detector.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7Y2601tc_YCs"
      },
      "outputs": [],
      "source": [
        "import cv2\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Model\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.layers import *\n",
        "from skimage.metrics import structural_similarity as ssim"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cd /content/\n",
        "!unzip /content/drive/MyDrive/CV_proj/train.zip -d ."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RshXnCQ-CPU6",
        "outputId": "c20f39d1-203d-4cea-fd43-ccb6fc36cd1c"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "  inflating: ./train/tejarat_249.jpg  \n",
            "  inflating: ./train/tejarat_249.txt  \n",
            "  inflating: ./train/tejarat_25.jpg  \n",
            "  inflating: ./train/tejarat_25.txt  \n",
            "  inflating: ./train/tejarat_26.jpg  \n",
            "  inflating: ./train/tejarat_26.txt  \n",
            "  inflating: ./train/tejarat_27.jpg  \n",
            "  inflating: ./train/tejarat_27.txt  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.layers.serialization import activation\n",
        "# input_layer = layers.Input(shape=(None, None, 3))\n",
        "# resize_layer = layers.Resizing(300, 300)(input_layer)\n",
        "vgg = tf.keras.applications.VGG16(classes=3, include_top=False, input_shape=(300, 300, 3))\n",
        "# for l in vgg.layers:\n",
        "#   if l.name == 'block4_conv3':\n",
        "#     x = l\n",
        "#     break\n",
        "\n",
        "classifier_1 = layers.Conv2D(28, (3, 3), name='classifier_1', padding='same')(vgg.output)\n",
        "# classifier_1 = layers.MaxPooling2D((37, 37))(classifier_1)\n",
        "r1 = layers.Reshape((-1, 28))(classifier_1)\n",
        "\n",
        "vgg_pool = layers.MaxPooling2D()(vgg.output)\n",
        "\n",
        "conv_layer1 = layers.Conv2D(1024, (3, 3), padding='same')(vgg_pool)\n",
        "conv_layer2 = layers.Conv2D(1024, (1, 1), padding='same')(conv_layer1)\n",
        "classifier_2 = layers.Conv2D(42, (3, 3), name='classifier_2', padding='same')(conv_layer2)\n",
        "classifier_2 = layers.MaxPooling2D((18, 18))(classifier_2)\n",
        "r2 = layers.Reshape((-1, 42))(classifier_2)\n",
        "\n",
        "pool_layer1 = layers.MaxPooling2D()(conv_layer2)\n",
        "conv_layer3 = layers.Conv2D(256, (1, 1), padding='same')(pool_layer1)\n",
        "conv_layer4 = layers.Conv2D(512, (3, 3), padding='same')(conv_layer3)\n",
        "classifier_3 = layers.Conv2D(42, (3, 3), name='classifier_3', padding='same')(conv_layer4)\n",
        "classifier_3 = layers.MaxPooling2D((9, 9))(classifier_3)\n",
        "r3 = layers.Reshape((-1, 42))(classifier_3)\n",
        "\n",
        "pool_layer2 = layers.MaxPooling2D()(conv_layer4)\n",
        "conv_layer5 = layers.Conv2D(128, (1, 1), padding='same')(pool_layer2)\n",
        "conv_layer6 = layers.Conv2D(256, (3, 3), padding='same')(conv_layer5)\n",
        "classifier_4 = layers.Conv2D(42, (3, 3), padding='same', name='classifier_4')(conv_layer6)\n",
        "classifier_4 = layers.MaxPooling2D((4, 4))(classifier_4)\n",
        "r4 = layers.Reshape((-1, 42))(classifier_4)\n",
        "\n",
        "pool_layer3 = layers.MaxPooling2D()(conv_layer6)\n",
        "conv_layer7 = layers.Conv2D(128, (1, 1), padding='same')(pool_layer3)\n",
        "conv_layer8 = layers.Conv2D(256, (3, 3), padding='same')(conv_layer7)\n",
        "classifier_5 = layers.Conv2D(28, (3, 3), padding='same', name='classifier_5')(conv_layer8)\n",
        "classifier_5 = layers.MaxPooling2D()(classifier_5)\n",
        "r5 = layers.Reshape((-1, 28))(classifier_5)\n",
        "\n",
        "conv_layer9 = layers.Conv2D(128, (1, 1), padding='same')(conv_layer8)\n",
        "conv_layer10 = layers.Conv2D(256, (3, 3), padding='same')(conv_layer9)\n",
