def reindex(nnindexer, verbose=True, memtrack=None):
r""" indexes all vectors with FLANN. """
num_vecs = nnindexer.num_indexed
notify_num = 1e6
verbose_ = ut.VERYVERBOSE or verbose or (not ut.QUIET
and num_vecs > notify_num)
if verbose_:
logger.info(
'[nnindex] ...building kdtree over %d points (this may take a sec).'
% num_vecs)
tt = ut.tic(msg='Building index')
idx2_vec = nnindexer.idx2_vec
flann_params = nnindexer.flann_params
if num_vecs == 0:
logger.info(
'WARNING: CANNOT BUILD FLANN INDEX OVER 0 POINTS. THIS MAY BE A SIGN OF A DEEPER ISSUE'
)
else:
if memtrack is not None:
memtrack.report('BEFORE BUILD FLANN INDEX')
nnindexer.flann.build_index(idx2_vec, **flann_params)
if memtrack is not None:
memtrack.report('AFTER BUILD FLANN INDEX')
if verbose_:
ut.toc(tt)
def reindex(self, verbose=True):
num_vecs = len(self.wx2_word)
if verbose:
print('[nnindex] ...building kdtree over %d points (this may take a sec).' % num_vecs)
tt = ut.tic(msg='Building vocab index')
if num_vecs == 0:
print('WARNING: CANNOT BUILD FLANN INDEX OVER 0 POINTS. THIS MAY BE A SIGN OF A DEEPER ISSUE')
else:
self.wordflann.build_index(self.wx2_word, **self.flann_params)
if verbose:
ut.toc(tt)
def reindex(self, verbose=True):
num_vecs = len(self.wx2_word)
if verbose:
print(
'[nnindex] ...building kdtree over %d points (this may take a sec).'
% num_vecs)
tt = ut.tic(msg='Building vocab index')
if num_vecs == 0:
print(
'WARNING: CANNOT BUILD FLANN INDEX OVER 0 POINTS. THIS MAY BE A SIGN OF A DEEPER ISSUE'
)
else:
self.wordflann.build_index(self.wx2_word, **self.flann_params)
if verbose:
ut.toc(tt)
def build(vocab, verbose=True):
num_vecs = len(vocab.wx_to_word)
if vocab.wordflann is None:
flannclass = pickle_flann.PickleFLANN
vocab.wordflann = flannclass()
if verbose:
logger.info(' ...build kdtree with %d points (may take a sec).' %
num_vecs)
tt = ut.tic(msg='Building vocab index')
if num_vecs == 0:
logger.info('WARNING: CANNOT BUILD FLANN INDEX OVER 0 POINTS.')
logger.info('THIS MAY BE A SIGN OF A DEEPER ISSUE')
else:
vocab.wordflann.build_index(vocab.wx_to_word, **vocab.flann_params)
if verbose:
ut.toc(tt)
def reindex(nnindexer, verbose=True, memtrack=None):
r""" indexes all vectors with FLANN. """
num_vecs = nnindexer.num_indexed
notify_num = 1E6
verbose_ = ut.VERYVERBOSE or verbose or (
not ut.QUIET and num_vecs > notify_num)
if verbose_:
print('[nnindex] ...building kdtree over %d points (this may take a sec).' % num_vecs)
tt = ut.tic(msg='Building index')
idx2_vec = nnindexer.idx2_vec
flann_params = nnindexer.flann_params
if num_vecs == 0:
print('WARNING: CANNOT BUILD FLANN INDEX OVER 0 POINTS. THIS MAY BE A SIGN OF A DEEPER ISSUE')
else:
if memtrack is not None:
memtrack.report('BEFORE BUILD FLANN INDEX')
nnindexer.flann.build_index(idx2_vec, **flann_params)
if memtrack is not None:
memtrack.report('AFTER BUILD FLANN INDEX')
if verbose_:
ut.toc(tt)
def TEST_SQL_NUMPY():
sqldb_fname = 'temp_test_sql_numpy.sqlite3'
sqldb_dpath = utool.util_cplat.get_app_resource_dir('ibeis', 'testfiles')
utool.ensuredir(sqldb_dpath)
utool.util_path.remove_file(join(sqldb_dpath, sqldb_fname), dryrun=False)
db = sqldbc.SQLDatabaseController(sqldb_dpath=sqldb_dpath,
sqldb_fname=sqldb_fname)
db.add_table('temp', [
('temp_id', 'INTEGER PRIMARY KEY'),
('temp_hash', 'NUMPY'),
])
tt = utool.tic()
feats_list = grab_numpy_testdata(shape=(3e3, 128), dtype=np.uint8)
print(' * numpy.new time=%r sec' % utool.toc(tt))
print('[TEST] insert numpy arrays')
tt = utool.tic()
feats_iter = ((feats, ) for feats in feats_list)
db.executemany(operation='''
INSERT
INTO temp
(
temp_hash
)
VALUES (?)
