168 lines
5.0 KiB
Python
168 lines
5.0 KiB
Python
# Client主进程
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# 读取摄像头数据并显示
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import MultiFunc_V2 as Func
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import cv2
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import threading
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import numpy as np
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import time
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import queue
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rtsp_ulr = 0 #"rtsp://192.168.1.206:554"
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# rtsp_ulr = "rtsp://admin：123456@192.168.1.123/video1"
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# 自定义无缓存读视频类
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class NetCameraCapture:
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"""Customized VideoCapture, always read latest frame """
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def __init__(self, camera_id):
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# "camera_id" is a int type id or string name
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self.cap = cv2.VideoCapture(camera_id)
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self.q = queue.Queue(maxsize=3)
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self.stop_threads = False # to gracefully close sub-thread
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th = threading.Thread(target=self._reader)
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th.daemon = True # 设置工作线程为后台运行
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th.start()
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# 实时读帧，只保存最后一帧
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def _reader(self):
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while not self.stop_threads:
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if self.cap.isOpened(): # 判断是否已经打开
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ret, frame = self.cap.read()
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if not ret:
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break
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if not self.q.empty():
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try:
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self.q.get_nowait()
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except queue.Empty:
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pass
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self.q.put(frame)
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def read(self):
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return True, self.q.get()
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def get(self, id):
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return self.cap.get(id)
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def release(self):
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self.stop_threads = True
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self.cap.release()
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class ClientThread(threading.Thread):
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def __init__(self):
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self._running = True # 定义线程状态变量
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super().__init__()
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def terminate(self):
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self._running = False
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def run(self):
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while self._running:
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if GlobalVar.event1.isSet():
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GlobalVar.faces, temp_name = Func.function(GlobalVar.frame)
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# 如果识别结果为空，且少于15张图片（约0.5s），则不更新name
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if len(temp_name) < 2 and GlobalVar.i < 15: # 识别结果为空
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GlobalVar.i = GlobalVar.i + 1
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else:
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GlobalVar.name = temp_name
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GlobalVar.i = 0
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time.sleep(0.02)
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class GlobalVar:
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event1 = threading.Event() # 用于主进程读取摄像头图像时，不会同时取用图像
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frame = []
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faces = np.array([])
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t = ClientThread() # 图片处理的子线程
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sk = Func.ClientNetworkThread() # 与服务器通信的子线程
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width = 0 # 图片宽度
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height = 0 # 图片高度
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name = "" # 识别结果
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i = 0
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def init():
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GlobalVar.event1.clear
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# 开启线程
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GlobalVar.t.start()
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GlobalVar.sk.start()
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def draw_frame():
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font = cv2.FONT_HERSHEY_COMPLEX
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cv2.putText(GlobalVar.frame, "Press 'q': Quit", (20, GlobalVar.height - 30), font, 0.8, (84, 255, 159), 1,
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cv2.LINE_AA)
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# 显示人脸识别结果
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cv2.putText(GlobalVar.frame, GlobalVar.name, (20, GlobalVar.height - 80), font, 0.8, (84, 255, 159), 1, cv2.LINE_AA)
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if len(GlobalVar.faces) != 0:
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cv2.putText(GlobalVar.frame, "Faces: " + str(len(GlobalVar.faces)), (20, 50), font, 1, (0, 0, 255), 1,
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cv2.LINE_AA)
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for kk, d in enumerate(GlobalVar.faces):
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# 绘制矩形框
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# cv2.rectangle(GlobalVar.frame, tuple([d.left(), d.top()]), tuple([d.right(), d.bottom()]),
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cv2.rectangle(GlobalVar.frame, (d[0], d[1]), (d[2], d[3]), (0, 255, 255), 2)
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# 显示人脸识别结果
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cv2.putText(GlobalVar.frame, GlobalVar.name, (d[0], d[1] - 10), font, 0.8, (0, 255, 255), 1, cv2.LINE_AA)
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else:
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cv2.putText(GlobalVar.frame, "Faces: " + str(0), (20, 50), font, 1, (0, 0, 255), 1, cv2.LINE_AA)
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def demo():
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# 初始化
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init()
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# 创建 cv2 摄像头对象
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# 获取摄像机来捕获实时流 参数0表示本台设备的序号为0的采集设备（camera）
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# capture = cv2.VideoCapture(0)
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# 使用网络摄像头
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capture = NetCameraCapture(rtsp_ulr)
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# 获取捕获图像分辨率
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GlobalVar.width, GlobalVar.height = int(capture.get(3)), int(capture.get(4))
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print("图像尺寸:", GlobalVar.width, "x", GlobalVar.height, "px")
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i = 0
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s1 = time.time()
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while i < 1000:
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i = i + 1
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GlobalVar.event1.clear() # 置为false，不允许子进程读取图片
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ret, GlobalVar.frame = capture.read()
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# if frame is read correctly ret is True
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if not ret:
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print("Can't receive frame (stream end?). Exiting ...")
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exit()
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GlobalVar.event1.set() # 置为true，允许子进程读取图片
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# 在图片上绘制文字和框
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draw_frame()
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# 窗口显示 show with opencv
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cv2.imshow("Face Recognition", GlobalVar.frame)
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if cv2.waitKey(1) == ord('q') or cv2.waitKey(1) == ord('Q'):
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# 关闭摄像头 释放窗口
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capture.release()
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cv2.destroyAllWindows()
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GlobalVar.t.terminate()
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break
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s2 = time.time()
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print(i, " 张图片共用时：", (s2 - s1))
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GlobalVar.t.terminate()
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GlobalVar.sk.terminate()
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if __name__ == "__main__":
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demo()
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