YoloV3-tiny_tf.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TensorFlow version: 2.1.0\n",
      "Eager execution: True\n",
      "Keras version: 2.2.4-tf\n",
      "Cuda version: 10.1\n",
      "Cudnn version: 7\n",
      "Num Physical GPUs Available:  0\n",
      "Num Logical GPUs Available:  0\n"
     ]
    }
   ],
   "source": [
    "import io\n",
    "import os\n",
    "\n",
    "from collections import defaultdict\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from keras import backend as K\n",
    "from keras.layers import Conv2D, Input, BatchNormalization, LeakyReLU, ZeroPadding2D, UpSampling2D, MaxPool2D, Activation\n",
    "from keras.layers.merge import add, concatenate\n",
    "from keras.models import Model\n",
    "from keras.utils import get_custom_objects\n",
    "from tensorflow.python.platform import build_info as tf_build_info\n",
    "\n",
    "print(\"TensorFlow version: {}\".format(tf.__version__))\n",
    "print(\"Eager execution: {}\".format(tf.executing_eagerly()))\n",
    "print(\"Keras version: {}\".format(tf.keras.__version__))\n",
    "print(\"Cuda version: {}\".format(tf_build_info.cuda_version_number))\n",
    "print(\"Cudnn version: {}\".format(tf_build_info.cudnn_version_number))\n",
    "print(\"Num Physical GPUs Available: \", len(tf.config.experimental.list_physical_devices('GPU')))\n",
    "print(\"Num Logical GPUs Available: \", len(tf.config.experimental.list_logical_devices('GPU')))\n",
    "\n",
    "# ------------------------------------------------------------\n",
    "# needs to be defined as activation class otherwise error\n",
    "# AttributeError: 'Activation' object has no attribute '__name__'    \n",
    "class Mish(Activation):\n",
    "    \n",
    "    def __init__(self, activation, **kwargs):\n",
    "        super(Mish, self).__init__(activation, **kwargs)\n",
    "        self.__name__ = 'mish'\n",
    "\n",
    "\n",
    "def mysoftplus(x):\n",
    "\n",
    "    mask_min = tf.cast((x<-20.0),tf.float32)\n",
    "    ymin = mask_min*tf.math.exp(x)\n",
    "\n",
    "    mask_max = tf.cast((x>20.0),tf.float32)\n",
    "    ymax = mask_max*x\n",
    "    \n",
    "    mask= tf.cast((abs(x)<=20.0),tf.float32)\n",
    "    y = mask*tf.math.log(tf.math.exp(x) + 1.0)\n",
    "    \n",
    "    return(ymin+ymax+y)    \n",
    "        \n",
    "\n",
    "\n",
    "def mish(x):\n",
    "    return (x* tf.math.tanh(mysoftplus(x)))\n",
    "    \n",
    "   \n",
    "    \n",
    "\n",
    "get_custom_objects().update({'mish': Mish(mish)})\n",
    "\n",
    "def _conv_block(inp, convs, skip=False):\n",
    "    x = inp\n",
    "    count = 0\n",
    "    \n",
    "    for conv in convs:\n",
    "        if count == (len(convs) - 2) and skip:\n",
    "            skip_connection = x\n",
    "        count += 1\n",
    "        \n",
    "        if conv['stride'] > 1: x = ZeroPadding2D(((1,0),(1,0)), name='zerop_' + str(conv['layer_idx']))(x)  # peculiar padding as darknet prefer left and top\n",
    "        \n",
    "        x = Conv2D(conv['filter'], \n",
    "                   conv['kernel'], \n",
    "                   strides=conv['stride'], \n",
    "                   padding='valid' if conv['stride'] > 1 else 'same', # peculiar padding as darknet prefer left and top\n",
    "                   name='convn_' + str(conv['layer_idx']) if conv['bnorm'] else 'conv_' + str(conv['layer_idx']),\n",
    "                   use_bias=True)(x)\n",
    "        \n",
    "        if conv['bnorm']: x = BatchNormalization(name='BN_' + str(conv['layer_idx']))(x)    \n",
    "        \n",
    "        if conv['activ'] == 1: x = LeakyReLU(alpha=0.1, name='leaky_' + str(conv['layer_idx']))(x)\n",
    "        if conv['activ'] == 2: x = Activation('mish', name='mish_' + str(conv['layer_idx']))(x) \n",
    "            \n",
    "    return add([skip_connection, x],  name='add_' + str(conv['layer_idx']+1)) if skip else x\n",
    "\n",
    "def make_yolov3_tiny_model():\n",
    "        \n",
    "    input_image = Input(shape=(416, 416, 3), name='input_0')\n",
    "\n",
    "    # Layer  0\n",
    "    x = _conv_block(input_image, [{'filter': 16, 'kernel': 3, 'stride': 1, 'bnorm': True, 'activ': 1, 'layer_idx': 0}])\n",
    "    \n",
    "    # Layer  1\n",
    "    x = MaxPool2D(pool_size=(2, 2), strides=2, padding='same', name = 'max_1')(x)\n",
    "    \n",
    "    # Layer  2\n",
    "    x = _conv_block(x, [{'filter': 32, 'kernel': 3, 'stride': 1, 'bnorm': True, 'activ': 1, 'layer_idx': 2}])\n",
    "  \n",
    "    # Layer  3\n",
    "    x = MaxPool2D(pool_size=(2, 2), strides=2, padding='same', name = 'max_3')(x)\n",
    "    \n",
    "    # Layer  4\n",
    "    x = _conv_block(x, [{'filter': 64, 'kernel': 3, 'stride': 1, 'bnorm': True, 'activ': 1, 'layer_idx': 4}])   \n",
