emotion_detector.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "emotion_detector(Completed).ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/akshat-suwalka/Emotion-Recognition/blob/master/emotion_detector.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l0NFViLiJ3Px"
      },
      "source": [
        "import pandas as pd\n",
        "import cv2\n",
        "import numpy as np\n",
        "\n",
        "dataset_path = 'fer2013.csv'\n",
        "image_size=(48,48)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ey_AwQN6J3QB"
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xf_x0f5-Zpxz"
      },
      "source": [
        "data = pd.read_csv(dataset_path)\n",
        "pixels = data['pixels'].tolist()\n",
        "face = [int(pixel) for pixel in pixels[0].split(' ')]\n",
        "face = np.asarray(face)\n",
        "face = np.resize(face,48*48)\n",
        "face = face.reshape(48, 48)\n",
        "face = cv2.resize(face.astype('uint8'),image_size)\n",
        "face = cv2.resize(face.astype('uint8'),image_size)\n",
        "face = face.astype('float32')\n",
        "face = np.asarray(face)\n",
        "face = np.expand_dims(face, -1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bodiEpeVJ3QL"
      },
      "source": [
        "def load_fer2013():\n",
        "    data = pd.read_csv(dataset_path)\n",
        "    pixels = data['pixels'].tolist()\n",
        "    width, height = 48, 48\n",
        "    faces = []\n",
        "    for pixel_sequence in pixels:\n",
        "        face = [int(pixel) for pixel in pixel_sequence.split(' ')]\n",
        "        face = np.asarray(face)\n",
        "        face = np.resize(face,48*48)\n",
        "        face = face.reshape(width, height)\n",
        "        face = cv2.resize(face.astype('uint8'),image_size)\n",
        "        faces.append(face.astype('float32'))\n",
        "    faces = np.asarray(faces)\n",
        "    faces = np.expand_dims(faces, -1)\n",
        "    emotions = pd.get_dummies(data['emotion']).as_matrix()\n",
        "    return faces, emotions\n",
        " \n",
        "def preprocess_input(x, v2=True):\n",
        "    x = x.astype('float32')\n",
        "    x = x / 255.0\n",
        "    if v2:\n",
        "        x = x - 0.5\n",
        "        x = x * 2.0\n",
        "    return x\n",
        " \n",
        "faces, emotions = load_fer2013()\n",
        "faces = preprocess_input(faces)\n",
        "xtrain, xtest,ytrain,ytest = train_test_split(faces, emotions,test_size=0.2,shuffle=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kPjJer6KJ3Qd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "5dc3d7aa-4f3d-472d-ebaa-e8f0635d9d49"
      },
      "source": [
        "from keras.callbacks import CSVLogger, ModelCheckpoint, EarlyStopping\n",
        "from keras.callbacks import ReduceLROnPlateau\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.layers import Activation, Convolution2D, Dropout, Conv2D\n",
        "from keras.layers import AveragePooling2D, BatchNormalization\n",
        "from keras.layers import GlobalAveragePooling2D\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Flatten\n",
        "from keras.models import Model\n",
        "from keras.layers import Input\n",
        "from keras.layers import MaxPooling2D\n",
        "from keras.layers import SeparableConv2D\n",
        "from keras import layers\n",
        "from keras.regularizers import l2\n",
        "import pandas as pd\n",
        "import cv2\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        " \n",
        "# parameters\n",
        "batch_size = 32\n",
        "num_epochs = 110\n",
        "input_shape = (48, 48, 1)\n",
        "verbose = 1\n",
        "num_classes = 7\n",
        "patience = 50\n",
        "base_path = r'C:\\Users\\Visitor\\Downloads\\Emotion-recognition-master\\Emotion-recognition-master\\models'\n",
        "l2_regularization=0.01\n",
        " \n",
        "# data generator\n",
        "data_generator = ImageDataGenerator(\n",
        "                        featurewise_center=False,\n",
        "                        featurewise_std_normalization=False,\n",
        "                        rotation_range=10,\n",
        "                        width_shift_range=0.1,\n",
        "                        height_shift_range=0.1,\n",
        "                        zoom_range=.1,\n",
        "                        horizontal_flip=True)\n",
        " \n",
        "# model parameters\n",
        "regularization = l2(l2_regularization)\n",
        " \n",
        "# base\n",
        "img_input = Input(input_shape)\n",
        "x = Conv2D(8, (3, 3), strides=(1, 1), kernel_regularizer=regularization, use_bias=False)(img_input)\n",
        "x = BatchNormalization()(x)\n",
        "x = Activation('relu')(x)\n",
        "x = Conv2D(8, (3, 3), strides=(1, 1), kernel_regularizer=regularization, use_bias=False)(x)\n",
        "x = BatchNormalization()(x)\n",
        "x = Activation('relu')(x)\n",
        " \n",
        "# module 1\n",
        "residual = Conv2D(16, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x)\n",
        "residual = BatchNormalization()(residual)\n",
        "x = SeparableConv2D(16, (3, 3), padding='same', kernel_regularizer=regularization, use_bias=False)(x)\n",
        "x = BatchNormalization()(x)\n",
        "x = Activation('relu')(x)\n",
        "x = SeparableConv2D(16, (3, 3), padding='same', kernel_regularizer=regularization, use_bias=False)(x)\n",
        "x = BatchNormalization()(x)\n",
        "x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)\n",
        "x = layers.add([x, residual])\n",
        " \n",
        "# module 2\n",
        "residual = Conv2D(32, (1, 1), strides=(2, 2), padding='same', use_bias=False)(x)\n",
