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：
效果图
原图
中间处理效果
最终结果
思路分析
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。
源码：定位棋盘位置
- from PIL import ImageGrab
- import numpy as np
- import cv2
- from glob import glob
- imglist = 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 = i
- i+=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 ImageGrab
- import numpy as np
- import cv2
- from glob import glob
- import os
- imglist = sorted(glob("screen/*.jpg"))
- a=0
- for 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 = i
- i+=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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原文链接：https://blog.csdn.net/mao_hui_fei/article/details/118363347
