def debug_nnindexer(nnindexer):
r"""
Makes sure the indexer has valid SIFT descriptors
"""
# FIXME: they might not agree if data has been added / removed
init_data, extra_data = nnindexer.flann.get_indexed_data()
with ut.Indenter('[NNINDEX_DEBUG]'):
print('extra_data = %r' % (extra_data,))
print('init_data = %r' % (init_data,))
print('nnindexer.max_distance_sqrd = %r' % (nnindexer.max_distance_sqrd,))
data_agrees = nnindexer.idx2_vec is nnindexer.flann.get_indexed_data()[0]
if data_agrees:
print('indexed_data agrees')
assert vt.check_sift_validity(init_data), 'bad SIFT properties'
assert data_agrees, 'indexed data does not agree'
def debug_nnindexer(nnindexer):
r"""
Makes sure the indexer has valid SIFT descriptors
"""
# FIXME: they might not agree if data has been added / removed
init_data, extra_data = nnindexer.flann.get_indexed_data()
with ut.Indenter('[NNINDEX_DEBUG]'):
print('extra_data = %r' % (extra_data,))
print('init_data = %r' % (init_data,))
print('nnindexer.max_distance_sqrd = %r' % (nnindexer.max_distance_sqrd,))
data_agrees = nnindexer.idx2_vec is nnindexer.flann.get_indexed_data()[0]
if data_agrees:
print('indexed_data agrees')
assert vt.check_sift_validity(init_data), 'bad SIFT properties'
assert data_agrees, 'indexed data does not agree'
def _assert_siftvec(sift):
import vtool as vt
assert vt.check_sift_validity(sift)
def detect_feats(img_fpath,
use_adaptive_scale=False,
nogravity_hack=False,
**kwargs):
r"""
driver function for detecting hessian affine keypoints from an image path.
extra parameters can be passed to the hessian affine detector by using
kwargs.
Args:
img_fpath (str): image file path on disk
use_adaptive_scale (bool):
nogravity_hack (bool):
Kwargs:
numberOfScales (int) : default=3
threshold (float) : default=5.33333333333
edgeEigenValueRatio (float) : default=10.0
border (int) : default=5
maxIterations (int) : default=16
convergenceThreshold (float) : default=0.05
smmWindowSize (int) : default=19
mrSize (float) : default=5.19615242271
spatialBins (int) : default=4
orientationBins (int) : default=8
maxBinValue (float) : default=0.2
initialSigma (float) : default=1.6
patchSize (int) : default=41
scale_min (float) : default=-1.0
scale_max (float) : default=-1.0
rotation_invariance (bool) : default=False
affine_invariance (bool) : default=True
Returns:
tuple : (kpts, vecs)
CommandLine:
python -m pyhesaff detect_feats
python -m pyhesaff detect_feats --show
python -m pyhesaff detect_feats --show --fname star.png
python -m pyhesaff detect_feats --show --fname zebra.jpg
python -m pyhesaff detect_feats --show --fname lena.png
python -m pyhesaff detect_feats --show --fname carl.jpg
python -m pyhesaff detect_feats --show --fname lena.png --ri
python -m pyhesaff detect_feats --show --fname lena.png --ai
python -m pyhesaff detect_feats --show --fname easy1.png --no-ai
python -m pyhesaff detect_feats --show --fname easy1.png --no-ai --numberOfScales=1 --verbose
python -m pyhesaff detect_feats --show --fname easy1.png --no-ai --scale-max=100 --verbose
python -m pyhesaff detect_feats --show --fname easy1.png --no-ai --scale-min=20 --verbose
python -m pyhesaff detect_feats --show --fname easy1.png --no-ai --scale-min=100 --verbose
python -m pyhesaff detect_feats --show --fname easy1.png --no-ai --scale-max=20 --verbose
python -m vtool.test_constrained_matching visualize_matches --show
python -m vtool.tests.dummy testdata_ratio_matches --show
