Esempio n. 1
0
def process_video(weight_path, video_path):
    print "\nLoading data from disk..."
    video = Video(vtype='face', face_predictor_path=FACE_PREDICTOR_PATH)
    if os.path.isfile(video_path):
        video.from_video(video_path)
    else:
        video.from_frames(video_path)
    print "Data loaded.\n"

    a = video.split_commands()
    show_square(video.sq[20:], video.avg_sq)

    ans_v = []
    ans_r = []

    if (a != []):
        for i in range(len(a)):
            if (i == 0):
                video.from_video_test(video_path, 0, a[i])
                v, r = predict_videos(video, weight_path)
                ans_v.append(v)
                ans_r.append(r)

            if (i == len(a) - 1):
                video.from_video_test(video_path, a[i], -1, last=True)
                v, r = predict_videos(video, weight_path)
                ans_v.append(v)
                ans_r.append(r)
                break

            video.from_video_test(video_path, a[i], a[i + 1])
            v, r = predict_videos(video, weight_path)
            ans_v.append(v)
            ans_r.append(r)
    return ans_v, ans_r
Esempio n. 2
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def predict(weight_path,
            video_path,
            absolute_max_string_len=32,
            output_size=28):
    print "\nLoading data from disk..."
    video = Video(vtype='face', face_predictor_path=FACE_PREDICTOR_PATH)
    if os.path.isfile(video_path):
        video.from_video(video_path)
    else:
        video.from_frames(video_path)
    print "Data loaded.\n"

    a = video.split_commands()
    show_square(video.sq[20:], video.avg_sq)

    if (a != []):
        for i in range(len(a)):
            if (i == len(a) - 1):
                a[i + 1] = len(a)
            video.from_video_test(video_path, a[i], a[i + 1])

        if K.image_data_format() == 'channels_first':
            img_c, frames_n, img_w, img_h = video.data.shape
        else:
            frames_n, img_w, img_h, img_c = video.data.shape

        lipnet = LipNet(img_c=img_c,
                        img_w=img_w,
                        img_h=img_h,
                        frames_n=frames_n,
                        absolute_max_string_len=absolute_max_string_len,
                        output_size=output_size)

        adam = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-08)

        lipnet.model.compile(loss={
            'ctc': lambda y_true, y_pred: y_pred
        },
                             optimizer=adam)
        lipnet.model.load_weights(weight_path)

        spell = Spell(path=PREDICT_DICTIONARY)
        decoder = Decoder(greedy=PREDICT_GREEDY,
                          beam_width=PREDICT_BEAM_WIDTH,
                          postprocessors=[labels_to_text, spell.sentence])

        X_data = np.array([video.data]).astype(np.float32) / 255
        input_length = np.array([len(video.data)])

        y_pred = lipnet.predict(X_data)
        result = decoder.decode(y_pred, input_length)[0]

    return (video, result)
Esempio n. 3
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def predict(video_path):
    print "\nLoading data from disk..."
    video = Video(vtype='face', face_predictor_path=FACE_PREDICTOR_PATH)
    if os.path.isfile(video_path):
        video.from_video(video_path)
    else:
        video.from_frames(video_path)
    print "Data loaded.\n"

    


    
    a = video.split_commands()

    ##slide disp
    d = 0
    for item in video.avg_sq:
        d += item
    d = d/len(video.avg_sq)

    print('Avarage dispertion(slide) = ', d)

    #disp all
    avg_sq = 0
    disp = 0
    for i in range(len(video.sq)):
        avg_sq += video.sq[i]
        disp += video.sq[i]**2

    avg_sq = ((avg_sq)*(avg_sq))/len(video.sq)
    disp = (disp - avg_sq)/len(video.sq)
    disp = np.sqrt(disp)

    print('disp = ', disp)

    #avarage square
    avg = 0
    for item in video.sq:
        avg += item
    avg = avg/len(video.sq)
    print('Avarage square = ', avg) 

     

   


    show_square(video.sq[20:],video.avg_sq)