def get_matching_pts_sift(im_path_1, im_path_2):
"""
Gets the matching points in the two images, using SIFT features.
"""
# Process and save features to file
params = "--edge-thresh 10 --peak-thresh 5 --verbose"
sift.process_image(im_path_1, im_path_1 + '.sift', params=params)
sift.process_image(im_path_2, im_path_2 + '.sift', params=params)
# Read features from the two images.
l1, d1 = sift.read_features_from_file(im_path_1 + '.sift')
l2, d2 = sift.read_features_from_file(im_path_2 + '.sift')
# matchscores will have an entry for each feature in im1.
# The entry will be 0 if there is not a match.
# If there is a match, the entry will be the index of the matching feature in im2.
matchscores = sift.match_twosided(d1, d2)
pts1, pts2 = get_matches(l1, l2, matchscores)
return pts1, pts2
This is the example graph illustration of matching images from Figure 2-10.
To download the images, see ch2_download_panoramio.py.
"""
download_path = "panoimages" # set this to the path where you downloaded the panoramio images
path = "C:\Users\SW\PycharmProjects\geotag\panoimages" # path to save thumbnails (pydot needs the full system path)
# list of downloaded filenames
imlist = imtools.get_imlist(download_path)
nbr_images = len(imlist)
# extract features
featlist = [imname[:-3] + 'sift' for imname in imlist]
for i, imname in enumerate(imlist):
if os.path.isfile(imname) == False: # File exist test
sift.process_image(imname, featlist[i])
matchscores = zeros((nbr_images, nbr_images))
for i in range(nbr_images):
for j in range(i, nbr_images): # only compute upper triangle
print 'comparing ', imlist[i], imlist[j]
l1, d1 = sift.read_features_from_file(featlist[i])
l2, d2 = sift.read_features_from_file(featlist[j])
matches = sift.match_twosided(d1, d2)
nbr_matches = sum(matches > 0)
print 'number of matches = ', nbr_matches
matchscores[i, j] = nbr_matches
# copy values
for i in range(nbr_images):
from PIL import Image
from numpy import *
from pylab import *
import numpy as np
from PCV.geometry import homography, camera,sfm
from PCV.localdescriptors import sift
import importlib
camera = importlib.reload(camera)
homography = importlib.reload(homography)
sfm = importlib.reload(sfm)
sift = importlib.reload(sift)
# 提取特征，注意读取图片的顺序！
im1 = array(Image.open('D:/study/machine_learning/images/yosemite2.jpg'))
sift.process_image('D:/study/machine_learning/images/yosemite2.jpg', 'im1.sift')
im2 = array(Image.open('D:/study/machine_learning/images/yosemite1.jpg'))
sift.process_image('D:/study/machine_learning/images/yosemite1.jpg', 'im2.sift')
l1, d1 = sift.read_features_from_file('im1.sift')
l2, d2 = sift.read_features_from_file('im2.sift')
matches = sift.match_twosided(d1, d2)
ndx = matches.nonzero()[0]
x1 = homography.make_homog(l1[ndx, :2].T)#将点集转化为齐次坐标表示
ndx2 = [int(matches[i]) for i in ndx]
x2 = homography.make_homog(l2[ndx2, :2].T)#将点集转化为齐次坐标表示
d1n = d1[ndx]
# -*- coding: utf-8 -*-
from PIL import Image
from pylab import *
from PCV.localdescriptors import sift
from PCV.localdescriptors import harris
# 添加中文字体支持
from matplotlib.font_manager import FontProperties
font = FontProperties(fname=r"c:\windows\fonts\SimSun.ttc", size=14)
imname = '../data/empire.jpg'
im = array(Image.open(imname).convert('L'))
sift.process_image(imname, 'empire.sift')
l1, d1 = sift.read_features_from_file('empire.sift')
figure()
gray()
subplot(131)
sift.plot_features(im, l1, circle=False)
title(u'SIFT特征',fontproperties=font)
subplot(132)
sift.plot_features(im, l1, circle=True)
title(u'用圆圈表示SIFT特征尺度',fontproperties=font)
# 检测harris角点
harrisim = harris.compute_harris_response(im)
subplot(133)
filtered_coords = harris.get_harris_points(harrisim, 6, 0.1)
imshow(im)
plot([p[1] for p in filtered_coords], [p[0] for p in filtered_coords], '*')
# If you have PCV installed, these imports should work
from PCV.geometry import homography, warp
from PCV.localdescriptors import sift
"""
This is the panorama example from section 3.3.
