imdb_age_predict-opencv.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    " \n",
    "import datetime\n",
    "import pandas as pd\n",
    "import os\n",
    "import warnings\n",
    "\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "from numpy import save, load\n",
    "\n",
    "from tensorflow import keras\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.optimizers import SGD, Adam\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, LearningRateScheduler, EarlyStopping, ReduceLROnPlateau, TensorBoard\n",
    "from tensorflow.keras import optimizers, losses, activations, models\n",
    "from tensorflow.keras.layers import Convolution2D, Dense, Input, Flatten, Dropout, MaxPooling2D, BatchNormalization, \\\n",
    "    GlobalMaxPool2D, Concatenate, GlobalMaxPooling2D, GlobalAveragePooling2D, Lambda, Conv2D\n",
    "from tensorflow.keras.applications.resnet50 import ResNet50\n",
    "from tensorflow.keras.applications.inception_resnet_v2 import InceptionResNetV2\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.models import Model, load_model\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from tensorflow.keras import backend as K\n",
    "from tensorflow.keras.callbacks import TensorBoard\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "\n",
    "config = tf.ConfigProto()\n",
    "config.gpu_options.allow_growth = True\n",
    "session = tf.Session(config=config)\n",
    "\n",
    "import cv2\n",
    "\n",
    "from tqdm import tqdm\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "logging.basicConfig(level=logging.DEBUG)\n",
    "warnings.simplefilter('ignore')\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Get_Croped_image(img,bb_data):\n",
    "    img_shape = img.shape\n",
    "    x = bb_data[0]\n",
    "    y = bb_data[1]\n",
    "    w = bb_data[2]\n",
    "    h = bb_data[3]\n",
    "    crop_img = img[y:y+h, x:x+w]\n",
    "    return crop_img\n",
    "\n",
    "\n",
    "datagen = ImageDataGenerator(\n",
    "    rotation_range=20,\n",
    "    width_shift_range=0.05,\n",
    "    height_shift_range=0.05,\n",
    "    horizontal_flip=True,\n",
    "    zoom_range=0.1)\n",
    "\n",
    "def augment(im_array):\n",
    "    im_array = datagen.random_transform(im_array)\n",
    "    return im_array\n",
    "\n",
    "\n",
    "def read_and_resize(dataFrame, input_shape, aug=True):\n",
    "    #filepath = image_data.path.values[0]\n",
    "    images = []\n",
    "    for index in dataFrame.index :\n",
    "        im_cv = cv2.imread(dataFrame[\"path\"][index])\n",
    "        bb_data = (dataFrame[\"x\"][index],dataFrame[\"y\"][index],dataFrame[\"w\"][index],dataFrame[\"h\"][index])\n",
    "        im_cv = Get_Croped_image(im_cv,bb_data)\n",
    "        im_cv = cv2.resize(im_cv,input_shape)\n",
    "        im_array = np.array(im_cv)\n",
    "        im_cv = np.array(im_array / (np.max(im_array)+ 0.001), dtype=\"float32\")\n",
    "        if aug:\n",
    "            im_cv = augment(im_cv)\n",
    "        images.append(im_cv)\n",
    "    return images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def gen(df, num_classes, input_shape, batch_size=4, aug=False):\n",
    "    df = df.sample(frac=1)\n",
    "    while True:\n",
    "        for _, batch in enumerate([df[i:i+batch_size] for i in range(0,df.shape[0],batch_size)]):\n",
    "            labels = np.array(batch.out_ages.values)\n",
    "            \n",
    "            #labels = labels[..., np.newaxis]\n",
    "            labels_categorical = []\n",
    "            for label in labels:\n",
    "                labels_categorical.append(to_categorical(label,num_classes=num_classes))\n",
    "            labels_categorical = np.array(labels_categorical)\n",
    "            #labels_categorical = labels_categorical[...,np.newaxis]\n",
    "            \n",
    "            #labels = labels[..., np.newaxis]    \n",
    "            images = np.array(read_and_resize(batch,input_shape=input_shape,aug=aug))\n",
    "            yield images, labels_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_model(optimizer,n_classes=1):\n",
    "\n",
    "    #base_model = keras.applications.nasnet.NASNetMobile(weights=\"./NASNet-mobile-no-top.h5\", include_top=False)\n",
    "    base_model = ResNet50(weights=\"imagenet\", include_top=False, input_tensor=Input(shape=(224,224,3)))#(Input(shape=(224, 224, 3)))\n",
    "    #for layer in base_model.layers:\n",
    "    #    layer.trainable = False\n",
