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179 lines
7.1 KiB
Python
179 lines
7.1 KiB
Python
# Copyright (c) 2022 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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from __future__ import absolute_import
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import copy
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import os.path as osp
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import sys
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import numpy as np
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from . import fd_logging as logging
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from .util import is_pic, get_num_workers
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class CocoDetection(object):
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"""读取MSCOCO格式的检测数据集，并对样本进行相应的处理，该格式的数据集同样可以应用到实例分割模型的训练中。
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Args:
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data_dir (str): 数据集所在的目录路径。
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ann_file (str): 数据集的标注文件，为一个独立的json格式文件。
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num_workers (int|str): 数据集中样本在预处理过程中的线程或进程数。默认为'auto'。当设为'auto'时，根据
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系统的实际CPU核数设置`num_workers`: 如果CPU核数的一半大于8，则`num_workers`为8，否则为CPU核数的一半。
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shuffle (bool): 是否需要对数据集中样本打乱顺序。默认为False。
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allow_empty (bool): 是否加载负样本。默认为False。
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empty_ratio (float): 用于指定负样本占总样本数的比例。如果小于0或大于等于1，则保留全部的负样本。默认为1。
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"""
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def __init__(self,
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data_dir,
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ann_file,
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num_workers='auto',
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shuffle=False,
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allow_empty=False,
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empty_ratio=1.):
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from pycocotools.coco import COCO
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self.data_dir = data_dir
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self.data_fields = None
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self.num_max_boxes = 1000
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self.num_workers = get_num_workers(num_workers)
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self.shuffle = shuffle
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self.allow_empty = allow_empty
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self.empty_ratio = empty_ratio
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self.file_list = list()
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neg_file_list = list()
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self.labels = list()
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coco = COCO(ann_file)
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self.coco_gt = coco
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img_ids = sorted(coco.getImgIds())
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cat_ids = coco.getCatIds()
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catid2clsid = dict({catid: i for i, catid in enumerate(cat_ids)})
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cname2clsid = dict({
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coco.loadCats(catid)[0]['name']: clsid
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for catid, clsid in catid2clsid.items()
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})
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for label, cid in sorted(cname2clsid.items(), key=lambda d: d[1]):
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self.labels.append(label)
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logging.info("Starting to read file list from dataset...")
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ct = 0
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for img_id in img_ids:
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is_empty = False
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img_anno = coco.loadImgs(img_id)[0]
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im_fname = osp.join(data_dir, img_anno['file_name'])
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if not is_pic(im_fname):
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continue
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im_w = float(img_anno['width'])
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im_h = float(img_anno['height'])
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ins_anno_ids = coco.getAnnIds(imgIds=img_id, iscrowd=False)
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instances = coco.loadAnns(ins_anno_ids)
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bboxes = []
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for inst in instances:
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x, y, box_w, box_h = inst['bbox']
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x1 = max(0, x)
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y1 = max(0, y)
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x2 = min(im_w - 1, x1 + max(0, box_w))
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y2 = min(im_h - 1, y1 + max(0, box_h))
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if inst['area'] > 0 and x2 >= x1 and y2 >= y1:
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inst['clean_bbox'] = [x1, y1, x2, y2]
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bboxes.append(inst)
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else:
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logging.warning(
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"Found an invalid bbox in annotations: "
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"im_id: {}, area: {} x1: {}, y1: {}, x2: {}, y2: {}."
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.format(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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gt_score = np.ones((num_bbox, 1), dtype=np.float32)
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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 reversed(list(enumerate(bboxes))):
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catid = box['category_id']
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gt_class[i][0] = 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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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]]
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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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gt_poly.pop(i)
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is_crowd = np.delete(is_crowd, i)
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gt_class = np.delete(gt_class, i)
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gt_bbox = 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 has_segmentation and not any(gt_poly) and not self.allow_empty:
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continue
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im_info = {
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'im_id': np.array([img_id]).astype('int32'),
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'image_shape': np.array([im_h, im_w]).astype('int32'),
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}
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label_info = {
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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_score': gt_score,
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'gt_poly': gt_poly,
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'difficult': difficult
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}
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if is_empty:
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neg_file_list.append({
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'image': im_fname,
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**
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im_info,
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**
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label_info
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})
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else:
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self.file_list.append({
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'image': im_fname,
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**
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im_info,
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**
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label_info
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})
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ct += 1
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self.num_max_boxes = max(self.num_max_boxes, len(instances))
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if not ct:
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logging.error(
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"No coco record found in %s' % (ann_file)", exit=True)
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self.pos_num = len(self.file_list)
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if self.allow_empty and neg_file_list:
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self.file_list += self._sample_empty(neg_file_list)
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logging.info(
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"{} samples in file {}, including {} positive samples and {} negative samples.".
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format(
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len(self.file_list), ann_file, self.pos_num,
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len(self.file_list) - self.pos_num))
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self.num_samples = len(self.file_list)
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self._epoch = 0
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