tf_handBbox_esitmation.ipynb

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  "metadata": {
    "colab": {
      "name": "tf-handBbox-esitmation.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "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/zaidalyafeai/Notebooks/blob/master/tf_handBbox_esitmation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "VwlrHpIgJLZb",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Introduction\n",
        "\n",
        "In this task we want to localize the right and left hands for each person that exists in a single frame. "
      ]
    },
    {
      "metadata": {
        "id": "SbtMmIt0vmb8",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Download The Dataset"
      ]
    },
    {
      "metadata": {
        "id": "X6BcOFxOJX1S",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We use the dataset fromt his [project](https://lmb.informatik.uni-freiburg.de/projects/hand3d/). Each single frame contains the pose estimation of each hand. We use that to extract a tight box for each hand.  "
      ]
    },
    {
      "metadata": {
        "id": "EdhKRW-Ri3B0",
        "colab_type": "code",
        "outputId": "2d5cccff-8ed3-4e40-8ef3-8ff2410070aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        }
      },
      "cell_type": "code",
      "source": [
        "!wget https://lmb.informatik.uni-freiburg.de/data/RenderedHandpose/RHD_v1-1.zip"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-02-16 12:34:41--  https://lmb.informatik.uni-freiburg.de/data/RenderedHandpose/RHD_v1-1.zip\n",
            "Resolving lmb.informatik.uni-freiburg.de (lmb.informatik.uni-freiburg.de)... 132.230.167.23\n",
            "Connecting to lmb.informatik.uni-freiburg.de (lmb.informatik.uni-freiburg.de)|132.230.167.23|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 7080690299 (6.6G) [application/zip]\n",
            "Saving to: ‘RHD_v1-1.zip’\n",
            "\n",
            "RHD_v1-1.zip        100%[===================>]   6.59G  37.6MB/s    in 3m 27s  \n",
            "\n",
            "2019-02-16 12:38:08 (32.7 MB/s) - ‘RHD_v1-1.zip’ saved [7080690299/7080690299]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "sce5-9GMkb-e",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!unzip RHD_v1-1.zip >> tmp.txt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "nDu1hia43GNv",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import os \n",
        "os.chdir('RHD_published_v2')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "JzaCJyYBIuRK",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Extract the Bounding Boxes \n",
        "\n",
        "At each image there exists at most to hands left and right, we normalize them and extract a tight bounding box for each hand then for each file we save the vector [boxL , boxR] where boxL is the bounding box for the left hand and boxR is the box for the right hand. "
      ]
    },
    {
      "metadata": {
        "id": "agUh1PE-lOUS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from PIL import Image  \n",
        "import pickle\n",
        "import os\n",
        "import numpy as np\n",
        "import scipy.misc\n",
        "import matplotlib.pyplot as plt\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "import cv2\n",
        "\n",
        "def get_bb(proj, size, vis):\n",
        "  w, h = size\n",
        "  \n",
        "  proj = proj[vis== 1]\n",
        "  if len(proj) == 0:\n",
        "    return [0, 0, 0, 0]\n",
        "  xmin = np.min(proj[:,0])\n",
        "  ymin = np.min(proj[:,1]) \n",
        "  xmax = np.max(proj[:,0])\n",
        "  ymax = np.max(proj[:,1]) \n",
        "  \n",
        "  \n",
        "  box = [xmin/w, ymin/h, xmax/w, ymax/h]\n",
        "  return box\n",
        "\n",
        "# chose between training and evaluation set\n",
        "# set = 'training'\n",
        "set = 'training'\n",
        "data = {}\n",
        "\n",
        "# load annotations of this set\n",
        "with open(os.path.join(set, 'anno_%s.pickle' % set), 'rb') as fi:\n",
        "    anno_all = pickle.load(fi)\n",
        "\n",
        "i = 0 \n",
        "# iterate samples of the set\n",
        "for sample_id, anno in anno_all.items():\n",
        "    # get info from annotation dictionary\n",
        "    kp_coord_uv = anno['uv_vis'][:, :2]  # u, v coordinates of 42 hand keypoints, pixel\n",
        "    kp_visible = (anno['uv_vis'][:, 2] == 1)  # visibility of the keypoints, boolean\n",
        "    kp_coord_xyz = anno['xyz']  # x, y, z coordinates of the keypoints, in meters\n",
        "    camera_intrinsic_matrix = anno['K']  # matrix containing intrinsic parameters\n",
        "\n",
        "    # Project world coordinates into the camera frame\n",
        "    kp_coord_uv_proj = np.matmul(kp_coord_xyz, np.transpose(camera_intrinsic_matrix))\n",
        "    kp_coord_uv_proj = kp_coord_uv_proj[:, :2] / kp_coord_uv_proj[:, 2:]\n",
        "    \n",
        "    #estimate bbox for the lefthand \n",
        "    path = os.path.join(set, 'color', '%.5d.png' % sample_id)\n",
        "    \n",
        "    img = Image.open(path)\n",
        "    w, h = img.size\n",
        "\n",
