视频投票网站怎么做,系统开发岗位职责,小程序开发教程,建设报名系统官方网站Python-OpenCV中的图像处理-图像特征 图像特征Harris角点检测亚像素级精度的角点检测Shi-Tomasi角点检测SIFT(Scale-Invariant Feature Transfrom)SURF(Speeded-Up Robust Features) 图像特征
特征理解特征检测特征描述
Harris角点检测
cv2.cornerHarris(img, blockSize, ks… Python-OpenCV中的图像处理-图像特征 图像特征Harris角点检测亚像素级精度的角点检测Shi-Tomasi角点检测SIFT(Scale-Invariant Feature Transfrom)SURF(Speeded-Up Robust Features) 图像特征
特征理解特征检测特征描述
Harris角点检测
cv2.cornerHarris(img, blockSize, ksize, k, borderType…) img输入图像数据类型为float32blockSize角点检测中要考虑的领域大小ksizeSobe求导中使用的窗口大小kHarris角点检测方程中的自由参数取值参数为 [0.04,0.06]borderType边界类型
import numpy as np
import cv2
from matplotlib import pyplot as plt# img cv2.imread(./resource/opencv/image/chessboard.png, cv2.IMREAD_COLOR)
img cv2.imread(./resource/opencv/image/pattern.png, cv2.IMREAD_COLOR)gray cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)gray np.float32(gray)# 输入图像必须是float32最后一个参数在0.04到0.05之间
dst cv2.cornerHarris(gray, 2, 3, 0.05)
dst cv2.dilate(dst, None)img[dst0.01*dst.max()] [0, 0, 255]cv2.imshow(dst, img)
cv2.waitKey(0)
cv2.destroyAllWindows()亚像素级精度的角点检测
cv2.cornerSubPix(img, corners, winSize, zeroZone, criteria) 最大精度的角点检测首先要找到 Harris角点然后将角点的重心传给这个函数进行修正。
import numpy as np
import cv2
from matplotlib import pyplot as pltimg cv2.imread(./resource/opencv/image/subpixel.png, cv2.IMREAD_COLOR)
gray cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)gray np.float32(gray)
dst cv2.cornerHarris(gray, 2, 3, 0.04)
dst cv2.dilate(dst, None)
ret, dst cv2.threshold(dst, 0.01*dst.max(), 255, 0)
dst np.uint8(dst)ret, labels, stats, centroids cv2.connectedComponentsWithStats(dst)criteria (cv2.TERM_CRITERIA_EPS cv2.TERM_CRITERIA_MAX_ITER, 100, 0.001)corners cv2.cornerSubPix(gray, np.float32(centroids), (5,5), (-1, -1), criteria)res np.hstack((centroids, corners))res np.int0(res)
img[res[:,1],res[:,0]][0,0,255]
img[res[:,3],res[:,2]][0,255,0]cv2.imshow(img, img)
cv2.waitKey(0)
cv2.destroyAllWindows()Harris 角点用红色像素标出绿色像素是修正后的角点。
Shi-Tomasi角点检测
cv2.goodFeatureToTrack()
import numpy as np
import cv2
from matplotlib import pyplot as pltimg cv2.imread(./resource/opencv/image/shitomasi_block.jpg, cv2.IMREAD_COLOR)
gray cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)corners cv2.goodFeaturesToTrack(gray, 25, 0.01, 10)corners np.int0(corners)for i in corners:x,y i.ravel()cv2.circle(img, (x,y), 3, 255, -1)plt.imshow(img)
plt.show()SIFT(Scale-Invariant Feature Transfrom) SIFT即尺度不变特征变换Scale-invariant feature transformSIFT是用于图像处理领域的一种描述。这种描述具有尺度不变性可在图像中检测出关键点是一种局部特征描述子。 cv2.SIFT_create() kp sift.detect(img, None)查找特征点kp, des sift.compute(img, kp)计算特征点kp, des sift.detectAndCompute(img, None) 直接找到特征点并计算描述符 cv2.drawKeypoints(img, kp, out_img, flagscv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS)画特征点 img : 输入图像kp图像特征点out_img输出图像flags cv2.DRAW_MATCHES_FLAGS_DEFAULT cv2.DRAW_MATCHES_FLAGS_DRAW_OVER_OUTIMG cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS
import numpy as np
import cv2# 读取图片
# img cv2.imread(./resource/opencv/image/home.jpg)
img cv2.imread(./resource/opencv/image/AverageMaleFace.jpg)
key_points img.copy()# 实例化SIFT算法
sift cv2.SIFT_create()# 得到特征点
kp sift.detect(img, None)
print(np.array(kp).shape)# 绘制特征点
cv2.drawKeypoints(img, kp, key_points, flagscv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS)# 图片展示
cv2.imshow(key points, key_points)
cv2.waitKey(0)
cv2.destroyAllWindows()# 保存图片
# cv2.imwrite(key_points.jpg, key_points)# 计算特征
kp, des sift.compute(img, kp)# 调试输出
print(des.shape)
print(des[0])cv2.imshow(kp, key_points)
cv2.waitKey(0)
cv2.destroyAllWindows() SURF(Speeded-Up Robust Features)
