def train(self, featurefiles, k=100, subsampling=10):
"""Train a vocabulary from features in files listed in |featurefiles| using
k-means with k words. Subsampling of training data can be used for speedup.
"""
image_count = len(featurefiles)
descr = []
descr.append(sift.read_features_from_file(featurefiles[0])[1])
descriptors = descr[0] # Stack features for k-means.
for i in numpy.arange(1, image_count):
descr.append(sift.read_features_from_file(featurefiles[i])[1])
descriptors = numpy.vstack((descriptors, descr[i]))
# Run k-means.
self.voc, distortion = vq.kmeans(descriptors[::subsampling, :], k, 1)
self.word_count = self.voc.shape[0]
# Project training data on vocabulary.
imwords = numpy.zeros((image_count, self.word_count))
for i in range(image_count):
imwords[i] = self.project(descr[i])
occurence_count = numpy.sum((imwords > 0)*1, axis=0)
self.idf = numpy.log(image_count / (occurence_count + 1.0))
self.trainingdata = featurefiles
def train(self,featurefiles,k=100,subsampling=10):
""" featurefilesに列挙されたファイルから特徴量を読み込み
k平均法とk個のビジュアルワードを用いてボキャブラリを
学習する。subsamplingで教師データを間引いて高速化可能 """
nbr_images = len(featurefiles)
# ファイルから特徴量を読み込む
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平均法：最後の数字で試行数を指定する
self.voc,distortion = kmeans(descriptors[::subsampling,:],k,1)
self.nbr_words = self.voc.shape[0]
# 教師画像を順番にボキャブラリに射影する
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 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 train(self, featurefiles, k=100, subsampling=10):
nbr_images = len(featurefiles)
descr = []
descr.append(sift.read_features_from_file(featurefiles[0])[1])
descriptors = descr[0]
print "begin loading image feature files..."
for i in np.arange(1, nbr_images):
descr.append(sift.read_features_from_file(featurefiles[i])[1])
# descriptors = np.vstack((descriptors, descr[i]))
descriptors = np.vstack((descriptors, descr[i][::subsampling,:]))
if i%100 == 0:
print i, "images have been loaded..."
print "finish loading image feature files!"
# self.voc, distortion = cluster.kmeans(descriptors[::subsampling,:], k, 1)
print "begin MiniBatchKMeans cluster....patient"
mbk = MiniBatchKMeans(k, init="k-means++", compute_labels=False, n_init=3, init_size=3*k)
# mbk.fit(descriptors[::subsampling,:])
mbk.fit(descriptors)
self.voc = mbk.cluster_centers_
print "cluster finish!"
self.nbr_word = self.voc.shape[0]
imwords = np.zeros((nbr_images, self.nbr_word))
for i in xrange(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.traindata = featurefiles
def get_sift_match(f1, f2): fn1, fext1 = os.path.splitext(os.path.basename(f1)) fn2, fext2 = os.path.splitext(os.path.basename(f2)) try: l1, d1 = sift.read_features_from_file(TMP_DIR + fn1 + '.key') l2, d2 = sift.read_features_from_file(TMP_DIR + fn2 + '.key') return sift.score(d1, d2) except: return 0.0
def get_krt(im1, im2):
ims = [im1, im2]
sifts = []
for x in range(2):
sifts.append(ims[x][:-3]+"sift")
# compute features
#sift.process_image('../../data/book_frontal.JPG','../../data/im0.sift')
sift.process_image(ims[0],sifts[0])
l0,d0 = sift.read_features_from_file(sifts[0])
#sift.process_image('../../data/book_perspective.JPG','../../data/im1.sift')
sift.process_image(ims[1],sifts[1])
l1,d1 = sift.read_features_from_file(sifts[1])
# 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]
print len(ndx2)
tp = homography.make_homog(l1[ndx2,:2].T)
model = homography.RansacModel()
H,ransac_data = homography.H_from_ransac(fp,tp,model)
# camera calibration
#K = camera.my_calibration((747,1000))
