loss: 0.1649 - val_accuracy: 0.9324\n",
            "Epoch 4/10\n",
            "75/75 - 31s - loss: 0.1472 - accuracy: 0.9426 - val_loss: 0.1208 - val_accuracy: 0.9522\n",
            "Epoch 5/10\n",
            "75/75 - 32s - loss: 0.1249 - accuracy: 0.9511 - val_loss: 0.1058 - val_accuracy: 0.9571\n",
            "Epoch 6/10\n",
            "75/75 - 32s - loss: 0.1058 - accuracy: 0.9581 - val_loss: 0.1031 - val_accuracy: 0.9581\n",
            "Epoch 7/10\n",
            "75/75 - 33s - loss: 0.0955 - accuracy: 0.9616 - val_loss: 0.0963 - val_accuracy: 0.9610\n",
            "Epoch 8/10\n",
            "75/75 - 34s - loss: 0.0812 - accuracy: 0.9666 - val_loss: 0.1069 - val_accuracy: 0.9609\n",
            "Epoch 9/10\n",
            "75/75 - 33s - loss: 0.0771 - accuracy: 0.9684 - val_loss: 0.0839 - val_accuracy: 0.9660\n",
            "Epoch 10/10\n",
            "75/75 - 33s - loss: 0.0688 - accuracy: 0.9714 - val_loss: 0.0961 - val_accuracy: 0.9654\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QK0o3JYyYoSi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e2a63469-c137-4b16-8f69-84af85b60ba3"
      },
      "source": [
        "dataset_path = os.path.join('rotated_3100/test')\n",
        "X_test, y_test = getData(dataset_path)\n",
        "# predict\n",
        "prediction = model.predict(X_test, verbose=2, batch_size=1)\n"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/utils.py:107: UserWarning: grayscale is deprecated. Please use color_mode = \"grayscale\"\n",
            "  warnings.warn('grayscale is deprecated. Please use '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "110/110 - 1s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tv6W6tlyYvma",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "outputId": "9bc039df-50ab-4ba8-abc7-781d2c70ccaf"
      },
      "source": [
        "\n",
        "plt.rcParams['figure.figsize'] = (12.0, 9.0)\n",
        "\n",
        "idx = np.random.randint(0, X_test.shape[0])\n",
        "\n",
        "img = np.empty(2, dtype=object)\n",
        "img[0] = X_test[idx] * 255\n",
        "img[1] = prediction[idx].reshape(prediction[idx].shape[0], prediction[idx].shape[1])\n",
        "\n",
        "img[1] *= 255\n",
        "for row in range(img[1].shape[1]):\n",
        "    for column in range(img[1].shape[0]):\n",
        "        img[1][column][row] = 255 if img[1][column][row] >= 125 else 0\n",
        "\n",
        "title = ['input image', 'predicted image']\n",
        "\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.imshow(img[0].astype('uint8'))\n",
        "plt.title(title[0])\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.imshow(img[1], cmap='gray')\n",
        "plt.title(title[1])\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2XXAuUi97r5Q"
      },
      "source": [
        "from google.colab import files\n",
        "files.download(\"trained_model.h5\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YgNiU-X3uM0G"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}