        "residual = BatchNormalization()(residual)\n",
        "x = SeparableConv2D(32, (3, 3), padding='same', kernel_regularizer=regularization, use_bias=False)(x)\n",
        "x = BatchNormalization()(x)\n",
        "x = Activation('relu')(x)\n",
        "x = SeparableConv2D(32, (3, 3), padding='same', kernel_regularizer=regularization, use_bias=False)(x)\n",
        "x = BatchNormalization()(x)\n",
        "x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)\n",
        "x = layers.add([x, residual])\n",
        " \n",
        "# module 3\n",
        "residual = Conv2D(64, (1, 1), strides=(2, 2),padding='same', use_bias=False)(x)\n",
        "residual = BatchNormalization()(residual)\n",
        "x = SeparableConv2D(64, (3, 3), padding='same',kernel_regularizer=regularization,use_bias=False)(x)\n",
        "x = BatchNormalization()(x)\n",
        "x = Activation('relu')(x)\n",
        "x = SeparableConv2D(64, (3, 3), padding='same',kernel_regularizer=regularization,use_bias=False)(x)\n",
        "x = BatchNormalization()(x)\n",
        "x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)\n",
        "x = layers.add([x, residual])\n",
        " \n",
        "# module 4\n",
        "residual = Conv2D(128, (1, 1), strides=(2, 2),padding='same', use_bias=False)(x)\n",
        "residual = BatchNormalization()(residual)\n",
        "x = SeparableConv2D(128, (3, 3), padding='same',kernel_regularizer=regularization,use_bias=False)(x)\n",
        "x = BatchNormalization()(x)\n",
        "x = Activation('relu')(x)\n",
        "x = SeparableConv2D(128, (3, 3), padding='same',kernel_regularizer=regularization,use_bias=False)(x)\n",
        "x = BatchNormalization()(x)\n",
        "x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)\n",
        "x = layers.add([x, residual])\n",
        "x = Conv2D(num_classes, (3, 3), padding='same')(x)\n",
        "x = GlobalAveragePooling2D()(x)\n",
        "\n",
        "output = Activation('softmax',name='predictions')(x)\n",
        "print(tf.shape(output))\n",
        "model = Model(img_input, output)\n",
        "model.compile(optimizer='adam', loss='categorical_crossentropy',metrics=['accuracy'])\n",
        "model.summary()\n",
        " \n",
        "# callbacks\n",
        "log_file_path = base_path + '_emotion_training.log'\n",
        "csv_logger = CSVLogger(log_file_path, append=False)\n",
        "early_stop = EarlyStopping('val_loss', patience=patience)\n",
        "reduce_lr = ReduceLROnPlateau('val_loss', factor=0.1, patience=int(patience/4), verbose=1)\n",
        "trained_models_path = base_path + '_mini_XCEPTION'\n",
        "model_names = trained_models_path + '.{epoch:02d}-{val_acc:.2f}.hdf5'\n",
        "model_checkpoint = ModelCheckpoint(model_names, 'val_loss', verbose=1,save_best_only=True)\n",
        "callbacks = [model_checkpoint, csv_logger, early_stop, reduce_lr]\n",
        "\n",
        "model.fit_generator(data_generator.flow(xtrain, ytrain,batch_size),\n",
        "                        steps_per_epoch=len(xtrain) / batch_size,\n",
        "                        epochs=num_epochs, verbose=1, callbacks=callbacks,\n",
        "                        validation_data=(xtest,ytest))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tensor(\"Shape_2:0\", shape=(2,), dtype=int32)\n",
            "Model: \"model_3\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_3 (InputLayer)            (None, 48, 48, 1)    0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_15 (Conv2D)              (None, 46, 46, 8)    72          input_3[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_29 (BatchNo (None, 46, 46, 8)    32          conv2d_15[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_13 (Activation)      (None, 46, 46, 8)    0           batch_normalization_29[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_16 (Conv2D)              (None, 44, 44, 8)    576         activation_13[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_30 (BatchNo (None, 44, 44, 8)    32          conv2d_16[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_14 (Activation)      (None, 44, 44, 8)    0           batch_normalization_30[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "separable_conv2d_17 (SeparableC (None, 44, 44, 16)   200         activation_14[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_32 (BatchNo (None, 44, 44, 16)   64          separable_conv2d_17[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "activation_15 (Activation)      (None, 44, 44, 16)   0           batch_normalization_32[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "separable_conv2d_18 (SeparableC (None, 44, 44, 16)   400         activation_15[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_33 (BatchNo (None, 44, 44, 16)   64          separable_conv2d_18[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_17 (Conv2D)              (None, 22, 22, 16)   128         activation_14[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_9 (MaxPooling2D)  (None, 22, 22, 16)   0           batch_normalization_33[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_31 (BatchNo (None, 22, 22, 16)   64          conv2d_17[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "add_9 (Add)                     (None, 22, 22, 16)   0           max_pooling2d_9[0][0]            \n",