python -m pyhesaff detect_feats --show --fname easy1.png --ai \
--verbose --rebuild-hesaff --scale-min=35 --scale-max=40 --no-rmbuild
python -m pyhesaff detect_feats --show --fname easy1.png --ai \
--verbose --scale-min=35 --scale-max=40&
python -m pyhesaff detect_feats --show --fname easy1.png --no-ai \
--verbose --scale-min=35 --scale-max=40&
python -m pyhesaff detect_feats --show --fname easy1.png --no-ai \
--verbose --scale-max=40 --darken .5
python -m pyhesaff detect_feats --show --fname easy1.png --no-ai \
--verbose --scale-max=30 --darken .5
python -m pyhesaff detect_feats --show --fname easy1.png --ai \
--verbose --scale-max=30 --darken .5
# DENSE KEYPOINTS
python -m pyhesaff detect_feats --show --fname lena.png \
--no-affine-invariance --numberOfScales=1 --maxPyramidLevels=1 \
--use_dense --dense_stride=64
python -m pyhesaff detect_feats --show --fname lena.png \
--no-affine-invariance --numberOfScales=1 --maxPyramidLevels=1 \
--use_dense --dense_stride=64 --rotation-invariance
python -m pyhesaff detect_feats --show --fname lena.png \
--affine-invariance --numberOfScales=1 --maxPyramidLevels=1 \
--use_dense --dense_stride=64
python -m pyhesaff detect_feats --show --fname lena.png \
--no-affine-invariance --numberOfScales=3 \
--maxPyramidLevels=2 --use_dense --dense_stride=32
python -m pyhesaff detect_feats --show --only_count=False
Example0:
>>> # ENABLE_DOCTEST
>>> # Test simple detect
>>> from pyhesaff._pyhesaff import * # NOQA
>>> import plottool as pt
>>> import utool as ut
>>> import vtool as vt
>>> TAU = 2 * np.pi
>>> fpath = ut.grab_test_imgpath(ut.get_argval('--fname', default='lena.png'))
>>> theta = ut.get_argval('--theta', float, 0) # TAU * 3 / 8)
>>> img_fpath = vt.rotate_image_ondisk(fpath, theta)
>>> kwargs = argparse_hesaff_params()
>>> print('kwargs = %r' % (kwargs,))
>>> (kpts_list, vecs_list) = detect_feats(img_fpath, **kwargs)
>>> kpts = kpts_list
>>> vecs = vecs_list
>>> # Show keypoints
>>> imgBGR = vt.imread(img_fpath)
>>> ut.quit_if_noshow()
>>> pt.interact_keypoints.ishow_keypoints(imgBGR, kpts, vecs, ori=True, ell_alpha=.4, color='distinct')
>>> pt.set_figtitle('Detect Kpts in Image')
>>> pt.show_if_requested()
"""
if __DEBUG__:
print('[hes] Detecting Keypoints')
print('[hes] use_adaptive_scale=%r' % (use_adaptive_scale, ))
print('[hes] nogravity_hack=%r' % (nogravity_hack, ))
print('[hes] kwargs=%s' % (ut.dict_str(kwargs), ))
# Load image
hesaff_ptr = _new_fpath_hesaff(img_fpath, **kwargs)
if __DEBUG__:
print('[hes] detect')
# Get num detected
nKpts = HESAFF_CLIB.detect(hesaff_ptr)
if __DEBUG__:
print('[hes] allocate')
# Allocate arrays
kpts = alloc_kpts(nKpts)
vecs = alloc_vecs(nKpts)
if __DEBUG__:
print('[hes] export')
# Populate arrays
HESAFF_CLIB.exportArrays(hesaff_ptr, nKpts, kpts, vecs)
HESAFF_CLIB.free_hesaff(hesaff_ptr)
if __DEBUG__:
import vtool as vt
assert vt.check_sift_validity(vecs)
if use_adaptive_scale: # Adapt scale if requested
if __DEBUG__:
print('[hes] adapt_scale')
kpts, vecs = adapt_scale(img_fpath, kpts)
if nogravity_hack:
if __DEBUG__:
print('[hes] adapt_rotation')
kpts, vecs = vtool_adapt_rotation(img_fpath, kpts)
return kpts, vecs
def detect_feats(img_fpath, use_adaptive_scale=False, nogravity_hack=False, **kwargs):
r"""
driver function for detecting hessian affine keypoints from an image path.
extra parameters can be passed to the hessian affine detector by using
kwargs.