        "    leftbb = get_bb(kp_coord_uv_proj[0:21,:], (w,h), anno['uv_vis'][0:21, 2])\n",
        "    rightbb = get_bb(kp_coord_uv_proj[21:, :], (w,h), anno['uv_vis'][21:, 2])\n",
        "    \n",
        "    \n",
        "    if max(leftbb) > 1 or max(rightbb) > 1:\n",
        "      continue\n",
        "   \n",
        "    data[path] = leftbb+rightbb"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "8x63jmHModXd",
        "colab_type": "code",
        "outputId": "0d51f26c-c5f6-482a-ff8d-76656f3748d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "len(data)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "41245"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "metadata": {
        "id": "Thbo1sfdvwx_",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Imports"
      ]
    },
    {
      "metadata": {
        "id": "ZlvQpexUvyOX",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We use tensorflow with eager execution. Hence, eager execution allows immediate evaluation of tensors without instintiating graph. "
      ]
    },
    {
      "metadata": {
        "id": "90oDq-StNAUz",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Input \n",
        "from tensorflow.keras.layers import Flatten, Dropout, BatchNormalization, Concatenate, Reshape, GlobalAveragePooling2D, Reshape\n",
        "from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2, preprocess_input\n",
        "import cv2\n",
        "import matplotlib.pyplot as plt \n",
        "import os \n",
        "import numpy as np\n",
        "from PIL import Image\n",
        "from random import shuffle\n",
        "import random\n",
        "import tensorflow.contrib.eager as tfe"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ydz_xg61O1T8",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "tf.enable_eager_execution()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "lDQMPv9wyy3W",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#Create A Dataset"
      ]
    },
    {
      "metadata": {
        "id": "DkEvAcn-yyzh",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Here we use `tf.data` for manipulating the data and use them for training"
      ]
    },
    {
      "metadata": {
        "id": "nsOgGrafJoF4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def parse_data(filename, label):\n",
        "  \n",
        "  image = tf.image.decode_jpeg(tf.read_file(filename), channels = 3)\n",
        "  \n",
        "  image = tf.image.convert_image_dtype(image, tf.float32)\n",
        "  \n",
        "  image = tf.image.resize_images(image, [IMG_SIZE, IMG_SIZE])\n",
        "  \n",
        "  label = tf.cast(label, tf.float32)\n",
        "  \n",
        "  return image, label"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Ga1bjmp3vnLs",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def create_dataset(ff, ll):\n",
        "\n",
        "  dataset = tf.data.Dataset.from_tensor_slices((ff, ll)).shuffle(len(ff) - 1)\n",
        "  dataset = dataset.map(parse_data, num_parallel_calls = 4)\n",
        "  dataset = dataset.batch(BATCH_SIZE)\n",
        "  \n",
        "  return dataset"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "MLwembJkv4nh",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Data Split"
      ]
    },
    {
      "metadata": {
        "id": "Zv7qXaXYzoaK",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We create a 10% split for the test data to be used for validation "
      ]
    },
    {
      "metadata": {
        "id": "czQrmixaUjsT",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "files = list(data.keys())\n",
        "labels = list(data.values())\n",
        "\n",
        "N = len(files)\n",
        "M = int(0.9 * N)\n",
        "\n",
        "#split files for images \n",
        "train_files = files[:M]\n",
        "test_files  = files[M:]\n",
        "\n",
        "#split labels\n",
        "train_labels = labels[:M]\n",
        "test_labels =  labels[M:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "71f4-MgLzkIA",
        "colab_type": "code",
        "outputId": "f46e010d-caa1-4f96-d802-660d870accdb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "cell_type": "code",
      "source": [
        "print('training', len(train_files))\n",
        "print('testing' , len(test_files))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "training 37120\n",
            "testing 4125\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "cOSh-CzQfquU",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "IMG_SIZE = 128\n",
        "BATCH_SIZE = 32\n",
        "\n",
        "train_dataset = create_dataset(train_files, train_labels)\n",
        "test_dataset = create_dataset(test_files, test_labels)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "svrjoXDczluq",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Visualization "
      ]
    },
    {
      "metadata": {
        "id": "iVEsbr-JknFO",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def plot_annot(img, boxes):  \n",
        "\n",
        "  img = img.numpy()\n",
        "  boxes = boxes.numpy() * IMG_SIZE\n",
        "  \n",
        "  leftbb = boxes[0:4]\n",
        "  rightbb = boxes[4:]\n",
        "\n",
