(X0, Y0, video_indices, classes)
Process features
Process features
Process features
def process_features(X0, Y0, video_indices, classes):
    ''' Process features '''
    # Convert features
    # From: raw feature of individual image.
    # To:   time-serials features calculated from multiple raw features
    #       of multiple adjacent images, including speed, normalized pos, etc.
    ADD_NOISE = False
    if ADD_NOISE:
        X1, Y1 = extract_multi_frame_features(
            X0, Y0, video_indices, WINDOW_SIZE, 
            is_adding_noise=True, is_print=True)
        X2, Y2 = extract_multi_frame_features(
            X0, Y0, video_indices, WINDOW_SIZE,
            is_adding_noise=False, is_print=True)
        X = np.vstack((X1, X2))
        Y = np.concatenate((Y1, Y2))
        return X, Y
    else:
        X, Y = extract_multi_frame_features(
            X0, Y0, video_indices, WINDOW_SIZE, 
            is_adding_noise=False, is_print=True)
        return X, Y
def process_features ( X0 , Y0 , video_indices , classes ) : # Convert features # From: raw feature of individual image. # To:   time-serials features calculated from multiple raw features #       of multiple adjacent images, including speed, normalized pos, etc. ADD_NOISE = False if ADD_NOISE : X1 , Y1 = extract_multi_frame_features ( X0 , Y0 , video_indices , WINDOW_SIZE , is_adding_noise = True , is_print = True ) X2 , Y2 = extract_multi_frame_features ( X0 , Y0 , video_indices , WINDOW_SIZE , is_adding_noise = False , is_print = True ) X = np . vstack ( ( X1 , X2 ) ) Y = np . concatenate ( ( Y1 , Y2 ) ) return X , Y else : X , Y = extract_multi_frame_features ( X0 , Y0 , video_indices , WINDOW_SIZE , is_adding_noise = False , is_print = True ) return X , Y