def learnCodebooksOPQ(pointsFilename, pointsCount, dim, M, K, vocFilename, ninit=20):
points = readXvecs(pointsFilename, dim, pointsCount)
R = np.identity(dim)
rotatedPoints = np.dot(points, R.T).astype('float32')
codebookDim = dim / M
codebooks = np.zeros((M, K, codebookDim), dtype='float32')
# init vocabs
for i in xrange(M):
perm = np.random.permutation(pointsCount)
codebooks[i, :, :] = rotatedPoints[perm[:K], codebookDim*i:codebookDim*(i+1)].copy()
# init assignments
assigns = np.zeros((pointsCount, M), dtype='int32')
for i in xrange(M):
(idx, dis) = ynumpy.knn(rotatedPoints[:,codebookDim*i:codebookDim*(i+1)].astype('float32'), codebooks[i,:,:], nt=30)
assigns[:,i] = idx.flatten()
for it in xrange(ninit):
approximations = reconstructPointsOPQ(assigns, codebooks)
errors = rotatedPoints - approximations
error = 0
for pid in xrange(pointsCount):
error += np.dot(errors[pid,:], errors[pid,:].T)
print 'Quantization error: ' + str(error / pointsCount)
U, s, V = np.linalg.svd(np.dot(approximations.T, points), full_matrices=False)
R = np.dot(U, V)
rotatedPoints = np.dot(points, R.T).astype('float32')
for m in xrange(M):
counts = np.bincount(assigns[:,m])
for k in xrange(K):
codebooks[m,k,:] = np.sum(rotatedPoints[assigns[:,m]==k,codebookDim*m:codebookDim*(m+1)], axis=0) / counts[k]
for m in xrange(M):
subpoints = rotatedPoints[:,codebookDim*m:codebookDim*(m+1)].copy()
(idx, dis) = ynumpy.knn(subpoints, codebooks[m,:,:], nt=30)
assigns[:,m] = idx.flatten()
error = 0
for m in xrange(M):
subpoints = rotatedPoints[:,m*codebookDim:(m+1)*codebookDim].copy()
(idx, dis) = ynumpy.knn(subpoints, codebooks[m,:,:], nt=2)
error += np.sum(dis.flatten())
print 'Quantization error: ' + str(error / pointsCount)
model = (codebooks, R)
vocFile = open(vocFilename, 'wb')
pickle.dump(model, vocFile)
def get_distances(train_images):
surf = cv2.SURF(hessianThreshold=500, extended=True)
image_descs = []
for fnames in train_images:
try:
img = cv2.imread(fnames,0);
kp, des = surf.detectAndCompute(img, None)
except:
continue
image_descs.append(des)
all_desc= np.vstack(image_descs)
k = 128
n_sample = k * 500
sample = all_desc
sample = sample.astype('float32')
mean = sample.mean(axis = 0)
sample = sample - mean
cov = np.dot(sample.T, sample)
eigvals, eigvecs = np.linalg.eig(cov)
perm = eigvals.argsort()
pca_transform = eigvecs[:, perm[32:128]]
sample = np.dot(sample, pca_transform)
gmm = ynumpy.gmm_learn(sample, k)
image_fvs = []
for image_desc in image_descs:
image_desc = np.dot(image_desc - mean, pca_transform)
fv = ynumpy.fisher(gmm, image_desc, include = 'mu')
image_fvs.append(fv)
image_fvs = np.vstack(image_fvs)
image_fvs = np.sign(image_fvs) * np.abs(image_fvs) ** 0.5
norms = np.sqrt(np.sum(image_fvs ** 2, 1))
image_fvs /= norms.reshape(-1, 1)
image_fvs[np.isnan(image_fvs)] = 100
query_imnos = range(0,len(image_fvs)-1);
query_fvs = image_fvs#[query_imnos]
results, distances = ynumpy.knn(query_fvs, image_fvs, nnn = len(image_fvs))
s_results = np.argsort(results, axis = 1)
s_distances = distances*0
for i in range(distances.shape[0]):
s_distances[i,:] = distances[i,s_results[i,:]]
return s_distances
def CompareImages(image_names, image_fvs): # get the indices of the query images (the subset of images that end in "0") query_imnos = [i for i, name in enumerate(image_names) if name[-1:] == "0"] # corresponding descriptors query_fvs = image_fvs[query_imnos] # get the 8 NNs for all query images in the image_fvs array results, distances = ynumpy.knn(query_fvs, image_fvs, nnn = 8) return query_imnos, results, distances
def getTrackNeighborsYaelYnumpy(feats, k):
from yael import ynumpy, yael
nrTracks = feats.shape[0]
print '\tFinding %d nearest neighbors, for %d trajectories' % (k,nrTracks),
sys.stdout.flush()
(simMatIDs, simMat) = ynumpy.knn(feats, feats, nnn=k+1, distance_type=12, nt=1)
print '.'
