def train(self, featurefiles, k=100, subsampling=10):
"""用含有k个单词的K-means列出在featurefiles中的特征文件训练处一个词汇。对训练数据下采样可以加快训练速度"""
nbr_images = len(featurefiles)
# 从文件中读取特征
descr = []
descr.append(sift.read_feature_from_file(featurefiles[0])[1])
# 将所有的特征并在一起，以便后面进行K-means聚类
descriptors = descr[0]
count = 0
for i in np.arange(1, nbr_images):
d1 = sift.read_feature_from_file(featurefiles[i])[1]
if d1.shape[1] == 0:
count += 1
continue
descr.append(d1)
descriptors = np.vstack((descriptors, descr[i - count]))
self.voc, distortion = kmeans(descriptors[::subsampling, :], k, 1)
self.nbr_words = self.voc.shape[0]
nbr_images = nbr_images - count
# 遍历所有的训练图像，并投影到词汇上
imwords = np.zeros((nbr_images, self.nbr_words))
for i in range(nbr_images):
imwords[i] = self.project(descr[i])
nbr_occurences = np.sum((imwords > 0) * 1, axis=0)
self.idf = np.log((1.0 * nbr_images) / (1.0 * nbr_occurences + 1))
self.trainingdata = featurefiles
def train(self, featurefiles, k=100, subsampling=10):
"""用含有k个单词的K-means列出在featurefiles中的特征文件训练处一个词汇。对训练数据下采样可以加快训练速度"""
nbr_images = len(featurefiles)
# 从文件中读取特征
descr = []
descr.append(sift.read_feature_from_file(featurefiles[0])[1])
# 将所有的特征并在一起，以便后面进行K-means聚类
descriptors = descr[0]
count=0
for i in np.arange(1, nbr_images):
d1 = sift.read_feature_from_file(featurefiles[i])[1]
if d1.shape[1] == 0:
count += 1
continue
descr.append(d1)
descriptors = np.vstack((descriptors, descr[i-count]))
self.voc, distortion = kmeans(descriptors[::subsampling, :], k, 1)
self.nbr_words = self.voc.shape[0]
nbr_images = nbr_images - count
# 遍历所有的训练图像，并投影到词汇上
imwords = np.zeros((nbr_images, self.nbr_words))
for i in range(nbr_images):
imwords[i] = self.project(descr[i])
nbr_occurences = np.sum((imwords>0)*1, axis=0)
self.idf = np.log((1.0*nbr_images)/(1.0*nbr_occurences+1))
self.trainingdata = featurefiles
def sift_matrix():
featurelist = sift_pan_desc_generator('/home/aurora/hdd/workspace/PycharmProjects/data/pcv_img/panoimages/')
imlist = getFiles('/home/aurora/hdd/workspace/PycharmProjects/data/pcv_img/panoimages/')
nbr_images = len(imlist)
matchscores = np.zeros((nbr_images, nbr_images))
for i in range(nbr_images):
for j in range(i, nbr_images):
print 'comparing ', imlist[i], imlist[j]
l1, d1 = sift.read_feature_from_file(featurelist[i])
l2, d2 = sift.read_feature_from_file(featurelist[j])
matches = sift.match_twosided(d1, d2)
nbr_matches = sum(matches > 0)
print 'number of matches = ', nbr_matches
matchscores[i, j] = nbr_matches
for i in range(nbr_images):
for j in range(i + 1, nbr_images):
matchscores[j, i] = matchscores[i, j]
np.save('pan_img_matchscore', matchscores)
def read_gesture_features_labels(path):
featlists = [os.path.join(path, f) for f in os.listdir(path) if f.endswith('.dsift')]
features = []
for featfile in featlists:
l, d = sift.read_feature_from_file(featfile)
features.append(d.flatten())
features = np.array(features, dtype=np.float64)
labels = [f.split('/')[-1][0] for f in featlists]
return features, np.array(labels)
def sift_matrix():
featurelist = sift_pan_desc_generator('/home/aurora/hdd/workspace/PycharmProjects/data/pcv_img/panoimages/')
imlist = getFiles('/home/aurora/hdd/workspace/PycharmProjects/data/pcv_img/panoimages/')
nbr_images = len(imlist)
matchscores = np.zeros((nbr_images, nbr_images))
for i in range(nbr_images):
for j in range(i, nbr_images):
print 'comparing ', imlist[i], imlist[j]
l1, d1 = sift.read_feature_from_file(featurelist[i])
l2, d2 = sift.read_feature_from_file(featurelist[j])
matches = sift.match_twosided(d1, d2)
nbr_matches = sum(matches > 0)
print 'number of matches = ', nbr_matches
matchscores[i, j] = nbr_matches
for i in range(nbr_images):
for j in range(i + 1, nbr_images):
matchscores[j, i] = matchscores[i, j]
np.save('pan_img_matchscore', matchscores)
def read_gesture_features_labels(path):
featlists = [
os.path.join(path, f) for f in os.listdir(path) if f.endswith('.dsift')
]
features = []
for featfile in featlists:
l, d = sift.read_feature_from_file(featfile)
features.append(d.flatten())
features = np.array(features, dtype=np.float64)
labels = [f.split('/')[-1][0] for f in featlists]
return features, np.array(labels)
else:
cmmd = str('sift ' + imagename + ' --output=' + resultname +
" --read-frames=tmp.frame")