            "                                                                 batch_normalization_31[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "separable_conv2d_19 (SeparableC (None, 22, 22, 32)   656         add_9[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_35 (BatchNo (None, 22, 22, 32)   128         separable_conv2d_19[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "activation_16 (Activation)      (None, 22, 22, 32)   0           batch_normalization_35[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "separable_conv2d_20 (SeparableC (None, 22, 22, 32)   1312        activation_16[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_36 (BatchNo (None, 22, 22, 32)   128         separable_conv2d_20[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_18 (Conv2D)              (None, 11, 11, 32)   512         add_9[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_10 (MaxPooling2D) (None, 11, 11, 32)   0           batch_normalization_36[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_34 (BatchNo (None, 11, 11, 32)   128         conv2d_18[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "add_10 (Add)                    (None, 11, 11, 32)   0           max_pooling2d_10[0][0]           \n",
            "                                                                 batch_normalization_34[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "separable_conv2d_21 (SeparableC (None, 11, 11, 64)   2336        add_10[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_38 (BatchNo (None, 11, 11, 64)   256         separable_conv2d_21[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "activation_17 (Activation)      (None, 11, 11, 64)   0           batch_normalization_38[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "separable_conv2d_22 (SeparableC (None, 11, 11, 64)   4672        activation_17[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_39 (BatchNo (None, 11, 11, 64)   256         separable_conv2d_22[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_19 (Conv2D)              (None, 6, 6, 64)     2048        add_10[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_11 (MaxPooling2D) (None, 6, 6, 64)     0           batch_normalization_39[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_37 (BatchNo (None, 6, 6, 64)     256         conv2d_19[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "add_11 (Add)                    (None, 6, 6, 64)     0           max_pooling2d_11[0][0]           \n",
            "                                                                 batch_normalization_37[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "separable_conv2d_23 (SeparableC (None, 6, 6, 128)    8768        add_11[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_41 (BatchNo (None, 6, 6, 128)    512         separable_conv2d_23[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "activation_18 (Activation)      (None, 6, 6, 128)    0           batch_normalization_41[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "separable_conv2d_24 (SeparableC (None, 6, 6, 128)    17536       activation_18[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_42 (BatchNo (None, 6, 6, 128)    512         separable_conv2d_24[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_20 (Conv2D)              (None, 3, 3, 128)    8192        add_11[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_12 (MaxPooling2D) (None, 3, 3, 128)    0           batch_normalization_42[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_40 (BatchNo (None, 3, 3, 128)    512         conv2d_20[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "add_12 (Add)                    (None, 3, 3, 128)    0           max_pooling2d_12[0][0]           \n",
            "                                                                 batch_normalization_40[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_21 (Conv2D)              (None, 3, 3, 7)      8071        add_12[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "global_average_pooling2d_3 (Glo (None, 7)            0           conv2d_21[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "predictions (Activation)        (None, 7)            0           global_average_pooling2d_3[0][0] \n",
            "==================================================================================================\n",
            "Total params: 58,423\n",
            "Trainable params: 56,951\n",
            "Non-trainable params: 1,472\n",
            "__________________________________________________________________________________________________\n",
            "Epoch 1/110\n",
            "60/59 [==============================] - 11s 177ms/step - loss: 2.0333 - acc: 0.2100 - val_loss: 2.1218 - val_acc: 0.1937\n",
            "\n",
            "Epoch 00001: val_loss improved from inf to 2.12178, saving model to C:\\Users\\Visitor\\Downloads\\Emotion-recognition-master\\Emotion-recognition-master\\models_mini_XCEPTION.01-0.19.hdf5\n",
            "Epoch 2/110\n",
            "60/59 [==============================] - 3s 56ms/step - loss: 1.9096 - acc: 0.2606 - val_loss: 2.2925 - val_acc: 0.1621\n",
            "\n",
            "Epoch 00002: val_loss did not improve from 2.12178\n",
            "Epoch 3/110\n",
            "60/59 [==============================] - 3s 54ms/step - loss: 1.8988 - acc: 0.2685 - val_loss: 2.2233 - val_acc: 0.2000\n",
            "\n",
            "Epoch 00003: val_loss did not improve from 2.12178\n",
            "Epoch 4/110\n",
            "60/59 [==============================] - 3s 54ms/step - loss: 1.8494 - acc: 0.2882 - val_loss: 2.3680 - val_acc: 0.1832\n",
            "\n",
            "Epoch 00004: val_loss did not improve from 2.12178\n",
            "Epoch 5/110\n",
            "60/59 [==============================] - 3s 53ms/step - loss: 1.8208 - acc: 0.3040 - val_loss: 1.9583 - val_acc: 0.2695\n",
            "\n",
            "Epoch 00005: val_loss improved from 2.12178 to 1.95828, saving model to C:\\Users\\Visitor\\Downloads\\Emotion-recognition-master\\Emotion-recognition-master\\models_mini_XCEPTION.05-0.27.hdf5\n",
            "Epoch 6/110\n",
            "60/59 [==============================] - 3s 53ms/step - loss: 1.7995 - acc: 0.3118 - val_loss: 1.8925 - val_acc: 0.2632\n",
