python-3.x - OpenCV 无法将模板与图像匹配(matchTemplate)

Question

所以我有一个图像

和一个模板

我想在图像中找到模板图像，但我的代码没有找到任何东西。我试过缩小尺寸但仍然没有检测到。请帮我举个例子:

import cv2
import imutils
import glob, os
import numpy as np

image = cv2.imread("mainimage.png")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
h, w = gray.shape[:2]
for file in glob.glob("template.png"):
    template = cv2.imread(file)
    template = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)
    found = None
    (tH, tW) = template.shape[:2]
    cv2.imshow("Template", template)

    for scale in np.linspace(1, 0.2, 20)[::-1]:
        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)
        (_, maxVal, _, maxLoc) = cv2.minMaxLoc(result)

        if found is None or maxVal > found[0]:
            found = (maxVal, maxLoc, r)

    (_, maxLoc, r) = found
    (startX, startY) = (int(maxLoc[0] * r), int(maxLoc[1] * r))
    (endX, endY) = (int((maxLoc[0] + tW) * r), int((maxLoc[1] + tH) * r))
    cv2.rectangle(image, (startX, startY), (endX, endY), (0, 0, 255), 2)
    cv2.imshow("Image", image)
    cv2.waitKey(0)

Answer 1

您的代码大部分都不错。在您发布的代码中，您以错误的方式缩放。您正在缩小主图像而不是增大它。此外，您需要对模板和图像都进行 Canny。

在您发布的图像中，模板 (160x160) 大于主图像中的区域 (88x88)。如果缩放 mainimage，则缩放因子应为 1.818。缩放模板可能会快得多。

import cv2
# import imutils
import glob, os
import numpy as np

image = cv2.imread("mainimage.png")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
h, w = gray.shape[:2]
for file in glob.glob("template.png"):
    template = cv2.imread(file)
    template = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)
    found = None
    (tH, tW) = template.shape[:2]
    # cv2.imshow("Template", template)

    tEdged = cv2.Canny(template, 50, 200)

    for scale in np.linspace(1, 2, 20):
        # resized = imutils.resize(gray, width=int(gray.shape[1] * scale))
        resized = cv2.resize(gray, dsize = (0,0), fx = scale, fy = 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, tEdged, cv2.TM_CCOEFF)
        (_, maxVal, _, maxLoc) = cv2.minMaxLoc(result)

        if found is None or maxVal > found[0]:
            found = (maxVal, maxLoc, r)

    (_, maxLoc, r) = found
    (startX, startY) = (int(maxLoc[0] * r), int(maxLoc[1] * r))
    (endX, endY) = (int((maxLoc[0] + tW) * r), int((maxLoc[1] + tH) * r))
    cv2.rectangle(image, (startX, startY), (endX, endY), (0, 0, 255), 2)

    # cv2.imshow("Image", image)
    cv2.imwrite('output.jpg', image)
    # ~ cv2.waitKey(0)

在我的电脑上，这段代码需要 6 秒才能运行。

关键点匹配+单应性

作为替代方案，关键点匹配 + 单应性对尺度不敏感。在下面的代码中，dst 保存包含找到的模板的边界框的点。对我来说，以下代码在 0.06 秒内执行:

import cv2
# import imutils
import glob, os
import numpy as np
import time

image = cv2.imread("mainimage.png")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
h, w = gray.shape[:2]

MIN_MATCH_COUNT = 3

start_time = time.time()

for file in glob.glob("template.png"):
    template = cv2.imread(file, 0)

    patchSize = 16

    orb = cv2.ORB_create(edgeThreshold = patchSize, 
                            patchSize = patchSize)
    kp1, des1 = orb.detectAndCompute(template, None)
    kp2, des2 = orb.detectAndCompute(gray, None)

    FLANN_INDEX_LSH = 6
    index_params= dict(algorithm = FLANN_INDEX_LSH,
               table_number = 6,
               key_size = 12,    
               multi_probe_level = 1)
    search_params = dict(checks = 50)

    flann = cv2.FlannBasedMatcher(index_params, search_params)
    matches = flann.knnMatch(des1,des2,k=2)
    # store all the good matches as per Lowe's ratio test.
    good = []

    for pair in matches:
        if len(pair) == 2:
            if pair[0].distance < 0.7*pair[1].distance:
                good.append(pair[0])

    print('len(good) ', len(good))
    print('match %03d, min_match %03d, kp %03d' % (len(good), MIN_MATCH_COUNT, len(kp1)))
    if len(good)>MIN_MATCH_COUNT:
        src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2)
        dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good ]).reshape(-1,1,2)
        M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC,5.0)
        matchesMask = mask.ravel().tolist()
        h,w = template.shape
        pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2)
        dst = cv2.perspectiveTransform(pts,M)

        # dst contains points of bounding box of template in image. 
        # draw a close polyline around the found template:
        image = cv2.polylines(image,[np.int32(dst)], 
                              isClosed = True,
                              color = (0,255,0),
                              thickness = 3, 
                              linetype = cv2.LINE_AA)                    
    else:
        print( "Not enough matches are found - {}/{}".format(len(good), MIN_MATCH_COUNT) )
        matchesMask = None

    draw_params = dict(matchColor = (0,255,0), # draw matches in green color
               singlePointColor = None,
               matchesMask = matchesMask, # draw only inliers
               flags = 2)

    if len(good)>MIN_MATCH_COUNT:
        output2 = cv2.drawMatches(template,kp1,gray,kp2,good,None,**draw_params)

    print('elapsed time ', time.time()-start_time)
    # cv2.imshow("Image", image)
    cv2.imwrite('output_homography.jpg', image)
    cv2.imwrite('output2.jpg', output2)

来自 cv2.drawMatches 函数的输出 2

关键点检测的重要参数之一是 patchSize。在代码中，我们对图像和模板都使用了 patchSize = 16。当您使 patchSize 更小时，您将获得更多关键点。你可以去的最小值是 2。但是当你变得太小时，你开始得到糟糕的匹配。我不确定如何找到最佳点。