def inplace_filter_results(self, filter_pat):
import utool as ut
self.filter_pats.append(filter_pat)
# Get zipflags
flags_list = self.pattern_filterflags(filter_pat)
# Check to see if there are any survivors
flags = ut.lmap(any, flags_list)
#
found_lines_list = ut.zipcompress(self.found_lines_list, flags_list)
found_lxs_list = ut.zipcompress(self.found_lxs_list, flags_list)
#
found_fpath_list = ut.compress(self.found_fpath_list, flags)
found_lines_list = ut.compress(found_lines_list, flags)
found_lxs_list = ut.compress(found_lxs_list, flags)
# In place modification
self.found_fpath_list = found_fpath_list
self.found_lines_list = found_lines_list
self.found_lxs_list = found_lxs_list
def inplace_filter_results(self, filter_pat):
import utool as ut
self.filter_pats.append(filter_pat)
# Get zipflags
flags_list = self.pattern_filterflags(filter_pat)
# Check to see if there are any survivors
flags = ut.lmap(any, flags_list)
#
found_lines_list = ut.zipcompress(self.found_lines_list, flags_list)
found_lxs_list = ut.zipcompress(self.found_lxs_list, flags_list)
#
found_fpath_list = ut.compress(self.found_fpath_list, flags)
found_lines_list = ut.compress(found_lines_list, flags)
found_lxs_list = ut.compress(found_lxs_list, flags)
# In place modification
self.found_fpath_list = found_fpath_list
self.found_lines_list = found_lines_list
self.found_lxs_list = found_lxs_list
def get_photobomber_map(ibs, aids, aid_to_nid=None):
"""
Builds map of which names that photobomb other names.
python -m wbia.gui.id_review_api --test-test_review_widget --show --db PZ_MTEST -a default:qindex=0
>>> import wbia
>>> dbdir = ut.truepath('~/lev/media/danger/GGR/GGR-IBEIS')
>>> ibs = wbia.opendb(dbdir='/home/joncrall/lev/media/danger/GGR/GGR-IBEIS')
>>> filter_kw = {
>>> 'multiple': False,
>>> 'minqual': 'good',
>>> 'is_known': True,
>>> 'min_pername': 2,
>>> 'view': ['right'],
>>> }
>>> aids = ibs.filter_annots_general(ibs.get_valid_aids(), filter_kw=filter_kw)
"""
ams_list = ibs.get_annotmatch_rowids_from_aid(aids)
flags_list = ibs.unflat_map(
ut.partial(ibs.get_annotmatch_prop, 'Photobomb'), ams_list)
pb_ams = ut.zipcompress(ams_list, flags_list)
has_pb_ams = [len(ams) > 0 for ams in pb_ams]
pb_ams_ = ut.compress(pb_ams, has_pb_ams)
# aids_ = ut.compress(aids, has_pb_ams)
pb_ams_flat = ut.flatten(pb_ams_)
pb_aids1_ = ibs.get_annotmatch_aid1(pb_ams_flat)
pb_aids2_ = ibs.get_annotmatch_aid2(pb_ams_flat)
pb_aid_pairs_ = list(zip(pb_aids1_, pb_aids2_))
if aid_to_nid is None:
pb_nid_pairs_ = ibs.unflat_map(ibs.get_annot_nids, pb_aid_pairs_)
else:
pb_nid_pairs_ = ibs.unflat_map(ut.partial(ut.take, aid_to_nid),
pb_aid_pairs_)
# invalid_aid_map = ut.ddict(set)
# for aid1, aid2 in pb_aid_pairs_:
# if aid1 != aid2:
# invalid_aid_map[aid1].add(aid2)
# invalid_aid_map[aid2].add(aid1)
invalid_nid_map = ut.ddict(set)
for nid1, nid2 in pb_nid_pairs_:
if nid1 != nid2:
invalid_nid_map[nid1].add(nid2)
invalid_nid_map[nid2].add(nid1)
return invalid_nid_map
def purge_ensure_one_annot_per_images(ibs):
"""
pip install Pipe
"""
# Purge all but one annotation
images = ibs.images()
# images.aids
groups = images._annot_groups
import numpy as np
# Take all but the largest annotations per images
large_masks = [
ut.index_to_boolmask([np.argmax(x)], len(x)) for x in groups.bbox_area
]
small_masks = ut.lmap(ut.not_list, large_masks)
# Remove all but the largets annotation
small_aids = ut.zipcompress(groups.aid, small_masks)
small_aids = ut.flatten(small_aids)
# Fix any empty images
images = ibs.images()
empty_images = ut.where(np.array(images.num_annotations) == 0)
logger.info('empty_images = %r' % (empty_images, ))
# list(map(basename, map(dirname, images.uris_original)))
def VecPipe(func):
import pipe
@pipe.Pipe
def wrapped(sequence):
return map(func, sequence)
# return (None if item is None else func(item) for item in sequence)
return wrapped
name_list = list(images.uris_original | VecPipe(dirname)
| VecPipe(basename))
aids_list = images.aids
ut.assert_all_eq(list(aids_list | VecPipe(len)))
annots = ibs.annots(ut.flatten(aids_list))
annots.names = name_list
def fix_duplicates(drive):
r"""
for every duplicate file passing a (eg avi) filter, remove the file
that is in the smallest directory. On a tie use the smallest dpath.
