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="") # Mapping integers back to original characters num_to_char = tf.keras.layers.StringLookup( vocabulary=char_to_num.get_vocabulary(), oov_token="", invert=True ) def load_video(path:str) -> List[float]: #print(path) 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]: #print(path) 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 splitting for windows 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