I am trying to detect

on my chessboard

But can't get it to detect the right piece. The piece is 59x83. It should be detected, but isn't. I guess i'm missing something here?

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()

CodePudding user response：

I have tried several setups and experimentally found this to work best:

1. Do shape matching ignoring the piece color.
2. Do color matching only on matching pieces.

1- Shape matching:

In a similar method to what you have listed, but using the morphological gradient to stabilize the matching, a response map is created. The map is thresholded to get the most likely locations (4 in our case as color is not checked).

2- Color matching:

Using the absolute pixel-wise difference followed by a threshold (needs tuning) we can decide whether the studied piece's color matches the provided template image.

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()

Images: Board image and template image after morphological gradient: