太原站扩建后的规模,建设网站运营成本,网站建设的公司哪家便宜,网站定制开发是什么意思文章目录 特征检测的基本概念Harris角点检测Shi-Tomasi角点检测SIFT关键点检测SIFT计算描述子SURF特征检测OBR特征检测暴力特征匹配FLANN特征匹配实战flann特征匹配图像查找图像拼接基础知识图像拼接实战 特征点检测与匹配是计算机视觉中非常重要的内容。不是所有图像操作都是对… 文章目录 特征检测的基本概念Harris角点检测Shi-Tomasi角点检测SIFT关键点检测SIFT计算描述子SURF特征检测OBR特征检测暴力特征匹配FLANN特征匹配实战flann特征匹配图像查找图像拼接基础知识图像拼接实战 特征点检测与匹配是计算机视觉中非常重要的内容。不是所有图像操作都是对每个像素进行处理有些只需使用4个顶点即可如图像的拼接、二维码定位等 特征检测的基本概念 Harris角点检测 详情见官方参考文档
# -*- coding: utf-8 -*-
import cv2
import numpy as npblockSize 2
ksize 3
k 0.04img cv2.imread(./chess.png)# 灰度化
gray cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)# Harris角点检测
dst cv2.cornerHarris(gray, blockSize, ksize, k)# Harris角点展示
img[dst 0.01 * dst.max()] [0, 255, 0]cv2.imshow(harris, img)
if cv2.waitKey(0) 0xff 27:cv2.destroyAllWindows()Shi-Tomasi角点检测 距离越大检测到的角数越少距离越小检测到的角数越多
# -*- coding: utf-8 -*-
import cv2
import numpy as np# Harris
# blockSize 2
# ksize 3
# k 0.04# Shi-Tomasi
maxCorners 1000
ql 0.01
minDistance 10img cv2.imread(./chess.png)# 灰度化
gray cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)# Harris角点检测
# dst cv2.cornerHarris(gray, blockSize, ksize, k)# Harris角点展示
# img[dst 0.01 * dst.max()] [0, 255, 0]corners cv2.goodFeaturesToTrack(gray, maxCorners, ql, minDistance)corners np.int0(corners)for i in corners:x, y i.ravel()cv2.circle(img, (x, y), 3, (0, 255, 0), -1)cv2.imshow(Tomasi, img)
if cv2.waitKey(0) 0xff 27:cv2.destroyAllWindows()SIFT关键点检测
详情见官方文档 The distinguishing qualities of an image that make it stand out are referred to as key points in an image. The key points of a particular image let us recognize objects and compare images. Detecting critical spots in a picture may be done using a variety of techniques and algorithms. We utilize the drawKeypoints() method in OpenCV to be able to draw the identified key points on a given picture. The input picture, keypoints, color, and flag are sent to the drawKeypoints() method. key points are the most important aspects of the detection. Even after the image is modified the key points remain the same. As of now, we can only use the SIRF_create() function as the surf function is patented. # -*- coding: utf-8 -*-
import cv2
import numpy as npimg cv2.imread(./chess.png)
# 灰度化
gray cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)# 创建SIFT对象
sift cv2.xfeatures2d.SIFT_create()
# 进行检测
kp sift.detect(gray, None)# 绘制keypoints
cv2.drawKeypoints(gray, kp, img)cv2.imshow(img, img)
if cv2.waitKey(0) 0xff 27:cv2.destroyAllWindows()sift cv2.xfeatures2d.SIFT_create() 实例化 参数说明sift为实例化的sift函数 kp sift.detect(gray, None) 找出图像中的关键点 参数说明: kp表示生成的关键点gray表示输入的灰度图 ret cv2.drawKeypoints(gray, kp, img) 在图中画出关键点 参数说明gray表示输入图片, kp表示关键点img表示输出的图片 kp, dst sift.compute(kp) 计算关键点对应的sift特征向量 参数说明kp表示输入的关键点dst表示输出的sift特征向量通常是128维的
SIFT计算描述子 # -*- coding: utf-8 -*-
import cv2
import numpy as npimg cv2.imread(./chess.png)
# 灰度化
gray cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)# 创建SIFT对象
sift cv2.xfeatures2d.SIFT_create()
# 进行检测
kp, des sift.detectAndCompute(gray, None)print(des)# 绘制keypoints
cv2.drawKeypoints(gray, kp, img)cv2.imshow(img, img)
if cv2.waitKey(0) 0xff 27:cv2.destroyAllWindows()SURF特征检测 # -*- coding: utf-8 -*-
import cv2
import numpy as npimg cv2.imread(./chess.png)
# 灰度化
gray cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)# 创建SIFT对象
# sift cv2.xfeatures2d.SIFT_create()# 创建SURF对象
surf cv2.xfeatures2d.SURF.create()# 进行检测
# kp, des sift.detectAndCompute(gray, None)
kp, des surf.detectAndCompute(gray, None)# print(des[0])# 绘制keypoints
cv2.drawKeypoints(gray, kp, img)cv2.imshow(img, img)
if cv2.waitKey(0) 0xff 27:cv2.destroyAllWindows()好消息SURF付费了不是开源的接口了需要大家自己造轮子写新的好算法
OBR特征检测 # -*- coding: utf-8 -*-
import cv2
import numpy as npimg cv2.imread(./chess.png)
# 灰度化
gray cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)# 创建SIFT对象
# sift cv2.xfeatures2d.SIFT_create()# 创建SURF对象
# surf cv2.xfeatures2d.SURF.create()# 创建ORB对象
orb cv2.ORB_create()# 进行检测
# kp, des sift.detectAndCompute(gray, None)
# kp, des surf.detectAndCompute(gray, None)
kp, des orb.detectAndCompute(gray, None)# print(des[0])# 绘制keypoints
cv2.drawKeypoints(gray, kp, img)cv2.imshow(img, img)
if cv2.waitKey(0) 0xff 27:cv2.destroyAllWindows()暴力特征匹配 # -*- coding: utf-8 -*-
import cv2
import numpy as np# img cv2.imread(./chess.png)
img1 cv2.imread(./opencv_search.png)
img2 cv2.imread(./opencv_orig.png)# 灰度化
gray1 cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
gray2 cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)# create SIFT feature extractor
# 创建SIFT对象
sift cv2.xfeatures2d.SIFT_create()# 创建SURF对象
# surf cv2.xfeatures2d.SURF.create()# 创建ORB对象
orb cv2.ORB_create()# detect features from the image
# 进行检测
# kp, des sift.detectAndCompute(gray, None)
# kp, des surf.detectAndCompute(gray, None)
# kp, des orb.detectAndCompute(gray, None)# draw the detected key points
kp1, des1 sift.detectAndCompute(gray1, None)
kp2, des2 sift.detectAndCompute(gray2, None)# print(des[0])# 绘制keypoints
# cv2.drawKeypoints(gray, kp, img)# 创建匹配器
bf cv2.BFMatcher(cv2.NORM_L1)
match bf.match(des1, des2)img3 cv2.drawMatches(img1, kp1, img2, kp2, match, None)cv2.imshow(img, img3)
if cv2.waitKey(0) 0xff 27:cv2.destroyAllWindows()FLANN特征匹配 实战flann特征匹配
参考的官网手册
# -*- coding: utf-8 -*-
import cv2
import numpy as np# queryImage
img1 cv2.imread(./opencv_search.png)
# trainImage
img2 cv2.imread(./opencv_orig.png)# 灰度化
g1 cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
