def _add_annotation(
annotation,
bbox,
theta,
species_name,
viewpoint,
interest,
decrease,
width,
height,
part_type=None,
):
# Transformation matrix
R = vt.rotation_around_bbox_mat3x3(theta, bbox)
# Get verticies of the annotation polygon
verts = vt.verts_from_bbox(bbox, close=True)
# Rotate and transform vertices
xyz_pts = vt.add_homogenous_coordinate(np.array(verts).T)
trans_pts = vt.remove_homogenous_coordinate(R.dot(xyz_pts))
new_verts = np.round(trans_pts).astype(np.int).T.tolist()
x_points = [pt[0] for pt in new_verts]
y_points = [pt[1] for pt in new_verts]
xmin = int(min(x_points) * decrease)
xmax = int(max(x_points) * decrease)
ymin = int(min(y_points) * decrease)
ymax = int(max(y_points) * decrease)
# Bounds check
xmin = max(xmin, 0)
ymin = max(ymin, 0)
xmax = min(xmax, width - 1)
ymax = min(ymax, height - 1)
# Get info
info = {}
w_ = xmax - xmin
h_ = ymax - ymin
if w_ < min_annot_size:
return
if h_ < min_annot_size:
return
if viewpoint != -1 and viewpoint is not None:
info['pose'] = viewpoint
if interest is not None:
info['interest'] = '1' if interest else '0'
if part_type is not None:
species_name = '%s+%s' % (
species_name,
part_type,
)
area = w_ * h_
logger.info('\t\tAdding %r with area %0.04f pixels^2' % (
species_name,
area,
))
annotation.add_object(species_name, (xmax, xmin, ymax, ymin), **info)
def align(bbox, theta, width, height):
# Transformation matrix
R = vt.rotation_around_bbox_mat3x3(theta, bbox)
# Get verticies of the annotation polygon
verts = vt.verts_from_bbox(bbox, close=True)
# Rotate and transform vertices
xyz_pts = vt.add_homogenous_coordinate(np.array(verts).T)
trans_pts = vt.remove_homogenous_coordinate(R.dot(xyz_pts))
new_verts = np.round(trans_pts).astype(np.int).T.tolist()
x_points = [pt[0] for pt in new_verts]
y_points = [pt[1] for pt in new_verts]
xmin = int(min(x_points))
xmax = int(max(x_points))
ymin = int(min(y_points))
ymax = int(max(y_points))
# Bounds check
xmin = max(xmin, 0)
ymin = max(ymin, 0)
xmax = min(xmax, width - 1)
ymax = min(ymax, height - 1)
xtl = xmin
ytl = ymin
w = xmax - xmin
h = ymax - ymin
return (
xtl,
ytl,
w,
h,
)
def _add_annotation_or_part(
image_index,
annot_index,
annot_uuid,
bbox,
theta,
species_name,
viewpoint,
interest,
annot_name,
decrease,
width,
height,
individuals,
part_index=None,
part_uuid=None,
):
is_part = part_index is not None
R = vt.rotation_around_bbox_mat3x3(theta, bbox)
verts = vt.verts_from_bbox(bbox, close=True)
xyz_pts = vt.add_homogenous_coordinate(np.array(verts).T)
trans_pts = vt.remove_homogenous_coordinate(R.dot(xyz_pts))
new_verts = np.round(trans_pts).astype(np.int).T.tolist()
x_points = [int(np.around(pt[0] * decrease)) for pt in new_verts]
y_points = [int(np.around(pt[1] * decrease)) for pt in new_verts]
segmentation = ut.flatten(list(zip(x_points, y_points)))
xmin = max(min(x_points), 0)
ymin = max(min(y_points), 0)
xmax = min(max(x_points), width - 1)
ymax = min(max(y_points), height - 1)
w = xmax - xmin
h = ymax - ymin
area = w * h
xtl_, ytl_, w_, h_ = bbox
xtl_ *= decrease
ytl_ *= decrease
w_ *= decrease
h_ *= decrease
annot_part = {
'bbox': [xtl_, ytl_, w_, h_],
'theta': theta,
'viewpoint': viewpoint,
'segmentation': [segmentation],
'segmentation_bbox': [xmin, ymin, w, h],
'area': area,
'iscrowd': 0,
'id': part_index if is_part else annot_index,
'image_id': image_index,
'category_id': category_dict[species_name],
'uuid': str(part_uuid if is_part else annot_uuid),
'individual_ids': individuals,
}
if is_part:
annot_part['annot_id'] = annot_index
else:
annot_part['isinterest'] = int(interest)
annot_part['name'] = annot_name
return annot_part, area
def export_to_coco(ibs,
species_list,
species_mapping={},
target_size=2400,
use_maximum_linear_dimension=True,
use_existing_train_test=True,
gid_list=None,
include_reviews=False,
require_named=True,
output_images=True,
**kwargs):
"""Create training COCO dataset for training models."""
