225 lines
8.0 KiB
Python
225 lines
8.0 KiB
Python
# 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 print_function
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import numpy as np
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import paddle
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from paddle.io import Dataset
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import os
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import cv2
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from ppcls.data import preprocess
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from ppcls.data.preprocess import transform
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from ppcls.utils import logger
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from .common_dataset import create_operators
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import os.path as osp
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import glob
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import re
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from PIL import Image
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class Market1501(Dataset):
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"""
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Market1501
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Reference:
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Zheng et al. Scalable Person Re-identification: A Benchmark. ICCV 2015.
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URL: http://www.liangzheng.org/Project/project_reid.html
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Dataset statistics:
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# identities: 1501 (+1 for background)
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# images: 12936 (train) + 3368 (query) + 15913 (gallery)
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"""
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_dataset_dir = 'market1501/Market-1501-v15.09.15'
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def __init__(self,
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image_root,
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cls_label_path,
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transform_ops=None,
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backend="cv2"):
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self._img_root = image_root
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self._cls_path = cls_label_path # the sub folder in the dataset
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self._dataset_dir = osp.join(image_root, self._dataset_dir,
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self._cls_path)
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self._check_before_run()
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if transform_ops:
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self._transform_ops = create_operators(transform_ops)
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self.backend = backend
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self._dtype = paddle.get_default_dtype()
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self._load_anno(relabel=True if 'train' in self._cls_path else False)
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def _check_before_run(self):
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"""Check if the file is available before going deeper"""
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if not osp.exists(self._dataset_dir):
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raise RuntimeError("'{}' is not available".format(
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self._dataset_dir))
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def _load_anno(self, relabel=False):
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img_paths = glob.glob(osp.join(self._dataset_dir, '*.jpg'))
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pattern = re.compile(r'([-\d]+)_c(\d)')
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self.images = []
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self.labels = []
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self.cameras = []
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pid_container = set()
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for img_path in sorted(img_paths):
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pid, _ = map(int, pattern.search(img_path).groups())
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if pid == -1: continue # junk images are just ignored
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pid_container.add(pid)
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pid2label = {pid: label for label, pid in enumerate(pid_container)}
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for img_path in sorted(img_paths):
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pid, camid = map(int, pattern.search(img_path).groups())
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if pid == -1: continue # junk images are just ignored
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assert 0 <= pid <= 1501 # pid == 0 means background
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assert 1 <= camid <= 6
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camid -= 1 # index starts from 0
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if relabel: pid = pid2label[pid]
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self.images.append(img_path)
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self.labels.append(pid)
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self.cameras.append(camid)
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self.num_pids, self.num_imgs, self.num_cams = get_imagedata_info(
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self.images, self.labels, self.cameras, subfolder=self._cls_path)
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def __getitem__(self, idx):
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try:
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img = Image.open(self.images[idx]).convert('RGB')
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if self.backend == "cv2":
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img = np.array(img, dtype="float32").astype(np.uint8)
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if self._transform_ops:
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img = transform(img, self._transform_ops)
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if self.backend == "cv2":
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img = img.transpose((2, 0, 1))
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return (img, self.labels[idx], self.cameras[idx])
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except Exception as ex:
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logger.error("Exception occured when parse line: {} with msg: {}".
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format(self.images[idx], ex))
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rnd_idx = np.random.randint(self.__len__())
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return self.__getitem__(rnd_idx)
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def __len__(self):
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return len(self.images)
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@property
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def class_num(self):
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return len(set(self.labels))
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class MSMT17(Dataset):
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"""
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MSMT17
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Reference:
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Wei et al. Person Transfer GAN to Bridge Domain Gap for Person Re-Identification. CVPR 2018.
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URL: http://www.pkuvmc.com/publications/msmt17.html
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Dataset statistics:
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# identities: 4101
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# images: 32621 (train) + 11659 (query) + 82161 (gallery)
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# cameras: 15
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"""
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_dataset_dir = 'msmt17/MSMT17_V1'
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def __init__(self, image_root, cls_label_path, transform_ops=None):
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self._img_root = image_root
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self._cls_path = cls_label_path # the sub folder in the dataset
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self._dataset_dir = osp.join(image_root, self._dataset_dir,
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self._cls_path)
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self._check_before_run()
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if transform_ops:
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self._transform_ops = create_operators(transform_ops)
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self._dtype = paddle.get_default_dtype()
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self._load_anno(relabel=True if 'train' in self._cls_path else False)
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def _check_before_run(self):
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"""Check if the file is available before going deeper"""
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if not osp.exists(self._dataset_dir):
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raise RuntimeError("'{}' is not available".format(
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self._dataset_dir))
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def _load_anno(self, relabel=False):
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img_paths = glob.glob(osp.join(self._dataset_dir, '*.jpg'))
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pattern = re.compile(r'([-\d]+)_c(\d+)')
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self.images = []
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self.labels = []
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self.cameras = []
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pid_container = set()
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for img_path in img_paths:
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pid, _ = map(int, pattern.search(img_path).groups())
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if pid == -1:
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continue # junk images are just ignored
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pid_container.add(pid)
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pid2label = {pid: label for label, pid in enumerate(pid_container)}
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for img_path in img_paths:
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pid, camid = map(int, pattern.search(img_path).groups())
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if pid == -1:
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continue # junk images are just ignored
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assert 1 <= camid <= 15
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camid -= 1 # index starts from 0
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if relabel:
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pid = pid2label[pid]
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self.images.append(img_path)
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self.labels.append(pid)
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self.cameras.append(camid)
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self.num_pids, self.num_imgs, self.num_cams = get_imagedata_info(
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self.images, self.labels, self.cameras, subfolder=self._cls_path)
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def __getitem__(self, idx):
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try:
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img = Image.open(self.images[idx]).convert('RGB')
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img = np.array(img, dtype="float32").astype(np.uint8)
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if self._transform_ops:
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img = transform(img, self._transform_ops)
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img = img.transpose((2, 0, 1))
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return (img, self.labels[idx], self.cameras[idx])
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except Exception as ex:
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logger.error("Exception occured when parse line: {} with msg: {}".
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format(self.images[idx], ex))
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rnd_idx = np.random.randint(self.__len__())
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return self.__getitem__(rnd_idx)
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def __len__(self):
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return len(self.images)
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@property
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def class_num(self):
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return len(set(self.labels))
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def get_imagedata_info(data, labels, cameras, subfolder='train'):
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pids, cams = [], []
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for _, pid, camid in zip(data, labels, cameras):
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pids += [pid]
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cams += [camid]
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pids = set(pids)
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cams = set(cams)
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num_pids = len(pids)
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num_cams = len(cams)
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num_imgs = len(data)
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print("Dataset statistics:")
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print(" ----------------------------------------")
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print(" subset | # ids | # images | # cameras")
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print(" ----------------------------------------")
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print(" {} | {:5d} | {:8d} | {:9d}".format(subfolder, num_pids,
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num_imgs, num_cams))
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print(" ----------------------------------------")
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return num_pids, num_imgs, num_cams
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