            "\n",
            "Epoch 00006: val_loss improved from 1.95828 to 1.89254, saving model to C:\\Users\\Visitor\\Downloads\\Emotion-recognition-master\\Emotion-recognition-master\\models_mini_XCEPTION.06-0.26.hdf5\n",
            "Epoch 7/110\n",
            "60/59 [==============================] - 3s 51ms/step - loss: 1.7602 - acc: 0.3243 - val_loss: 1.9500 - val_acc: 0.2463\n",
            "\n",
            "Epoch 00007: val_loss did not improve from 1.89254\n",
            "Epoch 8/110\n",
            "60/59 [==============================] - 3s 52ms/step - loss: 1.7481 - acc: 0.3395 - val_loss: 1.8528 - val_acc: 0.3074\n",
            "\n",
            "Epoch 00008: val_loss improved from 1.89254 to 1.85279, saving model to C:\\Users\\Visitor\\Downloads\\Emotion-recognition-master\\Emotion-recognition-master\\models_mini_XCEPTION.08-0.31.hdf5\n",
            "Epoch 9/110\n",
            "60/59 [==============================] - 3s 53ms/step - loss: 1.7080 - acc: 0.3602 - val_loss: 1.9736 - val_acc: 0.2989\n",
            "\n",
            "Epoch 00009: val_loss did not improve from 1.85279\n",
            "Epoch 10/110\n",
            "60/59 [==============================] - 3s 53ms/step - loss: 1.7094 - acc: 0.3633 - val_loss: 1.7565 - val_acc: 0.3411\n",
            "\n",
            "Epoch 00010: val_loss improved from 1.85279 to 1.75647, saving model to C:\\Users\\Visitor\\Downloads\\Emotion-recognition-master\\Emotion-recognition-master\\models_mini_XCEPTION.10-0.34.hdf5\n",
            "Epoch 11/110\n",
            "60/59 [==============================] - 3s 52ms/step - loss: 1.6832 - acc: 0.3770 - val_loss: 1.8361 - val_acc: 0.3053\n",
            "\n",
            "Epoch 00011: val_loss did not improve from 1.75647\n",
            "Epoch 12/110\n",
            "60/59 [==============================] - 3s 53ms/step - loss: 1.6563 - acc: 0.3805 - val_loss: 1.7349 - val_acc: 0.3705\n",
            "\n",
            "Epoch 00012: val_loss improved from 1.75647 to 1.73485, saving model to C:\\Users\\Visitor\\Downloads\\Emotion-recognition-master\\Emotion-recognition-master\\models_mini_XCEPTION.12-0.37.hdf5\n",
            "Epoch 13/110\n",
            "60/59 [==============================] - 3s 51ms/step - loss: 1.6099 - acc: 0.3977 - val_loss: 1.7351 - val_acc: 0.3642\n",
            "\n",
            "Epoch 00013: val_loss did not improve from 1.73485\n",
            "Epoch 14/110\n",
            "60/59 [==============================] - 3s 52ms/step - loss: 1.6273 - acc: 0.3853 - val_loss: 1.8022 - val_acc: 0.3158\n",
            "\n",
            "Epoch 00014: val_loss did not improve from 1.73485\n",
            "Epoch 15/110\n",
            "60/59 [==============================] - 3s 53ms/step - loss: 1.5822 - acc: 0.4029 - val_loss: 1.8234 - val_acc: 0.3116\n",
            "\n",
            "Epoch 00015: val_loss did not improve from 1.73485\n",
            "Epoch 16/110\n",
            "60/59 [==============================] - 3s 52ms/step - loss: 1.6216 - acc: 0.3947 - val_loss: 1.7468 - val_acc: 0.3516\n",
            "\n",
            "Epoch 00016: val_loss did not improve from 1.73485\n",
            "Epoch 17/110\n",
            "60/59 [==============================] - 3s 52ms/step - loss: 1.5401 - acc: 0.4276 - val_loss: 2.0391 - val_acc: 0.2863\n",
            "\n",
            "Epoch 00017: val_loss did not improve from 1.73485\n",
            "Epoch 18/110\n",
            "60/59 [==============================] - 3s 53ms/step - loss: 1.5763 - acc: 0.4081 - val_loss: 1.7736 - val_acc: 0.3495\n",
            "\n",
            "Epoch 00018: val_loss did not improve from 1.73485\n",
            "Epoch 19/110\n",
            "60/59 [==============================] - 3s 52ms/step - loss: 1.5244 - acc: 0.4228 - val_loss: 1.8667 - val_acc: 0.3558\n",
            "\n",
            "Epoch 00019: val_loss did not improve from 1.73485\n",
            "Epoch 20/110\n",
            "60/59 [==============================] - 3s 53ms/step - loss: 1.5157 - acc: 0.4417 - val_loss: 1.8073 - val_acc: 0.3326\n",
            "\n",
            "Epoch 00020: val_loss did not improve from 1.73485\n",
            "Epoch 21/110\n",
            "60/59 [==============================] - 3s 53ms/step - loss: 1.4672 - acc: 0.4588 - val_loss: 1.9666 - val_acc: 0.3747\n",
            "\n",
            "Epoch 00021: val_loss did not improve from 1.73485\n",
            "Epoch 22/110\n",
            "60/59 [==============================] - 3s 52ms/step - loss: 1.4692 - acc: 0.4599 - val_loss: 1.8417 - val_acc: 0.3684\n",
            "\n",
            "Epoch 00022: val_loss did not improve from 1.73485\n",
            "Epoch 23/110\n",
            "60/59 [==============================] - 3s 54ms/step - loss: 1.5024 - acc: 0.4566 - val_loss: 1.7772 - val_acc: 0.3684\n",
            "\n",
            "Epoch 00023: val_loss did not improve from 1.73485\n",
            "Epoch 24/110\n",
            "60/59 [==============================] - 3s 52ms/step - loss: 1.4602 - acc: 0.4658 - val_loss: 2.0336 - val_acc: 0.3242\n",
            "\n",
            "Epoch 00024: val_loss did not improve from 1.73485\n",
            "\n",
            "Epoch 00024: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "Epoch 25/110\n",
            "60/59 [==============================] - 3s 52ms/step - loss: 1.3651 - acc: 0.5069 - val_loss: 1.5646 - val_acc: 0.4421\n",
            "\n",
            "Epoch 00025: val_loss improved from 1.73485 to 1.56463, saving model to C:\\Users\\Visitor\\Downloads\\Emotion-recognition-master\\Emotion-recognition-master\\models_mini_XCEPTION.25-0.44.hdf5\n",
            "Epoch 26/110\n",