"""
# set paths to data folder
featname = ['../data/Univ' + str(i + 1) + '.sift' for i in range(5)]
imname = ['../data/Univ' + str(i + 1) + '.jpg' for i in range(5)]
# extract features and match
l = {}
d = {}
for i in range(5):
sift.process_image(imname[i], featname[i])
l[i], d[i] = sift.read_features_from_file(featname[i])
matches = {}
for i in range(4):
matches[i] = sift.match(d[i + 1], d[i])
# visualize the matches (Figure 3-11 in the book)
for i in range(4):
im1 = array(Image.open(imname[i]))
im2 = array(Image.open(imname[i + 1]))
figure()
sift.plot_matches(im2, im1, l[i + 1], l[i], matches[i], show_below=True)
# function to convert the matches to hom. points
def process_image(filename):
sift_filename = get_sift_filename(filename)
if not os.path.exists(sift_filename):
sift.process_image(filename, sift_filename)
return sift_filename
"""
This is the example graph illustration of matching images from Figure 2-10.
To download the images, see ch2_download_panoramio.py.
"""
download_path = "panoimages" # set this to the path where you downloaded the panoramio images
path = "/FULLPATH/panoimages/" # path to save thumbnails (pydot needs the full system path)
# list of downloaded filenames
imlist = imtools.get_imlist(download_path)
nbr_images = len(imlist)
# extract features
featlist = [imname[:-3] + 'sift' for imname in imlist]
for i, imname in enumerate(imlist):
sift.process_image(imname, featlist[i])
matchscores = zeros((nbr_images, nbr_images))
for i in range(nbr_images):
for j in range(i, nbr_images): # only compute upper triangle
print 'comparing ', imlist[i], imlist[j]
l1, d1 = sift.read_features_from_file(featlist[i])
l2, d2 = sift.read_features_from_file(featlist[j])
matches = sift.match_twosided(d1, d2)
nbr_matches = sum(matches > 0)
print 'number of matches = ', nbr_matches
matchscores[i, j] = nbr_matches
# copy values
for i in range(nbr_images):
def my_calibration(sz):
"""
Calibration function for the camera (iPhone4) used in this example.
"""
row, col = sz
fx = 2555 * col / 2592
fy = 2586 * row / 1936
K = diag([fx, fy, 1])
K[0, 2] = 0.5 * col
K[1, 2] = 0.5 * row
return K
# compute features
sift.process_image('../data/book_frontal.JPG', 'im0.sift')
l0, d0 = sift.read_features_from_file('im0.sift')
sift.process_image('../data/book_perspective.JPG', 'im1.sift')
l1, d1 = sift.read_features_from_file('im1.sift')
# match features and estimate homography
matches = sift.match_twosided(d0, d1)
ndx = matches.nonzero()[0]
fp = homography.make_homog(l0[ndx, :2].T)
ndx2 = [int(matches[i]) for i in ndx]
tp = homography.make_homog(l1[ndx2, :2].T)
model = homography.RansacModel()
H, inliers = homography.H_from_ransac(fp, tp, model)
from pylab import * from numpy import * from PIL import Image from PCV.localdescriptors import sift """ This is the twosided SIFT feature matching example from Section 2.2 (p 44). """ imname1 = 'data/climbing_1_small.jpg' imname2 = 'data/climbing_2_small.jpg' # process and save features to file sift.process_image(imname1, imname1 + '.sift') sift.process_image(imname2, imname2 + '.sift') # read features and match l1, d1 = sift.read_features_from_file(imname1 + '.sift') l2, d2 = sift.read_features_from_file(imname2 + '.sift') matchscores = sift.match_twosided(d1, d2) # load images and plot im1 = array(Image.open(imname1)) im2 = array(Image.open(imname2)) sift.plot_matches(im1, im2, l1, l2, matchscores, show_below=True) show()
'''
from PIL import Image
from pylab import *
import sfm
from PCV.geometry import camera, homography
from PCV.localdescriptors import sift
import tic
# calibration
K = array([[2394,0,932],[0,2398,628],[0,0,1]])
