def show_chipres(hs, res, cx, fnum=None, pnum=None, sel_fm=[], in_image=False, **kwargs):
'shows single annotated match result.'
qcx = res.qcx
#cx2_score = res.get_cx2_score()
cx2_fm = res.get_cx2_fm()
cx2_fs = res.get_cx2_fs()
#cx2_fk = res.get_cx2_fk()
#printDBG('[viz.show_chipres()] Showing matches from %s' % (vs_str))
#printDBG('[viz.show_chipres()] fnum=%r, pnum=%r' % (fnum, pnum))
# Test valid cx
printDBG('[viz] show_chipres()')
if np.isnan(cx):
nan_img = np.zeros((32, 32), dtype=np.uint8)
title = '(q%s v %r)' % (hs.cidstr(qcx), cx)
df2.imshow(nan_img, fnum=fnum, pnum=pnum, title=title)
return
fm = cx2_fm[cx]
fs = cx2_fs[cx]
#fk = cx2_fk[cx]
#vs_str = hs.vs_str(qcx, cx)
# Read query and result info (chips, names, ...)
if in_image:
# TODO: rectify build_transform2 with cc2
# clean up so its not abysmal
rchip1, rchip2 = [hs.cx2_image(_) for _ in [qcx, cx]]
kpts1, kpts2 = viz.cx2_imgkpts(hs, [qcx, cx])
else:
rchip1, rchip2 = hs.get_chip([qcx, cx])
kpts1, kpts2 = hs.get_kpts([qcx, cx])
# Build annotation strings / colors
lbl1 = 'q' + hs.cidstr(qcx)
lbl2 = hs.cidstr(cx)
if in_image:
# HACK!
lbl1 = None
lbl2 = None
# Draws the chips and keypoint matches
kwargs_ = dict(fs=fs, lbl1=lbl1, lbl2=lbl2, fnum=fnum,
pnum=pnum, vert=hs.prefs.display_cfg.vert)
kwargs_.update(kwargs)
# ax, xywh1, xywh2 = df2.show_chipmatch2(rchip1, rchip2, kpts1, kpts2, fm, **kwargs_)
ax, xywh1, xywh2 = show_chipmatch2(rchip1, rchip2, kpts1, kpts2, fm, **kwargs_)
x1, y1, w1, h1 = xywh1
x2, y2, w2, h2 = xywh2
if len(sel_fm) > 0:
# Draw any selected matches
_smargs = dict(rect=True, colors=df2.BLUE)
# df2.draw_fmatch(xywh1, xywh2, kpts1, kpts2, sel_fm, **_smargs)
draw_fmatch(xywh1, xywh2, kpts1, kpts2, sel_fm, **_smargs)
offset1 = (x1, y1)
offset2 = (x2, y2)
annotate_chipres(hs, res, cx, xywh2=xywh2, in_image=in_image, offset1=offset1, offset2=offset2, **kwargs)
return ax, xywh1, xywh2
def show_chipmatch2(rchip1,
rchip2,
kpts1,
kpts2,
fm=None,
fs=None,
title=None,
vert=None,
fnum=None,
pnum=None,
**kwargs):
'''Draws two chips and the feature matches between them. feature matches
kpts1 and kpts2 use the (x,y,a,c,d)
'''
printDBG('[df2] draw_matches2() fnum=%r, pnum=%r' % (fnum, pnum))
# get matching keypoints + offset
(h1, w1) = rchip1.shape[0:2] # get chip (h, w) dimensions
(h2, w2) = rchip2.shape[0:2]
# Stack the compared chips
match_img, woff, hoff = df2.stack_images(rchip1, rchip2, vert)
xywh1 = (0, 0, w1, h1)
xywh2 = (woff, hoff, w2, h2)
# Show the stacked chips
fig, ax = df2.imshow(match_img, title=title, fnum=fnum, pnum=pnum)
# Overlay feature match nnotations
draw_fmatch(xywh1, xywh2, kpts1, kpts2, fm, fs, **kwargs)
return ax, xywh1, xywh2
def test(hs, cx=0):
from hsviz import draw_func2 as df2
import os
if not 'cx' in vars():
cx = 0
# READ IMAGE AND ROI
cx2_roi = hs.tables.cx2_roi
cx2_gx = hs.tables.cx2_gx
gx2_gname = hs.tables.gx2_gname
#---
roi_ = cx2_roi[cx]
gx = cx2_gx[cx]
img_fname = gx2_gname[gx]
img_fpath = os.path.join(hs.dirs.img_dir, img_fname)
#---
print('testing segment')
seg_chip, img_mask = segment(img_fpath, roi_, new_size=None)
from hsviz import viz
viz.show_image(hs, gx, fnum=1, pnum=131, title='original', docla=True)