    "\n",
    "    # Layer  5\n",
    "    x = MaxPool2D(pool_size=(2, 2), strides=2, padding='same', name = 'max_5')(x)\n",
    "    \n",
    "    # Layer  6\n",
    "    x = _conv_block(x, [{'filter': 128, 'kernel': 3, 'stride': 1, 'bnorm': True, 'activ': 1, 'layer_idx': 6}])   \n",
    "    \n",
    "    # Layer  7\n",
    "    x = MaxPool2D(pool_size=(2, 2), strides=2, padding='same', name = 'max_7')(x)\n",
    "    \n",
    "    # Layer  8\n",
    "    x = _conv_block(x, [{'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True, 'activ': 1, 'layer_idx': 8}])   \n",
    "    layer_8 = x\n",
    "    \n",
    "    # Layer  9\n",
    "    x = MaxPool2D(pool_size=(2, 2), strides=2, padding='same', name = 'max_9')(x)\n",
    "    \n",
    "    # Layer  10\n",
    "    x = _conv_block(x, [{'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'activ': 1, 'layer_idx': 10}])\n",
    "    layer_10 = x\n",
    "    \n",
    "    # Layer  11\n",
    "    x = MaxPool2D(pool_size=(2, 2), strides=1, padding='same', name = 'max_11')(x)\n",
    "    \n",
    "    # Layer  12\n",
    "    x = _conv_block(x, [{'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'activ': 1, 'layer_idx': 12}])\n",
    "\n",
    "    \n",
    "    ###########\n",
    "    \n",
    "    # Layer  13\n",
    "    x = _conv_block(x, [{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'activ': 1, 'layer_idx': 13}]) \n",
    "    layer_13 = x\n",
    "        \n",
    "    # Layer  14\n",
    "    x = _conv_block(x, [{'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'activ': 1, 'layer_idx': 14}])     \n",
    "    \n",
    "    # Layer  15\n",
    "    x = _conv_block(x, [{'filter': 255, 'kernel': 1, 'stride': 1, 'bnorm': True, 'activ': 0, 'layer_idx': 15}])    \n",
    "    \n",
    "    # Layer  16\n",
    "    yolo_16 = x\n",
    "    \n",
    "    \n",
    "    # Layer  17\n",
    "    x = layer_13\n",
    "\n",
    "    # Layer  18\n",
    "    x = _conv_block(x, [{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'activ': 1, 'layer_idx': 18}])\n",
    "   \n",
    "    # Layer  19\n",
    "    x = UpSampling2D(size=(2, 2), name = 'upsamp_19')(x)\n",
    "\n",
    "    # Layer  20\n",
    "    x = concatenate([layer_8, x],  name='concatenate_20')\n",
    "    \n",
    "    # Layer  21\n",
    "    x = _conv_block(x, [{'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True, 'activ': 1, 'layer_idx': 21}])     \n",
    "    \n",
    "    # Layer  22\n",
    "    x = _conv_block(x, [{'filter': 255, 'kernel': 1, 'stride': 1, 'bnorm': True, 'activ': 0, 'layer_idx': 22}])    \n",
    "    \n",
    "    # Layer  23\n",
    "    yolo_23 = x   \n",
    "                                      \n",
    "    model = Model(input_image, [yolo_16, yolo_23], name = 'Yolo_v3_tiny')    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the model\n",
    "model = make_yolov3_tiny_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"Yolo_v3_tiny\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_0 (InputLayer)            (None, 416, 416, 3)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "convn_0 (Conv2D)                (None, 416, 416, 16) 448         input_0[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "BN_0 (BatchNormalization)       (None, 416, 416, 16) 64          convn_0[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_0 (LeakyReLU)             (None, 416, 416, 16) 0           BN_0[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "max_1 (MaxPooling2D)            (None, 208, 208, 16) 0           leaky_0[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "convn_2 (Conv2D)                (None, 208, 208, 32) 4640        max_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "BN_2 (BatchNormalization)       (None, 208, 208, 32) 128         convn_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_2 (LeakyReLU)             (None, 208, 208, 32) 0           BN_2[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "max_3 (MaxPooling2D)            (None, 104, 104, 32) 0           leaky_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "convn_4 (Conv2D)                (None, 104, 104, 64) 18496       max_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "BN_4 (BatchNormalization)       (None, 104, 104, 64) 256         convn_4[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_4 (LeakyReLU)             (None, 104, 104, 64) 0           BN_4[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "max_5 (MaxPooling2D)            (None, 52, 52, 64)   0           leaky_4[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "convn_6 (Conv2D)                (None, 52, 52, 128)  73856       max_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "BN_6 (BatchNormalization)       (None, 52, 52, 128)  512         convn_6[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_6 (LeakyReLU)             (None, 52, 52, 128)  0           BN_6[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "max_7 (MaxPooling2D)            (None, 26, 26, 128)  0           leaky_6[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "convn_8 (Conv2D)                (None, 26, 26, 256)  295168      max_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "BN_8 (BatchNormalization)       (None, 26, 26, 256)  1024        convn_8[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_8 (LeakyReLU)             (None, 26, 26, 256)  0           BN_8[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "max_9 (MaxPooling2D)            (None, 13, 13, 256)  0           leaky_8[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "convn_10 (Conv2D)               (None, 13, 13, 512)  1180160     max_9[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "BN_10 (BatchNormalization)      (None, 13, 13, 512)  2048        convn_10[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_10 (LeakyReLU)            (None, 13, 13, 512)  0           BN_10[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "max_11 (MaxPooling2D)           (None, 13, 13, 512)  0           leaky_10[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "convn_12 (Conv2D)               (None, 13, 13, 1024) 4719616     max_11[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "BN_12 (BatchNormalization)      (None, 13, 13, 1024) 4096        convn_12[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_12 (LeakyReLU)            (None, 13, 13, 1024) 0           BN_12[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "convn_13 (Conv2D)               (None, 13, 13, 256)  262400      leaky_12[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "BN_13 (BatchNormalization)      (None, 13, 13, 256)  1024        convn_13[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_13 (LeakyReLU)            (None, 13, 13, 256)  0           BN_13[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "convn_18 (Conv2D)               (None, 13, 13, 128)  32896       leaky_13[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "BN_18 (BatchNormalization)      (None, 13, 13, 128)  512         convn_18[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_18 (LeakyReLU)            (None, 13, 13, 128)  0           BN_18[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "upsamp_19 (UpSampling2D)        (None, 26, 26, 128)  0           leaky_18[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_20 (Concatenate)    (None, 26, 26, 384)  0           leaky_8[0][0]                    \n",
      "                                                                 upsamp_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "convn_14 (Conv2D)               (None, 13, 13, 512)  1180160     leaky_13[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "convn_21 (Conv2D)               (None, 26, 26, 256)  884992      concatenate_20[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "BN_14 (BatchNormalization)      (None, 13, 13, 512)  2048        convn_14[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "BN_21 (BatchNormalization)      (None, 26, 26, 256)  1024        convn_21[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "leaky_14 (LeakyReLU)            (None, 13, 13, 512)  0           BN_14[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "leaky_21 (LeakyReLU)            (None, 26, 26, 256)  0           BN_21[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "convn_15 (Conv2D)               (None, 13, 13, 255)  130815      leaky_14[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "convn_22 (Conv2D)               (None, 26, 26, 255)  65535       leaky_21[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "BN_15 (BatchNormalization)      (None, 13, 13, 255)  1020        convn_15[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "BN_22 (BatchNormalization)      (None, 26, 26, 255)  1020        convn_22[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 8,863,958\n",
      "Trainable params: 8,856,570\n",