This will filter all duplicate files in a folder into a single folder.
but... need to look at non-duplicates in that folder and decide if they
should be moved as well. So, should trigger on folders that have at
least 50% duplicate. Might not want to move curated folders.
Example:
cd ~/local/scripts
>>> from register_files import * # NOQA
>>> dpaths = ut.get_argval('--drives', type_=list, default=['E:/'])#'D:/', 'E:/', 'F:/'])
>>> drives = [Drive(root_dpath) for root_dpath in dpaths]
>>> E = drive = drives[0]
>>> #D, E, F = drives
"""
print('Fixing Duplicates in %r' % (drive,))
list_ = drive.fpath_hashX_list
multiindex_dict_ = build_multindex(list_)
duplicate_hashes = [
key for key, val in six.iteritems(multiindex_dict_)
if len(val) > 1
]
duplicate_idxs = ut.dict_take(multiindex_dict_, duplicate_hashes)
unflat_fpaths = ut.list_unflat_take(drive.fpath_list, duplicate_idxs)
# Check if any dups have been removed
still_exists = ut.unflat_map(exists, unflat_fpaths)
unflat_idxs2 = ut.zipcompress(duplicate_idxs, still_exists)
duplicate_idxs = [idxs for idxs in unflat_idxs2 if len(idxs) > 1]
# Look at duplicate files
unflat_fpaths = ut.list_unflat_take(drive.fpath_list, duplicate_idxs)
unflat_sizes = ut.list_unflat_take(drive.fpath_bytes_list, duplicate_idxs)
# Find highly coupled directories
if True:
coupled_dirs = []
for fpaths in unflat_fpaths:
#basedir = ut.longest_existing_path(commonprefix(fpaths))
dirs = sorted(list(map(dirname, fpaths)))
_list = list(range(len(dirs)))
idxs = ut.upper_diag_self_prodx(_list)
coupled_dirs.extend(list(map(tuple, ut.list_unflat_take(dirs, idxs))))
hist_ = ut.dict_hist(coupled_dirs)
coupled_idxs = ut.list_argsort(hist_.values())[::-1]
most_coupled = ut.take(list(hist_.keys()), coupled_idxs[0:100])
print('Coupled fpaths: ' + ut.list_str(most_coupled, nl=True))
print('%d unique files are duplicated' % (len(unflat_sizes),))
#print('Duplicate sizes: ' + ut.list_str(unflat_sizes[0:10], nl=True))
#print('Duplicate fpaths: ' + ut.list_str(unflat_fpaths[0:10], nl=True))
#print('Duplicate fpaths: ' + ut.list_str(unflat_fpaths[0::5], nl=True))
print('Duplicate fpaths: ' + ut.list_str(unflat_fpaths, nl=True))
# Find duplicate directories
dpath_list = list(drive.dpath_to_fidx.keys())
fidxs_list = ut.dict_take(drive.dpath_to_fidx, drive.dpath_list)
#exists_list = list(map(exists, drive.fpath_list))
#unflat_exists = ut.list_unflat_take(exists_list, fidxs_list)
fname_registry = [basename(fpath) for fpath in drive.fpath_list]
unflat_fnames = ut.list_unflat_take(fname_registry, fidxs_list)
def unsorted_list_hash(list_):
return ut.hashstr27(str(sorted(list_)))
unflat_fname_sets = list(map(unsorted_list_hash, ut.ProgIter(unflat_fnames, freq=10000)))
fname_based_duplicate_dpaths = []
multiindex_dict2_ = build_multindex(unflat_fname_sets)
fname_based_duplicate_hashes = [key for key, val in multiindex_dict2_.items() if len(val) > 1]
print('#fname_based_duplicate_dpaths = %r' % (len(fname_based_duplicate_hashes),))
fname_based_duplicate_didxs = ut.dict_take(multiindex_dict2_, fname_based_duplicate_hashes)
fname_based_duplicate_dpaths = ut.list_unflat_take(dpath_list, fname_based_duplicate_didxs)
print(ut.repr3(fname_based_duplicate_dpaths[0:10]))
def fix_duplicates(drive):
r"""
for every duplicate file passing a (eg avi) filter, remove the file
that is in the smallest directory. On a tie use the smallest dpath.
