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| import tensorflow as tf | |
| from typing import List | |
| import numpy as np | |
| 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) | |
| print(frames.shape) | |
| alignments = load_alignments(alignment_path) | |
| image_data = (frames * 255).astype(np.uint8) | |
| image_data = np.squeeze(image_data) | |
| return frames, alignments, image_data |