图像拼接技术与Python实现-优快云博客

本文链接：https://blog.youkuaiyun.com/weixin_44576543/article/details/102324411

本文介绍了图像拼接的基本流程，包括关键点检测、特征向量生成、匹配、优化及最终的图像拼接。提供了多张图片和两张图片拼接的Python代码资源，虽然存在一些问题，但可作为参考进行修改。详细步骤包括使用SIFT、SURF、DOG等算法抽取特征点，采用最近邻算法匹配并利用RANSAC去除误匹配，通过透视变换实现图像拼接。

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摘要：关于图像拼接的相关简介/代码图像拼接可以是两张图片的拼接也可以是多张图片的评价，大体可以分为关键点检测与生成相对应的特征向量；匹配关键点；最后把多张图片拼接在一起

多张图片拼接代码连接：https://github.com/kushalvyas/Python-Multiple-Image-Stitching 这个代码有点问题

两张图片拼接的完整代码(两个python文件)：cmd 命令
1 python stitch.py --first 路径A --second 路径B

panorama.py

# import the necessary packages
import numpy as np
import imutils
import cv2

class Stitcher:
	def __init__(self):
		# determine if we are using OpenCV v3.X
		self.isv3 = imutils.is_cv3()

	def stitch(self, images, ratio=0.75, reprojThresh=4.0, #参数的设置
		showMatches=False):
		# unpack the images, then detect keypoints and extract
		# local invariant descriptors from them
		(imageB, imageA) = images
		(kpsA, featuresA) = self.detectAndDescribe(imageA)
		(kpsB, featuresB) = self.detectAndDescribe(imageB)

		# match features between the two images
		M = self.matchKeypoints(kpsA, kpsB,
			featuresA, featuresB, ratio, reprojThresh)

		# if the match is None, then there aren't enough matched
		# keypoints to create a panorama
		if M is None:
			return None

		# otherwise, apply a perspective warp to stitch the images
		# together
		(matches, H, status) = M
		result = cv2.warpPerspective(imageA, H,
			(imageA.shape[1] + imageB.shape[1], imageA.shape[0]))
		result[0:imageB.shape[0], 0:imageB.shape[1]] = imageB

		# check to see if the keypoint matches should be visualized
		if showMatches:
			vis = self.drawMatches(imageA, imageB, kpsA, kpsB, matches,
				status)

			# return a tuple of the stitched image and the
			# visualization
			return (result, vis)

		# return the stitched image
		return result

	def detectAndDescribe(self, image):
		# convert the image to grayscale
		gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

		# check to see if we are using OpenCV 3.X
		if self.isv3:
			# detect and extract features from the image
			descriptor = cv2.xfeatures2d.SIFT_create()
			(kps, features) = descriptor.detectAndCo