semantic_Segmentation_with_UNET.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "semantic Segmentation with UNET.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyMmc8SR0KE0wHeTHB1UGVMU",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/anshupandey/Computer-Vision/blob/master/semantic_Segmentation_with_UNET.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bPcT96f8cV1s"
      },
      "source": [
        "# Dataset Preparation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "niY59w9Tb-uv"
      },
      "source": [
        "!wget -q https://www.dropbox.com/s/ghvpyv3hbw67cpt/camvid.tar"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PTZjycv0cbht"
      },
      "source": [
        "!wget -q http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/data/label_colors.txt"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ThaDb-w0cc4g",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9832548f-8244-4ed6-ed99-80faff50634d"
      },
      "source": [
        "!tar -xvf camvid.tar  > /dev/null; echo \" done.\""
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-vXUsyPKcfVN"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras.preprocessing import image\n",
        "import os\n",
        "import cv2"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lRcgopTWe4fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "f4012f56-7552-48df-aaca-c360ec480ddb"
      },
      "source": [
        "mask = plt.imread(\"/content/camvid/labels/0001TP_006690_P.png\")\n",
        "plt.imshow(mask)\n",
        "plt.show()"
      ],
      "execution_count": 5,
      "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7dd6KRp4m8rR"
      },
      "source": [
        ""
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A2XCQ2IBfIf0"
      },
      "source": [
        "# Modelling the UNET"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4bPizd5ffKQZ"
      },
      "source": [
        "from tensorflow.keras import models,layers"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RS8ObFEkfOCY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9761cb2e-23aa-4e5b-b00d-48431f41fdae"
      },
      "source": [
        "input_layer = layers.Input(shape=(128,128,3),name=\"image_input\")\n",
        "\n",
        "# Encoder Segment\n",
        "#block1\n",
        "x = layers.Conv2D(filters=64,kernel_size=(3,3),padding='same')(input_layer)\n",
        "x = layers.BatchNormalization()(x)\n",
        "x = layers.Activation(activation='relu')(x)\n",
        "x = layers.Conv2D(filters=64,kernel_size=(3,3),padding='same')(x)\n",
        "x = layers.BatchNormalization()(x)\n",
        "block1 = layers.Activation(activation='relu')(x)\n",
        "\n",
        "#block2\n",
        "x = layers.MaxPooling2D(pool_size=(2,2))(block1)\n",
        "x = layers.Conv2D(filters=128,kernel_size=(3,3),padding='same')(x)\n",
        "x = layers.BatchNormalization()(x)\n",
        "x = layers.Activation(activation='relu')(x)\n",
        "x = layers.Conv2D(filters=128,kernel_size=(3,3),padding='same')(x)\n",
        "x = layers.BatchNormalization()(x)\n",
        "block2 = layers.Activation(activation='relu')(x)\n",
        "\n",
        "#block3\n",
        "x = layers.MaxPooling2D(pool_size=(2,2))(block2)\n",
        "x = layers.Conv2D(filters=256,kernel_size=(3,3),padding='same')(x)\n",
        "x = layers.BatchNormalization()(x)\n",
        "x = layers.Activation(activation='relu')(x)\n",
        "x = layers.Conv2D(filters=256,kernel_size=(3,3),padding='same')(x)\n",
        "x = layers.BatchNormalization()(x)\n",
        "block3 = layers.Activation(activation='relu')(x)\n",
        "\n",
        "#block4\n",
        "x = layers.MaxPooling2D(pool_size=(2,2))(block3)\n",
        "x = layers.Conv2D(filters=512,kernel_size=(3,3),padding='same')(x)\n",
        "x = layers.BatchNormalization()(x)\n",
        "x = layers.Activation(activation='relu')(x)\n",
        "x = layers.Conv2D(filters=512,kernel_size=(3,3),padding='same')(x)\n",
        "x = layers.BatchNormalization()(x)\n",
        "block4 = layers.Activation(activation='relu')(x)\n",
        "block4 = layers.Dropout(0.5)(block4)\n",
        "\n",
        "## bottleneck\n",
        "x = layers.MaxPooling2D(pool_size=(2,2))(block4)\n",
        "x = layers.Conv2D(filters=1024,kernel_size=(3,3),padding='same')(x)\n",
        "x = layers.BatchNormalization()(x)\n",
        "x = layers.Activation(activation='relu')(x)\n",
        "x = layers.Conv2D(filters=1024,kernel_size=(3,3),padding='same')(x)\n",
        "x = layers.BatchNormalization()(x)\n",
        "x = layers.Activation(activation='relu')(x)\n",
        "bottleneck = layers.Dropout(0.5)(x)\n",
        "\n",
        "## Decoder segment\n",
        "\n",
        "#block5\n",
        "x = layers.Conv2DTranspose(filters=512,kernel_size=(3,3),padding='same',strides=(2,2))(bottleneck)\n",
