def testshow_extramargin_info(ibs, aid_list, arg_list, newsize_list, halfoffset_cs_list):
#cfpath, gfpath, bbox, theta, new_size, filter_list = tup
# TEMP TESTING
from vtool import chip as ctool
import plottool as pt
import vtool as vt
from ibeis.viz import viz_chip
index = 0
cfpath, gfpath, bbox, theta, new_size, filter_list = arg_list[index]
chipBGR = ctool.compute_chip(gfpath, bbox, theta, new_size, filter_list)
bbox_cs_list = [
(xo_pcs, yo_pcs, w_pcs, h_pcs)
for (w_pcs, h_pcs), (xo_pcs, yo_pcs) in zip(newsize_list, halfoffset_cs_list)
]
bbox_pcs = bbox_cs_list[index]
aid = aid_list[0]
print('new_size = %r' % (new_size,))
print('newsize_list[index] = %r' % (newsize_list[index],))
fnum = 1
viz_chip.show_chip(ibs, aid, pnum=(1, 3, 1), fnum=fnum, annote=False, in_image=True ,
title_suffix='\noriginal image')
viz_chip.show_chip(ibs, aid, pnum=(1, 3, 2), fnum=fnum, annote=False,
title_suffix='\noriginal chip')
bboxed_chip = vt.draw_verts(chipBGR,
vt.scaled_verts_from_bbox(bbox_pcs, theta, 1, 1))
pt.imshow(bboxed_chip, pnum=(1, 3, 3), fnum=fnum,
title='scaled chip with expanded margin.\n(orig margin drawn in orange)')
pt.show_if_requested()
def detect_feats_main():
import pyhesaff
from pyhesaff._pyhesaff import grab_test_imgpath
from pyhesaff._pyhesaff import argparse_hesaff_params
import cv2
import ubelt as ub
img_fpath = grab_test_imgpath(ub.argval('--fname', default='astro.png'))
kwargs = argparse_hesaff_params()
print('kwargs = %r' % (kwargs, ))
(kpts, vecs) = pyhesaff.detect_feats(img_fpath, **kwargs)
if ub.argflag('--show'):
# Show keypoints
imgBGR = cv2.imread(img_fpath)
default_showkw = dict(ori=False,
ell=True,
ell_linewidth=2,
ell_alpha=.4,
ell_color='distinct')
print('default_showkw = %r' % (default_showkw, ))
import utool as ut
showkw = ut.argparse_dict(default_showkw)
import plottool as pt
pt.interact_keypoints.ishow_keypoints(imgBGR, kpts, vecs, **showkw)
pt.show_if_requested()
def testshow_extramargin_info(gfpath, bbox_gs, theta, new_size, halfoffset_ms, mbbox_gs, margin_size):
import plottool as pt
import vtool as vt
imgBGR = vt.imread(gfpath)
chipBGR = compute_chip(gfpath, bbox_gs, theta, new_size, [])
mchipBGR = compute_chip(gfpath, mbbox_gs, theta, margin_size, [])
#index = 0
w_cs, h_cs = new_size
xo_ms, yo_ms = halfoffset_ms
bbox_ms = [xo_ms, yo_ms, w_cs, h_cs]
verts_gs = vt.scaled_verts_from_bbox(bbox_gs, theta, 1, 1)
expanded_verts_gs = vt.scaled_verts_from_bbox(mbbox_gs, theta, 1, 1)
expanded_verts_ms = vt.scaled_verts_from_bbox(bbox_ms, 0, 1, 1)
# topheavy
imgBGR = vt.draw_verts(imgBGR, verts_gs)
imgBGR = vt.draw_verts(imgBGR, expanded_verts_gs)
mchipBGR = vt.draw_verts(mchipBGR, expanded_verts_ms)
fnum = 1
pt.imshow(imgBGR, pnum=(1, 3, 1), fnum=fnum, title='original image')
pt.gca().set_xlabel(str(imgBGR.shape))
pt.imshow(chipBGR, pnum=(1, 3, 2), fnum=fnum, title='original chip')
pt.gca().set_xlabel(str(chipBGR.shape))
pt.imshow(mchipBGR, pnum=(1, 3, 3), fnum=fnum,
title='scaled chip with expanded margin.\n(orig margin drawn in orange)')
pt.gca().set_xlabel(str(mchipBGR.shape))
pt.show_if_requested()
def intra_encounter_matching():
import numpy as np
from scipy.sparse import coo_matrix, csgraph
qreq_, cm_list = testdata_workflow()
# qaids = [cm.qaid for cm in cm_list]
# top_aids = [cm.get_top_aids(5) for cm in cm_list]
aid_pairs = np.array([(cm.qaid, daid)
for cm in cm_list for daid in cm.get_top_aids(5)])
top_scores = ut.flatten([cm.get_top_scores(5) for cm in cm_list])
N = aid_pairs.max() + 1
mat = coo_matrix((top_scores, aid_pairs.T), shape=(N, N))
csgraph.connected_components(mat)
tree = csgraph.minimum_spanning_tree(mat) # NOQA
import plottool as pt
dense = mat.todense()
pt.imshow(dense / dense.max() * 255)
pt.show_if_requested()
# baseline jobid
import opengm
# https://github.com/opengm/opengm/blob/master/src/interfaces/python/examples/tutorial/OpenGM%20tutorial.ipynb
numVar = 10
unaries = np.ones([numVar, 3], dtype=opengm.value_type)
gm = opengm.gm(np.ones(numVar, dtype=opengm.label_type) * 3)
unary_fids = gm.addFunctions(unaries)
gm.addFactors(unary_fids, np.arange(numVar))
infParam = opengm.InfParam(
workflow=ut.ensure_ascii('(IC)(TTC-I,CC-I)'),
)
inf = opengm.inference.Multicut(gm, parameter=infParam)
visitor = inf.verboseVisitor(printNth=1, multiline=False)
inf.infer(visitor)
arg = inf.arg()
# gridVariableIndices = opengm.secondOrderGridVis(img.shape[0], img.shape[1])
# fid = gm.addFunction(regularizer)
# gm.addFactors(fid, gridVariableIndices)
# regularizer = opengm.pottsFunction([3, 3], 0.0, beta)
# gridVariableIndices = opengm.secondOrderGridVis(img.shape[0], img.shape[1])
# fid = gm.addFunction(regularizer)
# gm.addFactors(fid, gridVariableIndices)
unaries = np.random.rand(10, 10, 2)
potts = opengm.PottsFunction([2, 2], 0.0, 0.4)
gm = opengm.grid2d2Order(unaries=unaries, regularizer=potts)
inf = opengm.inference.GraphCut(gm)
inf.infer()
arg = inf.arg() # NOQA
"""
def intra_encounter_matching():
