def test_get_labels_lut_empty_subset(self):
lut = du.get_labels_lut(self.labels, [])
assert_is_instance(lut, np.ndarray)
assert_equals(lut.dtype, int)
assert_equals(lut.ndim, 1)
assert_equals(lut.shape, (len(self.labels)+1,))
assert_list_equal(lut.tolist(), [0]*(len(self.labels)+1))
def test_get_labels_lut_empty_labels(self):
lut = du.get_labels_lut([], self.subset)
assert_is_instance(lut, np.ndarray)
assert_equals(lut.dtype, int)
assert_equals(lut.ndim, 1)
assert_equals(lut.shape, (1,))
assert_list_equal(lut.tolist(), [0])
def test_get_labels_lut_empty_labels(self):
lut = du.get_labels_lut([], self.subset)
assert_is_instance(lut, np.ndarray)
assert_equals(lut.dtype, int)
assert_equals(lut.ndim, 1)
assert_equals(lut.shape, (1, ))
assert_list_equal(lut.tolist(), [0])
def test_get_labels_lut_empty_subset(self):
lut = du.get_labels_lut(self.labels, [])
assert_is_instance(lut, np.ndarray)
assert_equals(lut.dtype, int)
assert_equals(lut.ndim, 1)
assert_equals(lut.shape, (len(self.labels) + 1, ))
assert_list_equal(lut.tolist(), [0] * (len(self.labels) + 1))
def test_get_labels_lut(self):
lut = du.get_labels_lut(self.labels, self.subset)
assert_is_instance(lut, np.ndarray)
assert_equals(lut.dtype, int)
assert_equals(lut.ndim, 1)
assert_equals(lut.shape, (9+1+1,))
lut_expected = [0]*(9+1+1)
lut_expected[4] = 5
lut_expected[5] = 2
lut_expected[7] = 2
lut_expected[9] = 9
assert_list_equal(lut.tolist(), lut_expected)
def test_get_labels_lut(self):
lut = du.get_labels_lut(self.labels, self.subset)
assert_is_instance(lut, np.ndarray)
assert_equals(lut.dtype, int)
assert_equals(lut.ndim, 1)
assert_equals(lut.shape, (9 + 1 + 1, ))
lut_expected = [0] * (9 + 1 + 1)
lut_expected[4] = 5
lut_expected[5] = 2
lut_expected[7] = 2
lut_expected[9] = 9
assert_list_equal(lut.tolist(), lut_expected)
def pascal_context_to_lmdb(dir_imgs,
dir_segm_labels,
fpath_labels_list,
fpath_labels_list_subset,
dst_prefix,
dir_dst,
val_list=None):
'''
Fine intersection of filename in both directories and create
one lmdb directory for each
val_list - list of entities to exclude from train (validation subset e.g. ['2008_000002', '2010_000433'])
'''
if dst_prefix is None:
dst_prefix = ''
labels_list = get_labels_list(fpath_labels_list)
labels_59_list = get_labels_list(fpath_labels_list_subset)
#print labels_list
#print labels_59_list
labels_lut = du.get_labels_lut(labels_list, labels_59_list)
def apply_labels_lut(m):
return labels_lut[m]
paths_imgs = fs.gen_paths(dir_imgs, fs.filter_is_img)
paths_segm_labels = fs.gen_paths(dir_segm_labels)
paths_pairs = fs.fname_pairs(paths_imgs, paths_segm_labels)
paths_imgs, paths_segm_labels = map(list, zip(*paths_pairs))
#for a, b in paths_pairs:
# print a,b
if val_list is not None:
# do train/val split
train_idx, val_idx = du.get_train_val_split_from_names(paths_imgs, val_list)
# images
paths_imgs_train = [paths_imgs[i] for i in train_idx]
fpath_lmdb_imgs_train = os.path.join(dir_dst,
'%scontext_imgs_train_lmdb' % dst_prefix)
to_lmdb.imgs_to_lmdb(paths_imgs_train,
fpath_lmdb_imgs_train)
paths_imgs_val = [paths_imgs[i] for i in val_idx]
fpath_lmdb_imgs_val = os.path.join(dir_dst,
'%scontext_imgs_val_lmdb' % dst_prefix)
to_lmdb.imgs_to_lmdb(paths_imgs_val,
fpath_lmdb_imgs_val)
# ground truth
paths_segm_labels_train = [paths_segm_labels[i] for i in train_idx]
fpath_lmdb_segm_labels_train = os.path.join(dir_dst,
'%scontext_labels_train_lmdb' % dst_prefix)
to_lmdb.matfiles_to_lmdb(paths_segm_labels_train,
fpath_lmdb_segm_labels_train,
'LabelMap',
