点位偏差一直是一个很头疼的问题，但是由于摄像头和实际环境的局限性，我们不得不面对这个问题。对此，使用判别的方式进行一个仿射变换，是一种非常有效的方式，下图中图1是基准图，图2是目标图，图3是目标图仿射变换后得到的结果图。
可以看出效果非常的nice。
import cv2
import numpy as npdef get_good_match(des1,des2):bf = cv2.BFMatcher()matches = bf.knnMatch(des1, des2, k=2)good = []for m, n in matches:if m.distance < 0.75 * n.distance:good.append(m)return gooddef sift_kp(image):'''SIFT特征点检测'''height, width = image.shape[:2]size = (int(width * 0.2), int(height * 0.2))shrink = cv2.resize(image, size, interpolation=cv2.INTER_AREA)gray_image = cv2.cvtColor(shrink,cv2.COLOR_BGR2GRAY)sift = cv2.SIFT.create()kp, des = sift.detectAndCompute(gray_image, None)return kp,desdef siftImageAlignment(img1,img2):"""img1: cv2.imread后读取的图片数组，标准图；img2: cv2.imread后读取的图片数组，测试图。函数作用：把img2配准到img1上，返回变换后的img2。注意：img1和img2的size一定要相同。"""kp1,des1 = sift_kp(img1)kp2,des2 = sift_kp(img2)goodMatch = get_good_match(des1,des2)if len(goodMatch) > 4:ptsA= np.float32([kp1[m.queryIdx].pt for m in goodMatch]).reshape(-1, 1, 2)ptsB = np.float32([kp2[m.trainIdx].pt for m in goodMatch]).reshape(-1, 1, 2)ptsA = ptsA / 0.2ptsB = ptsB / 0.2ransacReprojThreshold = 4H, status =cv2.findHomography(ptsA,ptsB,cv2.RANSAC,ransacReprojThreshold)imgOut = cv2.warpPerspective(img2, H, (img1.shape[1],img1.shape[0]),flags=cv2.INTER_LINEAR + cv2.WARP_INVERSE_MAP)return imgOutelse:return img2def cv_imread(file_path):"""能读取中文路径的cv2读图函数。"""cv_img = cv2.imdecode(np.fromfile(file_path,dtype=np.uint8),-1)return cv_imgdef align(t0_path, t1_path):"""测试函数，分别输入标准图和测试图的路径，输出变换后的图和对比图。"""t0 = cv_imread(t0_path)t1 = cv_imread(t1_path)t1_img_align, _, _, ptsA, ptsB = siftImageAlignment(t0, t1)# # 把配准图写到本地# t1_new_bn = 'align_' + os.path.basename(t1_path)# cv2.imwrite('./pics/' + t1_new_bn, t1_img_align)# new_img = np.vstack((t0, t1, t1_img_align))# com_bn = 'compare_' + os.path.basename(t1_path)# cv2.imwrite('./pics/' + com_bn, new_img)return t1_img_alignif __name__ == "__main__":t0_path = r".\_1723957234138288128.jpg"t1_path = r".\1723957234138288128_20231115_200243.jpg"align(t0_path, t1_path)注意着仍然会出现一些不好的状况，但相似点寻找错误或者过小的时候。
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