def render_vocab(vocab):
"""
Renders the average patch of each word.
This is a quick visualization of the entire vocabulary.
CommandLine:
python -m ibeis.algo.smk.vocab_indexer render_vocab --show
Example:
>>> from ibeis.algo.smk.vocab_indexer import * # NOQA
>>> vocab = testdata_vocab('PZ_MTEST', num_words=64)
>>> all_words = vocab.render_vocab()
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> pt.qt4ensure()
>>> pt.imshow(all_words)
>>> ut.show_if_requested()
"""
import plottool as pt
wx_list = list(range(len(vocab)))
# wx_list = ut.strided_sample(wx_list, 64)
wx_list = ut.strided_sample(wx_list, 64)
word_patch_list = []
for wx in ut.ProgIter(wx_list, bs=True, lbl='building patches'):
word = vocab.wx_to_word[wx]
word_patch = vt.inverted_sift_patch(word, 64)
word_patch = pt.render_sift_on_patch(word_patch, word)
word_patch_list.append(word_patch)
all_words = vt.stack_square_images(word_patch_list)
return all_words
def visualize_vocab_word(ibs, invassign, wx, fnum=None):
"""
Example:
>>> from ibeis.new_annots import * # NOQA
>>> import plottool as pt
>>> pt.qt4ensure()
>>> ibs, aid_list, vocab = testdata_vocab()
>>> #aid_list = aid_list[0:1]
>>> fstack = StackedFeatures(ibs, aid_list)
>>> nAssign = 2
>>> invassign = fstack.inverted_assignment(vocab, nAssign)
>>> sortx = ut.argsort(invassign.num_list)[::-1]
>>> wx_list = ut.take(invassign.wx_list, sortx)
>>> wx = wx_list[0]
"""
import plottool as pt
pt.qt4ensure()
vecs = invassign.get_vecs(wx)
word = invassign.vocab.wx2_word[wx]
word_patches = invassign.get_patches(wx)
average_patch = np.mean(word_patches, axis=0)
average_vec = vecs.mean(axis=0)
average_vec = word
word
with_sift = True
fnum = 2
fnum = pt.ensure_fnum(fnum)
if with_sift:
patch_img = pt.render_sift_on_patch(average_patch, average_vec)
#sift_word_patches = [pt.render_sift_on_patch(patch, vec) for patch, vec in ut.ProgIter(list(zip(word_patches, vecs)))]
#stacked_patches = vt.stack_square_images(word_patches)
#stacked_patches = vt.stack_square_images(sift_word_patches)
else:
patch_img = average_patch
stacked_patches = vt.stack_square_images(word_patches)
solidbar = np.zeros((patch_img.shape[0], int(patch_img.shape[1] * .1), 3),
dtype=patch_img.dtype)
border_color = (100, 10, 10) # bgr, darkblue
if ut.is_float(solidbar):
solidbar[:, :, :] = (np.array(border_color) / 255)[None, None]
else:
solidbar[:, :, :] = np.array(border_color)[None, None]
word_img = vt.stack_image_list([patch_img, solidbar, stacked_patches],
vert=False,
modifysize=True)
pt.imshow(word_img, fnum=fnum)
#pt.imshow(patch_img, pnum=(1, 2, 1), fnum=fnum)
#patch_size = 64
#half_size = patch_size / 2
#pt.imshow(stacked_patches, pnum=(1, 2, 2), fnum=fnum)
pt.iup()
def visualize_vocab_word(ibs, invassign, wx, fnum=None):
"""
Example:
>>> from ibeis.new_annots import * # NOQA
>>> import plottool as pt
>>> pt.qt4ensure()
>>> ibs, aid_list, vocab = testdata_vocab()
>>> #aid_list = aid_list[0:1]
>>> fstack = StackedFeatures(ibs, aid_list)
>>> nAssign = 2
>>> invassign = fstack.inverted_assignment(vocab, nAssign)
>>> sortx = ut.argsort(invassign.num_list)[::-1]
>>> wx_list = ut.take(invassign.wx_list, sortx)
>>> wx = wx_list[0]
"""
import plottool as pt
pt.qt4ensure()
vecs = invassign.get_vecs(wx)
word = invassign.vocab.wx2_word[wx]
word_patches = invassign.get_patches(wx)
average_patch = np.mean(word_patches, axis=0)
average_vec = vecs.mean(axis=0)
average_vec = word
word
with_sift = True
fnum = 2
fnum = pt.ensure_fnum(fnum)
if with_sift:
patch_img = pt.render_sift_on_patch(average_patch, average_vec)
#sift_word_patches = [pt.render_sift_on_patch(patch, vec) for patch, vec in ut.ProgIter(list(zip(word_patches, vecs)))]
#stacked_patches = vt.stack_square_images(word_patches)
#stacked_patches = vt.stack_square_images(sift_word_patches)
else:
patch_img = average_patch
stacked_patches = vt.stack_square_images(word_patches)
solidbar = np.zeros((patch_img.shape[0], int(patch_img.shape[1] * .1), 3), dtype=patch_img.dtype)
border_color = (100, 10, 10) # bgr, darkblue
if ut.is_float(solidbar):
solidbar[:, :, :] = (np.array(border_color) / 255)[None, None]
else:
solidbar[:, :, :] = np.array(border_color)[None, None]
word_img = vt.stack_image_list([patch_img, solidbar, stacked_patches], vert=False, modifysize=True)
pt.imshow(word_img, fnum=fnum)
#pt.imshow(patch_img, pnum=(1, 2, 1), fnum=fnum)
#patch_size = 64
#half_size = patch_size / 2
#pt.imshow(stacked_patches, pnum=(1, 2, 2), fnum=fnum)
pt.iup()
def render_vocab(vocab, inva=None, use_data=False):
"""
Renders the average patch of each word.