df2.imshow(img_mask, fnum=1, pnum=132, title='mask')
df2.imshow(seg_chip, fnum=1, pnum=133, title='segmented')
def test(hs, cx=0):
from hsviz import draw_func2 as df2
import os
if not 'cx' in vars():
cx = 0
# READ IMAGE AND ROI
cx2_roi = hs.tables.cx2_roi
cx2_gx = hs.tables.cx2_gx
gx2_gname = hs.tables.gx2_gname
#---
roi_ = cx2_roi[cx]
gx = cx2_gx[cx]
img_fname = gx2_gname[gx]
img_fpath = os.path.join(hs.dirs.img_dir, img_fname)
#---
print('testing segment')
seg_chip, img_mask = segment(img_fpath, roi_, new_size=None)
from hsviz import viz
viz.show_image(hs, gx, fnum=1, pnum=131, title='original', docla=True)
df2.imshow(img_mask, fnum=1, pnum=132, title='mask')
df2.imshow(seg_chip, fnum=1, pnum=133, title='segmented')
def im(img, fnum=0):
from hsviz import draw_func2 as df2
df2.imshow(img, fnum=fnum)
df2.update()
def test_clean_mask(chip_mask):
from hsviz import draw_func2 as df2
mask = chip_mask
print('Cleaning')
mask2 = clean_mask(mask, 0, 3, .020)
mask3 = clean_mask(mask, 3, 0, .023)
mask4 = clean_mask(mask, 3, 3, .025)
mask5 = clean_mask(mask4, 2, 3, .025)
mask6 = clean_mask(mask5, 1, 0, .025)
mask7 = clean_mask(mask6, 1, 0, .025)
mask8 = clean_mask(mask7, 1, 0, .025)
mask9 = clean_mask(mask8, 1, 3, .025)
print('Drawing')
df2.imshow(mask, pnum=331)
df2.imshow(mask2, pnum=332)
df2.imshow(mask3, pnum=333)
df2.imshow(mask4, pnum=334)
df2.imshow(mask5, pnum=335)
df2.imshow(mask6, pnum=336)
df2.imshow(mask7, pnum=337)
df2.imshow(mask8, pnum=338)
df2.imshow(mask9, pnum=339)
print('Updating')
df2.update()
print('Done')
res = hs.query(cx)
nTop = 2
for tx in xrange(nTop):
cx2 = res.topN_cxs(hs)[tx]
dstimg1, dstimg2, args_, kwargs_ = test_result_coverage(
hs, res, cx2, scale_factor)
test_find_coverage_score(hs, res)
res.show_chipres(hs, cx2, fnum=fnum)
df2.set_figtitle('matching viz' + str(tx), incanvas=False)
fnum += 1
df2.show_chipmatch2(dstimg1, dstimg2, *args_, fnum=fnum, **kwargs_)
df2.set_figtitle('matching coverage' + str(tx))
fnum += 1
df2.imshow(srcimg, fnum=fnum, heatmap=True)
df2.set_figtitle('gaussian weights')
fnum += 1
df2.imshow(dstimg, fnum=fnum, heatmap=True)
df2.set_figtitle('chip coverage map')
fnum += 1
df2.imshow(dstimg_thresh, fnum=fnum, heatmap=True)
df2.set_figtitle('thresholded chip coverage map')
fnum += 1
viz.show_chip(hs, cx, fnum=fnum)
df2.set_figtitle('chip', incanvas=False)
fnum += 1
exec(viz.df2.present())
def im(img, fnum=0):
from hsviz import draw_func2 as df2
df2.imshow(img, fnum=fnum)
df2.update()
def test_clean_mask(chip_mask):
from hsviz import draw_func2 as df2
mask = chip_mask
print('Cleaning')
mask2 = clean_mask(mask, 0, 3, .020)
mask3 = clean_mask(mask, 3, 0, .023)
mask4 = clean_mask(mask, 3, 3, .025)
mask5 = clean_mask(mask4, 2, 3, .025)
mask6 = clean_mask(mask5, 1, 0, .025)
mask7 = clean_mask(mask6, 1, 0, .025)
mask8 = clean_mask(mask7, 1, 0, .025)
mask9 = clean_mask(mask8, 1, 3, .025)
print('Drawing')
df2.imshow(mask, pnum=331)
df2.imshow(mask2, pnum=332)
df2.imshow(mask3, pnum=333)
df2.imshow(mask4, pnum=334)
df2.imshow(mask5, pnum=335)
df2.imshow(mask6, pnum=336)
df2.imshow(mask7, pnum=337)
df2.imshow(mask8, pnum=338)
df2.imshow(mask9, pnum=339)
print('Updating')
df2.update()
print('Done')