Yolo-2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ashish/Envs/DCRNN/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential, Model\n",
    "from keras.layers import Dense, Conv2D, MaxPooling2D, Dropout, Flatten, BatchNormalization, Reshape, Input, Lambda\n",
    "from keras.layers import LeakyReLU\n",
    "from keras.preprocessing import image as Im\n",
    "import numpy as np\n",
    "from utils import *\n",
    "from config import anchors, class_names\n",
    "from keras.layers.merge import concatenate\n",
    "import tensorflow as tf\n",
    "import scipy.io\n",
    "import scipy.misc\n",
    "from matplotlib.pyplot import imshow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_model(input_shape):\n",
    "    input = Input(input_shape)\n",
    "\n",
    "    def space_to_depth_x2(x):\n",
    "        return tf.space_to_depth(x, block_size=2)\n",
    "\n",
    "    # Layer 1\n",
    "    x = Conv2D(32, (3,3), strides=(1,1), padding='same', name='conv_1', use_bias=False)(input)\n",
    "    x = BatchNormalization(name='norm_1')(x)\n",
    "    x = LeakyReLU(alpha=0.1)(x)\n",
    "    x = MaxPooling2D(pool_size=(2, 2))(x)\n",
    "\n",
    "    # Layer 2\n",
    "    x = Conv2D(64, (3,3), strides=(1,1), padding='same', name='conv_2', use_bias=False)(x)\n",
    "    x = BatchNormalization(name='norm_2')(x)\n",
    "    x = LeakyReLU(alpha=0.1)(x)\n",
    "    x = MaxPooling2D(pool_size=(2, 2))(x)\n",
    "\n",
    "    # Layer 3\n",
    "    x = Conv2D(128, (3,3), strides=(1,1), padding='same', name='conv_3', use_bias=False)(x)\n",
    "    x = BatchNormalization(name='norm_3')(x)\n",
    "    x = LeakyReLU(alpha=0.1)(x)\n",
    "\n",
    "    # Layer 4\n",
    "    x = Conv2D(64, (1,1), strides=(1,1), padding='same', name='conv_4', use_bias=False)(x)\n",
    "    x = BatchNormalization(name='norm_4')(x)\n",
    "    x = LeakyReLU(alpha=0.1)(x)\n",
    "\n",
    "    # Layer 5\n",
    "    x = Conv2D(128, (3,3), strides=(1,1), padding='same', name='conv_5', use_bias=False)(x)\n",
    "    x = BatchNormalization(name='norm_5')(x)\n",
    "    x = LeakyReLU(alpha=0.1)(x)\n",
    "    x = MaxPooling2D(pool_size=(2, 2))(x)\n",
    "\n",
    "    # Layer 6\n",
    "    x = Conv2D(256, (3,3), strides=(1,1), padding='same', name='conv_6', use_bias=False)(x)\n",
    "    x = BatchNormalization(name='norm_6')(x)\n",
    "    x = LeakyReLU(alpha=0.1)(x)\n",
    "\n",
    "    # Layer 7\n",
    "    x = Conv2D(128, (1,1), strides=(1,1), padding='same', name='conv_7', use_bias=False)(x)\n",
    "    x = BatchNormalization(name='norm_7')(x)\n",
    "    x = LeakyReLU(alpha=0.1)(x)\n",
    "\n",
    "    # Layer 8\n",
    "    x = Conv2D(256, (3,3), strides=(1,1), padding='same', name='conv_8', use_bias=False)(x)\n",
    "    x = BatchNormalization(name='norm_8')(x)\n",
    "    x = LeakyReLU(alpha=0.1)(x)\n",
    "    x = MaxPooling2D(pool_size=(2, 2))(x)\n",
    "\n",
    "    # Layer 9\n",
    "    x = Conv2D(512, (3,3), strides=(1,1), padding='same', name='conv_9', use_bias=False)(x)\n",
    "    x = BatchNormalization(name='norm_9')(x)\n",
    "    x = LeakyReLU(alpha=0.1)(x)\n",
    "\n",
    "    # Layer 10\n",
    "    x = Conv2D(256, (1,1), strides=(1,1), padding='same', name='conv_10', use_bias=False)(x)\n",
    "    x = BatchNormalization(name='norm_10')(x)\n",
    "    x = LeakyReLU(alpha=0.1)(x)\n",
    "\n",
    "    # Layer 11\n",
    "    x = Conv2D(512, (3,3), strides=(1,1), padding='same', name='conv_11', use_bias=False)(x)\n",
    "    x = BatchNormalization(name='norm_11')(x)\n",
    "    x = LeakyReLU(alpha=0.1)(x)\n",
    "\n",
    "    # Layer 12\n",
    "    x = Conv2D(256, (1,1), strides=(1,1), padding='same', name='conv_12', use_bias=False)(x)\n",
    "    x = BatchNormalization(name='norm_12')(x)\n",
    "    x = LeakyReLU(alpha=0.1)(x)\n",
    "\n",
    "    # Layer 13\n",
    "    x = Conv2D(512, (3,3), strides=(1,1), padding='same', name='conv_13', use_bias=False)(x)\n",
