python识别围棋定位棋盘位置

最近需要做一个围棋识别的项目，首先要将棋盘位置定位出来，效果图如下：

原图

中间处理效果

最终结果

我们利用python opencv的相关函数进行操作实现，根据棋盘颜色的特征，寻找到相关特征，将棋盘区域抠出来。最好从原始图像中将棋盘位置截取出来。

from PIL import ImageGrabimport numpy as npimport cv2from glob import globimglist = sorted(glob('screen/*.jpg'))for i in imglist:# while 1: img = cv2.imread(i) image = img.copy() w,h,c = img.shape img2 = np.zeros((w,h,c), np.uint8) img3 = np.zeros((w,h,c), np.uint8) # img = ImageGrab.grab() #bbox specifies specific region (bbox= x,y,width,height *starts top-left)hsv=cv2.cvtColor(img,cv2.COLOR_BGR2HSV) lower = np.array([10,0,0]) upper = np.array([40,255,255]) mask = cv2.inRange(hsv,lower,upper) erodeim = cv2.erode(mask,None,iterations=2) # 腐蚀 dilateim = cv2.dilate(erodeim,None,iterations=2) img = cv2.bitwise_and(img,img,mask=dilateim) frame = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) ret, dst = cv2.threshold(frame, 100, 255, cv2.THRESH_BINARY) contours,hierarchy = cv2.findContours(dst, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) cv2.imshow('0',img) i = 0 maxarea = 0 nextarea = 0 maxint = 0 for c in contours:if cv2.contourArea(c)>maxarea: maxarea = cv2.contourArea(c) maxint = ii+=1 #多边形拟合 epsilon = 0.02*cv2.arcLength(contours[maxint],True) if epsilon<1:continue#多边形拟合 approx = cv2.approxPolyDP(contours[maxint],epsilon,True) [[x1,y1]] = approx[0] [[x2,y2]] = approx[2] checkerboard = image[y1:y2,x1:x2] cv2.imshow('1',checkerboard) cv2.waitKey(1000)cv2.destroyAllWindows()

带保存图像

from PIL import ImageGrabimport numpy as npimport cv2from glob import globimport osimglist = sorted(glob('screen/*.jpg'))a=0for i in imglist:# while 1: a=a+1 img = cv2.imread(i) image = img.copy() w,h,c = img.shape img2 = np.zeros((w,h,c), np.uint8) img3 = np.zeros((w,h,c), np.uint8) # img = ImageGrab.grab() #bbox specifies specific region (bbox= x,y,width,height *starts top-left)hsv=cv2.cvtColor(img,cv2.COLOR_BGR2HSV) lower = np.array([10,0,0]) upper = np.array([40,255,255]) mask = cv2.inRange(hsv,lower,upper) erodeim = cv2.erode(mask,None,iterations=2) # 腐蚀 dilateim = cv2.dilate(erodeim,None,iterations=2) img = cv2.bitwise_and(img,img,mask=dilateim) frame = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) ret, dst = cv2.threshold(frame, 100, 255, cv2.THRESH_BINARY) contours,hierarchy = cv2.findContours(dst, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) # 保存图片的地址 img_file_1 = './temp' # 确认上述地址是否存在 if not os.path.exists(img_file_1):os.mkdir(img_file_1) cv2.imshow('0',img) cv2.imwrite(img_file_1 + '/' + ’temp_%d.jpg’%a, img) i = 0 maxarea = 0 nextarea = 0 maxint = 0 for c in contours:if cv2.contourArea(c)>maxarea: maxarea = cv2.contourArea(c) maxint = ii+=1 #多边形拟合 epsilon = 0.02*cv2.arcLength(contours[maxint],True) if epsilon<1:continue#多边形拟合 approx = cv2.approxPolyDP(contours[maxint],epsilon,True) [[x1,y1]] = approx[0] [[x2,y2]] = approx[2] checkerboard = image[y1:y2,x1:x2] cv2.imshow('1',checkerboard) cv2.waitKey(1000) # 保存图片的地址 img_file_2 = './checkerboard' # 确认上述地址是否存在 if not os.path.exists(img_file_2):os.mkdir(img_file_2) cv2.imwrite(img_file_2 + '/' + ’checkerboard_%d.jpg’%a, checkerboard)cv2.destroyAllWindows()

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