# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import copy try: from collections.abc import Sequence except Exception: from collections import Sequence import numpy as np from ppdet.core.workspace import register, serializable from .dataset import DetDataset from ppdet.utils.logger import setup_logger logger = setup_logger(__name__) __all__ = ['COCODataSet', 'SlicedCOCODataSet', 'SemiCOCODataSet'] @register @serializable class COCODataSet(DetDataset): """ Load dataset with COCO format. Args: dataset_dir (str): root directory for dataset. image_dir (str): directory for images. anno_path (str): coco annotation file path. data_fields (list): key name of data dictionary, at least have 'image'. sample_num (int): number of samples to load, -1 means all. load_crowd (bool): whether to load crowded ground-truth. False as default allow_empty (bool): whether to load empty entry. False as default empty_ratio (float): the ratio of empty record number to total record's, if empty_ratio is out of [0. ,1.), do not sample the records and use all the empty entries. 1. as default repeat (int): repeat times for dataset, use in benchmark. """ def __init__(self, dataset_dir=None, image_dir=None, anno_path=None, data_fields=['image'], sample_num=-1, load_crowd=False, allow_empty=False, empty_ratio=1., repeat=1): super(COCODataSet, self).__init__( dataset_dir, image_dir, anno_path, data_fields, sample_num, repeat=repeat) self.load_image_only = False self.load_semantic = False self.load_crowd = load_crowd self.allow_empty = allow_empty self.empty_ratio = empty_ratio def _sample_empty(self, records, num): # if empty_ratio is out of [0. ,1.), do not sample the records if self.empty_ratio < 0. or self.empty_ratio >= 1.: return records import random sample_num = min( int(num * self.empty_ratio / (1 - self.empty_ratio)), len(records)) records = random.sample(records, sample_num) return records def parse_dataset(self): anno_path = os.path.join(self.dataset_dir, self.anno_path) image_dir = os.path.join(self.dataset_dir, self.image_dir) assert anno_path.endswith('.json'), \ 'invalid coco annotation file: ' + anno_path from pycocotools.coco import COCO coco = COCO(anno_path) img_ids = coco.getImgIds() img_ids.sort() cat_ids = coco.getCatIds() records = [] empty_records = [] ct = 0 self.catid2clsid = dict({catid: i for i, catid in enumerate(cat_ids)}) self.cname2cid = dict({ coco.loadCats(catid)[0]['name']: clsid for catid, clsid in self.catid2clsid.items() }) if 'annotations' not in coco.dataset: self.load_image_only = True logger.warning('Annotation file: {} does not contains ground truth ' 'and load image information only.'.format(anno_path)) for img_id in img_ids: img_anno = coco.loadImgs([img_id])[0] im_fname = img_anno['file_name'] im_w = float(img_anno['width']) im_h = float(img_anno['height']) im_path = os.path.join(image_dir, im_fname) if image_dir else im_fname is_empty = False if not os.path.exists(im_path): logger.warning('Illegal image file: {}, and it will be ' 'ignored'.format(im_path)) continue if im_w < 0 or im_h < 0: logger.warning('Illegal width: {} or height: {} in annotation, ' 'and im_id: {} will be ignored'.format( im_w, im_h, img_id)) continue coco_rec = { 'im_file': im_path, 'im_id': np.array([img_id]), 'h': im_h, 'w': im_w, } if 'image' in self.data_fields else {} if not self.load_image_only: ins_anno_ids = coco.getAnnIds( imgIds=[img_id], iscrowd=None if self.load_crowd else False) instances = coco.loadAnns(ins_anno_ids) bboxes = [] is_rbox_anno = False for inst in instances: # check gt bbox if inst.get('ignore', False): continue if 'bbox' not in inst.keys(): continue else: if not any(np.array(inst['bbox'])): continue x1, y1, box_w, box_h = inst['bbox'] x2 = x1 + box_w y2 = y1 + box_h eps = 1e-5 if inst['area'] > 0 and x2 - x1 > eps and y2 - y1 > eps: inst['clean_bbox'] = [ round(float(x), 3) for x in [x1, y1, x2, y2] ] bboxes.append(inst) else: logger.warning( 'Found an invalid bbox in annotations: im_id: {}, ' 'area: {} x1: {}, y1: {}, x2: {}, y2: {}.'