            "60/59 [==============================] - 3s 52ms/step - loss: 1.3355 - acc: 0.5063 - val_loss: 1.5214 - val_acc: 0.4547\n",
            "\n",
            "Epoch 00026: val_loss improved from 1.56463 to 1.52142, saving model to C:\\Users\\Visitor\\Downloads\\Emotion-recognition-master\\Emotion-recognition-master\\models_mini_XCEPTION.26-0.45.hdf5\n",
            "Epoch 27/110\n",
            "60/59 [==============================] - 3s 52ms/step - loss: 1.3138 - acc: 0.5149 - val_loss: 1.5108 - val_acc: 0.4463\n",
            "\n",
            "Epoch 00027: val_loss improved from 1.52142 to 1.51084, saving model to C:\\Users\\Visitor\\Downloads\\Emotion-recognition-master\\Emotion-recognition-master\\models_mini_XCEPTION.27-0.45.hdf5\n",
            "Epoch 28/110\n",
            "60/59 [==============================] - 3s 53ms/step - loss: 1.3261 - acc: 0.5157 - val_loss: 1.5310 - val_acc: 0.4421\n",
            "\n",
            "Epoch 00028: val_loss did not improve from 1.51084\n",
            "Epoch 29/110\n",
            "60/59 [==============================] - 3s 53ms/step - loss: 1.3133 - acc: 0.5209 - val_loss: 1.5226 - val_acc: 0.4421\n",
            "\n",
            "Epoch 00029: val_loss did not improve from 1.51084\n",
            "Epoch 30/110\n",
            "60/59 [==============================] - 3s 53ms/step - loss: 1.3062 - acc: 0.5229 - val_loss: 1.5033 - val_acc: 0.4674\n",
            "\n",
            "Epoch 00030: val_loss improved from 1.51084 to 1.50333, saving model to C:\\Users\\Visitor\\Downloads\\Emotion-recognition-master\\Emotion-recognition-master\\models_mini_XCEPTION.30-0.47.hdf5\n",
            "Epoch 31/110\n",
            "60/59 [==============================] - 3s 54ms/step - loss: 1.3100 - acc: 0.5358 - val_loss: 1.5144 - val_acc: 0.4547\n",
            "\n",
            "Epoch 00031: val_loss did not improve from 1.50333\n",
            "Epoch 32/110\n",
            "60/59 [==============================] - 3s 54ms/step - loss: 1.3173 - acc: 0.5139 - val_loss: 1.5189 - val_acc: 0.4463\n",
            "\n",
            "Epoch 00032: val_loss did not improve from 1.50333\n",
            "Epoch 33/110\n",
            "60/59 [==============================] - 3s 50ms/step - loss: 1.3006 - acc: 0.5181 - val_loss: 1.5210 - val_acc: 0.4400\n",
            "\n",
            "Epoch 00033: val_loss did not improve from 1.50333\n",
            "Epoch 34/110\n",
            "60/59 [==============================] - 3s 51ms/step - loss: 1.2952 - acc: 0.5290 - val_loss: 1.5423 - val_acc: 0.4526\n",
            "\n",
            "Epoch 00034: val_loss did not improve from 1.50333\n",
            "Epoch 35/110\n",
            "60/59 [==============================] - 3s 52ms/step - loss: 1.3019 - acc: 0.5189 - val_loss: 1.5197 - val_acc: 0.4463\n",
            "\n",
            "Epoch 00035: val_loss did not improve from 1.50333\n",
            "Epoch 36/110\n",
            "60/59 [==============================] - 3s 50ms/step - loss: 1.2758 - acc: 0.5386 - val_loss: 1.5361 - val_acc: 0.4463\n",
            "\n",
            "Epoch 00036: val_loss did not improve from 1.50333\n",
            "Epoch 37/110\n",
            "60/59 [==============================] - 3s 50ms/step - loss: 1.2749 - acc: 0.5235 - val_loss: 1.5128 - val_acc: 0.4358\n",
            "\n",
            "Epoch 00037: val_loss did not improve from 1.50333\n",
            "Epoch 38/110\n",
            "60/59 [==============================] - 3s 51ms/step - loss: 1.2758 - acc: 0.5306 - val_loss: 1.5283 - val_acc: 0.4400\n",
            "\n",
            "Epoch 00038: val_loss did not improve from 1.50333\n",
            "Epoch 39/110\n",
            "60/59 [==============================] - 3s 51ms/step - loss: 1.2580 - acc: 0.5391 - val_loss: 1.5051 - val_acc: 0.4484\n",
            "\n",
            "Epoch 00039: val_loss did not improve from 1.50333\n",
            "Epoch 40/110\n",
            "60/59 [==============================] - 3s 50ms/step - loss: 1.2500 - acc: 0.5477 - val_loss: 1.5396 - val_acc: 0.4379\n",
            "\n",
            "Epoch 00040: val_loss did not improve from 1.50333\n",
            "Epoch 41/110\n",
            "60/59 [==============================] - 3s 50ms/step - loss: 1.2538 - acc: 0.5452 - val_loss: 1.5106 - val_acc: 0.4547\n",
            "\n",
            "Epoch 00041: val_loss did not improve from 1.50333\n",
            "Epoch 42/110\n",
            "60/59 [==============================] - 3s 50ms/step - loss: 1.2719 - acc: 0.5333 - val_loss: 1.5282 - val_acc: 0.4526\n",
            "\n",
            "Epoch 00042: val_loss did not improve from 1.50333\n",
            "\n",
            "Epoch 00042: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
            "Epoch 43/110\n",
            "60/59 [==============================] - 3s 51ms/step - loss: 1.2454 - acc: 0.5570 - val_loss: 1.5213 - val_acc: 0.4547\n",
            "\n",
            "Epoch 00043: val_loss did not improve from 1.50333\n",
            "Epoch 44/110\n",
            "60/59 [==============================] - 3s 50ms/step - loss: 1.2508 - acc: 0.5411 - val_loss: 1.5231 - val_acc: 0.4568\n",
            "\n",
            "Epoch 00044: val_loss did not improve from 1.50333\n",
            "Epoch 45/110\n",
            "60/59 [==============================] - 3s 51ms/step - loss: 1.2356 - acc: 0.5551 - val_loss: 1.5210 - val_acc: 0.4589\n",
            "\n",
            "Epoch 00045: val_loss did not improve from 1.50333\n",
            "Epoch 46/110\n",
            "60/59 [==============================] - 3s 52ms/step - loss: 1.2544 - acc: 0.5335 - val_loss: 1.5189 - val_acc: 0.4547\n",
            "\n",
            "Epoch 00046: val_loss did not improve from 1.50333\n",
            "Epoch 47/110\n",