        "  img = cv2.rectangle(img, (int(leftbb[0]), int(leftbb[1])), (int(leftbb[2]), int(leftbb[3])), (1, 0, 0), 2)\n",
        "  img = cv2.rectangle(img, (int(rightbb[0]), int(rightbb[1])), (int(rightbb[2]), int(rightbb[3])), (0, 0, 1), 2)\n",
        "\n",
        "\n",
        "  plt.axis('off')\n",
        "  plt.imshow(img)\n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3MH_bDFTMcCb",
        "colab_type": "code",
        "outputId": "25ebdbd9-6a78-400a-be8b-22e7942e02f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "cell_type": "code",
      "source": [
        "for x, y in train_dataset:\n",
        "  plot_annot(x[0], y[0])\n",
        "  break"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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k4jon0DLEa1PptGHDGC+rbtnzF1l4OY6AUn+/MfWTabQqAQVyY6Y9yIuvHiIi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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "bjoZC5r90UWO",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Create a model "
      ]
    },
    {
      "metadata": {
        "id": "y6pLAol7J9zM",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We use a ResNet model with 12 convolutiona layer. At the end we flatten the features and output a vector of size 8 for the bounding box predictions for each hand. "
      ]
    },
    {
      "metadata": {
        "id": "2RXE17PjgS0G",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def conv_block(fs, x, activation = 'relu'):\n",
        "  conv  = Conv2D(fs, (3, 3), padding = 'same', activation = activation)(x)\n",
        "  bnrm   = BatchNormalization()(conv)\n",
        "  return bnrm\n",
        "\n",
        "def residual_block(fs, x):\n",
        "  y = conv_block(fs, x)\n",
        "  y = conv_block(fs, y)\n",
        "  return Concatenate(axis = -1)([x, y])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "w52nLfQxquch",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "outputId": "7bb13141-eb2e-4f9f-fd47-c8333ec543ef"
      },
      "cell_type": "code",
      "source": [
        "inp = Input(shape = (IMG_SIZE, IMG_SIZE, 3))\n",
        "\n",
        "block1 = residual_block(16, inp)\n",
        "pool1  = MaxPooling2D(pool_size = (2, 2))(block1)\n",
        "block2 = residual_block(32, pool1)\n",
        "pool2  = MaxPooling2D(pool_size = (2, 2))(block2)\n",
        "block3 = residual_block(64, pool2)\n",
        "pool3  = MaxPooling2D(pool_size = (2, 2))(block3)\n",
        "block4 = residual_block(128, pool3)\n",
        "pool4  = MaxPooling2D(pool_size = (2, 2))(block4)\n",
        "block5 = residual_block(256, pool4)\n",
        "pool5  = MaxPooling2D(pool_size = (2, 2))(block5)\n",
        "block6 = residual_block(512, pool5)\n",
        "pool6  = MaxPooling2D(pool_size = (2, 2))(block6)\n",
        "\n",
        "flatten = Flatten()(pool6)\n",
        "\n",
        "dense = Dense(256, activation = 'relu')(flatten)\n",
        "droput = Dropout(0.5)(dense)\n",
        "out = Dense(8, activation = 'sigmoid')(droput)\n",
        "\n",
        "#create a model with one input and two outputs \n",
        "model = tf.keras.models.Model(inputs = inp, outputs = out)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/layers/core.py:143: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "rIHWmzXPRP8l",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1667
        },
        "outputId": "c11b5d49-6dd5-46a2-e304-a9375c379596"
      },
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            (None, 128, 128, 3)  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d (Conv2D)                 (None, 128, 128, 16) 448         input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_v1 (BatchNo (None, 128, 128, 16) 64          conv2d[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_1 (Conv2D)               (None, 128, 128, 16) 2320        batch_normalization_v1[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_v1_1 (Batch (None, 128, 128, 16) 64          conv2d_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "concatenate (Concatenate)       (None, 128, 128, 19) 0           input_1[0][0]                    \n",
            "                                                                 batch_normalization_v1_1[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D)    (None, 64, 64, 19)   0           concatenate[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_2 (Conv2D)               (None, 64, 64, 32)   5504        max_pooling2d[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_v1_2 (Batch (None, 64, 64, 32)   128         conv2d_2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_3 (Conv2D)               (None, 64, 64, 32)   9248        batch_normalization_v1_2[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_v1_3 (Batch (None, 64, 64, 32)   128         conv2d_3[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_1 (Concatenate)     (None, 64, 64, 51)   0           max_pooling2d[0][0]              \n",