.format( img_id, float(inst['area']), x1, y1, x2, y2)) num_bbox = len(bboxes) if num_bbox <= 0 and not self.allow_empty: continue elif num_bbox <= 0: is_empty = True gt_bbox = np.zeros((num_bbox, 4), dtype=np.float32) gt_class = np.zeros((num_bbox, 1), dtype=np.int32) is_crowd = np.zeros((num_bbox, 1), dtype=np.int32) gt_poly = [None] * num_bbox has_segmentation = False for i, box in enumerate(bboxes): catid = box['category_id'] gt_class[i][0] = self.catid2clsid[catid] gt_bbox[i, :] = box['clean_bbox'] is_crowd[i][0] = box['iscrowd'] # check RLE format if 'segmentation' in box and box['iscrowd'] == 1: gt_poly[i] = [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]] elif 'segmentation' in box and box['segmentation']: if not np.array(box['segmentation'] ).size > 0 and not self.allow_empty: bboxes.pop(i) gt_poly.pop(i) np.delete(is_crowd, i) np.delete(gt_class, i) np.delete(gt_bbox, i) else: gt_poly[i] = box['segmentation'] has_segmentation = True if has_segmentation and not any( gt_poly) and not self.allow_empty: continue gt_rec = { 'is_crowd': is_crowd, 'gt_class': gt_class, 'gt_bbox': gt_bbox, 'gt_poly': gt_poly, } for k, v in gt_rec.items(): if k in self.data_fields: coco_rec[k] = v # TODO: remove load_semantic if self.load_semantic and 'semantic' in self.data_fields: seg_path = os.path.join(self.dataset_dir, 'stuffthingmaps', 'train2017', im_fname[:-3] + 'png') coco_rec.update({'semantic': seg_path}) logger.debug('Load file: {}, im_id: {}, h: {}, w: {}.'.format( im_path, img_id, im_h, im_w)) if is_empty: empty_records.append(coco_rec) else: records.append(coco_rec) ct += 1 if self.sample_num > 0 and ct >= self.sample_num: break assert ct > 0, 'not found any coco record in %s' % (anno_path) logger.info('Load [{} samples valid, {} samples invalid] in file {}.'. format(ct, len(img_ids) - ct, anno_path)) if self.allow_empty and len(empty_records) > 0: empty_records = self._sample_empty(empty_records, len(records)) records += empty_records self.roidbs = records @register @serializable class SlicedCOCODataSet(COCODataSet): """Sliced COCODataSet""" def __init__( self, dataset_dir=None, image_dir=None, anno_path=None, data_fields=['image'], sample_num=-1, load_crowd=False, allow_empty=False, empty_ratio=1., repeat=1, sliced_size=[640, 640], overlap_ratio=[0.25, 0.25], ): super(SlicedCOCODataSet, self).__init__( dataset_dir=dataset_dir, image_dir=image_dir, anno_path=anno_path, data_fields=data_fields, sample_num=sample_num, load_crowd=load_crowd, allow_empty=allow_empty, empty_ratio=empty_ratio, repeat=repeat, ) self.sliced_size = sliced_size self.overlap_ratio = overlap_ratio def parse_dataset(self): anno_path = os.path.join(self.dataset_dir, self.anno_path) image_dir = os.path.join(self.dataset_dir, self.image_dir) assert anno_path.endswith('.json'), \ 'invalid coco annotation file: ' + anno_path from pycocotools.coco import COCO coco = COCO(anno_path) img_ids = coco.getImgIds() img_ids.sort() cat_ids = coco.getCatIds() records = [] empty_records = [] ct = 0 ct_sub = 0 self.catid2clsid = dict({catid: i for i, catid in enumerate(cat_ids)}) self.cname2cid = dict({ coco.loadCats(catid)[0]['name']: clsid for catid, clsid in self.catid2clsid.items() }) if 'annotations' not in coco.dataset: self.load_image_only = True logger.warning('Annotation file: {} does not contains ground truth ' 'and load image information only.'