def show_ori_image(gori,
weights,
patch,
gradx=None,
grady=None,
gauss=None,
fnum=None):
"""
python -m pyhesaff._pyhesaff --test-test_rot_invar --show --nocpp
"""
import plottool as pt
if fnum is None:
fnum = pt.next_fnum()
print('gori.max = %r' % gori.max())
assert gori.max() <= TAU
assert gori.min() >= 0
bgr_ori = pt.color_orimag(gori, weights, False, encoding='bgr')
print('bgr_ori.max = %r' % bgr_ori.max())
#ut.embed()
bgr_ori = (255 * bgr_ori).astype(np.uint8)
print('bgr_ori.max = %r' % bgr_ori.max())
#bgr_ori = np.array(bgr_ori, dtype=np.uint8)
legend = pt.make_ori_legend_img()
#gorimag_, woff, hoff = vt.stack_images(bgr_ori, legend, vert=False, modifysize=True)
import vtool as vt
gorimag_, offsets, sftup = vt.stack_images(bgr_ori,
legend,
vert=False,
modifysize=True,
return_offset=True,
return_sf=True)
(woff, hoff) = offsets[1]
if patch is None:
pt.imshow(gorimag_, fnum=fnum)
else:
pt.imshow(gorimag_,
fnum=fnum,
pnum=(3, 1, 1),
title='colored by orientation')
#pt.imshow(patch, fnum=fnum, pnum=(2, 2, 1))
#gradx, grady = np.cos(gori + TAU / 4.0), np.sin(gori + TAU / 4.0)
if gradx is not None and grady is not None:
if weights is not None:
gradx *= weights
grady *= weights
pt.imshow(np.array(gradx * 255, dtype=np.uint8),
fnum=fnum,
pnum=(3, 3, 4))
pt.imshow(np.array(grady * 255, dtype=np.uint8),
fnum=fnum,
pnum=(3, 3, 5))
#pt.imshow(bgr_ori, pnum=(2, 2, 4))
pt.draw_vector_field(gradx, grady, pnum=(3, 3, 6), invert=True)
pt.imshow(patch, fnum=fnum, pnum=(3, 1, 3))
def show_ori_image(gori, weights, patch, gradx=None, grady=None, gauss=None, fnum=None):
"""
python -m pyhesaff._pyhesaff --test-test_rot_invar --show --nocpp
"""
import plottool as pt
if fnum is None:
fnum = pt.next_fnum()
print('gori.max = %r' % gori.max())
assert gori.max() <= TAU
assert gori.min() >= 0
bgr_ori = pt.color_orimag(gori, weights, False, encoding='bgr')
print('bgr_ori.max = %r' % bgr_ori.max())
#ut.embed()
bgr_ori = (255 * bgr_ori).astype(np.uint8)
print('bgr_ori.max = %r' % bgr_ori.max())
#bgr_ori = np.array(bgr_ori, dtype=np.uint8)
legend = pt.make_ori_legend_img()
#gorimag_, woff, hoff = vt.stack_images(bgr_ori, legend, vert=False, modifysize=True)
import vtool as vt
gorimag_, offsets, sftup = vt.stack_images(bgr_ori, legend, vert=False,
modifysize=True,
return_offset=True,
return_sf=True)
(woff, hoff) = offsets[1]
if patch is None:
pt.imshow(gorimag_, fnum=fnum)
else:
pt.imshow(gorimag_, fnum=fnum, pnum=(3, 1, 1), title='colored by orientation')
#pt.imshow(patch, fnum=fnum, pnum=(2, 2, 1))
#gradx, grady = np.cos(gori + TAU / 4.0), np.sin(gori + TAU / 4.0)
if gradx is not None and grady is not None:
if weights is not None:
gradx *= weights
grady *= weights
pt.imshow(np.array(gradx * 255, dtype=np.uint8), fnum=fnum, pnum=(3, 3, 4))
pt.imshow(np.array(grady * 255, dtype=np.uint8), fnum=fnum, pnum=(3, 3, 5))
#pt.imshow(bgr_ori, pnum=(2, 2, 4))
pt.draw_vector_field(gradx, grady, pnum=(3, 3, 6), invert=True)
pt.imshow(patch, fnum=fnum, pnum=(3, 1, 3))
def TEST_figure1(wpatch, gradx, grady, gmag, gori, hist, centers):
from plottool import draw_func2 as df2
import plottool
import vtool.patch as ptool
print('[rotinvar] 4) Draw histogram with interpolation annotations')
fnum = 1
gorimag = plottool.color_orimag(gori, gmag, True)
nRow, nCol = (2, 7)
df2.figure(fnum=1, pnum=(nRow, 1, nRow), doclf=True, docla=True)
plottool.draw_hist_subbin_maxima(hist, centers)
df2.set_xlabel('grad orientation (radians)')
df2.set_ylabel('grad magnitude')
df2.set_title('dominant orientations')
print('[rotinvar] 5) Show patch, gradients, magintude, and orientation')
df2.imshow(wpatch, pnum=(nRow, nCol, 1), fnum=fnum, title='patch')
df2.draw_vector_field(gradx,
grady,
pnum=(nRow, nCol, 2),
fnum=fnum,
title='gori (vec)')
df2.imshow(gorimag, pnum=(nRow, nCol, 3), fnum=fnum, title='gori (col)')
df2.imshow(np.abs(gradx), pnum=(nRow, nCol, 4), fnum=fnum, title='gradx')
df2.imshow(np.abs(grady), pnum=(nRow, nCol, 5), fnum=fnum, title='grady')
df2.imshow(gmag, pnum=(nRow, nCol, 6), fnum=fnum, title='gmag')
gpatch = ptool.gaussian_patch(shape=gori.shape)
df2.imshow(gpatch * 255,
pnum=(nRow, nCol, 7),
fnum=fnum,
title='gauss weights',
cmap_='hot')
#gpatch3 = np.dstack((gpatch, gpatch, gpatch))
#df2.draw_vector_field(gradx * gpatch, grady * gpatch, pnum=(nRow, nCol, 8), fnum=fnum, title='gori (vec)')
#df2.imshow(gorimag * gpatch3, pnum=(nRow, nCol, 9), fnum=fnum, title='gori (col)')
#df2.imshow(gradx * gpatch, pnum=(nRow, nCol, 10), fnum=fnum, title='gradx')
#df2.imshow(grady * gpatch, pnum=(nRow, nCol, 11), fnum=fnum, title='grady')
#df2.imshow(gmag * gpatch, pnum=(nRow, nCol, 12), fnum=fnum, title='gmag')
return locals()