Args:
img_fpath (str): image file path on disk
use_adaptive_scale (bool):
nogravity_hack (bool):
Kwargs:
numberOfScales (int) : default=3
threshold (float) : default=5.33333333333
edgeEigenValueRatio (float) : default=10.0
border (int) : default=5
maxIterations (int) : default=16
convergenceThreshold (float) : default=0.05
smmWindowSize (int) : default=19
mrSize (float) : default=5.19615242271
spatialBins (int) : default=4
orientationBins (int) : default=8
maxBinValue (float) : default=0.2
initialSigma (float) : default=1.6
patchSize (int) : default=41
scale_min (float) : default=-1.0
scale_max (float) : default=-1.0
rotation_invariance (bool) : default=False
affine_invariance (bool) : default=True
Returns:
tuple : (kpts, vecs)
CommandLine:
python -m pyhesaff detect_feats
python -m pyhesaff detect_feats --show
python -m pyhesaff detect_feats --show --fname star.png
python -m pyhesaff detect_feats --show --fname lena.png
python -m pyhesaff detect_feats --show --fname carl.jpg
python -m pyhesaff detect_feats --show --fname lena.png --ri
python -m pyhesaff detect_feats --show --fname lena.png --ai
python -m pyhesaff detect_feats --show --fname easy1.png --no-ai
python -m pyhesaff detect_feats --show --fname easy1.png --no-ai --numberOfScales=1 --verbose
python -m pyhesaff detect_feats --show --fname easy1.png --no-ai --scale-max=100 --verbose
python -m pyhesaff detect_feats --show --fname easy1.png --no-ai --scale-min=20 --verbose
python -m pyhesaff detect_feats --show --fname easy1.png --no-ai --scale-min=100 --verbose
python -m pyhesaff detect_feats --show --fname easy1.png --no-ai --scale-max=20 --verbose
python -m vtool.test_constrained_matching visualize_matches --show
python -m vtool.tests.dummy testdata_ratio_matches --show
python -m pyhesaff detect_feats --show --fname easy1.png --ai \
--verbose --rebuild-hesaff --scale-min=35 --scale-max=40 --no-rmbuild
python -m pyhesaff detect_feats --show --fname easy1.png --ai \
--verbose --scale-min=35 --scale-max=40&
python -m pyhesaff detect_feats --show --fname easy1.png --no-ai \
--verbose --scale-min=35 --scale-max=40&
python -m pyhesaff detect_feats --show --fname easy1.png --no-ai \
--verbose --scale-max=40 --darken .5
python -m pyhesaff detect_feats --show --fname easy1.png --no-ai \
--verbose --scale-max=30 --darken .5
python -m pyhesaff detect_feats --show --fname easy1.png --ai \
--verbose --scale-max=30 --darken .5
# DENSE KEYPOINTS
python -m pyhesaff detect_feats --show --fname lena.png \
--no-affine-invariance --numberOfScales=1 --maxPyramidLevels=1 \
--use_dense --dense_stride=64
python -m pyhesaff detect_feats --show --fname lena.png \
--no-affine-invariance --numberOfScales=1 --maxPyramidLevels=1 \
--use_dense --dense_stride=64 --rotation-invariance
python -m pyhesaff detect_feats --show --fname lena.png \
--affine-invariance --numberOfScales=1 --maxPyramidLevels=1 \
--use_dense --dense_stride=64
python -m pyhesaff detect_feats --show --fname lena.png \
--no-affine-invariance --numberOfScales=3 \
--maxPyramidLevels=2 --use_dense --dense_stride=32
python -m pyhesaff detect_feats --show --only_count=False
Example0:
>>> # ENABLE_DOCTEST
>>> # Test simple detect
>>> from pyhesaff._pyhesaff import * # NOQA
>>> import plottool as pt
>>> import utool as ut
>>> import vtool as vt
>>> TAU = 2 * np.pi
>>> fpath = ut.grab_test_imgpath(ut.get_argval('--fname', default='lena.png'))
>>> theta = ut.get_argval('--theta', float, 0) # TAU * 3 / 8)
>>> img_fpath = vt.rotate_image_ondisk(fpath, theta)
>>> kwargs = argparse_hesaff_params()
>>> (kpts_list, vecs_list) = detect_feats(img_fpath, **kwargs)
>>> kpts = kpts_list
>>> vecs = vecs_list
>>> # Show keypoints
>>> imgBGR = vt.imread(img_fpath)
>>> ut.quit_if_noshow()
>>> pt.interact_keypoints.ishow_keypoints(imgBGR, kpts, vecs, ori=True, ell_alpha=.4, color='distinct')
>>> pt.set_figtitle('Detect Kpts in Image')
>>> pt.show_if_requested()
"""
if __DEBUG__:
print('[hes] Detecting Keypoints')
print('[hes] use_adaptive_scale=%r' % (use_adaptive_scale,))
print('[hes] nogravity_hack=%r' % (nogravity_hack,))
print('[hes] kwargs=%s' % (ut.dict_str(kwargs),))
# Load image
hesaff_ptr = _new_fpath_hesaff(img_fpath, **kwargs)
if __DEBUG__:
print('[hes] detect')
# Get num detected
nKpts = HESAFF_CLIB.detect(hesaff_ptr)
if __DEBUG__:
print('[hes] allocate')
# Allocate arrays
kpts = alloc_kpts(nKpts)
vecs = alloc_vecs(nKpts)
if __DEBUG__:
print('[hes] export')
# Populate arrays
HESAFF_CLIB.exportArrays(hesaff_ptr, nKpts, kpts, vecs)
HESAFF_CLIB.free_hesaff(hesaff_ptr)
if __DEBUG__:
import vtool as vt
assert vt.check_sift_validity(vecs)
if use_adaptive_scale: # Adapt scale if requested
if __DEBUG__:
print('[hes] adapt_scale')
kpts, vecs = adapt_scale(img_fpath, kpts)
if nogravity_hack:
if __DEBUG__:
print('[hes] adapt_rotation')
kpts, vecs = vtool_adapt_rotation(img_fpath, kpts)
return kpts, vecs
def _assert_siftvec(sift):
import vtool as vt
assert vt.check_sift_validity(sift)