        "x = layers.concatenate([x,block4],axis=3)\n",
        "x = layers.Conv2D(filters=512,kernel_size=(3,3),padding='same')(x)\n",
        "x = layers.BatchNormalization()(x)\n",
        "x = layers.Activation(activation='relu')(x)\n",
        "x = layers.Conv2D(filters=512,kernel_size=(3,3),padding='same')(x)\n",
        "x = layers.BatchNormalization()(x)\n",
        "block5 = layers.Activation(activation='relu')(x)\n",
        "block5 = layers.Dropout(0.5)(block5)\n",
        "\n",
        "#block6\n",
        "x = layers.Conv2DTranspose(filters=256,kernel_size=(3,3),padding='same',strides=(2,2))(block5)\n",
        "x = layers.concatenate([x,block3],axis=3)\n",
        "x = layers.Conv2D(filters=256,kernel_size=(3,3),padding='same')(x)\n",
        "x = layers.BatchNormalization()(x)\n",
        "x = layers.Activation(activation='relu')(x)\n",
        "x = layers.Conv2D(filters=256,kernel_size=(3,3),padding='same')(x)\n",
        "x = layers.BatchNormalization()(x)\n",
        "block6 = layers.Activation(activation='relu')(x)\n",
        "\n",
        "#block7\n",
        "x = layers.Conv2DTranspose(filters=128,kernel_size=(3,3),padding='same',strides=(2,2))(block6)\n",
        "x = layers.concatenate([x,block2],axis=3)\n",
        "x = layers.Conv2D(filters=128,kernel_size=(3,3),padding='same')(x)\n",
        "x = layers.BatchNormalization()(x)\n",
        "x = layers.Activation(activation='relu')(x)\n",
        "x = layers.Conv2D(filters=128,kernel_size=(3,3),padding='same')(x)\n",
        "x = layers.BatchNormalization()(x)\n",
        "block7 = layers.Activation(activation='relu')(x)\n",
        "\n",
        "#block8\n",
        "x = layers.Conv2DTranspose(filters=64,kernel_size=(3,3),padding='same',strides=(2,2))(block7)\n",
        "x = layers.concatenate([x,block1],axis=3)\n",
        "x = layers.Conv2D(filters=64,kernel_size=(3,3),padding='same')(x)\n",
        "x = layers.BatchNormalization()(x)\n",
        "x = layers.Activation(activation='relu')(x)\n",
        "x = layers.Conv2D(filters=64,kernel_size=(3,3),padding='same')(x)\n",
        "x = layers.BatchNormalization()(x)\n",
        "block8 = layers.Activation(activation='relu')(x)\n",
        "\n",
        "# outputlayer\n",
        "output_layer = layers.Conv2D(filters=32,kernel_size=(1,1),activation='softmax')(block8)\n",
        "\n",
        "model = models.Model(inputs=input_layer,outputs=output_layer)\n",
        "model.summary()\n"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "image_input (InputLayer)        [(None, 128, 128, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d (Conv2D)                 (None, 128, 128, 64) 1792        image_input[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization (BatchNorma (None, 128, 128, 64) 256         conv2d[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "activation (Activation)         (None, 128, 128, 64) 0           batch_normalization[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_1 (Conv2D)               (None, 128, 128, 64) 36928       activation[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_1 (BatchNor (None, 128, 128, 64) 256         conv2d_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_1 (Activation)       (None, 128, 128, 64) 0           batch_normalization_1[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D)    (None, 64, 64, 64)   0           activation_1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_2 (Conv2D)               (None, 64, 64, 128)  73856       max_pooling2d[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_2 (BatchNor (None, 64, 64, 128)  512         conv2d_2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_2 (Activation)       (None, 64, 64, 128)  0           batch_normalization_2[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_3 (Conv2D)               (None, 64, 64, 128)  147584      activation_2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_3 (BatchNor (None, 64, 64, 128)  512         conv2d_3[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_3 (Activation)       (None, 64, 64, 128)  0           batch_normalization_3[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2D)  (None, 32, 32, 128)  0           activation_3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_4 (Conv2D)               (None, 32, 32, 256)  295168      max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_4 (BatchNor (None, 32, 32, 256)  1024        conv2d_4[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_4 (Activation)       (None, 32, 32, 256)  0           batch_normalization_4[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_5 (Conv2D)               (None, 32, 32, 256)  590080      activation_4[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_5 (BatchNor (None, 32, 32, 256)  1024        