Source code for common.vision.datasets.reid.msmt17
"""
@author: Baixu Chen
@contact: cbx_99_hasta@outlook.com
"""
from .basedataset import BaseImageDataset
from typing import Callable
from PIL import Image
import os
import os.path as osp
from common.vision.datasets._util import download
[docs]class MSMT17(BaseImageDataset):
"""MSMT17 dataset from `Person Transfer GAN to Bridge Domain Gap for Person Re-Identification (CVPR 2018)
<https://arxiv.org/pdf/1711.08565.pdf>`_.
Dataset statistics:
- identities: 4101
- images: 32621 (train) + 11659 (query) + 82161 (gallery)
- cameras: 15
Args:
root (str): Root directory of dataset
verbose (bool, optional): If true, print dataset statistics after loading the dataset. Default: True
"""
dataset_dir = '.'
archive_name = 'MSMT17_V1.zip'
dataset_url = 'https://cloud.tsinghua.edu.cn/f/55d7e5aa3c224f49b908/?dl=1'
def __init__(self, root, verbose=True):
super(MSMT17, 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)
self.train = self.process_dir(self.train_dir)
self.query = self.process_dir(self.query_dir)
self.gallery = self.process_dir(self.gallery_dir)
if verbose:
print("=> MSMT17 loaded")
self.print_dataset_statistics(self.train, self.query, self.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(self.gallery)
def process_dir(self, dir_path):
image_list = os.listdir(dir_path)
dataset = []
pid_container = set()
for image_path in image_list:
pid, cid, _ = image_path.split('_')
pid = int(pid)
cid = int(cid[1:]) - 1 # index starts from 0
full_image_path = osp.join(dir_path, image_path)
dataset.append((full_image_path, pid, cid))
pid_container.add(pid)
# check if pid starts from 0 and increments with 1
for idx, pid in enumerate(pid_container):
assert idx == pid, "See code comment for explanation"
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 = Image.open(image_path)
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)
translated_image.save(translated_image_path)