    "\n",
    "    x = base_model.output\n",
    "    x = Flatten()(x)\n",
    "    x = Dense(350,activation=\"relu\")(x)\n",
    "    #x = Dense(100,activation=\"relu\")(x)\n",
    "    x = Dropout(0.2)(x)\n",
    "    if n_classes == 1:\n",
    "        x = Dense(n_classes, activation=\"sigmoid\")(x)\n",
    "    else:\n",
    "        x = Dense(n_classes, activation=\"softmax\")(x)\n",
    "\n",
    "    base_model = Model(base_model.input, x, name=\"base_model\")\n",
    "    if n_classes == 1:\n",
    "        base_model.compile(loss=\"binary_crossentropy\", metrics=['acc'], optimizer=optimizer)\n",
    "    else:\n",
    "        base_model.compile(loss=\"categorical_crossentropy\", metrics=['acc'], optimizer=optimizer)\n",
    "\n",
    "    return base_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading train and test CSV files \n",
      "Reading Done.\n",
      "Generating callback_list\n",
      "15\n",
      "Done Generating callbacklist.\n",
      "generating Model\n",
      "WARNING:tensorflow:From /mnt/sda4/vamsik1211/Data/ML/lib/python3.7/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /mnt/sda4/vamsik1211/Data/ML/lib/python3.7/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done generating model\n"
     ]
    }
   ],
   "source": [
    "if __name__ == \"__main__\":\n",
    "    base_path = \"./Dataset-copy/\"\n",
    "\n",
    "    dict_age = {'(0, 2)' : 0,\n",
    "                '(3, 5)' : 1,\n",
    "                '(6, 10)' : 2,\n",
    "                '(11, 15)' : 3,\n",
    "                '(16, 20)' : 4,\n",
    "                '(21, 30)' : 5,\n",
    "                '(31, 40)' : 6,\n",
    "                '(41, 50)' : 7,\n",
    "                '(51, 60)' : 8,\n",
    "                '(61, 70)' : 9,\n",
    "                '(71, 80)' : 10,\n",
    "                 '(81, 90)' : 11,\n",
    "                 '(91, 100)' : 12}\n",
    "\n",
    "    bag = 3\n",
    "\n",
    "    all_indexes = list(range(5))\n",
    "    \n",
    "    accuracies = []\n",
    "    print(\"Reading train and test CSV files \")\n",
    "    train_df = pd.read_csv(\"croped_filter_expanded_data_age_range_update.csv\")\n",
    "    #test_df = pd.read_csv(\"test_gender_filtered_data_with_path.csv\")\n",
    "    tr_tr, tr_val = train_test_split(train_df, test_size=0.01)\n",
    "    tr_unique_ages = tr_tr['out_ages'].unique()\n",
    "    num_classes = len(tr_unique_ages)\n",
    "    tr_unique_ages.sort()\n",
    "    #print(\"Unique ages are: \",val_unique_ages)\n",
    "    print(\"Reading Done.\")\n",
    "    cnt_ave = 0\n",
    "    predictions = 0\n",
    "#     print(\"Extracting test labels and test images from files\")\n",
    "#     test_images = load(\"imdb_test_images.npy\")\n",
    "#     test_labels = load(\"imdb_test_labels.npy\")\n",
    "#     print(\"Extracting Done.\")\n",
    "    #tr_tr, tr_val = train_test_split(train_df, test_size=0.1,random_state = 100)\n",
    "    file_path = \"imdb_age_recog_weights.h5\"\n",
    "    \n",
    "    print(\"Generating callback_list\")\n",
    "    \n",
    "#     log_dir=\"./logs/\" + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
    "#     tensorboard_callback = TensorBoard(log_dir=log_dir, histogram_freq=1)\n",
    "    \n",
    "    checkpoint = ModelCheckpoint(file_path, monitor='val_acc', verbose=1, save_best_only=True, mode='max')\n",
    "\n",
    "    #early = EarlyStopping(monitor=\"val_acc\", mode=\"max\", patience=5)\n",
    "\n",
    "    reduce_on_plateau = ReduceLROnPlateau(monitor=\"val_loss\",\n",
    "                                          mode=\"min\", \n",
    "                                          factor=0.1,\n",
    "                                          #cooldown=0,\n",
    "                                          patience=7,\n",
    "                                          verbose=1,\n",
    "                                          min_lr=0.00001)\n",
    "    tensorboard = TensorBoard(log_dir='./logs/imdb_age_recog')\n",
    "\n",
    "    callbacks_list = [checkpoint,\n",
    "                      reduce_on_plateau,\n",
    "                      tensorboard\n",
    "                      #tensorboard_callback\n",
    "                      #early\n",
    "                     ]  # early\n",
    "    print(len(train_df[\"out_ages\"].unique()))\n",
    "    print(\"Done Generating callbacklist.\")\n",
    "    print(\"generating Model\")\n",
    "    optimizer = Adam(lr=0.0001)\n",
