前言
本文提供一个功能完备的OpenCv视频播放处理类，供大家使用。（支持，保存视频、日志打印、视频裁剪、Q键退出、是否播放，选择使用摄像头或者视频, 播放完释放资源，进度条打印，日志打印。。。
【Opencv工程开发所用类】VideoTracker 视频摄像头
VideoTracker.py
# --time**2022.8.13
# --** worker:优快云大气层煮月亮
# --** email:2642898145@qq.com
import cv2
import time
# import torch
import warnings
import numpy as np
from PIL import Image
from loguru import logger
import os
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
class VideoTracker(object):
def __init__(self, cam=-1, video_path='', save_path='', use_frame=[0, -1], display=True):
self.display = display
self.use_frame = use_frame
self.video_path = video_path
self.cam = cam
if self.cam != -1:
print("Using webcam :" + str(self.cam))
self.vdo = cv2.VideoCapture(self.cam)
else:
print("Using video :" + str(self.video_path))
self.vdo = cv2.VideoCapture()
self.save_path = save_path
self.frame_interval = 1
self.use_cuda = True
#use_cuda = self.use_cuda and torch.cuda.is_available()
#if not use_cuda:
#warnings.warn("Running in cpu mode which maybe very slow!", UserWarning)
def __enter__(self):
if self.cam != -1:
ret, frame = self.vdo.read()
assert ret, "Error: Camera error"
self.im_width = frame.shape[0]
self.im_height = frame.shape[1]
self.count_frame = int(-1)
else:
assert os.path.isfile(self.video_path), "Path error"
self.vdo.open(self.video_path)
self.im_width = int(self.vdo.get(cv2.CAP_PROP_FRAME_WIDTH))
self.im_height = int(self.vdo.get(cv2.CAP_PROP_FRAME_HEIGHT))
self.count_frame = int(self.vdo.get(cv2.CAP_PROP_FRAME_COUNT))
assert self.vdo.isOpened()
if self.save_path != '':
os.makedirs(self.save_path, exist_ok=True)
# path of saved video and results
self.save_video_path = os.path.join(self.save_path, "results.avi")
# create video writer
fourcc = cv2.VideoWriter_fourcc(*'MJPG')
self.writer = cv2.VideoWriter(self.save_video_path, fourcc, 24, (self.im_width, self.im_height))
# logging
logger.info("Save results to {}".format(self.save_path))
return self
def __exit__(self, exc_type, exc_value, exc_traceback):
if exc_type:
print(exc_type, exc_value, exc_traceback)
# 释放视频资源
if hasattr(self, 'vdo') and self.vdo.isOpened():
self.vdo.release()
print("Video resource released.")
# 释放视频写入器资源
if hasattr(self, 'writer'):
self.writer.release()
print("Video writer resource released.")
def run(self):
idx_frame = 0
all_costTime = 0
while self.vdo.grab():
idx_frame += 1
start_iter_frame_id = int(self.count_frame * self.use_frame[0])
end_iter_frame_id = int(self.count_frame * self.use_frame[1])
# 展示用
self.show_count_frames = end_iter_frame_id
if idx_frame % self.frame_interval:
continue
if idx_frame < start_iter_frame_id:
continue
if idx_frame > end_iter_frame_id:
break
start = time.time()
ref, ori_im = self.vdo.retrieve()
if ref is True:
# start your code from here
# -----------end-----------
if self.display:
cv2.imshow("frame", ori_im)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
if self.save_path:
self.writer.write(ori_im)
# logging
end = time.time() - start if time.time() - start != 0 else 1
all_costTime += end - start
if self.display:
if self.cam != -1:
logger.info("frame schedule:<{}/-1> ({:.2f} ms), fps: {:.03f}"
.format(idx_frame, end - start, 1 / (end - start)))
else:
logger.info("frame schedule:<{}/{}> ({:.2f} ms), fps: {:.03f}"
.format(idx_frame, self.show_count_frames , end - start, 1 / (end - start)))
else:
self.print_progress_bar(idx_frame, self.show_count_frames, prefix='Progress:', suffix='Complete', length=50)
logger.info("ALL_COST_TIME:{:.3f}s".format(all_costTime))
def print_progress_bar(self, iteration, total, prefix='', suffix='', length=50, fill='█'):
"""
Args:
iteration (int): 当前迭代次数
total (int): 总迭代次数
prefix (str): 进度条前缀文本
suffix (str): 进度条后缀文本
length (int): 进度条长度
fill (str): 用于填充进度条的字符
"""
percent = ("{0:.1f}").format(100 * (iteration / float(total)))
filled_length = int(length * iteration // total)
bar = fill * filled_length + '-' * (length - filled_length)
sys.stdout.write('\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix))
sys.stdout.flush()
def pil_to_cv2(self, pil_image):
"""
Convert a PIL Image to an OpenCV image (NumPy array).
Args:
pil_image (PIL.Image): The image in PIL format.
Returns:
numpy.ndarray: The image in OpenCV format.
"""
# Convert the PIL image to a NumPy array
pil_image_np = np.array(pil_image)
# Convert RGB to BGR
cv2_image = cv2.cvtColor(pil_image_np, cv2.COLOR_RGB2BGR)
return cv2_image
def cv2_to_pil(self, cv2_image):
"""
Convert an OpenCV image (NumPy array) to a PIL Image.
Args:
cv2_image (numpy.ndarray): The image in OpenCV format.
Returns:
PIL.Image: The image in PIL format.
"""
# OpenCV uses BGR by default, whereas PIL uses RGB, so we need to convert the image
cv2_image_rgb = cv2.cvtColor(cv2_image, cv2.COLOR_BGR2RGB)
# Convert the NumPy array to a PIL image
pil_image = Image.fromarray(cv2_image_rgb)
return pil_image
Camer
#using camer
from VideoCapture import VideoTracker
if __name__ == '__main__':
# select the camer you want to use
with VideoTracker(cam=0) as vdo_trk:
vdo_trk.run()
Video
#using video
from VideoCapture import VideoTracker
if __name__ == '__main__':
# select the video you want to use
'''
@video_path<str> -> |视频路径
@use_frame<list> -> |取值范围[0,1],如果设置为【0，0.5】那么意思就是只使用该视频的前半段。
@display<bool> -> |是否播放视频。
'''
with VideoTracker(video_path='test.mp4',use_frame=[0, 1]) as vdo_trk:
vdo_trk.run()
# save video
只需要在VideoTracker中设置参数save_path，save_path是要存放保存视频的位置，如：'datasets/out_video/'
from VideoCapture import VideoTracker
if __name__ == '__main__':
# select the video you want to use
with VideoTracker(video_path='test.mp4',save_path='database/output/', use_frame=[0, -1]) as vdo_trk:
vdo_trk.run()
# crop video
use_frame=[0,-1]，默认播放所有视频。
裁剪视频的原理非常简单，比如将use_frame=[10,100]，且设置save_path，就是只处理视频[10,100]帧，并且保存视频。也就是说最后的输出视频是只有[10,100]帧的视频。
from VideoCapture import VideoTracker
if __name__ == '__main__':
# select the video you want to use
with VideoTracker(video_path='test.mp4',save_path='database/output/', use_frame=[10, 100]) as vdo_trk:
vdo_trk.run()
【OUTPUT】
一些关于OpenCv的参数：
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