鲁棒特征及应用

参考：https://zhuanlan.zhihu.com/p/70385018

sift

import cv2 as cv
img = cv.imread('D:/lena.jpg')
gray = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
sift = cv.xfeatures2d.SIFT_create()

kp,des = sift.detectAndCompute(gray,None)
img=cv.drawKeypoints(gray,kp,img)
print(des.shape)

cv.imshow("SIFT", img) # cv.imwrite('sift_keypoints.jpg',img) 
cv.waitKey() 
cv.destroyAllWindows()

　

descriptors_shape：(1073, 128)

 surf

from __future__ import print_function
import cv2 as cv
import numpy as np

img1 = cv.imread('D:/lena.jpg', cv.IMREAD_GRAYSCALE)

#-- Step 1: Detect the keypoints using SURF Detector, compute the descriptors
minHessian = 800
detector = cv.xfeatures2d_SURF.create(hessianThreshold=minHessian)
keypoints1, descriptors1 = detector.detectAndCompute(img1, None)

img = cv.drawKeypoints(img1, keypoints1, img1)
print(descriptors1)
print(descriptors1.shape)
cv.imshow("keypoints", img)
cv.waitKey()
cv.destroyAllWindows()

 descriptors_shape：(277, 64)

flann match　

随便定义一个图像翻转操作。

def transform(origin):
    h, w = origin.shape
    generate_img = np.zeros(origin.shape)
    for i in range(h):
        for j in range(w):
            generate_img[i, w - 1 - j] = origin[i, j]
    return generate_img.astype(np.uint8)

import cv2 as cv
import numpy as np
import argparse

def transform(origin):
    h, w = origin.shape
    generate_img = np.zeros(origin.shape)
    for i in range(h):
        for j in range(w):
            generate_img[i, w - 1 - j] = origin[i, j]
    cv.imwrite('D:/lena_in_scene.jpg', generate_img.astype(np.uint8))
    return 0

parser = argparse.ArgumentParser(description='Code for Feature Matching with FLANN tutorial.')
parser.add_argument('--input1', help='Path to input image 1.', default='D:/lena.jpg')
parser.add_argument('--input2', help='Path to input image 2.', default='D:/lena_in_scene.jpg')
args = parser.parse_args()
img1 = cv.imread(args.input1, cv.IMREAD_GRAYSCALE)
transform(img1)
img2 = cv.imread(args.input2, cv.IMREAD_GRAYSCALE)
if img1 is None or img2 is None:
    print('Could not open or find the images!')
    exit(0)
#-- Step 1: Detect the keypoints using SURF Detector, compute the descriptors
minHessian = 300
detector = cv.xfeatures2d_SURF.create(hessianThreshold=minHessian)
keypoints1, descriptors1 = detector.detectAndCompute(img1, None)
keypoints2, descriptors2 = detector.detectAndCompute(img2, None)
#-- Step 2: Matching descriptor vectors with a FLANN based matcher
# Since SURF is a floating-point descriptor NORM_L2 is used
matcher = cv.DescriptorMatcher_create(cv.DescriptorMatcher_FLANNBASED)
knn_matches = matcher.knnMatch(descriptors1, descriptors2, 2)
#-- Filter matches using the Lowe's ratio test
ratio_thresh = 0.7
good_matches = []
for m,n in knn_matches:
    if m.distance < ratio_thresh * n.distance:
        good_matches.append(m)
#-- Draw matches
img_matches = np.empty((max(img1.shape[0], img2.shape[0]), img1.shape[1]+img2.shape[1], 3), dtype=np.uint8)
cv.drawMatches(img1, keypoints1, img2, keypoints2, good_matches, img_matches, flags=cv.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
#-- Show detected matches
cv.imshow('Good Matches', img_matches)
cv.waitKey()

 图片匹配

from __future__ import print_function
import cv2 as cv
import numpy as np
import argparse