import numpy as np
from scipy.sparse import coo_matrix, csgraph
qreq_, cm_list = testdata_workflow()
# qaids = [cm.qaid for cm in cm_list]
# top_aids = [cm.get_top_aids(5) for cm in cm_list]
aid_pairs = np.array([(cm.qaid, daid) for cm in cm_list
for daid in cm.get_top_aids(5)])
top_scores = ut.flatten([cm.get_top_scores(5) for cm in cm_list])
N = aid_pairs.max() + 1
mat = coo_matrix((top_scores, aid_pairs.T), shape=(N, N))
csgraph.connected_components(mat)
tree = csgraph.minimum_spanning_tree(mat) # NOQA
import plottool as pt
dense = mat.todense()
pt.imshow(dense / dense.max() * 255)
pt.show_if_requested()
# baseline jobid
import opengm
# https://github.com/opengm/opengm/blob/master/src/interfaces/python/examples/tutorial/OpenGM%20tutorial.ipynb
numVar = 10
unaries = np.ones([numVar, 3], dtype=opengm.value_type)
gm = opengm.gm(np.ones(numVar, dtype=opengm.label_type) * 3)
unary_fids = gm.addFunctions(unaries)
gm.addFactors(unary_fids, np.arange(numVar))
infParam = opengm.InfParam(workflow=ut.ensure_ascii('(IC)(TTC-I,CC-I)'), )
inf = opengm.inference.Multicut(gm, parameter=infParam)
visitor = inf.verboseVisitor(printNth=1, multiline=False)
inf.infer(visitor)
arg = inf.arg()
# gridVariableIndices = opengm.secondOrderGridVis(img.shape[0], img.shape[1])
# fid = gm.addFunction(regularizer)
# gm.addFactors(fid, gridVariableIndices)
# regularizer = opengm.pottsFunction([3, 3], 0.0, beta)
# gridVariableIndices = opengm.secondOrderGridVis(img.shape[0], img.shape[1])
# fid = gm.addFunction(regularizer)
# gm.addFactors(fid, gridVariableIndices)
unaries = np.random.rand(10, 10, 2)
potts = opengm.PottsFunction([2, 2], 0.0, 0.4)
gm = opengm.grid2d2Order(unaries=unaries, regularizer=potts)
inf = opengm.inference.GraphCut(gm)
inf.infer()
arg = inf.arg() # NOQA
"""
def testshow_extramargin_info(ibs, aid_list, arg_list, newsize_list,
halfoffset_cs_list):
#cfpath, gfpath, bbox, theta, new_size, filter_list = tup
# TEMP TESTING
from vtool import chip as ctool
import plottool as pt
import vtool as vt
from ibeis.viz import viz_chip
index = 0
cfpath, gfpath, expanded_bbox, theta, expanded_new_size, filter_list = arg_list[
index]
expanded_chipBGR = ctool.compute_chip(gfpath, expanded_bbox, theta,
expanded_new_size, filter_list)
bbox_cs_list = [
(xo_pcs, yo_pcs, w_pcs, h_pcs)
for (w_pcs, h_pcs), (xo_pcs,
yo_pcs) in zip(newsize_list, halfoffset_cs_list)
]
bbox_pcs = bbox_cs_list[index]
aid = aid_list[0]
#print('new_size = %r' % (new_size,))
print('newsize_list[index] = %r' % (newsize_list[index], ))
fnum = 1
viz_chip.show_chip(ibs,
aid,
pnum=(1, 3, 1),
fnum=fnum,
annote=False,
in_image=True,
title_suffix='\noriginal image')
viz_chip.show_chip(ibs,
aid,
pnum=(1, 3, 2),
fnum=fnum,
annote=False,
title_suffix='\noriginal chip')
bboxed_chip = vt.draw_verts(
expanded_chipBGR, vt.scaled_verts_from_bbox(bbox_pcs, theta, 1, 1))
pt.imshow(
bboxed_chip,
pnum=(1, 3, 3),
fnum=fnum,
title='scaled chip with expanded margin.\n(orig margin drawn in orange)'
)
pt.gca().set_xlabel(str(bboxed_chip.shape))
pt.show_if_requested()
def testshow_colors(rgb_list, gray=ut.get_argflag('--gray')):
import plottool as pt
import vtool as vt
block = np.zeros((5, 5, 3))
block_list = [block + color[0:3] for color in rgb_list]
#print(ut.list_str(block_list))
#print(ut.list_str(rgb_list))
stacked_block = vt.stack_image_list(block_list, vert=False)
# convert to bgr
stacked_block = stacked_block[:, :, ::-1]
uint8_img = (255 * stacked_block).astype(np.uint8)
if gray:
import cv2
uint8_img = cv2.cvtColor(uint8_img, cv2.COLOR_RGB2GRAY)
pt.imshow(uint8_img)
pt.show_if_requested()
def testshow_extramargin_info(gfpath, bbox_gs, theta, new_size, halfoffset_ms,
mbbox_gs, margin_size):
import plottool as pt
import vtool as vt
imgBGR = vt.imread(gfpath)
chipBGR = compute_chip(gfpath, bbox_gs, theta, new_size, [])
mchipBGR = compute_chip(gfpath, mbbox_gs, theta, margin_size, [])
#index = 0
w_cs, h_cs = new_size
xo_ms, yo_ms = halfoffset_ms
bbox_ms = [xo_ms, yo_ms, w_cs, h_cs]
verts_gs = vt.scaled_verts_from_bbox(bbox_gs, theta, 1, 1)
expanded_verts_gs = vt.scaled_verts_from_bbox(mbbox_gs, theta, 1, 1)
expanded_verts_ms = vt.scaled_verts_from_bbox(bbox_ms, 0, 1, 1)
# topheavy
imgBGR = vt.draw_verts(imgBGR, verts_gs)
imgBGR = vt.draw_verts(imgBGR, expanded_verts_gs)
mchipBGR = vt.draw_verts(mchipBGR, expanded_verts_ms)
fnum = 1
pt.imshow(imgBGR, pnum=(1, 3, 1), fnum=fnum, title='original image')
pt.gca().set_xlabel(str(imgBGR.shape))
pt.imshow(chipBGR, pnum=(1, 3, 2), fnum=fnum, title='original chip')
pt.gca().set_xlabel(str(chipBGR.shape))
pt.imshow(
mchipBGR,
pnum=(1, 3, 3),
fnum=fnum,
title='scaled chip with expanded margin.\n(orig margin drawn in orange)'
)
pt.gca().set_xlabel(str(mchipBGR.shape))
pt.show_if_requested()
def drive_test_script(ibs):
r"""
Test script where we drive around and take pictures of animals
both in a given database and not in a given databse to make sure
the system works.