    "    x = BatchNormalization(name='norm_13')(x)\n",
    "    x = LeakyReLU(alpha=0.1)(x)\n",
    "\n",
    "    skip_connection = x\n",
    "\n",
    "    x = MaxPooling2D(pool_size=(2, 2))(x)\n",
    "\n",
    "    # Layer 14\n",
    "    x = Conv2D(1024, (3,3), strides=(1,1), padding='same', name='conv_14', use_bias=False)(x)\n",
    "    x = BatchNormalization(name='norm_14')(x)\n",
    "    x = LeakyReLU(alpha=0.1)(x)\n",
    "\n",
    "    # Layer 15\n",
    "    x = Conv2D(512, (1,1), strides=(1,1), padding='same', name='conv_15', use_bias=False)(x)\n",
    "    x = BatchNormalization(name='norm_15')(x)\n",
    "    x = LeakyReLU(alpha=0.1)(x)\n",
    "\n",
    "    # Layer 16\n",
    "    x = Conv2D(1024, (3,3), strides=(1,1), padding='same', name='conv_16', use_bias=False)(x)\n",
    "    x = BatchNormalization(name='norm_16')(x)\n",
    "    x = LeakyReLU(alpha=0.1)(x)\n",
    "\n",
    "    # Layer 17\n",
    "    x = Conv2D(512, (1,1), strides=(1,1), padding='same', name='conv_17', use_bias=False)(x)\n",
    "    x = BatchNormalization(name='norm_17')(x)\n",
    "    x = LeakyReLU(alpha=0.1)(x)\n",
    "\n",
    "    # Layer 18\n",
    "    x = Conv2D(1024, (3,3), strides=(1,1), padding='same', name='conv_18', use_bias=False)(x)\n",
    "    x = BatchNormalization(name='norm_18')(x)\n",
    "    x = LeakyReLU(alpha=0.1)(x)\n",
    "\n",
    "    # Layer 19\n",
    "    x = Conv2D(1024, (3,3), strides=(1,1), padding='same', name='conv_19', use_bias=False)(x)\n",
    "    x = BatchNormalization(name='norm_19')(x)\n",
    "    x = LeakyReLU(alpha=0.1)(x)\n",
    "\n",
    "    # Layer 20\n",
    "    x = Conv2D(1024, (3,3), strides=(1,1), padding='same', name='conv_20', use_bias=False)(x)\n",
    "    x = BatchNormalization(name='norm_20')(x)\n",
    "    x = LeakyReLU(alpha=0.1)(x)\n",
    "\n",
    "    # Layer 21\n",
    "    skip_connection = Conv2D(64, (1,1), strides=(1,1), padding='same', name='conv_21', use_bias=False)(skip_connection)\n",
    "    skip_connection = BatchNormalization(name='norm_21')(skip_connection)\n",
    "    skip_connection = LeakyReLU(alpha=0.1)(skip_connection)\n",
    "    skip_connection = Lambda(space_to_depth_x2)(skip_connection)\n",
    "\n",
    "    x = concatenate([skip_connection, x])\n",
    "\n",
    "    # Layer 22\n",
    "    x = Conv2D(1024, (3,3), strides=(1,1), padding='same', name='conv_22', use_bias=False)(x)\n",
    "    x = BatchNormalization(name='norm_22')(x)\n",
    "    x = LeakyReLU(alpha=0.1)(x)\n",
    "\n",
    "    out = Conv2D(5 * (4 + 1 + 80), (1,1), strides=(1,1), padding='same', name='conv_23')(x)\n",
    "\n",
    "    model = Model(inputs = input, outputs = out)\n",
    "    model.summary()\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def yolo_non_max_suppression(scores, boxes, classes, max_boxes = 10, iou_threshold = 0.5):\n",
    "    max_boxes_tensor = K.variable(max_boxes, dtype='int32')\n",
    "    K.get_session().run(tf.variables_initializer([max_boxes_tensor])) \n",
    " \n",
    "    nms_indices = tf.image.non_max_suppression(boxes, scores, max_boxes, iou_threshold=0.5)\n",
    "    scores = K.gather(scores, nms_indices)\n",
    "    boxes = K.gather(boxes, nms_indices)\n",
    "    classes = K.gather(classes, nms_indices)\n",
    "    \n",
    "    return scores, boxes, classes\n",
    "\n",
    "\n",
    "def yolo_eval(yolo_outputs, image_shape = (720., 1280.), max_boxes=10, score_threshold=.6, iou_threshold=.5):\n",
    "\n",
    "    box_confidence, box_xy, box_wh, box_class_probs = yolo_outputs\n",
    "\n",
    "\n",
    "    boxes = yolo_boxes_to_corners(box_xy, box_wh)\n",
    "\n",
    "    scores, boxes, classes = yolo_filter_boxes(box_confidence, boxes, box_class_probs, threshold=score_threshold)\n",
    "    \n",
    "    boxes = scale_boxes(boxes, image_shape)\n",
    "\n",
    "    scores, boxes, classes = yolo_non_max_suppression(scores, boxes, classes, max_boxes=max_boxes, iou_threshold=iou_threshold)\n",
    "        \n",