''',
params_iter=feats_iter)
print(' * execute insert time=%r sec' % utool.toc(tt))
print('[TEST] save sql database')
tt = utool.tic()
#db.cur.commit()
db.connection.commit()
print(' * commit time=%r sec' % utool.toc(tt))
print('[TEST] read from sql database')
tt = utool.tic()
db.cur.execute('SELECT temp_hash FROM temp', [])
print(' * execute select time=%r sec' % utool.toc(tt))
tt = utool.tic()
result_list = _results_gen(db.cur)
print(' * iter results time=%r sec' % utool.toc(tt))
print(' * memory(result_list) = %s' %
utool.byte_str2(utool.get_object_size(result_list)))
del result_list
#print('[TEST] result_list=%r' % result_list)
print('[TEST] dump sql database')
tt = utool.tic()
db.dump('temp.dump.txt')
print(' * dump time=%r sec' % utool.toc(tt))
#with open('temp.dump.txt') as file_:
# print(file_.read())
return locals()
def TEST_SQL_NUMPY():
sqldb_fname = 'temp_test_sql_numpy.sqlite3'
sqldb_dpath = utool.util_cplat.get_app_resource_dir('ibeis', 'testfiles')
utool.ensuredir(sqldb_dpath)
utool.util_path.remove_file(join(sqldb_dpath, sqldb_fname), dryrun=False)
db = sqldbc.SQLDatabaseController(sqldb_dpath=sqldb_dpath,
sqldb_fname=sqldb_fname)
db.schema('temp', [
('temp_id', 'INTEGER PRIMARY KEY'),
('temp_hash', 'NUMPY'),
])
tt = utool.tic()
feats_list = grab_numpy_testdata(shape=(3e3, 128), dtype=np.uint8)
print(' * numpy.new time=%r sec' % utool.toc(tt))
print('[TEST] insert numpy arrays')
tt = utool.tic()
feats_iter = ((feats, ) for feats in feats_list)
db.executemany(operation='''
INSERT
INTO temp
(
temp_hash
)
VALUES (?)
''', params_iter=feats_iter)
print(' * execute insert time=%r sec' % utool.toc(tt))
print('[TEST] save sql database')
tt = utool.tic()
#db.cur.commit()
db.connection.commit()
print(' * commit time=%r sec' % utool.toc(tt))
print('[TEST] read from sql database')
tt = utool.tic()
db.cur.execute('SELECT temp_hash FROM temp', [])
print(' * execute select time=%r sec' % utool.toc(tt))
tt = utool.tic()
result_list = _results_gen(db.cur)
print(' * iter results time=%r sec' % utool.toc(tt))
print(' * memory(result_list) = %s' % utool.byte_str2(utool.get_object_size(result_list)))
del result_list
#print('[TEST] result_list=%r' % result_list)
print('[TEST] dump sql database')
tt = utool.tic()
db.dump('temp.dump.txt')
print(' * dump time=%r sec' % utool.toc(tt))
#with open('temp.dump.txt') as file_:
# print(file_.read())
return locals()
def _check_for_double_click(iqrw, qtindex):
threshold = 0.50 # seconds
distance = utool.toc(iqrw.tt)
print('Pressed %r' % (distance,))
col = qtindex.column()
model = qtindex.model()
colname = model.get_header_name(col)
if distance <= threshold:
if colname == 'status':
iqrw.view.clicked.emit(qtindex)
iqrw._on_click(qtindex)
else:
#iqrw.view.doubleClicked.emit(qtindex)
iqrw._on_doubleclick(qtindex)
iqrw.tt = utool.tic()
def _akmeans_iterate(data, centroids, datax2_clusterx_old, max_iters,
