python-3.x - 比较来自 OpenCV matchTemplate 的 maxVal 以进行动态模板匹配

Question

我正在尝试与cv2.matchTemplate. 模板是：

我与之匹配的图像是：

我正在调整模板的大小和角度，然后将它们抓取maxVal并存储在maxLoc其中作为键。然后我从. 当我遍历所有键并将它们绘制在图像上时，有些人发现米奇头比.cv2.matchTemplatedictmaxValmax(data.keys())result = cv2.matchTemplate(image, resized_template, cv2.TM_CCORR_NORMED)max(data.keys())

这是“最佳”匹配的区域：

#Find best match from scores
best_match = max(scores.keys())
#Output image
image_copy = color_image.copy()
#Create rectangle around best match
cv2.rectangle(image_copy, (data[best_match][1][0], data[best_match][1][1]), (data[best_match][1][0] + data[best_match][0][0], data[best_match][1][1] + data[best_match][0][1]), (255, 255, 255), 2)
plt.imshow(image_copy)

但这个区域似乎更准确地定位它：

image_copy = color_image.copy()
for n in list(data.keys())[69:70:1]:
    #Create rectangle around best match
    cv2.rectangle(image_copy, (data[n][1][0], data[n][1][1]), (data[n][1][0] + data[n][0][0], data[n][1][1] + data[n][0][1]), (255, 255, 255), 2)
    cv2.imwrite('output_1.jpg', image_copy)
    plt.imshow(image_copy)

我是否错过了解释maxValfromcv2.matchTemplate实际代表的内容？如果是这样，我如何根据某些值或标准选择实际的“最佳”区域？这是完整的代码：

#Import packages 
import numpy as np 
import matplotlib.pyplot as plt 
import cv2
import imutils
import os

#Import template
template = cv2.imread(templates_dir + '\\' + os.listdir(templates_dir)[3])
gray_template = cv2.cvtColor(template, cv2.COLOR_RGB2GRAY)

#Import image
image = cv2.imread(images_dir + '\\' + os.listdir(images_dir)[5])
color_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)

#Image dims
image_h = image.shape[1]
image_w = image.shape[0]

#Processing loop scaling the template
#Scores dict
scores = {}
#Data dict of structure (maxVal: (template dims, locs, angle))
data = {}

#Edge images 
template = cv2.Canny(gray_template, 80, 200)
image = cv2.Canny(gray_image, 80, 200)

#Loop through different template scales
for scale in np.linspace(0.2, 1.5, 15):
    #Loop through different template rotations
    for angle in np.linspace(0, 360, 25)[:-1]:
        #Rotate template
        template_rotated = imutils.rotate(template, angle)
        #Resize template
        resized_template = imutils.resize(template_rotated, width = int(template_rotated.shape[0] * scale))
        #Dims of resized template
        template_h = resized_template.shape[1]
        template_w = resized_template.shape[0]
        #Break from loop if the template becomes bigger than the image
        if template_h > image_h or template_w > image_w:
            break
        #Run template through image
        result = cv2.matchTemplate(image, resized_template, cv2.TM_CCORR_NORMED)
        #Get matching score and location
        (_, maxVal, _, maxLoc) = cv2.minMaxLoc(result)
        #Add correlation value and result to scores
        scores[str(maxVal)] = result
        #Add info to data
        data[str(maxVal)] = ((template_h, template_w), (maxLoc[0], maxLoc[1]), angle)
    
#Find best match from scores
best_match = max(scores.keys())

#Output image
image_copy = color_image.copy()
#Create rectangle around best match
cv2.rectangle(image_copy, (data[best_match][1][0], data[best_match][1][1]), (data[best_match][1][0] + data[best_match][0][0], data[best_match][1][1] + data[best_match][0][1]), (0, 0, 255), 2)
plt.imshow(image_copy)

标签： python-3.xopencvtemplate-matching