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