self.upload_button = tk.Button(self.left_frame, text='上传图像', command=self.load_image) self.upload_button.pack(side='left', padx=10, pady=10) self.match_button = tk.Button(self.left_frame, text='图像匹配', command=self.match_images) self.match_button.pack(side='left', padx=1...
基于第三和第四部分进行图像拼接,代码如下: defsiftImage(img_1,img_2):kp_img_1,kp_1,des_1=sift_kp_des(img_1)kp_img_2,kp_2,des_2=sift_kp_des(img_2)good_kp=get_good_match(des_1,des_2)iflen(good_kp)>4:ptsA=np.float32([kp_1[m.queryIdx].ptformingood_kp]).reshape(-1,1,...
inlier_threshold =2.5# Distance threshold to identify inliers with homography checkfori, minenumerate(matched1):# Create the homogeneous pointcol = np.ones((3,1), dtype=np.float64) col[0:2,0] = m.pt# Project from image 1 to image 2col = np.dot(homography, col) col /= col[2,0]...
image = cv2.imread("poker.jpg") gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) #转化为灰度图 #选取图像的一个区域作为匹配模板,如下是一个菱形的区域 template = gray[75:105, 235:265] #第三个参数是将图片标准化,防止外界环境影响。计算匹配度,计算结果更准确 match = cv2.matchTemplate(gray, tem...
img=imread(originalImageName,1); templ=imread(matchImageName,1); /// 创建窗口 namedWindow(image_window,CV_WINDOW_AUTOSIZE); namedWindow(result_window,CV_WINDOW_AUTOSIZE); /// 创建滑动条 char*trackbar_label="Method: \n 0: SQDIFF \n 1: SQDIFF NORMED \n 2: TM CCORR \n 3: TM CCORR ...
img_match=cv.imread("./image/baidu-ico.png")img=cv.imread("./image/baidu-sousuo.png")template_matching(img_match,img,cv.TM_SQDIFF)cv.waitKey(0)cv.destroyAllWindows() 第二章:六大模板匹配算法 在一些复杂的场景下,从简单的平方差算法到更复杂的相关系数算法,匹配的准确率会不断提高,但是计算量...
string matchImageName ="small1.png";/// 加载原图像和模板块img =imread( originalImageName,1); templ =imread( matchImageName,1);/// 创建窗体namedWindow( image_window, CV_WINDOW_AUTOSIZE );namedWindow( result_window, CV_WINDOW_AUTOSIZE );/// 创建滑动条char* trackbar_label ="Method: \n...
imageMatches, // the image produced Scalar(255,255,255), Scalar(255,255,255), vector<char>(), 2); imshow("Matches",imageMatches); //根据筛选出的匹配得到对应点的index vector<int> pointIndexes1; vector<int> pointIndexes2; for (vector<DMatch>::const_iterator it= selMatches.begin(); ...
target_gray=cv.cvtColor(target_image,cv.COLOR_BGR2GRAY) tpl_gray=np.float32(tpl_gray/255.0) target_gray=np.float32(target_gray/255.0) tpl_sum,tpl_sqsum=cv.integral2(tpl_gray) t_sum,t_sqsum=cv.integral2(target_gray) matcher=NCCTemplateMatch([tpl_gray],[target_gray],[0.85], ...
match=cv2.matchShapes(reference_contours[0],target_contours[0],cv2.CONTOURS_MATCH_I1) 在上述示例中,我们首先读取参考轮廓图像和目标轮廓图像,并通过边缘检测和轮廓寻找获取轮廓。然后,使用matchShapes函数计算轮廓之间的相似度。CONTOURS_MATCH_I1参数表示使用I1方法进行轮廓匹配。