from datetime import date
import datetime
import random
import json
print('Received species_mapping = %r' % (species_mapping, ))
print('Using species_list = %r' % (species_list, ))
current_year = int(date.today().year)
datadir = abspath(join(ibs.get_cachedir(), 'coco'))
annotdir = join(datadir, 'annotations')
imagedir = join(datadir, 'images')
image_dir_dict = {
'train': join(imagedir, 'train%s' % (current_year, )),
'val': join(imagedir, 'val%s' % (current_year, )),
'test': join(imagedir, 'test%s' % (current_year, )),
}
ut.delete(datadir)
ut.ensuredir(datadir)
ut.ensuredir(annotdir)
ut.ensuredir(imagedir)
for dataset in image_dir_dict:
ut.ensuredir(image_dir_dict[dataset])
info = {
'description': 'Wild Me %s Dataset' % (ibs.dbname, ),
# 'url' : 'http://www.greatgrevysrally.com',
'url': 'http://www.wildme.org',
'version': '1.0',
'year': current_year,
'contributor': 'Wild Me, Jason Parham <*****@*****.**>',
'date_created': datetime.datetime.utcnow().isoformat(' '),
'ibeis_database_name': ibs.get_db_name(),
'ibeis_database_uuid': str(ibs.get_db_init_uuid()),
}
licenses = [
{
'url': 'http://creativecommons.org/licenses/by-nc-nd/2.0/',
'id': 3,
'name': 'Attribution-NonCommercial-NoDerivs License',
},
]
assert len(species_list) == len(
set(species_list)), 'Cannot have duplicate species in species_list'
category_dict = {}
categories = []
for index, species in enumerate(sorted(species_list)):
species = species_mapping.get(species, species)
categories.append({
'id': index,
'name': species,
'supercategory': 'animal',
})
category_dict[species] = index
output_dict = {}
for dataset in ['train', 'val', 'test']:
output_dict[dataset] = {
'info': info,
'licenses': licenses,
'categories': categories,
'images': [],
'annotations': [],
}
# Get all gids and process them
if gid_list is None:
aid_list = ibs.get_valid_aids()
species_list_ = ibs.get_annot_species(aid_list)
flag_list = [
species_mapping.get(species_, species_) in species_list
for species_ in species_list_
]
aid_list = ut.compress(aid_list, flag_list)
if require_named:
nid_list = ibs.get_annot_nids(aid_list)
flag_list = [nid >= 0 for nid in nid_list]
aid_list = ut.compress(aid_list, flag_list)
gid_list = sorted(list(set(ibs.get_annot_gids(aid_list))))
# Make a preliminary train / test split as imagesets or use the existing ones
if not use_existing_train_test:
ibs.imageset_train_test_split(**kwargs)
train_gid_set = set(general_get_imageset_gids(ibs, 'TRAIN_SET', **kwargs))
test_gid_set = set(general_get_imageset_gids(ibs, 'TEST_SET', **kwargs))
image_index = 1
annot_index = 1
aid_dict = {}
print('Exporting %d images' % (len(gid_list), ))
for gid in gid_list:
if gid in test_gid_set:
dataset = 'test'
elif gid in train_gid_set:
state = random.uniform(0.0, 1.0)
if state <= 0.75:
dataset = 'train'
else:
dataset = 'val'
else:
raise AssertionError(
'All gids must be either in the TRAIN_SET or TEST_SET imagesets'
)
width, height = ibs.get_image_sizes(gid)
if target_size is None:
decrease = 1.0
else:
condition = width > height if use_maximum_linear_dimension else width < height
if condition:
ratio = height / width
decrease = target_size / width
width = target_size
height = int(target_size * ratio)
else:
ratio = width / height
decrease = target_size / height
height = target_size
width = int(target_size * ratio)
image_path = ibs.get_image_paths(gid)
image_filename = '%012d.jpg' % (image_index, )
image_filepath = join(image_dir_dict[dataset], image_filename)
if output_images:
_image = ibs.get_images(gid)
_image = vt.resize(_image, (width, height))
vt.imwrite(image_filepath, _image)
output_dict[dataset]['images'].append({
'license':
3,
# 'file_name' : image_filename,
'file_name':
basename(ibs.get_image_uris_original(gid)),
'coco_url':
None,
'height':
height,
'width':
width,
'date_captured':
ibs.get_image_datetime_str(gid).replace('/', '-'),
'flickr_url':
None,
