Source code for

@author: Baixu Chen
from .basedataset import BaseImageDataset
from typing import Callable
from PIL import Image
import os
import os.path as osp
import glob
import re
from import download

[docs]class Market1501(BaseImageDataset): """Market1501 dataset from `Scalable Person Re-identification: A Benchmark (ICCV 2015) <>`_. Dataset statistics: - identities: 1501 (+1 for background) - images: 12936 (train) + 3368 (query) + 15913 (gallery) - cameras: 6 Args: root (str): Root directory of dataset verbose (bool, optional): If true, print dataset statistics after loading the dataset. Default: True """ dataset_dir = 'Market-1501-v15.09.15' archive_name = 'Market-1501-v15.09.15.tgz' dataset_url = '' def __init__(self, root, verbose=True): super(Market1501, self).__init__() download(root, self.dataset_dir, self.archive_name, self.dataset_url) self.relative_dataset_dir = self.dataset_dir self.dataset_dir = osp.join(root, self.dataset_dir) self.train_dir = osp.join(self.dataset_dir, 'bounding_box_train') self.query_dir = osp.join(self.dataset_dir, 'query') self.gallery_dir = osp.join(self.dataset_dir, 'bounding_box_test') required_files = [self.dataset_dir, self.train_dir, self.query_dir, self.gallery_dir] self.check_before_run(required_files) train = self.process_dir(self.train_dir, relabel=True) query = self.process_dir(self.query_dir, relabel=False) gallery = self.process_dir(self.gallery_dir, relabel=False) if verbose: print("=> Market1501 loaded") self.print_dataset_statistics(train, query, gallery) self.train = train self.query = query = gallery self.num_train_pids, self.num_train_imgs, self.num_train_cams = self.get_imagedata_info(self.train) self.num_query_pids, self.num_query_imgs, self.num_query_cams = self.get_imagedata_info(self.query) self.num_gallery_pids, self.num_gallery_imgs, self.num_gallery_cams = self.get_imagedata_info( def process_dir(self, dir_path, relabel=False): img_paths = glob.glob(osp.join(dir_path, '*.jpg')) pattern = re.compile(r'([-\d]+)_c(\d)') pid_container = set() for img_path in img_paths: pid, _ = map(int, if pid == -1: continue # junk images are just ignored pid_container.add(pid) pid2label = {pid: label for label, pid in enumerate(pid_container)} dataset = [] for img_path in img_paths: pid, cid = map(int, if pid == -1: continue # junk images are just ignored assert 0 <= pid <= 1501 # pid == 0 means background assert 1 <= cid <= 6 cid -= 1 # index starts from 0 if relabel: pid = pid2label[pid] dataset.append((img_path, pid, cid)) return dataset
[docs] def translate(self, transform: Callable, target_root: str): """ Translate an image and save it into a specified directory Args: transform (callable): a transform function that maps images from one domain to another domain target_root (str): the root directory to save images """ os.makedirs(target_root, exist_ok=True) translated_dataset_dir = osp.join(target_root, self.relative_dataset_dir) translated_train_dir = osp.join(translated_dataset_dir, 'bounding_box_train') translated_query_dir = osp.join(translated_dataset_dir, 'query') translated_gallery_dir = osp.join(translated_dataset_dir, 'bounding_box_test') print("Translating dataset with image to image transform...") self.translate_dir(transform, self.train_dir, translated_train_dir) self.translate_dir(None, self.query_dir, translated_query_dir) self.translate_dir(None, self.gallery_dir, translated_gallery_dir) print("Translation process is done, save dataset to {}".format(translated_dataset_dir))
def translate_dir(self, transform, origin_dir: str, target_dir: str): image_list = os.listdir(origin_dir) for image_name in image_list: if not image_name.endswith(".jpg"): continue image_path = osp.join(origin_dir, image_name) image = translated_image_path = osp.join(target_dir, image_name) translated_image = image if transform: translated_image = transform(image) os.makedirs(os.path.dirname(translated_image_path), exist_ok=True)


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