CommandLine:
python -m ibeis.viz.viz_image --test-drive_test_script
python -m ibeis.viz.viz_image --test-drive_test_script --db PZ_MTEST --show
python -m ibeis.viz.viz_image --test-drive_test_script --db GIR_Tanya --show
python -m ibeis.viz.viz_image --test-drive_test_script --db GIR_Master0 --show
python -m ibeis.viz.viz_image --test-drive_test_script --db PZ_Master0 --show
python -m ibeis.viz.viz_image --test-drive_test_script --db PZ_FlankHack --show
python -m ibeis.viz.viz_image --test-drive_test_script --db PZ_FlankHack --show
python -m ibeis.viz.viz_image --test-drive_test_script --dbdir /raid/work2/Turk/GIR_Master --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.viz.viz_image import * # NOQA
>>> import ibeis
>>> ibs = ibeis.opendb()
>>> drive_test_script(ibs)
"""
import ibeis
aid_list = ibeis.testdata_aids(a='default:pername=1')
print('Running with (annot) aid_list = %r' % (aid_list))
gid_list = ibs.get_annot_gids(aid_list)
print('Running with (image) gid_list = %r' % (gid_list))
avuuid_list = ibs.get_annot_visual_uuids(aid_list)
guuid_list = ibs.get_image_uuids(gid_list)
print('Running with annot_visual_uuid_list = %s' % (ut.list_str(zip(aid_list, avuuid_list))))
print('Running with image_uuid_list = %s' % (ut.list_str(zip(gid_list, guuid_list))))
for gid, aid in ut.ProgressIter(zip(gid_list, aid_list), lbl='progress '):
print('\ngid, aid, nid = %r, %r, %r' % (gid, aid, ibs.get_annot_nids(aid),))
show_image(ibs, gid, annote=False, rich_title=True)
pt.show_if_requested()
def fourier_devtest(img):
r"""
Args:
img (ndarray[uint8_t, ndim=2]): image data
CommandLine:
python -m vtool.quality_classifier --test-fourier_devtest --show
References:
http://opencv-python-tutroals.readthedocs.org/en/latest/py_tutorials/py_imgproc/py_transforms/py_fourier_transform/py_fourier_transform.html
http://cns-alumni.bu.edu/~slehar/fourier/fourier.html
Example:
>>> # DISABLE_DOCTEST
>>> from vtool.quality_classifier import * # NOQA
>>> import vtool as vt
>>> # build test data
>>> img_fpath = ut.grab_test_imgpath('lena.png')
>>> img = vt.imread(img_fpath, grayscale=True)
>>> # execute function
>>> magnitude_spectrum = fourier_devtest(img)
"""
import plottool as pt
def pad_img(img):
rows, cols = img.shape
nrows = cv2.getOptimalDFTSize(rows)
ncols = cv2.getOptimalDFTSize(cols)
right = ncols - cols
bottom = nrows - rows
bordertype = cv2.BORDER_CONSTANT
nimg = cv2.copyMakeBorder(img, 0, bottom, 0, right, bordertype, value=0)
return nimg
def convert_to_fdomain(img):
dft = cv2.dft(img.astype(np.float32), flags=cv2.DFT_COMPLEX_OUTPUT)
#dft_shift = np.fft.fftshift(dft)
return dft
def convert_from_fdomain(dft):
img = cv2.idft(dft)
img = cv2.magnitude(img[:, :, 0], img[:, :, 1])
img /= img.max()
return img * 255.0
def get_fdomain_mag(dft_shift):
magnitude_spectrum = np.log(cv2.magnitude(dft_shift[:, :, 0], dft_shift[:, :, 1]))
return magnitude_spectrum
def imgstats(img):
print('stats:')
print(' dtype = %r ' % (img.dtype,))
print(' ' + ut.get_stats_str(img, axis=None))
nimg = pad_img(img)
dft = convert_to_fdomain(nimg)
#freq_domain = np.fft.fft2(img)
#freq_domain_shift = np.fft.fftshift(freq_domain)
rows, cols = nimg.shape
crow, ccol = rows / 2 , cols / 2
# create a mask first, center square is 1, remaining all zeros
mask = np.zeros((rows, cols, 2), np.uint8)
mask[crow - 30:crow + 30, ccol - 30:ccol + 30] = 1
dft_mask = np.fft.ifftshift(np.fft.fftshift(dft) * mask)
img_back = convert_from_fdomain(dft_mask)
imgstats(dft)
imgstats(mask)
imgstats(nimg)
imgstats(nimg)
print('nimg.shape = %r' % (nimg.shape,))
print('dft_shift.shape = %r' % (dft.shape,))
if ut.show_was_requested():
#import plottool as pt
next_pnum = pt.make_pnum_nextgen(nRows=3, nCols=2)
pt.imshow(nimg, pnum=next_pnum(), title='nimg')
pt.imshow(20 * get_fdomain_mag(dft), pnum=next_pnum(), title='mag(f)')
pt.imshow(20 * get_fdomain_mag(dft_mask), pnum=next_pnum(), title='dft_mask')
pt.imshow(img_back, pnum=next_pnum(), title='img_back')
pt.show_if_requested()
def test_rot_invar():
r"""
CommandLine:
python -m pyhesaff test_rot_invar --show --rebuild-hesaff --no-rmbuild
python -m pyhesaff test_rot_invar --show --nocpp
python -m vtool.tests.dummy testdata_ratio_matches --show --ratio_thresh=1.0 --rotation_invariance --rebuild-hesaff
python -m vtool.tests.dummy testdata_ratio_matches --show --ratio_thresh=1.1 --rotation_invariance --rebuild-hesaff
Example:
>>> # DISABLE_DODCTEST
