def show_keypoints(chip, kpts, fnum=0, pnum=None, **kwargs):
r"""
Args:
chip (ndarray[uint8_t, ndim=2]): annotation image data
kpts (ndarray[float32_t, ndim=2]): keypoints
fnum (int): figure number(default = 0)
pnum (tuple): plot number(default = None)
Kwargs:
ddd, title, figtitle, interpolation, cmap, heatmap, data_colorbar,
darken, update, redraw_image, docla, doclf, projection, sel_fx
CommandLine:
python -m plottool.viz_keypoints --exec-show_keypoints
Example:
>>> # DISABLE_DOCTEST
>>> from plottool.viz_keypoints import * # NOQA
>>> import vtool as vt
>>> kpts, vecs, chip = testdata_kpts()
>>> fnum = 0
>>> pnum = None
>>> result = show_keypoints(chip, kpts, fnum, pnum)
>>> print(result)
"""
#printDBG('[df2.show_kpts] %r' % (kwargs.keys(),))
fig, ax = df2.imshow(chip, fnum=fnum, pnum=pnum, **kwargs)
_annotate_kpts(kpts, **kwargs)
ph.set_plotdat(ax, 'viztype', 'keypoints')
ph.set_plotdat(ax, 'kpts', kpts)
if kwargs.get('ddd', False):
ph.draw()
def show_chip(ibs, aid, in_image=False, annote=True, **kwargs):
""" Driver function to show chips """
printDBG('[viz] show_chip()')
vh.ibsfuncs.assert_valid_aids(ibs, (aid,))
#utool.embed()
# Get chip
chip = vh.get_chips(ibs, aid, in_image, **kwargs)
# Create chip title
chip_text = vh.get_annot_texts(ibs, [aid], **kwargs)[0]
# Draw chip
fig, ax = df2.imshow(chip, **kwargs)
# Populate axis user data
vh.set_ibsdat(ax, 'viztype', 'chip')
vh.set_ibsdat(ax, 'aid', aid)
if annote and not kwargs.get('nokpts', False):
# Get and draw keypoints
kpts = vh.get_kpts(ibs, aid, in_image, **kwargs)
_annotate_kpts(kpts, **kwargs)
df2.upperleft_text(chip_text, color=kwargs.get('text_color', None))
if not kwargs.get('notitle', False):
ax.set_title(chip_text)
if in_image:
gid = ibs.get_annot_gids(aid)
aid_list = ibs.get_image_aids(gid)
annotekw = viz_image.get_annot_annotations(ibs, aid_list, sel_aids=[aid])
viz_image2.draw_image_overlay(ax, **annotekw)
def _draw_chip(px, title, chip, inliers, kpts1_m, kpts2_m, H1=None):
if isinstance(px, tuple):
pnum = px
df2.imshow(chip, title=title, fnum=fnum, pnum=pnum)
px = pnum[2]
else:
df2.imshow(chip, title=title, fnum=fnum, pnum=pnum2_(px))
if kpts1_m is not None:
_draw_kpts(kpts1_m, color=color1_dark, H=H1, **out_kwargs)
draw_inlier_kpts(kpts1_m, inliers, color1, H=H1)
if kpts2_m is not None:
_draw_kpts(kpts2_m, color=color2_dark, **out_kwargs)
draw_inlier_kpts(kpts2_m, inliers, color2)
if kpts2_m is not None and kpts1_m is not None and show_lines:
__fm = np.vstack((inliers, inliers)).T
df2.draw_lines2(kpts1_m, kpts2_m, __fm,
color_list=[custom_constants.ORANGE], lw=2,
line_alpha=1,
H1=H1)
return px + 1
def show_image(img, bbox_list=[], title='', theta_list=None,
text_list=None, sel_list=None, draw_lbls=True,
fnum=None, annote=True, **kwargs):
""" Driver function to show images """
# Shows an image with annotations
if fnum is None:
fnum = df2.next_fnum()
fig, ax = df2.imshow(img, title=title, fnum=fnum, docla=True, **kwargs)
df2.remove_patches(ax)
if annote:
draw_image_overlay(ax, bbox_list, theta_list, text_list, sel_list,
draw_lbls)
return fig, ax
def _draw_chip(px, title, chip, inliers, kpts1_m, kpts2_m, H1=None):
if isinstance(px, tuple):
pnum = px
df2.imshow(chip, title=title, fnum=fnum, pnum=pnum)
px = pnum[2]
else:
