# The implementation is adopted from er-muyue/DeFRCN # made publicly available under the MIT License at # https://github.com/er-muyue/DeFRCN/blob/main/defrcn/data/meta_voc.py import os import xml.etree.ElementTree as ET import numpy as np from detectron2.data import DatasetCatalog, MetadataCatalog from detectron2.structures import BoxMode from fvcore.common.file_io import PathManager # PASCAL VOC categories PASCAL_VOC_ALL_CATEGORIES = { 1: [ 'aeroplane', 'bicycle', 'boat', 'bottle', 'car', 'cat', 'chair', 'diningtable', 'dog', 'horse', 'person', 'pottedplant', 'sheep', 'train', 'tvmonitor', 'bird', 'bus', 'cow', 'motorbike', 'sofa', ], 2: [ 'bicycle', 'bird', 'boat', 'bus', 'car', 'cat', 'chair', 'diningtable', 'dog', 'motorbike', 'person', 'pottedplant', 'sheep', 'train', 'tvmonitor', 'aeroplane', 'bottle', 'cow', 'horse', 'sofa', ], 3: [ 'aeroplane', 'bicycle', 'bird', 'bottle', 'bus', 'car', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'person', 'pottedplant', 'train', 'tvmonitor', 'boat', 'cat', 'motorbike', 'sheep', 'sofa', ] } PASCAL_VOC_NOVEL_CATEGORIES = { 1: ['bird', 'bus', 'cow', 'motorbike', 'sofa'], 2: ['aeroplane', 'bottle', 'cow', 'horse', 'sofa'], 3: ['boat', 'cat', 'motorbike', 'sheep', 'sofa'] } PASCAL_VOC_BASE_CATEGORIES = { 1: [ 'aeroplane', 'bicycle', 'boat', 'bottle', 'car', 'cat', 'chair', 'diningtable', 'dog', 'horse', 'person', 'pottedplant', 'sheep', 'train', 'tvmonitor', ], 2: [ 'bicycle', 'bird', 'boat', 'bus', 'car', 'cat', 'chair', 'diningtable', 'dog', 'motorbike', 'person', 'pottedplant', 'sheep', 'train', 'tvmonitor', ], 3: [ 'aeroplane', 'bicycle', 'bird', 'bottle', 'bus', 'car', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'person', 'pottedplant', 'train', 'tvmonitor', ] } def load_filtered_voc_instances(name: str, root: str, dirname: str, split: str, classnames: str): """ Load Pascal VOC detection annotations to Detectron2 format. Args: dirname: Contain "Annotations", "ImageSets", "JPEGImages" split (str): one of "train", "test", "val", "trainval" """ is_shots = 'shot' in name dicts = [] if is_shots: fileids = {} # split_dir = os.path.join("datasets", "vocsplit") split_dir = os.path.join(root, 'vocsplit') shot = name.split('_')[-2].split('shot')[0] seed = int(name.split('_seed')[-1]) split_dir = os.path.join(split_dir, 'seed{}'.format(seed)) for cls in classnames: with PathManager.open( os.path.join(split_dir, 'box_{}shot_{}_train.txt'.format(shot, cls))) as f: fileids_ = np.loadtxt(f, dtype=np.str_).tolist() if isinstance(fileids_, str): fileids_ = [fileids_] fileids_ = [ fid.split('/')[-1].split('.jpg')[0] for fid in fileids_ ] fileids[cls] = fileids_ for cls, fileids_ in fileids.items(): dicts_ = [] for fileid in fileids_: year = '2012' if '_' in fileid else '2007' # dirname = os.path.join("datasets", "VOC{}".format(year)) # anno_file = os.path.join(dirname, "Annotations", fileid + ".xml") # jpeg_file = os.path.join(dirname, "JPEGImages", fileid + ".jpg") dir_voc = os.path.join(root, 'VOC{}'.format(year)) anno_file = os.path.join(dir_voc, 'Annotations', fileid + '.xml') jpeg_file = os.path.join(dir_voc, 'JPEGImages', fileid + '.jpg') tree = ET.parse(anno_file) for obj in tree.findall('object'): r = { 'file_name': jpeg_file, 'image_id': fileid, 'height': int(tree.findall('./size/height')[0].text), 'width': int(tree.findall('./size/width')[0].text), } cls_ = obj.find('name').text if cls != cls_: continue bbox = obj.find('bndbox') bbox = [ float(bbox.find(x).text) for x in ['xmin', 'ymin', 'xmax', 'ymax'] ] bbox[0] -= 1.0 bbox[1] -= 1.0 instances = [{ 'category_id': classnames.index(cls), 'bbox': bbox, 'bbox_mode': BoxMode.XYXY_ABS, }] r['annotations'] = instances dicts_.append(r) if len(dicts_) > int(shot): dicts_ = np.random.choice(dicts_, int(shot), replace=False) dicts.extend(dicts_) else: with PathManager.open( os.path.join(root, dirname, 'ImageSets', 'Main', split + '.txt')) as f: fileids = np.loadtxt(f, dtype=np.str_) for fileid in fileids: anno_file = os.path.join(root, dirname, 'Annotations', fileid + '.xml') jpeg_file = os.path.join(root, dirname, 'JPEGImages', fileid + '.jpg') tree = ET.parse(anno_file) r = { 'file_name': jpeg_file, 'image_id': fileid, 'height': int(tree.findall('./size/height')[0].text), 'width': int(tree.findall('./size/width')[0].text), } instances = [] for obj in tree.findall('object'): cls = obj.find('name').text if not (cls in classnames): continue bbox = obj.find('bndbox') bbox = [ float(bbox.find(x).text) for x in ['xmin', 'ymin', 'xmax', 'ymax'] ] bbox[0] -= 1.0 bbox[1] -= 1.0 instances.append({ 'category_id': classnames.index(cls), 'bbox': bbox, 'bbox_mode': BoxMode.XYXY_ABS, }) r['annotations'] = instances dicts.append(r) return dicts def register_meta_voc(name, root, dirname, split, year, keepclasses, sid): if keepclasses.startswith('base_novel'): thing_classes = PASCAL_VOC_ALL_CATEGORIES[sid] elif keepclasses.startswith('base'): thing_classes = PASCAL_VOC_BASE_CATEGORIES[sid] elif keepclasses.startswith('novel'): thing_classes = PASCAL_VOC_NOVEL_CATEGORIES[sid] DatasetCatalog.register( name, lambda: load_filtered_voc_instances(name, root, dirname, split, thing_classes), ) MetadataCatalog.get(name).set( thing_classes=thing_classes, dirname=os.path.join(root, dirname), year=year, split=split, base_classes=PASCAL_VOC_BASE_CATEGORIES[sid], novel_classes=PASCAL_VOC_NOVEL_CATEGORIES[sid], ) def register_all_voc(root='datasets'): METASPLITS = [ ('voc_2007_trainval_base1', 'VOC2007', 'trainval', 'base1', 1), ('voc_2007_trainval_base2', 'VOC2007', 'trainval', 'base2', 2), ('voc_2007_trainval_base3', 'VOC2007', 'trainval', 'base3', 3), ('voc_2012_trainval_base1', 'VOC2012', 'trainval', 'base1', 1), ('voc_2012_trainval_base2', 'VOC2012', 'trainval', 'base2', 2), ('voc_2012_trainval_base3', 'VOC2012', 'trainval', 'base3', 3), ('voc_2007_trainval_all1', 'VOC2007', 'trainval', 'base_novel_1', 1), ('voc_2007_trainval_all2', 'VOC2007', 'trainval', 'base_novel_2', 2), ('voc_2007_trainval_all3', 'VOC2007', 'trainval', 'base_novel_3', 3), ('voc_2012_trainval_all1', 'VOC2012', 'trainval', 'base_novel_1', 1), ('voc_2012_trainval_all2', 'VOC2012', 'trainval', 'base_novel_2', 2), ('voc_2012_trainval_all3', 'VOC2012', 'trainval', 'base_novel_3', 3), ('voc_2007_test_base1', 'VOC2007', 'test', 'base1', 1), ('voc_2007_test_base2', 'VOC2007', 'test', 'base2', 2), ('voc_2007_test_base3', 'VOC2007', 'test', 'base3', 3), ('voc_2007_test_novel1', 'VOC2007', 'test', 'novel1', 1), ('voc_2007_test_novel2', 'VOC2007', 'test', 'novel2', 2), ('voc_2007_test_novel3', 'VOC2007', 'test', 'novel3', 3), ('voc_2007_test_all1', 'VOC2007', 'test', 'base_novel_1', 1), ('voc_2007_test_all2', 'VOC2007', 'test', 'base_novel_2', 2), ('voc_2007_test_all3', 'VOC2007', 'test', 'base_novel_3', 3), ] for prefix in ['all', 'novel']: for sid in range(1, 4): for shot in [1, 2, 3, 5, 10]: for year in [2007, 2012]: for seed in range(30): seed = '_seed{}'.format(seed) name = 'voc_{}_trainval_{}{}_{}shot{}'.format( year, prefix, sid, shot, seed) dirname = 'VOC{}'.format(year) img_file = '{}_{}shot_split_{}_trainval'.format( prefix, shot, sid) keepclasses = ('base_novel_{}'.format(sid) if prefix == 'all' else 'novel{}'.format(sid)) METASPLITS.append( (name, dirname, img_file, keepclasses, sid)) for name, dirname, split, keepclasses, sid in METASPLITS: if name in DatasetCatalog: continue year = 2007 if '2007' in name else 2012 register_meta_voc( name, root, dirname, split, year, keepclasses, sid, ) MetadataCatalog.get(name).evaluator_type = 'pascal_voc'