templateimage的数据库
我正在尝试编写一个python脚本，它使用cv2库来模板匹配图像。我已经弄清楚了如何在图像中检测模板的一个示例，考虑到模板是否具有不同的比例，但我似乎无法弄清楚如何在图像中找到模板的多个示例，同时考虑到比例。
我试着找到第一个匹配，阻止它，然后再次运行扫描，但第二次扫描无法正确找到模板。image with bounding boxesimage to find
有谁知道我能做些什么来检测这两个示例？
下面是我运行的代码：

import numpy as np
import imutils
import glob
import cv2
from imutils.object_detection import non_max_suppression

imagen_a_detectar = r"C:\Users\OMEN\OneDrive\Documentos\TEC\PROYECTO RESIDEO\REPO\SCRUMsinCUM\software\Deteccion Iconos\flecha50.jpg"
imagen_a_comparar = r"C:\Users\OMEN\OneDrive\Documentos\TEC\PROYECTO RESIDEO\REPO\SCRUMsinCUM\software\Deteccion Iconos\HomeAuto3.png"

template = cv2.imread(imagen_a_detectar)
template = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)
template = cv2.Canny(template, 50, 200)
(tH, tW) = template.shape[:2]

for imagePath in glob.glob(imagen_a_comparar):
    image = cv2.imread(imagePath)
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    found = None
    threshold = 0.9

    for scale in np.linspace(0.2, 1.0, 20)[::-1]:
        scalem = scale
        resized = imutils.resize(gray, width = int(gray.shape[1] * scale))
        r = gray.shape[1] / float(resized.shape[1])

        if resized.shape[0] < tH or resized.shape[1] < tW:
            break
        edged = cv2.Canny(resized, 50, 200)

        result = cv2.matchTemplate(edged, template, cv2.TM_CCOEFF_NORMED)
        min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)

        if found is None or max_val > found[0]:
            found = (max_val, max_loc, r)

    (_, max_loc, r) = found
    (startX, startY) = (int(max_loc[0] * r), int(max_loc[1] * r))
    (endX, endY) = (int((max_loc[0] + tW) * r), int((max_loc[1] + tH) * r))
    m1 = cv2.rectangle(image, (startX, startY), (endX, endY), (0, 0, 255), -1)

    gray2 = cv2.cvtColor(m1, cv2.COLOR_BGR2GRAY)
    resized2 = imutils.resize(gray2, width = int(gray.shape[1] * scalem))
    r2 = gray.shape[1] / float(resized.shape[1])
    edged2 = cv2.Canny(resized2, 50, 200)

    result2 = cv2.matchTemplate(edged2, template, cv2.TM_CCOEFF_NORMED)
    min_val2, max_val2, min_loc2, max_loc2 = cv2.minMaxLoc(result2)
    found2 = (max_val2, max_loc2, r2)
    
    (_, max_loc2, r2) = found2
    (startX2, startY2) = (int(max_loc2[0] * r2), int(max_loc2[1] * r2))
    (endX2, endY2) = (int((max_loc2[0] + tW) * r2), int((max_loc2[1] + tH) * r2))

    m2 = cv2.rectangle(m1, (startX2, startY2), (endX2, endY2), (0, 0, 255), -1)

    cv2.imshow("Image", m2)
    cv2.waitKey(0)
    print(str(endX) + ' ' +str(endY)+ ' ' +str(r))

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