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float32
  • blockSize：角点检测中要考虑的领域大小
  • ksize：Sobe求导中使用的窗口大小
  • k：Harris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[dst>0.01*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 transform，SIFT），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, flags=cv2.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, flags=cv2.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)