g2 cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)# 创建SIFT特征检测器
# Initiate SIFT detector
sift cv2.xfeatures2d.SIFT_create()# 计算描述子与特征点
# find the keypoints and descriptors with SIFT
kp1, des1 sift.detectAndCompute(g1, None)
kp2, des2 sift.detectAndCompute(g2, None)# 创建匹配器
# FLANN parameters
index_params dict(algorithm 1, trees 5)
search_params dict(checks 50)
flann cv2.FlannBasedMatcher(index_params, search_params)# 对描述子进行匹配计算
matchs flann.knnMatch(des1, des2, k 2)good []
# ratio test as per Lowes paper
for i, (m, n) in enumerate(matchs):if m.distance 0.7 * n.distance:good.append(m)ret cv2.drawMatchesKnn(img1, kp1, img2, kp2, [good], None)cv2.imshow(res, ret)
if cv2.waitKey(0) 0xff 27:cv2.destroyAllWindows()图像查找 # -*- coding: utf-8 -*-
import cv2
import numpy as npimg1 cv2.imread(./opencv_search.png)
img2 cv2.imread(./opencv_orig.png)MIN_MATCH_COUNT 4# 灰度化
g1 cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
g2 cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)# 创建SIFT特征检测器
sift cv2.xfeatures2d.SIFT_create()# 计算描述子与特征点
kp1, des1 sift.detectAndCompute(g1, None)
kp2, des2 sift.detectAndCompute(g2, None)# 创建匹配器
index_params dict(algorithm 1, trees 5)
search_params dict(checks 50)
flann cv2.FlannBasedMatcher(index_params, search_params)# 对描述子进行匹配计算
matchs flann.knnMatch(des1, des2, k 2)good []
for i, (m, n) in enumerate(matchs):if m.distance 0.7 * n.distance:good.append(m)if len(good) MIN_MATCH_COUNT:src_pts np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2)dst_pts np.float32([ kp2[m.trainIdx].pt for m in good ]).reshape(-1,1,2)M, mask cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)h,w img1.shape[:2]pts np.float32([ [0, 0], [0, h-1], [w-1, h-1], [w-1, 0]]).reshape(-1,1,2)dst cv2.perspectiveTransform(pts, M)cv2.polylines(img2, [np.int32(dst)], True, (0, 255, 0))
else:print(the number of good is less than 4.)exit()ret cv2.drawMatchesKnn(img1, kp1, img2, kp2, [good], None)cv2.imshow(res, ret)
if cv2.waitKey(0) 0xff 27:cv2.destroyAllWindows()图像拼接基础知识 (0,0)是第二张图的左上角
图像拼接实战
# -*- coding: utf-8 -*-
import cv2
import numpy as np#第一步读取文件将图片设置成一样大小640x480
#第二步找特征点描述子计算单应性矩阵
#第三步根据单应性矩阵对图像进行变换然后平移
#第四步拼接并输出最终结果img1 cv2.imread(map1.png)
img2 cv2.imread(map2.png)img1 cv2.resize(img1, (640, 480))
img2 cv2.resize(img2, (640, 480))inputs np.hstack((img1, img2))
cv2.imshow(input_img, inputs)
if cv2.waitKey(0) 0xff 27:cv2.destroyAllWindows()# -*- coding: utf-8 -*-
import cv2
import numpy as np#第一步读取文件将图片设置成一样大小640x480
#第二步找特征点描述子计算单应性矩阵
#第三步根据单应性矩阵对图像进行变换然后平移
