| import tensorflow as tf |
| from typing import List |
| import cv2 |
| import os |
|
|
| vocab = [x for x in "abcdefghijklmnopqrstuvwxyz'?!123456789 "] |
| char_to_num = tf.keras.layers.StringLookup(vocabulary=vocab, oov_token="") |
| |
| num_to_char = tf.keras.layers.StringLookup( |
| vocabulary=char_to_num.get_vocabulary(), oov_token="", invert=True |
| ) |
|
|
| def load_video(path:str) -> List[float]: |
| |
| cap = cv2.VideoCapture(path) |
| frames = [] |
| for _ in range(int(cap.get(cv2.CAP_PROP_FRAME_COUNT))): |
| ret, frame = cap.read() |
| frame = tf.image.rgb_to_grayscale(frame) |
| frames.append(frame[190:236,80:220,:]) |
| cap.release() |
| |
| mean = tf.math.reduce_mean(frames) |
| std = tf.math.reduce_std(tf.cast(frames, tf.float32)) |
| return tf.cast((frames - mean), tf.float32) / std |
| |
| def load_alignments(path:str) -> List[str]: |
| |
| with open(path, 'r') as f: |
| lines = f.readlines() |
| tokens = [] |
| for line in lines: |
| line = line.split() |
| if line[2] != 'sil': |
| tokens = [*tokens,' ',line[2]] |
| return char_to_num(tf.reshape(tf.strings.unicode_split(tokens, input_encoding='UTF-8'), (-1)))[1:] |
|
|
| def load_data(path: str): |
| path = bytes.decode(path.numpy()) |
| file_name = path.split('/')[-1].split('.')[0] |
| |
| file_name = path.split('\\')[-1].split('.')[0] |
| video_path = os.path.join('..','data','s1',f'{file_name}.mpg') |
| alignment_path = os.path.join('..','data','alignments','s1',f'{file_name}.align') |
| frames = load_video(video_path) |
| alignments = load_alignments(alignment_path) |
| |
| return frames, alignments |