return simMatIDs[:,1:], simMat[:,1:]
def GetKnn(ID):
print ID
info = Info.GetVideoInfo(ID)
frame_sift_lst = [
x for x in sorted(os.listdir(info['frame_sift_path']))
if x.endswith('.sift')
]
pano_sift_lst = [
x for x in sorted(os.listdir(info['pano_sift_path']))
if x.endswith('.sift')
]
#print pano_sift_lst
frame_desc = []
pano_desc = []
for one in frame_sift_lst:
f_name = info['frame_sift_path'] + '/' + one
desc = ReadSift.ReadSift(f_name)[1]
if desc.size == 0:
desc = np.zeros((0, 128), dtype='uint8')
frame_desc.append(desc)
for one in pano_sift_lst:
f_name = info['pano_sift_path'] + '/' + one
desc = ReadSift.ReadSift(f_name)[1]
if desc.size == 0:
desc = np.zeros((0, 128), dtype='uint8')
pano_desc.append(desc)
data = np.load(Info.Config.ROOT_PATH + '/gmm_2step.npz')
gmm = (data['a'], data['b'], data['c'])
mean = data['mean']
pca_transform = data['pca_transform']
image_fvs = []
for image_dec in (frame_desc + pano_desc):
image_dec = np.dot(image_dec - mean, pca_transform)
fv = ynumpy.fisher(gmm, image_dec, include='mu')
image_fvs.append(fv)
image_fvs = np.vstack(image_fvs)
image_fvs = np.sign(image_fvs) * np.abs(image_fvs)**0.5
norms = np.sqrt(np.sum(image_fvs**2, 1))
image_fvs /= norms.reshape(-1, 1)
image_fvs[np.isnan(image_fvs)] = 100
frame_fvs = image_fvs[0:len(frame_sift_lst)]
pano_fvs = image_fvs[len(frame_sift_lst):]
results, distances = ynumpy.knn(frame_fvs, pano_fvs, nnn=10)
#print results
#print distances
np.save(info['pano_path'] + '/fisher_results', results)
def getQuantizationErrorPQ(testFilename, dim, pointsCount, codebooksFilename):
codebooks = pickle.load(open(codebooksFilename, 'rb'))
points = readXvecs(testFilename, dim, pointsCount)
M = codebooks.shape[0]
if dim % M != 0:
raise Exception('Dim is not a multiple of M!')