flann_params, ave_unchanged_thresh, ave_unchanged_iterwin):
""" Helper function which continues the iterations of akmeans """
num_data = data.shape[0]
num_clusters = centroids.shape[0]
# Keep track of how many points have changed in each iteration
xx2_unchanged = np.zeros(ave_unchanged_iterwin, dtype=centroids.dtype) + len(data)
print('[akmeans] Running akmeans: data.shape=%r ; num_clusters=%r' %
(data.shape, num_clusters))
print('[akmeans] * max_iters = %r ' % max_iters)
print('[akmeans] * ave_unchanged_iterwin=%r ; ave_unchanged_thresh=%r' %
(ave_unchanged_thresh, ave_unchanged_iterwin))
#print('[akmeans] Printing akmeans info in format: time (iterx, ave(#changed), #unchanged)')
xx = 0
for xx in range(0, max_iters):
tt = ut.tic()
ut.print_('...tic')
# 1) Find each datapoints nearest cluster center
(datax2_clusterx, _dist) = nn.ann_flann_once(centroids, data, 1, flann_params)
ellapsed = ut.toc(tt)
ut.print_('...toc(%.2fs)' % ellapsed)
# 2) Compute new cluster centers
centroids = _compute_cluster_centers(num_data, num_clusters, data, centroids, datax2_clusterx)
# 3) Check for convergence (no change of cluster index)
#ut.print_('+')
num_changed = (datax2_clusterx_old != datax2_clusterx).sum()
xx2_unchanged[xx % ave_unchanged_iterwin] = num_changed
ave_unchanged = xx2_unchanged.mean()
#ut.print_(' (%d, %.2f, %d)\n' % (xx, ave_unchanged, num_changed))
if ave_unchanged < ave_unchanged_thresh:
break
else: # Iterate
datax2_clusterx_old = datax2_clusterx
#if xx % 5 == 0:
# sys.stdout.flush()
if xx == max_iters:
print('[akmeans] * AKMEANS: converged in %d/%d iters' % (xx + 1, max_iters))
else:
print('[akmeans] * AKMEANS: reached the maximum iterations after in %d/%d iters' % (xx + 1, max_iters))
sys.stdout.flush()
return (datax2_clusterx, centroids)
def detect_opencv_keypoints():
import cv2
import vtool as vt
import numpy as np # NOQA
#img_fpath = ut.grab_test_imgpath(ut.get_argval('--fname', default='lena.png'))
img_fpath = ut.grab_test_imgpath(
ut.get_argval('--fname', default='zebra.png'))
imgBGR = vt.imread(img_fpath)
imgGray = cv2.cvtColor(imgBGR, cv2.COLOR_BGR2GRAY)
def from_cv2_kpts(cv2_kp):
kp = (cv2_kp.pt[0], cv2_kp.pt[1], cv2_kp.size, 0, cv2_kp.size,
cv2_kp.angle)
return kp
print('\n'.join(ut.search_module(cv2, 'create', recursive=True)))
detect_factory = {
#'BLOB': cv2.SimpleBlobDetector_create,
#'HARRIS' : HarrisWrapper.create,
#'SIFT': cv2.xfeatures2d.SIFT_create, # really DoG
'SURF': cv2.xfeatures2d.SURF_create, # really harris corners
'MSER': cv2.MSER_create,
#'StarDetector_create',
}
extract_factory = {
'SIFT': cv2.xfeatures2d.SIFT_create,
'SURF': cv2.xfeatures2d.SURF_create,
#'DAISY': cv2.xfeatures2d.DAISY_create,
'FREAK': cv2.xfeatures2d.FREAK_create,
#'LATCH': cv2.xfeatures2d.LATCH_create,
#'LUCID': cv2.xfeatures2d.LUCID_create,
#'ORB': cv2.ORB_create,
}
mask = None
type_to_kpts = {}
type_to_desc = {}
key = 'BLOB'
key = 'MSER'
for key in detect_factory.keys():
factory = detect_factory[key]
extractor = factory()
# For MSERS need to adapt shape and then convert into a keypoint repr
if hasattr(extractor, 'detectRegions'):
# bboxes are x,y,w,h
regions, bboxes = extractor.detectRegions(imgGray)
# ellipse definition from [Fitzgibbon95]
# http://www.bmva.org/bmvc/1995/bmvc-95-050.pdf p518
# ell = [c_x, c_y, R_x, R_y, theta]
# (cx, cy) = conic center
# Rx and Ry = conic radii
# theta is the counterclockwise angle
fitz_ellipses = [cv2.fitEllipse(mser) for mser in regions]
# http://answers.opencv.org/question/19015/how-to-use-mser-in-python/
#hulls = [cv2.convexHull(p.reshape(-1, 1, 2)) for p in regions]
#hull_ells = [cv2.fitEllipse(hull[:, 0]) for hull in hulls]
kpts_ = []
for ell in fitz_ellipses:
((cx, cy), (rx, ry), degrees) = ell
theta = np.radians(degrees) # opencv lives in radians
S = vt.scale_mat3x3(rx, ry)
T = vt.translation_mat3x3(cx, cy)
R = vt.rotation_mat3x3(theta)
#R = np.eye(3)
invVR = T.dot(R.dot(S))
kpt = vt.flatten_invV_mats_to_kpts(np.array([invVR]))[0]
kpts_.append(kpt)
kpts_ = np.array(kpts_)
tt = ut.tic('Computing %r keypoints' % (key, ))
try:
cv2_kpts = extractor.detect(imgGray, mask)
except Exception as ex:
ut.printex(ex,
'Failed to computed %r keypoints' % (key, ),
iswarning=True)
pass
else:
ut.toc(tt)
type_to_kpts[key] = cv2_kpts
print(list(type_to_kpts.keys()))
print(ut.depth_profile(list(type_to_kpts.values())))
print('type_to_kpts = ' + ut.repr3(type_to_kpts, truncate=True))
cv2_kpts = type_to_kpts['MSER']
kp = cv2_kpts[0] # NOQA
#cv2.fitEllipse(cv2_kpts[0])
cv2_kpts = type_to_kpts['SURF']
for key in extract_factory.keys():
factory = extract_factory[key]
extractor = factory()
tt = ut.tic('Computing %r descriptors' % (key, ))
try:
filtered_cv2_kpts, desc = extractor.compute(imgGray, cv2_kpts)
except Exception as ex:
ut.printex(ex,
'Failed to computed %r descriptors' % (key, ),
iswarning=True)
pass
else:
ut.toc(tt)
type_to_desc[key] = desc
print(list(type_to_desc.keys()))
print(ut.depth_profile(list(type_to_desc.values())))
print('type_to_desc = ' + ut.repr3(type_to_desc, truncate=True))
def create_databse():
def _randstr(size=6, chars=string.ascii_uppercase + string.digits):
return ''.join(random.choice(chars) for _ in range(size))
sqldb_fname = 'data_test_qt.sqlite3'
sqldb_dpath = utool.util_cplat.get_app_resource_dir('ibeis', 'testfiles')
utool.ensuredir(sqldb_dpath)
utool.util_path.remove_file(join(sqldb_dpath, sqldb_fname), dryrun=False)
db = SQLDatabaseControl.SQLDatabaseController(sqldb_dpath=sqldb_dpath,
sqldb_fname=sqldb_fname)
encounters = [
('encounter_id', 'INTEGER PRIMARY KEY'),
('encounter_name', 'TEXT'),
]
db.add_table('encounters', encounters)
rows = 1 * (10 ** 3)
feats_iter = ( (_randstr(), ) for i in range(rows) )
print('[TEST] insert encounters')
tt = utool.tic()
db.executemany(operation='''
INSERT
INTO encounters
(
encounter_name
)
VALUES (?)