            "                                                                 batch_normalization_v1_3[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2D)  (None, 32, 32, 51)   0           concatenate_1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_4 (Conv2D)               (None, 32, 32, 64)   29440       max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_v1_4 (Batch (None, 32, 32, 64)   256         conv2d_4[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_5 (Conv2D)               (None, 32, 32, 64)   36928       batch_normalization_v1_4[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_v1_5 (Batch (None, 32, 32, 64)   256         conv2d_5[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_2 (Concatenate)     (None, 32, 32, 115)  0           max_pooling2d_1[0][0]            \n",
            "                                                                 batch_normalization_v1_5[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2D)  (None, 16, 16, 115)  0           concatenate_2[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_6 (Conv2D)               (None, 16, 16, 128)  132608      max_pooling2d_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_v1_6 (Batch (None, 16, 16, 128)  512         conv2d_6[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_7 (Conv2D)               (None, 16, 16, 128)  147584      batch_normalization_v1_6[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_v1_7 (Batch (None, 16, 16, 128)  512         conv2d_7[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_3 (Concatenate)     (None, 16, 16, 243)  0           max_pooling2d_2[0][0]            \n",
            "                                                                 batch_normalization_v1_7[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2D)  (None, 8, 8, 243)    0           concatenate_3[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_8 (Conv2D)               (None, 8, 8, 256)    560128      max_pooling2d_3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_v1_8 (Batch (None, 8, 8, 256)    1024        conv2d_8[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_9 (Conv2D)               (None, 8, 8, 256)    590080      batch_normalization_v1_8[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_v1_9 (Batch (None, 8, 8, 256)    1024        conv2d_9[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_4 (Concatenate)     (None, 8, 8, 499)    0           max_pooling2d_3[0][0]            \n",
            "                                                                 batch_normalization_v1_9[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2D)  (None, 4, 4, 499)    0           concatenate_4[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_10 (Conv2D)              (None, 4, 4, 512)    2299904     max_pooling2d_4[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_v1_10 (Batc (None, 4, 4, 512)    2048        conv2d_10[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_11 (Conv2D)              (None, 4, 4, 512)    2359808     batch_normalization_v1_10[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_v1_11 (Batc (None, 4, 4, 512)    2048        conv2d_11[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_5 (Concatenate)     (None, 4, 4, 1011)   0           max_pooling2d_4[0][0]            \n",
            "                                                                 batch_normalization_v1_11[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_5 (MaxPooling2D)  (None, 2, 2, 1011)   0           concatenate_5[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "flatten (Flatten)               (None, 4044)         0           max_pooling2d_5[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "dense (Dense)                   (None, 256)          1035520     flatten[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dropout (Dropout)               (None, 256)          0           dense[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "dense_1 (Dense)                 (None, 8)            2056        dropout[0][0]                    \n",
            "==================================================================================================\n",
            "Total params: 7,219,640\n",
            "Trainable params: 7,215,608\n",
            "Non-trainable params: 4,032\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "vHkY6K9qKSKm",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Loss and gradient "
      ]
    },
    {
      "metadata": {
        "id": "Pjyk0wmk0sSP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def loss(pred, y):  \n",
        "  return tf.losses.absolute_difference(y, pred) \n",
        "           \n",
        "def grad(model, x, y):\n",
        "  #record the gradient\n",
        "  with tf.GradientTape() as tape:\n",
        "    pred = model(x)\n",
        "    value = loss(pred, y)\n",
        "  #return the gradient of the loss function with respect to the model variables \n",
        "  return tape.gradient(value, model.trainable_variables)\n",
        "\n",
        "optimizer = tf.train.AdamOptimizer()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "gD_D94PnKUGA",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Evaluation metric "
      ]
    },
    {
      "metadata": {
        "id": "WHuelB-wKWOV",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We use intersection over union to evaluate how good we are estimating the generatd bounding boxes."
