07 计算机视觉

07 计算机视觉

import cv2
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

模板匹配

模板匹配和卷积原理很像，模板在原图像上从原点开始滑动，计算模板与（图像被模板覆盖的地方）的差别程度，这个差别程度的计算方法在opencv里有6种，然后将每次计算的结果放入一个矩阵里，作为结果输出。假如原图形是AxB大小，而模板是axb大小，则输出结果的矩阵是(A-a+1)x(B-b+1)

# 模板匹配
img = cv2.imread("lena.jpg",0)
template = cv2.imread("face.jpg",0)
h,w = template.shape[:2]

img.shape

(263, 263)

template.shape

(110, 85)

• TM_SQDIFF：计算平方不同，计算出来的值越小，越相关
• TM_CCORR：计算相关性，计算出来的值越大，越相关
• TM_CCOEFF：计算相关系数，计算出来的值越大，越相关
• TM_SQDIFF_NORMED：计算归一化平方不同，计算出来的值越接近0，越相关
• TM_CCORR_NORMED：计算归一化相关性，计算出来的值越接近1，越相关
• TM_CCOEFF_NORMED：计算归一化相关系数，计算出来的值越接近1，越相关

公式：.3.1/df/dfb/group__imgproc__object.html#ga3a7850640f1fe1f58fe91a2d7583695d

methods = ['cv2.TM_CCOEFF', 'cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCORR','cv2.TM_CCORR_NORMED', 'cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED']

res = cv2.matchTemplate(img, template, cv2.TM_SQDIFF)
res.shape 

(154, 179)

min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)

min_val

39168.0

max_val

74403584.0

min_loc

(107, 89)

max_loc

(159, 62)

for meth in methods:img2 = py()# 匹配方法的真值method = eval(meth)print (method)res = cv2.matchTemplate(img, template, method)min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)# 如果是平方差匹配TM_SQDIFF或归一化平方差匹配TM_SQDIFF_NORMED，取最小值if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:top_left = min_locelse:top_left = max_locbottom_right = (top_left[0] + w, top_left[1] + h)# 画矩形angle(img2, top_left, bottom_right, 255, 2)plt.subplot(121), plt.imshow(res, cmap='gray')icks([]), icks([])  # 隐藏坐标轴plt.subplot(122), plt.imshow(img2, cmap='gray')icks([]), icks([])plt.suptitle(meth)plt.show()

4

5

2

3

0

1

匹配多个对象

img_rgb = cv2.imread('mario.jpg')
img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)
template = cv2.imread('mario_coin.jpg', 0)
h, w = template.shape[:2]res = cv2.matchTemplate(img_gray, template, cv2.TM_CCOEFF_NORMED)
threshold = 0.8
# 取匹配程度大于%80的坐标
loc = np.where(res >= threshold)
for pt in zip(*loc[::-1]):  # *号表示可选参数bottom_right = (pt[0] + w, pt[1] + angle(img_rgb, pt, bottom_right, (0, 0, 255), 2)cv2.imshow('img_rgb', img_rgb)
cv2.waitKey(0)
cv2.destroyAllWindows()plt.imshow(img_rgb)

<matplotlib.image.AxesImage at 0x2a809ae5eb8>