            "60/59 [==============================] - 3s 51ms/step - loss: 1.2404 - acc: 0.5487 - val_loss: 1.5192 - val_acc: 0.4547\n",
            "\n",
            "Epoch 00047: val_loss did not improve from 1.50333\n",
            "Epoch 48/110\n",
            "60/59 [==============================] - 3s 52ms/step - loss: 1.2583 - acc: 0.5460 - val_loss: 1.5216 - val_acc: 0.4547\n",
            "\n",
            "Epoch 00048: val_loss did not improve from 1.50333\n",
            "Epoch 49/110\n",
            "60/59 [==============================] - 3s 52ms/step - loss: 1.2513 - acc: 0.5488 - val_loss: 1.5190 - val_acc: 0.4547\n",
            "\n",
            "Epoch 00049: val_loss did not improve from 1.50333\n",
            "Epoch 50/110\n",
            "60/59 [==============================] - 3s 51ms/step - loss: 1.2418 - acc: 0.5511 - val_loss: 1.5203 - val_acc: 0.4526\n",
            "\n",
            "Epoch 00050: val_loss did not improve from 1.50333\n",
            "Epoch 51/110\n",
            "60/59 [==============================] - 3s 51ms/step - loss: 1.2612 - acc: 0.5479 - val_loss: 1.5185 - val_acc: 0.4526\n",
            "\n",
            "Epoch 00051: val_loss did not improve from 1.50333\n",
            "Epoch 52/110\n",
            "60/59 [==============================] - 3s 51ms/step - loss: 1.2350 - acc: 0.5526 - val_loss: 1.5175 - val_acc: 0.4547\n",
            "\n",
            "Epoch 00052: val_loss did not improve from 1.50333\n",
            "Epoch 53/110\n",
            "60/59 [==============================] - 3s 50ms/step - loss: 1.2474 - acc: 0.5335 - val_loss: 1.5206 - val_acc: 0.4442\n",
            "\n",
            "Epoch 00053: val_loss did not improve from 1.50333\n",
            "Epoch 54/110\n",
            "60/59 [==============================] - 3s 52ms/step - loss: 1.2493 - acc: 0.5425 - val_loss: 1.5168 - val_acc: 0.4526\n",
            "\n",
            "Epoch 00054: val_loss did not improve from 1.50333\n",
            "\n",
            "Epoch 00054: ReduceLROnPlateau reducing learning rate to 1.0000000656873453e-06.\n",
            "Epoch 55/110\n",
            "60/59 [==============================] - 3s 50ms/step - loss: 1.2409 - acc: 0.5458 - val_loss: 1.5171 - val_acc: 0.4526\n",
            "\n",
            "Epoch 00055: val_loss did not improve from 1.50333\n",
            "Epoch 56/110\n",
            "60/59 [==============================] - 3s 49ms/step - loss: 1.2618 - acc: 0.5424 - val_loss: 1.5178 - val_acc: 0.4547\n",
            "\n",
            "Epoch 00056: val_loss did not improve from 1.50333\n",
            "Epoch 57/110\n",
            "60/59 [==============================] - 3s 51ms/step - loss: 1.2229 - acc: 0.5637 - val_loss: 1.5173 - val_acc: 0.4505\n",
            "\n",
            "Epoch 00057: val_loss did not improve from 1.50333\n",
            "Epoch 58/110\n",
            "60/59 [==============================] - 3s 51ms/step - loss: 1.2435 - acc: 0.5458 - val_loss: 1.5178 - val_acc: 0.4547\n",
            "\n",
            "Epoch 00058: val_loss did not improve from 1.50333\n",
            "Epoch 59/110\n",
            "60/59 [==============================] - 3s 51ms/step - loss: 1.2281 - acc: 0.5510 - val_loss: 1.5167 - val_acc: 0.4526\n",
            "\n",
            "Epoch 00059: val_loss did not improve from 1.50333\n",
            "Epoch 60/110\n",
            "60/59 [==============================] - 3s 52ms/step - loss: 1.2309 - acc: 0.5520 - val_loss: 1.5169 - val_acc: 0.4526\n",
            "\n",
            "Epoch 00060: val_loss did not improve from 1.50333\n",
            "Epoch 61/110\n",
            "60/59 [==============================] - 3s 50ms/step - loss: 1.2450 - acc: 0.5402 - val_loss: 1.5175 - val_acc: 0.4505\n",
            "\n",
            "Epoch 00061: val_loss did not improve from 1.50333\n",
            "Epoch 62/110\n",
            "60/59 [==============================] - 3s 50ms/step - loss: 1.2476 - acc: 0.5432 - val_loss: 1.5180 - val_acc: 0.4505\n",
            "\n",
            "Epoch 00062: val_loss did not improve from 1.50333\n",
            "Epoch 63/110\n",
            "60/59 [==============================] - 3s 49ms/step - loss: 1.2340 - acc: 0.5505 - val_loss: 1.5167 - val_acc: 0.4547\n",
            "\n",
            "Epoch 00063: val_loss did not improve from 1.50333\n",
            "Epoch 64/110\n",
            "60/59 [==============================] - 3s 51ms/step - loss: 1.2586 - acc: 0.5365 - val_loss: 1.5174 - val_acc: 0.4526\n",
            "\n",
            "Epoch 00064: val_loss did not improve from 1.50333\n",
            "Epoch 65/110\n",
            "60/59 [==============================] - 3s 50ms/step - loss: 1.2464 - acc: 0.5460 - val_loss: 1.5166 - val_acc: 0.4526\n",
            "\n",
            "Epoch 00065: val_loss did not improve from 1.50333\n",
            "Epoch 66/110\n",
            "60/59 [==============================] - 3s 50ms/step - loss: 1.2608 - acc: 0.5342 - val_loss: 1.5164 - val_acc: 0.4547\n",
            "\n",
            "Epoch 00066: val_loss did not improve from 1.50333\n",
            "\n",
            "Epoch 00066: ReduceLROnPlateau reducing learning rate to 1.0000001111620805e-07.\n",
            "Epoch 67/110\n",
            "60/59 [==============================] - 3s 51ms/step - loss: 1.2388 - acc: 0.5450 - val_loss: 1.5167 - val_acc: 0.4547\n",
            "\n",
            "Epoch 00067: val_loss did not improve from 1.50333\n",
            "Epoch 68/110\n",
            "60/59 [==============================] - 3s 53ms/step - loss: 1.2417 - acc: 0.5383 - val_loss: 1.5169 - val_acc: 0.4547\n",
            "\n",
            "Epoch 00068: val_loss did not improve from 1.50333\n",
            "Epoch 69/110\n",