'id':
image_index,
'ibeis_image_uuid':
str(ibs.get_image_uuids(gid)),
})
print('Copying:\n%r\n%r\n%r\n\n' % (
image_path,
image_filepath,
(width, height),
))
aid_list = ibs.get_image_aids(gid)
bbox_list = ibs.get_annot_bboxes(aid_list)
theta_list = ibs.get_annot_thetas(aid_list)
species_name_list = ibs.get_annot_species_texts(aid_list)
viewpoint_list = ibs.get_annot_viewpoints(aid_list)
nid_list = ibs.get_annot_nids(aid_list)
seen = 0
zipped = zip(aid_list, bbox_list, theta_list, species_name_list,
viewpoint_list, nid_list)
for aid, bbox, theta, species_name, viewpoint, nid in zipped:
species_name = species_mapping.get(species_name, species_name)
if species_name is None:
continue
if species_name not in species_list:
continue
if require_named and nid < 0:
continue
# Transformation matrix
R = vt.rotation_around_bbox_mat3x3(theta, bbox)
verts = vt.verts_from_bbox(bbox, close=True)
xyz_pts = vt.add_homogenous_coordinate(np.array(verts).T)
trans_pts = vt.remove_homogenous_coordinate(R.dot(xyz_pts))
new_verts = np.round(trans_pts).astype(np.int).T.tolist()
x_points = [int(np.around(pt[0] * decrease)) for pt in new_verts]
y_points = [int(np.around(pt[1] * decrease)) for pt in new_verts]
segmentation = ut.flatten(list(zip(x_points, y_points)))
xmin = max(min(x_points), 0)
ymin = max(min(y_points), 0)
xmax = min(max(x_points), width - 1)
ymax = min(max(y_points), height - 1)
w = xmax - xmin
h = ymax - ymin
area = w * h
# individuals = ibs.get_name_aids(ibs.get_annot_nids(aid))
reviews = ibs.get_review_rowids_from_single([aid])[0]
user_list = ibs.get_review_identity(reviews)
aid_tuple_list = ibs.get_review_aid_tuple(reviews)
decision_list = ibs.get_review_decision_str(reviews)
ids = []
decisions = []
zipped = zip(user_list, aid_tuple_list, decision_list)
for user, aid_tuple, decision in zipped:
if 'user:web' not in user:
continue
match = list(set(aid_tuple) - set([aid]))
assert len(match) == 1
ids.append(match[0])
decisions.append(decision.lower())
xtl_, ytl_, w_, h_ = bbox
xtl_ *= decrease
ytl_ *= decrease
w_ *= decrease
h_ *= decrease
annot = {
'bbox': [xtl_, ytl_, w_, h_],
'theta': theta,
'viewpoint': viewpoint,
'segmentation': [segmentation],
'segmentation_bbox': [xmin, ymin, w, h],
'area': area,
'iscrowd': 0,
'image_id': image_index,
'category_id': category_dict[species_name],
'id': annot_index,
'ibeis_annot_uuid': str(ibs.get_annot_uuids(aid)),
'ibeis_annot_name': str(ibs.get_annot_name_texts(aid)),
# 'individual_ids' : individuals,
}
if include_reviews:
annot['review_ids'] = list(zip(ids, decisions))
output_dict[dataset]['annotations'].append(annot)
seen += 1
print('\t\tAdding %r with area %0.04f pixels^2' % (
species_name,
area,
))
aid_dict[aid] = annot_index
annot_index += 1
# assert seen > 0
image_index += 1
for dataset in output_dict:
annots = output_dict[dataset]['annotations']
for index in range(len(annots)):
annot = annots[index]
# Map internal aids to external annot index
# individual_ids = annot['individual_ids']
# individual_ids_ = []
# for individual_id in individual_ids:
# if individual_id not in aid_dict:
# continue
# individual_id_ = aid_dict[individual_id]
# individual_ids_.append(individual_id_)
# annot['individual_ids'] = individual_ids_
# Map reviews
if include_reviews:
review_ids = annot['review_ids']
review_ids_ = []
for review in review_ids:
review_id, review_decision = review
if review_id not in aid_dict:
continue
review_id_ = aid_dict[review_id]
review_ = (
review_id_,
review_decision,
)
review_ids_.append(review_)
annot['review_ids'] = review_ids_
# Store
output_dict[dataset]['annotations'][index] = annot
for dataset in output_dict:
json_filename = 'instances_%s%s.json' % (
dataset,
current_year,
)
json_filepath = join(annotdir, json_filename)
with open(json_filepath, 'w') as json_file:
json.dump(output_dict[dataset], json_file)
print('...completed')
return datadir