def dev_cls(back):
""" Help -> Developer Mode"""
print('[back] dev_cls')
print('\n'.join([''] * 100))
back.refresh_state()
from plottool import draw_func2 as df2
df2.update()
def on_click_inside(self, event, ax):
index = ph.get_plotdat(ax, 'index')
print('index = %r' % (index, ))
if index is not None:
if self.MOUSE_BUTTONS[event.button] == 'right':
if self.context_option_funcs is not None:
#if event.button == 3:
options = self.context_option_funcs[index]()
self.show_popup_menu(options, event)
elif self.MOUSE_BUTTONS[event.button] == 'left':
#bbox_list = ph.get_plotdat(ax, 'bbox_list')
gpath = self.gpath_list[index]
if ut.is_funclike(gpath):
print('gpath_isfunklike')
print('gpath = %r' % (gpath, ))
import plottool as pt
fnum = pt.next_fnum()
gpath(fnum=fnum)
df2.update()
else:
bbox_list = self.bboxes_list[index]
print('Bbox of figure: %r' % (bbox_list, ))
theta_list = self.thetas_list[index]
print('theta_list = %r' % (theta_list, ))
#img = mpimg.imread(gpath)
if isinstance(gpath, six.string_types):
img = vt.imread(gpath)
else:
img = gpath
fnum = df2.next_fnum()
mc = interact_annotations.ANNOTATIONInteraction(
img,
index,
self.update_images,
bbox_list=bbox_list,
theta_list=theta_list,
fnum=fnum)
mc.start()
self.mc = mc
# """wait for accept
# have a flag to tell if a bbox has been changed, on the bbox
# list that is brought it" on accept: viz_image2.show_image
# callback
# """
df2.update()
print('Clicked: ax: num=%r' % index)
def on_click_inside(self, event, ax):
index = ph.get_plotdat(ax, 'index')
print('index = %r' % (index,))
if index is not None:
if self.MOUSE_BUTTONS[event.button] == 'right':
if self.context_option_funcs is not None:
#if event.button == 3:
options = self.context_option_funcs[index]()
self.show_popup_menu(options, event)
elif self.MOUSE_BUTTONS[event.button] == 'left':
#bbox_list = ph.get_plotdat(ax, 'bbox_list')
gpath = self.gpath_list[index]
if ut.is_funclike(gpath):
print('gpath_isfunklike')
print('gpath = %r' % (gpath,))
import plottool as pt
fnum = pt.next_fnum()
gpath(fnum=fnum)
df2.update()
else:
bbox_list = self.bboxes_list[index]
print('Bbox of figure: %r' % (bbox_list,))
theta_list = self.thetas_list[index]
print('theta_list = %r' % (theta_list,))
#img = mpimg.imread(gpath)
if isinstance(gpath, six.string_types):
img = vt.imread(gpath)
else:
img = gpath
fnum = df2.next_fnum()
mc = interact_annotations.ANNOTATIONInteraction(
img, index, self.update_images, bbox_list=bbox_list,
theta_list=theta_list, fnum=fnum)
mc.start()
self.mc = mc
# """wait for accept
# have a flag to tell if a bbox has been changed, on the bbox
# list that is brought it" on accept: viz_image2.show_image
# callback
# """
df2.update()
print('Clicked: ax: num=%r' % index)
def __init__(self, ibs, gid, next_callback=None, prev_callback=None, rows_updated_callback=lambda: None, reset_window=True):
self.ibs = ibs
self.gid = gid
self.rows_updated_callback = rows_updated_callback
img = ibs.get_images(self.gid)
self.aid_list = ibs.get_image_aids(self.gid)
bbox_list = ibs.get_annot_bboxes(self.aid_list)
theta_list = ibs.get_annot_thetas(self.aid_list)
species_list = ibs.get_annot_species(self.aid_list)
self.interact_ANNOTATIONS = interact_annotations.ANNOTATIONInteraction(
img,
bbox_list=bbox_list,
theta_list=theta_list,
species_list=species_list,
callback=self.callback,
default_species=self.ibs.cfg.detect_cfg.species,
next_callback=next_callback,
prev_callback=prev_callback,
fnum=12,
#figure_to_use=None if reset_window else self.interact_ANNOTATIONS.fig,
)
df2.update()
def plot_scores2(hs, qcx_list, fnum=1):
print('[dev] plot_scores(fnum=%r)' % fnum)
qcx2_res = get_qcx2_res(hs, qcx_list)
all_score_list = []
gtscore_ys = []
gtscore_xs = []
gtscore_ranks = []
EXCLUDE_ZEROS = False
N = 1
# Append all scores to a giant list
for res in qcx2_res.itervalues():
