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OpenCV版本:4.5.3.56 算法实现思路: 图像拼接全景轮廓提取轮廓最小正矩形腐蚀处理裁剪代码实现: import cv2 import numpy as np def stitch(image): # 图像拼接 # stitcher = cv2.createStitcher(False) # OpenCV 3.X.X.X使用该方法 stitcher = cv2.Stitcher_create(cv2.Stitcher_PANORAMA) # OpenCV 4.X.X.X使用该方法,cv2.Stitcher_create()也可以 status, pano = stitcher.stitch(image) # 黑边处理 if status == cv2.Stitcher_OK: # 全景图轮廓提取 stitched = cv2.copyMakeBorder(pano, 10, 10, 10, 10, cv2.BORDER_CONSTANT, (0, 0, 0)) gray = cv2.cvtColor(stitched, cv2.COLOR_BGR2GRAY) thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY)[1] cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0] # 轮廓最小正矩形 mask = np.zeros(thresh.shape, dtype="uint8") (x, y, w, h) = cv2.boundingRect(cnts[0]) # 取出list中的轮廓二值图,类型为numpy.ndarray cv2.rectangle(mask, (x, y), (x + w, y + h), 255, -1) # 腐蚀处理,直到minRect的像素值都为0 minRect = mask.copy() sub = mask.copy() while cv2.countNonZero(sub) > 0: minRect = cv2.erode(minRect, None) sub = cv2.subtract(minRect, thresh) # 提取minRect轮廓并裁剪 cnts = cv2.findContours(minRect, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0] (x, y, w, h) = cv2.boundingRect(cnts[0]) stitched = stitched[y:y + h, x:x + w] cv2.imshow('stitched', stitched) cv2.imwrite('stitched.jpg', stitched) cv2.waitKey(0) cv2.destroyAllWindows() else: print('图像匹配的特征点不足') if __name__ == "__main__": image1 = cv2.imread('data/space1.jpg') image2 = cv2.imread('data/space2.jpg') image3 = cv2.imread('data/space3.jpg') image = image1, image2, image3 stitch(image)原图: OpenCV-Python的stitch实现了图像拼接方法。在OpenCV 3.X.X.X系列版本中,使用cv2.createStitcher。在OpenCV 4.X.X.X系列版本中,使用cv2.Stitcher_create或者cv2.Stitcher_create,两者用法一致。 stitch有两个返回值,一个是status,表示是否拼接成功;另一个是pano,当图像匹配的特征点足够时,拼接成功,返回全景图,当图像匹配的特征点不够时,拼接失败,返回None。效果如下: 全景图拼接完成后,会出现图像边界外的黑色像素(0),使全景图不完美。可采取如下方法去除黑边:全景图轮廓提取、轮廓最小正矩形、腐蚀处理。 全景图轮廓提取 # 全景图轮廓提取 stitched = cv2.copyMakeBorder(pano, 10, 10, 10, 10, cv2.BORDER_CONSTANT, (0, 0, 0)) gray = cv2.cvtColor(stitched, cv2.COLOR_BGR2GRAY) thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY)[1] cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0]创建了两个mask副本: minRect,最小mask,将慢慢缩小尺寸,直到它可以放入全景图的内部sub,判断minRect是否全黑(0)通过cv2.erode()腐蚀minRect,直到sub为全黑(0)。用类似动图演示,上面为sub,下面为minRect: OpenCV: samples/python/stitching.py Image Stitching with OpenCV and Python - PyImageSearch 使用OpenCV和Python拼接图像_W_Tortoise的博客-CSDN博客 |
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