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375 lines
14 KiB
375 lines
14 KiB
3 years ago
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# 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
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import os.path as osp
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import random
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import numpy as np
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from collections import OrderedDict
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from paddle.io import Dataset
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from paddlers.utils import logging, get_num_workers, get_encoding, path_normalization, is_pic
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from paddlers.transforms import ImgDecoder, MixupImage
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from paddlers.tools import YOLOAnchorCluster
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class COCODetection(Dataset):
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"""读取COCO格式的检测数据集，并对样本进行相应的处理。
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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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image_dir (str): 描述数据集图片文件路径。
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anno_path (str): COCO标注文件路径。
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label_list (str): 描述数据集包含的类别信息文件路径。
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transforms (paddlers.det.transforms): 数据集中每个样本的预处理/增强算子。
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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
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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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image_dir,
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anno_path,
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label_list,
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transforms=None,
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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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# matplotlib.use() must be called *before* pylab, matplotlib.pyplot,
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# or matplotlib.backends is imported for the first time
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# pycocotools import matplotlib
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import matplotlib
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matplotlib.use('Agg')
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from pycocotools.coco import COCO
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super(COCODetection, self).__init__()
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self.data_dir = data_dir
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self.data_fields = None
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self.transforms = copy.deepcopy(transforms)
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self.num_max_boxes = 50
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self.use_mix = False
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if self.transforms is not None:
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for op in self.transforms.transforms:
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if isinstance(op, MixupImage):
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self.mixup_op = copy.deepcopy(op)
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self.use_mix = True
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self.num_max_boxes *= 2
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break
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self.batch_transforms = None
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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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annotations = dict()
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annotations['images'] = list()
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annotations['categories'] = list()
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annotations['annotations'] = list()
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cname2cid = OrderedDict()
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label_id = 0
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with open(label_list, 'r', encoding=get_encoding(label_list)) as f:
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for line in f.readlines():
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cname2cid[line.strip()] = label_id
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label_id += 1
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self.labels.append(line.strip())
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for k, v in cname2cid.items():
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annotations['categories'].append({
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'supercategory': 'component',
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'id': v + 1,
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'name': k
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})
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anno_path = path_normalization(os.path.join(self.data_dir, anno_path))
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image_dir = path_normalization(os.path.join(self.data_dir, 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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ct = 0
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catid2clsid = dict({catid: i for i, catid in enumerate(cat_ids)})
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cname2cid = 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 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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if not os.path.exists(im_path):
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logging.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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logging.warning(
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'Illegal width: {} or height: {} in annotation, '
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'and im_id: {} will be ignored'.format(im_w, im_h, img_id))
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continue
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im_info = {
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'image': im_path,
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'im_id': np.array([img_id]),
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'image_shape': np.array(
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[im_h, im_w], dtype=np.int32)
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}
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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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is_crowds = []
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gt_classes = []
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gt_bboxs = []
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gt_scores = []
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difficults = []
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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 box
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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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else:
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logging.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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is_crowds.append([inst['iscrowd']])
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gt_classes.append([inst['category_id']])
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gt_bboxs.append(inst['clean_bbox'])
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gt_scores.append([1.])
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difficults.append([0])
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annotations['annotations'].append({
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'iscrowd': inst['iscrowd'],
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'image_id': int(inst['image_id']),
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'bbox': inst['clean_bbox'],
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'area': inst['area'],
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'category_id': inst['category_id'],
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'id': inst['id'],
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'difficult': 0
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})
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label_info = {
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'is_crowd': np.array(is_crowds),
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'gt_class': np.array(gt_classes),
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'gt_bbox': np.array(gt_bboxs).astype(np.float32),
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'gt_score': np.array(gt_scores).astype(np.float32),
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'difficult': np.array(difficults),
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}
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if label_info['gt_bbox'].size > 0:
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self.file_list.append({ ** im_info, ** label_info})
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annotations['images'].append({
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'height': im_h,
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'width': im_w,
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'id': int(im_info['im_id']),
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'file_name': osp.split(im_info['image'])[1]
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})
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else:
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neg_file_list.append({ ** im_info, ** label_info})
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ct += 1
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if self.use_mix:
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self.num_max_boxes = max(self.num_max_boxes, 2 * len(instances))
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else:
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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' % (file_list)", 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), anno_path, 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.coco_gt = COCO()
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self.coco_gt.dataset = annotations
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self.coco_gt.createIndex()
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self._epoch = 0
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def __getitem__(self, idx):
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sample = copy.deepcopy(self.file_list[idx])
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if self.data_fields is not None:
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sample = {k: sample[k] for k in self.data_fields}
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if self.use_mix and (self.mixup_op.mixup_epoch == -1 or
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self._epoch < self.mixup_op.mixup_epoch):
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if self.num_samples > 1:
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mix_idx = random.randint(1, self.num_samples - 1)
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mix_pos = (mix_idx + idx) % self.num_samples
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else:
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mix_pos = 0
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sample_mix = copy.deepcopy(self.file_list[mix_pos])
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if self.data_fields is not None:
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sample_mix = {k: sample_mix[k] for k in self.data_fields}
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sample = self.mixup_op(sample=[
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ImgDecoder(to_rgb=False)(sample),
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ImgDecoder(to_rgb=False)(sample_mix)
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])
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sample = self.transforms(sample)
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return sample
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def __len__(self):
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return self.num_samples
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def set_epoch(self, epoch_id):
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self._epoch = epoch_id
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def cluster_yolo_anchor(self,
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num_anchors,
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image_size,
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cache=True,
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cache_path=None,
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iters=300,
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gen_iters=1000,
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thresh=.25):
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"""
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Cluster YOLO anchors.
