def make_wordfigures(ibs, metrics, invindex, figdir, wx_sample, wx2_dpath):
"""
Builds mosaics of patches assigned to words in sample
ouptuts them to disk
"""
from plottool import draw_func2 as df2
import vtool as vt
import parse
vocabdir = join(figdir, 'vocab_patches2')
ut.ensuredir(vocabdir)
dump_word_patches(ibs, vocabdir, invindex, wx_sample, metrics)
# COLLECTING PART --- collects patches in word folders
#vocabdir
seldpath = vocabdir + '_selected'
ut.ensurepath(seldpath)
# stack for show
for wx, dpath in ut.progiter(six.iteritems(wx2_dpath),
lbl='Dumping Word Images:',
num=len(wx2_dpath),
freq=1,
backspace=False):
#df2.rrr()
fpath_list = ut.ls(dpath)
fname_list = [basename(fpath_) for fpath_ in fpath_list]
patch_list = [vt.imread(fpath_) for fpath_ in fpath_list]
# color each patch by nid
nid_list = [
int(parse.parse('{}_nid={nid}_{}', fname)['nid'])
for fname in fname_list
]
nid_set = set(nid_list)
nid_list = np.array(nid_list)
if len(nid_list) == len(nid_set):
# no duplicate names
newpatch_list = patch_list
else:
# duplicate names. do coloring
sortx = nid_list.argsort()
patch_list = np.array(patch_list, dtype=object)[sortx]
fname_list = np.array(fname_list, dtype=object)[sortx]
nid_list = nid_list[sortx]
colors = (255 *
np.array(df2.distinct_colors(len(nid_set)))).astype(
np.int32)
color_dict = dict(zip(nid_set, colors))
wpad, hpad = 3, 3
newshape_list = [
tuple(
(np.array(patch.shape) + (wpad * 2, hpad * 2, 0)).tolist())
for patch in patch_list
]
color_list = [color_dict[nid_] for nid_ in nid_list]
newpatch_list = [
np.zeros(shape) + color[None, None]
for shape, color in zip(newshape_list, color_list)
]
for patch, newpatch in zip(patch_list, newpatch_list):
newpatch[wpad:-wpad, hpad:-hpad, :] = patch
#img_list = patch_list
#bigpatch = vt.stack_image_recurse(patch_list)
#bigpatch = vt.stack_image_list(patch_list, vert=False)
bigpatch = vt.stack_square_images(newpatch_list)
bigpatch_fpath = join(seldpath, basename(dpath) + '_patches.png')
#
def _dictstr(dict_):
str_ = ut.dict_str(dict_, newlines=False)
str_ = str_.replace('\'', '').replace(': ', '=').strip('{},')
return str_
figtitle = '\n'.join([
'wx=%r' % wx,
'stat(pdist): %s' % _dictstr(metrics.wx2_pdist_stats[wx]),
'stat(wdist): %s' % _dictstr(metrics.wx2_wdist_stats[wx]),
])
metrics.wx2_nMembers[wx]
df2.figure(fnum=1, doclf=True, docla=True)
fig, ax = df2.imshow(bigpatch, figtitle=figtitle)
#fig.show()
df2.set_figtitle(figtitle)
df2.adjust_subplots(top=.878, bottom=0)
df2.save_figure(1, bigpatch_fpath)
def test_grabcut_on_aid(aid):
chip_fpath = ibs.get_annot_chip_fpath(aid)
probchip_fpath = ibs.get_annot_probchip_fpath(aid)
chip_img = cv2.imread(chip_fpath)
probchip_img = cv2.imread(probchip_fpath, flags=cv2.IMREAD_GRAYSCALE)
label_values = [cv2.GC_BGD, cv2.GC_PR_BGD, cv2.GC_PR_FGD, cv2.GC_FGD]
def probchip_to_grabcut_labels(probchip_img, w, h):
scaled_probchip = cv2.resize(probchip_img, dsize=(w, h))
mask = ((len(label_values) - 1) * (scaled_probchip / 255)).astype(np.uint8)
# Except for one center pixel
#mask[mask.shape[0] // 2, mask.shape[1] // 2] = 3
label_mask = mask.copy()
for index, value in enumerate(label_values):
label_mask[mask == index] = value
# No certainty
label_mask[label_mask == cv2.GC_FGD] = cv2.GC_PR_FGD