    "    return scores, boxes, classes\n",
    "\n",
    "def get_spaced_colors(n):\n",
    "    max_value = 255**3\n",
    "    interval = int(max_value / n)\n",
    "    colors = [hex(I)[2:].zfill(6) for I in range(0, max_value, interval)]\n",
    "    \n",
    "    return [(int(i[:2], 16), int(i[2:4], 16), int(i[4:], 16)) for i in colors]\n",
    "\n",
    "\n",
    "def preprocess_test_image(img_path, model_image_size=(608,608)):\n",
    "    original_image = Im.load_img(img_path)\n",
    "    test_image = Im.load_img(img_path, target_size = model_image_size)\n",
    "    test_data = Im.img_to_array(test_image)\n",
    "    test_data /= 255.0\n",
    "    test_data = np.expand_dims(test_data, axis = 0)\n",
    "    return original_image, test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 608, 608, 3)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv_1 (Conv2D)                 (None, 608, 608, 32) 864         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "norm_1 (BatchNormalization)     (None, 608, 608, 32) 128         conv_1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_1 (LeakyReLU)       (None, 608, 608, 32) 0           norm_1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 304, 304, 32) 0           leaky_re_lu_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv_2 (Conv2D)                 (None, 304, 304, 64) 18432       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "norm_2 (BatchNormalization)     (None, 304, 304, 64) 256         conv_2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_2 (LeakyReLU)       (None, 304, 304, 64) 0           norm_2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)  (None, 152, 152, 64) 0           leaky_re_lu_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv_3 (Conv2D)                 (None, 152, 152, 128 73728       max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "norm_3 (BatchNormalization)     (None, 152, 152, 128 512         conv_3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_3 (LeakyReLU)       (None, 152, 152, 128 0           norm_3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv_4 (Conv2D)                 (None, 152, 152, 64) 8192        leaky_re_lu_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "norm_4 (BatchNormalization)     (None, 152, 152, 64) 256         conv_4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_4 (LeakyReLU)       (None, 152, 152, 64) 0           norm_4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv_5 (Conv2D)                 (None, 152, 152, 128 73728       leaky_re_lu_4[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "norm_5 (BatchNormalization)     (None, 152, 152, 128 512         conv_5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_5 (LeakyReLU)       (None, 152, 152, 128 0           norm_5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)  (None, 76, 76, 128)  0           leaky_re_lu_5[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv_6 (Conv2D)                 (None, 76, 76, 256)  294912      max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "norm_6 (BatchNormalization)     (None, 76, 76, 256)  1024        conv_6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_6 (LeakyReLU)       (None, 76, 76, 256)  0           norm_6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv_7 (Conv2D)                 (None, 76, 76, 128)  32768       leaky_re_lu_6[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "norm_7 (BatchNormalization)     (None, 76, 76, 128)  512         conv_7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_7 (LeakyReLU)       (None, 76, 76, 128)  0           norm_7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv_8 (Conv2D)                 (None, 76, 76, 256)  294912      leaky_re_lu_7[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "norm_8 (BatchNormalization)     (None, 76, 76, 256)  1024        conv_8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_8 (LeakyReLU)       (None, 76, 76, 256)  0           