#第四步拼接并输出最终结果MIN_MATCH_COUNT 8def stitch_image(img1, img2, H):# 1. 获得每张图片的四个角点# 2. 对图片进行变换单应性矩阵使图进行旋转平移# 3. 创建一张大图将两张图拼接到一起# 4. 将结果输出#获得原始图的高/宽h1, w1 img1.shape[:2]h2, w2 img2.shape[:2]img1_dims np.float32([[0,0], [0, h1], [w1, h1], [w1, 0]]).reshape(-1, 1, 2)img2_dims np.float32([[0,0], [0, h2], [w2, h2], [w2, 0]]).reshape(-1, 1, 2)img1_transform cv2.perspectiveTransform(img1_dims, H)print(img1_dims)print(img2_dims)print(img1_transform)def get_homo(img1, img2):#1. 创建特征转换对象#2. 通过特征转换对象获得特征点和描述子#3. 创建特征匹配器#4. 进行特征匹配#5. 过滤特征找出有效的特征匹配点# 创建SIFT特征检测器sift cv2.xfeatures2d.SIFT_create()# 计算描述子与特征点kp1, des1 sift.detectAndCompute(img1, None)kp2, des2 sift.detectAndCompute(img2, None)# 创建特征匹配器bf cv2.BFMatcher()# 对描述子进行匹配计算matchs bf.knnMatch(des1, des2, k 2)verify_matches []for i, (m, n) in enumerate(matchs):if m.distance 0.8 * n.distance:verify_matches.append(m)if len(verify_matches) MIN_MATCH_COUNT:img1_pts []img2_pts []for m in verify_matches:img1_pts.append(kp1[m.queryIdx].pt)img2_pts.append(kp2[m.trainIdx].pt)#[(x1, y1), (x2, y2), ...]#[[x1, y1], [x2, y2], ...] img1_pts np.float32(img1_pts).reshape(-1, 1, 2)img2_pts np.float32(img2_pts).reshape(-1, 1, 2)# 获取单应性矩阵H, mask cv2.findHomography(img1_pts, img2_pts, cv2.RANSAC, 5.0)return Helse:print(err: Not enough matches!)exit()img1 cv2.imread(map1.png)
img2 cv2.imread(map2.png)img1 cv2.resize(img1, (640, 480))
img2 cv2.resize(img2, (640, 480))inputs np.hstack((img1, img2))#获得单应性矩阵
H get_homo(img1, img2)#进行图像拼接
result_image stitch_image(img1, img2, H)cv2.imshow(input_img, inputs)
if cv2.waitKey(0) 0xff 27:cv2.destroyAllWindows()# -*- coding: utf-8 -*-
import cv2
import numpy as np#第一步读取文件将图片设置成一样大小640x480
#第二步找特征点描述子计算单应性矩阵
#第三步根据单应性矩阵对图像进行变换然后平移
#第四步拼接并输出最终结果MIN_MATCH_COUNT 8def stitch_image(img1, img2, H):# 1. 获得每张图片的四个角点# 2. 对图片进行变换单应性矩阵使图进行旋转平移# 3. 创建一张大图将两张图拼接到一起# 4. 将结果输出#获得原始图的高/宽h1, w1 img1.shape[:2]h2, w2 img2.shape[:2]img1_dims np.float32([[0,0], [0, h1], [w1, h1], [w1, 0]]).reshape(-1, 1, 2)img2_dims np.float32([[0,0], [0, h2], [w2, h2], [w2, 0]]).reshape(-1, 1, 2)img1_transform cv2.perspectiveTransform(img1_dims, H)# print(img1_dims)# print(img2_dims)# print(img1_transform)result_dims np.concatenate((img2_dims, img1_transform), axis0)# print(result_dims)[x_min, y_min] np.int32(result_dims.min(axis0).ravel()-0.5)[x_max, y_max ] np.int32(result_dims.max(axis0).ravel()0.5)#平移的距离transform_dist [-x_min, -y_min]result_img cv2.warpPerspective(img1, H, (x_max-x_min, y_max-y_min))return result_imgdef get_homo(img1, img2):#1. 创建特征转换对象#2. 通过特征转换对象获得特征点和描述子#3. 创建特征匹配器#4. 进行特征匹配#5. 过滤特征找出有效的特征匹配点# 创建SIFT特征检测器sift cv2.xfeatures2d.SIFT_create()# 计算描述子与特征点kp1, des1 sift.detectAndCompute(img1, None)kp2, des2 sift.detectAndCompute(img2, None)# 创建特征匹配器bf cv2.BFMatcher()# 对描述子进行匹配计算matchs bf.knnMatch(des1, des2, k 2)verify_matches []for i, (m, n) in enumerate(matchs):if m.distance 0.8 * n.distance:verify_matches.append(m)if len(verify_matches) MIN_MATCH_COUNT:img1_pts []img2_pts []for m in verify_matches:img1_pts.append(kp1[m.queryIdx].pt)img2_pts.append(kp2[m.trainIdx].pt)#[(x1, y1), (x2, y2), ...]