[kp.pt for kp in kps]
时间: 2024-01-11 17:03:45 浏览: 28
This is a list comprehension in Python that creates a new list containing the .pt domains of all the items in the original list 'kps'.
For example, if kps = ['google.com', 'yahoo.pt', 'bing.pt'], then [kp.pt for kp in kps] would return ['google.pt', 'yahoo.pt', 'bing.pt'].
It works by iterating through each item in the 'kps' list, assigning it to the variable 'kp', and then taking the string after the last '.' character (assuming it is the top-level domain) and adding the '.pt' suffix to it.
相关问题
list< cv::Point2f > keypoints; vector<cv::KeyPoint> kps; cv::Ptr<cv::FastFeatureDetector> detector = cv::FastFeatureDetector::create(); detector->detect(color, kps); for (auto kp : kps) keypoints.push_back(kp.pt); last_color = color;这段代码什么意思?
这段代码使用OpenCV库中的FAST特征检测器来检测输入图像color中的关键点。首先,声明了两个类型为cv::Point2f的变量keypoints和类型为std::vector<cv::KeyPoint>的变量kps。然后使用cv::FastFeatureDetector::create()创建了一个FAST特征检测器的指针detector。接下来,调用detector->detect(color, kps)来检测输入图像color中的特征点,并将检测到的特征点存储在kps中。最后,使用一个for循环将kps中的关键点的坐标(kp.pt)存储在keypoints中,并且将color赋值给last_color变量。
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()