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