棋子检测 On checkboard Opencv

Question

我正在尝试检测

在我的棋盘上

但是无法让它检测到正确的部分。这件作品是 59x83。它应该被检测到，但没有。 我想我在这里遗漏了什么？

import cv2
import numpy as np

# Load the chess board and chess piece images
img_board = cv2.imread('ccom.png')
img_piece = cv2.imread('bbis.png')

# Convert both images to grayscale
img_board_gray = cv2.cvtColor(img_board, cv2.COLOR_BGR2GRAY)
img_piece_gray = cv2.cvtColor(img_piece, cv2.COLOR_BGR2GRAY)

# Apply morphological operations to extract the chess piece from the board
kernel = np.ones((5, 5), np.uint8)
img_piece_mask = cv2.erode(img_piece_gray, kernel, iterations=1)
img_piece_mask = cv2.dilate(img_piece_mask, kernel, iterations=1)

# Find the matching location on the board
result = cv2.matchTemplate(img_board_gray, img_piece_mask, cv2.TM_SQDIFF)
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)

# Draw a rectangle around the matching location
top_left = min_loc
bottom_right = (top_left[0] + img_piece.shape[1], top_left[1] + img_piece.shape[0])
cv2.rectangle(img_board, top_left, bottom_right, (0, 0, 255), 2)

# Show the result
cv2.imshow('Result', img_board)
cv2.waitKey(0)
cv2.destroyAllWindows()

Answer 1

我尝试了几种设置，并通过实验发现这种设置效果最好：

1. 忽略棋子颜色进行形状匹配。
2. 只对匹配的部分进行颜色匹配。

   1- 形状匹配：

   使用与您列出的方法类似的方法，但使用 morphological gradient 来稳定匹配，创建响应映射。对地图进行阈值处理以获取最可能的位置（在我们的示例中为 4，因为未检查颜色）。

   2-配色：

   使用绝对像素差异后跟一个阈值（需要调整），我们可以决定所研究的作品的颜色是否与提供的模板图像匹配。

   import cv2
   import numpy as np
   
   def draw_results(img, rects):
       for r in rects:
           print(r)
           cv2.rectangle(img, (r[0], r[1]), (r[0] + r[2], r[1] + r[3]), (0, 0, 255), 2)
   
   def check_color(img, temp, rect):
       y0, y1, x0, x1 = rect[1], rect[1] + rect[3], rect[0], rect[0] + rect[2]    
       crop = (img[y0 : y1, x0 : x1]).copy()
       diff = cv2.absdiff(temp, crop)
       avg_diff = cv2.mean(diff)[0] / 255        
       return avg_diff < 0.4 # a tricky threshold
   
   def find_template_multiple(img, temp):
       rects = []
       w, h = temp.shape[1], temp.shape[0]
   
       result = cv2.matchTemplate(img, temp, cv2.TM_CCOEFF_NORMED)
       threshold = 0.5 # matching threshold, relatively stable.
       loc = np.where( res >= threshold)
   
       for pt in zip(*loc[::-1]):
           rects.append((pt[0], pt[1], w, h))    
   
       #Perform a simple non-max suppression 
       rects, _ = cv2.groupRectangles(rects, 1, 1)
   
       #Flatten list of list to list of elements
       rects = [r for r in rects]   
   
       return rects
   
   
   # Load the chess board and chess piece images
   img_board = cv2.imread('board.png')
   img_piece = cv2.imread('template.png')
   
   # Convert both images to grayscale
   img_board_gray = cv2.cvtColor(img_board, cv2.COLOR_BGR2GRAY)
   img_piece_gray = cv2.cvtColor(img_piece, cv2.COLOR_BGR2GRAY)
   
   s = 3
   kernel = np.ones((s, s), np.uint8)
   #morphological gradient stabilizes the template matching by focusing on the shape's edges rather than its content.
   img_board_gray_grad = cv2.morphologyEx(img_board_gray, cv2.MORPH_GRADIENT, kernel)
   img_piece_gray_grad = cv2.morphologyEx(img_piece_gray, cv2.MORPH_GRADIENT, kernel)
   
   
   rects = find_template_multiple(img_board_gray_grad, img_piece_gray_grad)
   
   matching_color_list = [check_color(img_board_gray, img_piece_gray, r) for r in rects]
   
   #Keep only matching color rectangles.
   matching_color_rects = [r for (r, is_matching) in zip(rects, matching_color_list) if is_matching]
   
   
   draw_results(img_board, matching_color_rects)
   
   
   # Show the result
   cv2.imshow('Result', img_board)
   cv2.waitKey(0)
   cv2.destroyAllWindows()