descriptor = cv2.xfeatures2d.SIFT_create()
时间: 2024-05-24 09:11:39 浏览: 12
This code creates a SIFT (Scale-Invariant Feature Transform) descriptor object using the xfeatures2d module of OpenCV. SIFT is a popular feature detection and extraction algorithm used in computer vision and image processing tasks. The SIFT descriptor creates a set of distinctive, invariant feature points in an image that can be used for tasks such as object recognition, image stitching, and 3D reconstruction. The .create() method initializes the SIFT descriptor object with default parameters.
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import cv2 # 读取两幅待处理的图像 img1 = cv2.imread('image1.jpg', cv2.IMREAD_GRAYSCALE) img2 = cv2.imread('image2.jpg', cv2.IMREAD_GRAYSCALE) # 对图像进行高斯模糊 img1 = cv2.GaussianBlur(img1, (5, 5), 0) img2 = cv2.GaussianBlur(img2, (5, 5), 0) # 使用Shi-Tomasi算法检测特征点 corners1 = cv2.goodFeaturesToTrack(img1, 100, 0.01, 10) corners2 = cv2.goodFeaturesToTrack(img2, 100, 0.01, 10) # 对特征点进行亚像素定位 corners1 = cv2.cornerSubPix(img1, corners1, (5, 5), (-1, -1), criteria=(cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)) corners2 = cv2.cornerSubPix(img2, corners2, (5, 5), (-1, -1), criteria=(cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)) # 对特征点进行匹配 matcher = cv2.DescriptorMatcher_create(cv2.DESCRIPTOR_MATCHER_BRUTEFORCE_HAMMING) kps1, descs1 = sift.detectAndCompute(img1, None) kps2, descs2 = sift.detectAndCompute(img2, None) matches = matcher.match(descs1, descs2) # 使用RANSAC算法进行匹配点筛选 src_pts = np.float32([kps1[m.queryIdx].pt for m in matches]).reshape(-1, 1, 2) dst_pts = np.float32([kps2[m.trainIdx].pt for m in matches]).reshape(-1, 1, 2) M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0) # 对图像进行配准和拼接 result = cv2.warpPerspective(img1, M, (img1.shape[1] + img2.shape[1], img1.shape[0])) result[0:img2.shape[0], 0:img2.shape[1]] = img2 # 显示结果 cv2.imshow('Result', result) cv2.waitKey() cv2.destroyAllWindows()改进这段代码使其输出特征点连线图和拼接图
import cv2
import numpy as np
# 读取两幅待处理的图像
img1 = cv2.imread('image1.jpg', cv2.IMREAD_GRAYSCALE)
img2 = cv2.imread('image2.jpg', cv2.IMREAD_GRAYSCALE)
# 对图像进行高斯模糊
img1 = cv2.GaussianBlur(img1, (5, 5), 0)
img2 = cv2.GaussianBlur(img2, (5, 5), 0)
# 使用Shi-Tomasi算法检测特征点
corners1 = cv2.goodFeaturesToTrack(img1, 100, 0.01, 10)
corners2 = cv2.goodFeaturesToTrack(img2, 100, 0.01, 10)
# 对特征点进行亚像素定位
corners1 = cv2.cornerSubPix(img1, corners1, (5, 5), (-1, -1), criteria=(cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001))
corners2 = cv2.cornerSubPix(img2, corners2, (5, 5), (-1, -1), criteria=(cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001))
# 对特征点进行匹配
sift = cv2.xfeatures2d.SIFT_create()
matcher = cv2.DescriptorMatcher_create(cv2.DESCRIPTOR_MATCHER_BRUTEFORCE_HAMMING)
kps1, descs1 = sift.detectAndCompute(img1, None)
kps2, descs2 = sift.detectAndCompute(img2, None)
matches = matcher.match(descs1, descs2)
# 使用RANSAC算法进行匹配点筛选
src_pts = np.float32([kps1[m.queryIdx].pt for m in matches]).reshape(-1, 1, 2)
dst_pts = np.float32([kps2[m.trainIdx].pt for m in matches]).reshape(-1, 1, 2)
M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
# 画出特征点连线图
matchesMask = mask.ravel().tolist()
h, w = img1.shape
draw_params = dict(matchColor=(0, 255, 0), singlePointColor=None, matchesMask=matchesMask, flags=2)
img3 = cv2.drawMatches(img1, kps1, img2, kps2, matches, None, **draw_params)
cv2.imshow('Feature Matching', img3)
# 对图像进行配准和拼接
result = cv2.warpPerspective(img1, M, (img1.shape[1] + img2.shape[1], img1.shape[0]))
result[0:img2.shape[0], 0:img2.shape[1]] = img2
# 显示结果
cv2.imshow('Result', result)
cv2.waitKey()
cv2.destroyAllWindows()
cv2.DescriptorMatcher_create()函数,创建欧式距离匹配器的代码
以下是使用cv2.DescriptorMatcher_create()函数创建欧式距离匹配器的代码:
```
import cv2
# 创建SIFT特征提取器和描述符
sift = cv2.xfeatures2d.SIFT_create()
# 读取图片并提取特征
img1 = cv2.imread('img1.jpg')
gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
kp1, des1 = sift.detectAndCompute(gray1, None)
img2 = cv2.imread('img2.jpg')
gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
kp2, des2 = sift.detectAndCompute(gray2, None)
# 创建欧式距离匹配器
matcher = cv2.DescriptorMatcher_create(cv2.DESCRIPTOR_MATCHER_BRUTEFORCE)
# 匹配特征
matches = matcher.match(des1, des2)
# 绘制匹配结果
img_matches = cv2.drawMatches(img1, kp1, img2, kp2, matches, None)
cv2.imshow("Matches", img_matches)
cv2.waitKey(0)
cv2.destroyAllWindows()
```
在上面的代码中,我们首先使用SIFT特征提取器和描述符提取了两张图片的特征。然后,我们使用cv2.DescriptorMatcher_create()函数创建了一个欧式距离匹配器,这里我们使用了cv2.DESCRIPTOR_MATCHER_BRUTEFORCE参数来指定使用暴力匹配器。接着,我们使用matcher.match()函数对两张图片的描述符进行匹配,并将匹配结果绘制出来。最后,我们使用cv2.imshow()函数显示匹配结果,并使用cv2.waitKey()函数等待用户按下键盘任意键,最后使用cv2.destroyAllWindows()函数关闭所有窗口。
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