Repository navigation
Expand file tree
/
Copy pathread_frame.py
More file actions
101 lines (76 loc) · 3.38 KB
/
Copy pathread_frame.py
File metadata and controls
101 lines (76 loc) · 3.38 KB
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
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
import cv2
import concurrent.futures
import argparse
import logging
import threading
import time
from time import sleep
def process_frame(video_path, start_frame, end_frame, output_list, lock):
cap = cv2.VideoCapture(video_path)
logging.info(f"Thread {start_frame}: starting")
thread_output = [] # 用于存储每个线程的输出
cap.set(cv2.CAP_PROP_POS_FRAMES, start_frame)
for _ in range(start_frame, end_frame):
# 获取帧。grap 好像不能跳帧 cap.set(cv2.CAP_PROP_POS_FRAMES, start_frame)
ret, _ = cap.read()
if not ret:
logging.info(f"Thread {start_frame}: failed to retrieve frame")
break
# 获取时间戳
timestamp = cap.get(cv2.CAP_PROP_POS_MSEC) / 1000.0
thread_output.append(
f"{time.ctime()} Thread {start_frame}: Timestamp - {timestamp}")
# 模拟处理帧的过程，越慢多线程优势越明显
sleep(5)
# 设置视频流的位置，直接跳过一些帧
cap.set(cv2.CAP_PROP_POS_FRAMES, cap.get(cv2.CAP_PROP_POS_FRAMES) + 20)
if cap.get(cv2.CAP_PROP_POS_FRAMES) >= end_frame:
logging.info(f"Thread {start_frame}: reached end frame")
break
# 将每个线程的输出添加到共享的列表中
# 使用锁确保对共享列表的安全访问
with lock:
output_list.extend(thread_output)
cap.release() # 关闭视频文件
logging.info(f"Thread {start_frame}: finishing")
def main(video_path, num_threads):
cap = cv2.VideoCapture(video_path)
# 获取视频的总帧数
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
# 计算每个线程处理的帧范围
frames_per_thread = total_frames // num_threads
frame_ranges = [(i * frames_per_thread + 1, min((i + 1) *
frames_per_thread, total_frames)) for i in range(num_threads)]
logging.info(f"Frame ranges: {frame_ranges}")
# 创建共享的列表，用于存储每个线程的输出
output_list = []
# 创建线程锁
lock = threading.Lock()
# 使用线程池
with concurrent.futures.ThreadPoolExecutor(max_workers=num_threads) as executor:
# 提交帧处理任务给线程池
futures = [executor.submit(process_frame, video_path, start, end, output_list, lock)
for start, end in frame_ranges]
# 等待所有任务完成
concurrent.futures.wait(futures)
logging.info("All threads completed.")
# logging.info(f"Output list: {output_list.size()}")
# 打印所有线程的输出
# for output in output_list:
# print(output)
cap.release()
cv2.destroyAllWindows()
# python3 read_frame.py /Users/frank/Movies/out.mp4 --num_threads 4
if __name__ == "__main__":
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s %(message)s',
datefmt='%Y-%m-%d %H:%M:%S')
# 添加命令行参数解析
parser = argparse.ArgumentParser(
description='Process video frames and get timestamps.')
parser.add_argument('video_path', type=str, help='Path to the video file.')
parser.add_argument('--num_threads', type=int,
default=2, help='Number of threads.')
args = parser.parse_args()
# 调用主函数
main(args.video_path, args.num_threads)