      "Non-trainable params: 7,388\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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l4A8ePCipdABUqt7Z2VkZhuGuulVMPB4vWBErnU7r1q1b6unpcbdlMhkFg0GNj48X9JI5fPhwQeDWiO319PRocnJS4XDYfX6uXbsm0zSrCs7S6bQk6ciRIxW/F9gJBEAAAAAAmk57e3vB7/mv/X5/wT83Hi9Jhw4dkmVZ8vv96uzsVCwWK9j/3HPPyTRNdXV1ybZtdXd3yzRNXb16VRcuXJD06VLwL7/8soaGhnb2A+6Qo0ePSpI++OCDit63tram0dHRLcOshx56SCdPnpRhGAqHw/rd735XEKJJ0vnz50vO0dPV1dXQ7Unr99g0TbW3t7tDujY+K+Vy7oFzT4DdZuRK9UsDAACApxmGocXFRXcoA7Abrly5osHBwZLDY2rN+RLfDF+DBgcHJUmLi4v3dB6nd9LY2FjF77UsS0tLS/fUPu2tC4fD8vv9Vd2HjfjzG9WgBxAAAAAAeFgwGNTNmzcLhrGVI5lMamJiokZVtVZ7qVRKqVRKwWBwV9oDiiEAAgAAANASNs4b1Cp8Pp+i0aguXry45Zw3+RKJhB5++OEdXSGsVdtbXl7W3NycotGofD5fzdsDSiEAAgAAADZIpVKKRCKyLOuelnJOJpMKh8PuRLThcFipVEqZTKauS0Sn02mFQiEZhqFQKFQwsa6kgslzN/7Mzs7Ktu2Skwo3so3zBrWStrY2xWIx3bhxo6zje3p63Amkd4OX27NtWxcuXFBbW9uutAeUQgAEAAAA5JmdnVU4HNa+fft06dKlqueKCYfDWlhY0NDQkLs89Llz55ROp+saPmSzWaVSKV2+fFlra2s6ceKETp48WTBxbi6X0+rqqvt6bW3N/QynTp1SJBLR0NBQ0/WiKbVcd6vw+Xw7Mv8MKjM2Nkb4g4ZAAAQAAAD8USgU0tramrsseGdnZ1XncXr6XL58uaCXQVtbm0zT1O3bt3eq5IrdunXLXTnJ5/O5y1lbllVwXP4X1vxhK4FAQNFoVNL63DLN2BMIAFoRARAAAADKls1mFY/H3eFAkUikrGM2zr0Sj8fdwMG2bRmGIcuylE6nlUwmNw07cszOzrrb0un0lueplLMc9NTUVMl5OsLh8LbLRieTSU1PT285uWyxeUd267oFAoGiNY2Ojm75ufK1tbXpRz/6kWzb1q1bt8p+HwCgfgiAAAAAULahoSG99dZb7hCaf/u3f9sUiAwNDenDDz90hxHZtv3/2bv/2Dbuw/7/r2vieIvRkUgLyahXpygyCwY2KGsxR1rbCJGDATZ2TLtZrn6U9QpQAjWkXQLzj0YgYRgUnPwhokYdIIJEfIBAkEw4+6PRITMKWNokDAkdzIC4zX9UCNKIRdOJwBByAba2aXrfP/y9MymR4g9LJHV6PgDCvuOb7/f77hQlfOX9o2SkSCgU0tDQkCzLUjqdlmma2tjYkGVZevnll9XT06OlpSVJUjQaLZmqc/HiRUWjUa2tren555/fsZ56ZDIZTU5O6uzZs5qdnXWDla1r49TirbfekiR9+ctf3rHc1ilIzbpvW0c1OfWfPXu2ruv86le/Kkn6p3/6p7o+BwBoERsAAAAHkiR7fn6+5vLXr1+3Jdmbm5vuuXfeecc2TdM9XlpaKltGkn39+vWStrf+p+jWc9Fo1JZk5/N591w+n7ej0Whd9dRiamrKlmSvra257YTDYVuS/c4779RVVyPtN/u+bW3bNM2S8rVeSyPXOj8/X/dnDqrh4WF7eHi41d1AG6r39zdg27b9cDNCJgAAAOx/CwsLkkrXhunp6dHi4qJ7/MYbb2wrc/LkSffzznoztTh37pwmJyd18+ZN93N37tzRuXPnGr+ICiKRiCS506N8Pp/C4bCmp6f1+uuv7/lW0a28b1evXtXExETTt6d2rhmVOVMZuVcAdoNh2wdw+XsAAADIMAzNz89reHi45vLS9qlLtZTZer5cuXLnnPVunJApFospHo/v+Jla+tlov2sxPj6u6elp5fP5mkOVZt83RyqV0scff6zR0dG6+iXdmzrm9/sVjUbL1l3JwsKCRkZGai4PoLx6fn8DEmsAAQAAoEbOzlGZTKZqmXLbg9ezyLBjeHjYXfMmm83q1KlTdddRC6dv5Xa0cq6pVs5aOh988EHNn2nFfctkMrp7927F8KeaO3fuSJKeeeaZhj5vb9mSndf21/DwsIaHh1veD17t9wIaQQAEAACAmjghxfT0tBuUZLNZjY+Pu2Wc/xv9/vvvu+ecsgMDA3W32d/fL0l6/fXX9fbbb+vpp59urPNVOH0rDm2cftf7f9hN05Rpmpqenq5YJpvNKpFIuMfNvm+5XE63bt0qGbmTyWRKnuVOcrmcrl69KtM03bYAAO2NAAgAAAA1ee6559xgw+/3yzAMvfzyy3rxxRfdMmfOnJFpmrpy5Yo7muXmzZsKh8NuUFA8ysUJOYpH3hS/39HRoWg0qunpaf3yl78smVJVTz3V9Pf3KxqNKhaLuZ+7ceOGTNMsWX+nlm3gJSmZTOqXv/ylxsfHtb6+XvJeNpvV888/r2Aw6J5r9n0LhUKKRCIlW8Y/+eSTJTuBFddd/PdMJqNQKOReJwBgfyAAAgAAQE06OjqUTCYVjUYl3dtq/MUXX9SJEyfcMj6fT8lkUqZpqrOz011D5pVXXnHLdHZ2un/3+/0lf259X5K7ePHWqVj11lNNPB7f1u+5ubm66nB0dHRobm5OZ8+e1Y9+9CM3ZAkEAvrpT3+qV199tWTB52bet0uXLsmyrLL97urqknRv7Z/iup3AzzAM3bp1SxMTE1pcXCy5BgBAe2MRaAAAgAOq3kWggd3gLALN15DqnMWy5+fnW9wTtBt+f6MRjAACAAAAAADwOAIgAAAAAAAAjyMAAgAAgGcVL3K80wvwolwuV7LbHNpHIpEoWVwdaAYCIAAAAHiWbds1vXBwFAqFPQ399rr+WuVyOV26dKlkEfBUKqVAICDDMDQ+Pl7XTnnFMplMSYA6Pj5e8n42m9X4+Lj73vLy8gNdSzPbKxQKSqfTmp2dVSAQKFum1vYsy3LvdyAQUCqVct979tlnFQwGG34GQCMIgAAAAAAcGKurq/u6/loUCgWFQiFduHDB3aVvdnZWHR0dWlxclG3b6uvrUygUUiaTqbv+d999t+T47NmzJW1nMhm99tpryufz6uvr0+nTpyvuPNdu7U1NTemtt97S2NhY2TpqbS+RSCgQCCgej8u2bcXjcQ0NDbkjsrq7uzUxMaFQKMRIIDQNARAAAACAA6FQKGh2dnbf1l+rZDKp7u5u9fT0uOfGxsZKRpsMDg7KsizFYrG66z969GjJCLriUUarq6vusc/n0+DgoCRVHE3Tbu3F43HF4/GK79faXiQSkXQv6Cn+c2VlxS3T09OjY8eOKZlMNtRXoF4EQAAAAADaXqFQUCqVcqcBzc7OlgQa5dZ02npuamrKHanhnM/lcu5UHeneSBlnas/6+voD1y9JsVisoaClEblcTpFIRM8880zJ+ZmZGS0sLGwrf+zYsbrqz2azCgQCisViSqfT294vDmeKhcPhutppVXvV1Nre1NSUJLl9zmazkrQtXBoYGFAkEmEqGJqCAAgAAABA2wsGg/r4449l27Y2NzdlWVbJ9JnNzc1tn9nY2Cg5Lv7y7Ywm6ezsVCAQkGVZSqfTGh0dVT6flyR1dXW5IVCj9Tfb7du3JUlPPPFEyfnR0VEtLi66x8511RuUOFPGJicn1dvbq0AgsGN44Tyf4mlb7dxevSq1d/HiRUWjUfX29iqdTuvtt9/W5uamOxLI4Twn57kBe4kACAAAAEBbW15elmVZeu655yRJHR0dmpiYkGVZunnzpntuq+PHj1etuzikcaZM+Xw+NxhxRvQ0Wr9UfVrRbnLWy6nWt7m5Oa2trW0LJKoxTVP5fF5ra2uKRqOyLEtvvvlmxfJ37tyRaZp6+umn62qnVe3Va6f24vG4wuGwent7dffuXR0+fHhbGZ/PJ0klo82AvUIABAAAAKCtvfHGG5JKQ5iTJ09KUtlpTbvBCUactVz2i8nJyapllpeXde7cubrDH4fP51N3d7fi8bhmZmZ2XHD56tWrmpiYcIOO/dBePXZqL5FIqK+vzx1RFgwGty347Hxuv/2cYX8iAAIAAADQ1qanp7edc744P8juUgfVo48+2nD4s9X58+crPoNUKiXTNEsWo95v7e1kp/ZSqZQikYjOnDkjn8+nYDAoy7J048aNpvQNKIcACAAAAEBbcxbeLbf2y14t9tus+pstlUrtakBSPF2uWCaT0d27dzU6OrprbbWivUqqtTc0NCTpflDZ2dkp6d5ubECrEAABAAAAaGvDw8OSpPfff98950ylGRgY2JM2nTVZmrWY8G5xdp/aOtXI4WxbvlsKhcK2Z5DL5XTr1q2SdY8ymYzGx8f3XXvl1NLe1t3CnCCo0i5i0Wh0D3oKlCIAAgAAANDWzpw5I9M0deXKFXcU0M2bNxUOh9Xf3++Wc0aGOOFN8bbhzpfz4tFEiUSipJ1UKiXpXsgwNzcn0zRLvrA3Wn8zt4E/ceKEpMoBUKW+JBIJGYbh7rpVTiqV0vLysnuczWa1urpa8gxyuZxCoZAikYgMw3BfTz75ZEmY1o7tOYrv3db7WGt7L7zwgnsN0v2fFed88TVJ0qlTp6r2C3hQBEAAAAAA2prP51MymZRpmurs7JRhGJKkV155paTcSy+9JNM01dXVJcuy1NPTI9M0df36dV2+fFnS/a3ar127pmAwWPL5kydPKhAIyO/36/jx45qbm9vV+pvhqaeekiR9+OGHdX0un88rHA7vGFQdOXJEp0+flmEYisVi+uijj7aNaLl06VLFNXq6urrauj1JMgxDfr/fPfb7/e7PWz3t9ff3a2lpSSsrKzIMQ6+//rqWlpZKwivp/nNynhuwlwy7eN9DAAAAHBiGYWh+ft6dXgM0w8LCgkZGRtROX0OcL/jt1CdJGhkZkSTNz8/X9Tln5NHFixfrbjMQCGhxcbHuzzXK6+1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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.keras.utils.plot_model( model, show_shapes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Model\n",
    "import struct\n",
    "\n",
    "class WeightReader:\n",
