python+OpenCV 特征点检测

# -*- coding: utf-8 -*-
from pylab import *
from PIL import Image
from numpy import *
from scipy.ndimage import filters

print 'hello'

def compute_harris_response(im,sigma=3):
    """ Compute the Harris corner detector response function 
        for each pixel in a graylevel image. """

    # derivatives
    imx = zeros(im.shape)
    filters.gaussian_filter(im, (sigma,sigma), (0,1), imx)
    imy = zeros(im.shape)
    filters.gaussian_filter(im, (sigma,sigma), (1,0), imy)

    # compute components of the Harris matrix
    Wxx = filters.gaussian_filter(imx*imx,sigma)
    Wxy = filters.gaussian_filter(imx*imy,sigma)
    Wyy = filters.gaussian_filter(imy*imy,sigma)

    # determinant and trace
    Wdet = Wxx*Wyy - Wxy**2
    Wtr = Wxx + Wyy

    return Wdet / Wtr


def get_harris_points(harrisim,min_dist=10,threshold=0.1):
    """ Return corners from a Harris response image
        min_dist is the minimum number of pixels separating 
        corners and image boundary. """

    # find top corner candidates above a threshold
    corner_threshold = harrisim.max() * threshold
    harrisim_t = (harrisim > corner_threshold) * 1

    # get coordinates of candidates
    coords = array(harrisim_t.nonzero()).T

    # ...and their values
    candidate_values = [harrisim[c[0],c[1]] for c in coords]

    # sort candidates (reverse to get descending order)
    index = argsort(candidate_values)[::-1]

    # store allowed point locations in array
    allowed_locations = zeros(harrisim.shape)
    allowed_locations[min_dist:-min_dist,min_dist:-min_dist] = 1

    # select the best points taking min_distance into account
    filtered_coords = []
    for i in index:
        if allowed_locations[coords[i,0],coords[i,1]] == 1:
            filtered_coords.append(coords[i])
            allowed_locations[(coords[i,0]-min_dist):(coords[i,0]+min_dist), 
                        (coords[i,1]-min_dist):(coords[i,1]+min_dist)] = 0

    return filtered_coords


def plot_harris_points(image,filtered_coords):
    """ Plots corners found in image. """

    figure()
    gray()
    imshow(image)
    plot([p[1] for p in filtered_coords],
                [p[0] for p in filtered_coords],'*')
    axis('off')
    show()


def get_descriptors(image,filtered_coords,wid=5):
    """ For each point return pixel values around the point
        using a neighbourhood of width 2*wid+1. (Assume points are 
        extracted with min_distance > wid). """

    desc = []
    for coords in filtered_coords:
        patch = image[coords[0]-wid:coords[0]+wid+1,
                            coords[1]-wid:coords[1]+wid+1].flatten()
        desc.append(patch)

    return desc


def match(desc1,desc2,threshold=0.5):
    """ For each corner point descriptor in the first image, 
        select its match to second image using
        normalized cross correlation. """

    n = len(desc1[0])

    # pair-wise distances
    d = -ones((len(desc1),len(desc2)))
    for i in range(len(desc1)):
        for j in range(len(desc2)):
            d1 = (desc1[i] - mean(desc1[i])) / std(desc1[i])
            d2 = (desc2[j] - mean(desc2[j])) / std(desc2[j])
            ncc_value = sum(d1 * d2) / (n-1) 
            if ncc_value > threshold:
                d[i,j] = ncc_value

    ndx = argsort(-d)
    matchscores = ndx[:,0]

    return matchscores


def match_twosided(desc1,desc2,threshold=0.5):
    """ Two-sided symmetric version of match(). """

    matches_12 = match(desc1,desc2,threshold)
    matches_21 = match(desc2,desc1,threshold)

    ndx_12 = where(matches_12 >= 0)[0]

    # remove matches that are not symmetric
    for n in ndx_12:
        if matches_21[matches_12[n]] != n:
            matches_12[n] = -1

    return matches_12


def appendimages(im1,im2):
    """ Return a new image that appends the two images side-by-side. """

    # select the image with the fewest rows and fill in enough empty rows
    rows1 = im1.shape[0]    
    rows2 = im2.shape[0]

    if rows1 < rows2:
        im1 = concatenate((im1,zeros((rows2-rows1,im1.shape[1]))),axis=0)
    elif rows1 > rows2:
        im2 = concatenate((im2,zeros((rows1-rows2,im2.shape[1]))),axis=0)
    # if none of these cases they are equal, no filling needed.

    return concatenate((im1,im2), axis=1)


def plot_matches(im1,im2,locs1,locs2,matchscores,show_below=True):
    """ Show a figure with lines joining the accepted matches 
        input: im1,im2 (images as arrays), locs1,locs2 (feature locations), 
        matchscores (as output from 'match()'), 
        show_below (if images should be shown below matches). """

    im3 = appendimages(im1,im2)
    if show_below:
        im3 = vstack((im3,im3))

    imshow(im3)

    cols1 = im1.shape[1]
    for i,m in enumerate(matchscores):
        if m>0:
            plot([locs1[i][1],locs2[m][1]+cols1],[locs1[i][0],locs2[m][0]],'c')
    axis('off')

def imresize(im,sz):
    """    Resize an image array using PIL. """
    pil_im = Image.fromarray(uint8(im))

    return array(pil_im.resize(sz))
"""
Example of detecting Harris corner points (Figure 2-1 in the book).
"""

