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379 lines
14 KiB
379 lines
14 KiB
# 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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from collections import OrderedDict |
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import numpy as np |
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from .base import BaseDataset |
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from paddlers.utils import logging, get_encoding, norm_path, is_pic |
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from paddlers.transforms import DecodeImg, MixupImage |
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from paddlers.tools import YOLOAnchorCluster |
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class COCODetDataset(BaseDataset): |
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""" |
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Dataset with COCO annotations for detection tasks. |
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Args: |
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data_dir (str): Root directory of the dataset. |
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image_dir (str): Directory that contains the images. |
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ann_path (str): Path to COCO annotations. |
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transforms (paddlers.transforms.Compose): Data preprocessing and data augmentation operators to apply. |
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label_list (str, optional): Path of the file that contains the category names. Defaults to None. |
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num_workers (int|str, optional): Number of processes used for data loading. If `num_workers` is 'auto', |
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the number of workers will be automatically determined according to the number of CPU cores: If |
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there are more than 16 cores，8 workers will be used. Otherwise, the number of workers will be half |
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the number of CPU cores. Defaults: 'auto'. |
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shuffle (bool, optional): Whether to shuffle the samples. Defaults to False. |
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allow_empty (bool, optional): Whether to add negative samples. Defaults to False. |
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empty_ratio (float, optional): Ratio of negative samples. If `empty_ratio` is smaller than 0 or not less |
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than 1, keep all generated negative samples. Defaults to 1.0. |
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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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transforms, |
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label_list, |
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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(COCODetDataset, self).__init__(data_dir, label_list, transforms, |
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num_workers, shuffle) |
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self.data_fields = None |
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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.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 = norm_path(os.path.join(self.data_dir, anno_path)) |
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image_dir = norm_path(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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DecodeImg(to_rgb=False)(sample), |
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DecodeImg(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[int]|int): [h, w] or an int value that corresponds to the shape [image_size, image_size]. |
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cache (bool, optional): Whether to use cache. Defaults to True. |
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cache_path (str|None, optional): Path of cache directory. If None, use `dataset.data_dir`. |
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Defaults to None. |
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iters (int, optional): Iterations of k-means algorithm. Defaults to 300. |
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gen_iters (int, optional): Iterations of genetic algorithm. Defaults to 1000. |
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thresh (float, optional): Anchor scale threshold. Defaults to 0.25. |
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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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""" |
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Generate and add negative samples. |
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Args: |
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image_dir (str): Directory that contains images. |
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empty_ratio (float|None, optional): Ratio of negative samples. If `empty_ratio` is smaller than |
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0 or not less than 1, keep all generated negative samples. Defaults to 1.0. |
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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 ValueError("{} 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 = { |
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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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'difficult': difficult |
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} |
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if 'gt_poly' in self.file_list[0]: |
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label_info['gt_poly'] = [] |
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neg_file_list.append({'image': im_fname, ** im_info, ** label_info}) |
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if neg_file_list: |
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self.allow_empty = True |
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self.file_list += self._sample_empty(neg_file_list) |
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logging.info( |
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"{} negative samples added. Dataset contains {} positive samples and {} negative samples.". |
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format( |
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len(self.file_list) - self.num_samples, 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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def _sample_empty(self, neg_file_list): |
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if 0. <= self.empty_ratio < 1.: |
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import random |
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total_num = len(self.file_list) |
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neg_num = total_num - self.pos_num |
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sample_num = min((total_num * self.empty_ratio - neg_num) // |
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(1 - self.empty_ratio), len(neg_file_list)) |
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return random.sample(neg_file_list, sample_num) |
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else: |
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return neg_file_list
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