    "    model = get_model( optimizer,n_classes=len(train_df[\"out_ages\"].unique()))\n",
    "    print(\"Done generating model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Fit_generator\n",
      "Epoch 1/200\n",
      "   98/33410 [..............................] - ETA: 5:27:40 - loss: 3.4327 - acc: 0.1410"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-0458e5808a86>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      9\u001b[0m                         \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m                         \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtr_tr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;31m#int(10740.75),\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m                         \u001b[0mvalidation_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtr_val\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m                         \u001b[0;31m#use_multiprocessing=True\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m                        )\n",
      "\u001b[0;32m/mnt/sda4/vamsik1211/Data/ML/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m   1294\u001b[0m         \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1295\u001b[0m         \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1296\u001b[0;31m         steps_name='steps_per_epoch')\n\u001b[0m\u001b[1;32m   1297\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1298\u001b[0m   def evaluate_generator(self,\n",
      "\u001b[0;32m/mnt/sda4/vamsik1211/Data/ML/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_generator.py\u001b[0m in \u001b[0;36mmodel_iteration\u001b[0;34m(model, data, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch, mode, batch_size, steps_name, **kwargs)\u001b[0m\n\u001b[1;32m    263\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    264\u001b[0m       \u001b[0mis_deferred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_compiled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 265\u001b[0;31m       \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mbatch_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    266\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_outs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    267\u001b[0m         \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mbatch_outs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/mnt/sda4/vamsik1211/Data/ML/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training.py\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[0;34m(self, x, y, sample_weight, class_weight, reset_metrics)\u001b[0m\n\u001b[1;32m   1015\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_sample_weight_modes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample_weights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1016\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_train_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1017\u001b[0;31m       \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1018\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1019\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mreset_metrics\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/mnt/sda4/vamsik1211/Data/ML/lib/python3.7/site-packages/tensorflow_core/python/keras/backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   3474\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3475\u001b[0m     fetched = self._callable_fn(*array_vals,\n\u001b[0;32m-> 3476\u001b[0;31m                                 run_metadata=self.run_metadata)\n\u001b[0m\u001b[1;32m   3477\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_fetch_callbacks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3478\u001b[0m     output_structure = nest.pack_sequence_as(\n",
      "\u001b[0;32m/mnt/sda4/vamsik1211/Data/ML/lib/python3.7/site-packages/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1470\u001b[0m         ret = tf_session.TF_SessionRunCallable(self._session._session,\n\u001b[1;32m   1471\u001b[0m                                                \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1472\u001b[0;31m                                                run_metadata_ptr)\n\u001b[0m\u001b[1;32m   1473\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1474\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "print(\"Running Fit_generator\")\n",
    "batch_size = 11\n",
    "input_shape=(224,224)\n",