>>> from pyhesaff._pyhesaff import * # NOQA
>>> test_rot_invar()
"""
import cv2
import utool as ut
import vtool as vt
import plottool as pt
TAU = 2 * np.pi
fnum = pt.next_fnum()
NUM_PTS = 5 # 9
theta_list = np.linspace(0, TAU, NUM_PTS, endpoint=False)
nRows, nCols = pt.get_square_row_cols(len(theta_list), fix=True)
next_pnum = pt.make_pnum_nextgen(nRows, nCols)
# Expand the border a bit around star.png
pad_ = 100
img_fpath = ut.grab_test_imgpath('star.png')
img_fpath2 = vt.pad_image_ondisk(img_fpath, pad_, value=26)
for theta in theta_list:
print('-----------------')
print('theta = %r' % (theta,))
#theta = ut.get_argval('--theta', type_=float, default=TAU * 3 / 8)
img_fpath = vt.rotate_image_ondisk(img_fpath2, theta, borderMode=cv2.BORDER_REPLICATE)
if not ut.get_argflag('--nocpp'):
(kpts_list_ri, vecs_list2) = detect_feats(img_fpath, rotation_invariance=True)
kpts_ri = ut.strided_sample(kpts_list_ri, 2)
(kpts_list_gv, vecs_list1) = detect_feats(img_fpath, rotation_invariance=False)
kpts_gv = ut.strided_sample(kpts_list_gv, 2)
# find_kpts_direction
imgBGR = vt.imread(img_fpath)
kpts_ripy = vt.find_kpts_direction(imgBGR, kpts_gv, DEBUG_ROTINVAR=False)
# Verify results stdout
#print('nkpts = %r' % (len(kpts_gv)))
#print(vt.kpts_repr(kpts_gv))
#print(vt.kpts_repr(kpts_ri))
#print(vt.kpts_repr(kpts_ripy))
# Verify results plot
pt.figure(fnum=fnum, pnum=next_pnum())
pt.imshow(imgBGR)
#if len(kpts_gv) > 0:
# pt.draw_kpts2(kpts_gv, ori=True, ell_color=pt.BLUE, ell_linewidth=10.5)
ell = False
rect = True
if not ut.get_argflag('--nocpp'):
if len(kpts_ri) > 0:
pt.draw_kpts2(kpts_ri, rect=rect, ell=ell, ori=True,
ell_color=pt.RED, ell_linewidth=5.5)
if len(kpts_ripy) > 0:
pt.draw_kpts2(kpts_ripy, rect=rect, ell=ell, ori=True,
ell_color=pt.GREEN, ell_linewidth=3.5)
#print('\n'.join(vt.get_ori_strs(np.vstack([kpts_gv, kpts_ri, kpts_ripy]))))
#ut.embed(exec_lines=['pt.update()'])
pt.set_figtitle('green=python, red=C++')
pt.show_if_requested()
def test_sver_wrapper():
"""
Test to ensure cpp and python agree and that cpp is faster
CommandLine:
python -m vtool.sver_c_wrapper --test-test_sver_wrapper
python -m vtool.sver_c_wrapper --test-test_sver_wrapper --rebuild-sver
python -m vtool.sver_c_wrapper --test-test_sver_wrapper --show
python -m vtool.sver_c_wrapper --test-test_sver_wrapper --show --dummy
python -m vtool.sver_c_wrapper --test-test_sver_wrapper --show --fname1=easy1.png --fname2=easy2.png
python -m vtool.sver_c_wrapper --test-test_sver_wrapper --show --fname1=easy1.png --fname2=hard3.png
python -m vtool.sver_c_wrapper --test-test_sver_wrapper --show --fname1=carl.jpg --fname2=hard3.png
Example:
>>> # ENABLE_DOCTEST
>>> from vtool.sver_c_wrapper import * # NOQA
>>> test_sver_wrapper()
Ignore:
%timeit call_python_version(*args)
%timeit get_affine_inliers_cpp(*args)
"""
import vtool.spatial_verification as sver
import vtool.tests.dummy as dummy
xy_thresh_sqrd = ktool.KPTS_DTYPE(.4)
scale_thresh_sqrd = ktool.KPTS_DTYPE(2.0)
ori_thresh = ktool.KPTS_DTYPE(TAU / 4.0)
keys = 'xy_thresh_sqrd, scale_thresh_sqrd, ori_thresh'.split(', ')
print(ut.dict_str(ut.dict_subset(locals(), keys)))
def report_errors():
pass
if ut.get_argflag('--dummy'):
testtup = dummy.testdata_dummy_matches()
(kpts1, kpts2, fm_input, fs_input, rchip1, rchip2) = testtup
fm_input = fm_input.astype(fm_dtype)
#fm_input = fm_input[0:10].astype(fm_dtype)
#fs_input = fs_input[0:10].astype(np.float32)
else:
fname1 = ut.get_argval('--fname1', type_=str, default='easy1.png')
fname2 = ut.get_argval('--fname2', type_=str, default='easy2.png')
testtup = dummy.testdata_ratio_matches(fname1, fname2)
(kpts1, kpts2, fm_input, fs_input, rchip1, rchip2) = testtup
# pack up call to aff hypothesis
import vtool as vt
import scipy.stats.mstats
scales1 = vt.get_scales(kpts1.take(fm_input.T[0], axis=0))
scales2 = vt.get_scales(kpts2.take(fm_input.T[1], axis=0))
#fs_input = 1 / scipy.stats.mstats.gmean(np.vstack((scales1, scales2)))
fs_input = scipy.stats.mstats.gmean(np.vstack((scales1, scales2)))
print('fs_input = ' + ut.numpy_str(fs_input))
#fs_input[0:-9] = 0
#fs_input = np.ones(len(fm_input), dtype=fs_dtype)
#ut.embed()
#fs_input = scales1 * scales2
args = (kpts1, kpts2, fm_input, fs_input, xy_thresh_sqrd,
scale_thresh_sqrd, ori_thresh)
ex_list = []
try:
with ut.Indenter('[TEST1] '):
inlier_tup = vt.compare_implementations(
sver.get_affine_inliers,
get_affine_inliers_cpp,