''', params_iter=feats_iter)
print(' * execute insert time=%r sec' % utool.toc(tt))
##############################################
headers = [
('data_id', 'INTEGER PRIMARY KEY'),
('encounter_id', 'INT'),
('data_float', 'FLOAT'),
('data_int', 'INT'),
('data_text', 'TEXT'),
('data_text2', 'TEXT'),
]
db.add_table('data', headers)
col_name_list = [ column[0] for column in headers ]
col_type_list = [ str ] * len(col_name_list)
col_edit_list = [ False, True, True, True, True, True ]
col_nice_list = [
'ID',
'Encounter ID',
'TEST Float',
'TEST Int',
'TEST String 1',
'TEST String 2',
]
rows = 1 * (10 ** 4)
feats_iter = ((random.randint(0, 1000), random.uniform(0.0, 1.0), random.randint(0, 100), _randstr(), _randstr())
for i in range(rows) )
print('[TEST] insert data')
tt = utool.tic()
db.executemany(operation='''
INSERT
INTO data
(
encounter_id,
data_float,
data_int,
data_text,
data_text2
)
VALUES (?,?,?,?,?)
''', params_iter=feats_iter)
print(' * execute insert time=%r sec' % utool.toc(tt))
return col_name_list, col_type_list, col_edit_list, col_nice_list, db
def detect_opencv_keypoints():
import cv2
import vtool as vt
import numpy as np # NOQA
#img_fpath = ut.grab_test_imgpath(ut.get_argval('--fname', default='lena.png'))
img_fpath = ut.grab_test_imgpath(ut.get_argval('--fname', default='zebra.png'))
imgBGR = vt.imread(img_fpath)
imgGray = cv2.cvtColor(imgBGR, cv2.COLOR_BGR2GRAY)
def from_cv2_kpts(cv2_kp):
kp = (cv2_kp.pt[0], cv2_kp.pt[1], cv2_kp.size, 0, cv2_kp.size, cv2_kp.angle)
return kp
print('\n'.join(ut.search_module(cv2, 'create', recursive=True)))
detect_factory = {
#'BLOB': cv2.SimpleBlobDetector_create,
#'HARRIS' : HarrisWrapper.create,
#'SIFT': cv2.xfeatures2d.SIFT_create, # really DoG
'SURF': cv2.xfeatures2d.SURF_create, # really harris corners
'MSER': cv2.MSER_create,
#'StarDetector_create',
}
extract_factory = {
'SIFT': cv2.xfeatures2d.SIFT_create,
'SURF': cv2.xfeatures2d.SURF_create,
#'DAISY': cv2.xfeatures2d.DAISY_create,
'FREAK': cv2.xfeatures2d.FREAK_create,
#'LATCH': cv2.xfeatures2d.LATCH_create,
#'LUCID': cv2.xfeatures2d.LUCID_create,
#'ORB': cv2.ORB_create,
}
mask = None
type_to_kpts = {}
type_to_desc = {}
key = 'BLOB'
key = 'MSER'
for key in detect_factory.keys():
factory = detect_factory[key]
extractor = factory()
# For MSERS need to adapt shape and then convert into a keypoint repr
if hasattr(extractor, 'detectRegions'):
# bboxes are x,y,w,h
regions, bboxes = extractor.detectRegions(imgGray)
# ellipse definition from [Fitzgibbon95]
# http://www.bmva.org/bmvc/1995/bmvc-95-050.pdf p518
# ell = [c_x, c_y, R_x, R_y, theta]
# (cx, cy) = conic center
# Rx and Ry = conic radii
# theta is the counterclockwise angle
fitz_ellipses = [cv2.fitEllipse(mser) for mser in regions]
# http://answers.opencv.org/question/19015/how-to-use-mser-in-python/
#hulls = [cv2.convexHull(p.reshape(-1, 1, 2)) for p in regions]
#hull_ells = [cv2.fitEllipse(hull[:, 0]) for hull in hulls]
kpts_ = []
for ell in fitz_ellipses:
((cx, cy), (rx, ry), degrees) = ell
theta = np.radians(degrees) # opencv lives in radians