tic.k('start')
# load images and compute featuers
im1 = array(Image.open('./images/salcatraz1.jpg'))
sift.process_image('./images/salcatraz1.jpg','./images/salcatraz1.sift')
l1,d1 = sift.read_features_from_file('./images/salcatraz1.sift')
im2 = array(Image.open('./images/salcatraz2.jpg'))
sift.process_image('./images/salcatraz2.jpg','./images/salcatraz2.sift')
l2,d2 = sift.read_features_from_file('./images/salcatraz2.sift')
tic.k('loadd sifts')
print '{} / {} features'.format(len(d1), len(d2))
# match features
matches = sift.match_twosided(d1,d2)
ndx = matches.nonzero()[0]
tic.k('matched')
from PCV.geometry import homography, sfm
from PCV.localdescriptors import sift
import numpy as np
from PIL import Image
from pylab import *
K = np.array([[2394, 0, 932], [0, 2398, 628], [0, 0, 1]])
imname = '../data/alcatraz1.jpg'
im2name = '../data/alcatraz2.jpg'
im1 = np.array(Image.open(imname))
sift.process_image(imname, 'im1.sift')
l1, d1 = sift.read_features_from_file('im1.sift')
im2 = np.array(Image.open(im2name))
sift.process_image(im2name, 'im2.sift')
l2, d2 = sift.read_features_from_file('im2.sift')
matches = sift.match_twosided(d1, d2)
ndx = matches.nonzero()[0]
x1 = homography.make_homog(l1[ndx, :2].T)
ndx2 = [int(matches[i]) for i in ndx]
x2 = homography.make_homog(l2[ndx2, :2].T)
x1n = np.dot(np.linalg.inv(K), x1)
x2n = np.dot(np.linalg.inv(K), x2)
model = sfm.RansacModel()
E, inliners = sfm.F_from_ransac(x1n, x2n, model)
# -*- coding: utf-8 -*-
from PIL import Image
from pylab import *
from PCV.localdescriptors import sift
from PCV.localdescriptors import harris
# 添加中文字体支持
from matplotlib.font_manager import FontProperties
font = FontProperties(fname=r"c:\windows\fonts\SimSun.ttc", size=14)
imname = '../data/empire.jpg'
im = array(Image.open(imname).convert('L'))
sift.process_image(imname, 'empire.sift')
l1, d1 = sift.read_features_from_file('empire.sift')
figure()
gray()
subplot(131)
sift.plot_features(im, l1, circle=False)
title(u'SIFT特征', fontproperties=font)
subplot(132)
sift.plot_features(im, l1, circle=True)
title(u'用圆圈表示SIFT特征尺度', fontproperties=font)
# 检测harris角点
harrisim = harris.compute_harris_response(im)
subplot(133)
filtered_coords = harris.get_harris_points(harrisim, 6, 0.1)
imshow(im)
plot([p[1] for p in filtered_coords], [p[0] for p in filtered_coords], '*')
# compute features
# sift.process_image('./data/simage10.jpg','./data/simage10.sift')
# l0,d0 = sift.read_features_from_file('./data/simage10.sift')
#
# sift.process_image('./data/simage11.jpg','./data/simage11.sift')
# l1,d1 = sift.read_features_from_file('./data/simage11.sift')
# sift.process_image('./data/image1.JPG', './data/im2.sift')
# l0,d0 = sift.read_features_from_file('./data/im2.sift')
#
# sift.process_image('./data/image2.JPG', './data/im3.sift')
# l1,d1 = sift.read_features_from_file('./data/im3.sift')
sift.process_image('./data/s20160720_113416.JPG', './data/im2.sift')
l0,d0 = sift.read_features_from_file('./data/im2.sift')
sift.process_image('./data/s20160720_113436.JPG', './data/im3.sift')
l1,d1 = sift.read_features_from_file('./data/im3.sift')
# match features and estimate homography
matches = sift.match_twosided(d0,d1)
# im0 = array(Image.open('./data/book_frontal.JPG'))
# im1 = array(Image.open('./data/book_perspective.JPG'))
# im0 = array(Image.open('./data/simage10.JPG'))
# im1 = array(Image.open('./data/simage11.JPG'))
# figure()
# sift.plot_matches(im0,im1,l0,l1, matches, show_below=True)
# show()
from PIL import Image
from PCV.localdescriptors import sift
"""
This is the twosided SIFT feature matching example from Section 2.2 (p 44).