# 读入图像
im = array(Image.open('swan.jpg').convert('L'))

# 检测harris角点
harrisim = compute_harris_response(im)

# Harris响应函数
harrisim1 = 255 - harrisim

figure()
gray()

#画出Harris响应图
subplot(141)
imshow(harrisim1)
print harrisim1.shape
axis('off')
axis('equal')


threshold = [0.01, 0.05, 0.1]
for i, thres in enumerate(threshold):
    filtered_coords = get_harris_points(harrisim, 6, thres)
    subplot(1, 4, i+2)
    imshow(im)
    print im.shape
    plot([p[1] for p in filtered_coords], [p[0] for p in filtered_coords], '*')
    axis('off')

#原书采用的PCV中PCV harris模块
#harris.plot_harris_points(im, filtered_coords)

# plot only 200 strongest
# harris.plot_harris_points(im, filtered_coords[:200])


# Figure 2-2下面的图
im1 = array(Image.open("swan.jpg").convert("L"))
im2 = array(Image.open("swan.jpg").convert("L"))
# resize to make matching faster
im1 = imresize(im1, (im1.shape[1]/2, im1.shape[0]/2))
im2 = imresize(im2, (im2.shape[1]/2, im2.shape[0]/2))

wid = 5
harrisim = compute_harris_response(im1, 5)
filtered_coords1 = get_harris_points(harrisim, wid+1)
d1 = get_descriptors(im1, filtered_coords1, wid)

harrisim = compute_harris_response(im2, 5)
filtered_coords2 = get_harris_points(harrisim, wid+1)
d2 = get_descriptors(im2, filtered_coords2, wid)

print 'starting matching'
matches = match_twosided(d1, d2)

figure()
gray() 
plot_matches(im1, im2, filtered_coords1, filtered_coords2, matches)
show()

# -*- coding: utf-8 -*-
"""
Created on Sat Jun 11 23:21:18 2016

@author: season
"""

import cv2
import numpy as np


filename = 'swan.jpg'
img = cv2.imread(filename)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

gray = np.float32(gray)
dst = cv2.cornerHarris(gray,2,3,0.04)

#result is dilated for marking the corners, not important
dst = cv2.dilate(dst,None)

# Threshold for an optimal value, it may vary depending on the image.
img[dst>0.01*dst.max()]=[0,0,255]

cv2.imshow('dst',img)
if cv2.waitKey(0) & 0xff == 27:
    cv2.destroyAllWindows()

#test cv2 and numpy package
print cv2.__version__
a = np.arange(10)
print(a)

# -*- coding: utf-8 -*-
"""
Created on Sat Jun 11 20:22:51 2016

@author: season
"""

import cv2

import numpy as np
#import pdb
#pdb.set_trace()#turn on the pdb prompt

#test cv2 and numpy package
print cv2.__version__
a = np.arange(10)
print(a)

img = cv2.imread('swan.jpg')
gray= cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

sift = cv2.SIFT()
kp = sift.detect(gray,None)

img=cv2.drawKeypoints(gray,kp)

cv2.imwrite('sift_keypoints.jpg',img)
cv2.imshow("sift_keypoint",img)
cv2.waitKey(0)
cv2.destroyAllWindows()

# -*- coding: utf-8 -*-
"""
Created on Wed Jun 15 22:05:44 2016

@author: Administrator
"""
import cv2


import numpy

opencv_haystack =cv2.imread('woman.jpg')
opencv_needle =cv2.imread('face.jpg')

ngrey = cv2.cvtColor(opencv_needle, cv2.COLOR_BGR2GRAY)
hgrey = cv2.cvtColor(opencv_haystack, cv2.COLOR_BGR2GRAY)

# build feature detector and descriptor extractor
hessian_threshold = 85
detector = cv2.SURF(hessian_threshold)
(hkeypoints, hdescriptors) = detector.detect(hgrey, None, useProvidedKeypoints = False)
(nkeypoints, ndescriptors) = detector.detect(ngrey, None, useProvidedKeypoints = False)

# extract vectors of size 64 from raw descriptors numpy arrays
rowsize = len(hdescriptors) / len(hkeypoints)
if rowsize > 1:
    hrows = numpy.array(hdescriptors, dtype = numpy.float32).reshape((-1, rowsize))
    nrows = numpy.array(ndescriptors, dtype = numpy.float32).reshape((-1, rowsize))
    #print hrows.shape, nrows.shape
else:
    hrows = numpy.array(hdescriptors, dtype = numpy.float32)
    nrows = numpy.array(ndescriptors, dtype = numpy.float32)
    rowsize = len(hrows[0])

