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# load the weights for the optimal model
lstm_model.load_weights('biLSTM.h5')

# Converts a class vector (integers) to binary class matrix
# for use with categorical_crossentropy
# IMPORTANT for CATEGORICAL_CROSSENTROPY!
y_test = np_utils.to_categorical(y_test, 2)

# generator for validating the LSTM model
test_gen = generate_batch(X_test, y_test, batch_size, expected_frames)

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import os
import cv2
import glob

# Set image dir of your choice
imageDir = r'dataset\nervous_frames\_0\\'

# Read in files and sort
filenames = os.listdir(imageDir)
filenames.sort(key= lambda x: int(x[:-4]))

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# create the bidirectional LSTM model
# create a LSTM model
lstm_model = Sequential()
lstm_model.add(Bidirectional(LSTM(units=256, return_sequences=False),
                             input_shape=(n_frames, n_tokens)))
lstm_model.add(Dense(512, activation='relu'))
lstm_model.add(Dropout(0.5))


# two categories: happy/amused smiles and nervous/awkward smiles

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# combine these features
features = np.concatenate((features_happy, features_awkward), axis=0)

# generate corresponding labels
labels = np.concatenate((np.ones(len(features_happy)), np.zeros(len(features_awkward))), axis=0)

# save features and labels to file
np.savez_compressed('dataset', f=features, l=labels)

# split the dataset into training and testing

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def extract_features(iDirName, cnn_model):
    # get face detector from dlib and initialize the face aligner
    detector = dlib.get_frontal_face_detector()

    # open the video file
    iFramePaths = [os.path.join(iDirName, iFramePath) for iFramePath in os.listdir(iDirName)]
    # an empty list to hold the results
    features = []

    # process each frame

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def extract_features_dir(ddir, cnn_model):
    # check the existence of the directory
    if not os.path.exists(ddir):
        raise ValueError('The directory "{}" does not exist!'.format(ddir))

    # create an empty list to hold the result
    features = []

    # get the list of image dirs in this
    iDirNames = [os.path.join(ddir, idir) for idir in os.listdir(ddir)]

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# construct the path for the directory of happy smiles
dir_happy = os.path.join('dataset', 'happy_frames\\')

# construct the path for the directory of awkward smiles
dir_awkward = os.path.join('dataset', 'nervous_frames\\')

# create the pre-trained VGG-Face model
cnn_model = VGGFace(model='resnet50', include_top=False, input_shape=(224, 224, 3), weights=None)

# load the weights from the pre-trained model

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import pandas as pd
import os
from matplotlib import pyplot as plt
import numpy as np

def read_csvs(dirPath):
    # Reads in the OpenFace output csv files

    # read input directory
    files = os.listdir(dirPath)

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FeatureExtraction.exe -fdir "C:\dataset\happy_frames\_1"

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ffmpeg -loglevel warning -i cropped.mp4 %d.jpg