conv2d_5[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_5 (Activation)       (None, 32, 32, 256)  0           batch_normalization_5[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2D)  (None, 16, 16, 256)  0           activation_5[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_6 (Conv2D)               (None, 16, 16, 512)  1180160     max_pooling2d_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_6 (BatchNor (None, 16, 16, 512)  2048        conv2d_6[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_6 (Activation)       (None, 16, 16, 512)  0           batch_normalization_6[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_7 (Conv2D)               (None, 16, 16, 512)  2359808     activation_6[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_7 (BatchNor (None, 16, 16, 512)  2048        conv2d_7[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_7 (Activation)       (None, 16, 16, 512)  0           batch_normalization_7[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "dropout (Dropout)               (None, 16, 16, 512)  0           activation_7[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2D)  (None, 8, 8, 512)    0           dropout[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_8 (Conv2D)               (None, 8, 8, 1024)   4719616     max_pooling2d_3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_8 (BatchNor (None, 8, 8, 1024)   4096        conv2d_8[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_8 (Activation)       (None, 8, 8, 1024)   0           batch_normalization_8[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_9 (Conv2D)               (None, 8, 8, 1024)   9438208     activation_8[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_9 (BatchNor (None, 8, 8, 1024)   4096        conv2d_9[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_9 (Activation)       (None, 8, 8, 1024)   0           batch_normalization_9[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "dropout_1 (Dropout)             (None, 8, 8, 1024)   0           activation_9[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_transpose (Conv2DTranspo (None, 16, 16, 512)  4719104     dropout_1[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate (Concatenate)       (None, 16, 16, 1024) 0           conv2d_transpose[0][0]           \n",
            "                                                                 dropout[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_10 (Conv2D)              (None, 16, 16, 512)  4719104     concatenate[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_10 (BatchNo (None, 16, 16, 512)  2048        conv2d_10[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_10 (Activation)      (None, 16, 16, 512)  0           batch_normalization_10[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_11 (Conv2D)              (None, 16, 16, 512)  2359808     activation_10[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_11 (BatchNo (None, 16, 16, 512)  2048        conv2d_11[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_11 (Activation)      (None, 16, 16, 512)  0           batch_normalization_11[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "dropout_2 (Dropout)             (None, 16, 16, 512)  0           activation_11[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_transpose_1 (Conv2DTrans (None, 32, 32, 256)  1179904     dropout_2[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_1 (Concatenate)     (None, 32, 32, 512)  0           conv2d_transpose_1[0][0]         \n",
            "                                                                 activation_5[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_12 (Conv2D)              (None, 32, 32, 256)  1179904     concatenate_1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_12 (BatchNo (None, 32, 32, 256)  1024        conv2d_12[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_12 (Activation)      (None, 32, 32, 256)  0           batch_normalization_12[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_13 (Conv2D)              (None, 32, 32, 256)  590080      activation_12[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_13 (BatchNo (None, 32, 32, 256)  1024        conv2d_13[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_13 (Activation)      (None, 32, 32, 256)  0           batch_normalization_13[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_transpose_2 (Conv2DTrans (None, 64, 64, 128)  295040      activation_13[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_2 (Concatenate)     (None, 64, 64, 256)  0           conv2d_transpose_2[0][0]         \n",