args,
lbl1='py',
lbl2='c',
output_lbl=('aff_inliers_list', 'aff_errors_list', 'Aff_mats'))
out_inliers, out_errors, out_mats = inlier_tup
except AssertionError as ex:
ex_list.append(ex)
raise
try:
import functools
with ut.Indenter('[TEST2] '):
bestinlier_tup = vt.compare_implementations(
functools.partial(sver.get_best_affine_inliers, forcepy=True),
get_best_affine_inliers_cpp,
args,
show_output=True,
lbl1='py',
lbl2='c',
output_lbl=('bestinliers', 'besterror', 'bestmat'))
bestinliers, besterror, bestmat = bestinlier_tup
except AssertionError as ex:
ex_list.append(ex)
raise
if len(ex_list) > 0:
raise AssertionError('some tests failed. see previous stdout')
#num_inliers_list = np.array(map(len, out_inliers_c))
#best_argx = num_inliers_list.argmax()
##best_inliers_py = out_inliers_py[best_argx]
#best_inliers_c = out_inliers_c[best_argx]
if ut.show_was_requested():
import plottool as pt
fm_output = fm_input.take(bestinliers, axis=0)
fnum = pt.next_fnum()
pt.figure(fnum=fnum, doclf=True, docla=True)
pt.show_chipmatch2(rchip1,
rchip2,
kpts1,
kpts2,
fm_input,
ell_linewidth=5,
fnum=fnum,
pnum=(2, 1, 1))
pt.show_chipmatch2(rchip1,
rchip2,
kpts1,
kpts2,
fm_output,
ell_linewidth=5,
fnum=fnum,
pnum=(2, 1, 2))
pt.show_if_requested()
def test_sver_wrapper():
"""
Test to ensure cpp and python agree and that cpp is faster
CommandLine:
python -m vtool.sver_c_wrapper --test-test_sver_wrapper
python -m vtool.sver_c_wrapper --test-test_sver_wrapper --rebuild-sver
python -m vtool.sver_c_wrapper --test-test_sver_wrapper --show
python -m vtool.sver_c_wrapper --test-test_sver_wrapper --show --dummy
python -m vtool.sver_c_wrapper --test-test_sver_wrapper --show --fname1=easy1.png --fname2=easy2.png
python -m vtool.sver_c_wrapper --test-test_sver_wrapper --show --fname1=easy1.png --fname2=hard3.png
python -m vtool.sver_c_wrapper --test-test_sver_wrapper --show --fname1=carl.jpg --fname2=hard3.png
Example:
>>> # ENABLE_DOCTEST
>>> from vtool.sver_c_wrapper import * # NOQA
>>> test_sver_wrapper()
Ignore:
%timeit call_python_version(*args)
%timeit get_affine_inliers_cpp(*args)
"""
import vtool.spatial_verification as sver
import vtool.tests.dummy as dummy
xy_thresh_sqrd = ktool.KPTS_DTYPE(.4)
scale_thresh_sqrd = ktool.KPTS_DTYPE(2.0)
ori_thresh = ktool.KPTS_DTYPE(TAU / 4.0)
keys = 'xy_thresh_sqrd, scale_thresh_sqrd, ori_thresh'.split(', ')
print(ut.dict_str(ut.dict_subset(locals(), keys)))
def report_errors():
pass
if ut.get_argflag('--dummy'):
testtup = dummy.testdata_dummy_matches()
(kpts1, kpts2, fm_input, fs_input, rchip1, rchip2) = testtup
fm_input = fm_input.astype(fm_dtype)
#fm_input = fm_input[0:10].astype(fm_dtype)
#fs_input = fs_input[0:10].astype(np.float32)
else:
fname1 = ut.get_argval('--fname1', type_=str, default='easy1.png')
fname2 = ut.get_argval('--fname2', type_=str, default='easy2.png')
testtup = dummy.testdata_ratio_matches(fname1, fname2)
(kpts1, kpts2, fm_input, fs_input, rchip1, rchip2) = testtup
# pack up call to aff hypothesis
import vtool as vt
import scipy.stats.mstats
scales1 = vt.get_scales(kpts1.take(fm_input.T[0], axis=0))
scales2 = vt.get_scales(kpts2.take(fm_input.T[1], axis=0))
#fs_input = 1 / scipy.stats.mstats.gmean(np.vstack((scales1, scales2)))
fs_input = scipy.stats.mstats.gmean(np.vstack((scales1, scales2)))
print('fs_input = ' + ut.numpy_str(fs_input))
#fs_input[0:-9] = 0
#fs_input = np.ones(len(fm_input), dtype=fs_dtype)
#ut.embed()
#fs_input = scales1 * scales2
args = (kpts1, kpts2, fm_input, fs_input, xy_thresh_sqrd, scale_thresh_sqrd, ori_thresh)
ex_list = []
try:
with ut.Indenter('[TEST1] '):
inlier_tup = vt.compare_implementations(
sver.get_affine_inliers,
get_affine_inliers_cpp,
args, lbl1='py', lbl2='c',
output_lbl=('aff_inliers_list', 'aff_errors_list', 'Aff_mats')
)
out_inliers, out_errors, out_mats = inlier_tup
except AssertionError as ex:
ex_list.append(ex)
raise
try:
import functools
with ut.Indenter('[TEST2] '):
bestinlier_tup = vt.compare_implementations(
functools.partial(sver.get_best_affine_inliers, forcepy=True),
get_best_affine_inliers_cpp,
args, show_output=True, lbl1='py', lbl2='c',
output_lbl=('bestinliers', 'besterror', 'bestmat')
)
bestinliers, besterror, bestmat = bestinlier_tup
except AssertionError as ex:
ex_list.append(ex)
raise
if len(ex_list) > 0:
raise AssertionError('some tests failed. see previous stdout')
#num_inliers_list = np.array(map(len, out_inliers_c))