    "    def __init__(self, weight_file):\n",
    "        with open(weight_file, 'rb') as w_f:\n",
    "            major,    = struct.unpack('i', w_f.read(4))\n",
    "            minor,    = struct.unpack('i', w_f.read(4))\n",
    "            revision, = struct.unpack('i', w_f.read(4))\n",
    "\n",
    "            if (major*10 + minor) >= 2 and major < 1000 and minor < 1000:\n",
    "                print(\"reading 64 bytes\")\n",
    "                w_f.read(8)\n",
    "            else:\n",
    "                print(\"reading 32 bytes\")\n",
    "                w_f.read(4)\n",
    "\n",
    "            transpose = (major > 1000) or (minor > 1000)\n",
    "            \n",
    "            binary = w_f.read()\n",
    "\n",
    "        self.offset = 0\n",
    "        self.all_weights = np.frombuffer(binary, dtype='float32')\n",
    "        \n",
    "    def read_bytes(self, size):\n",
    "        self.offset = self.offset + size\n",
    "        return self.all_weights[self.offset-size:self.offset]\n",
    "\n",
    "    def load_weights(self, model):\n",
    "        count = 0\n",
    "        ncount = 0\n",
    "        for i in range(23):\n",
    "            try:\n",
    "\n",
    "                conv_layer = model.get_layer('convn_' + str(i))\n",
    "                filter = conv_layer.kernel.shape[-1]\n",
    "                nweights = np.prod(conv_layer.kernel.shape) # kernel*kernel*c*filter\n",
    "                \n",
    "                print(\"loading weights of convolution #\" + str(i)+ \"- nb parameters: \"+str(nweights+filter))             \n",
    "                \n",
    "                if i  in [15, 22]:\n",
    "                    print(\"Special processing for layer \"+ str(i))\n",
    "                    bias  = self.read_bytes(filter) # bias\n",
    "                    weights = self.read_bytes(nweights) # weights\n",
    "                \n",
    "                else:                    \n",
    "                    bias  = self.read_bytes(filter) # bias\n",
    "                    scale = self.read_bytes(filter) # scale\n",
    "                    mean  = self.read_bytes(filter) # mean\n",
    "                    var   = self.read_bytes(filter) # variance\n",
    "                    weights = self.read_bytes(nweights) # weights\n",
    "                    \n",
    "                    bias = bias - scale  * mean / (np.sqrt(var + 0.00001)) #normalize bias\n",
    "\n",
    "                    weights = np.reshape(weights,(filter,int(nweights/filter)))  #normalize weights\n",
    "                    A = scale / (np.sqrt(var + 0.00001))\n",
    "                    A= np.expand_dims(A,axis=0)\n",
    "                    weights = weights* A.T\n",
    "                    weights = np.reshape(weights,(nweights))\n",
    "                \n",
    "\n",
    "                weights = weights.reshape(list(reversed(conv_layer.get_weights()[0].shape)))                 \n",
    "                weights = weights.transpose([2,3,1,0])\n",
    "                \n",
    "                if len(conv_layer.get_weights()) > 1:\n",
    "                    a=conv_layer.set_weights([weights, bias])\n",
    "                else:    \n",
    "                    a=conv_layer.set_weights([weights])\n",
    "                \n",
    "                count = count+1\n",
    "                ncount = ncount+nweights+filter\n",
    "             \n",
    "            except ValueError:\n",
    "                print(\"no convolution #\" + str(i)) \n",
    "        \n",
    "        print(count, \"Conv normalized layers loaded \", ncount, \" parameters\")\n",
    "    \n",
    "    def reset(self):\n",
    "        self.offset = 0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reading 64 bytes\n",
      "loading weights of convolution #0- nb parameters: 448\n",
      "no convolution #1\n",
      "loading weights of convolution #2- nb parameters: 4640\n",
      "no convolution #3\n",
      "loading weights of convolution #4- nb parameters: 18496\n",
      "no convolution #5\n",
      "loading weights of convolution #6- nb parameters: 73856\n",
      "no convolution #7\n",
      "loading weights of convolution #8- nb parameters: 295168\n",
      "no convolution #9\n",
      "loading weights of convolution #10- nb parameters: 1180160\n",
      "no convolution #11\n",
      "loading weights of convolution #12- nb parameters: 4719616\n",
      "loading weights of convolution #13- nb parameters: 262400\n",
      "loading weights of convolution #14- nb parameters: 1180160\n",
      "loading weights of convolution #15- nb parameters: 130815\n",
      "Special processing for layer 15\n",
      "no convolution #16\n",
      "no convolution #17\n",
      "loading weights of convolution #18- nb parameters: 32896\n",