"""
imname1 = '../data/climbing_1_small.jpg'
imname2 = '../data/climbing_2_small.jpg'
# process and save features to file
#sift.process_image(imname1, 'climbing_1_small.sift')
#sift.process_image(imname2, 'climbing_2_small.sift')
sift.process_image(imname1, imname1+'.sift')
sift.process_image(imname2, imname2+'.sift')
# read features and match
l1, d1 = sift.read_features_from_file('climbing_1_small.sift')
l2, d2 = sift.read_features_from_file('climbing_2_small.sift')
#matchscores = sift.match(d1, d2)
matchscores = sift.match_twosided(d1, d2)
# load images and plot
im1 = array(Image.open(imname1))
im2 = array(Image.open(imname2))
sift.plot_matches(im1, im2, l1, l2, matchscores, show_below=True)
show()
from PCV.localdescriptors import sift
if len(sys.argv) >= 3:
im1f, im2f = sys.argv[1], sys.argv[2]
else:
im1f = r'e:\Study\pythonxyProject\sift\images/02.jpg'
im2f = r'e:\Study\pythonxyProject\sift\images/04.jpg'
# im1f = '../data/crans_1_small.jpg'
# im2f = '../data/crans_2_small.jpg'
# im1f = '../data/climbing_1_small.jpg'
# im2f = '../data/climbing_2_small.jpg'
im1 = array(Image.open(im1f))
im2 = array(Image.open(im2f))
sift.process_image(im1f, 'out_sift_1.txt')
l1, d1 = sift.read_features_from_file('out_sift_1.txt')
figure()
gray()
subplot(121)
sift.plot_features(im1, l1, circle=False)
sift.process_image(im2f, 'out_sift_2.txt')
l2, d2 = sift.read_features_from_file('out_sift_2.txt')
subplot(122)
sift.plot_features(im2, l2, circle=False)
matches = sift.match(d1, d2)
matches = sift.match_twosided(d1, d2)
print '{} matches'.format(len(matches.nonzero()[0]))
import glob
import os
from PCV.localdescriptors import sift
for f in glob.glob('out/*.jpg'):
base = os.path.splitext(os.path.basename(f))[0]
siftpath = os.path.join('out', base + '.sift')
if os.path.exists(siftpath):
continue
print 'processing', f
sift.process_image(f, siftpath)
from pylab import *
from PIL import Image
from PCV.localdescriptors import sift
"""
This is the twosided SIFT feature matching example from Section 2.2 (p 44).