norm_8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2D)  (None, 38, 38, 256)  0           leaky_re_lu_8[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv_9 (Conv2D)                 (None, 38, 38, 512)  1179648     max_pooling2d_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "norm_9 (BatchNormalization)     (None, 38, 38, 512)  2048        conv_9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_9 (LeakyReLU)       (None, 38, 38, 512)  0           norm_9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv_10 (Conv2D)                (None, 38, 38, 256)  131072      leaky_re_lu_9[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "norm_10 (BatchNormalization)    (None, 38, 38, 256)  1024        conv_10[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_10 (LeakyReLU)      (None, 38, 38, 256)  0           norm_10[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv_11 (Conv2D)                (None, 38, 38, 512)  1179648     leaky_re_lu_10[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "norm_11 (BatchNormalization)    (None, 38, 38, 512)  2048        conv_11[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_11 (LeakyReLU)      (None, 38, 38, 512)  0           norm_11[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv_12 (Conv2D)                (None, 38, 38, 256)  131072      leaky_re_lu_11[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "norm_12 (BatchNormalization)    (None, 38, 38, 256)  1024        conv_12[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_12 (LeakyReLU)      (None, 38, 38, 256)  0           norm_12[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv_13 (Conv2D)                (None, 38, 38, 512)  1179648     leaky_re_lu_12[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "norm_13 (BatchNormalization)    (None, 38, 38, 512)  2048        conv_13[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_13 (LeakyReLU)      (None, 38, 38, 512)  0           norm_13[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2D)  (None, 19, 19, 512)  0           leaky_re_lu_13[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv_14 (Conv2D)                (None, 19, 19, 1024) 4718592     max_pooling2d_5[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "norm_14 (BatchNormalization)    (None, 19, 19, 1024) 4096        conv_14[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_14 (LeakyReLU)      (None, 19, 19, 1024) 0           norm_14[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv_15 (Conv2D)                (None, 19, 19, 512)  524288      leaky_re_lu_14[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "norm_15 (BatchNormalization)    (None, 19, 19, 512)  2048        conv_15[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_15 (LeakyReLU)      (None, 19, 19, 512)  0           norm_15[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv_16 (Conv2D)                (None, 19, 19, 1024) 4718592     leaky_re_lu_15[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "norm_16 (BatchNormalization)    (None, 19, 19, 1024) 4096        conv_16[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_16 (LeakyReLU)      (None, 19, 19, 1024) 0           norm_16[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv_17 (Conv2D)                (None, 19, 19, 512)  524288      leaky_re_lu_16[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "norm_17 (BatchNormalization)    (None, 19, 19, 512)  2048        conv_17[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_17 (LeakyReLU)      (None, 19, 19, 512)  0           norm_17[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv_18 (Conv2D)                (None, 19, 19, 1024) 4718592     leaky_re_lu_17[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "norm_18 (BatchNormalization)    (None, 19, 19, 1024) 4096        conv_18[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_18 (LeakyReLU)      (None, 19, 19, 1024) 0           norm_18[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv_19 (Conv2D)                (None, 19, 