      "no convolution #19\n",
      "no convolution #20\n",
      "loading weights of convolution #21- nb parameters: 884992\n",
      "loading weights of convolution #22- nb parameters: 65535\n",
      "Special processing for layer 22\n",
      "13 Conv normalized layers loaded  8849182  parameters\n"
     ]
    }
   ],
   "source": [
    "# Get and compute the weights\n",
    "weight_reader = WeightReader('models/yolo/yolov3-tiny.weights')\n",
    "weight_reader.load_weights(model)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Le volume dans le lecteur C s'appelle Windows\n",
      " Le num‚ro de s‚rie du volume est CCDC-AB26\n",
      "\n",
      " R‚pertoire de C:\\Users\\edh\\yolo4\\Yolov-4\\models\\yolo\n",
      "\n",
      "11/07/2020  12:02        35ÿ584ÿ360 yolov3-tiny.h5\n",
      "11/07/2020  10:22       258ÿ880ÿ344 yolov4.h5\n",
      "               2 fichier(s)      294ÿ464ÿ704 octets\n",
      "               0 R‚p(s)  297ÿ033ÿ768ÿ960 octets libres\n"
     ]
    }
   ],
   "source": [
    "# save the model to file\n",
    "! rm models\\yolo\\yolov3-tiny.h5\n",
    "\n",
    "model.save('models/yolo/yolov3-tiny.h5')\n",
    "! dir models\\yolo\\*.h5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\edh\\Anaconda3\\lib\\site-packages\\keras\\engine\\saving.py:341: UserWarning: No training configuration found in save file: the model was *not* compiled. Compile it manually.\n",
      "  warnings.warn('No training configuration found in save file: '\n"
     ]
    }
   ],
   "source": [
    "# Load the model\n",
    "from keras.models import load_model, Model\n",
    "yolo_model = load_model(\"models/yolo/yolov3-tiny.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_labels(labels_path):\n",
    "    with open(labels_path) as f:\n",
    "        labels = f.readlines()\n",
    "    labels = [c.strip() for c in labels]\n",
    "    return labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['person', 'bicycle', 'car', 'motorbike', 'aeroplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'sofa', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tvmonitor', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush']\n",
      "nb labels:  80\n"
     ]
    }
   ],
   "source": [
    "# Load the labels\n",
    "labels = read_labels(\"models/yolo/coco_classes.txt\")\n",
    "print(labels)\n",
    "print(\"nb labels: \",len(labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import expand_dims\n",
    "from keras.preprocessing.image import load_img\n",
    "from keras.preprocessing.image import img_to_array\n",
    "\n",
    "# load and prepare an image\n",
    "def load_image_pixels(filename, shape):\n",
    "    # load the image to get its shape\n",
    "    image = load_img(filename)\n",
    "    width, height = image.size\n",
    "    # load the image with the required size\n",
    "    image = load_img(filename, interpolation = 'bilinear', target_size=shape)\n",
    "    # convert to numpy array\n",
    "    image = img_to_array(image)\n",
    "    # scale pixel values to [0, 1]\n",
    "    image = image.astype('float32')\n",
    "    image /= 255.0\n",
    "\n",
    "    # add a dimension so that we have one sample\n",
    "    image = expand_dims(image, 0)\n",
    "    \n",
    "    return image, width, height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image initial size:  300 168\n",
      "input image (1, 416, 416, 3)\n"
     ]
    }
   ],
   "source": [
    "# Pre-process the image\n",
    "input_w, input_h = 416, 416\n",
    "photo_filename = 'images/6dogs.jpg'\n",
    "image, image_w, image_h = load_image_pixels(photo_filename, (input_w, input_h))\n",
    "print(\"image initial size: \", image_w, image_h)\n",
    "print(\"input image\",image.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output [(1, 13, 13, 255), (1, 26, 26, 255)]\n"
     ]
    }
   ],
   "source": [
    "# Run the model\n",
    "yhat = yolo_model.predict(image)\n",
    "\n",
    "print(\"output\",[a.shape for a in yhat])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "class BoundBox:\n",
    "    def __init__(self, xmin, ymin, xmax, ymax, objness = None, classes = None):\n",
    "        self.xmin = xmin\n",
    "        self.ymin = ymin\n",
    "        self.xmax = xmax\n",
    "        self.ymax = ymax\n",
    "        self.objness = objness\n",
    "        self.classes = classes\n",
    "        self.label = -1\n",
    "        self.score = -1\n",
    " \n",
    "    def get_label(self):\n",
    "        if self.label == -1:\n",
    "            self.label = np.argmax(self.classes)\n",
    " \n",
    "        return self.label\n",
    " \n",
    "    def get_score(self):\n",
    "        if self.score == -1:\n",