This is a quick visualization of the entire vocabulary.
CommandLine:
python -m ibeis.algo.smk.vocab_indexer render_vocab --show
python -m ibeis.algo.smk.vocab_indexer render_vocab --show --use-data
python -m ibeis.algo.smk.vocab_indexer render_vocab --show --debug-depc
Example:
>>> from ibeis.algo.smk.vocab_indexer import * # NOQA
>>> ibs, aid_list, voab = testdata_vocab('PZ_MTEST', num_words=10000)
>>> use_data = ut.get_argflag('--use-data')
>>> vocab = inva.vocab
>>> all_words = vocab.render_vocab(inva, use_data=use_data)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> pt.qt4ensure()
>>> pt.imshow(all_words)
>>> ut.show_if_requested()
"""
# Get words with the most assignments
sortx = ut.argsort(list(inva.wx_to_num.values()))[::-1]
wx_list = ut.take(list(inva.wx_to_num.keys()), sortx)
wx_list = ut.strided_sample(wx_list, 64)
word_patch_list = []
for wx in ut.ProgIter(wx_list, bs=True, lbl='building patches'):
word = inva.vocab.wx_to_word[wx]
if use_data:
word_patch = inva.get_word_patch(wx)
else:
word_patch = vt.inverted_sift_patch(word, 64)
import plottool as pt
word_patch = pt.render_sift_on_patch(word_patch, word)
word_patch_list.append(word_patch)
#for wx, p in zip(wx_list, word_patch_list):
# inva._word_patches[wx] = p
all_words = vt.stack_square_images(word_patch_list)
return all_words
def render_inverted_vocab(inva, ibs, use_data=False):
"""
Renders the average patch of each word.
This is a visualization of the entire vocabulary.
CommandLine:
python -m ibeis.algo.smk.inverted_index render_inverted_vocab --show
python -m ibeis.algo.smk.inverted_index render_inverted_vocab --show --use-data
python -m ibeis.algo.smk.inverted_index render_inverted_vocab --show --debug-depc
Example:
>>> from ibeis.algo.smk.inverted_index import * # NOQA
>>> qreq_, inva = testdata_inva()
>>> ibs = qreq_.ibs
>>> all_words = inva.render_inverted_vocab(ibs)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> pt.qt4ensure()
>>> pt.imshow(all_words)
>>> ut.show_if_requested()
"""
import plottool as pt
# Get words with the most assignments
vocab = ibs.depc['vocab'].get_row_data([inva.vocab_rowid], 'words')[0]
wx_list = ut.strided_sample(inva.wx_list, 64)
word_patch_list = []
for wx in ut.ProgIter(wx_list, bs=True, lbl='building patches'):
word = vocab.wx_to_word[wx]
word_patch = inva.get_word_patch(wx, ibs)
word_patch = pt.render_sift_on_patch(word_patch, word)
word_patch_list.append(word_patch)
all_words = vt.stack_square_images(word_patch_list)
return all_words
def render_inverted_vocab_word(inva, wx, ibs, fnum=None):
"""
Creates a visualization of a visual word. This includes the average patch,
the SIFT-like representation of the centroid, and some of the patches that
were assigned to it.
CommandLine:
python -m ibeis.algo.smk.inverted_index render_inverted_vocab_word --show
Example:
>>> from ibeis.algo.smk.inverted_index import * # NOQA
>>> import plottool as pt
>>> qreq_, inva = testdata_inva()
>>> ibs = qreq_.ibs
>>> wx_list = list(inva.wx_to_aids.keys())
>>> wx = wx_list[0]
>>> ut.qtensure()
>>> fnum = 2
>>> fnum = pt.ensure_fnum(fnum)
>>> # Interactive visualization of many words
>>> for wx in ut.InteractiveIter(wx_list):
>>> word_img = inva.render_inverted_vocab_word(wx, ibs, fnum)
>>> pt.imshow(word_img, fnum=fnum, title='Word %r/%r' % (wx, '?'))
>>> pt.update()
"""
import plottool as pt
# Create the contributing patch image
word_patches = inva.get_patches(wx, ibs)
word_patches_ = ut.strided_sample(word_patches, 64)
stacked_patches = vt.stack_square_images(word_patches_)
# Create the average word image
vocab = ibs.depc['vocab'].get_row_data([inva.vocab_rowid], 'words')[0]
word = vocab.wx_to_word[wx]
average_patch = np.mean(word_patches, axis=0)
#vecs = inva.get_vecs(wx)
#assert np.allclose(word, vecs.mean(axis=0))
with_sift = True
if with_sift:
patch_img = pt.render_sift_on_patch(average_patch, word)
else:
patch_img = average_patch
# Stack them together
solidbar = np.zeros(
(patch_img.shape[0], int(patch_img.shape[1] * .1), 3),
dtype=patch_img.dtype)
border_color = (100, 10, 10) # bgr, darkblue
if ut.is_float(solidbar):
solidbar[:, :, :] = (np.array(border_color) / 255)[None, None]
else:
solidbar[:, :, :] = np.array(border_color)[None, None]
patch_img2 = vt.inverted_sift_patch(word)
# Fix types
patch_img = vt.rectify_to_uint8(patch_img)
patch_img2 = vt.rectify_to_uint8(patch_img2)
solidbar = vt.rectify_to_uint8(solidbar)
stacked_patches = vt.rectify_to_uint8(stacked_patches)
# Stack everything together
patch_img2, patch_img = vt.make_channels_comparable(
patch_img2, patch_img)
img_list = [patch_img, solidbar, patch_img2, solidbar, stacked_patches]
word_img = vt.stack_image_list(img_list, vert=False, modifysize=True)
return word_img