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231 lines
8.5 KiB
231 lines
8.5 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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import os |
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import numpy as np |
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import xml.etree.ElementTree as ET |
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from paddlers.models.ppdet.core.workspace import register, serializable |
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from .dataset import DetDataset |
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from paddlers.models.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 VOCDataSet(DetDataset): |
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""" |
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Load dataset with PascalVOC format. |
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Notes: |
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`anno_path` must contains xml file and image file path for annotations. |
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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): voc 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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label_list (str): if use_default_label is False, will load |
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mapping between category and class index. |
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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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""" |
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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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label_list=None, |
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allow_empty=False, |
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empty_ratio=1.): |
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super(VOCDataSet, 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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self.label_list = label_list |
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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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# mapping category name to class id |
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# first_class:0, second_class:1, ... |
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records = [] |
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empty_records = [] |
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ct = 0 |
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cname2cid = {} |
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if self.label_list: |
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label_path = os.path.join(self.dataset_dir, self.label_list) |
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if not os.path.exists(label_path): |
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raise ValueError("label_list {} does not exists".format( |
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label_path)) |
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with open(label_path, 'r') as fr: |
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label_id = 0 |
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for line in fr.readlines(): |
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cname2cid[line.strip()] = label_id |
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label_id += 1 |
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else: |
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cname2cid = pascalvoc_label() |
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with open(anno_path, 'r') as fr: |
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while True: |
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line = fr.readline() |
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if not line: |
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break |
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img_file, xml_file = [os.path.join(image_dir, x) \ |
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for x in line.strip().split()[:2]] |
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if not os.path.exists(img_file): |
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logger.warning( |
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'Illegal image file: {}, and it will be ignored'.format( |
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img_file)) |
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continue |
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if not os.path.isfile(xml_file): |
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logger.warning( |
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'Illegal xml file: {}, and it will be ignored'.format( |
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xml_file)) |
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continue |
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tree = ET.parse(xml_file) |
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if tree.find('id') is None: |
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im_id = np.array([ct]) |
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else: |
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im_id = np.array([int(tree.find('id').text)]) |
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objs = tree.findall('object') |
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im_w = float(tree.find('size').find('width').text) |
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im_h = float(tree.find('size').find('height').text) |
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if im_w < 0 or im_h < 0: |
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logger.warning( |
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'Illegal width: {} or height: {} in annotation, ' |
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'and {} will be ignored'.format(im_w, im_h, xml_file)) |
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continue |
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num_bbox, i = len(objs), 0 |
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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.zeros((num_bbox, 1), dtype=np.float32) |
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difficult = np.zeros((num_bbox, 1), dtype=np.int32) |
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for obj in objs: |
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cname = obj.find('name').text |
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# user dataset may not contain difficult field |
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_difficult = obj.find('difficult') |
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_difficult = int( |
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_difficult.text) if _difficult is not None else 0 |
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x1 = float(obj.find('bndbox').find('xmin').text) |
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y1 = float(obj.find('bndbox').find('ymin').text) |
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x2 = float(obj.find('bndbox').find('xmax').text) |
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y2 = float(obj.find('bndbox').find('ymax').text) |
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x1 = max(0, x1) |
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y1 = max(0, y1) |
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x2 = min(im_w - 1, x2) |
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y2 = min(im_h - 1, y2) |
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if x2 > x1 and y2 > y1: |
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gt_bbox[i, :] = [x1, y1, x2, y2] |
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gt_class[i, 0] = cname2cid[cname] |
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gt_score[i, 0] = 1. |
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difficult[i, 0] = _difficult |
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i += 1 |
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else: |
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logger.warning( |
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'Found an invalid bbox in annotations: xml_file: {}' |
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', x1: {}, y1: {}, x2: {}, y2: {}.'.format( |
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xml_file, x1, y1, x2, y2)) |
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gt_bbox = gt_bbox[:i, :] |
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gt_class = gt_class[:i, :] |
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gt_score = gt_score[:i, :] |
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difficult = difficult[:i, :] |
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voc_rec = { |
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'im_file': img_file, |
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'im_id': im_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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gt_rec = { |
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'gt_class': gt_class, |
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'gt_score': gt_score, |
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'gt_bbox': gt_bbox, |
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'difficult': difficult |
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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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voc_rec[k] = v |
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if len(objs) == 0: |
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empty_records.append(voc_rec) |
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else: |
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records.append(voc_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 voc record in %s' % (self.anno_path) |
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logger.debug('{} samples in file {}'.format(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, self.cname2cid = records, cname2cid |
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def get_label_list(self): |
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return os.path.join(self.dataset_dir, self.label_list) |
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def pascalvoc_label(): |
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labels_map = { |
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'aeroplane': 0, |
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'bicycle': 1, |
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'bird': 2, |
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'boat': 3, |
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'bottle': 4, |
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'bus': 5, |
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'car': 6, |
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'cat': 7, |
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'chair': 8, |
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'cow': 9, |
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'diningtable': 10, |
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'dog': 11, |
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'horse': 12, |
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'motorbike': 13, |
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'person': 14, |
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'pottedplant': 15, |
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'sheep': 16, |
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'sofa': 17, |
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'train': 18, |
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'tvmonitor': 19 |
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} |
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return labels_map
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