            "60/59 [==============================] - 3s 52ms/step - loss: 1.2297 - acc: 0.5726 - val_loss: 1.5174 - val_acc: 0.4547\n",
            "\n",
            "Epoch 00069: val_loss did not improve from 1.50333\n",
            "Epoch 70/110\n",
            "60/59 [==============================] - 3s 51ms/step - loss: 1.2640 - acc: 0.5511 - val_loss: 1.5171 - val_acc: 0.4547\n",
            "\n",
            "Epoch 00070: val_loss did not improve from 1.50333\n",
            "Epoch 71/110\n",
            "60/59 [==============================] - 3s 52ms/step - loss: 1.2578 - acc: 0.5375 - val_loss: 1.5177 - val_acc: 0.4484\n",
            "\n",
            "Epoch 00071: val_loss did not improve from 1.50333\n",
            "Epoch 72/110\n",
            "60/59 [==============================] - 3s 52ms/step - loss: 1.2296 - acc: 0.5432 - val_loss: 1.5176 - val_acc: 0.4526\n",
            "\n",
            "Epoch 00072: val_loss did not improve from 1.50333\n",
            "Epoch 73/110\n",
            "60/59 [==============================] - 3s 51ms/step - loss: 1.2301 - acc: 0.5466 - val_loss: 1.5167 - val_acc: 0.4547\n",
            "\n",
            "Epoch 00073: val_loss did not improve from 1.50333\n",
            "Epoch 74/110\n",
            "60/59 [==============================] - 3s 51ms/step - loss: 1.2406 - acc: 0.5508 - val_loss: 1.5168 - val_acc: 0.4547\n",
            "\n",
            "Epoch 00074: val_loss did not improve from 1.50333\n",
            "Epoch 75/110\n",
            "60/59 [==============================] - 3s 51ms/step - loss: 1.2223 - acc: 0.5518 - val_loss: 1.5174 - val_acc: 0.4547\n",
            "\n",
            "Epoch 00075: val_loss did not improve from 1.50333\n",
            "Epoch 76/110\n",
            "60/59 [==============================] - 3s 50ms/step - loss: 1.2470 - acc: 0.5387 - val_loss: 1.5166 - val_acc: 0.4547\n",
            "\n",
            "Epoch 00076: val_loss did not improve from 1.50333\n",
            "Epoch 77/110\n",
            "60/59 [==============================] - 3s 50ms/step - loss: 1.2378 - acc: 0.5478 - val_loss: 1.5183 - val_acc: 0.4505\n",
            "\n",
            "Epoch 00077: val_loss did not improve from 1.50333\n",
            "Epoch 78/110\n",
            "60/59 [==============================] - 3s 50ms/step - loss: 1.2400 - acc: 0.5543 - val_loss: 1.5169 - val_acc: 0.4547\n",
            "\n",
            "Epoch 00078: val_loss did not improve from 1.50333\n",
            "\n",
            "Epoch 00078: ReduceLROnPlateau reducing learning rate to 1.000000082740371e-08.\n",
            "Epoch 79/110\n",
            "60/59 [==============================] - 3s 53ms/step - loss: 1.2360 - acc: 0.5517 - val_loss: 1.5175 - val_acc: 0.4526\n",
            "\n",
            "Epoch 00079: val_loss did not improve from 1.50333\n",
            "Epoch 80/110\n",
            "60/59 [==============================] - 3s 50ms/step - loss: 1.2331 - acc: 0.5511 - val_loss: 1.5169 - val_acc: 0.4547\n",
            "\n",
            "Epoch 00080: val_loss did not improve from 1.50333\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f1f21ec9400>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aCX97U5wPpWn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "8f409eff-7ac1-41d2-e147-83c5f0a74769"
      },
      "source": [
        "\n",
        "from keras.models import load_model\n",
        "import imutils\n",
        "import cv2\n",
        "import numpy as np\n",
        "import os\n",
        "import sys\n",
        "import IPython\n",
        "from IPython.display import HTML, Image\n",
        "from google.colab.output import eval_js\n",
        "import urllib\n",
        "import html\n",
        "from google.colab.patches import cv2_imshow\n",
        "def take_photo(filename='photo.jpg', quality=0.8):\n",
        "  js = IPython.display.Javascript('''\n",
        "    async function takePhoto(quality) {\n",
        "      const div = document.createElement('div');\n",
        "\n",
        "      const video = document.createElement('video');\n",
        "      video.style.display = 'block';\n",
        "      const stream = await navigator.mediaDevices.getUserMedia({video: true});\n",
        "\n",
        "      document.body.appendChild(div);\n",
        "      div.appendChild(video);\n",
        "      video.srcObject = stream;\n",
        "      await video.play();\n",
        "\n",
        "      // Resize the output to fit the video element.\n",
        "      google.colab.output.setIframeHeight(document.documentElement.scrollHeight, true);\n",
        "\n",
        "      const canvas = document.createElement('canvas');\n",
        "      canvas.width = video.videoWidth;\n",
        "      canvas.height = video.videoHeight;\n",
        "      canvas.getContext('2d').drawImage(video, 0, 0);\n",
        "      stream.getVideoTracks()[0].stop();\n",
        "      div.remove();\n",
        "      return canvas.toDataURL('image/jpeg', quality);\n",
        "    }\n",
        "    ''')\n",
        "  display(js)\n",
        "  data = eval_js('takePhoto({})'.format(quality))\n",
        "  resp = urllib.request.urlopen(data)\n",
        "  image = np.asarray(bytearray(resp.read()), dtype=\"uint8\")\n",
        "  image = cv2.imdecode(image, cv2.IMREAD_COLOR)\n",
        "  # return the image\n",
        "  return image\n",
        "\n",
        "# to capture an image\n",
        "img = take_photo()\n",
        "print(type(img))\n",
        "\n",
        "from PIL import Image\n",
        "im = Image.fromarray(img)\n",
        "im.save('y.jpeg')\n",
        "\n",
        "%matplotlib inline\n",
        "from matplotlib import pyplot as plt\n",
        "plt.imshow(cv2.imread('y.jpeg'), interpolation='nearest')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function takePhoto(quality) {\n",