            "                                                                 activation_3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_14 (Conv2D)              (None, 64, 64, 128)  295040      concatenate_2[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_14 (BatchNo (None, 64, 64, 128)  512         conv2d_14[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_14 (Activation)      (None, 64, 64, 128)  0           batch_normalization_14[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_15 (Conv2D)              (None, 64, 64, 128)  147584      activation_14[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_15 (BatchNo (None, 64, 64, 128)  512         conv2d_15[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_15 (Activation)      (None, 64, 64, 128)  0           batch_normalization_15[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_transpose_3 (Conv2DTrans (None, 128, 128, 64) 73792       activation_15[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_3 (Concatenate)     (None, 128, 128, 128 0           conv2d_transpose_3[0][0]         \n",
            "                                                                 activation_1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_16 (Conv2D)              (None, 128, 128, 64) 73792       concatenate_3[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_16 (BatchNo (None, 128, 128, 64) 256         conv2d_16[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_16 (Activation)      (None, 128, 128, 64) 0           batch_normalization_16[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_17 (Conv2D)              (None, 128, 128, 64) 36928       activation_16[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_17 (BatchNo (None, 128, 128, 64) 256         conv2d_17[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_17 (Activation)      (None, 128, 128, 64) 0           batch_normalization_17[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_18 (Conv2D)              (None, 128, 128, 32) 2080        activation_17[0][0]              \n",
            "==================================================================================================\n",
            "Total params: 34,538,912\n",
            "Trainable params: 34,527,136\n",
            "Non-trainable params: 11,776\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pi813Q16jXMQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a9a4fc27-299d-47e0-9865-932ec2ee1a16"
      },
      "source": [
        "from tensorflow.keras.utils import plot_model\n",
        "plot_model(model)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fykWr7aPjdSN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0ed99168-da1d-46b1-b207-8886c7fea484"
      },
      "source": [
        "img_dir = \"/content/camvid/images\"\n",
        "mask_dir = \"/content/camvid/labels\"\n",
        "img_list = os.listdir(img_dir)\n",
        "mask_list = os.listdir(mask_dir)\n",
        "print(len(mask_list))\n",
        "print(len(img_list))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "701\n",
            "702\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y-qvm841k7LF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c3053979-96a4-41e8-a173-bfb6bf0d8c48"
      },
      "source": [
        "os.remove(img_dir+\"/test.txt\")\n",
        "img_list = os.listdir(img_dir)\n",
        "mask_list = os.listdir(mask_dir)\n",
        "print(len(mask_list))\n",
        "print(len(img_list))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "701\n",
            "701\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ipEID0ikp7Qy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a7b16781-74f5-402a-f712-344a473e3ef1"
      },
      "source": [
        "img_list = os.listdir(img_dir)\n",
        "mask_list = os.listdir(mask_dir)\n",
        "print(len(mask_list))\n",
        "print(len(img_list))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "701\n",
            "701\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q3p2ZaiLmC8U"
      },
      "source": [
        "# Custom image Data Generator"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E0aYov5VlMMW"
      },
      "source": [
        "from tensorflow.keras.utils import Sequence\n",
        "\n",
        "samples = len(mask_list)\n",
        "batch_size=8\n",
        "class custom_image_data_generator(Sequence):\n",
        "  def __init__(self,xd,yd,batch_size):\n",
        "    self.x,self.y = xd,yd\n",
        "    self.batch_size = batch_size\n",
        "    \n",
        "  def __len__(self):\n",
        "    return int(np.ceil(len(self.x) / float(self.batch_size)))\n",
        "\n",
        "  def __getitem__(self, idx):\n",
        "        idx = np.random.randint(0,samples,batch_size)    \n",
        "        batchx,batchy = [],[]\n",
        "        for i in idx:\n",
        "          img = image.load_img(img_dir+\"/\"+img_list[i],target_size=(128,128))\n",
        "          img = image.img_to_array(img)/255\n",
        "          mask = image.load_img(mask_dir+\"/\"+img_list[i][:-4]+\"_P.png\",target_size=(128,128),grayscale=True)\n",