K = camera.my_calibration((Image.open(im2).size))
# 3D points at plane z=0 with sides of length 0.2
box = cube.cube_points([0,0,0.1],0.1)
# project bottom square in first image
cam1 = camera.Camera( hstack((K,dot(K,array([[0],[0],[-1]])) )) )
# first points are the bottom square
box_cam1 = cam1.project(homography.make_homog(box[:,:5]))
# use H to transfer points to the second image
print dot(H,box_cam1)
box_trans = homography.normalize(dot(H,box_cam1))
# compute second camera matrix from cam1 and H
cam2 = camera.Camera(dot(H,cam1.P))
A = dot(linalg.inv(K),cam2.P[:,:3])
A = array([A[:,0],A[:,1],cross(A[:,0],A[:,1])]).T
cam2.P[:,:3] = dot(K,A)
# project with the second camera
box_cam2 = cam2.project(homography.make_homog(box))
# test: projecting point on z=0 should give the same
point = array([1,1,0,1]).T
print homography.normalize(dot(dot(H,cam1.P),point))
print cam2.project(point)
import pickle
with open('%s.pkl' % ims[1][:-4],'w') as f:
pickle.dump(K,f)
pickle.dump(dot(linalg.inv(K),cam2.P),f)
sys.stderr.write("K and Rt dumped to %s.pkl\n" % ims[1][:-4])
def cbir_train(train_path, voc_name, db_name, n_subsample=2000, n_cluster=2000, subfeatsampling=10):
voc_name = voc_name + '_' + str(n_subsample) + '_' + str(n_cluster) + '_' + str(subfeatsampling)
db_name = db_name[:-3] + '_' + str(n_subsample) + '_' + str(n_cluster) + '_' + str(subfeatsampling) + db_name[-3:]
imlist, featlist = cbir_utils.create_imglist_featlist(train_path)
imlist = imlist[:n_subsample]
featlist = featlist[:n_subsample]
### generate sift feature
nbr_images = len(imlist)
''''''
for i in range(nbr_images):
sift.process_image(imlist[i], featlist[i], mask = True)
### generate visual word
voc = visual_word.Vocabulary(voc_name)
voc.train(featlist, n_cluster, subfeatsampling)
with open(voc_name+'.pkl', 'wb') as f:
cPickle.dump(voc, f)
print 'vocabulary is', voc.name, voc.nbr_word
### generate image index
with open(voc_name+'.pkl', 'rb') as f:
voc = cPickle.load(f)
indx = image_search.Indexer(db_name, voc)
indx.create_tables()
for i in range(nbr_images):
locs, descr = sift.read_features_from_file(featlist[i])
indx.add_to_index(imlist[i], descr)
indx.db_commit()
print 'generate index finish!'
print 'training over'
def get_descriptors(img):
# ImageObjectet var, aminek van mar filename_keypoints attributuma
'''
returns the image as array, the location of features, and the descriptors
'''
loc,desc = sift.read_features_from_file(img.filename_keypoints)
return loc, desc
def __main__:
nbr_images = len(imlist)
featlist = [ imlist[i][:-3] + 'sif' for i in range(nbr_images))
for i in range(nbr_images):
sift.process_image(imlist[i],featlist[i])
voc = vocabularly.Vocabulary('ukbenchtest')
voc.train(featlist,1000,10)
with open('vocabulary.pkl', 'wb') as f:
pickle.dump(voc,f)
print 'vocabulary is:', voc.name, voc.nbr_wods
nbr_images = len(imlist)
with open('vocabulary.pkl', 'rb') as f:
voc = pickle.load(f)
indx = imagesearch.Indexer('test.db',voc)
indx.create_tables()
for i in range(nbr_images)[:100]:
locs,descr = sift.read_features_from_file(featlist[i])
indx.add_to_index(imlist[i],descr)
indx.db_commit()
con = sqlite.connect('test.db')
print con.execute('select count (filename) from imlist').fetchone()
print con.execute('select * from imlist').fetchone()
src = imagesearch.Searcher('test.db')
locs,descr = sift.read_features_from_file(featlist[0])
iw = voc.project(descr)
print 'ask using a histogram...'
print src.candidates_from_histogram(iw)[:10]
print 'try a query...'
