forked from PulseFocusPlatform/PulseFocusPlatform
361 lines
13 KiB
Python
361 lines
13 KiB
Python
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# Copyright (c) 2021 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 cv2
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import numpy as np
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from collections import OrderedDict
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try:
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from collections.abc import Sequence
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except Exception:
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from collections import Sequence
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from .dataset import DetDataset, _make_dataset, _is_valid_file
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from ppdet.core.workspace import register, serializable
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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 MOTDataSet(DetDataset):
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"""
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Load dataset with MOT format.
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Args:
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dataset_dir (str): root directory for dataset.
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image_lists (str|list): mot data image lists, muiti-source mot dataset.
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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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Notes:
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MOT datasets root directory following this:
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dataset/mot
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|——————image_lists
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| |——————caltech.train
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| |——————caltech.val
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| |——————mot16.train
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| |——————mot17.train
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| ......
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|——————Caltech
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|——————MOT17
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|——————......
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All the MOT datasets have the following structure:
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Caltech
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|——————images
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| └——————00001.jpg
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| |—————— ...
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| └——————0000N.jpg
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└——————labels_with_ids
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└——————00001.txt
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|—————— ...
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└——————0000N.txt
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or
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MOT17
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|——————images
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| └——————train
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| └——————test
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└——————labels_with_ids
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└——————train
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"""
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def __init__(self,
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dataset_dir=None,
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image_lists=[],
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data_fields=['image'],
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sample_num=-1):
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super(MOTDataSet, self).__init__(
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dataset_dir=dataset_dir,
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data_fields=data_fields,
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sample_num=sample_num)
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self.dataset_dir = dataset_dir
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self.image_lists = image_lists
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if isinstance(self.image_lists, str):
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self.image_lists = [self.image_lists]
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self.roidbs = None
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self.cname2cid = None
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def get_anno(self):
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if self.image_lists == []:
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return
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# only used to get categories and metric
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return os.path.join(self.dataset_dir, 'image_lists',
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self.image_lists[0])
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def parse_dataset(self):
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self.img_files = OrderedDict()
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self.img_start_index = OrderedDict()
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self.label_files = OrderedDict()
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self.tid_num = OrderedDict()
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self.tid_start_index = OrderedDict()
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img_index = 0
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for data_name in self.image_lists:
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# check every data image list
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image_lists_dir = os.path.join(self.dataset_dir, 'image_lists')
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assert os.path.isdir(image_lists_dir), \
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"The {} is not a directory.".format(image_lists_dir)
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list_path = os.path.join(image_lists_dir, data_name)
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assert os.path.exists(list_path), \
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"The list path {} does not exist.".format(list_path)
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# record img_files, filter out empty ones
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with open(list_path, 'r') as file:
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self.img_files[data_name] = file.readlines()
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self.img_files[data_name] = [
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os.path.join(self.dataset_dir, x.strip())
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for x in self.img_files[data_name]
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]
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self.img_files[data_name] = list(
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filter(lambda x: len(x) > 0, self.img_files[data_name]))
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self.img_start_index[data_name] = img_index
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img_index += len(self.img_files[data_name])
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# record label_files
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self.label_files[data_name] = [
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x.replace('images', 'labels_with_ids').replace(
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'.png', '.txt').replace('.jpg', '.txt')
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for x in self.img_files[data_name]
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]
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for data_name, label_paths in self.label_files.items():
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max_index = -1
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for lp in label_paths:
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lb = np.loadtxt(lp)
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if len(lb) < 1:
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continue
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if len(lb.shape) < 2:
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img_max = lb[1]
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else:
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img_max = np.max(lb[:, 1])
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if img_max > max_index:
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max_index = img_max
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self.tid_num[data_name] = int(max_index + 1)
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last_index = 0
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for i, (k, v) in enumerate(self.tid_num.items()):
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self.tid_start_index[k] = last_index
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last_index += v
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self.total_identities = int(last_index + 1)
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self.num_imgs_each_data = [len(x) for x in self.img_files.values()]
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self.total_imgs = sum(self.num_imgs_each_data)
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logger.info('=' * 80)
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logger.info('MOT dataset summary: ')
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logger.info(self.tid_num)
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logger.info('total images: {}'.format(self.total_imgs))
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logger.info('image start index: {}'.format(self.img_start_index))
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logger.info('total identities: {}'.format(self.total_identities))
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logger.info('identity start index: {}'.format(self.tid_start_index))
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logger.info('=' * 80)
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records = []
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cname2cid = mot_label()
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for img_index in range(self.total_imgs):
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for i, (k, v) in enumerate(self.img_start_index.items()):
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if img_index >= v:
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data_name = list(self.label_files.keys())[i]
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start_index = v
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img_file = self.img_files[data_name][img_index - start_index]
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lbl_file = self.label_files[data_name][img_index - start_index]
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if not os.path.exists(img_file):
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logger.warning('Illegal image file: {}, and it will be ignored'.
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format(img_file))
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continue
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if not os.path.isfile(lbl_file):
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logger.warning('Illegal label file: {}, and it will be ignored'.
