forked from PulseFocusPlatform/PulseFocusPlatform
242 lines
9.7 KiB
Python
242 lines
9.7 KiB
Python
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# 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 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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@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. 1. as default
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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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super(COCODataSet, self).__init__(dataset_dir, image_dir, anno_path,
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data_fields, sample_num)
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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 = int(num * self.empty_ratio / (1 - self.empty_ratio))
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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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# read rbox anno or not
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is_rbox_anno = True if len(inst['bbox']) == 5 else False
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if is_rbox_anno:
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xc, yc, box_w, box_h, angle = inst['bbox']
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x1 = xc - box_w / 2.0
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y1 = yc - box_h / 2.0
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x2 = x1 + box_w
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y2 = y1 + box_h
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else:
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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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if is_rbox_anno:
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inst['clean_rbox'] = [xc, yc, box_w, box_h, angle]
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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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if is_rbox_anno:
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gt_rbox = np.zeros((num_bbox, 5), dtype=np.float32)
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gt_theta = np.zeros((num_bbox, 1), dtype=np.int32)
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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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difficult = np.zeros((num_bbox, 1), dtype=np.int32)
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gt_poly = [None] * num_bbox
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has_segmentation = 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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# xc, yc, w, h, theta
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if is_rbox_anno:
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gt_rbox[i, :] = box['clean_rbox']
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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], ]
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elif 'segmentation' in box and box['segmentation']:
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gt_poly[i] = box['segmentation']
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has_segmentation = 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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if is_rbox_anno:
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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_rbox': gt_rbox,
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'gt_poly': gt_poly,
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}
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else:
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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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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.debug('{} samples in file {}'.format(ct, anno_path))
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if 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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