cx2_score = res.cx2_score
# Get gt scores first
#gt_cxs = hs.get_other_indexed_cxs(res.qcx)
gt_cxs = np.array(res.topN_cxs(hs, N=N, only_gt=True))
gt_ys = cx2_score[gt_cxs]
if EXCLUDE_ZEROS:
nonzero_cxs = np.where(cx2_score != 0)[0]
gt_cxs = gt_cxs[gt_ys != 0]
gt_ranks = res.get_gt_ranks(gt_cxs)
gt_cxs = np.array(utool.list_index(nonzero_cxs, gt_cxs))
gt_ys = gt_ys[gt_ys != 0]
score_list = cx2_score[nonzero_cxs].tolist()
else:
score_list = cx2_score.tolist()
gt_ranks = res.get_gt_ranks(gt_cxs)
gtscore_ys.extend(gt_ys)
gtscore_xs.extend(gt_cxs + len(all_score_list))
gtscore_ranks.extend(gt_ranks)
# Append all scores
all_score_list.extend(score_list)
all_score_list = np.array(all_score_list)
gtscore_ranks = np.array(gtscore_ranks)
gtscore_ys = np.array(gtscore_ys)
# Sort all chipmatch scores
allx_sorted = all_score_list.argsort() # mapping from sortedallx to allx
allscores_sorted = all_score_list[allx_sorted]
# Change the groundtruth positions to correspond to sorted cmatch scores
# Find position of gtscore_xs in allx_sorted
gtscore_sortxs = utool.list_index(allx_sorted, gtscore_xs)
gtscore_sortxs = np.array(gtscore_sortxs)
# Draw and info
rank_bounds = [
(0, 1),
(1, 5),
(5, None)
]
rank_colors = [
df2.TRUE_GREEN,
df2.UNKNOWN_PURP,
df2.FALSE_RED
]
print('[dev] matching chipscore stats: ' + utool.stats_str(all_score_list))
df2.figure(fnum=fnum, doclf=True, docla=True)
# Finds the knee
df2.plot(allscores_sorted, color=df2.ORANGE, label='all scores')
# get positions which are within rank bounds
for count, ((low, high), rankX_color) in reversed(list(enumerate(zip(rank_bounds, rank_colors)))):
rankX_flag_low = gtscore_ranks >= low
if high is not None:
rankX_flag_high = gtscore_ranks < high
rankX_flag = np.logical_and(rankX_flag_low, rankX_flag_high)
else:
rankX_flag = rankX_flag_low
rankX_allgtx = np.where(rankX_flag)[0]
rankX_gtxs = gtscore_sortxs[rankX_allgtx]
rankX_gtys = gtscore_ys[rankX_allgtx]
rankX_label = '%d <= gt rank' % low
if high is not None:
rankX_label += ' < %d' % high
if len(rankX_gtxs) > 0:
df2.plot(rankX_gtxs, rankX_gtys, 'o', color=rankX_color, label=rankX_label)
rowid = qcx2_res.itervalues().next().rowid
df2.set_logyscale_from_data(allscores_sorted)
df2.set_xlabel('chipmatch index')
df2.dark_background()
df2.set_figtitle('matching scores\n' + rowid)
df2.legend(loc='upper left')
#xmin = 0
#xmax = utool.order_of_magnitude_ceil(len(allscores_sorted))
#print('len_ = %r' % (len(allscores_sorted),))
#print('xmin = %r' % (xmin,))
#print('xmax = %r' % (xmax,))
#df2.gca().set_xlim(xmin, xmax)
df2.update()
#utool.embed()
# Second Plot
#data = sorted(zip(gtscore_sortxs, gtscore_ys, gtscore_ranks))
#gtxs = [x for (x, y, z) in data]
#gtys = [y for (x, y, z) in data]
#gtrs = [z for (x, y, z) in data]
#nongtxs = np.setdiff1d(np.arange(gtxs[0], gtxs[-1]), gtxs)
#min_ = min(gtxs)
#max_ = len(allscores_sorted)
#len_ = max_ - min_
#normsum = 0
#ratsum = 0
#gtxs = np.array(gtxs)
#nongtxs = np.array(nongtxs)
#for ix in xrange(min_, max_):
#nongtxs_ = nongtxs[nongtxs >= ix]
#gtxs_ = gtxs[gtxs >= ix]
#numer = allscores_sorted[gtxs_].sum()
#denom = allscores_sorted[nongtxs_].sum()
#ratio = (1 + numer) / (denom + 1)
#total_support = (len(gtxs_) + len(nongtxs_))
#normrat = ratio / total_support
#ratsum += ratio
#normsum += normrat
#print(total_support)
#print(ratsum / len_)
#print(normsum / len_)
#print(ratsum / allscores_sorted[min_:max_].sum())
#print(normsum / allscores_sorted[min_:max_].sum())
#index_gap = np.diff(gtxs)
#score_gap = np.diff(gtys)
#badness = (index_gap - 1) * score_gap
#np.arange(len(gtxs))
fnum += 1
return fnum
def query_last_feature():
viz.show_nearest_descriptors(ibs, qaid, last_state.last_fx, df2.next_fnum())
fig3 = df2.gcf()
ih.connect_callback(fig3, 'button_press_event', _click_matches_click)
df2.update()