print src.query(imlist[0])[:10]
def runSurf(self):
#save a grayscale image
im = self.image.convert("L")
im.save(self.filename + "_gray.pgm", "PPM")
surfexec = surfpath + " -i " + self.filename + "_gray.pgm" + " -o " + self.filename + "_result.txt"
print surfexec
os.system(surfexec)
self.locators, self.descriptors = sift.read_features_from_file(self.filename + "_result.txt")
def train(self,featurefiles,k=100,subsampling=10):
""" featurefilesに列挙されたファイルから特徴量を読み込み
k平均法とk個のビジュアルワードを用いてボキャブラリを
学習する。subsamplingで教師データを間引いて高速化可能 """
nbr_images = len(featurefiles)
# ファイルから特徴量を読み込む
#points = []
descr = []
descr.append(sift.read_features_from_file(featurefiles[0])[1])
# optional.view feature points.
#points.append( np.array(sift.read_features_from_file(featurefiles[0])[0][:,0:2]) ) # stock of x,y axis value
descriptors = descr[0] #stack all features for k-means
#pointors = points[0]
for i in arange(1,nbr_images):
descr.append(sift.read_features_from_file(featurefiles[i])[1])
#points.append( np.array(sift.read_features_from_file(featurefiles[i])[0][:,0:2]) ) # stock of x,y axis value
descriptors = vstack((descriptors,descr[i]))
# k平均法：最後の数字で試行数を指定する
self.voc,distortion = kmeans(descriptors[::subsampling,:],k,1)
self.nbr_words = self.voc.shape[0]
# 重心を保存しておく
with open('voc_centroid.pkl','wb') as f:
pickle.dump(self.voc,f)
"""
# ワードとx,y座標の辞書作成
dic = []
for i in xrange(len(nbr_images)):
dic[i] = {}
dic[i][]
"""
# 教師画像を順番にボキャブラリに射影する
imwords = zeros((nbr_images,self.nbr_words))
for i in xrange(1): #xrange( nbr_images ):
# imwords[i] = self.project(descr[i], points[i]) # PLSAを使う場合はこちらを使用する
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 plot_sift_feature(im):
#imname = ’empire.jpg’
#im1 = array(Image.open(imname).convert(’L’))
tmpFile = 'tmp.sift'
sift.process_image(im,tmpFile)
l1,d1 = sift.read_features_from_file(tmpFile)
figure()
gray()
sift.plot_features(im,l1,circle=True)
show()
def read_feature_labels(path):
featlist = [os.path.join(path, f) for f in os.listdir(path) if f.endswith('.dsift')]
features = []
for featfile in featlist:
l, d = sift.read_features_from_file(featfile)
features.append(d.flatten())
features = array(features)
return features
def runsift(self):
#save a grayscale image
imsize = self.image.size
im = self.image.resize((imsize[0]/10, imsize[1]/10))
im = im.convert("L")
im.save(self.filename + "_gray.pgm", "PPM")
siftexec = siftpath + self.filename + "_gray.pgm >" + self.filename + "_result.txt"
print siftexec
os.system(siftexec)
self.locators, self.descriptors = sift.read_features_from_file(self.filename + "_result.txt")
def extractSift(input_files):
all_features_dict = {}
for i, fname in enumerate(input_files):
features_fname = fname + '.sift'
if exists(features_fname) == False:
print "calculating sift features for", fname
sift.process_image(fname, features_fname)
print "gathering sift features for", fname,
locs, descriptors = sift.read_features_from_file(features_fname)
print descriptors.shape
all_features_dict[fname] = descriptors
return all_features_dict
def get_sift_lowe(img):
features_fname = img + '.sift'
if os.path.isfile(features_fname) == False:
is_size_zero = sift.process_image(img, features_fname)
if is_size_zero:
os.remove(features_fname)
sift.process_image(img, features_fname)
if os.path.isfile(features_fname) and os.path.getsize(features_fname) == 0:
os.remove(features_fname)
sift.process_image(img, features_fname)
locs, desc = sift.read_features_from_file(features_fname)
return desc
def read_feature_labels(path):
featlist = [os.path.join(path, f) for f in os.listdir(path) if f.endswith('.' + typeFeats)]
features = []
for featfile in featlist:
l, d = sift.read_features_from_file(featfile)
features.append(d.flatten())
features = array(features)
labels = [featfile.split('/')[-1][0] for featfile in featlist]
return features, featlist
def extractSift(input_files,target_folder):
all_features_dict = {}
count=0
for i,fname in enumerate(input_files):
features_fname = target_folder+'/'+fname.split('/')[2].split('.')[0]+'.sift'
if exists(features_fname) == False:
print("Calculating sift features for ",fname)
sift.process_image(fname, features_fname,count)
count+=1
locs, descriptors = sift.read_features_from_file(features_fname)
all_features_dict[fname] = (locs,descriptors)
os.chdir('..')