df2.imshow(chip, title=title, fnum=fnum, pnum=pnum2_(px))
if kpts1_m is not None:
_draw_kpts(kpts1_m, color=color1_dark, H=H1, **out_kwargs)
draw_inlier_kpts(kpts1_m, inliers, color1, H=H1)
if kpts2_m is not None:
_draw_kpts(kpts2_m, color=color2_dark, **out_kwargs)
draw_inlier_kpts(kpts2_m, inliers, color2)
if kpts2_m is not None and kpts1_m is not None and show_lines:
__fm = np.vstack((inliers, inliers)).T
df2.draw_lines2(kpts1_m,
kpts2_m,
__fm,
color_list=[custom_constants.ORANGE],
lw=2,
line_alpha=1,
H1=H1)
return px + 1
def plot_score_matrix(allres):
print('[viz] plotting score matrix')
score_matrix = allres.score_matrix
title = 'Score Matrix\n' + allres.title_suffix
# Find inliers
#inliers = util.find_std_inliers(score_matrix)
#max_inlier = score_matrix[inliers].max()
# Trunate above 255
score_img = np.copy(score_matrix)
#score_img[score_img < 0] = 0
#score_img[score_img > 255] = 255
#dim = 0
#score_img = util.norm_zero_one(score_img, dim=dim)
# Make colors
scores = score_img.flatten()
colors = df2.scores_to_color(scores, logscale=True)
cmap = df2.scores_to_cmap(scores, colors)
df2.figure(fnum=FIGNUM, doclf=True, title=title)
# Show score matrix
df2.imshow(score_img, fnum=FIGNUM, cmap=cmap)
# Colorbar
df2.colorbar(scores, colors)
df2.set_xlabel('database')
df2.set_ylabel('queries')
def plot_score_matrix(allres):
print('[viz] plotting score matrix')
score_matrix = allres.score_matrix
title = 'Score Matrix\n' + allres.title_suffix
# Find inliers
#inliers = util.find_std_inliers(score_matrix)
#max_inlier = score_matrix[inliers].max()
# Trunate above 255
score_img = np.copy(score_matrix)
#score_img[score_img < 0] = 0
#score_img[score_img > 255] = 255
#dim = 0
#score_img = util.norm_zero_one(score_img, dim=dim)
# Make colors
scores = score_img.flatten()
colors = df2.scores_to_color(scores, logscale=True)
cmap = df2.scores_to_cmap(scores, colors)
df2.figure(fnum=FIGNUM, doclf=True, title=title)
# Show score matrix
df2.imshow(score_img, fnum=FIGNUM, cmap=cmap)
# Colorbar
df2.colorbar(scores, colors)
df2.set_xlabel('database')
df2.set_ylabel('queries')
def show_image(img,
bbox_list=[],
title='',
theta_list=None,
text_list=None,
sel_list=None,
draw_lbls=True,
fnum=None,
annote=True,
**kwargs):
""" Driver function to show images """
# Shows an image with annotations
if fnum is None:
fnum = df2.next_fnum()
fig, ax = df2.imshow(img, title=title, fnum=fnum, docla=True, **kwargs)
df2.remove_patches(ax)
if annote:
draw_image_overlay(ax, bbox_list, theta_list, text_list, sel_list,
draw_lbls)
return fig, ax
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()
def draw_feat_row(
chip,
fx,
kp,
sift,
fnum,
nRows,
nCols=None,
px=None,
prevsift=None,
origsift=None,
aid=None,
info="",
type_=None,
shape_labels=False,
vecfield=False,
multicolored_arms=False,
draw_chip=False,
draw_warped=True,
draw_unwarped=True,
draw_desc=True,
rect=True,
ori=True,
pts=False,
**kwargs
):
"""
draw_feat_row
SeeAlso:
ibeis.viz.viz_nearest_descriptors
~/code/ibeis/ibeis/viz/viz_nearest_descriptors.py
CommandLine:
# Use this to find the fx you want to visualize
python -m plottool.interact_keypoints --test-ishow_keypoints --show --fname zebra.png
# Use this to visualize the featrow
python -m plottool.viz_featrow --test-draw_feat_row --show
