Python OpenCV实现围棋棋盘自动定位与截取

苏见山

发布时间：2026-10-14| 3人浏览过

原创

本文介绍利用Python和OpenCV库进行围棋棋盘图像识别与定位的技术方案。通过HSV颜色空间转换、形态学处理及轮廓检测算法，自动提取棋盘区域并截取保存，适用于自动化对弈或棋局分析场景。

近期开发了一个围棋图像识别项目，首要任务是准确定位棋盘在图像中的位置，具体效果见下图。

效果图

原始输入图像

中间处理阶段效果

最终定位结果

思路分析

本方案基于Python OpenCV库实现，主要依据棋盘的颜色特征进行识别，从而提取出棋盘区域。最佳实践是从原始图像中直接截取包含棋盘的位置信息。

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()

包含图像保存功能的版本


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()

以上就是关于利用Python识别并定位围棋棋盘位置的技术分享，更多相关Python围棋定位内容可查阅本站往期文章或继续浏览后续内容，感谢支持。