基于python及图像识别的围棋棋盘棋子识别1——定位棋盘位置

「爱情、让人受尽委屈。」 2022-10-11 12:18 263阅读 0赞

系列文章：  
1、[基于python及图像识别的围棋棋盘棋子识别1——定位棋盘位置][python_1]  
2、[基于python及图像识别的围棋棋盘棋子识别2——定位棋子位置及识别棋子颜色][python_2]  
3、[基于python及图像识别的围棋棋盘棋子识别3——耗时优化（一行代码速度提高600倍）][python_3_600]  
4、[基于python及图像识别的围棋棋盘棋子识别4——源码及使用说明][python_4]

### 基于python及图像识别的围棋棋盘棋子识别1——定位棋盘位置 ###

*  效果图
 *   *  原图
     *  中间处理效果
     *  最终结果
 *  思路分析
 *  源码：定位棋盘位置
 *   *  带保存图像

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 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21hb19odWlfZmVp_size_16_color_FFFFFF_t_70]

## 中间处理效果 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21hb19odWlfZmVp_size_16_color_FFFFFF_t_70 1]

## 最终结果 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21hb19odWlfZmVp_size_16_color_FFFFFF_t_70 2]

# 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()

[python_1]: https://blog.csdn.net/mao_hui_fei/article/details/118363347
[python_2]: https://blog.csdn.net/mao_hui_fei/article/details/118481877
[python_3_600]: https://blog.csdn.net/mao_hui_fei/article/details/118495923
[python_4]: https://blog.csdn.net/mao_hui_fei/article/details/118546899
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