os.system(cmmd)
print 'processed', imagename, 'to', resultname
if __name__ == '__main__':
"""可以使用欧几里得距离来计算dsift之间的距离"""
process_image_dsift(
'/home/aurora/hdd/workspace/PycharmProjects/data/pcv_img/climbing_1_small.jpg',
'climbing_1.sift', 90, 40, True)
process_image_dsift(
'/home/aurora/hdd/workspace/PycharmProjects/data/pcv_img/climbing_2_small.jpg',
'climbing_2.sift', 90, 40, True)
l1, d1 = sift.read_feature_from_file('climbing_1.sift')
l2, d2 = sift.read_feature_from_file('climbing_2.sift')
img1 = np.array(
Image.open(
'/home/aurora/hdd/workspace/PycharmProjects/data/pcv_img/climbing_1_small.jpg'
).convert('L'))
print 'starting matching'
matches = sift.match_twosided(d1, d2)
plt.figure()
plt.gray()
sift.plot_features(img1, l1, True)
# img1 = np.array(Image.open('/home/aurora/hdd/workspace/PycharmProjects/data/pcv_img/climbing_1_small.jpg').convert('L'))
# img2 = np.array(Image.open('/home/aurora/hdd/workspace/PycharmProjects/data/pcv_img/climbing_2_small.jpg').convert('L'))
# sift.plot_matches(img1, img2, l1, l2, matches)
print xx.shape
frame = np.array([xx, yy, scale*np.ones(xx.shape[0]), np.zeros(xx.shape[0])])
print frame.shape
np.savetxt('tmp.frame', frame.T, fmt='%03.3f')
if force_orientation:
cmmd = str('sift '+imagename+' --output='+resultname+" --read-frames=tmp.frame --orientations")
else:
cmmd = str('sift '+imagename+' --output='+resultname+" --read-frames=tmp.frame")
os.system(cmmd)
print 'processed', imagename, 'to', resultname
if __name__=='__main__':
"""可以使用欧几里得距离来计算dsift之间的距离"""
process_image_dsift('/home/aurora/hdd/workspace/PycharmProjects/data/pcv_img/climbing_1_small.jpg', 'climbing_1.sift', 90, 40, True)
process_image_dsift('/home/aurora/hdd/workspace/PycharmProjects/data/pcv_img/climbing_2_small.jpg', 'climbing_2.sift', 90, 40, True)
l1, d1 = sift.read_feature_from_file('climbing_1.sift')
l2, d2 = sift.read_feature_from_file('climbing_2.sift')
img1 = np.array(Image.open('/home/aurora/hdd/workspace/PycharmProjects/data/pcv_img/climbing_1_small.jpg').convert('L'))
print 'starting matching'
matches = sift.match_twosided(d1, d2)
plt.figure()
plt.gray()
sift.plot_features(img1, l1, True)
# img1 = np.array(Image.open('/home/aurora/hdd/workspace/PycharmProjects/data/pcv_img/climbing_1_small.jpg').convert('L'))
# img2 = np.array(Image.open('/home/aurora/hdd/workspace/PycharmProjects/data/pcv_img/climbing_2_small.jpg').convert('L'))
# sift.plot_matches(img1, img2, l1, l2, matches)
plt.show()
url = '/home/aurora/hdd/workspace/PycharmProjects/data/pcv_img/first1000/'
imlists = vocabulary.get_img_list(url)
feature = vocabulary.get_feature_list(url)
nbr_images = len(imlists)
with open('vocabulary-new.pkl', 'rb') as f:
voc = pickle.load(f)
src = imagesearch.Searcher('test.db', voc)
q_ind = 50
nbr_results = 20
res_reg = [w[1] for w in src.query(imlists[q_ind])[:nbr_images]]
print 'top matches (regular):', res_reg
q_locs, q_descr = sift.read_feature_from_file(feature[q_ind])
fp = homography.make_homog(q_locs[:, :2].T)
model = homography.RansacModel()
rank = {}
# load image features for result
#载入候选图像的特征
for ndx in res_reg[1:]:
locs,descr = sift.read_feature_from_file(feature[ndx]) # because 'ndx' is a rowid of the DB that starts at 1
# get matches
matches = sift.match(q_descr,descr)
ind = matches.nonzero()[0]
ind2 = matches[ind]
tp = homography.make_homog(locs[:,:2].T)
# compute homography, count inliers. if not enough matches return empty list
try:
import homography
import cam
import sift
#compute the feature
sift.process_image('/home/wangkai/Pictures/book_frontal.jpg','im0.sift')
l0,d0 = sift.read_feature_from_file('im0.sift')
sift.process_image('/home/wangkai/Pictures/book_prespective.jpg','im1.sift')
l1,d1 = sift.read_feature_from_file('im1.sift')
#match the feature, compute the homography
matches = sift.match_twoside(d0,d1)
ndx = matches.nozeros()[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 = homography.H_from_ransac(fp,tp,model)
def cube_points(c,wid):
"""create a list of points for draw cube"""
p = []
#bottom
p.addpen([c[0]-wid,c[1]-wid,c[2]-wid])
p.addpen([c[0]-wid,c[1]+wid,c[2]-wid])
p.addpen([c[0]+wid,c[1]+wid,c[2]-wid])
p.addpen([c[0]+wid,c[1]-wid,c[2]-wid])
p.addpen([c[0]-wid,c[1]-wid,c[2]-wid])#to draw close image,as the first
#top