label_mask[label_mask == cv2.GC_BGD] = cv2.GC_PR_BGD
return label_mask
def grabcut_labels_to_probchip(label_mask):
image_mask = label_mask.copy()
label_colors = np.linspace(0, 255, len(label_values)).astype(np.uint8)
for value, color in zip(label_values, label_colors):
image_mask[label_mask == value] = (color)
return image_mask
def grabcut_from_probchip(chip_img, label_mask):
rect = (0, 0, w, h)
bgd_model = np.zeros((1, 13 * 5), np.float64)
fgd_model = np.zeros((1, 13 * 5), np.float64)
num_iters = 5
mode = cv2.GC_INIT_WITH_MASK
# label_mask is an outvar
label_mask_ = label_mask.copy()
print(label_values)
print(np.unique(label_mask_))
with ut.Timer('grabcut'):
cv2.grabCut(chip_img, label_mask_, rect, bgd_model, fgd_model, num_iters, mode=mode)
#is_foreground = (label_mask == cv2.GC_FGD) + (label_mask == cv2.GC_PR_FGD)
#is_foreground = (label_mask_ == cv2.GC_FGD) # + (label_mask == cv2.GC_PR_FGD)
return label_mask_
(h, w) = chip_img.shape[0:2]
label_mask = probchip_to_grabcut_labels(probchip_img, w, h)
label_mask_ = grabcut_from_probchip(chip_img, label_mask)
float_mask = grabcut_labels_to_probchip(label_mask_) / 255.0
segmented_chip = chip_img * float_mask[:, :, None]
next_pnum = df2.make_pnum_nextgen(2, 3)
df2.imshow(chip_img, fnum=1, pnum=next_pnum())
df2.imshow(probchip_img, fnum=1, pnum=next_pnum())
df2.imshow(grabcut_labels_to_probchip(label_mask), fnum=1, pnum=next_pnum())
df2.imshow(segmented_chip, fnum=1, pnum=next_pnum())
df2.imshow(255 * (float_mask), fnum=1, pnum=next_pnum())
df2.imshow(chip_img * (float_mask > .6)[:, :, None], fnum=1, pnum=next_pnum())
df2.present()
def make_wordfigures(ibs, metrics, invindex, figdir, wx_sample, wx2_dpath):
"""
Builds mosaics of patches assigned to words in sample
ouptuts them to disk
"""
from plottool import draw_func2 as df2
import vtool as vt
import parse
vocabdir = join(figdir, 'vocab_patches2')
ut.ensuredir(vocabdir)
dump_word_patches(ibs, vocabdir, invindex, wx_sample, metrics)
# COLLECTING PART --- collects patches in word folders
#vocabdir
seldpath = vocabdir + '_selected'
ut.ensurepath(seldpath)
# stack for show
for wx, dpath in ut.progiter(six.iteritems(wx2_dpath), lbl='Dumping Word Images:', num=len(wx2_dpath), freq=1, backspace=False):
#df2.rrr()
fpath_list = ut.ls(dpath)
fname_list = [basename(fpath_) for fpath_ in fpath_list]
patch_list = [gtool.imread(fpath_) for fpath_ in fpath_list]
# color each patch by nid
nid_list = [int(parse.parse('{}_nid={nid}_{}', fname)['nid']) for fname in fname_list]
nid_set = set(nid_list)
nid_list = np.array(nid_list)
if len(nid_list) == len(nid_set):
# no duplicate names
newpatch_list = patch_list
else:
# duplicate names. do coloring
sortx = nid_list.argsort()
patch_list = np.array(patch_list, dtype=object)[sortx]
fname_list = np.array(fname_list, dtype=object)[sortx]
nid_list = nid_list[sortx]
colors = (255 * np.array(df2.distinct_colors(len(nid_set)))).astype(np.int32)
color_dict = dict(zip(nid_set, colors))
wpad, hpad = 3, 3
newshape_list = [tuple((np.array(patch.shape) + (wpad * 2, hpad * 2, 0)).tolist()) for patch in patch_list]
color_list = [color_dict[nid_] for nid_ in nid_list]
newpatch_list = [np.zeros(shape) + color[None, None] for shape, color in zip(newshape_list, color_list)]