"""
imname1 = '../data/climbing_1_small.jpg'
imname2 = '../data/climbing_2_small.jpg'
# process and save features to file
sift.process_image(imname1, './climbing_1_small.sift')
sift.process_image(imname2, './climbing_2_small.sift')
# sift.process_image(imname1, imname1+'.sift')
# sift.process_image(imname2, imname2+'.sift')
# read features and match
l1, d1 = sift.read_features_from_file('./climbing_1_small.sift')
l2, d2 = sift.read_features_from_file('./climbing_2_small.sift')
#matchscores = sift.match(d1, d2)
matchscores = sift.match_twosided(d1, d2)
# load images and plot
im1 = array(Image.open(imname1))
im2 = array(Image.open(imname2))
sift.plot_matches(im1, im2, l1, l2, matchscores, show_below=True)
show()
@author: liaoyuhua
'''
from PIL import Image
from numpy import *
from pylab import *
from scipy import *
from PCV.localdescriptors import sift
from PCV.geometry import homography,warp
if __name__ == '__main__':
featname = [r'D:\Python_Computer_Vision\data\Univ'+str(i+1)+'.sift' for i in range(5)]
imname = [r'D:\Python_Computer_Vision\data\Univ'+str(i+1)+'.png' for i in range(5)]
l = {}
d = {}
for i in range(5):
sift.process_image(imname[i],featname[i])
l[i],d[i] = sift.read_features_from_file(featname[i])
matches = {}
for i in range(4):
matches[i] = sift.match(d[i+1],d[i])
# function to convert the matches to hom. points
def convert_points(j):
ndx = matches[j].nonzero()[0]
print array([l[j+1][ndx,1],l[j+1][ndx,0]],'f')
fp = homography.make_homog(array([l[j+1][ndx,1],l[j+1][ndx,0]],'f'))
ndx2 = [int(matches[j][i]) for i in ndx]
tp = homography.make_homog(array([l[j][ndx2,1],l[j][ndx2,0]],'f'))
return fp,tp
# estimate the homographies
model = homography.RansacModel()
fp,tp = convert_points(1)
# -*- coding: utf-8 -*-
import pickle
from PCV.imagesearch import vocabulary
from PCV.tools.imtools import get_imlist
from PCV.localdescriptors import sift
#获取图像列表
imlist = get_imlist('./first500/')
nbr_images = len(imlist)
#获取特征列表
featlist = [imlist[i][:-3] + 'sift' for i in range(nbr_images)]
#提取文件夹下图像的sift特征
for i in range(nbr_images):
sift.process_image(imlist[i], featlist[i])
#生成词汇
voc = vocabulary.Vocabulary('ukbenchtest')
voc.train(featlist, 1000, 10)
#保存词汇
# saving vocabulary
with open('./first500/vocabulary.pkl', 'wb') as f:
pickle.dump(voc, f)
print('vocabulary is:', voc.name, voc.nbr_words)
# -*- coding: utf-8 -*-
import pickle
from PCV.imagesearch import vocabulary
from PCV.tools.imtools import get_imlist
from PCV.localdescriptors import sift
imlist = get_imlist('./first500/')
nbr_images = len(imlist)
featlist = [imlist[i][:-3]+'sift' for i in range(nbr_images)]
for i in range(nbr_images):
sift.process_image(imlist[i], featlist[i])
voc = vocabulary.Vocabulary('ukbench')
voc.train(featlist, 1000, 10)
# saving vocabulary
with open('./first500/vocabulary.pkl', 'wb') as f:
pickle.dump(voc, f)
print 'vocabulary is:', voc.name, voc.nbr_words
def my_calibration(sz):
"""
Calibration function for the camera (iPhone4) used in this example.
