Python OpenCV：使用matchTemplate

Question

我有两个表面，一个大表

和较小的一个

我试图通过将template matching tutorial视为灰度图像来调整this GoogleDocs document。

我需要更新教程以在x和y中独立扩展，我已完成此操作但添加了一个额外的循环。我的代码是：

# Section 1
1. Item 1
# Section 2 
1. Item 2

然而，我发现“最佳拟合”始终位于最大import pandas as pd import numpy as np import cv2 import matplotlib.pyplot as plt import matplotlib.patches as patches # If True shows each iteration of the template matching Visualise = True # Load in image and template image = pd.read_excel('TemplateMatching_exampleData.xlsx',sheetname="radial_template").as_matrix().astype(np.float32) template = pd.read_excel('TemplateMatching_exampleData.xlsx',sheetname="radial_image").as_matrix().T.astype(np.float32) # Save a raw copy of the template template_raw = template # Rescale the template to approximate the same range in values as the image template = template - np.mean(template) template = (template/np.max(template)) * np.max(image) # Get the height and width of the template (tH, tW) = template.shape[:2] # initialize the bookkeeping variable to keep track of the matched region found = None # If visualise = True then initialise the figure to show the iteration if Visualise: fig1 = plt.figure() ax1 = fig1.add_subplot(111) # loop over the scales of the image for scale_width in np.linspace(0.1, 2, 20): for scale_height in np.linspace(0.1, 3, 20)[::-1]: # resize the image according to the scale, and keep track # of the ratio of the resizing resized = cv2.resize(image, ( int(image.shape[0] * scale_height), int(image.shape[1] * scale_width) )) r_h = image.shape[0] / float(resized.shape[0]) r_w = image.shape[1] / float(resized.shape[1]) # if the resized image is smaller than the template, then break # from the loop if resized.shape[0] < tH or resized.shape[1] < tW: break # apply template matching to find the template in the image result = cv2.matchTemplate(resized, template, cv2.TM_CCOEFF) (_, maxVal, _, maxLoc) = cv2.minMaxLoc(result) # check to see if the iteration should be visualized if Visualise: ax1.clear() ax1.imshow(resized) ax1.add_patch(patches.Rectangle((maxLoc[0], maxLoc[1]), tW, tH, fill=False, edgecolor = 'red')) plt.show() plt.draw() plt.pause(0.05) # fig1.waitforbuttonpress() # if we have found a new maximum correlation value, then update # the bookkeeping variable if found is None or maxVal > found[0]: found = (maxVal, maxLoc, r_w, r_h, scale_width, scale_height) # unpack the bookkeeping varaible and compute the (x, y) coordinates # of the bounding box based on the resized ratio (_, maxLoc, r_w, r_h, scale_width, scale_height) = found (startX, startY) = (int(maxLoc[0] * r_w), int(maxLoc[1] * r_h)) (endX, endY) = (int((maxLoc[0] + tW) * r_w), int((maxLoc[1] + tH) * r_h)) # draw a bounding box around the detected result and display the image figure = plt.figure() ax1 = figure.add_subplot(111) ax1.imshow(image) ax1.add_patch(patches.Rectangle((startX,startY), endX-startX, endY-startY, fill=False, edgecolor = 'red')) plt.show() plt.draw() # show the matching image segment and template together plt.figure() plt.subplot(121) plt.imshow(image[startX:endX, startY:endY]) plt.title('Image') plt.subplot(122) plt.imshow(template) plt.title('Template') 值，无论我将此值设置为什么，但我无法弄清楚原因。我猜测它是如何测量拟合的结果，但我不太了解scale_width能够纠正它并且我已经为此困扰了好几天。

请帮我修改代码吗？

我已将我的Excel数据中的数据复制到Customize Kotlinx。

Answer 1

从问题开始可以解决

首先，需要完成一些簿记 - 似乎image和template在Excel文档中切换，而且似乎template实际上是旋转的90度。要获得工作的内容，我会手动将模板缩放到大致正确的大小，并确保可以找到模板。请注意，我将每个工作表导出为.csv文件并更正了名称。