else:
codebooksDim = dim / M
errors = np.zeros(pointsCount)
for m in xrange(M):
codebook = codebooks[m,:,:]
subpoints = points[:,m*codebooksDim:(m+1)*codebooksDim].copy()
(idx, dis) = ynumpy.knn(subpoints, codebook, nt=3)
errors += np.reshape(dis, pointsCount)
return np.mean(errors)
def encodeDatasetPQ(baseFilename, pointsCount, vocabFilename, codeFilename, threadsCount=30):
codebooks = pickle.load(open(vocabFilename, 'rb'))
M = codebooks.shape[0]
dim = codebooks.shape[2] * M
vocabDim = codebooks.shape[2]
codes = np.zeros((pointsCount, M), dtype='int32')
basePoints = readXvecs(baseFilename, dim, pointsCount)
for m in xrange(M):
codebook = codebooks[m,:,:]
subpoints = basePoints[:,m*vocabDim:(m+1)*vocabDim].copy()
(idx, dis) = ynumpy.knn(subpoints, codebook, nt=threadsCount)
codes[:,m] = idx.flatten()
codeFile = open(codeFilename, 'w')
pickle.dump(codes, codeFile)
codeFile.close()
def getQuantizationErrorOPQ(codebooksFilename, pointsFilename, pointsCount):
model = pickle.load(open(codebooksFilename, 'rb'))
R = model[1]
codebooks = model[0]
codebookDim = codebooks.shape[2]
M = codebooks.shape[0]
dim = codebookDim * M
points = readXvecs(pointsFilename, dim, pointsCount)
rotatedPoints = np.dot(points, R.T).astype('float32')
errors = 0.0
for m in xrange(M):
subpoints = rotatedPoints[:,m*dim/M:(m+1)*dim/M].copy()
(idx, dis) = ynumpy.knn(subpoints, codebooks[m,:,:], nt=2)
errors += np.sum(dis.flatten())
print errors / pointsCount
def getQuantizationErrorPQ(testFilename, dim, pointsCount, codebooksFilename):
codebooks = pickle.load(open(codebooksFilename, 'rb'))
points = readXvecs(testFilename, dim, pointsCount)
M = codebooks.shape[0]
if dim % M != 0:
raise Exception('Dim is not a multiple of M!')
else:
codebooksDim = dim / M
errors = np.zeros(pointsCount)
for m in xrange(M):
codebook = codebooks[m, :, :]
subpoints = points[:, m * codebooksDim:(m + 1) * codebooksDim].copy()
(idx, dis) = ynumpy.knn(subpoints, codebook, nt=3)
errors += np.reshape(dis, pointsCount)
return np.mean(errors)
def getTrackNeighborsYaelYnumpy(feats, k):
from yael import ynumpy, yael
nrTracks = feats.shape[0]
print '\tFinding %d nearest neighbors, for %d trajectories' % (k,
nrTracks),
sys.stdout.flush()
(simMatIDs, simMat) = ynumpy.knn(feats,
feats,
nnn=k + 1,
distance_type=12,
nt=1)
print '.'
return simMatIDs[:, 1:], simMat[:, 1:]
def encodeDatasetOPQ(baseFilename, pointsCount, vocabFilename, codeFilename, threadsCount=30):
model = pickle.load(open(vocabFilename, 'rb'))
codebooks = model[0]
R = model[1]
M = codebooks.shape[0]
dim = codebooks.shape[2] * M
codes = np.zeros((pointsCount, M), dtype='int32')
basePoints = readXvecs(baseFilename, dim, pointsCount)
basePoints = np.dot(basePoints, R.T).astype('float32')
error = 0
for m in xrange(M):
subpoints = basePoints[:,m*dim/M:(m+1)*dim/M].copy()
(idx, dis) = ynumpy.knn(subpoints, codebooks[m,:,:], nt=threadsCount)
codes[:,m] = idx.flatten()
error += np.sum(dis.flatten())
codeFile = open(codeFilename, 'wb')
pickle.dump(codes, codeFile)
codeFile.close()
def GetKnn(ID):
print ID
info = Info.GetVideoInfo(ID)
frame_sift_lst = [x for x in sorted(os.listdir(info['frame_sift_path'])) if x.endswith('.sift')]