for patch, newpatch in zip(patch_list, newpatch_list):
newpatch[wpad:-wpad, hpad:-hpad, :] = patch
#img_list = patch_list
#bigpatch = vt.stack_image_recurse(patch_list)
#bigpatch = vt.stack_image_list(patch_list, vert=False)
bigpatch = vt.stack_square_images(newpatch_list)
bigpatch_fpath = join(seldpath, basename(dpath) + '_patches.png')
#
def _dictstr(dict_):
str_ = ut.dict_str(dict_, newlines=False)
str_ = str_.replace('\'', '').replace(': ', '=').strip('{},')
return str_
figtitle = '\n'.join([
'wx=%r' % wx,
'stat(pdist): %s' % _dictstr(metrics.wx2_pdist_stats[wx]),
'stat(wdist): %s' % _dictstr(metrics.wx2_wdist_stats[wx]),
])
metrics.wx2_nMembers[wx]
df2.figure(fnum=1, doclf=True, docla=True)
fig, ax = df2.imshow(bigpatch, figtitle=figtitle)
#fig.show()
df2.set_figtitle(figtitle)
df2.adjust_subplots(top=.878, bottom=0)
df2.save_figure(1, bigpatch_fpath)
def test_grabcut_on_aid(aid):
chip_fpath = ibs.get_annot_chip_fpath(aid)
probchip_fpath = ibs.get_annot_probchip_fpath(aid)
chip_img = vt.imread(chip_fpath)
probchip_img = vt.imread(probchip_fpath, grayscale=True)
label_values = [cv2.GC_BGD, cv2.GC_PR_BGD, cv2.GC_PR_FGD, cv2.GC_FGD]
def probchip_to_grabcut_labels(probchip_img, w, h):
scaled_probchip = cv2.resize(probchip_img, dsize=(w, h))
mask = ((len(label_values) - 1) * (scaled_probchip / 255)).astype(
np.uint8)
# Except for one center pixel
#mask[mask.shape[0] // 2, mask.shape[1] // 2] = 3
label_mask = mask.copy()
for index, value in enumerate(label_values):
label_mask[mask == index] = value
# No certainty
label_mask[label_mask == cv2.GC_FGD] = cv2.GC_PR_FGD
label_mask[label_mask == cv2.GC_BGD] = cv2.GC_PR_BGD
return label_mask
def grabcut_labels_to_probchip(label_mask):
image_mask = label_mask.copy()
label_colors = np.linspace(0, 255,
len(label_values)).astype(np.uint8)
for value, color in zip(label_values, label_colors):
image_mask[label_mask == value] = (color)
return image_mask
def grabcut_from_probchip(chip_img, label_mask):
rect = (0, 0, w, h)
bgd_model = np.zeros((1, 13 * 5), np.float64)
fgd_model = np.zeros((1, 13 * 5), np.float64)
num_iters = 5
mode = cv2.GC_INIT_WITH_MASK
# label_mask is an outvar
label_mask_ = label_mask.copy()
print(label_values)
print(np.unique(label_mask_))
with ut.Timer('grabcut'):
cv2.grabCut(chip_img,
label_mask_,
rect,
bgd_model,
fgd_model,
num_iters,
mode=mode)
#is_foreground = (label_mask == cv2.GC_FGD) + (label_mask == cv2.GC_PR_FGD)
#is_foreground = (label_mask_ == cv2.GC_FGD) # + (label_mask == cv2.GC_PR_FGD)
return label_mask_
(h, w) = chip_img.shape[0:2]
label_mask = probchip_to_grabcut_labels(probchip_img, w, h)
label_mask_ = grabcut_from_probchip(chip_img, label_mask)
float_mask = grabcut_labels_to_probchip(label_mask_) / 255.0
segmented_chip = chip_img * float_mask[:, :, None]
next_pnum = df2.make_pnum_nextgen(2, 3)
df2.imshow(chip_img, fnum=1, pnum=next_pnum())
df2.imshow(probchip_img, fnum=1, pnum=next_pnum())
df2.imshow(grabcut_labels_to_probchip(label_mask),
fnum=1,
pnum=next_pnum())
df2.imshow(segmented_chip, fnum=1, pnum=next_pnum())
df2.imshow(255 * (float_mask), fnum=1, pnum=next_pnum())
df2.imshow(chip_img * (float_mask > .6)[:, :, None],
fnum=1,
pnum=next_pnum())
df2.present()