    "            self.score = self.classes[self.get_label()]\n",
    " \n",
    "        return self.score\n",
    " \n",
    "def _sigmoid(x):\n",
    "    return 1. / (1. + np.exp(-x))\n",
    " \n",
    "def decode_netout(netout, anchors, obj_thresh, net_h, net_w, nb_box, scales_x_y):\n",
    "    grid_h, grid_w = netout.shape[:2]  \n",
    "    netout = netout.reshape((grid_h, grid_w, nb_box, -1))\n",
    "    nb_class = netout.shape[-1] - 5 # 5 = bx,by,bh,bw,pc\n",
    "\n",
    "    print(\"grid_h,grid_w: \",grid_h,grid_w)   \n",
    "    print(\"nb class: \",nb_class)   \n",
    "    \n",
    "    boxes = []\n",
    "    netout[..., :2] = _sigmoid(netout[..., :2]) # x, y\n",
    "    netout[..., :2] = netout[..., :2]*scales_x_y - 0.5*(scales_x_y - 1.0) # scale x, y\n",
    "\n",
    "    netout[..., 4:] = _sigmoid(netout[..., 4:]) # objectness + classes probabilities\n",
    "\n",
    "    for i in range(grid_h*grid_w):\n",
    "\n",
    "        row = i / grid_w\n",
    "        col = i % grid_w\n",
    "        \n",
    "        \n",
    "        for b in range(nb_box):\n",
    "            # 4th element is objectness\n",
    "            objectness = netout[int(row)][int(col)][b][4]\n",
    "\n",
    "            if(objectness > obj_thresh):\n",
    "                print(\"objectness: \",objectness)                \n",
    "            \n",
    "                # first 4 elements are x, y, w, and h\n",
    "                x, y, w, h = netout[int(row)][int(col)][b][:4]\n",
    "                x = (col + x) / grid_w # center position, unit: image width\n",
    "                y = (row + y) / grid_h # center position, unit: image height\n",
    "                w = anchors[2 * b + 0] * np.exp(w) / net_w # unit: image width\n",
    "                h = anchors[2 * b + 1] * np.exp(h) / net_h # unit: image height            \n",
    "            \n",
    "                # last elements are class probabilities\n",
    "                classes = objectness*netout[int(row)][col][b][5:]\n",
    "                classes *= classes > obj_thresh\n",
    "                box = BoundBox(x-w/2, y-h/2, x+w/2, y+h/2, objectness, classes)           \n",
    "                boxes.append(box)\n",
    "    return boxes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "grid_h,grid_w:  13 13\n",
      "nb class:  80\n",
      "objectness:  0.70689446\n",
      "objectness:  0.5728171\n",
      "objectness:  0.3412685\n",
      "objectness:  0.39486772\n",
      "objectness:  0.45261085\n",
      "objectness:  0.26239383\n",
      "grid_h,grid_w:  26 26\n",
      "nb class:  80\n",
      "nb boxes detected;  6\n"
     ]
    }
   ],
   "source": [
    "# Compute the Yolo layers\n",
    "obj_thresh = 0.25\n",
    "anchors = [[81,82,  135,169,  344,319], [10,14,  23,27,  37,58]]\n",
    "scales_x_y = [1.0, 1.0]\n",
    "boxes = list()\n",
    "\n",
    "for i in range(len(anchors)):\n",
    "    # decode the output of the network\n",
    "    boxes += decode_netout(yhat[i][0], anchors[i], obj_thresh, input_h, input_w, 3, scales_x_y[i])\n",
    "\n",
    "print(\"nb boxes detected; \",len(boxes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def correct_yolo_boxes(boxes, image_h, image_w, net_h, net_w):\n",
    " new_w, new_h = net_w, net_h\n",
    " for i in range(len(boxes)):\n",
    "        x_offset, x_scale = (net_w - new_w)/2./net_w, float(new_w)/net_w\n",
    "        y_offset, y_scale = (net_h - new_h)/2./net_h, float(new_h)/net_h\n",
    "        boxes[i].xmin = int((boxes[i].xmin - x_offset) / x_scale * image_w)\n",
    "        boxes[i].xmax = int((boxes[i].xmax - x_offset) / x_scale * image_w)\n",
    "        boxes[i].ymin = int((boxes[i].ymin - y_offset) / y_scale * image_h)\n",
    "        boxes[i].ymax = int((boxes[i].ymax - y_offset) / y_scale * image_h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Correct the boxes according the inital size of the image\n",
    "correct_yolo_boxes(boxes, image_h, image_w, input_h, input_w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _interval_overlap(interval_a, interval_b):\n",
    "    x1, x2 = interval_a\n",
    "    x3, x4 = interval_b\n",
    "    if x3 < x1:\n",
    "        if x4 < x1:\n",
    "            return 0\n",
    "        else:\n",
    "            return min(x2,x4) - x1\n",
    "    else:\n",
    "        if x2 < x3:\n",
    "            return 0\n",
    "        else:\n",
    "            return min(x2,x4) - x3\n",
    " \n",
    "def bbox_iou(box1, box2):\n",
    "    intersect_w = _interval_overlap([box1.xmin, box1.xmax], [box2.xmin, box2.xmax])\n",
    "    intersect_h = _interval_overlap([box1.ymin, box1.ymax], [box2.ymin, box2.ymax])\n",
    "    intersect = intersect_w * intersect_h\n",
    "    w1, h1 = box1.xmax-box1.xmin, box1.ymax-box1.ymin\n",