pano_sift_lst = [x for x in sorted(os.listdir(info['pano_sift_path'])) if x.endswith('.sift')]
#print pano_sift_lst
frame_desc = []
pano_desc = []
for one in frame_sift_lst:
f_name = info['frame_sift_path'] + '/' + one
desc = ReadSift.ReadSift(f_name)[1]
if desc.size == 0:
desc = np.zeros((0, 128), dtype = 'uint8')
frame_desc.append(desc)
for one in pano_sift_lst:
f_name = info['pano_sift_path'] + '/' + one
desc = ReadSift.ReadSift(f_name)[1]
if desc.size == 0:
desc = np.zeros((0, 128), dtype = 'uint8')
pano_desc.append(desc)
data = np.load(Info.Config.ROOT_PATH + '/gmm_2step.npz')
gmm = (data['a'], data['b'], data['c'])
mean = data['mean']
pca_transform = data['pca_transform']
image_fvs = []
for image_dec in (frame_desc + pano_desc):
image_dec = np.dot(image_dec - mean, pca_transform)
fv = ynumpy.fisher(gmm, image_dec, include = 'mu')
image_fvs.append(fv)
image_fvs = np.vstack(image_fvs)
image_fvs = np.sign(image_fvs) * np.abs(image_fvs) ** 0.5
norms = np.sqrt(np.sum(image_fvs ** 2, 1))
image_fvs /= norms.reshape(-1,1)
image_fvs[np.isnan(image_fvs)] = 100
frame_fvs = image_fvs[0:len(frame_sift_lst)]
pano_fvs = image_fvs[len(frame_sift_lst):]
results, distances = ynumpy.knn(frame_fvs, pano_fvs, nnn = 10)
#print results
#print distances
np.save(info['pano_path'] + '/fisher_results', results)
def encodeDatasetPQ(baseFilename,
pointsCount,
vocabFilename,
codeFilename,
threadsCount=30):
codebooks = pickle.load(open(vocabFilename, 'rb'))
M = codebooks.shape[0]
dim = codebooks.shape[2] * M
vocabDim = codebooks.shape[2]
codes = np.zeros((pointsCount, M), dtype='int32')
basePoints = readXvecs(baseFilename, dim, pointsCount)
for m in xrange(M):
codebook = codebooks[m, :, :]
subpoints = basePoints[:, m * vocabDim:(m + 1) * vocabDim].copy()
(idx, dis) = ynumpy.knn(subpoints, codebook, nt=threadsCount)
codes[:, m] = idx.flatten()
codeFile = open(codeFilename, 'w')
pickle.dump(codes, codeFile)
codeFile.close()
def bovw(codebook, X, nt=1):
inds, dists = ynumpy.knn(X, codebook, nnn=1, distance_type=2, nt=1)
bins, _ = np.histogram(inds[:, 0],
bins=INTERNAL_PARAMETERS['bovw_codebook_k'])
return bins
nnn = 2
base = numpy.array([range(i, i+d) for i in range(5)],
dtype = numpy.float32)
queries = numpy.array([[x + 0.25 for x in range(i, i+d)]
for i in range(nq)],
dtype = numpy.float32)
print "base="
print base
print "queries="
print queries
idx, dis = ynumpy.knn(base, queries, nnn, distance_type = 1)
print "indices="
print idx
print "distances="
print dis
try:
# v, meta = ynumpy.siftgeo_read('/Users/matthijs//Desktop/papers/lhl/trunk/data/test_query_10k.siftgeo')
v, meta = ynumpy.siftgeo_read('/scratch2/bigimbaz/dataset/holidays/siftgeo/hesaff_norm/128300.siftgeo')
v = v.astype('float32')
except Exception, e:
# L2 normalize
norms = np.sqrt(np.sum(image_fvs ** 2, 1))
image_fvs /= norms.reshape(-1, 1)
# handle images with 0 local descriptor (100 = far away from "normal" images)
image_fvs[np.isnan(image_fvs)] = 100
# get the indices of the query images (the subset of images that end in "00")
query_imnos = [i for i, name in enumerate(image_names) if name[-2:] == "00"]
# corresponding descriptors
query_fvs = image_fvs[query_imnos]
