全景图像拼接；实现特征点检测与描述子

import numpy as np import cv2 class Stitcher: #拼接函数 def stitch(self, images, ratio=0.75, reprojThresh=4.0,showMatches=False): #获取输入图片 (imageB, imageA) = images #检测A、B图片的SIFT关键特征点，并计算特征描述子 (kpsA, featuresA) = self.detectAndDescribe(imageA) (kpsB, featuresB) = self.detectAndDescribe(imageB) # 匹配两张图片的所有特征点，返回匹配结果 M = self.matchKeypoints(kpsA, kpsB, featuresA, featuresB, ratio, reprojThresh) # 如果返回结果为空，没有匹配成功的特征点，退出算法 if M is None: return None # 否则，提取匹配结果 # H是3x3视角变换矩阵 (matches, H, status) = M # 将图片A进行视角变换，result是变换后图片 result = cv2.warpPerspective(imageA, H, (imageA.shape[1] + imageB.shape[1], imageA.shape[0])) self.cv_show('result', result) # 将图片B传入result图片最左端 result[0:imageB.shape[0], 0:imageB.shape[1]] = imageB self.cv_show('result', result) # 检测是否需要显示图片匹配 if showMatches: # 生成匹配图片 vis = self.drawMatches(imageA, imageB, kpsA, kpsB, matches, status) # 返回结果 return (result, vis) # 返回匹配结果 return result def cv_show(self,name,img): cv2.imshow(name, img) cv2.waitKey(0) cv2.destroyAllWindows() def detectAndDescribe(self, image): # 将彩色图片转换成灰度图 gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # 建立SIFT生成器 descriptor = cv2.xfeatures2d.SIFT_create() # 检测SIFT特征点，并计算描述子 (kps, features) = descriptor.detectAndCompute(image, None) # 将结果转换成NumPy数组 kps = np.float32([kp.pt for kp in kps]) # 返回特征点集，及对应的描述特征 return (kps, features) def matchKeypoints(self, kpsA, kpsB, featuresA, featuresB, ratio, reprojThresh): # 建立暴力匹配器 matcher = cv2.BFMatcher() # 使用KNN检测来自A、B图的SIFT特征匹配对，K=2 rawMatches = matcher.knnMatch(featuresA, featuresB, 2) matches = [] for m in rawMatches: # 当最近距离跟次近距离的比值小于ratio值时，保留此匹配对 if len(m) == 2 and m[0].distance < m[1].distance * ratio: # 存储两个点在featuresA, featuresB中的索引值 matches.append((m[0].trainIdx, m[0].queryIdx)) # 当筛选后的匹配对大于4时，计算视角变换矩阵 if len(matches) > 4: # 获取匹配对的点坐标 ptsA = np.float32([kpsA[i] for (_, i) in matches]) ptsB = np.float32([kpsB[i] for (i, _) in matches]) # 计算视角变换矩阵 (H, status) = cv2.findHomography(ptsA, ptsB, cv2.RANSAC, reprojThresh) # 返回结果 return (matches, H, status) # 如果匹配对小于4时，返回None return None def drawMatches(self, imageA, imageB, kpsA, kpsB, matches, status): # 初始化可视化图片，将A、B图左右连接到一起 (hA, wA) = imageA.shape[:2] (hB, wB) = imageB.shape[:2] vis = np.zeros((max(hA, hB), wA + wB, 3), dtype="uint8") vis[0:hA, 0:wA] = imageA vis[0:hB, wA:] = imageB # 联合遍历，画出匹配对 for ((trainIdx, queryIdx), s) in zip(matches, status): # 当点对匹配成功时，画到可视化图上 if s == 1: # 画出匹配对 ptA = (int(kpsA[queryIdx][0]), int(kpsA[queryIdx][1])) ptB = (int(kpsB[trainIdx][0]) + wA, int(kpsB[trainIdx][1])) cv2.line(vis, ptA, ptB, (0, 255, 0), 1) # 返回可视化结果 return vis