return all_features_dict
def read_gesture_features_labels(path):
featlist = [os.path.join(path,f) for f in os.listdir(path) if f.endswith('.dsift')]
# print featlist
#read the feature
features = []
for featfile in featlist:
l,d = sift.read_features_from_file(featfile)
features.append(d.flatten())
features = np.array(features)
#create labels
labels = [featfile1.split('/')[-1][0] for featfile1 in featlist]
return features,np.array(labels)
def find_matches(image_names, root):
l = {}
d = {}
n = len(image_names)
for i, im in enumerate(image_names):
resultname = os.path.join(root, '{}.sift'.format(im))
if not os.path.isfile(resultname):
sift.process_image(os.path.join(root, '{}.png'.format(im)), resultname)
l[i], d[i] = sift.read_features_from_file(resultname)
matches = {}
for i in range(n - 1):
matches[i] = sift.match(d[i + 1], d[i])
return matches, l, d
def read_gesture_feature_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)
def match_keys(self, sceneFile, modelFile): sceneFeat = sift.read_features_from_file(sceneFile)[1] try: modelFeat = sift.read_features_from_file(modelFile)[1] except: return desc1=sceneFeat desc2=modelFeat # pdb.set_trace() # t1=time.time() dist_ratio=.6 desc1_shape = desc1.shape[0] matchscores=np.zeros(desc1_shape) dotProds = np.dot(desc1,desc2.T) dotProds *=.9999 acos_dots= np.arccos(dotProds) indx = np.argsort(acos_dots,axis=1) range_ind = range(desc1_shape) # sig_keys = np.array(acos_dots[0,indx[:,0]] < dist_ratio * acos_dots[0,indx[:,1]]) sig_keys = np.array(acos_dots[range_ind,indx[:,0]] < dist_ratio * acos_dots[range_ind,indx[:,1]]) sig_keys_ind = np.nonzero(sig_keys)[0] matchscores[sig_keys_ind] = indx[sig_keys_ind,0] # print 'T1:',time.time()-t1 # t2=time.time() # other=sift.match(desc1,desc2) # print 'T2:',time.time()-t2 # pdb.set_trace() return matchscores
def compute_homography():
# compute features
#sift.process_image('../../data/book_frontal.JPG','../../data/im0.sift')
im1='../../data/space_front.jpg'
im2='../../data/space_perspective.jpg'
im1='../../data/mag_front.jpg'
im2='../../data/mag_perspective.jpg'
ims = [im1, im2]
sifts = []
for k in range(2):
sifts.append(ims[k][:-4]+".sift")
l0,d0 = sift.read_features_from_file(sifts[0])
l1,d1 = sift.read_features_from_file(sifts[1])
# 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,ransac_data = homography.H_from_ransac(fp,tp,model)
return H
def extractSift(input_files):
all_features_dict = {}
count = 0
for i,fname in enumerate(input_files):
# path to store resulting sift files
features_fname = 'sift_output/'+fname.split('/')[2].split('.')[0]+'.sift'
if count == 0:
os.chdir('siftDemoV4')
print("Calculating sift features for ",fname)
sift.process_image(fname,features_fname,count)
count+=1
locs, descriptors = sift.read_features_from_file(features_fname)
all_features_dict[fname] = descriptors
os.chdir('..')