This will filter all duplicate files in a folder into a single folder.
but... need to look at non-duplicates in that folder and decide if they
should be moved as well. So, should trigger on folders that have at
least 50% duplicate. Might not want to move curated folders.
Example:
cd ~/local/scripts
>>> from register_files import * # NOQA
>>> dpaths = ut.get_argval('--drives', type_=list, default=['E:/'])#'D:/', 'E:/', 'F:/'])
>>> drives = [Drive(root_dpath) for root_dpath in dpaths]
>>> E = drive = drives[0]
>>> #D, E, F = drives
"""
print('Fixing Duplicates in %r' % (drive, ))
list_ = drive.fpath_hashX_list
multiindex_dict_ = build_multindex(list_)
duplicate_hashes = [
key for key, val in six.iteritems(multiindex_dict_) if len(val) > 1
]
duplicate_idxs = ut.dict_take(multiindex_dict_, duplicate_hashes)
unflat_fpaths = ut.list_unflat_take(drive.fpath_list, duplicate_idxs)
# Check if any dups have been removed
still_exists = ut.unflat_map(exists, unflat_fpaths)
unflat_idxs2 = ut.zipcompress(duplicate_idxs, still_exists)
duplicate_idxs = [idxs for idxs in unflat_idxs2 if len(idxs) > 1]
# Look at duplicate files
unflat_fpaths = ut.list_unflat_take(drive.fpath_list, duplicate_idxs)
unflat_sizes = ut.list_unflat_take(drive.fpath_bytes_list,
duplicate_idxs)
# Find highly coupled directories
if True:
coupled_dirs = []
for fpaths in unflat_fpaths:
#basedir = ut.longest_existing_path(commonprefix(fpaths))
dirs = sorted(list(map(dirname, fpaths)))
_list = list(range(len(dirs)))
idxs = ut.upper_diag_self_prodx(_list)
coupled_dirs.extend(
list(map(tuple, ut.list_unflat_take(dirs, idxs))))
hist_ = ut.dict_hist(coupled_dirs)
coupled_idxs = ut.list_argsort(hist_.values())[::-1]
most_coupled = ut.take(list(hist_.keys()), coupled_idxs[0:100])
print('Coupled fpaths: ' + ut.repr2(most_coupled, nl=True))
print('%d unique files are duplicated' % (len(unflat_sizes), ))
#print('Duplicate sizes: ' + ut.repr2(unflat_sizes[0:10], nl=True))
#print('Duplicate fpaths: ' + ut.repr2(unflat_fpaths[0:10], nl=True))
#print('Duplicate fpaths: ' + ut.repr2(unflat_fpaths[0::5], nl=True))
print('Duplicate fpaths: ' + ut.repr2(unflat_fpaths, nl=True))
# Find duplicate directories
dpath_list = list(drive.dpath_to_fidx.keys())
fidxs_list = ut.dict_take(drive.dpath_to_fidx, drive.dpath_list)
#exists_list = list(map(exists, drive.fpath_list))
#unflat_exists = ut.list_unflat_take(exists_list, fidxs_list)
fname_registry = [basename(fpath) for fpath in drive.fpath_list]
unflat_fnames = ut.list_unflat_take(fname_registry, fidxs_list)
def unsorted_list_hash(list_):
return ut.hashstr27(str(sorted(list_)))
unflat_fname_sets = list(
map(unsorted_list_hash, ut.ProgIter(unflat_fnames, freq=10000)))
fname_based_duplicate_dpaths = []
multiindex_dict2_ = build_multindex(unflat_fname_sets)
fname_based_duplicate_hashes = [
key for key, val in multiindex_dict2_.items() if len(val) > 1
]
print('#fname_based_duplicate_dpaths = %r' %
(len(fname_based_duplicate_hashes), ))
fname_based_duplicate_didxs = ut.dict_take(
multiindex_dict2_, fname_based_duplicate_hashes)
fname_based_duplicate_dpaths = ut.list_unflat_take(
dpath_list, fname_based_duplicate_didxs)
print(ut.repr3(fname_based_duplicate_dpaths[0:10]))