标签：hiera 腐蚀 arch orm col 获取 transform com 遍历
如下图：
提取后：
这里可以加载网络摄像头对视频图像进行逐帧处理动态检测，在图书馆，我手机上模拟的网络摄像头和电脑不在同一热点，这里就直接拍了张照片进行测试。
①原图片太大了，对图像缩小一点
img=cv2.imread("C:/Users/31132/Desktop/mtest.jpg") print(img.shape) x,y=img.shape[0],img.shape[1] frameWidth=x//6 frameHeight=y//6 imgRsize=cv2.resize(img,(frameWidth,frameHeight))
②对图像进行预处理，包括转换为灰度图像，高斯模糊，边缘检测，膨胀，腐蚀等，获取图像较为清晰的轮廓
def preProcessing(img): imgGray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) #灰度图像 imgBlur=cv2.GaussianBlur(imgGray,(5,5),1) #高斯模糊 imgCanny=cv2.Canny(imgBlur,200,200) #边缘检测 kernal=np.ones((5,5)) imgDial=cv2.dilate(imgCanny,kernal,iterations=2) #膨胀 imgThres=cv2.erode(imgDial,kernal,iterations=1) #腐蚀 return imgThres
效果如下：
③绘制轮廓，检测图像位置，获取四个楞角
def getContours(img): coutours,hierarchy=cv2.findContours(img,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE) biggest=np.array([]) maxArea=0 for cnt in coutours: area=cv2.contourArea(cnt) if area>5000: # cv2.drawContours(imgcontours, cnt, -1, (0, 0, 255), 5) #周长 peri=cv2.arcLength(cnt,True) #拟合轮廓点集 approx=cv2.approxPolyDP(cnt,0.03*peri,True) if area>maxArea and len(approx)==4: maxArea=area biggest=approx print(biggest) cv2.drawContours(imgcontours, biggest, -1, (255,140,0),22) return biggest
④根据四个点的大小调整位置并作透视转化
def getWarp(img,biggest): newbig=np.zeros_like(biggest) newbig[0]=biggest[1] newbig[1]=biggest[0] newbig[2]=biggest[2] newbig[3] = biggest[3] print(‘new‘,newbig) waitdots = np.float32(newbig) resultd = np.float32([[0, 0], [frameWidth, 0], [0, frameHeight], [frameWidth, frameHeight]]) martix = cv2.getPerspectiveTransform(waitdots, resultd) imgout = cv2.warpPerspective(img, martix, (frameWidth, frameHeight)) return imgout
所有代码
#day05 #加载视频，网络摄像头 # cap=cv2.VideoCapture("http://192.168.137.116:4747/video") # cap.set(3,frameWidth) # cap.set(4,frameHeight) # cap.set(10,150) def getContours(img): coutours,hierarchy=cv2.findContours(img,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE) biggest=np.array([]) maxArea=0 for cnt in coutours: area=cv2.contourArea(cnt) if area>5000: # cv2.drawContours(imgcontours, cnt, -1, (0, 0, 255), 5) #周长 peri=cv2.arcLength(cnt,True) #拟合轮廓点集 approx=cv2.approxPolyDP(cnt,0.03*peri,True) if area>maxArea and len(approx)==4: maxArea=area biggest=approx print(biggest) cv2.drawContours(imgcontours, biggest, -1, (255,140,0),22) return biggest def preProcessing(img): imgGray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) imgBlur=cv2.GaussianBlur(imgGray,(5,5),1) imgCanny=cv2.Canny(imgBlur,200,200) kernal=np.ones((5,5)) imgDial=cv2.dilate(imgCanny,kernal,iterations=2) imgThres=cv2.erode(imgDial,kernal,iterations=1) return imgThres def getWarp(img,biggest): newbig=np.zeros_like(biggest) newbig[0]=biggest[1] newbig[1]=biggest[0] newbig[2]=biggest[2] newbig[3] = biggest[3] print(‘new‘,newbig) waitdots = np.float32(newbig) resultd = np.float32([[0, 0], [frameWidth, 0], [0, frameHeight], [frameWidth, frameHeight]]) martix = cv2.getPerspectiveTransform(waitdots, resultd) imgout = cv2.warpPerspective(img, martix, (frameWidth, frameHeight)) return imgout #图像显示：遍历帧 # while True: # success,img=cap.read() img=cv2.imread("C:/Users/31132/Desktop/mtest.jpg") print(img.shape) x,y=img.shape[0],img.shape[1] frameWidth=x//6 frameHeight=y//6 imgRsize=cv2.resize(img,(frameWidth,frameHeight)) imgcontours=imgRsize.copy() imgThres=preProcessing(imgRsize) biggest=getContours(imgThres) imgOut=getWarp(imgRsize,biggest) cv2.imshow("Video",imgOut) cv2.waitKey(0)
标签：hiera 腐蚀 arch orm col 获取 transform com 遍历
原文地址：https://www.cnblogs.com/XiaoGao128/p/13955328.html