#best_argx = num_inliers_list.argmax()
##best_inliers_py = out_inliers_py[best_argx]
#best_inliers_c = out_inliers_c[best_argx]
if ut.show_was_requested():
import plottool as pt
fm_output = fm_input.take(bestinliers, axis=0)
fnum = pt.next_fnum()
pt.figure(fnum=fnum, doclf=True, docla=True)
pt.show_chipmatch2(rchip1, rchip2, kpts1, kpts2, fm_input, ell_linewidth=5, fnum=fnum, pnum=(2, 1, 1))
pt.show_chipmatch2(rchip1, rchip2, kpts1, kpts2, fm_output, ell_linewidth=5, fnum=fnum, pnum=(2, 1, 2))
pt.show_if_requested()
def intra_encounter_matching():
qreq_, cm_list = testdata_workflow()
# qaids = [cm.qaid for cm in cm_list]
# top_aids = [cm.get_top_aids(5) for cm in cm_list]
import numpy as np
from scipy.sparse import coo_matrix, csgraph
aid_pairs = np.array([(cm.qaid, daid) for cm in cm_list for daid in cm.get_top_aids(5)])
top_scores = ut.flatten([cm.get_top_scores(5) for cm in cm_list])
N = aid_pairs.max() + 1
mat = coo_matrix((top_scores, aid_pairs.T), shape=(N, N))
csgraph.connected_components(mat)
tree = csgraph.minimum_spanning_tree(mat) # NOQA
import plottool as pt
dense = mat.todense()
pt.imshow(dense / dense.max() * 255)
pt.show_if_requested()
# load image and convert to LAB
img_fpath = str(ut.grab_test_imgpath(str('lena.png')))
img = vigra.impex.readImage(img_fpath)
imgLab = vigra.colors.transform_RGB2Lab(img)
superpixelDiameter = 15 # super-pixel size
slicWeight = 15.0 # SLIC color - spatial weight
labels, nseg = vigra.analysis.slicSuperpixels(imgLab, slicWeight,
superpixelDiameter)
labels = vigra.analysis.labelImage(labels)-1
# get 2D grid graph and RAG
gridGraph = graphs.gridGraph(img.shape[0:2])
rag = graphs.regionAdjacencyGraph(gridGraph, labels)
nodeFeatures = rag.accumulateNodeFeatures(imgLab)
nodeFeaturesImg = rag.projectNodeFeaturesToGridGraph(nodeFeatures)
nodeFeaturesImg = vigra.taggedView(nodeFeaturesImg, "xyc")
nodeFeaturesImgRgb = vigra.colors.transform_Lab2RGB(nodeFeaturesImg)
#from sklearn.cluster import MiniBatchKMeans, KMeans
from sklearn import mixture
nCluster = 3
g = mixture.GMM(n_components=nCluster)
g.fit(nodeFeatures[:,:])
clusterProb = g.predict_proba(nodeFeatures)
import numpy
#https://github.com/opengm/opengm/blob/master/src/interfaces/python/examples/tutorial/Irregular%20Factor%20Graphs.ipynb
#https://github.com/opengm/opengm/blob/master/src/interfaces/python/examples/tutorial/Hard%20and%20Soft%20Constraints.ipynb
clusterProbImg = rag.projectNodeFeaturesToGridGraph(clusterProb.astype(numpy.float32))
clusterProbImg = vigra.taggedView(clusterProbImg, "xyc")
# strength of potts regularizer
beta = 40.0
# graphical model with as many variables
# as superpixels, each has 3 states
gm = opengm.gm(numpy.ones(rag.nodeNum,dtype=opengm.label_type)*nCluster)
# convert probabilites to energies
probs = numpy.clip(clusterProb, 0.00001, 0.99999)
costs = -1.0*numpy.log(probs)
# add ALL unaries AT ONCE
fids = gm.addFunctions(costs)
gm.addFactors(fids,numpy.arange(rag.nodeNum))
# add a potts function
regularizer = opengm.pottsFunction([nCluster]*2,0.0,beta)
fid = gm.addFunction(regularizer)
# get variable indices of adjacent superpixels
# - or "u" and "v" node id's for edges
uvIds = rag.uvIds()
uvIds = numpy.sort(uvIds,axis=1)
# add all second order factors at once
gm.addFactors(fid,uvIds)
# get super-pixels with slic on LAB image
import opengm
# Matching Graph
cost_matrix = np.array([
[0.5, 0.6, 0.2, 0.4, 0.1],
[0.0, 0.5, 0.2, 0.9, 0.2],
[0.0, 0.0, 0.5, 0.1, 0.1],
[0.0, 0.0, 0.0, 0.5, 0.1],
[0.0, 0.0, 0.0, 0.0, 0.5],
])
cost_matrix += cost_matrix.T
number_of_labels = 5
num_annots = 5
cost_matrix = (cost_matrix * 2) - 1
#gm = opengm.gm(number_of_labels)
gm = opengm.gm(np.ones(num_annots) * number_of_labels)
aids = np.arange(num_annots)
aid_pairs = np.array([(a1, a2) for a1, a2 in ut.iprod(aids, aids) if a1 != a2], dtype=np.uint32)
aid_pairs.sort(axis=1)
# 2nd order function
fid = gm.addFunction(cost_matrix)
gm.addFactors(fid, aid_pairs)
Inf = opengm.inference.BeliefPropagation
#Inf = opengm.inference.Multicut
parameter = opengm.InfParam(steps=10, damping=0.5, convergenceBound=0.001)
parameter = opengm.InfParam()
inf = Inf(gm, parameter=parameter)
class PyCallback(object):
def __init__(self,):
self.labels=[]
pass
def begin(self,inference):
print("begin of inference")
pass
def end(self,inference):
self.labels.append(inference.arg())
pass
def visit(self,inference):
gm=inference.gm()