.format(anno_path)) try: import sahi from sahi.slicing import slice_image except Exception as e: logger.error( 'sahi not found, plaese install sahi. ' 'for example: `pip install sahi`, see https://github.com/obss/sahi.' ) raise e sub_img_ids = 0 for img_id in img_ids: img_anno = coco.loadImgs([img_id])[0] im_fname = img_anno['file_name'] im_w = float(img_anno['width']) im_h = float(img_anno['height']) im_path = os.path.join(image_dir, im_fname) if image_dir else im_fname is_empty = False if not os.path.exists(im_path): logger.warning('Illegal image file: {}, and it will be ' 'ignored'.format(im_path)) continue if im_w < 0 or im_h < 0: logger.warning('Illegal width: {} or height: {} in annotation, ' 'and im_id: {} will be ignored'.format( im_w, im_h, img_id)) continue slice_image_result = sahi.slicing.slice_image( image=im_path, slice_height=self.sliced_size[0], slice_width=self.sliced_size[1], overlap_height_ratio=self.overlap_ratio[0], overlap_width_ratio=self.overlap_ratio[1]) sub_img_num = len(slice_image_result) for _ind in range(sub_img_num): im = slice_image_result.images[_ind] coco_rec = { 'image': im, 'im_id': np.array([sub_img_ids + _ind]), 'h': im.shape[0], 'w': im.shape[1], 'ori_im_id': np.array([img_id]), 'st_pix': np.array( slice_image_result.starting_pixels[_ind], dtype=np.float32), 'is_last': 1 if _ind == sub_img_num - 1 else 0, } if 'image' in self.data_fields else {} records.append(coco_rec) ct_sub += sub_img_num ct += 1 if self.sample_num > 0 and ct >= self.sample_num: break assert ct > 0, 'not found any coco record in %s' % (anno_path) logger.info('{} samples and slice to {} sub_samples in file {}'.format( ct, ct_sub, anno_path)) if self.allow_empty and len(empty_records) > 0: empty_records = self._sample_empty(empty_records, len(records)) records += empty_records self.roidbs = records @register @serializable class SemiCOCODataSet(COCODataSet): """Semi-COCODataSet used for supervised and unsupervised dataSet""" def __init__(self, dataset_dir=None, image_dir=None, anno_path=None, data_fields=['image'], sample_num=-1, load_crowd=False, allow_empty=False, empty_ratio=1., repeat=1, supervised=True): super(SemiCOCODataSet, self).__init__( dataset_dir, image_dir, anno_path, data_fields, sample_num, load_crowd, allow_empty, empty_ratio, repeat) self.supervised = supervised self.length = -1 # defalut -1 means all def parse_dataset(self): anno_path = os.path.join(self.dataset_dir, self.anno_path) image_dir = os.path.join(self.dataset_dir, self.image_dir) assert anno_path.endswith('.json'), \ 'invalid coco annotation file: ' + anno_path from pycocotools.coco import COCO coco = COCO(anno_path) img_ids = coco.getImgIds() img_ids.sort() cat_ids = coco.getCatIds() records = [] empty_records = [] ct = 0 self.catid2clsid = dict({catid: i for i, catid in enumerate(cat_ids)}) self.cname2cid = dict({ coco.loadCats(catid)[0]['name']: clsid for catid, clsid in self.catid2clsid.items() }) if 'annotations' not in coco.dataset or self.supervised == False: self.load_image_only = True logger.warning('Annotation file: {} does not contains ground truth ' 'and load image information only.'.format(anno_path)) for img_id in img_ids: img_anno = coco.loadImgs([img_id])[0] im_fname = img_anno['file_name'] im_w = float(img_anno['width']) im_h = float(img_anno['height']) im_path = os.path.join(image_dir, im_fname) if image_dir else im_fname is_empty = False if not os.path.exists(im_path): logger.warning('Illegal image file: {}, and it will be ' 'ignored'.format(im_path)) continue if im_w < 0 or im_h < 0: logger.warning('Illegal width: {} or height: {} in annotation, ' 'and im_id: {} will be ignored'.format( im_w, im_h, img_id)) continue coco_rec = { 'im_file': im_path, 'im_id': np.array([img_id]), 'h': im_h, 'w': im_w, } if 'image' in self.data_fields else {} if not self.load_image_only: ins_anno_ids = coco.getAnnIds( imgIds=[img_id], iscrowd=None