return all_features_dict
def read_gesture_features_labels(path):
# .dsiftで終わるすべてのファイル名のリストを作る
featlist = [os.path.join(path,f) for f in os.listdir(path) if f.endswith('.dsift')]
#特徴量を読み込む
features = []
for featfile in featlist:
l,d = sift.read_features_from_file(featfile)
features.append(d.flatten())
features = np.array(features)
#ラベルを生成する
labels = [featfile.split('/')[-1][0] for featfile in featlist]
return features, np.array(labels)
def _match():
imlist = []
featlist = []
nbr_image = len(imlist)
matchscores = zeros((nbr_images, nbr_images))
for i in xrange(nbr_images):
for j in xrange(i, nbr_image): # 上三角成分だけを計算する
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 or matches = ', nbr_matches
matchscores[j,i] = nbr_matches
# 値をコピーする
for i in xrange(nbr_images):
for j in xrange(i+1, nbr_images): # 対角成分はコピー不要
matchscores[j,i] = matchscores[i,j]
def read_feature_labels(path):
if typeFeats == 'dsift':
featlist = [os.path.join(path,f) for f in os.listdir(path) if f.endswith('.dsift')]
elif typeFeats == 'sift':
featlist = [os.path.join(path,f) for f in os.listdir(path) if f.endswith('.sift')]
else:
print_error()
features = []
for featfile in featlist:
l,d = sift.read_features_from_file(featfile)
features.append(d.flatten())
features = array(features)
labels = [featfile.split('/')[-1][0] for featfile in featlist]
return features,array(labels)
def extractSift(input_files):
print "extracting Sift features"
all_features_dict = {}
for i, fname in enumerate(input_files):
rest_of_path = fname[:-(len(os.path.basename(fname)))]
rest_of_path = os.path.join(rest_of_path, "sift")
rest_of_path = os.path.join(rest_of_path, os.path.basename(fname))
features_fname = rest_of_path + '.sift'
if os.path.exists(features_fname) == False:
# print "calculating sift features for", fname
sift.process_image(fname, features_fname)
# print "gathering sift features for", fname,
locs, descriptors = sift.read_features_from_file(features_fname)
# print descriptors.shape
all_features_dict[fname] = descriptors
return all_features_dict
def extractMF(filename):
features_fname = filename + '.sift'
sift.process_image(filename, features_fname)
locs, descriptors = sift.read_features_from_file(features_fname)
sh = min(locs.shape[0], 1000)
res = np.zeros((sh,SIZE_LOCAL_FEATURE)).astype(np.float32)
extra = [20,False,True,False,0,0,0]
WIN = 5
for i in range(sh):
x = np.int32(round(locs[i][0]))
y = np.int32(round(locs[i][1]))
I = Image.open(filename)
Nx,Ny = I.size
a = sg.spec(I.crop((max(x-WIN,0),max(y-WIN,0),min(x+WIN,Nx-1),min(y+WIN,Ny-1))),extra)
res[i] = a
print res.shape
return res
def extractSift(input_files):
print "extracting Sift features"
all_features_dict = {}
#all_features = zeros([1,128])
for i, fname in enumerate(input_files):
features_fname = fname + '.sift'
if exists(features_fname) == False:
print "calculating sift features for", fname
sift.process_image(fname, features_fname)
locs, descriptors = sift.read_features_from_file(features_fname)
# print descriptors.shape
all_features_dict[fname] = descriptors
# if all_features.shape[0] == 1:
# all_features = descriptors
# else:
# all_features = concatenate((all_features, descriptors), axis = 0)
return all_features_dict
#!/usr/bin/env python
# -*- coding=utf8 -*-
"""
# Author: Yoghourt.Lee->lvcr
# Created Time : Wed 25 Apr 2018 06:25:41 PM CST
# File Name: automaticallyFindMatchingCorrespondenceUseSIFT.py
# Description: 使用 SIFT 特征自动找到匹配对应
"""
import sift
featname = ['Univ' + str(i+1) + '.sift' for i in range(5)]
imname = ['Univ' + str(i+1) + '.jpg' fpr 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])
with open('vocabulary.pkl', 'rb') as f:
voc = pickle.load(f)
src = imagesearch.Searcher('test.db', voc)