              "      const div = document.createElement('div');\n",
              "\n",
              "      const video = document.createElement('video');\n",
              "      video.style.display = 'block';\n",
              "      const stream = await navigator.mediaDevices.getUserMedia({video: true});\n",
              "\n",
              "      document.body.appendChild(div);\n",
              "      div.appendChild(video);\n",
              "      video.srcObject = stream;\n",
              "      await video.play();\n",
              "\n",
              "      // Resize the output to fit the video element.\n",
              "      google.colab.output.setIframeHeight(document.documentElement.scrollHeight, true);\n",
              "\n",
              "      const canvas = document.createElement('canvas');\n",
              "      canvas.width = video.videoWidth;\n",
              "      canvas.height = video.videoHeight;\n",
              "      canvas.getContext('2d').drawImage(video, 0, 0);\n",
              "      stream.getVideoTracks()[0].stop();\n",
              "      div.remove();\n",
              "      return canvas.toDataURL('image/jpeg', quality);\n",
              "    }\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "<class 'numpy.ndarray'>\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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w5mfsAvU9tHPVt9EHQ1gng86OsvKzc/uBVWX/CHHtCLzWCukW9rdP6vys3ecz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\neLMGMV6ri51fLaHGKgp4mSB2nyEvwTTcxEADTgw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W68omyrLw/KS55OrhtlAj1VkoTc6S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Ww1XWEpZJsfGdtKAw4vJsY111FbFbprIbzWUBeC8BqhDA25bzFXUzaMaTiMdGFDzrYzb7qOD33o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\noHuU64jOC1cRL6IZy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eWhsMqwfPyMhS1ZNku2HZTzCpQDE0ELB1K1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IoASAt4PI0xJHq4vdKMfdLoz3C4TczzUav+3EmOZyJgGuxoGm16YsTMNmFsu0TkzddK2r9qY4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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0c0qTJ4_8wU-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "outputId": "6739818a-95be-45f3-833c-23b55fcedd44"
      },
      "source": [
        "from keras.preprocessing.image import img_to_array\n",
        "from keras.models import load_model\n",
        "import imutils\n",
        "from google.colab.patches import cv2_imshow\n",
        "import cv2\n",
        "import numpy as np\n",
        "import sys\n",
        "from matplotlib import pyplot as plt\n",
        "from google.colab.patches import cv2_imshow\n",
        " \n",
        "# parameters for loading data and images\n",
        "detection_model_path = 'haarcascade_frontalface_default.xml'\n",
        "emotion_model_path = 'fer2013_mini_XCEPTION.110-0.65.hdf5'\n",
        "\n",
        "img_path = 'y.jpeg'\n",
        " \n",
        "# hyper-parameters for bounding boxes shape\n",
        "# loading models\n",
        "face_detection = cv2.CascadeClassifier(detection_model_path)\n",
        "emotion_classifier = load_model(emotion_model_path, compile=False)\n",
        "#emotion_classifier = model\n",
        "EMOTIONS = [\"angry\",\"disgust\",\"scared\", \"happy\", \"sad\", \"surprised\",\"neutral\"]\n",
        " \n",
        "#reading the frame\n",
        "orig_frame = cv2.imread(img_path)\n",
        "frame = cv2.imread(img_path,0)\n",
        "faces = face_detection.detectMultiScale(frame,scaleFactor=1.1,minNeighbors=5,minSize=(30,30),flags=cv2.CASCADE_SCALE_IMAGE)\n",
        "\n",
        "print(len(faces))\n",
        "print(type(orig_frame))\n",
        "\n",
        "if len(faces) > 0:\n",
        "    faces = sorted(faces, reverse=True,key=lambda x: (x[2] - x[0]) * (x[3] - x[1]))[0]\n",
        "    (fX, fY, fW, fH) = faces\n",
        "    roi = frame[fY:fY + fH, fX:fX + fW]\n",
        "    roi = cv2.resize(roi, (64, 64))\n",
        "    roi = roi.astype(\"float\") / 255.0\n",
        "    roi = img_to_array(roi)\n",
        "    roi = np.expand_dims(roi, axis=0)\n",
        "    preds = emotion_classifier.predict(roi)[0]\n",
        "    emotion_probability = np.max(preds)\n",
        "    label = EMOTIONS[preds.argmax()]\n",
        "    print(label)\n",
        "    cv2.putText(orig_frame, label, (fX, fY - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)\n",
        "    cv2.rectangle(orig_frame, (fX, fY), (fX + fW, fY + fH),(0, 0, 255), 2)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "%matplotlib inline\n",
        "plt.imshow(orig_frame, interpolation='nearest')\n",
        "cv2.imwrite('test_output/'+img_path.split('/')[-1],orig_frame)\n",
        "if (cv2.waitKey(2000) & 0xFF == ord('q')):\n",
        "    sys.exit(\"Thanks\")\n",
        "cv2.destroyAllWindows()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1\n",
            "<class 'numpy.ndarray'>\n",
            "happy\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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w5mfsAvU9tHPVt9EHQ1gng86OsvKzc/uBVWX/CHHtCLzWCukW9rdP6vys3ecz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\neLMGMV6ri51fLaHGKgp4mSB2nyEvwTTcxEADTgw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FurKJMi88P2ouqUS4DWqkOgmlMVjy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AOgUPjpXBkom9vkh50DtDWnezpfVRxYBEXsvQcLxUMCNGHsVmgpKxK5e3qfm4/fmmXlHGrLAI1W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\nXGO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V7GQ9gzJetS/c8xpsxXZ23LCaWd1clbsD0NPzn06QOKxwIgyrNfuzGevlkiv7l0kp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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}