labelVector=inference.arg()
print("energy %r" % (gm.evaluate(labelVector),))
self.labels.append(labelVector)
pass
callback=PyCallback()
visitor=inf.pythonVisitor(callback,visitNth=1)
inf.infer(visitor)
print(callback.labels)
# baseline jobid
# https://github.com/opengm/opengm/blob/master/src/interfaces/python/examples/tutorial/OpenGM%20tutorial.ipynb
numVar = 10
unaries = np.ones([numVar, 3], dtype=opengm.value_type)
gm = opengm.gm(np.ones(numVar, dtype=opengm.label_type) * 3)
unary_fids = gm.addFunctions(unaries)
gm.addFactors(unary_fids, np.arange(numVar))
infParam = opengm.InfParam(
workflow=ut.ensure_ascii('(IC)(TTC-I,CC-I)'),
)
inf = opengm.inference.Multicut(gm, parameter=infParam)
visitor = inf.verboseVisitor(printNth=1, multiline=False)
inf.infer(visitor)
arg = inf.arg()
# gridVariableIndices = opengm.secondOrderGridVis(img.shape[0], img.shape[1])
# fid = gm.addFunction(regularizer)
# gm.addFactors(fid, gridVariableIndices)
# regularizer = opengm.pottsFunction([3, 3], 0.0, beta)
# gridVariableIndices = opengm.secondOrderGridVis(img.shape[0], img.shape[1])
# fid = gm.addFunction(regularizer)
# gm.addFactors(fid, gridVariableIndices)
unaries = np.random.rand(10, 10, 2)
potts = opengm.PottsFunction([2, 2], 0.0, 0.4)
gm = opengm.grid2d2Order(unaries=unaries, regularizer=potts)
inf = opengm.inference.GraphCut(gm)
inf.infer()
arg = inf.arg() # NOQA
"""
def test_featweight_worker():
"""
test function
python -m ibeis.algo.preproc.preproc_featweight --test-gen_featweight_worker --show --cnn
"""
import ibeis
qreq_ = ibeis.main_helpers.testdata_qreq_(defaultdb='PZ_MTEST', p=['default:fw_detector=cnn'], qaid_override=[1])
ibs = qreq_.ibs
config2_ = qreq_.qparams
lazy = True
aid_list = qreq_.get_external_qaids()
#aid_list = ibs.get_valid_aids()[0:30]
kpts_list = ibs.get_annot_kpts(aid_list)
chipsize_list = ibs.get_annot_chip_sizes(aid_list, config2_=config2_)
probchip_fpath_list = preproc_probchip.compute_and_write_probchip(ibs,
aid_list,
lazy=lazy,
config2_=config2_)
print('probchip_fpath_list = %r' % (probchip_fpath_list,))
probchip_list = [vt.imread(fpath, grayscale=True) if exists(fpath) else None
for fpath in probchip_fpath_list]
_iter = list(zip(aid_list, kpts_list, probchip_list, chipsize_list))
_iter = ut.InteractiveIter(_iter, enabled=ut.get_argflag('--show'))
for aid, kpts, probchip, chipsize in _iter:
#kpts = kpts_list[0]
#aid = aid_list[0]
#probchip = probchip_list[0]
#chipsize = chipsize_list[0]
tup = (aid, kpts, probchip, chipsize)
(aid, weights) = gen_featweight_worker(tup)
if aid == 3 and ibs.get_dbname() == 'testdb1':
# Run Asserts if not interactive
weights_03_test = weights[0:3]
print('weights[0:3] = %r' % (weights_03_test,))
#weights_03_target = [ 0.098, 0.155, 0.422]
#weights_03_target = [ 0.324, 0.407, 0.688]
#weights_thresh = [ 0.09, 0.09, 0.09]
#ut.assert_almost_eq(weights_03_test, weights_03_target, weights_thresh)
ut.assert_inbounds(weights_03_test, 0, 1)
if not ut.show_was_requested():
break
if ut.show_was_requested():
import plottool as pt
#sfx, sfy = (probchip.shape[1] / chipsize[0], probchip.shape[0] / chipsize[1])
#kpts_ = vt.offset_kpts(kpts, (0, 0), (sfx, sfy))
pnum_ = pt.make_pnum_nextgen(1, 3) # *pt.get_square_row_cols(4))
fnum = 1
pt.figure(fnum=fnum, doclf=True)
###
pt.imshow(ibs.get_annot_chips(aid, config2_=config2_), pnum=pnum_(0), fnum=fnum)
if ut.get_argflag('--numlbl'):
pt.gca().set_xlabel('(1)')
###
pt.imshow(probchip, pnum=pnum_(2), fnum=fnum)
if ut.get_argflag('--numlbl'):
pt.gca().set_xlabel('(2)')
#pt.draw_kpts2(kpts_, ell_alpha=.4, color_list=pt.ORANGE)
###
#pt.imshow(probchip, pnum=pnum_(3), fnum=fnum)
#color_list = pt.draw_kpts2(kpts_, weights=weights, ell_alpha=.7, cmap_='jet')
#cb = pt.colorbar(weights, color_list)
#cb.set_label('featweights')
###
pt.imshow(ibs.get_annot_chips(aid, config2_=qreq_.qparams), pnum=pnum_(1), fnum=fnum)
#color_list = pt.draw_kpts2(kpts, weights=weights, ell_alpha=.3, cmap_='jet')
color_list = pt.draw_kpts2(kpts, weights=weights, ell_alpha=.3)
cb = pt.colorbar(weights, color_list)
cb.set_label('featweights')
if ut.get_argflag('--numlbl'):
pt.gca().set_xlabel('(3)')
#pt.draw_kpts2(kpts, ell_alpha=.4)
pt.draw()
pt.show_if_requested()
def test_rot_invar():
r"""
CommandLine:
python -m pyhesaff test_rot_invar --show --rebuild-hesaff --no-rmbuild
python -m pyhesaff test_rot_invar --show --nocpp
