标签:nal uri wait element put nump col bre sdi
这是来自一段动态物体检测的代码,首先将动态物体检测出来并画框,然后将画框的图片以视频的形式保存下来。
import cv2 import numpy as np camera = cv2.VideoCapture("F:/7.mp4") # 判断视频是否打开 if (camera.isOpened()): print(‘Open‘) else: print(‘摄像头未打开‘) # 测试用,查看视频size size = (int(camera.get(cv2.CAP_PROP_FRAME_WIDTH)), int(camera.get(cv2.CAP_PROP_FRAME_HEIGHT))) print(‘size:‘+repr(size)) es = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (9, 4)) kernel = np.ones((5, 5), np.uint8) background = None a = 0 b = 0 fourcc = cv2.VideoWriter_fourcc(‘M‘, ‘P‘, ‘4‘, ‘2‘) out = cv2.VideoWriter(‘F:\output0.avi‘,fourcc, 20.0, (1280,720)) while True: # 读取视频流 grabbed, frame_lwpCV = camera.read() if frame_lwpCV is None: break gray_lwpCV = cv2.cvtColor(frame_lwpCV, cv2.COLOR_BGR2GRAY) gray_lwpCV = cv2.GaussianBlur(gray_lwpCV, (21, 21), 0) # 将第一帧设置为整个输入的背景 if background is None: background = gray_lwpCV continue diff = cv2.absdiff(gray_lwpCV, background) background = gray_lwpCV diff = cv2.threshold(diff, 9, 255, cv2.THRESH_BINARY)[1] diff = cv2.dilate(diff, es, iterations=2) # 形态学膨胀 # 显示矩形框 # 该函数计算一幅图像中目标的轮廓 image, contours, hierarchy = cv2.findContours(diff.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) aaa = 0 for c in contours: aaa += 1 if cv2.contourArea(c) < 2000: # 对于矩形区域,只显示大于给定阈值的轮廓 continue (x, y, w, h) = cv2.boundingRect(c) # 该函数计算矩形的边界框 #print(x, y, w, h) cv2.rectangle(frame_lwpCV, (x, y), (x+w, y+h), (0, 255, 0), 2) out.write(frame_lwpCV) cv2.imshow(‘contours‘, frame_lwpCV) cv2.imshow(‘dis‘, diff) key = cv2.waitKey(1) & 0xFF # 按‘q‘健退出循环 if key == ord(‘q‘): break camera.release() cv2.destroyAllWindows()
标签:nal uri wait element put nump col bre sdi
原文地址:https://www.cnblogs.com/czz0508/p/10837773.html