def find_overlap_annots(ibs1, ibs2, method='annots'):
"""
Finds the aids of annotations in ibs1 that are also in ibs2
ibs1 = wbia.opendb('PZ_Master1')
ibs2 = wbia.opendb('PZ_MTEST')
"""
if method == 'images':
images1, images2 = ibs1.images(), ibs2.images()
idxs1, idxs2 = ut.isect_indices(images1.uuids, images2.uuids)
isect_images1 = images1.take(idxs1)
annot_uuids = ut.flatten(isect_images1.annot_uuids)
isect_annots1 = ibs1.annots(uuids=annot_uuids)
elif method == 'annots':
annots1, annots2 = ibs1.annots(), ibs2.annots()
idxs1, idxs2 = ut.isect_indices(annots1.uuids, annots2.uuids)
isect_annots1 = annots1.take(idxs1)
return isect_annots1.aids
def remerge_subset():
"""
Assumes ibs1 is an updated subset of ibs2.
Re-merges ibs1 back into ibs2.
TODO: annotmatch table must have non-directional edges for this to work.
I.e. u < v
Ignore:
# Ensure annotmatch and names are up to date with staging
# Load graph
import ibei
ibs = wbia.opendb('PZ_PB_RF_TRAIN')
infr = wbia.AnnotInference(aids='all', ibs=ibs, verbose=3)
infr.reset_feedback('staging', apply=True)
infr.relabel_using_reviews()
# Check deltas
infr.wbia_name_group_delta_info()
infr.wbia_delta_info()
# Write if it looks good
infr.write_wbia_annotmatch_feedback()
infr.write_wbia_name_assignment()
Ignore:
import wbia
ibs = wbia.opendb('PZ_Master1')
infr = wbia.AnnotInference(ibs, 'all')
infr.reset_feedback('annotmatch', apply=True)
CommandLine:
python -m wbia.dbio.export_subset remerge_subset
"""
import wbia
ibs1 = wbia.opendb('PZ_PB_RF_TRAIN')
ibs2 = wbia.opendb('PZ_Master1')
gids1, gids2 = ibs1.images(), ibs2.images()
idxs1, idxs2 = ut.isect_indices(gids1.uuids, gids2.uuids)
isect_gids1, isect_gids2 = gids1.take(idxs1), gids2.take(idxs2)
assert all(
set.issubset(set(a1), set(a2))
for a1, a2 in zip(isect_gids1.annot_uuids, isect_gids2.annot_uuids))
annot_uuids = ut.flatten(isect_gids1.annot_uuids)
# aids1 = ibs1.annots(ibs1.get_annot_aids_from_uuid(annot_uuids), asarray=True)
# aids2 = ibs2.annots(ibs2.get_annot_aids_from_uuid(annot_uuids), asarray=True)
aids1 = ibs1.annots(uuids=annot_uuids, asarray=True)
aids2 = ibs2.annots(uuids=annot_uuids, asarray=True)
import numpy as np
to_aids2 = dict(zip(aids1, aids2))
# to_aids1 = dict(zip(aids2, aids1))
# Step 1) Update individual annot properties
# These annots need updates
# np.where(aids1.visual_uuids != aids2.visual_uuids)
# np.where(aids1.semantic_uuids != aids2.semantic_uuids)
annot_unary_props = [
# 'yaws', 'bboxes', 'thetas', 'qual', 'species', 'unary_tags']
'yaws',
'bboxes',
'thetas',
'qual',
'species',
'case_tags',
'multiple',
'age_months_est_max',
'age_months_est_min', # 'sex_texts'
]
to_change = {}
for key in annot_unary_props:
prop1 = getattr(aids1, key)
prop2 = getattr(aids2, key)
diff_idxs = set(np.where(prop1 != prop2)[0])
if diff_idxs:
diff_prop1 = ut.take(prop1, diff_idxs)
diff_prop2 = ut.take(prop2, diff_idxs)
logger.info('key = %r' % (key, ))
logger.info('diff_prop1 = %r' % (diff_prop1, ))
logger.info('diff_prop2 = %r' % (diff_prop2, ))
to_change[key] = diff_idxs
if to_change:
changed_idxs = ut.unique(ut.flatten(to_change.values()))
logger.info('Found %d annots that need updated properties' %
len(changed_idxs))
logger.info('changing unary attributes: %r' % (to_change, ))
if False and ut.are_you_sure('apply change'):
for key, idxs in to_change.items():
subaids1 = aids1.take(idxs)
subaids2 = aids2.take(idxs)
prop1 = getattr(subaids1, key)
# prop2 = getattr(subaids2, key)
setattr(subaids2, key, prop1)
else:
logger.info('Annot properties are in sync. Nothing to change')
# Step 2) Update annotmatch - pairwise relationships
infr1 = wbia.AnnotInference(aids=aids1.aids,
ibs=ibs1,
verbose=3,
autoinit=False)
# infr2 = wbia.AnnotInference(aids=ibs2.annots().aids, ibs=ibs2, verbose=3)
aids2 = ibs2.get_valid_aids(is_known=True)
infr2 = wbia.AnnotInference(aids=aids2, ibs=ibs2, verbose=3)
infr2.reset_feedback('annotmatch', apply=True)