此外，我将图像设置为平均值为零，标准偏差为1，只需减去平均值并除以标准偏差即可。这应该使图像保持大致相同的分布，以便在原始数组中沿着高度不同的值进行良好匹配。

import cv2
import numpy as np

img = np.genfromtxt('radial_img.csv', delimiter=',').astype(np.float32)
tmp = np.genfromtxt('radial_tmp.csv', delimiter=',').astype(np.float32)
tmp = np.rot90(tmp)
tmp = cv2.resize(tmp, None, fx=0.5, fy=0.33)

img = (img - np.mean(img))/np.std(img)
tmp = (tmp - np.mean(tmp))/np.std(tmp)

ccorr = cv2.matchTemplate(img, tmp, cv2.TM_CCORR)
tl = cv2.minMaxLoc(ccorr)[3]
h, w = tmp.shape[:2]
br = (tl[0]+w, tl[1]+h)

matched = cv2.normalize(img, None, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX)
matched = cv2.merge([matched, matched, matched])
cv2.rectangle(matched, tl, br, (0, 255, 0))
cv2.imshow('matched0.png', matched)
cv2.waitKey()

大！有用！请注意，这里的模板实际上非常小，比它应该的小，但我们仍然得到了粗略的位置，所以我们知道我们在正确的轨道上。

循环使用多种尺寸

现在我们只需创建您制作的循环并多次调整模板大小，直到我们获得最佳匹配。但请注意，模板匹配的方法都具有与模板大小相关的比例，因此较小的模板将具有较小的错误。因此，我们需要使用_NORMED方法来确保结果主要是尺度不变的。我认为使用列表理解来构建所有已调整大小的图像，然后循环遍历所有这些图像，而不是创建多个for循环并在内部调整大小。随着我们的进展，我们可以存储最好的结果，然后在我们完成后显示最佳结果。请注意，此处的所有规范化都严格用于可视化;没有必要使用OpenCV函数：

import cv2
import numpy as np

img = np.genfromtxt('radial_img.csv', delimiter=',').astype(np.float32)
tmp = np.genfromtxt('radial_tmp.csv', delimiter=',').astype(np.float32)
tmp = np.rot90(tmp)

img = (img - np.mean(img))/np.std(img)
tmp = (tmp - np.mean(tmp))/np.std(tmp)

sz_ranges = np.linspace(0.1, 2.0, 19)
resized_tmps = [cv2.resize(tmp, None, fx=i, fy=j)
                for i in sz_ranges for j in sz_ranges]
n_tmps = len(resized_tmps)

for rs_tmp, k in zip(resized_tmps, range(n_tmps)):
    ccorr = cv2.matchTemplate(img, rs_tmp, cv2.TM_CCORR_NORMED)
    match_val, match_loc = cv2.minMaxLoc(ccorr)[1::2]
    if k == 0:
        best_match_val = match_val
    if match_val > best_match_val:
        best_match_val = match_val
        best_match_loc = match_loc
        best_match = k

best_match_tmp = resized_tmps[best_match]
best_match_tmp = cv2.normalize(best_match_tmp, None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8)
best_match_tmp = cv2.merge([best_match_tmp, best_match_tmp, best_match_tmp])

h, w = best_match_tmp.shape[:2]
best_match_loc_end = (best_match_loc[0]+w, best_match_loc[1]+h)
matched = cv2.normalize(img, None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8)
matched = cv2.merge([matched, matched, matched])
cv2.rectangle(matched, best_match_loc, best_match_loc_end, (0, 255, 0))

cv2.imshow('matched1.png', matched)
cv2.waitKey()

cv2.imshow('besttmp.png', best_match_tmp)
cv2.waitKey()

再一次，它有效！

这是与最佳匹配的模板：

通过视觉检查看起来是正确的。最匹配模板的高度和宽度为(85, 99)，基本上只是原始模板，有一点水平拉伸。