金融网站建站,中企潍坊分公司网站,品牌建设工作方案,购买了网站空间如何进入import cv2 #opencv读取的格式是BGR
import numpy as np
import matplotlib.pyplot as plt#Matplotlib是RGB
%matplotlib inline
模版匹配
模版匹配和卷积原理很像#xff0c;模版在原图像上从原点开始滑动#xff0c;计算模版与#xff08;图像被模版覆盖的地方#xff…import cv2 #opencv读取的格式是BGR
import numpy as np
import matplotlib.pyplot as plt#Matplotlib是RGB
%matplotlib inline
模版匹配
模版匹配和卷积原理很像模版在原图像上从原点开始滑动计算模版与图像被模版覆盖的地方的差别层度这个差别成都的计算方法在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。假如原图案是A×B大小而模版是a×b大小则输出结果的矩阵是A-a1×B-b1 face.jpg
lena.jpg
#模版匹配
img cv2.imread(lena.jpg,0)
template cv2.imread(face.jpg,0)
h,w template.shape[:2] 查看相关参数
img.shape
template.shape
TM_SQDIFF 计算平方不同计算出来的值越小越相关TM_COORR :计算相关性计算出来的值越大越相关TM_CCOEFF : 计算相关系统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
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.shapemin_val,max_val,min_loc,max_loc cv2.minMaxLoc(res)min_valmax_valmin_locmax_loc for meth in methods:img2 img.copy()#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cv2.rectangle(img2,top_left,bottom_right,255,2)plt.subplot(121),plt.imshow(res,cmapgray)plt.xticks([]),plt.yticks([]) #隐藏坐标轴plt.subplot(122),plt.imshow(img2,cmap gray)plt.xticks([]),plt.yticks([])plt.suptitle(meth)plt.show()
运行出的结果 匹配多个目标对象 mario_coin.jpg
mario.jpg 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
#匹配成都大于%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] h)cv2.rectangle(img_rgb,pt,bottom_right,(0,0,255),2)cv2.imshow(img_rgb,img_rgb)
cv2.waitKey(0)
运行结果