"""
row, col = sz
fx = 2555*col/2592
fy = 2586*row/1936
K = diag([fx, fy, 1])
K[0, 2] = 0.5*col
K[1, 2] = 0.5*row
return K
# compute features
sift.process_image('../data/book_frontal.JPG', 'im0.sift')
l0, d0 = sift.read_features_from_file('im0.sift')
sift.process_image('../data/book_perspective.JPG', 'im1.sift')
l1, d1 = sift.read_features_from_file('im1.sift')
# match features and estimate homography
matches = sift.match_twosided(d0, d1)
ndx = matches.nonzero()[0]
fp = homography.make_homog(l0[ndx, :2].T)
ndx2 = [int(matches[i]) for i in ndx]
tp = homography.make_homog(l1[ndx2, :2].T)
model = homography.RansacModel()
H, inliers = homography.H_from_ransac(fp, tp, model)
import glob
import os
from PCV.localdescriptors import sift
for f in glob.glob('out/*.jpg'):
base = os.path.splitext(os.path.basename(f))[0]
siftpath = os.path.join('out', base + '.sift')
if os.path.exists(siftpath):
continue
print('processing', f)
sift.process_image(f, siftpath)
from pylab import *
from PIL import Image
from PCV.localdescriptors import sift
"""
This is the twosided SIFT feature matching example from Section 2.2 (p 44).
"""
imname1 = '../data/climbing_1_small.jpg'
imname2 = '../data/climbing_2_small.jpg'
# process and save features to file
sift.process_image(imname1, 'climbing_1_small.sift')
sift.process_image(imname2, 'climbing_2_small.sift')
#sift.process_image(imname1, imname1+'.sift')
#sift.process_image(imname2, imname2+'.sift')
# read features and match
l1, d1 = sift.read_features_from_file('climbing_1_small.sift')
l2, d2 = sift.read_features_from_file('climbing_2_small.sift')
matchscores = sift.match_twosided(d1, d2)
# load images and plot
im1 = array(Image.open(imname1))
im2 = array(Image.open(imname2))
sift.plot_matches(im1, im2, l1, l2, matchscores, show_below=True)
show()
def process_image(filename):
sift_filename = get_sift_filename(filename)
if not os.path.exists(sift_filename):
sift.process_image(filename, sift_filename)
return sift_filename
import os
root = "D:\\homework\\homework\\house\\11\\"
"""
This is the panorama example from section 3.3.
"""
# set paths to data folder
featname = ['../data/wanren/uu' + str(i + 1) + '.sift' for i in range(5)]
imname = ['../data/wanren/uu' + str(i + 1) + '.jpg' for i in range(5)]
# extract features and match
l = {}
d = {}
for i in range(5):
sift.process_image(root + imname[i], root + featname[i])
l[i], d[i] = sift.read_features_from_file(featname[i])
matches = {}
for i in range(4):
matches[i] = sift.match(d[i + 1], d[i])
# visualize the matches (Figure 3-11 in the book)
for i in range(4):
im1 = array(Image.open(imname[i]))
im2 = array(Image.open(imname[i + 1]))
figure()
sift.plot_matches(im2, im1, l[i + 1], l[i], matches[i], show_below=True)
# function to convert the matches to hom. points
from PCV.geometry import sfm, homography
from matplotlib.pyplot import plot, axis, show, imshow, figure, gray
from PCV.geometry import camera
from numpy import array, loadtxt, genfromtxt, ones, vstack, dot
from PIL import Image
# calibration
from PCV.localdescriptors import sift
K = array([[2394, 0, 932], [0, 2398, 628], [0, 0, 1]])
print "computing sift.."
# load images and compute features
im1 = array(Image.open('../dataset_alcatraz/alcatraz1.jpg'))
sift.process_image('../dataset_alcatraz/alcatraz1.jpg',
'../dataset_alcatraz/alcatraz1.sift')
l1, d1 = sift.read_features_from_file('dataset_alcatraz/alcatraz1.sift')
im2 = array(Image.open('../dataset_alcatraz/alcatraz2.jpg'))
sift.process_image('../dataset_alcatraz/alcatraz2.jpg',
'../dataset_alcatraz/alcatraz2.sift')
l2, d2 = sift.read_features_from_file('../dataset_alcatraz/alcatraz2.sift')
print "sift.. done"
print "match features..."
# match features
print "\tsearching matches"
matches = sift.match_twosided(d1, d2)
ndx = matches.nonzero()[0]
# make homogeneous and normalize with inv(K)