python 碎片图像拼接_python实现图像拼接

本文实例为大家分享了python实现图像拼接的具体代码，供大家参考，具体内容如下

1.待拼接的图像

2. 基于SIFT特征点和RANSAC方法得到的图像特征点匹配结果

3.图像变换结果

4.代码及注意事项

import cv2

import numpy as np

def cv_show(name, image):

cv2.imshow(name, image)

cv2.waitKey(0)

cv2.destroyAllWindows()

def detectAndCompute(image):

image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

sift = cv2.xfeatures2d.SIFT_create()

(kps, features) = sift.detectAndCompute(image, None)

kps = np.float32([kp.pt for kp in kps]) # 得到的点需要进一步转换才能使用

return (kps, features)

def matchKeyPoints(kpsA, kpsB, featuresA, featuresB, ratio = 0.75, reprojThresh = 4.0):

# ratio是最近邻匹配的推荐阈值

# reprojThresh是随机取样一致性的推荐阈值

matcher = cv2.BFMatcher()

rawMatches = matcher.knnMatch(featuresA, featuresB, 2)

matches = []

for m in rawMatches:

if len(m) == 2 and m[0].distance < ratio * m[1].distance:

matches.append((m[0].queryIdx, m[0].trainIdx))

kpsA = np.float32([kpsA[m[0]] for m in matches]) # 使用np.float32转化列表

kpsB = np.float32([kpsB[m[1]] for m in matches])

(M, status) = cv2.findHomography(kpsA, kpsB, cv2.RANSAC, reprojThresh)

return (M, matches, status) # 并不是所有的点都有匹配解，它们的状态存在status中

def stich(imgA, imgB, M):

result = cv2.warpPerspective(imgA, M, (imgA.shape[1] + imgB.shape[1], imgA.shape[0]))

result[0:imageA.shape[0], 0:imageB.shape[1]] = imageB

cv_show(‘result’, result)

def drawMatches(imgA, imgB, kpsA, kpsB, matches, status):

(hA, wA) = imgA.shape[0:2]

(hB, wB) = imgB.shape[0:2]

# 注意这里的3通道和uint8类型

drawImg = np.zeros((max(hA, hB), wA + wB, 3), ‘uint8’)

drawImg[0:hB, 0:wB] = imageB

drawImg[0:hA, wB:] = imageA

for ((queryIdx, trainIdx),s) in zip(matches, status):

if s == 1:

# 注意将float32 –> int

pt1 = (int(kpsB[trainIdx][0]), int(kpsB[trainIdx][1]))

pt2 = (int(kpsA[trainIdx][0]) + wB, int(kpsA[trainIdx][1]))

cv2.line(drawImg, pt1, pt2, (0, 0, 255))

cv_show(“drawImg”, drawImg)

# 读取图像

imageA = cv2.imread(‘./right_01.png’)

cv_show(“imageA”, imageA)

imageB = cv2.imread(‘./left_01.png’)

cv_show(“imageB”, imageB)

# 计算SIFT特征点和特征向量

(kpsA, featuresA) = detectAndCompute(imageA)

(kpsB, featuresB) = detectAndCompute(imageB)

# 基于最近邻和随机取样一致性得到一个单应性矩阵

(M, matches, status) = matchKeyPoints(kpsA, kpsB, featuresA, featuresB)

# 绘制匹配结果

drawMatches(imageA, imageB, kpsA, kpsB, matches, status)

# 拼接

stich(imageA, imageB, M)

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持聚米学院。