img_object = cv.imread('D:/box.png', cv.IMREAD_GRAYSCALE)
img_scene = cv.imread('D:/box_in_scene.png', cv.IMREAD_GRAYSCALE)
if img_object is None or img_scene is None:
    print('Could not open or find the images!')
    exit(0)
#-- Step 1: Detect the keypoints using SURF Detector, compute the descriptors
minHessian = 300
detector = cv.xfeatures2d_SURF.create(hessianThreshold=minHessian)
keypoints_obj, descriptors_obj = detector.detectAndCompute(img_object, None)
keypoints_scene, descriptors_scene = detector.detectAndCompute(img_scene, None)
#-- Step 2: Matching descriptor vectors with a FLANN based matcher
# Since SURF is a floating-point descriptor NORM_L2 is used
matcher = cv.DescriptorMatcher_create(cv.DescriptorMatcher_FLANNBASED)
knn_matches = matcher.knnMatch(descriptors_obj, descriptors_scene, 2)
#-- Filter matches using the Lowe's ratio test
ratio_thresh = 0.75
good_matches = []
for m,n in knn_matches:
    if m.distance < ratio_thresh * n.distance:
        good_matches.append(m)
#-- Draw matches
img_matches = np.empty((max(img_object.shape[0], img_scene.shape[0]), img_object.shape[1]+img_scene.shape[1], 3), dtype=np.uint8)
cv.drawMatches(img_object, keypoints_obj, img_scene, keypoints_scene, good_matches, img_matches, flags=cv.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
#-- Localize the object
obj = np.empty((len(good_matches),2), dtype=np.float32)
scene = np.empty((len(good_matches),2), dtype=np.float32)
for i in range(len(good_matches)):
    #-- Get the keypoints from the good matches
    obj[i,0] = keypoints_obj[good_matches[i].queryIdx].pt[0]
    obj[i,1] = keypoints_obj[good_matches[i].queryIdx].pt[1]
    scene[i,0] = keypoints_scene[good_matches[i].trainIdx].pt[0]
    scene[i,1] = keypoints_scene[good_matches[i].trainIdx].pt[1]
H, _ =  cv.findHomography(obj, scene, cv.RANSAC)
#-- Get the corners from the image_1 ( the object to be "detected" )
obj_corners = np.empty((4,1,2), dtype=np.float32)
obj_corners[0,0,0] = 0
obj_corners[0,0,1] = 0
obj_corners[1,0,0] = img_object.shape[1]
obj_corners[1,0,1] = 0
obj_corners[2,0,0] = img_object.shape[1]
obj_corners[2,0,1] = img_object.shape[0]
obj_corners[3,0,0] = 0
obj_corners[3,0,1] = img_object.shape[0]
print(H)
scene_corners = cv.perspectiveTransform(obj_corners, H)

#-- Draw lines between the corners (the mapped object in the scene - image_2 )
cv.line(img_matches, (int(scene_corners[0,0,0] + img_object.shape[1]), int(scene_corners[0,0,1])),\
    (int(scene_corners[1,0,0] + img_object.shape[1]), int(scene_corners[1,0,1])), (0,255,0), 4)
cv.line(img_matches, (int(scene_corners[1,0,0] + img_object.shape[1]), int(scene_corners[1,0,1])),\
    (int(scene_corners[2,0,0] + img_object.shape[1]), int(scene_corners[2,0,1])), (0,255,0), 4)
cv.line(img_matches, (int(scene_corners[2,0,0] + img_object.shape[1]), int(scene_corners[2,0,1])),\
    (int(scene_corners[3,0,0] + img_object.shape[1]), int(scene_corners[3,0,1])), (0,255,0), 4)
cv.line(img_matches, (int(scene_corners[3,0,0] + img_object.shape[1]), int(scene_corners[3,0,1])),\
    (int(scene_corners[0,0,0] + img_object.shape[1]), int(scene_corners[0,0,1])), (0,255,0), 4)
#-- Show detected matches
cv.imshow('Good Matches & Object detection', img_matches)
cv.imwrite('D:/boxx.png',img_matches)
cv.waitKey()

透视变换矩阵：

[[ 5.75824358e-01 -4.35665519e-01  3.83253304e+02]
 [ 4.86504147e-01  5.86534791e-01  1.53078867e+02]
 [ 7.79314893e-05  5.76010867e-05  1.00000000e+00]]

 harris

from __future__ import print_function
import cv2 as cv
import numpy as np
import argparse
source_window = 'Source image'
corners_window = 'Corners detected'
max_thresh = 255
def cornerHarris_demo(val):
    thresh = val
    # Detector parameters
    blockSize = 2
    apertureSize = 3
    k = 0.04
    # Detecting corners
    dst = cv.cornerHarris(src_gray, blockSize, apertureSize, k)
    # Normalizing
    dst_norm = np.empty(dst.shape, dtype=np.float32)
    cv.normalize(dst, dst_norm, alpha=0, beta=255, norm_type=cv.NORM_MINMAX)
    dst_norm_scaled = cv.convertScaleAbs(dst_norm)
    # Drawing a circle around corners
    for i in range(dst_norm.shape[0]):
        for j in range(dst_norm.shape[1]):
            if int(dst_norm[i,j]) > thresh:
                cv.circle(dst_norm_scaled, (j,i), 5, (0,255,128), 2)
    # Showing the result
    cv.namedWindow(corners_window)
    cv.imshow(corners_window, dst_norm_scaled)
# Load source image and convert it to gray
parser = argparse.ArgumentParser(description='Code for Harris corner detector tutorial.')
parser.add_argument('--input', help='Path to input image.', default='D:/box.png')
args = parser.parse_args()
src = cv.imread(args.input)
if src is None:
    print('Could not open or find the image:', args.input)
    exit(0)
src_gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
# Create a window and a trackbar
cv.namedWindow(source_window)
thresh = 200 # initial threshold
cv.createTrackbar('Threshold: ', source_window, thresh, max_thresh, cornerHarris_demo)
cv.imshow(source_window, src)
cornerHarris_demo(thresh)
cv.waitKey()