# kNN training - learn mapping from hrow to hkeypoints index
samples = hrows
responses = numpy.arange(len(hkeypoints), dtype = numpy.float32)
#print len(samples), len(responses)
knn = cv2.KNearest()
knn.train(samples,responses)

# retrieve index and value through enumeration
count = 1

for i, descriptor in enumerate(nrows):
    descriptor = numpy.array(descriptor, dtype = numpy.float32).reshape((1, rowsize))
    #print i, descriptor.shape, samples[0].shape
    retval, results, neigh_resp, dists = knn.find_nearest(descriptor, 1)
    res, dist =  int(results[0][0]), dists[0][0]
    #print res, dist

    if dist < 0.1:
        count = count+1
        # draw matched keypoints in red color
        color = (0, 0, 255)
#    else:
#        # draw unmatched in blue color
#        color = (255, 0, 0)
    # draw matched key points on haystack image
        x,y = hkeypoints[res].pt
        center = (int(x),int(y))
        cv2.circle(opencv_haystack,center,2,color,-1)
        # draw matched key points on needle image
        x,y = nkeypoints[i].pt
        center = (int(x),int(y))
        cv2.circle(opencv_needle,center,2,color,-1)


cv2.imshow("Input Image", opencv_haystack)
cv2.waitKey(0)
cv2.imshow("The match Result", opencv_needle)
cv2.waitKey(0)

print count
if count>40:
    print "Yes Success!"
else:
    print "False Face!"
#cv2.waitKey(0)
#cv2.destroyAllWindows()

# -*- coding: utf-8 -*-
"""
Created on Thu Jun 16 11:11:18 2016

@author: Administrator
"""

import numpy as np
import cv2
#from matplotlib import pyplot as plt
print cv2.__version__


img1 = cv2.imread('woman.jpg',0)          # queryImage
img2 = cv2.imread('face.jpg',0) # trainImage
def drawMatches(img1, kp1, img2, kp2, matches):
    """
    My own implementation of cv2.drawMatches as OpenCV 2.4.9
    does not have this function available but it's supported in
    OpenCV 3.0.0

    This function takes in two images with their associated 
    keypoints, as well as a list of DMatch data structure (matches) 
    that contains which keypoints matched in which images.

    An image will be produced where a montage is shown with
    the first image followed by the second image beside it.

    Keypoints are delineated with circles, while lines are connected
    between matching keypoints.

    img1,img2 - Grayscale images
    kp1,kp2 - Detected list of keypoints through any of the OpenCV keypoint 
              detection algorithms
    matches - A list of matches of corresponding keypoints through any
              OpenCV keypoint matching algorithm
    """

    # Create a new output image that concatenates the two images together
    # (a.k.a) a montage
    rows1 = img1.shape[0]
    cols1 = img1.shape[1]
    rows2 = img2.shape[0]
    cols2 = img2.shape[1]

    out = np.zeros((max([rows1,rows2]),cols1+cols2,3), dtype='uint8')

    # Place the first image to the left
    out[:rows1,:cols1] = np.dstack([img1, img1, img1])

    # Place the next image to the right of it
    out[:rows2,cols1:] = np.dstack([img2, img2, img2])

    # For each pair of points we have between both images
    # draw circles, then connect a line between them
    for mat in matches:

        # Get the matching keypoints for each of the images
        img1_idx = mat.queryIdx
        img2_idx = mat.trainIdx

        # x - columns
        # y - rows
        (x1,y1) = kp1[img1_idx].pt
        (x2,y2) = kp2[img2_idx].pt

        # Draw a small circle at both co-ordinates
        # radius 4
        # colour blue
        # thickness = 1
        cv2.circle(out, (int(x1),int(y1)), 4, (255, 0, 0), 1)   
        cv2.circle(out, (int(x2)+cols1,int(y2)), 4, (255, 0, 0), 1)

        # Draw a line in between the two points
        # thickness = 1
        # colour blue
        cv2.line(out, (int(x1),int(y1)), (int(x2)+cols1,int(y2)), (255, 0, 0), 1)


    # Show the image
    cv2.imshow('Matched Features', out)
    cv2.waitKey(0)
    cv2.destroyWindow('Matched Features')

    # Also return the image if you'd like a copy
    return out

# Initiate SIFT detector
orb = cv2.ORB()

# find the keypoints and descriptors with SIFT
kp1, des1 = orb.detectAndCompute(img1,None)
kp2, des2 = orb.detectAndCompute(img2,None)
# create BFMatcher object
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)

# Match descriptors.
matches = bf.match(des1,des2)

# Sort them in the order of their distance.
matches = sorted(matches, key = lambda x:x.distance)

# Draw first 10 matches.
img3 = drawMatches(img1,kp1,img2,kp2,matches[:10])

cv2.imshow('dst',img3)
if cv2.waitKey(0) & 0xff == 27:
    cv2.destroyAllWindows()
#plt.imshow(img3),plt.show()


'''
draw match 函数在下面的链接中有自己的实现，我直接复制过来了

http://stackoverflow.com/questions/20259025/module-object-has-no-attribute-drawmatches-opencv-python
'''