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)
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