Lip Reading Tutorial Test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 7. Using the Model on Other Dataset\n",
    "# Load keras package needed\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import keras\n",
    "import numpy as np\n",
    "from numpy import argmax\n",
    "import os\n",
    "import cv2\n",
    "from keras.utils import np_utils\n",
    "from keras.models import Sequential, Model, load_model\n",
    "from keras.preprocessing import image as image_utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load Model ( MobileNet )\n",
    "from keras.utils.generic_utils import CustomObjectScope\n",
    "with CustomObjectScope({'relu6': keras.applications.mobilenet.relu6,'DepthwiseConv2D': keras.applications.mobilenet.DepthwiseConv2D}):\n",
    "    model = load_model('Lib_Reading_10Frame_Model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 Label number of Dataset is: 95\n",
      "1 Label number of Dataset is: 91\n",
      "2 Label number of Dataset is: 97\n",
      "3 Label number of Dataset is: 84\n",
      "4 Label number of Dataset is: 91\n",
      "5 Label number of Dataset is: 95\n",
      "6 Label number of Dataset is: 88\n",
      "7 Label number of Dataset is: 127\n",
      "Total Number of Data is 768\n"
     ]
    }
   ],
   "source": [
    "data = []\n",
    "label = []\n",
    "n_labels = 8\n",
    "Totalnb = 0\n",
    "data_nb = [0,0,0,0,0,0,0,0]\n",
    "\n",
    "# Load Dataset\n",
    "for i in range(8):\n",
    "    nb = 0\n",
    "    for root, dirs, files in os.walk('normalized SVM/dataset/' + str(i+1)):  # set directory\n",
    "        for name in dirs:\n",
    "            nb = nb + 1\n",
    "    print(i,\"Label number of Dataset is:\",nb)\n",
    "    Totalnb = Totalnb + nb\n",
    "    data_nb[i] = data_nb[i] + nb\n",
    "    for j in range(nb-1):\n",
    "        temp = []\n",
    "        for k in range(10):\n",
    "            name = 'normalized SVM/dataset/' + str(i+1) + '/' + str(j+1) + '/' + str(k+1) + '.jpg'\n",
    "            img = cv2.imread(name)\n",
    "            res = cv2.resize(img, dsize=(img_col, img_row), interpolation=cv2.INTER_CUBIC)\n",
    "            temp.append(res)\n",
    "        label.append(i)        \n",
    "        data.append(temp)\n",
    "print(\"Total Number of Data is\",Totalnb)\n",
    "Train_label = np.eye(n_labels)[label]\n",
    "Train_data = np.array(data) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(Train_label.shape[0])\n",
    "np.random.shuffle(x)\n",
    "Train_label = Train_label[x]\n",
    "Train_data = Train_data[x]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "[0] [0. 0. 0. 0. 0. 0. 0. 1.]\n",
      "[5] [0. 0. 0. 0. 0. 0. 0. 1.]\n",
      "[5] [0. 0. 0. 0. 0. 0. 0. 1.]\n",
      "[5] [0. 0. 0. 0. 0. 0. 0. 1.]\n",
      "[5] [0. 0. 0. 0. 0. 0. 0. 1.]\n",
      "[5] [0. 0. 0. 0. 0. 0. 0. 1.]\n",
      "[5] [0. 0. 0. 0. 0. 0. 0. 1.]\n",
      "[5] [0. 0. 0. 0. 0. 0. 0. 1.]\n",
      "[5] [0. 0. 0. 0. 0. 0. 0. 1.]\n",
      "[5] [0. 0. 0. 0. 0. 0. 0. 1.]\n",
      "[5] [0. 0. 0. 0. 0. 0. 0. 1.]\n",
      "[5] [0. 0. 0. 0. 0. 0. 0. 1.]\n",
      "[5] [0. 0. 0. 0. 0. 0. 0. 1.]\n",
      "[5] [0. 0. 0. 0. 0. 0. 0. 1.]\n",
      "[5] [0. 0. 0. 0. 0. 0. 0. 1.]\n",
      "[5] [0. 0. 0. 0. 0. 0. 0. 1.]\n",
      "[5] [0. 0. 0. 0. 0. 0. 0. 1.]\n",
      "[5] [0. 0. 0. 0. 0. 0. 0. 1.]\n",
      "[5] [0. 0. 0. 0. 0. 0. 0. 1.]\n",
      "[5] [0. 0. 0. 0. 0. 0. 0. 1.]\n",
      "[5] [0. 0. 0. 0. 0. 0. 0. 1.]\n",
      "[7] [0. 0. 0. 0. 0. 0. 0. 1.]\n",
      "[0] [0. 0. 0. 0. 0. 0. 0. 1.]\n",
      "[7] [0. 0. 0. 0. 0. 0. 0. 1.]\n",
      "[5] [0. 0. 0. 0. 0. 0. 0. 1.]\n",
      "[7] [0. 0. 0. 0. 0. 0. 0. 1.]\n",
      "[0] [0. 0. 0. 0. 0. 0. 0. 1.]\n",
      "Number of correct answers is 396 into 768 Test Acc is 0.515625\n",
      "Number of correct answers for each classes is  46 0 79 52 59 89 20 51\n",
      "Accuary of each classes is  0.4842105263157895 0.0 0.8144329896907216 0.6190476190476191 0.6483516483516484 0.9368421052631579 0.22727272727272727 0.4015748031496063\n"
     ]
    }
   ],
   "source": [
    "npp = 0\n",
    "n0 = 0\n",
    "n1 = 0\n",
    "n2 = 0\n",
    "n3 = 0\n",
    "n4 = 0\n",
    "n5 = 0\n",
    "n6 = 0\n",
    "n7 = 0\n",
    "\n",
    "for i in range(Train_label.shape[0]):\n",
    "    # Test model by each image timesteps\n",
    "    Train_dataOne = Train_data[i]\n",
    "    Train_dataOne = np.expand_dims(Train_dataOne, axis=0)\n",
    "    # Use model class probability\n",
    "    Y_data = model.predict(Train_dataOne, batch_size = 1)\n",
    "    # Use model class\n",
    "    y_classes = Y_data.argmax(axis=-1)\n",
    "    # print result predicted class / model input label ( One_hot Code )\n",
    "    print(y_classes, Train_label[i])\n",
    "    if (Train_label[i][y_classes] == 1):\n",
    "        npp = npp + 1\n",
    "        if (y_classes == 0):\n",
    "            n0 = n0 + 1\n",
    "        if (y_classes == 1):\n",
    "            n1 = n1 + 1\n",
    "        if (y_classes == 2):\n",
    "            n2 = n2 + 1\n",
    "        if (y_classes == 3):\n",
    "            n3 = n3 + 1\n",
    "        if (y_classes == 4):\n",
    "            n4 = n4 + 1\n",
    "        if (y_classes == 5):\n",
    "            n5 = n5 + 1\n",
    "        if (y_classes == 6):\n",
    "            n6 = n6 + 1\n",
    "        if (y_classes == 7):\n",
    "            n7 = n7 + 1\n",
    "\n",
    "# print Total Result\n",
    "print(\"Number of correct answers is\", npp,\"into\", 768, \"Test Acc is\",npp/768)\n",
    "print(\"Number of correct answers for each classes is \",n0,n1,n2,n3,n4,n5,n6,n7)\n",
    "print(\"Accuary of each classes is \",n0/data_nb[0],n1/data_nb[1],n2/data_nb[2],n3/data_nb[3],n4/data_nb[4],n5/data_nb[5],n6/data_nb[6],n7/data_nb[7])"
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