        "          mask = image.img_to_array(mask)\n",
        "          batchx.append(img)\n",
        "          batchy.append(mask)\n",
        "        return np.array(batchx),np.array(batchy)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OEpiccavmxcP"
      },
      "source": [
        "model.compile(optimizer='adam',loss=\"sparse_categorical_crossentropy\",\n",
        "              metrics=['sparse_categorical_accuracy'])\n",
        "train_generator = custom_image_data_generator(img_list,mask_list,batch_size=8)\n",
        "model.fit_generator(train_generator,epochs=10,verbose=True )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J-I3LCMA9x3u"
      },
      "source": [
        "# Making Predictions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XXjCVzPAm3Q-"
      },
      "source": [
        "!wget -q https://raw.githubusercontent.com/anshupandey/Computer-Vision/master/Image%20Segmentation/test_images.zip"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KNvkkeVrT5hj"
      },
      "source": [
        "!unzip test_images.zip"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oj_w4lezT54g"
      },
      "source": [
        "\n",
        "test_dir = 'test_images/'\n",
        "image_list = os.listdir(test_dir)\n",
        "print(f'{len(image_list)} frames found')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g1dNwbmUdMCK"
      },
      "source": [
        "## Preparing labels and colors"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "piML2Ix8dhhi"
      },
      "source": [
        "def clean_labels(line):\n",
        "  if len(line.strip().split(\"\\t\"))==2:\n",
        "    col,lab = line.strip().split(\"\\t\")\n",
        "  else:\n",
        "    col,_,lab = line.strip().split(\"\\t\")\n",
        "  col = tuple(int(i) for i in col.split(\" \"))\n",
        "  return col,lab"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "abnhVLE7kD-4"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "90d_8UhJc-95"
      },
      "source": [
        "labels = open(\"label_colors.txt\").readlines()\n",
        "labels[:5]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yJYXE6NIkEfV"
      },
      "source": [
        "col,lab = labels[0].strip().split(\"\\t\")\n",
        "print(col)\n",
        "print(lab)\n",
        "col = tuple(int(i) for i in col.split(\" \"))\n",
        "print(col)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "me3P_NBKkjxz"
      },
      "source": [
        "labels[4].strip().split(\"\\t\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XnUJTskGdWtl"
      },
      "source": [
        "clean_labels(labels[0])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W2Mt40PgeHt8"
      },
      "source": [
        "label_colors,label_names = zip(*[clean_labels(line) for line in labels])\n",
        "label_colors,label_names = list(label_colors),list(label_names)\n",
        "print(len(label_colors))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "06YhXdQWeqfZ"
      },
      "source": [
        "label_colors[:5],label_names[:5]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fLHpi4XYetbO"
      },
      "source": [
        "dictionary = {K:V for K,V in enumerate(label_colors)}\n",
        "print(dictionary)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7fzP0uGAT9Q4"
      },
      "source": [
        "# Assign different objects with colurs with the help of id2code\n",
        "def color_label(a): \n",
        "    print(a.shape)\n",
        "    r,c=a.shape\n",
        "    res = np.zeros((r,c,3), 'uint8')\n",
        "    for j in range(r): \n",
        "        for k in range(c):\n",
        "            var = dictionary[a[j,k]]\n",
        "            res[j,k] = var\n",
        "    return res"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aqrkLseuT9fa"
      },
      "source": [
        "test = image.load_img('test_images/0016E5_07981.png')\n",
        "imh = image.img_to_array(test)\n",
        "dims = imh.shape\n",
        "x = cv2.resize(imh, (128, 128))\n",
        "x = np.float32(x)/255.\n",
        "##########get the predictions\n",
        "z = model.predict(np.expand_dims(x, axis=0))\n",
        "\n",
        "z = np.squeeze(z)\n",
        "z = cv2.resize(z, (dims[1], dims[0]))\n",
        "y = np.argmax(z, axis=2)\n",
        "print(y.shape)\n",
        "vb= color_label(y)\n",
        "\n",
        "fig = plt.figure(figsize=(10, 15))\n",
        "ax = fig.add_subplot(121)\n",
        "ax.set_title(f'Result {imh.shape}')\n",
        "ax.imshow(np.uint8(imh))\n",
        "\n",
        "ax2 = fig.add_subplot(122)\n",
        "ax2.set_title(f'Mask {vb.shape}')\n",
        "ax2.imshow(vb)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1YnhH16xT_ht"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}