    "model.fit_generator(gen(tr_tr,num_classes, input_shape, batch_size=batch_size, aug=True), \n",
    "                        validation_data=gen(tr_val, num_classes, input_shape, aug=True), \n",
    "                        epochs=200, \n",
    "                        verbose=1, \n",
    "                        #workers=6,\n",
    "                        callbacks=callbacks_list,\n",
    "                        steps_per_epoch=int(len(tr_tr)/batch_size),#int(10740.75), \n",
    "                        validation_steps=int(len(tr_val)),\n",
    "                        #use_multiprocessing=True\n",
    "                       )\n",
    "    #model.save(file_path)\n",
    "print(\"Trained Model saved  to disk\")"
   ]
  },
  {
   "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": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"imdb_age_recog_weights_resnet50_resume_training.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3713"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tr_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Get_Croped_image(img,bb_data):\n",
    "    #img_shape = img.shape\n",
    "    #cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)\n",
    "    x = bb_data[0]\n",
    "    y = bb_data[1]\n",
    "    w = bb_data[2]\n",
    "    h = bb_data[3]\n",
    "    #print(img)\n",
    "    crop_img = img[y:y+h, x:x+w]\n",
    "    return crop_img\n",
    "\n",
    "\n",
    "datagen = ImageDataGenerator(\n",
    "    rotation_range=20,\n",
    "    width_shift_range=0.05,\n",
    "    height_shift_range=0.05,\n",
    "    horizontal_flip=True,\n",
    "    #brightness_range=[1,1.02],\n",
    "    zoom_range=0.1)\n",
    "\n",
    "def augment(im_array):\n",
    "    im_array = datagen.random_transform(im_array)\n",
    "    return im_array\n",
    "\n",
    "\n",
    "def read_and_resize(dataFrame, input_shape=(128,128)):\n",
    "    #filepath = image_data.path.values[0]\n",
    "    images = []\n",
    "    for index in dataFrame.index :\n",
    "        im_cv = cv2.imread(dataFrame[\"path\"][index])\n",
    "        #im_cv = cv2.cvtColor(im_cv, cv2.COLOR_RGB2GRAY)\n",
    "        #cv2.imshow(\"image\", im_cv)\n",
    "        cv2.waitKey(0)\n",
    "        \n",
    "        \n",
    "        bb_data = (dataFrame[\"x\"][index],dataFrame[\"y\"][index],dataFrame[\"w\"][index],dataFrame[\"h\"][index])\n",
    "        #print(dataFrame[\"path\"][index])\n",
    "        im_cv = Get_Croped_image(im_cv,bb_data)\n",
    "        im_cv = cv2.resize(im_cv,input_shape)\n",
    "        im_means = cv2.mean(im_cv)\n",
    "        im_array = np.array(im_cv)\n",
    "        \n",
    "        im_cv = np.array(im_array / (np.max(im_array)+ 0.001), dtype=\"float32\")\n",
    "        im_cv = augment(im_cv)\n",
    "    \n",
    "        images.append(im_cv)\n",
    "        \n",
    "    return images\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def batch(data_set, batch_size):\n",
    "#     \"\"\"Yield successive n-sized chunks from l.\"\"\"\n",
    "#     for i in range(0, len(data_set), batch_size):\n",
    "#         return data_set[i:i + batch_size]\n",
    "\n",
    "\n",
    "\n",
    "def gen(df, batch_size=4, aug=True):\n",
    "    #df = df.sample(frac=1)\n",
    "\n",
    "#     dict_age = {'(0, 2)' : 0,\n",
    "#                 '(3, 5)' : 1,\n",
    "#                 '(6, 10)' : 2,\n",
    "#                 '(11, 15)' : 3,\n",
    "#                 '(16, 20)' : 4,\n",
    "#                 '(21, 30)' : 5,\n",
    "#                 '(31, 40)' : 6,\n",
    "#                 '(41, 50)' : 7,\n",
    "#                 '(51, 60)' : 8,\n",
    "#                 '(61, 70)' : 9,\n",
    "#                 '(71, 80)' : 10,\n",
    "#                  '(81, 90)' : 11,\n",
    "#                  '(91, 100)' : 12}\n",
    "\n",
    "    while True:\n",
    "        for _, batch in enumerate([df[i:i+batch_size] for i in range(0,df.shape[0],batch_size)]):\n",
    "            labels = np.array(batch.out_ages.values)\n",
    "\n",
    "            #labels = labels[..., np.newaxis]\n",
    "            labels_categorical = []\n",
    "            for label in labels:\n",
    "                labels_categorical.append(to_categorical(label,num_classes=100))\n",
    "            labels_categorical = np.array(labels_categorical)\n",
    "            if aug:\n",
    "                \n",
    "                images = read_and_resize(batch)\n",
    "                \n",