# get the 8 NNs for all query images in the image_fvs array
results, distances = ynumpy.knn(query_fvs, image_fvs, nnn = 8)
aps = []
for qimno, qres in zip(query_imnos, results):
qname = image_names[qimno]
# collect the positive results in the dataset
# the positives have the same prefix as the query image
positive_results = set([i for i, name in enumerate(image_names)
if name != qname and name[:4] == qname[:4]])
#
# ranks of positives. We skip the result #0, assumed to be the query image
ranks = [i for i, res in enumerate(qres[1:]) if res in positive_results]
#
# accumulate trapezoids with this basis
recall_step = 1.0 / len(positive_results)
ap = 0
# "power normalization"
dataset = numpy.sqrt(numpy.abs(dataset)) * numpy.sign(dataset)
# per-Fisher vector L2 normalization
norms = numpy.sqrt((dataset**2).sum(axis=1))
dataset /= norms[:, None]
print("Searching 1 image per group")
# select the images that will serve as queries
query_idx = numpy.nonzero(image_range % 4 == 0)[0]
queries = dataset[query_idx].copy()
# compute all search results at once
results, distances = ynumpy.knn(queries, dataset, nnn=4)
if show:
fig = plt.figure()
for i, query_results in zip(query_idx, results):
imno = image_range[i]
results_imno = image_range[query_results]
n_ok = (results_imno / 4 == imno / 4).sum()
print(" Image %d:" % imno, results_imno, "n_ok=", n_ok)
if show:
fig.canvas.set_window_title("Query image %d" % imno)
for idx, j in enumerate([i] + list(query_results)):
imagename = "%s/ukbench%05d.jpg" % (image_directory,
image_range[j])
def bovw(codebook, X, nt=1):
inds, dists = ynumpy.knn(X, codebook, nnn=1, distance_type=2, nt=1)
bins, _ = np.histogram(inds[:,0], bins=INTERNAL_PARAMETERS['bovw_codebook_k'])
return bins
n = 5
nq = 3
nnn = 2
base = numpy.array([range(i, i + d) for i in range(5)], dtype=numpy.float32)
queries = numpy.array([[x + 0.25 for x in range(i, i + d)] for i in range(nq)],
dtype=numpy.float32)
print "base="
print base
print "queries="
print queries
idx, dis = ynumpy.knn(base, queries, nnn, distance_type=1)
print "indices="
print idx
print "distances="
print dis
try:
# v, meta = ynumpy.siftgeo_read('/Users/matthijs//Desktop/papers/lhl/trunk/data/test_query_10k.siftgeo')
v, meta = ynumpy.siftgeo_read(
'/scratch2/bigimbaz/dataset/holidays/siftgeo/hesaff_norm/128300.siftgeo'
)
v = v.astype('float32')
# "power normalization"
dataset = numpy.sqrt(numpy.abs(dataset)) * numpy.sign(dataset)
# per-Fisher vector L2 normalization
norms = numpy.sqrt((dataset ** 2).sum(axis=1))
dataset /= norms[:, None]
print("Searching 1 image per group")
# select the images that will serve as queries
query_idx = numpy.nonzero(image_range % 4 == 0)[0]
queries = dataset[query_idx].copy()
# compute all search results at once
results, distances = ynumpy.knn(queries, dataset, nnn=4)
if show:
fig = plt.figure()
for i, query_results in zip(query_idx, results):
imno = image_range[i]
results_imno = image_range[query_results]
n_ok = (results_imno / 4 == imno / 4).sum()
print(" Image %d:" % imno, results_imno, "n_ok=", n_ok)
if show:
fig.canvas.set_window_title("Query image %d" % imno)
for idx, j in enumerate([i] + list(query_results)):
imagename = "%s/ukbench%05d.jpg" % (
image_directory, image_range[j])