def train(self,featurefiles,k=100,subsampling=10):
""" Train a vocabulary from features in files listed
in featurefiles using k-means with k number of words.
Subsampling of training data can be used for speedup. """
nbr_images = len(featurefiles)
# read the features from file
descr = []
descr.append(sift.read_features_from_file(featurefiles[0])[1])
descriptors = descr[0] #stack all features for k-means
for i in arange(1,nbr_images):
descr.append(sift.read_features_from_file(featurefiles[i])[1])
descriptors = vstack((descriptors,descr[i]))
# k-means: last number determines number of runs
self.voc,distortion = kmeans(descriptors[::subsampling,:],k,1)
self.nbr_words = self.voc.shape[0]
# go through all training images and project on vocabulary
imwords = zeros((nbr_images,self.nbr_words))
for i in range( nbr_images ):
imwords[i] = self.project(descr[i])
nbr_occurences = sum( (imwords > 0)*1 ,axis=0)
self.idf = log( (1.0*nbr_images) / (1.0*nbr_occurences+1) )
self.trainingdata = featurefiles
def read_gesture_features_labels(path):
# create list of all files ending in .dsift
featlist = [
os.path.join(path, f) for f in os.listdir(path) if f.endswith('.dsift')
]
# read the features
features = []
for featfile in featlist:
l, d = sift.read_features_from_file(featfile)
features.append(d.flatten())
features = array(features)
# create labels
labels = [featfile.split('/')[-1][0] for featfile in featlist]
return features, array(labels)
from PIL import Image
from pylab import *
from numpy import *
from pcv.localdescriptors import dsift, sift
"""
This is the dense SIFT illustration, it will reproduce the plot
in Figure 8-2.
"""
dsift.process_image_dsift('../data/empire.jpg', 'empire.sift', 90, 40, True)
l, d = sift.read_features_from_file('empire.sift')
im = array(Image.open('../data/empire.jpg'))
sift.plot_features(im, l, True)
show()
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)
# camera calibration
from pcv.tools import imtools
"""
This will process all hand gesture images with the dense SIFT descriptor.
Assumes you downloaded the hand images to ..data/hand_gesture.
The plot at the end generates one of the images of Figure 8-3.
"""
path = '../data/hand_gesture/train/'
imlist = []
for filename in os.listdir(path):
if os.path.splitext(filename)[1] == '.ppm':
imlist.append(path+filename)
# process images at fixed size (50,50)
for filename in imlist:
featfile = filename[:-3]+'dsift'
dsift.process_image_dsift(filename, featfile, 10, 5, resize=(50,50))
# show an image with features
l,d = sift.read_features_from_file(featfile)
im = array(Image.open(filename).resize((50,50)))
print im.shape
figure()
sift.plot_features(im, l, True)
show()
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 convert_points(j):
# 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):
for j in range(i + 1, nbr_images): # no need to copy diagonal
matchscores[j, i] = matchscores[i, j]
threshold = 2 # min number of matches needed to create link
g = pydot.Dot(graph_type='graph') # don't want the default directed graph
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()