forked from PulseFocusPlatform/PulseFocusPlatform
206 lines
7.3 KiB
Python
206 lines
7.3 KiB
Python
# 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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import xml.etree.ElementTree as ET
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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 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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"""
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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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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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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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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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gt_bbox = []
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gt_class = []
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gt_score = []
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difficult = []
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for i, obj in enumerate(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.append([x1, y1, x2, y2])
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gt_class.append([cname2cid[cname]])
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gt_score.append([1.])
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difficult.append([_difficult])
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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 = np.array(gt_bbox).astype('float32')
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gt_class = np.array(gt_class).astype('int32')
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gt_score = np.array(gt_score).astype('float32')
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difficult = np.array(difficult).astype('int32')
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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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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 len(records) > 0, 'not found any voc record in %s' % (
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self.anno_path)
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logger.debug('{} samples in file {}'.format(ct, anno_path))
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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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