python -m plottool.viz_featrow --test-draw_feat_row --show --fname zebra.png --fx=121 --feat-all --no-sift
python -m plottool.viz_featrow --test-draw_feat_row --dpath figures --save ~/latex/crall-candidacy-2015/figures/viz_featrow.jpg
Example:
>>> # DISABLE_DOCTEST
>>> from plottool.viz_featrow import * # NOQA
>>> import plottool as pt
>>> # build test data
>>> kpts, vecs, imgBGR = pt.viz_keypoints.testdata_kpts()
>>> chip = imgBGR
>>> print('There are %d features' % (len(vecs)))
>>> fx = ut.get_argval('--fx', type_=int, default=0)
>>> kp = kpts[fx]
>>> sift = vecs[fx]
>>> fnum = 1
>>> nRows = 1
>>> nCols = 2
>>> px = 0
>>> hack = ut.get_argflag('--feat-all')
>>> sift = sift if not ut.get_argflag('--no-sift') else None
>>> draw_desc = sift is not None
>>> kw = dict(
>>> prevsift=None, origsift=None, aid=None, info='', type_=None,
>>> shape_labels=False, vecfield=False, multicolored_arms=True,
>>> draw_chip=hack, draw_unwarped=hack, draw_warped=True, draw_desc=draw_desc
>>> )
>>> # execute function
>>> result = draw_feat_row(chip, fx, kp, sift, fnum, nRows, nCols, px,
>>> rect=False, ori=False, pts=False, **kw)
>>> # verify results
>>> print(result)
>>> pt.show_if_requested()
"""
import numpy as np
import vtool as vt
# should not need ncols here
if nCols is not None:
if ut.VERBOSE:
print("Warning nCols is no longer needed")
# assert nCols_ == nCols
nCols = draw_chip + draw_unwarped + draw_warped + draw_desc
pnum_ = df2.make_pnum_nextgen(nRows, nCols, start=px)
# pnum_ = df2.get_pnum_func(nRows, nCols, base=1)
# countgen = itertools.count(1)
# pnumgen_ = df2.make_pnum_nextgen(nRows, nCols, base=1)
def _draw_patch(**kwargs):
return df2.draw_keypoint_patch(
chip,
kp,
sift,
rect=rect,
ori=ori,
pts=pts,
ori_color=custom_constants.DEEP_PINK,
multicolored_arms=multicolored_arms,
**kwargs
)
# Feature strings
xy_str, shape_str, scale, ori_str = ph.kp_info(kp)
if draw_chip:
pnum = pnum_()
df2.imshow(chip, fnum=fnum, pnum=pnum)
kpts_kw = dict(ell_linewidth=5, ell_alpha=1.0)
kpts_kw.update(kwargs)
df2.draw_kpts2([kp], **kpts_kw)
if draw_unwarped:
# Draw the unwarped selected feature
# ax = _draw_patch(fnum=fnum, pnum=pnum_(px + six.next(countgen)))
# pnum = pnum_(px + six.next(countgen)
pnum = pnum_()
ax = _draw_patch(fnum=fnum, pnum=pnum)
ph.set_plotdat(ax, "viztype", "unwarped")
ph.set_plotdat(ax, "aid", aid)
ph.set_plotdat(ax, "fx", fx)
if shape_labels:
unwarped_lbl = "affine feature invV =\n" + shape_str + "\n" + ori_str
custom_figure.set_xlabel(unwarped_lbl, ax)
if draw_warped:
# Draw the warped selected feature
# ax = _draw_patch(fnum=fnum, pnum=pnum_(px + six.next(countgen)), warped=True)
pnum = pnum_()
ax = _draw_patch(fnum=fnum, pnum=pnum, warped=True, **kwargs)
ph.set_plotdat(ax, "viztype", "warped")
ph.set_plotdat(ax, "aid", aid)
ph.set_plotdat(ax, "fx", fx)
if shape_labels:
warped_lbl = ("warped feature\n" + "fx=%r scale=%.1f\n" + "%s") % (fx, scale, xy_str)
else:
warped_lbl = ""
warped_lbl += info
custom_figure.set_xlabel(warped_lbl, ax)
if draw_desc:
border_color = {
"None": None,
"query": None,
"match": custom_constants.BLUE,
"norm": custom_constants.ORANGE,
}.get(str(type_).lower(), None)
if border_color is not None:
df2.draw_border(ax, color=border_color)
# Draw the SIFT representation
# pnum = pnum_(px + six.next(countgen))
pnum = pnum_()