    "                \n",
    "                \n",
    "                    #images = np.array([augment(read_and_resize(file_path)) for file_path in batch.path.values])\n",
    "#                images = []\n",
    "#                 for index in range(len(batch)):\n",
    "#                     print(type(batch))\n",
    "#                     file_path = batch.path.values\n",
    "#                     temp_img = augment(read_and_resize(batch.path.values,batch.face_loc.values))\n",
    "#                     #print(temp_img.shape)\n",
    "#                     images.append(temp_img)\n",
    "\n",
    "                \n",
    "    \n",
    "            else:\n",
    "                images = np.array([read_and_resize(file_path) for file_path in batch.path.values])\n",
    "\n",
    "\n",
    "            #labels = np.array([dict_age[g] for g in batch.out_ages.values])\n",
    "#             images = np.array(images)\n",
    "            return images, labels_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"croped_filter_expanded_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./Dataset-copy/imdb_faces/imdb_crop/65/nm0924965_rm2828254976_1984-0-0_2004.jpg\n",
      "./Dataset-copy/imdb_faces/imdb_crop/04/nm0000404_rm1126270720_1937-12-21_2010.jpg\n",
      "./Dataset-copy/imdb_faces/imdb_crop/23/nm0000323_rm673361920_1933-3-14_2012.jpg\n",
      "./Dataset-copy/imdb_faces/imdb_crop/40/nm0326040_rm696358912_1962-7-7_2005.jpg\n"
     ]
    }
   ],
   "source": [
    "img = gen(df[0:5])\n",
    "#cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "im = cv2.imread(\"./Dataset-copy/imdb_faces/imdb_crop/65/nm0924965_rm2828254976_1984-0-0_2004.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img[1][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_and_resize(filepath, input_shape=(64, 64)):\n",
    "    im = Image.open((filepath)).convert('RGB')\n",
    "    im = im.resize(input_shape)\n",
    "    #im_means = cv2.mean(im)\n",
    "    #blob = cv2.dnn.blobFromImage(im, 1, input_shape, im_means)\n",
    "    im_array = np.array(im, dtype=\"uint8\")#[..., ::-1]\n",
    "#     im_array[0] = im_array[0] - im_means[0]\n",
    "#     im_array[1] = im_array[1] - im_means[1]\n",
    "#     im_array[2] = im_array[2] - im_means[2]\n",
    "    return np.array(im_array / (np.max(im_array)+ 0.001), dtype=\"float32\")\n",
    "\n",
    "datagen = ImageDataGenerator(\n",
    "    rotation_range=20,\n",
    "    width_shift_range=0.1,\n",
    "    height_shift_range=0.1,\n",
    "    horizontal_flip=True,\n",
    "    #brightness_range=[0.2,1.5],\n",
    "    zoom_range=0.2)\n",
    "\n",
    "def augment(im_array):\n",
    "    im_array = datagen.random_transform(im_array)\n",
    "    return im_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen(df, batch_size=1, aug=False):\n",
    "    df = df.sample(frac=1)\n",
    "\n",
    "    dict_age = {'(0, 2)' : 0,\n",
    "                '(3, 5)' : 1,\n",
    "                '(6, 10)' : 2,\n",
    "                '(11, 15)' : 3,\n",
    "                '(16, 20)' : 4,\n",
    "                '(21, 30)' : 5,\n",
    "                '(31, 40)' : 6,\n",
    "                '(41, 50)' : 7,\n",
    "                '(51, 60)' : 8,\n",
    "                '(61, 70)' : 9,\n",
    "                '(71, 80)' : 10,\n",
    "                 '(81, 90)' : 11,\n",
    "                 '(91, 100)' : 12}\n",
    "\n",
    "    while True:\n",
    "        for i, batch in enumerate([df[i:i+batch_size] for i in range(0,df.shape[0],batch_size)]):\n",
    "            if aug:\n",
    "                images = np.array([augment(read_and_resize(file_path)) for file_path in batch.path.values])\n",
    "            else:\n",
    "                images = np.array([read_and_resize(file_path) for file_path in batch.path.values])\n",
    "\n",
    "\n",
    "            #labels = np.array([dict_age[g] for g in batch.out_ages.values])\n",
    "            labels = np.array(batch.out_ages.values)\n",
    "\n",
    "            labels = labels[..., np.newaxis]\n",
    "\n",