lut=apply_labels_lut)
paths_segm_labels_val = [paths_segm_labels[i] for i in val_idx]
fpath_lmdb_segm_labels_val = os.path.join(dir_dst,
'%scontext_labels_val_lmdb' % dst_prefix)
to_lmdb.matfiles_to_lmdb(paths_segm_labels_val,
fpath_lmdb_segm_labels_val,
'LabelMap',
lut=apply_labels_lut)
return len(paths_imgs_train), len(paths_imgs_val),\
fpath_lmdb_imgs_train, fpath_lmdb_segm_labels_train, fpath_lmdb_imgs_val, fpath_lmdb_segm_labels_val
else:
fpath_lmdb_imgs = os.path.join(dir_dst,
'%scontext_imgs_lmdb' % dst_prefix)
to_lmdb.imgs_to_lmdb(paths_imgs,
fpath_lmdb_imgs)
fpath_lmdb_segm_labels = os.path.join(dir_dst,
'%scontext_labels_lmdb' % dst_prefix)
to_lmdb.matfiles_to_lmdb(paths_segm_labels,
fpath_lmdb_segm_labels,
'LabelMap',
lut=apply_labels_lut)
return len(paths_imgs), fpath_lmdb_imgs, fpath_lmdb_segm_labels
def pascal_context_to_lmdb(dir_imgs,
dir_segm_labels,
fpath_labels_list,
fpath_labels_list_subset,
dst_prefix,
dir_dst,
val_list=None):
'''
Fine intersection of filename in both directories and create
one lmdb directory for each
val_list - list of entities to exclude from train (validation subset e.g. ['2008_000002', '2010_000433'])
'''
if dst_prefix is None:
dst_prefix = ''
print 'begin'
labels_list = get_labels_list(fpath_labels_list)
labels_59_list = get_labels_list(fpath_labels_list_subset)
#print labels_list
#print labels_59_list
labels_lut = du.get_labels_lut(labels_list, labels_59_list)
def apply_labels_lut(m):
return labels_lut[m]
print 'plus loin'
paths_imgs = fs.gen_paths(dir_imgs, fs.filter_is_img)
paths_segm_labels = fs.gen_paths(dir_segm_labels)
paths_pairs = fs.fname_pairs(paths_imgs, paths_segm_labels)
paths_imgs, paths_segm_labels = map(list, zip(*paths_pairs))
print 'avant if'
#for a, b in paths_pairs:
# print a,b
if val_list is not None:
# do train/val split
train_idx, val_idx = du.get_train_val_split_from_names(
paths_imgs, val_list)
# images
print 'begin images'
print 'train'
paths_imgs_train = [paths_imgs[i] for i in train_idx]
fpath_lmdb_imgs_train = os.path.join(
dir_dst, '%scontext_imgs_train_lmdb' % dst_prefix)
to_lmdb.imgs_to_lmdb(paths_imgs_train, fpath_lmdb_imgs_train)
print 'val'
paths_imgs_val = [paths_imgs[i] for i in val_idx]
fpath_lmdb_imgs_val = os.path.join(
dir_dst, '%scontext_imgs_val_lmdb' % dst_prefix)
to_lmdb.imgs_to_lmdb(paths_imgs_val, fpath_lmdb_imgs_val)
# ground truth
print 'begin labels'
print 'train'
paths_segm_labels_train = [paths_segm_labels[i] for i in train_idx]
fpath_lmdb_segm_labels_train = os.path.join(
dir_dst, '%scontext_labels_train_lmdb' % dst_prefix)
to_lmdb.matfiles_to_lmdb(paths_segm_labels_train,
fpath_lmdb_segm_labels_train,
'LabelMap',
lut=apply_labels_lut)
print 'val'
paths_segm_labels_val = [paths_segm_labels[i] for i in val_idx]
fpath_lmdb_segm_labels_val = os.path.join(
dir_dst, '%scontext_labels_val_lmdb' % dst_prefix)
to_lmdb.matfiles_to_lmdb(paths_segm_labels_val,
fpath_lmdb_segm_labels_val,
'LabelMap',
lut=apply_labels_lut)
return len(paths_imgs_train), len(paths_imgs_val),\
fpath_lmdb_imgs_train, fpath_lmdb_segm_labels_train, fpath_lmdb_imgs_val, fpath_lmdb_segm_labels_val
else:
print 'dans else'
fpath_lmdb_imgs = os.path.join(dir_dst,
'%scontext_imgs_lmdb' % dst_prefix)
to_lmdb.imgs_to_lmdb(paths_imgs, fpath_lmdb_imgs)
fpath_lmdb_segm_labels = os.path.join(
dir_dst, '%scontext_labels_lmdb' % dst_prefix)
to_lmdb.matfiles_to_lmdb(paths_segm_labels,
fpath_lmdb_segm_labels,
'LabelMap',
lut=apply_labels_lut)
return len(paths_imgs), fpath_lmdb_imgs, fpath_lmdb_segm_labels
