Opencv matchTemplate 和 np.where()：仅保留唯一值

继带有马里奥硬币的 opencv 教程 https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_template_matching/py_template_matching.html我留下了将小图像检测到大图像中的点列表。然而，在同一对象周围的多个区域为正的意义上，存在大量“重复”。

我想通过一个从元组列表中删除冗余元素的函数来删除这些“重复项”：

如果这个新函数的“margin”参数是 10 像素，那么元组(522, 114) and (523, 114)被认为是重复的，因为abs(522-523)<10 and abs(114-114)<10.

这将使我能够根据结果列表的长度来计算唯一检测到的对象。

根据这些数据，预期结果是[(522, 114),(191, 178)]。我怎样才能写出这样的函数呢？

npwhere = [(522, 114),
    (523, 114),
    (524, 114),
    (522, 115),
    (523, 115),
    (524, 115),
    (522, 116),
    (523, 116),
    (524, 116),
    (523, 117),
    (191, 178),
    (192, 178),
    (193, 178)]

关于我正在使用的代码的更多信息（可能与这个问题无关）

   result = cv2.matchTemplate(targetimg, templateimg, cv2.TM_CCOEFF_NORMED)
    loc = np.where( result >= threshold)
    for pt in zip(*loc[::-1]):
        print(pt)
        cv2.rectangle(scr, pt, (pt[0] + w, pt[1] + h), (0,0,255), 2)

这是 Python/OpenCV 中提供的多模板匹配的替代方法https://www.pyimagesearch.com/2021/03/29/multi-template-matching-with-opencv/ https://www.pyimagesearch.com/2021/03/29/multi-template-matching-with-opencv/。我借用了那个例子中的图像。

我的方法简单地包括迭代峰值抑制（通过屏蔽模板匹配相关图像），直到匹配值达到阈值或实现给定数量的匹配。我将最大匹配数设置为 10，将匹配值的停止阈值设置为 0.95（1 个为完美匹配）。我还将遮罩区域的半径设置为模板最大尺寸的 1/4。

Input:

模板：

import cv2
import numpy as np

# read image
img = cv2.imread('8diamond.png')

# read template
tmplt = cv2.imread('1diamond.png')
hh, ww, cc = tmplt.shape

# set arguments
match_thresh = 0.95               # stopping threshold for match value
num_matches = 10                  # stopping threshold for number of matches
match_radius = max(hh,ww)//4      # approx radius of match peaks

# get correlation surface from template matching
corrimg = cv2.matchTemplate(img,tmplt,cv2.TM_CCORR_NORMED)
hc, wc = corrimg.shape

# get locations of all peaks higher than match_thresh for up to num_matches
imgcopy = img.copy()
corrcopy = corrimg.copy()
for i in range(1, num_matches):
    # get max value and location of max
    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(corrcopy)
    x1 = max_loc[0]
    y1 = max_loc[1]
    x2 = x1 + ww
    y2 = y1 + hh
    loc = str(x1) + "," + str(y1)
    if max_val > match_thresh:
        print("match number:", i, "match value:", max_val, "match x,y:", loc)
        # draw draw blue bounding box to define match location
        cv2.rectangle(imgcopy, (x1,y1), (x2,y2), (255,0,0), 2)
        # draw black filled circle over copy of corrimg 
        cv2.circle(corrcopy, (x1,y1), match_radius, 0, -1)
        i = i + 1
    else:
        break
    
# save results
# power of 4 exaggeration of correlation image to emphasize peaks
cv2.imwrite('8diamond_multi_template_corr.png', (255*cv2.pow(corrimg,4)).clip(0,255).astype(np.uint8))
cv2.imwrite('8diamond_multi_template_corr_masked.png', (255*cv2.pow(corrcopy,4)).clip(0,255).astype(np.uint8))
cv2.imwrite('8diamond_multi_template_match.png', imgcopy)


# show results
# power of 4 exaggeration of correlation image to emphasize peaks
cv2.imshow('image', img)
cv2.imshow('template', tmplt)
cv2.imshow('corr', cv2.pow(corrimg,4))
cv2.imshow('corr masked', cv2.pow(corrcopy,4))
cv2.imshow('result', imgcopy)
cv2.waitKey(0)
cv2.destroyAllWindows()

模板匹配的相关图像：

显示屏蔽区域的最终屏蔽相关图像：

输入的结果匹配：

文本输出：

match number: 1 match value: 0.9954221844673157 match x,y: 70,84
match number: 2 match value: 0.9926357865333557 match x,y: 112,132
match number: 3 match value: 0.9903871417045593 match x,y: 157,89
match number: 4 match value: 0.9901180267333984 match x,y: 67,173
match number: 5 match value: 0.9888120889663696 match x,y: 63,261
match number: 6 match value: 0.9862607717514038 match x,y: 108,219
match number: 7 match value: 0.9856082797050476 match x,y: 152,178
match number: 8 match value: 0.9837512969970703 match x,y: 146,261