        "classifier_6 = layers.Conv2D(28, (3, 3), padding='same', name='classifier_6')(conv_layer10)\n",
        "classifier_6 = layers.MaxPooling2D()(classifier_6)\n",
        "r6 = layers.Reshape((-1, 28))(classifier_6)\n",
        "\n",
        "conct_layer = layers.Concatenate()([r1, r2, r3, r4, r5, r6])\n",
        "# rl = conct_layer\n",
        "# class_prediction = layers.Conv2D(3, (3, 3), padding='same', activation='softmax')(rl)\n",
        "# box_prediction = layers.Conv2D(8, (3, 3), padding='same', activation='linear')(rl)\n",
        "\n",
        "# output_layer = layers.Concatenate()([class_prediction, box_prediction])\n",
        "# output_layer = layers.Conv2D(12, (37,37))(conctl)\n",
        "# output_layer = layers.Reshape((-1, 12))(output_layer)\n",
        "\n",
        "model = Model(inputs=vgg.input, outputs=[conct_layer])\n",
        "model.summary()"
      ],
      "metadata": {
        "id": "c0_g4O00_kYU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def iou_loss(y_true, y_pred):\n",
        "    # Extract the ground truth and predicted bounding boxes\n",
        "    true_box = y_true[..., :4]\n",
        "    pred_box = y_pred[..., :4]\n",
        "\n",
        "    # Calculate the intersection and union areas\n",
        "    intersection = tf.reduce_sum(tf.minimum(true_box, pred_box), axis=-1)\n",
        "    union = tf.reduce_sum(tf.maximum(true_box, pred_box), axis=-1)\n",
        "\n",
        "    # Calculate the IOU\n",
        "    iou = intersection / (union + tf.keras.backend.epsilon())\n",
        "\n",
        "    # Calculate the IOU loss\n",
        "    iou_loss = 1.0 - iou\n",
        "\n",
        "    return iou_loss\n",
        "# def custom_loss(y_pred, y_gt, smooth=1):\n",
        "#   return"
      ],
      "metadata": {
        "id": "zigs2CaXQqU3"
      },
      "execution_count": 160,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "base_dir = '/content/'\n",
        "train_dir = base_dir + 'train/'\n",
        "\n",
        "images, labels = [], []\n",
        "for (dirpath, dirname, filenames) in os.walk(train_dir):\n",
        "  for filename in filenames:\n",
        "    if filename.split('.')[1] != 'txt':\n",
        "      path = dirpath + filename\n",
        "      img = cv2.imread(path)\n",
        "      img = cv2.resize(img, (300, 300))\n",
        "      images.append(img)\n",
        "      txt_path = dirpath + filename.split('.')[0] + '.txt'\n",
        "      with open(txt_path, 'r') as f:\n",
        "        lines = f.readlines()\n",
        "        txt = []\n",
        "        # for l in lines:\n",
        "        #   txt.append(list(map(float,l.split())))\n",
        "        a = list(map(float,lines[0].split()))[1::]\n",
        "        b = list(map(float,lines[1].split()))[1::]\n",
        "        txt.append([1., 0.] + [float(it) for it in a])\n",
        "        txt.append([0., 1.] + [float(it) for it in b])\n",
        "      txt = np.array(txt)\n",
        "      # txt = txt.reshape((-1, 12))\n",
        "      labels.append(txt)\n",
        "\n",
        "images = np.array(images)\n",
        "labels = np.array(labels)"
      ],
      "metadata": {
        "id": "FSXAlk_yPTaB"
      },
      "execution_count": 266,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(images.shape)\n",
        "print(labels.shape)\n",
        "print(labels[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QxVGeL1vSSp7",
        "outputId": "a037e8ea-d0eb-42ff-ec29-d1647ed3462e"
      },
      "execution_count": 267,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1300, 300, 300, 3)\n",
            "(1300, 2, 6)\n",
            "[[1.         0.         0.4990099  0.71473354 0.8019802  0.09404389]\n",
            " [0.         1.         0.22178218 0.89341693 0.16831683 0.04702194]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "images[0: 600] = images[0:600]/255."
      ],
      "metadata": {
        "id": "X0gPoUIHT3Xd"
      },
      "execution_count": 268,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "images[600::] = images[600::]/255."
      ],
      "metadata": {
        "id": "6YMhF3KOkBRN"
      },
      "execution_count": 269,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "datagen = tf.keras.preprocessing.image.ImageDataGenerator(horizontal_flip=True, fill_mode='nearest', validation_split=0.2)\n",
        "\n",
        "datagen.fit(images)"
      ],
      "metadata": {
        "id": "-qeamvlvaAbr"
      },
      "execution_count": 249,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam', loss=iou_loss, metrics=['accuracy'])\n",
        "model.fit(images, labels, epochs=20, validation_split=0.2, batch_size=16, shuffle=True)"
      ],
      "metadata": {
        "id": "eHV72XILSr-r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_images = [cv2.imread(base_dir+'test/2.jpg'), cv2.imread(base_dir+'test/8.jpg')]\n",
        "for i in range(len(test_images)):\n",
        "  test_images[i] = cv2.resize(test_images[i], (300, 300))\n",
        "test_images = np.array(test_images)\n",
        "print(test_images.shape)\n",
        "print(test_images[0].shape)\n",
        "prediction = model.predict(test_images)"
      ],
      "metadata": {
        "id": "hhwGrQZhTqJt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prediction[1]"
      ],
      "metadata": {
        "id": "Zo5ezgTkU0ZF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class NMSLayer(layers.Layer):\n",
        "  def __init__(self, iou_threshold=0.5):\n",
        "        super(NMSLayer, self).__init__()\n",
        "        self.iou_threshold = iou_threshold\n",
        "\n",
        "  def call(self, inputs):\n",
        "      # Extract the bounding box predictions and scores from the inputs\n",
        "      boxes, scores = inputs[0], inputs[1]\n",
        "\n",
        "      # Apply non-maximum suppression\n",
        "      selected_indices = tf.image.non_max_suppression(\n",
        "          boxes, scores, max_output_size=100, iou_threshold=self.iou_threshold\n",
        "      )\n",
        "\n",
        "      # Gather the selected bounding boxes and scores\n",
        "      selected_boxes = tf.gather(boxes, selected_indices)\n",
        "      selected_scores = tf.gather(scores, selected_indices)\n",
        "\n",
        "      # Return the selected bounding boxes and scores\n",
        "      return [selected_boxes, selected_scores]"
      ],
      "metadata": {
        "id": "m7U-UyEAcd2A"
      },
      "execution_count": 200,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input_layer = layers.Input(shape=(300, 300, 3))\n",
        "resnet = tf.keras.applications.ResNet50(classes=3, include_top=False, input_shape=(300, 300, 3))(input_layer)\n",
        "conv1 = layers.Conv2D(1024, (3, 3))(resnet)\n",
        "conv2 = layers.Conv2D(512, (3, 3))(conv1)\n",
        "conv3 = layers.Conv2D(256, (3, 3))(conv2)\n",
        "flat = layers.Flatten()(conv3)\n",
        "dense = layers.Dense(4, activation='softmax')(flat)\n",
        "classifier_model = Model(inputs=input_layer, outputs=dense)\n",
        "# classifier_model.summary()\n",
        "#############################\n",
        "vgg = tf.keras.applications.VGG16(classes=3, include_top=False, input_shape=(300, 300, 3))(input_layer)\n",
        "rconv1 = layers.Conv2D(1024, (3, 3), padding='same')(vgg)\n",
        "rconv2 = layers.Conv2D(256, (3, 3), padding='same')(rconv1)\n",
        "f1 = layers.Flatten()(rconv2)\n",
        "d1 = layers.Dense(4, activation='linear')(f1)\n",
        "rconv3 = layers.Conv2D(1024, (3, 3), padding='same')(rconv2)\n",
        "rconv4 = layers.Conv2D(512, (3, 3), padding='same')(rconv3)\n",
        "f2 = layers.Flatten()(rconv4)\n",
        "d2 = layers.Dense(4, activation='linear')(f2)\n",
        "conct = layers.Concatenate()([d1, d2])\n",
        "resh = layers.Reshape((-1, 4))(conct)\n",
        "\n",
        "regression_model = Model(inputs=input_layer, outputs=resh)\n",
        "# regression_model.summary()\n",
        "###############################\n",
        "output = layers.Concatenate()([layers.Reshape((2,2))(classifier_model.output), regression_model.output])\n",
        "# output = layers.Reshape((2,1))(output)\n",
        "model = Model(inputs=input_layer, outputs=output)\n",
        "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "model.summary()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FD0BdEulXjbm",
        "outputId": "fe2be36b-3000-4d8d-f435-d5cfee221233"
      },
      "execution_count": 285,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_145\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_242 (InputLayer)         [(None, 300, 300, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " vgg16 (Functional)             (None, 9, 9, 512)    14714688    ['input_242[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_1276 (Conv2D)           (None, 9, 9, 1024)   4719616     ['vgg16[0][0]']                  \n",