sift_as_vecfield = ph.SIFT_OR_VECFIELD or vecfield
if sift_as_vecfield:
custom_figure.figure(fnum=fnum, pnum=pnum)
df2.draw_keypoint_gradient_orientations(chip, kp, sift=sift)
else:
if sift.dtype.type == np.uint8:
sigtitle = "sift histogram" if (px % 3) == 0 else ""
ax = df2.plot_sift_signature(sift, sigtitle, fnum=fnum, pnum=pnum)
else:
sigtitle = "descriptor vector" if (px % 3) == 0 else ""
ax = df2.plot_descriptor_signature(sift, sigtitle, fnum=fnum, pnum=pnum)
ax._hs_viztype = "histogram"
# dist_list = ['L1', 'L2', 'hist_isect', 'emd']
# dist_list = ['L2', 'hist_isect']
# dist_list = ['L2']
# dist_list = ['bar_L2_sift', 'cos_sift']
# dist_list = ['L2_sift', 'bar_cos_sift']
dist_list = ["L2_sift"]
dist_str_list = []
if origsift is not None:
distmap_orig = vt.compute_distances(sift, origsift, dist_list)
dist_str_list.append(
"query_dist: "
+ ", ".join(["(%s, %s)" % (key, formatdist(val)) for key, val in six.iteritems(distmap_orig)])
)
if prevsift is not None:
distmap_prev = vt.compute_distances(sift, prevsift, dist_list)
dist_str_list.append(
"prev_dist: "
+ ", ".join(["(%s, %s)" % (key, formatdist(val)) for key, val in six.iteritems(distmap_prev)])
)
dist_str = "\n".join(dist_str_list)
custom_figure.set_xlabel(dist_str)
return px + nCols
def draw_feat_row(chip,
fx,
kp,
sift,
fnum,
nRows,
nCols=None,
px=None,
prevsift=None,
origsift=None,
aid=None,
info='',
type_=None,
shape_labels=False,
vecfield=False,
multicolored_arms=False,
draw_chip=False,
draw_warped=True,
draw_unwarped=True,
draw_desc=True,
rect=True,
ori=True,
pts=False,
**kwargs):
"""
draw_feat_row
SeeAlso:
ibeis.viz.viz_nearest_descriptors
~/code/ibeis/ibeis/viz/viz_nearest_descriptors.py
CommandLine:
# Use this to find the fx you want to visualize
python -m plottool.interact_keypoints --test-ishow_keypoints --show --fname zebra.png
# Use this to visualize the featrow
python -m plottool.viz_featrow --test-draw_feat_row --show
python -m plottool.viz_featrow --test-draw_feat_row --show --fname zebra.png --fx=121 --feat-all --no-sift
python -m plottool.viz_featrow --test-draw_feat_row --dpath figures --save ~/latex/crall-candidacy-2015/figures/viz_featrow.jpg
Example:
>>> # DISABLE_DOCTEST
>>> from plottool.viz_featrow import * # NOQA
>>> import plottool as pt
>>> # build test data
>>> kpts, vecs, imgBGR = pt.viz_keypoints.testdata_kpts()
>>> chip = imgBGR
>>> print('There are %d features' % (len(vecs)))
>>> fx = ut.get_argval('--fx', type_=int, default=0)
>>> kp = kpts[fx]
>>> sift = vecs[fx]
>>> fnum = 1
>>> nRows = 1
>>> nCols = 2
>>> px = 0
>>> hack = ut.get_argflag('--feat-all')
>>> sift = sift if not ut.get_argflag('--no-sift') else None
>>> draw_desc = sift is not None
>>> kw = dict(
>>> prevsift=None, origsift=None, aid=None, info='', type_=None,
>>> shape_labels=False, vecfield=False, multicolored_arms=True,
>>> draw_chip=hack, draw_unwarped=hack, draw_warped=True, draw_desc=draw_desc
>>> )
>>> # execute function
>>> result = draw_feat_row(chip, fx, kp, sift, fnum, nRows, nCols, px,
>>> rect=False, ori=False, pts=False, **kw)
>>> # verify results
>>> print(result)
>>> pt.show_if_requested()
"""
import numpy as np
import vtool as vt
# should not need ncols here
if nCols is not None:
if ut.VERBOSE:
print('Warning nCols is no longer needed')
#assert nCols_ == nCols
nCols = (draw_chip + draw_unwarped + draw_warped + draw_desc)
pnum_ = df2.make_pnum_nextgen(nRows, nCols, start=px)
#pnum_ = df2.get_pnum_func(nRows, nCols, base=1)
#countgen = itertools.count(1)
#pnumgen_ = df2.make_pnum_nextgen(nRows, nCols, base=1)
def _draw_patch(**kwargs):
return df2.draw_keypoint_patch(chip,
kp,
sift,
rect=rect,
ori=ori,
pts=pts,
ori_color=custom_constants.DEEP_PINK,
multicolored_arms=multicolored_arms,
**kwargs)
# Feature strings
xy_str, shape_str, scale, ori_str = ph.kp_info(kp)
if draw_chip:
pnum = pnum_()
df2.imshow(chip, fnum=fnum, pnum=pnum)
kpts_kw = dict(ell_linewidth=5, ell_alpha=1.0)
kpts_kw.update(kwargs)
df2.draw_kpts2([kp], **kpts_kw)
if draw_unwarped:
# Draw the unwarped selected feature
#ax = _draw_patch(fnum=fnum, pnum=pnum_(px + six.next(countgen)))
#pnum = pnum_(px + six.next(countgen)
pnum = pnum_()
ax = _draw_patch(fnum=fnum, pnum=pnum)
ph.set_plotdat(ax, 'viztype', 'unwarped')
ph.set_plotdat(ax, 'aid', aid)
ph.set_plotdat(ax, 'fx', fx)
if shape_labels:
unwarped_lbl = 'affine feature invV =\n' + shape_str + '\n' + ori_str
custom_figure.set_xlabel(unwarped_lbl, ax)
if draw_warped:
# Draw the warped selected feature
#ax = _draw_patch(fnum=fnum, pnum=pnum_(px + six.next(countgen)), warped=True)
pnum = pnum_()
ax = _draw_patch(fnum=fnum, pnum=pnum, warped=True, **kwargs)
ph.set_plotdat(ax, 'viztype', 'warped')
ph.set_plotdat(ax, 'aid', aid)
ph.set_plotdat(ax, 'fx', fx)
if shape_labels:
warped_lbl = ('warped feature\n' + 'fx=%r scale=%.1f\n' +
'%s') % (fx, scale, xy_str)
else:
warped_lbl = ''
warped_lbl += info
custom_figure.set_xlabel(warped_lbl, ax)
if draw_desc:
border_color = {
'None': None,
'query': None,
'match': custom_constants.BLUE,
'norm': custom_constants.ORANGE
}.get(str(type_).lower(), None)
if border_color is not None:
df2.draw_border(ax, color=border_color)
# Draw the SIFT representation
#pnum = pnum_(px + six.next(countgen))
pnum = pnum_()
sift_as_vecfield = ph.SIFT_OR_VECFIELD or vecfield
if sift_as_vecfield:
custom_figure.figure(fnum=fnum, pnum=pnum)
df2.draw_keypoint_gradient_orientations(chip, kp, sift=sift)
else:
if sift.dtype.type == np.uint8:
sigtitle = 'sift histogram' if (px % 3) == 0 else ''
ax = df2.plot_sift_signature(sift,
sigtitle,
fnum=fnum,
pnum=pnum)
else:
sigtitle = 'descriptor vector' if (px % 3) == 0 else ''
ax = df2.plot_descriptor_signature(sift,
sigtitle,
fnum=fnum,
pnum=pnum)
ax._hs_viztype = 'histogram'
#dist_list = ['L1', 'L2', 'hist_isect', 'emd']
#dist_list = ['L2', 'hist_isect']
#dist_list = ['L2']
#dist_list = ['bar_L2_sift', 'cos_sift']
#dist_list = ['L2_sift', 'bar_cos_sift']
dist_list = ['L2_sift']
dist_str_list = []
if origsift is not None:
distmap_orig = vt.compute_distances(sift, origsift, dist_list)
dist_str_list.append('query_dist: ' + ', '.join([
'(%s, %s)' % (key, formatdist(val))
for key, val in six.iteritems(distmap_orig)
]))
if prevsift is not None:
distmap_prev = vt.compute_distances(sift, prevsift, dist_list)
dist_str_list.append('prev_dist: ' + ', '.join([
'(%s, %s)' % (key, formatdist(val))
for key, val in six.iteritems(distmap_prev)
]))
dist_str = '\n'.join(dist_str_list)
custom_figure.set_xlabel(dist_str)
return px + nCols