# map feedback from ibs1 onto ibs2 using ibs2 aids.
fb1 = infr1.read_wbia_annotmatch_feedback()
fb1_t = {(to_aids2[u], to_aids2[v]): val for (u, v), val in fb1.items()}
fb1_df_t = infr2._pandas_feedback_format(fb1_t).drop('am_rowid', axis=1)
# Add transformed feedback into ibs2
infr2.add_feedback_from(fb1_df_t)
# Now ensure that dummy connectivity exists to preserve origninal names
# from wbia.algo.graph import nx_utils
# for (u, v) in infr2.find_mst_edges('name_label'):
# infr2.draw_aids((u, v))
# cc1 = infr2.pos_graph.connected_to(u)
# cc2 = infr2.pos_graph.connected_to(v)
# logger.info(nx_utils.edges_cross(infr2.graph, cc1, cc2))
# infr2.neg_redundancy(cc1, cc2)
# infr2.pos_redundancy(cc2)
infr2.relabel_using_reviews(rectify=True)
infr2.apply_nondynamic_update()
if False:
infr2.wbia_delta_info()
infr2.wbia_name_group_delta_info()
if len(list(infr2.inconsistent_components())) > 0:
raise NotImplementedError('need to fix inconsistencies first')
# Make it so it just loops until inconsistencies are resolved
infr2.prioritize()
infr2.qt_review_loop()
else:
infr2.write_wbia_staging_feedback()
infr2.write_wbia_annotmatch_feedback()
infr2.write_wbia_name_assignment()
# if False:
# # Fix any inconsistency
# infr2.start_qt_interface(loop=False)
# test_nodes = [5344, 5430, 5349, 5334, 5383, 2280, 2265, 2234, 5399,
# 5338, 2654]
# import networkx as nx
# nx.is_connected(infr2.graph.subgraph(test_nodes))
# # infr = wbia.AnnotInference(aids=test_nodes, ibs=ibs2, verbose=5)
# # randomly sample some new labels to verify
# import wbia.guitool as gt
# from wbia.gui import inspect_gui
# gt.ensure_qapp()
# ut.qtensure()
# old_groups = ut.group_items(name_delta.index.tolist(), name_delta['old_name'])
# del old_groups['____']
# new_groups = ut.group_items(name_delta.index.tolist(), name_delta['new_name'])
# from wbia.algo.hots import simulate
# c = simulate.compare_groups(
# list(new_groups.values()),
# list(old_groups.values()),
# )
# ut.map_vals(len, c)
# for aids in c['pred_splits']:
# old_nids = ibs2.get_annot_nids(aids)
# new_nids = ut.take_column(infr2.gen_node_attrs('name_label', aids), 1)
# split_aids = ut.take_column(ut.group_items(aids, new_nids).values(), 0)
# aid1, aid2 = split_aids[0:2]
# if False:
# inspect_gui.show_vsone_tuner(ibs2, aid1, aid2)
# infr2.start_qt_interface(loop=False)
# if False:
# # import wbia
# ibs1 = wbia.opendb('PZ_PB_RF_TRAIN')
# infr1 = wbia.AnnotInference(aids='all', ibs=ibs1, verbose=3)
# infr1.initialize_graph()
# # infr1.reset_feedback('staging')
# infr1.reset_feedback('annotmatch')
# infr1.apply_feedback_edges()
# infr1.relabel_using_reviews()
# infr1.apply_review_inference()
# infr1.start_qt_interface(loop=False)
# delta = infr2.match_state_delta()
# logger.info('delta = %r' % (delta,))
# infr2.ensure_mst()
# infr2.relabel_using_reviews()
# infr2.apply_review_inference()
# mst_edges = infr2.find_mst_edges()
# set(infr2.graph.edges()).intersection(mst_edges)
return
"""