python -m vtool.tests.dummy testdata_ratio_matches --show --ratio_thresh=1.0 --rotation_invariance --rebuild-hesaff
python -m vtool.tests.dummy testdata_ratio_matches --show --ratio_thresh=1.1 --rotation_invariance --rebuild-hesaff
Example:
>>> # DISABLE_DODCTEST
>>> from pyhesaff._pyhesaff import * # NOQA
>>> test_rot_invar()
"""
import cv2
import vtool as vt
import plottool as pt
TAU = 2 * np.pi
fnum = pt.next_fnum()
NUM_PTS = 5 # 9
theta_list = np.linspace(0, TAU, NUM_PTS, endpoint=False)
nRows, nCols = pt.get_square_row_cols(len(theta_list), fix=True)
next_pnum = pt.make_pnum_nextgen(nRows, nCols)
# Expand the border a bit around star.png
pad_ = 100
img_fpath = grab_test_imgpath('star.png')
img_fpath2 = vt.pad_image_ondisk(img_fpath, pad_, value=26)
for theta in theta_list:
print('-----------------')
print('theta = %r' % (theta, ))
img_fpath = vt.rotate_image_ondisk(img_fpath2,
theta,
border_mode=cv2.BORDER_REPLICATE)
if not ub.argflag('--nocpp'):
(kpts_list_ri, vecs_list2) = detect_feats(img_fpath,
rotation_invariance=True)
kpts_ri = kpts_list_ri[0:2]
(kpts_list_gv, vecs_list1) = detect_feats(img_fpath,
rotation_invariance=False)
kpts_gv = kpts_list_gv[0:2]
# find_kpts_direction
imgBGR = vt.imread(img_fpath)
kpts_ripy = vt.find_kpts_direction(imgBGR,
kpts_gv,
DEBUG_ROTINVAR=False)
# Verify results stdout
#print('nkpts = %r' % (len(kpts_gv)))
#print(vt.kpts_repr(kpts_gv))
#print(vt.kpts_repr(kpts_ri))
#print(vt.kpts_repr(kpts_ripy))
# Verify results plot
pt.figure(fnum=fnum, pnum=next_pnum())
pt.imshow(imgBGR)
#if len(kpts_gv) > 0:
# pt.draw_kpts2(kpts_gv, ori=True, ell_color=pt.BLUE, ell_linewidth=10.5)
ell = False
rect = True
if not ub.argflag('--nocpp'):
if len(kpts_ri) > 0:
pt.draw_kpts2(kpts_ri,
rect=rect,
ell=ell,
ori=True,
ell_color=pt.RED,
ell_linewidth=5.5)
if len(kpts_ripy) > 0:
pt.draw_kpts2(kpts_ripy,
rect=rect,
ell=ell,
ori=True,
ell_color=pt.GREEN,
ell_linewidth=3.5)
pt.set_figtitle('green=python, red=C++')
pt.show_if_requested()
def fourier_devtest(img):
r"""
Args:
img (ndarray[uint8_t, ndim=2]): image data
CommandLine:
python -m vtool.quality_classifier --test-fourier_devtest --show
References:
http://opencv-python-tutroals.readthedocs.org/en/latest/py_tutorials/py_imgproc/py_transforms/py_fourier_transform/py_fourier_transform.html
http://cns-alumni.bu.edu/~slehar/fourier/fourier.html
Example:
>>> # DISABLE_DOCTEST
>>> from vtool.quality_classifier import * # NOQA
>>> import vtool as vt
>>> # build test data
>>> img_fpath = ut.grab_test_imgpath('lena.png')
>>> img = vt.imread(img_fpath, grayscale=True)
>>> # execute function
>>> magnitude_spectrum = fourier_devtest(img)
"""
import plottool as pt
def pad_img(img):
rows, cols = img.shape
nrows = cv2.getOptimalDFTSize(rows)
ncols = cv2.getOptimalDFTSize(cols)
right = ncols - cols
bottom = nrows - rows
bordertype = cv2.BORDER_CONSTANT
nimg = cv2.copyMakeBorder(img,
0,
bottom,
0,
right,
bordertype,
value=0)
return nimg
def convert_to_fdomain(img):
dft = cv2.dft(img.astype(np.float32), flags=cv2.DFT_COMPLEX_OUTPUT)
#dft_shift = np.fft.fftshift(dft)
return dft
def convert_from_fdomain(dft):
img = cv2.idft(dft)
img = cv2.magnitude(img[:, :, 0], img[:, :, 1])
img /= img.max()
return img * 255.0
def get_fdomain_mag(dft_shift):
magnitude_spectrum = np.log(
cv2.magnitude(dft_shift[:, :, 0], dft_shift[:, :, 1]))
return magnitude_spectrum
def imgstats(img):
print('stats:')
print(' dtype = %r ' % (img.dtype, ))
print(' ' + ut.get_stats_str(img, axis=None))
nimg = pad_img(img)
dft = convert_to_fdomain(nimg)
#freq_domain = np.fft.fft2(img)
#freq_domain_shift = np.fft.fftshift(freq_domain)
rows, cols = nimg.shape
crow, ccol = rows / 2, cols / 2
# create a mask first, center square is 1, remaining all zeros
mask = np.zeros((rows, cols, 2), np.uint8)
mask[crow - 30:crow + 30, ccol - 30:ccol + 30] = 1
dft_mask = np.fft.ifftshift(np.fft.fftshift(dft) * mask)
img_back = convert_from_fdomain(dft_mask)
imgstats(dft)
imgstats(mask)
imgstats(nimg)
imgstats(nimg)
print('nimg.shape = %r' % (nimg.shape, ))
print('dft_shift.shape = %r' % (dft.shape, ))
if ut.show_was_requested():
#import plottool as pt
next_pnum = pt.make_pnum_nextgen(nRows=3, nCols=2)
pt.imshow(nimg, pnum=next_pnum(), title='nimg')
pt.imshow(20 * get_fdomain_mag(dft), pnum=next_pnum(), title='mag(f)')
pt.imshow(20 * get_fdomain_mag(dft_mask),
pnum=next_pnum(),
title='dft_mask')
pt.imshow(img_back, pnum=next_pnum(), title='img_back')
pt.show_if_requested()