# 查询图像的索引号和返回的搜索结果数目
q_ind = 50
nbr_results = 20
# 常规查询
res_reg = [w[1] for w in src.query(imlist[q_ind])[:nbr_results]]
print 'top matches (regular):', res_reg
# 载入查询图像特征
q_locs, q_descr = sift.read_features_from_file(featlist[q_ind])
fp = homography.make_homog(q_locs[:, :2].T)
# 用RANSAC 模型拟合单应性
model = homography.RansacModel()
rank = {}
# 载入搜索结果的图像特征
for ndx in res_reg[1:]:
locs, descr = sift.read_features_from_file(featlist[ndx])
# 获取匹配数
matches = sift.match(q_descr, descr)
ind = matches.nonzero()[0]
ind2 = matches[ind]
tp = homography.make_homog(locs[:, :2].T)
import sift
from PIL import Image
import os
from numpy import *
from pylab import *
# import cv2
imname = 'tesla-model-s3.jpg'
# img = cv2.imread(imname)
# cv.imshow(img)
im1 = array(Image.open(imname).convert('L'))
# print(im1)
sift.process_image(imname, 'tesla-model-s3.sift')
l1, d1 = sift.read_features_from_file('tesla-model-s3.sift')
figure()
gray()
sift.plot_features(im1, l1, circle=True)
show()
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from PIL import Image
from numpy import array
import sift
from pylab import *
imname = 'Penguins.jpg'
im1 = array(Image.open(imname).convert('L'))
sift.process_image(imname, 'Penguins.sift')
l1, d1 = sift.read_features_from_file('Penguins.sift')
figure()
gray()
sift.plot_features(im1, l1, circle=True)
show()
##########################################
nbr_images = len(imlist)
# Get SIFT Features for each image
for i in range(nbr_images):
im1 = array(Image.open(imlist[i]).convert('L'))
siftName = basename(imlist[i])
sift.process_image(imlist[i], "features/" + siftName + ".sift")
featlist.append("features/" + siftName + ".sift")
matchscores = zeros((nbr_images, nbr_images))
for i in range(nbr_images):
for j in range(i, nbr_images):
if imlist[i] != imlist[j]:
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
for i in range(nbr_images):
for j in range(i + 1, nbr_images):
matchscores[j, i] = matchscores[i, j]
import homography
import sfm
import sift
from pylab import *
from PIL import Image
# 标定矩阵
K = array([[2394, 0, 932], [0, 2398, 628], [0, 0, 1]])
# 载入图像，并计算特征
im1 = array(Image.open('../data/alcatraz1.jpg'))
sift.process_image('../data/alcatraz1.jpg', 'im1.sift')
l1, d1 = sift.read_features_from_file('im1.sift')
im2 = array(Image.open('../data/alcatraz2.jpg'))
sift.process_image('../data/alcatraz2.jpg', 'im2.sift')
l2, d2 = sift.read_features_from_file('im2.sift')
# 匹配特征
matches = sift.match_twosided(d1, d2)
ndx = matches.nonzero()[0]
# 使用齐次坐标表示，并使用 inv(K) 归一化
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 = dot(inv(K), x1)
x2n = dot(inv(K), x2)
# 使用 RANSAC 方法估计 E
model = sfm.RansacModel()
E, inliers = sfm.F_from_ransac(x1n, x2n, model)
# 计算照相机矩阵(P2 是 4 个解的列表)
P1 = array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]])
P2 = sfm.compute_P_from_essential(E)
p.append([c[0] - wid, c[1] - wid, c[2] + wid])
# 为了绘制闭合图像，和第一个相同
# 竖直边
p.append([c[0] - wid, c[1] - wid, c[2] + wid])
p.append([c[0] - wid, c[1] + wid, c[2] + wid])
p.append([c[0] - wid, c[1] + wid, c[2] - wid])
p.append([c[0] + wid, c[1] + wid, c[2] - wid])
p.append([c[0] + wid, c[1] + wid, c[2] + wid])
p.append([c[0] + wid, c[1] - wid, c[2] + wid])
p.append([c[0] + wid, c[1] - wid, c[2] - wid])
return array(p).T
# 计算特征
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')
# 匹配特征，并计算单应性矩阵
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 = homography.H_from_ransac(fp, tp, model)
im0 = array(Image.open('../data/book_frontal.JPG'))
def cal(queryname , judge):
features = {}
i = 0
model = zeros(128)
for dirs , root , files in os.walk(datapath):
for filename in files:
#sift.process_image(datapath + filename , 'temp.sift')
l , d = sift.read_features_from_file('./sifts/' + filename[:-4] + '.sift')
