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121 lines
5.0 KiB
121 lines
5.0 KiB
import math
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import queue
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import cv2
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import threading
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import time
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from myutils.ConfigManager import myCongif
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import subprocess as sp
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class VideoCaptureWithFPS:
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'''视频捕获的封装类，是一个通道一个'''
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def __init__(self, source):
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self.source = source
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self.width = None
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self.height = None
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# GStreamer --- 内存占用太高，且工作环境的部署也不简单
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# self.pipeline = (
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# "rtspsrc location=rtsp://192.168.3.102/live1 protocols=udp latency=100 ! "
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# "rtph264depay !"
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# " h264parse !"
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# " avdec_h264 !"
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# " videoconvert !"
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# " appsink"
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# )
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#self.cap = cv2.VideoCapture(self.pipeline, cv2.CAP_GSTREAMER)
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#FFmpeg --更加定制化的使用--但要明确宽高。。。
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# self.ffmpeg_cmd = [
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# 'ffmpeg',
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# '-rtsp_transport', 'udp',
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# '-i', 'rtsp://192.168.3.102/live1',
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# '-f', 'image2pipe',
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# '-pix_fmt', 'bgr24',
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# '-vcodec', 'rawvideo', '-'
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# ]
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# self.pipe = sp.Popen(self.ffmpeg_cmd, stdout=sp.PIPE, bufsize=10 ** 8)
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# opencv -- 后端默认使用的就是FFmpeg -- 不支持UDP
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self.cap = cv2.VideoCapture(self.source)
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if self.cap.isOpened(): #若没有打开成功，在读取画面的时候，已有判断和处理 -- 这里也要检查下内存的释放情况
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self.width = int(self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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self.height = int(self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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print(self.width,self.height)
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#self.fps = fps # 线程保持最大帧率的刷新画面---过高的帧率会影响CPU性能，但过地的帧率会造成帧积压
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self.fps = math.ceil(self.cap.get(cv2.CAP_PROP_FPS)/float(myCongif.get_data("verify_rate")))-1 #向上取整。
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#print(self.fps)
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self.running = True
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self.frame_queue = queue.Queue(maxsize=1)
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#self.frame = None
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#self.read_lock = threading.Lock()
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self.thread = threading.Thread(target=self.update)
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self.thread.start()
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def update(self):
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icount = 0
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while self.running:
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ret, frame = self.cap.read()
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if not ret:
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icount += 1
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if icount > 5: #重连
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self.cap.release()
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self.cap = cv2.VideoCapture(self.source)
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#self.cap = cv2.VideoCapture(self.pipeline, cv2.CAP_GSTREAMER)
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if self.cap.isOpened():
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self.width = int(self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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self.height = int(self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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print(self.width,self.height)
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# self.fps = fps # 线程保持最大帧率的刷新画面---过高的帧率会影响CPU性能，但过地的帧率会造成帧积压
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self.fps = math.ceil(
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self.cap.get(cv2.CAP_PROP_FPS) / float(myCongif.get_data("verify_rate"))) -1 # 向上取整。
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icount = 0
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else:
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#self.frame = None
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sleep_time = myCongif.get_data("cap_sleep_time")
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print(f"{self.source}视频流，将于{sleep_time}秒后重连！")
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time.sleep(sleep_time)
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continue
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#resized_frame = cv2.resize(frame, (int(self.width / 2), int(self.height / 2)))
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# with self.read_lock:
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# self.frame = frame
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if self.frame_queue.full():
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try:
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#print("采集线程丢帧")
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self.frame_queue.get(timeout=0.01) #这里不get的好处是，模型线程不会有None
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except queue.Empty: #为空不处理
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pass
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self.frame_queue.put(frame)
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# 跳过指定数量的帧以避免积压
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for _ in range(self.fps):
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self.cap.grab()
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# time.sleep(self.fps) #按照视频源的帧率进行休眠
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#print("Frame updated at:",time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
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def read(self):
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'''
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直接读视频原画面
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:param type: 0-大多数情况读取，1-绘制区域时读取一帧，但当前帧不丢，还是回队列
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:return:
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'''
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# with self.read_lock:
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# frame = self.frame.copy() if self.frame is not None else None
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# if frame is not None:
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# return True, frame
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# else:
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# return False, None
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if not self.frame_queue.empty():
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try:
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frame = self.frame_queue.get(timeout=0.05)
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except queue.Empty:
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#print("cap-frame None")
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return False, None
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else:
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#print("cap-frame None")
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return False, None
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return True, frame
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def release(self):
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self.running = False
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self.thread.join()
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self.cap.release()
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