#[[x1, y1], [x2, y2], ...] img1_pts np.float32(img1_pts).reshape(-1, 1, 2)img2_pts np.float32(img2_pts).reshape(-1, 1, 2)# 获取单应性矩阵H, mask cv2.findHomography(img1_pts, img2_pts, cv2.RANSAC, 5.0)return Helse:print(err: Not enough matches!)exit()img1 cv2.imread(map1.png)
img2 cv2.imread(map2.png)img1 cv2.resize(img1, (640, 480))
img2 cv2.resize(img2, (640, 480))inputs np.hstack((img1, img2))#获得单应性矩阵
H get_homo(img1, img2)#进行图像拼接
result_image stitch_image(img1, img2, H)cv2.imshow(input_img, result_image)
if cv2.waitKey(0) 0xff 27:cv2.destroyAllWindows()# -*- coding: utf-8 -*-
import cv2
import numpy as np#第一步读取文件将图片设置成一样大小640x480
#第二步找特征点描述子计算单应性矩阵
#第三步根据单应性矩阵对图像进行变换然后平移
#第四步拼接并输出最终结果MIN_MATCH_COUNT 8def stitch_image(img1, img2, H):# 1. 获得每张图片的四个角点# 2. 对图片进行变换单应性矩阵使图进行旋转平移# 3. 创建一张大图将两张图拼接到一起# 4. 将结果输出#获得原始图的高/宽h1, w1 img1.shape[:2]h2, w2 img2.shape[:2]img1_dims np.float32([[0,0], [0, h1], [w1, h1], [w1, 0]]).reshape(-1, 1, 2)img2_dims np.float32([[0,0], [0, h2], [w2, h2], [w2, 0]]).reshape(-1, 1, 2)img1_transform cv2.perspectiveTransform(img1_dims, H)# print(img1_dims)# print(img2_dims)# print(img1_transform)result_dims np.concatenate((img2_dims, img1_transform), axis0)# print(result_dims)[x_min, y_min] np.int32(result_dims.min(axis0).ravel()-0.5)[x_max, y_max ] np.int32(result_dims.max(axis0).ravel()0.5)#平移的距离transform_dist [-x_min, -y_min]#[1, 0, dx]#[0, 1, dy] #[0, 0, 1 ]transform_array np.array([[1, 0, transform_dist[0]],[0, 1, transform_dist[1]],[0, 0, 1]])result_img cv2.warpPerspective(img1, transform_array.dot(H), (x_max-x_min, y_max-y_min))return result_imgdef get_homo(img1, img2):#1. 创建特征转换对象#2. 通过特征转换对象获得特征点和描述子#3. 创建特征匹配器#4. 进行特征匹配#5. 过滤特征找出有效的特征匹配点# 创建SIFT特征检测器sift cv2.xfeatures2d.SIFT_create()# 计算描述子与特征点kp1, des1 sift.detectAndCompute(img1, None)kp2, des2 sift.detectAndCompute(img2, None)# 创建特征匹配器bf cv2.BFMatcher()# 对描述子进行匹配计算matchs bf.knnMatch(des1, des2, k 2)verify_matches []for i, (m, n) in enumerate(matchs):if m.distance 0.8 * n.distance:verify_matches.append(m)if len(verify_matches) MIN_MATCH_COUNT:img1_pts []img2_pts []for m in verify_matches:img1_pts.append(kp1[m.queryIdx].pt)img2_pts.append(kp2[m.trainIdx].pt)#[(x1, y1), (x2, y2), ...]#[[x1, y1], [x2, y2], ...] img1_pts np.float32(img1_pts).reshape(-1, 1, 2)img2_pts np.float32(img2_pts).reshape(-1, 1, 2)# 获取单应性矩阵H, mask cv2.findHomography(img1_pts, img2_pts, cv2.RANSAC, 5.0)return Helse:print(err: Not enough matches!)exit()img1 cv2.imread(map1.png)
