棋盘上的棋子检测 Opencv

Question

我正在尝试检测

在我的棋盘上

但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. 忽略棋子的颜色进行形状匹配。
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 map。 map 被阈值化以获得最可能的位置（在我们的例子中是 4，因为颜色没有被选中）。

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()

图像：形态梯度后的板图和模板图：

Answer 2

我不确定是否需要任何形态学操作。 您在制作模板图像方面做得很好，并且它具有透明背景。 您可以使用文件的透明通道为模板制作遮罩。

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', cv2.IMREAD_UNCHANGED)

mask = img_piece[:,:,3] # use the inverted transparency channel for mask


# 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)
h, w = img_piece_gray.shape

# 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)


result = cv2.matchTemplate(img_board_gray, img_piece_gray, cv2.TM_SQDIFF_NORMED, mask=mask)
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)

while min_val < 0.1:

    # 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)

    #overwrite the portion of the result that has the match:
    h1 = top_left[1]-h//2
    h1 = np.clip(h1, 0, result.shape[0])

    h2 = top_left[1] + h//2 + 1
    h2 = np.clip(h2, 0, result.shape[0])

    w1 = top_left[0] - w//2
    w1 = np.clip(w1, 0, result.shape[1])

    w2 = top_left[0] + w//2 + 1
    w2 = np.clip(w2, 0, result.shape[1])
    result[h1:h2, w1:w2] = 1  # poison the result in the vicinity of this match so it isn't found again

    # look for next match
    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)

cv2.imwrite('output.png', img_board)

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

我将IMREAD_UNCHANGED标志添加到imread以导致 OpenCV 读取透明度数据。 这对您非常有帮助，因为您现在可以匹配两个黑人主教。

过去，您只能对TM_SQDIFF和TM_CCORR_NORMED使用模板掩码，但我认为这些限制在版本 4.x 中已被删除。 我用TM_SQDIFF替换了TM_SQDIFF_NORMED 。 NORMED 版本将结果值转换为 0 到 1 之间，感觉很像百分比分数。

我使用了这个答案和这个答案中的一个技巧来覆盖results ，这样你就可以找到更多的匹配项