if self.load_crowd else False) instances = coco.loadAnns(ins_anno_ids) bboxes = [] is_rbox_anno = False for inst in instances: # check gt bbox if inst.get('ignore', False): continue if 'bbox' not in inst.keys(): continue else: if not any(np.array(inst['bbox'])): continue x1, y1, box_w, box_h = inst['bbox'] x2 = x1 + box_w y2 = y1 + box_h eps = 1e-5 if inst['area'] > 0 and x2 - x1 > eps and y2 - y1 > eps: inst['clean_bbox'] = [ round(float(x), 3) for x in [x1, y1, x2, y2] ] bboxes.append(inst) else: logger.warning( 'Found an invalid bbox in annotations: im_id: {}, ' 'area: {} x1: {}, y1: {}, x2: {}, y2: {}.'.format( img_id, float(inst['area']), x1, y1, x2, y2)) num_bbox = len(bboxes) if num_bbox <= 0 and not self.allow_empty: continue elif num_bbox <= 0: is_empty = True gt_bbox = np.zeros((num_bbox, 4), dtype=np.float32) gt_class = np.zeros((num_bbox, 1), dtype=np.int32) is_crowd = np.zeros((num_bbox, 1), dtype=np.int32) gt_poly = [None] * num_bbox has_segmentation = False for i, box in enumerate(bboxes): catid = box['category_id'] gt_class[i][0] = self.catid2clsid[catid] gt_bbox[i, :] = box['clean_bbox'] is_crowd[i][0] = box['iscrowd'] # check RLE format if 'segmentation' in box and box['iscrowd'] == 1: gt_poly[i] = [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]] elif 'segmentation' in box and box['segmentation']: if not np.array(box['segmentation'] ).size > 0 and not self.allow_empty: bboxes.pop(i) gt_poly.pop(i) np.delete(is_crowd, i) np.delete(gt_class, i) np.delete(gt_bbox, i) else: gt_poly[i] = box['segmentation'] has_segmentation = True if has_segmentation and not any( gt_poly) and not self.allow_empty: continue gt_rec = { 'is_crowd': is_crowd, 'gt_class': gt_class, 'gt_bbox': gt_bbox, 'gt_poly': gt_poly, } for k, v in gt_rec.items(): if k in self.data_fields: coco_rec[k] = v # TODO: remove load_semantic if self.load_semantic and 'semantic' in self.data_fields: seg_path = os.path.join(self.dataset_dir, 'stuffthingmaps', 'train2017', im_fname[:-3] + 'png') coco_rec.update({'semantic': seg_path}) logger.debug('Load file: {}, im_id: {}, h: {}, w: {}.'.format( im_path, img_id, im_h, im_w)) if is_empty: empty_records.append(coco_rec) else: records.append(coco_rec) ct += 1 if self.sample_num > 0 and ct >= self.sample_num: break assert ct > 0, 'not found any coco record in %s' % (anno_path) logger.info('Load [{} samples valid, {} samples invalid] in file {}.'. format(ct, len(img_ids) - ct, anno_path)) if self.allow_empty and len(empty_records) > 0: empty_records = self._sample_empty(empty_records, len(records)) records += empty_records self.roidbs = records if self.supervised: logger.info(f'Use {len(self.roidbs)} sup_samples data as LABELED') else: if self.length > 0: # unsup length will be decide by sup length all_roidbs = self.roidbs.copy() selected_idxs = [ np.random.choice(len(all_roidbs)) for _ in range(self.length) ] self.roidbs = [all_roidbs[i] for i in selected_idxs] logger.info( f'Use {len(self.roidbs)} unsup_samples data as UNLABELED') def __getitem__(self, idx): n = len(self.roidbs) if self.repeat > 1: idx %= n # data batch roidb = copy.deepcopy(self.roidbs[idx]) if self.mixup_epoch == 0 or self._epoch < self.mixup_epoch: idx = np.random.randint(n) roidb = [roidb, copy.deepcopy(self.roidbs[idx])] elif self.cutmix_epoch == 0 or self._epoch < self.cutmix_epoch: idx = np.random.randint(n) roidb = [roidb, copy.deepcopy(self.roidbs[idx])] elif self.mosaic_epoch == 0 or self._epoch < self.mosaic_epoch: roidb = [roidb, ] + [ copy.deepcopy(self.roidbs[np.random.randint(n)]) for _ in range(4) ] if isinstance(roidb, Sequence): for r in roidb: r['curr_iter'] = self._curr_iter else: roidb['curr_iter'] = self._curr_iter self._curr_iter += 1 return self.transform(roidb)