      ]
    },
    {
      "metadata": {
        "id": "eDS_jzi5uWcL",
        "colab_type": "code",
        "outputId": "6343e818-ed2c-4e44-c2aa-9d002217ea2e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "def iou(boxA, boxB):\n",
        "  #evaluate the intersection points \n",
        "  xA = tf.maximum(boxA[:, 0], boxB[:, 0])\n",
        "  yA = tf.maximum(boxA[:, 1], boxB[:, 1])\n",
        "  xB = tf.minimum(boxA[:, 2], boxB[:, 2])\n",
        "  yB = tf.minimum(boxA[:, 3], boxB[:, 3])\n",
        "\n",
        "  # compute the area of intersection rectangle\n",
        "  interArea = tf.maximum(0, xB - xA + 1) * tf.maximum(0, yB - yA + 1)\n",
        "\n",
        "  # compute the area of both the prediction and ground-truth\n",
        "  # rectangles\n",
        "  boxAArea = (boxA[:, 2] - boxA[:, 0] + 1) * (boxA[:, 3] - boxA[:, 1] + 1)\n",
        "  boxBArea = (boxB[:, 2] - boxB[:, 0] + 1) * (boxB[:, 3] - boxB[:, 1] + 1)\n",
        "\n",
        "  #compute the union \n",
        "  unionArea = (boxAArea + boxBArea - interArea)\n",
        "\n",
        "  # return the intersection over union value\n",
        "  return tf.reduce_mean(interArea / unionArea)\n",
        "\n",
        "def mean_iou(pred, y):\n",
        "  boxLA = y[:, :4]  \n",
        "  boxLB = pred[:,:4] \n",
        "  \n",
        "  boxRA = y[:, 4:]\n",
        "  boxRB = pred[:, 4:]\n",
        "  \n",
        "  return (iou(boxLA, boxLB) + iou(boxRA, boxRB))/2"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tf.Tensor(1.0, shape=(), dtype=float32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "OMsC-cM_KoC3",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We record the metrics at each epoch"
      ]
    },
    {
      "metadata": {
        "id": "OP_YD6dpVR2Q",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "epochs = 50\n",
        "\n",
        "#initialize the history to record the metrics \n",
        "train_loss_history = tfe.metrics.Mean('train_loss')\n",
        "train_iou_history  = tfe.metrics.Mean('train_iou')\n",
        "\n",
        "test_loss_history = tfe.metrics.Mean('test_loss')\n",
        "test_iou_history  = tfe.metrics.Mean('test_iou')\n",
        "\n",
        "best_iou = 0.0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "VfXeuuGvOnWP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "for i in range(1, epochs + 1):\n",
        "  \n",
        "  for x, y in train_dataset:\n",
        "    pred = model(x)\n",
        "    grads = grad(model, x, y)\n",
        "\n",
        "    #update the paramters of the model \n",
        "    optimizer.apply_gradients(zip(grads, model.trainable_variables), global_step = tf.train.get_or_create_global_step())\n",
        "\n",
        "    #record the metrics of the current batch \n",
        "    loss_value = loss(pred, y)\n",
        "    iou_value  = mean_iou(pred, y)\n",
        "    \n",
        "    #calcualte the metrics of the current batch\n",
        "    train_loss_history(loss_value)\n",
        "    train_iou_history(iou_value)\n",
        "    \n",
        "  #loop over the test dataset \n",
        "  for x, y in test_dataset:\n",
        "    pred = model(x)\n",
        "    \n",
        "    #calcualte the metrics of the current batch \n",
        "    loss_value = loss(pred, y)\n",
        "    iou_value  = mean_iou(pred, y)\n",
        "    \n",
        "    #record the values of the metrics \n",
        "    test_loss_history(loss_value)\n",
        "    test_iou_history(iou_value)\n",
        "    \n",
        "  #print out the results \n",
        "  print(\"epoch: [{0:d}/{1:d}], Train: [loss: {2:0.4f}, iou: {3:0.4f}], Test: [loss: {4:0.4f}, iou: {5:0.4f}]\".\n",