features[filename] = d
i += 1
for i in range(len(d)):
model = vstack((model , d[i]))
model = delete(model , 0 , 0)
query = zeros(128)
sift.process_image(datapath + queryname , 'temp.sift')
l , d = sift.read_features_from_file('temp.sift')
for i in range(len(d)):
query = vstack((query , d[i]))
query = delete(query , 0 , 0)
whitened = whiten(model)
k , num = kmeans(whitened , 10)
modelarr , dum = vq(model , k)
queryarr , disnum = vq(query , k)
temp = [0] * 10
for i in modelarr:
temp[i] += 1
bignum = 0
big = -1
for i in range(10):
if temp[i] > bignum:
big = temp[i]
big = i
temp = [0] * 10
for i in queryarr:
temp[i] += 1
countdict = {}
for k , v in features.iteritems():
countdict[k] = [0] * 10
featurearr , desnum = vq(features[k] , k)
for i in featurearr:
countdict[k][i] += 1
result = {}
for k , v in countdict.iteritems():
numsum = 0
for i in range(10):
if i == big:
temp[i] = 0
val = countdict[k][i]
numsum += (val - temp[i]) * (val - temp[i])
result[k] = numsum
result = sorted([(v , k) for (k , v) in result.items()] , reverse = False)
if judge:
for i in result[:10]:
print i
else:
return result[:10]
import sift
import imagesearch
"""After ch07_buildindex.py has built an index in test.db, this program can
query it.
"""
imlist = imtools.get_imlist('/Users/thakis/Downloads/ukbench/first1000')[:100]
imcount = len(imlist)
featlist = [imlist[i][:-3] + 'sift' for i in range(imcount)]
with open('vocabulary.pkl', 'rb') as f:
voc = pickle.load(f)
searcher = imagesearch.Searcher('test.db', voc)
locs, descr = sift.read_features_from_file(featlist[0])
imwords = voc.project(descr)
print 'ask using a histogram...'
print searcher.candidates_from_histogram(imwords)[:10]
print 'try a query...'
res = searcher.query(imlist[0])[:10]
print res
print 'score:'
# Score a small subset, so this runs fast.
print imagesearch.compute_ukbench_score(searcher, imlist[:10])
# Plot images most similar to imlist[0].
imagesearch.plot_results(searcher, [r[1] for r in res[:6]])
"""
Created on Mon Aug 15 14:10:37 2016
@author: user
"""
from PIL import Image
from pylab import *
from numpy import *
import sift
imname = 'baby_1.jpg'
im1 = array(Image.open(imname).convert('L'))
imshow(im1)
sift.process_image(imname, 'baby.sift')
l1, d1 = sift.read_features_from_file('baby.sift')
figure()
gray()
sift.plot_features(im1, l1, circle=True)
show()
imname1 = 'climbing_1_small.jpg'
imname2 = '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
l2, d2 = sift.read_features_from_file(imname1 + '.sift')
l3, d3 = sift.read_features_from_file(imname2 + '.sift')
matchscores = sift.match_twosided(d2, d3)
if len(sys.argv) >= 3:
im1f, im2f = sys.argv[1], sys.argv[2]
else:
# im1f = '../data/sf_view1.jpg'
# im2f = '../data/sf_view2.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])))
figure()
def get_all_features(self):
# pdb.set_trace()
for i in range(self.fileCount):
filename = self.sift_filenames[i]
#read_features_from_file returns [locs,desciptors].Only look at descriptors
self.all_features.append(sift.read_features_from_file(filename)[1])
import sift
import os
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
im1name = 'crans_1_small.jpg'
im1sift = im1name + '.sift'
im1 = np.array(Image.open(im1name).convert('L'))
sift.process_image(im1name, im1sift)
l1, d1 = sift.read_features_from_file(im1sift)
print(l1.shape, d1.shape)
im2name = 'crans_2_small.jpg'
im2sift = im2name + '.sift'
im2 = np.array(Image.open(im2name).convert('L'))
sift.process_image(im2name, im2sift)
l2, d2 = sift.read_features_from_file(im2sift)
print(l2.shape, d2.shape)
print("start matching..")
matches = sift.match_twosided(d1, d2)
plt.figure()
plt.gray()
sift.plot_matches(im1, im2, l1, l2, matches)
plt.show()