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Reference:
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https://github.com/ultralytics/yolov5/blob/master/utils/autoanchor.py
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Args:
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num_anchors (int): number of clusters
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image_size (list or int): [h, w], being an int means image height and image width are the same.
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cache (bool): whether using cache
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cache_path (str or None, optional): cache directory path. If None, use `data_dir` of dataset.
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iters (int, optional): iters of kmeans algorithm
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gen_iters (int, optional): iters of genetic algorithm
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threshold (float, optional): anchor scale threshold
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verbose (bool, optional): whether print results
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"""
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if cache_path is None:
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cache_path = self.data_dir
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cluster = YOLOAnchorCluster(
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num_anchors=num_anchors,
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dataset=self,
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image_size=image_size,
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cache=cache,
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cache_path=cache_path,
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iters=iters,
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gen_iters=gen_iters,
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thresh=thresh)
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anchors = cluster()
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return anchors
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def add_negative_samples(self, image_dir, empty_ratio=1):
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"""将背景图片加入训练
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Args:
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image_dir (str)：背景图片所在的文件夹目录。
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empty_ratio (float or None): 用于指定负样本占总样本数的比例。如果为None，保留数据集初始化是设置的`empty_ratio`值，
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否则更新原有`empty_ratio`值。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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import cv2
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if not osp.isdir(image_dir):
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raise Exception("{} is not a valid image directory.".format(
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image_dir))
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if empty_ratio is not None:
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self.empty_ratio = empty_ratio
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image_list = os.listdir(image_dir)
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max_img_id = max(len(self.file_list) - 1, max(self.coco_gt.getImgIds()))
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neg_file_list = list()
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for image in image_list:
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if not is_pic(image):
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continue
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gt_bbox = np.zeros((0, 4), dtype=np.float32)
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gt_class = np.zeros((0, 1), dtype=np.int32)
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gt_score = np.zeros((0, 1), dtype=np.float32)
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is_crowd = np.zeros((0, 1), dtype=np.int32)
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difficult = np.zeros((0, 1), dtype=np.int32)
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max_img_id += 1
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im_fname = osp.join(image_dir, image)
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img_data = cv2.imread(im_fname, cv2.IMREAD_UNCHANGED)
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im_h, im_w, im_c = img_data.shape
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im_info = {
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'im_id': np.asarray([max_img_id]),
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'image_shape': np.array(
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[im_h, im_w], dtype=np.int32)
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}
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label_info = {
|
||
|
|
'is_crowd': is_crowd,
|
||
|
|
'gt_class': gt_class,
|
||
|
|
'gt_bbox': gt_bbox,
|
||
|
|
'gt_score': gt_score,
|
||
|
|
'difficult': difficult
|
||
|
|
}
|
||
|
|
if 'gt_poly' in self.file_list[0]:
|
||
|
|
label_info['gt_poly'] = []
|
||
|
|
|
||
|
|
neg_file_list.append({'image': im_fname, ** im_info, ** label_info})
|
||
|
|
if neg_file_list:
|
||
|
|
self.allow_empty = True
|
||
|
|
self.file_list += self._sample_empty(neg_file_list)
|
||
|
|
logging.info(
|
||
|
|
"{} negative samples added. Dataset contains {} positive samples and {} negative samples.".
|
||
|
|
format(
|
||
|
|
len(self.file_list) - self.num_samples, self.pos_num,
|
||
|
|
len(self.file_list) - self.pos_num))
|
||
|
|
self.num_samples = len(self.file_list)
|
||
|
|
|
||
|
|
def _sample_empty(self, neg_file_list):
|
||
|
|
if 0. <= self.empty_ratio < 1.:
|
||
|
|
import random
|
||
|
|
total_num = len(self.file_list)
|
||
|
|
neg_num = total_num - self.pos_num
|
||
|
|
sample_num = min((total_num * self.empty_ratio - neg_num) //
|
||
|
|
(1 - self.empty_ratio), len(neg_file_list))
|
||
|
|
return random.sample(neg_file_list, sample_num)
|
||
|
|
else:
|
||
|
|
return neg_file_list
|