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format(lbl_file))
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continue
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labels = np.loadtxt(lbl_file, dtype=np.float32).reshape(-1, 6)
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# each row in labels (N, 6) is [gt_class, gt_identity, cx, cy, w, h]
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cx, cy = labels[:, 2], labels[:, 3]
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w, h = labels[:, 4], labels[:, 5]
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gt_bbox = np.stack((cx, cy, w, h)).T.astype('float32')
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gt_class = labels[:, 0:1].astype('int32')
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gt_score = np.ones((len(labels), 1)).astype('float32')
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gt_ide = labels[:, 1:2].astype('int32')
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for i, _ in enumerate(gt_ide):
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if gt_ide[i] > -1:
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gt_ide[i] += self.tid_start_index[data_name]
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mot_rec = {
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'im_file': img_file,
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'im_id': img_index,
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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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'gt_ide': gt_ide,
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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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mot_rec[k] = v
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records.append(mot_rec)
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if self.sample_num > 0 and img_index >= self.sample_num:
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break
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assert len(records) > 0, 'not found any mot record in %s' % (
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self.image_lists)
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self.roidbs, self.cname2cid = records, cname2cid
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def mot_label():
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labels_map = {'person': 0}
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return labels_map
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@register
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@serializable
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class MOTImageFolder(DetDataset):
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def __init__(self,
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task,
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dataset_dir=None,
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data_root=None,
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image_dir=None,
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sample_num=-1,
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keep_ori_im=False,
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**kwargs):
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super(MOTImageFolder, self).__init__(
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dataset_dir, image_dir, sample_num=sample_num)
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self.task = task
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self.data_root = data_root
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self.keep_ori_im = keep_ori_im
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self._imid2path = {}
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self.roidbs = None
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def check_or_download_dataset(self):
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return
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def parse_dataset(self, ):
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if not self.roidbs:
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self.roidbs = self._load_images()
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def _parse(self):
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image_dir = self.image_dir
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if not isinstance(image_dir, Sequence):
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image_dir = [image_dir]
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images = []
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for im_dir in image_dir:
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if os.path.isdir(im_dir):
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im_dir = os.path.join(self.dataset_dir, im_dir)
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images.extend(_make_dataset(im_dir))
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elif os.path.isfile(im_dir) and _is_valid_file(im_dir):
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images.append(im_dir)
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return images
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def _load_images(self):
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images = self._parse()
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ct = 0
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records = []
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for image in images:
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assert image != '' and os.path.isfile(image), \
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"Image {} not found".format(image)
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if self.sample_num > 0 and ct >= self.sample_num:
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break
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rec = {'im_id': np.array([ct]), 'im_file': image}
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if self.keep_ori_im:
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rec.update({'keep_ori_im': 1})
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self._imid2path[ct] = image
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ct += 1
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records.append(rec)
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assert len(records) > 0, "No image file found"
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return records
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def get_imid2path(self):
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return self._imid2path
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def set_images(self, images):
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self.image_dir = images
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self.roidbs = self._load_images()
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def _is_valid_video(f, extensions=('.mp4', '.avi', '.mov', '.rmvb', 'flv')):
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return f.lower().endswith(extensions)
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@register
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@serializable
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class MOTVideoDataset(DetDataset):
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"""
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Load MOT dataset with MOT format from video for inference.
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Args:
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video_file (str): path of the video file
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dataset_dir (str): root directory for dataset.
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keep_ori_im (bool): whether to keep original image, default False.
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Set True when used during MOT model inference while saving
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images or video, or used in DeepSORT.
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"""
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def __init__(self,
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video_file='',
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dataset_dir=None,
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keep_ori_im=False,
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**kwargs):
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super(MOTVideoDataset, self).__init__(dataset_dir=dataset_dir)
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self.video_file = video_file
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self.dataset_dir = dataset_dir
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self.keep_ori_im = keep_ori_im
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self.roidbs = None
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def parse_dataset(self, ):
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if not self.roidbs:
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self.roidbs = self._load_video_images()
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def _load_video_images(self):
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self.cap = cv2.VideoCapture(self.video_file)
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self.vn = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))
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self.frame_rate = int(round(self.cap.get(cv2.CAP_PROP_FPS)))
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logger.info('Length of the video: {:d} frames'.format(self.vn))
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res = True
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ct = 0
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records = []
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while res:
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res, img = self.cap.read()
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image = np.ascontiguousarray(img, dtype=np.float32)
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image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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im_shape = image.shape
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rec = {
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'im_id': np.array([ct]),
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'image': image,
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'h': im_shape[0],
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'w': im_shape[1],
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'im_shape': np.array(
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im_shape[:2], dtype=np.float32),
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'scale_factor': np.array(
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[1., 1.], dtype=np.float32),
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}
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if self.keep_ori_im:
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rec.update({'ori_image': image})
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ct += 1
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records.append(rec)
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records = records[:-1]
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assert len(records) > 0, "No image file found"
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return records
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def set_video(self, video_file):
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self.video_file = video_file
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assert os.path.isfile(self.video_file) and _is_valid_video(self.video_file), \
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"wrong or unsupported file format: {}".format(self.video_file)
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self.roidbs = self._load_video_images()
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