S = vt.scale_mat3x3(rx, ry)
T = vt.translation_mat3x3(cx, cy)
R = vt.rotation_mat3x3(theta)
#R = np.eye(3)
invVR = T.dot(R.dot(S))
kpt = vt.flatten_invV_mats_to_kpts(np.array([invVR]))[0]
kpts_.append(kpt)
kpts_ = np.array(kpts_)
tt = ut.tic('Computing %r keypoints' % (key,))
try:
cv2_kpts = extractor.detect(imgGray, mask)
except Exception as ex:
ut.printex(ex, 'Failed to computed %r keypoints' % (key,), iswarning=True)
pass
else:
ut.toc(tt)
type_to_kpts[key] = cv2_kpts
print(list(type_to_kpts.keys()))
print(ut.depth_profile(list(type_to_kpts.values())))
print('type_to_kpts = ' + ut.repr3(type_to_kpts, truncate=True))
cv2_kpts = type_to_kpts['MSER']
kp = cv2_kpts[0] # NOQA
#cv2.fitEllipse(cv2_kpts[0])
cv2_kpts = type_to_kpts['SURF']
for key in extract_factory.keys():
factory = extract_factory[key]
extractor = factory()
tt = ut.tic('Computing %r descriptors' % (key,))
try:
filtered_cv2_kpts, desc = extractor.compute(imgGray, cv2_kpts)
except Exception as ex:
ut.printex(ex, 'Failed to computed %r descriptors' % (key,), iswarning=True)
pass
else:
ut.toc(tt)
type_to_desc[key] = desc
print(list(type_to_desc.keys()))
print(ut.depth_profile(list(type_to_desc.values())))
print('type_to_desc = ' + ut.repr3(type_to_desc, truncate=True))
def create_databse():
def _randstr(size=6, chars=string.ascii_uppercase + string.digits):
return ''.join(random.choice(chars) for _ in range(size))
sqldb_fname = 'data_test_qt.sqlite3'
sqldb_dpath = utool.util_cplat.get_app_resource_dir('ibeis', 'testfiles')
utool.ensuredir(sqldb_dpath)
utool.util_path.remove_file(join(sqldb_dpath, sqldb_fname), dryrun=False)
db = SQLDatabaseControl.SQLDatabaseController(sqldb_dpath=sqldb_dpath,
sqldb_fname=sqldb_fname)
imagesets = [
('imageset_id', 'INTEGER PRIMARY KEY'),
('imageset_name', 'TEXT'),
]
db.add_table('imagesets', imagesets)
rows = 1 * (10**3)
feats_iter = ((_randstr(), ) for i in range(rows))
print('[TEST] insert imagesets')
tt = utool.tic()
db.executemany(operation='''
INSERT
INTO imagesets
(
imageset_name
)
VALUES (?)
''',
params_iter=feats_iter)
print(' * execute insert time=%r sec' % utool.toc(tt))
##############################################
headers = [
('data_id', 'INTEGER PRIMARY KEY'),
('imageset_id', 'INT'),
('data_float', 'FLOAT'),
('data_int', 'INT'),
('data_text', 'TEXT'),
('data_text2', 'TEXT'),
]
db.add_table('data', headers)
col_name_list = [column[0] for column in headers]
col_type_list = [str] * len(col_name_list)
col_edit_list = [False, True, True, True, True, True]
col_nice_list = [
'ID',
'ImageSet ID',
'TEST Float',
'TEST Int',
'TEST String 1',
'TEST String 2',
]
rows = 1 * (10**4)
feats_iter = ((random.randint(0, 1000), random.uniform(0.0, 1.0),
random.randint(0, 100), _randstr(), _randstr())
for i in range(rows))
print('[TEST] insert data')
tt = utool.tic()
db.executemany(operation='''
INSERT
INTO data
(
imageset_id,
data_float,
data_int,
data_text,
data_text2
)
VALUES (?,?,?,?,?)
''',
params_iter=feats_iter)
print(' * execute insert time=%r sec' % utool.toc(tt))
return col_name_list, col_type_list, col_edit_list, col_nice_list, db