指针式仪表倾斜校正opencv算法python代码及仪表图像（包含倾斜的和模板图像）

import numpy as np import cv2 from matplotlib import pyplot as plt #https://www.javaroad.cn/questions/347518#toolbar-title # FIXME: doesn't work def deskew(): im_out = cv2.warpPerspective(img1, M, (img2.shape[1], img2.shape[0])) plt.imshow(im_out, 'gray') plt.show() # resizing images to improve speed factor = 0.4 img1 = cv2.resize(cv2.imread("./img/zheng2.png", 0), None, fx=factor, fy=factor, interpolation=cv2.INTER_CUBIC) img2 = cv2.resize(cv2.imread("./img/xie2.png", 0), None, fx=factor, fy=factor, interpolation=cv2.INTER_CUBIC) #有专利，SURF_create，SIFT_create可以直接跑 ''' 1. 卸载已有安装opencv-python： pip uninstall opencv-python 2. 安装opencv-contrib-python 3.2版本以下： pip install opencv-contrib-python==3.4.2 也可以不降低版本号，进行编译，详细流程见链接 https://blog.csdn.net/m0_50736744/article/details/129351648 ''' surf = cv2.xfeatures2d.SIFT_create() kp1, des1 = surf.detectAndCompute(img1, None) kp2, des2 = surf.detectAndCompute(img2, None) FLANN_INDEX_KDTREE = 0 index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5) search_params = dict(checks=50) flann = cv2.FlannBasedMatcher(index_params, search_params) matches = flann.knnMatch(des1, des2, k=2) # store all the good matches as per Lowe's ratio test. good = [] for m, n in matches: if m.distance < 0.7 * n.distance: good.append(m) MIN_MATCH_COUNT = 10 if len(good) > MIN_MATCH_COUNT: src_pts = np.float32([kp1[m.queryIdx].pt for m in good ]).reshape(-1, 1, 2) dst_pts = np.float32([kp2[m.trainIdx].pt for m in good ]).reshape(-1, 1, 2) M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0) matchesMask = mask.ravel().tolist() h, w = img1.shape pts = np.float32([[0, 0], [0, h - 1], [w - 1, h - 1], [w - 1, 0]]).reshape(-1, 1, 2) dst = cv2.perspectiveTransform(pts, M) deskew() img2 = cv2.polylines(img2, [np.int32(dst)], True, 255, 3, cv2.LINE_AA) else: print("Not enough matches are found - %d/%d" % (len(good), MIN_MATCH_COUNT)) matchesMask = None # show matching keypoints draw_params = dict(matchColor=(0, 255, 0), # draw matches in green color singlePointColor=None, matchesMask=matchesMask, # draw only inliers flags=2) img3 = cv2.drawMatches(img1, kp1, img2, kp2, good, None, **draw_params) plt.imshow(img3, 'gray') plt.show()