19, 1024) 9437184     leaky_re_lu_18[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "norm_19 (BatchNormalization)    (None, 19, 19, 1024) 4096        conv_19[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv_21 (Conv2D)                (None, 38, 38, 64)   32768       leaky_re_lu_13[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_19 (LeakyReLU)      (None, 19, 19, 1024) 0           norm_19[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "norm_21 (BatchNormalization)    (None, 38, 38, 64)   256         conv_21[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv_20 (Conv2D)                (None, 19, 19, 1024) 9437184     leaky_re_lu_19[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_21 (LeakyReLU)      (None, 38, 38, 64)   0           norm_21[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "norm_20 (BatchNormalization)    (None, 19, 19, 1024) 4096        conv_20[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "lambda_1 (Lambda)               (None, 19, 19, 256)  0           leaky_re_lu_21[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_20 (LeakyReLU)      (None, 19, 19, 1024) 0           norm_20[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 19, 19, 1280) 0           lambda_1[0][0]                   \n",
      "                                                                 leaky_re_lu_20[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv_22 (Conv2D)                (None, 19, 19, 1024) 11796480    concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "norm_22 (BatchNormalization)    (None, 19, 19, 1024) 4096        conv_22[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "leaky_re_lu_22 (LeakyReLU)      (None, 19, 19, 1024) 0           norm_22[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv_23 (Conv2D)                (None, 19, 19, 425)  435625      leaky_re_lu_22[0][0]             \n",
      "==================================================================================================\n",
      "Total params: 50,983,561\n",
      "Trainable params: 50,962,889\n",
      "Non-trainable params: 20,672\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = load_model(input_shape = (608,608,3))\n",
    "model.load_weights('weights/weights.h5')\n",
    "anchors = np.array(anchors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 3 boxes for test_person.jpg\n",
      "person 0.79 (190, 101) (271, 377)\n",
      "dog 0.79 (71, 259) (205, 355)\n",
      "horse 0.89 (401, 130) (604, 351)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ashish/Envs/DCRNN/lib/python3.6/site-packages/ipykernel_launcher.py:20: DeprecationWarning: `imread` is deprecated!\n",
      "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
      "Use ``imageio.imread`` instead.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "image_test, image_data = preprocess_test_image(\"images/person.jpg\", model_image_size = (608, 608))\n",
    "\n",
    "image_shape = image_test.size\n",
    "image_shape = tuple(float(i) for i in reversed(image_test.size))\n",
    "\n",
    "yolo_outputs = yolo_head(model.output, anchors, len(class_names))\n",
    "scores, boxes, classes = yolo_eval(yolo_outputs, image_shape)\n",
    "\n",
    "sess = K.get_session()\n",
    "out_scores, out_boxes, out_classes = sess.run([scores, boxes, classes], feed_dict={\n",
    "    model.input: image_data\n",
    "})\n",
    "\n",
    "print('Found {} boxes for {}'.format(len(out_boxes), \"test_person.jpg\"))\n",
    "\n",
    "colors = get_spaced_colors(len(class_names))\n",
    "draw_boxes(image_test, out_scores, out_boxes, out_classes, class_names, colors)\n",
    "image_test.save(os.path.join(\"output_images\", \"test_person.jpg\"), quality=90)\n",
    "output_image = scipy.misc.imread(os.path.join(\"output_images\", \"test_person.jpg\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f38f025fc50>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(output_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}