extract features

extract_features.py

def model_cnn_load():
    model = VGG16(weights="imagenet", include_top=True, input_shape=(224, 224, 3))
    out = model.layers[-2].output
    model_final = Model(inputs=model.input, outputs=out)
    return model_final


def load_image(path):
    img = cv2.imread(path)
    img = cv2.resize(img, (224, 224))
    return img


def extract_features(video, model):
    """
    :param video: The video whose frames are to be extracted to convert into a numpy array
    :param model: the pretrained vgg16 model
    :return: numpy array of size 4096x80
    """
    video_id = video.split(".")[0]
    print(video_id)
    print(f'Processing video {video}')

    image_list = video_to_frames(video)
    samples = np.round(np.linspace(
        0, len(image_list) - 1, 80))
    image_list = [image_list[int(sample)] for sample in samples]
    images = np.zeros((len(image_list), 224, 224, 3))
    for i in range(len(image_list)):
        img = load_image(image_list[i])
        images[i] = img
    images = np.array(images)
    fc_feats = model.predict(images, batch_size=128)
    img_feats = np.array(fc_feats)
    # cleanup
    shutil.rmtree(os.path.join(config.test_path, 'temporary_images'))
    return img_feats


def extract_feats_pretrained_cnn():
    """
    saves the numpy features from all the videos
    """
    model = model_cnn_load()
    print('Model loaded')

    if not os.path.isdir(os.path.join(config.test_path, 'feat')):
        os.mkdir(os.path.join(config.test_path, 'feat'))

    video_list = os.listdir(os.path.join(config.test_path, 'video'))
    for video in video_list:

        outfile = os.path.join(config.test_path, 'features_dir', video.split(".")[0] + '.npy')
        img_feats = extract_features(video, model)
        np.save(outfile, img_feats)