img2 cv2.imread(map2.png)img1 cv2.resize(img1, (640, 480))
img2 cv2.resize(img2, (640, 480))inputs np.hstack((img1, img2))#获得单应性矩阵
H get_homo(img1, img2)#进行图像拼接
result_image stitch_image(img1, img2, H)cv2.imshow(input_img, result_image)
if cv2.waitKey(0) 0xff 27:cv2.destroyAllWindows()# -*- coding: utf-8 -*-
import cv2
import numpy as np#第一步读取文件将图片设置成一样大小640x480
#第二步找特征点描述子计算单应性矩阵
#第三步根据单应性矩阵对图像进行变换然后平移
#第四步拼接并输出最终结果MIN_MATCH_COUNT 8def stitch_image(img1, img2, H):# 1. 获得每张图片的四个角点# 2. 对图片进行变换单应性矩阵使图进行旋转平移# 3. 创建一张大图将两张图拼接到一起# 4. 将结果输出#获得原始图的高/宽h1, w1 img1.shape[:2]h2, w2 img2.shape[:2]img1_dims np.float32([[0,0], [0, h1], [w1, h1], [w1, 0]]).reshape(-1, 1, 2)img2_dims np.float32([[0,0], [0, h2], [w2, h2], [w2, 0]]).reshape(-1, 1, 2)img1_transform cv2.perspectiveTransform(img1_dims, H)# print(img1_dims)# print(img2_dims)# print(img1_transform)result_dims np.concatenate((img2_dims, img1_transform), axis0)# print(result_dims)[x_min, y_min] np.int32(result_dims.min(axis0).ravel()-0.5)[x_max, y_max ] np.int32(result_dims.max(axis0).ravel()0.5)#平移的距离transform_dist [-x_min, -y_min]# 齐次坐标#[1, 0, dx]#[0, 1, dy] #[0, 0, 1 ]transform_array np.array([[1, 0, transform_dist[0]],[0, 1, transform_dist[1]],[0, 0, 1]])result_img cv2.warpPerspective(img1, transform_array.dot(H), (x_max-x_min, y_max-y_min))result_img[transform_dist[1]:transform_dist[1]h2, transform_dist[0]:transform_dist[0]w2] img2return result_imgdef get_homo(img1, img2):#1. 创建特征转换对象#2. 通过特征转换对象获得特征点和描述子#3. 创建特征匹配器#4. 进行特征匹配#5. 过滤特征找出有效的特征匹配点# 创建SIFT特征检测器sift cv2.xfeatures2d.SIFT_create()# 计算描述子与特征点kp1, des1 sift.detectAndCompute(img1, None)kp2, des2 sift.detectAndCompute(img2, None)# 创建特征匹配器bf cv2.BFMatcher()# 对描述子进行匹配计算matchs bf.knnMatch(des1, des2, k 2)verify_matches []for i, (m, n) in enumerate(matchs):if m.distance 0.8 * n.distance:verify_matches.append(m)if len(verify_matches) MIN_MATCH_COUNT:img1_pts []img2_pts []for m in verify_matches:img1_pts.append(kp1[m.queryIdx].pt)img2_pts.append(kp2[m.trainIdx].pt)#[(x1, y1), (x2, y2), ...]#[[x1, y1], [x2, y2], ...] img1_pts np.float32(img1_pts).reshape(-1, 1, 2)img2_pts np.float32(img2_pts).reshape(-1, 1, 2)# 获取单应性矩阵H, mask cv2.findHomography(img1_pts, img2_pts, cv2.RANSAC, 5.0)return Helse:print(err: Not enough matches!)exit()img1 cv2.imread(map1.png)
img2 cv2.imread(map2.png)img1 cv2.resize(img1, (640, 480))
img2 cv2.resize(img2, (640, 480))inputs np.hstack((img1, img2))#获得单应性矩阵
H get_homo(img1, img2)#进行图像拼接
result_image stitch_image(img1, img2, H)cv2.imshow(input_img, result_image)
if cv2.waitKey(0) 0xff 27:cv2.destroyAllWindows()之后我会持续更新如果喜欢我的文章请记得一键三连哦点赞关注收藏你的每一个赞每一份关注每一次收藏都将是我前进路上的无限动力 ↖(▔▽▔)↗感谢支持