            "                                                                                                  \n",
            " resnet50 (Functional)          (None, 10, 10, 2048  23587712    ['input_242[0][0]']              \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv2d_1277 (Conv2D)           (None, 9, 9, 256)    2359552     ['conv2d_1276[0][0]']            \n",
            "                                                                                                  \n",
            " conv2d_1273 (Conv2D)           (None, 8, 8, 1024)   18875392    ['resnet50[0][0]']               \n",
            "                                                                                                  \n",
            " conv2d_1278 (Conv2D)           (None, 9, 9, 1024)   2360320     ['conv2d_1277[0][0]']            \n",
            "                                                                                                  \n",
            " conv2d_1274 (Conv2D)           (None, 6, 6, 512)    4719104     ['conv2d_1273[0][0]']            \n",
            "                                                                                                  \n",
            " conv2d_1279 (Conv2D)           (None, 9, 9, 512)    4719104     ['conv2d_1278[0][0]']            \n",
            "                                                                                                  \n",
            " conv2d_1275 (Conv2D)           (None, 4, 4, 256)    1179904     ['conv2d_1274[0][0]']            \n",
            "                                                                                                  \n",
            " flatten_40 (Flatten)           (None, 20736)        0           ['conv2d_1277[0][0]']            \n",
            "                                                                                                  \n",
            " flatten_41 (Flatten)           (None, 41472)        0           ['conv2d_1279[0][0]']            \n",
            "                                                                                                  \n",
            " flatten_39 (Flatten)           (None, 4096)         0           ['conv2d_1275[0][0]']            \n",
            "                                                                                                  \n",
            " dense_90 (Dense)               (None, 4)            82948       ['flatten_40[0][0]']             \n",
            "                                                                                                  \n",
            " dense_91 (Dense)               (None, 4)            165892      ['flatten_41[0][0]']             \n",
            "                                                                                                  \n",
            " dense_89 (Dense)               (None, 4)            16388       ['flatten_39[0][0]']             \n",
            "                                                                                                  \n",
            " concatenate_124 (Concatenate)  (None, 8)            0           ['dense_90[0][0]',               \n",
            "                                                                  'dense_91[0][0]']               \n",
            "                                                                                                  \n",
            " reshape_468 (Reshape)          (None, 2, 2)         0           ['dense_89[0][0]']               \n",
            "                                                                                                  \n",
            " reshape_467 (Reshape)          (None, 2, 4)         0           ['concatenate_124[0][0]']        \n",
            "                                                                                                  \n",
            " concatenate_125 (Concatenate)  (None, 2, 6)         0           ['reshape_468[0][0]',            \n",
            "                                                                  'reshape_467[0][0]']            \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 77,500,620\n",
            "Trainable params: 77,447,500\n",
            "Non-trainable params: 53,120\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(images, labels, epochs=20, validation_split=0.2, batch_size=16, shuffle=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PSUfRvoVg0iJ",
        "outputId": "3334d248-f3a8-499d-fc9b-f1c30d8a1643"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "43/65 [==================>...........] - ETA: 14s - loss: 27.4378 - accuracy: 0.0400"
          ]
        }
      ]
    }
  ]
}