    "            return images, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'NoneType' object is not subscriptable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-4a877be68963>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdestroyAllWindows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-16-cd671c3bd7f5>\u001b[0m in \u001b[0;36mgen\u001b[0;34m(df, batch_size, aug)\u001b[0m\n\u001b[1;32m     30\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0maug\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m                 \u001b[0mimages\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mread_and_resize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-15-a77d226af8cf>\u001b[0m in \u001b[0;36mread_and_resize\u001b[0;34m(dataFrame, input_shape)\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m         \u001b[0mbb_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdataFrame\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"x\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdataFrame\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"y\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdataFrame\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"w\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdataFrame\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"h\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m         \u001b[0mim_cv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGet_Croped_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mim_cv\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbb_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     35\u001b[0m         \u001b[0mim_cv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mim_cv\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0minput_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m         \u001b[0mim_means\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mim_cv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-15-a77d226af8cf>\u001b[0m in \u001b[0;36mGet_Croped_image\u001b[0;34m(img, bb_data)\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbb_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbb_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0mcrop_img\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mcrop_img\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'NoneType' object is not subscriptable"
     ]
    }
   ],
   "source": [
    "img = gen(df[0:5])\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(64, 64, 3)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(img[0][0]).shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(np.array(img[0][0]))\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"croped_filter_expanded_data.csv\")"
   ]
  },
  {
   "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": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "def read_and_resize(filepath, input_shape=(256, 256)):\n",
    "    im_cv = cv2.imread(filepath)\n",
    "    im = Image.open((filepath)).convert('RGB')\n",
    "    im = im.resize(input_shape)\n",
    "    im_means = cv2.mean(im_cv)\n",
    "    #blob = cv2.dnn.blobFromImage(im, 1, input_shape, im_means)\n",
    "    im_array = np.array(im, dtype=\"uint8\")#[..., ::-1]\n",
    "    im_array[0] = im_array[0] - im_means[0]\n",
    "    im_array[1] = im_array[1] - im_means[1]\n",
    "    im_array[2] = im_array[2] - im_means[2]\n",
    "\n",
    "    return np.array(im_array / (np.max(im_array)+ 0.001), dtype=\"float32\")#blob/max(blob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "image = read_and_resize(\"./Dataset-copy/imdb_faces/imdb_crop/00/nm0000100_rm1264031744_1955-1-6_2007.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f7a4b686f90>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "def read_and_resize(filepath, input_shape=(224,224)):\n",
    "    im_cv = cv2.imread(filepath)\n",
    "    im_means = cv2.mean(im_cv)\n",
    "    im_cv = cv2.resize(im_cv,input_shape)\n",
    "    #im_cv -= im_means\n",
    "    a,b,c = (cv2.split(im_cv))\n",
    "    #im_max = cv2.max(a,b,c)\n",
    "    im_cv[0] = im_cv[0] - im_means[0]\n",
    "    im_cv[1] = im_cv[1] - im_means[1]\n",
    "    im_cv[2] = im_cv[2] - im_means[2]\n",
    "    im_array = np.array(im_cv)\n",
    "    im_max = np.max(a)\n",
    "    im_cv[0] = im_cv[0]/im_max\n",
    "    im_max = np.max(b)\n",
    "    im_cv[1] = im_cv[1]/im_max\n",
    "    im_max = np.max(c)\n",
    "    im_cv[2] = im_cv[2]/im_max\n",
    "    return  im_cv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "image = read_and_resize(\"./Dataset-copy/imdb_faces/imdb_crop/20/nm0000120_rm102300416_1962-1-17_2014.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(224, 224, 3)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image.shape"
   ]
  },
  {
   "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
}