    "    w2, h2 = box2.xmax-box2.xmin, box2.ymax-box2.ymin\n",
    "    union = w1*h1 + w2*h2 - intersect\n",
    "    return float(intersect) / union\n",
    "\n",
    "def do_nms(boxes, nms_thresh):\n",
    "    if len(boxes) > 0:\n",
    "        nb_class = len(boxes[0].classes)\n",
    "    else:\n",
    "        return\n",
    "    for c in range(nb_class):\n",
    "        sorted_indices = np.argsort([-box.classes[c] for box in boxes])\n",
    "        for i in range(len(sorted_indices)):\n",
    "            index_i = sorted_indices[i]\n",
    "            if boxes[index_i].classes[c] == 0: continue\n",
    "            for j in range(i+1, len(sorted_indices)):\n",
    "                index_j = sorted_indices[j]\n",
    "                if bbox_iou(boxes[index_i], boxes[index_j]) >= nms_thresh:\n",
    "                    boxes[index_j].classes[c] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nb boxes remaining;  6\n"
     ]
    }
   ],
   "source": [
    "# Suppress the non Maximal boxes\n",
    "do_nms(boxes, 0.5)\n",
    "print(\"nb boxes remaining; \",len(boxes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import colorsys\n",
    "import random\n",
    "\n",
    "def generate_colors(class_names):\n",
    "    hsv_tuples = [(x / len(class_names), 1., 1.) for x in range(len(class_names))]\n",
    "    colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))\n",
    "    colors = list(map(lambda x: (int(x[0] ), int(x[1] ), int(x[2] )), colors))\n",
    "    random.seed(10101)  # Fixed seed for consistent colors across runs.\n",
    "    random.shuffle(colors)  # Shuffle colors to decorrelate adjacent classes.\n",
    "    random.seed(None)  # Reset seed to default.\n",
    "    return colors\n",
    "\n",
    "# get all of the results above a threshold\n",
    "def get_boxes(boxes, labels, thresh, colors):\n",
    "    v_boxes, v_labels, v_scores, v_colors = list(), list(), list(), list()\n",
    "    # enumerate all boxes\n",
    "    for box in boxes:\n",
    "        # enumerate all possible labels\n",
    "        for i in range(len(labels)):\n",
    "\n",
    "            # check if the threshold for this label is high enough\n",
    "            if box.classes[i] > thresh:\n",
    "                v_boxes.append(box)\n",
    "                v_labels.append(labels[i])\n",
    "                v_scores.append(box.classes[i]*100)\n",
    "                v_colors.append(colors[i])\n",
    "                # don't break, many labels may trigger for one box\n",
    "    return v_boxes, v_labels, v_scores, v_colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nb boxes remaining;  4\n"
     ]
    }
   ],
   "source": [
    "# Get the details of the detected objects for a threshold > 0.6\n",
    "class_threshold = 0.25\n",
    "colors = generate_colors(labels)\n",
    "v_boxes, v_labels, v_scores, v_colors = get_boxes(boxes, labels, class_threshold, colors)\n",
    "print(\"nb boxes remaining; \",len(v_boxes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot\n",
    "from matplotlib.patches import Rectangle\n",
    "\n",
    "# draw all results\n",
    "def draw_boxes(filename, v_boxes, v_labels, v_scores, v_colors):\n",
    "    # load the image\n",
    "    data = pyplot.imread(filename)\n",
    "    # plot the image\n",
    "    pyplot.imshow(data)\n",
    "    # get the context for drawing boxes\n",
    "    ax = pyplot.gca()\n",
    "    # plot each box\n",
    "    for i in range(len(v_boxes)):\n",
    "        box = v_boxes[i]\n",
    "        # get coordinates\n",
    "        y1, x1, y2, x2 = box.ymin, box.xmin, box.ymax, box.xmax\n",
    "        # calculate width and height of the box\n",
    "        width, height = x2 - x1, y2 - y1\n",
    "        # create the shape\n",
    "        rect = Rectangle((x1, y1), width, height, fill=False, color=v_colors[i])\n",
    "        # draw the box\n",
    "        ax.add_patch(rect)\n",
    "        # draw text and score in top left corner\n",
    "        label = \"%s (%.3f)\" % (v_labels[i], v_scores[i])\n",
    "        pyplot.text(x1, y1, label, color='white')\n",
    "    # show the plot\n",
    "    pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dog 69.46498155593872 13 97 53 121\n",
      "dog 55.883872509002686 95 164 52 125\n",
      "dog 35.331496596336365 159 233 51 131\n",
      "dog 39.756783843040466 203 281 56 134\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw the result\n",
    "for i in range(len(v_boxes)):\n",
    "    print(v_labels[i], v_scores[i],  v_boxes[i].xmin, v_boxes[i].xmax, v_boxes[i].ymin, v_boxes[i].ymax)\n",
    "# draw what we found\n",
    "draw_boxes(photo_filename, v_boxes, v_labels, v_scores, v_colors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}