File size: 1,848 Bytes
8a0213d 797f917 8a0213d 929eedf 4f62405 8a0213d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 |
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())
print(path)
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 |