        "       format(i, epochs, train_loss_history.result(), train_iou_history.result(), \n",
        "              test_loss_history.result(), test_iou_history.result()))\n",
        "  \n",
        "  current_iou = test_iou_history.result().numpy()\n",
        "  \n",
        "  #save the best model \n",
        "  if best_iou  < current_iou:\n",
        "    best_iou = current_iou\n",
        "    print('saving best model with iou', best_iou)\n",
        "    model.save('keras.h5')\n",
        "    \n",
        "  #clear the history after each epoch \n",
        "  train_loss_history.init_variables()\n",
        "  train_iou_history.init_variables()\n",
        "  \n",
        "  test_loss_history.init_variables()\n",
        "  test_iou_history.init_variables()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "98gqCzUnKw3e",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Save the best model"
      ]
    },
    {
      "metadata": {
        "id": "x6a1fmzZm6A9",
        "colab_type": "code",
        "outputId": "dac6291c-29e8-4b46-fe82-ae294aae8d4b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import load_model\n",
        "best_model = load_model('keras.h5')"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:No training configuration found in save file: the model was *not* compiled. Compile it manually.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "WhG6NYoW309V",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Visualization "
      ]
    },
    {
      "metadata": {
        "id": "fmktaatL6UT6",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#visualize the predicted bounding box\n",
        "def plot_pred(img_id):\n",
        "  \n",
        "  raw = cv2.imread(img_id)[:,:,::-1]\n",
        "  \n",
        "  h, w = (512, 512)\n",
        "  \n",
        "  img = cv2.resize(raw, (IMG_SIZE, IMG_SIZE)).astype('float32')\n",
        "  img = np.expand_dims(img, 0)/255. \n",
        "  \n",
        "  boxes = best_model(img).numpy()[0]\n",
        "\n",
        "  raw = cv2.resize(raw, (w, h))\n",
        "    \n",
        "  leftbb = boxes[:4]*w\n",
        "  rightbb = boxes[4:]*w\n",
        "  \n",
        "  print(leftbb)\n",
        "  print(rightbb)\n",
        "  img = cv2.rectangle(raw, (int(leftbb[0]), int(leftbb[1])), (int(leftbb[2]), int(leftbb[3])), (255, 0, 0), 3)\n",
        "  img = cv2.rectangle(raw, (int(rightbb[0]), int(rightbb[1])), (int(rightbb[2]), int(rightbb[3])), (0, 0, 255), 3)\n",
        "\n",
        "\n",
        "  plt.axis('off')\n",
        "  plt.imshow(img)\n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "HFxxleaoofqz",
        "colab_type": "code",
        "outputId": "9fb958b6-b791-4095-eb62-737387092bba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        }
      },
      "cell_type": "code",
      "source": [
        "img_id = np.random.choice(test_files)\n",
        "plot_pred(img_id)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[478.8169  214.33978 508.4085  309.2115 ]\n",
            "[257.33472 188.45944 385.61972 305.6182 ]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3jfEWXzPWL/+AfVLJlJU95H6jP9yeNo70+oMVLMmVOabU4kD9GF23KQ1D0rRO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\nupSeb2TGF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tUdlAKGgWM2pGHbeB8ydpzUIggs3lsRvUNtgMJBrPCR2kRRig+B7JeYjKyv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M0tYqJRVEI+RIJJJVapin2BJbOtdjTdce+pQy8ywuAzln+r5vWOln+NLqfK23oeubxkAmM8fE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rmi7SdOpyN/+F9/4P3P7/nLX/7Cy8sLy8sLUJTQs3g9aa0ZR8Uw3gkd66h4fPzIZTpTm1bO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\nGbqi/QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}