164 lines
6.3 KiB
Python
164 lines
6.3 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import glob
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import re
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import sys
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import urllib
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import tarfile
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import zipfile
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import os.path as osp
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from scipy.io import loadmat
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import numpy as np
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import h5py
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from scipy.misc import imsave
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from torchreid.utils.iotools import mkdir_if_missing, write_json, read_json
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from .bases import BaseImageDataset
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class VIPeR(BaseImageDataset):
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"""
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VIPeR
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Reference:
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Gray et al. Evaluating appearance models for recognition, reacquisition, and tracking. PETS 2007.
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URL: https://vision.soe.ucsc.edu/node/178
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Dataset statistics:
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# identities: 632
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# images: 632 x 2 = 1264
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# cameras: 2
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"""
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dataset_dir = 'viper'
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def __init__(self, root='data', split_id=0, verbose=True, **kwargs):
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super(VIPeR, self).__init__()
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self.dataset_dir = osp.join(root, self.dataset_dir)
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self.dataset_url = 'http://users.soe.ucsc.edu/~manduchi/VIPeR.v1.0.zip'
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self.cam_a_path = osp.join(self.dataset_dir, 'VIPeR', 'cam_a')
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self.cam_b_path = osp.join(self.dataset_dir, 'VIPeR', 'cam_b')
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self.split_path = osp.join(self.dataset_dir, 'splits.json')
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self._download_data()
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self._check_before_run()
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self._prepare_split()
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splits = read_json(self.split_path)
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if split_id >= len(splits):
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raise ValueError("split_id exceeds range, received {}, but expected between 0 and {}".format(split_id, len(splits)-1))
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split = splits[split_id]
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train = split['train']
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query = split['query'] # query and gallery share the same images
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gallery = split['gallery']
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train = [tuple(item) for item in train]
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query = [tuple(item) for item in query]
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gallery = [tuple(item) for item in gallery]
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if verbose:
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print("=> VIPeR loaded")
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self.print_dataset_statistics(train, query, gallery)
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self.train = train
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self.query = query
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self.gallery = gallery
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self.num_train_pids, self.num_train_imgs, self.num_train_cams = self.get_imagedata_info(self.train)
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self.num_query_pids, self.num_query_imgs, self.num_query_cams = self.get_imagedata_info(self.query)
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self.num_gallery_pids, self.num_gallery_imgs, self.num_gallery_cams = self.get_imagedata_info(self.gallery)
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def _download_data(self):
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if osp.exists(self.dataset_dir):
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print("This dataset has been downloaded.")
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return
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print("Creating directory {}".format(self.dataset_dir))
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mkdir_if_missing(self.dataset_dir)
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fpath = osp.join(self.dataset_dir, osp.basename(self.dataset_url))
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print("Downloading VIPeR dataset")
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urllib.urlretrieve(self.dataset_url, fpath)
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print("Extracting files")
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zip_ref = zipfile.ZipFile(fpath, 'r')
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zip_ref.extractall(self.dataset_dir)
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zip_ref.close()
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def _check_before_run(self):
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"""Check if all files are 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(self.dataset_dir))
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if not osp.exists(self.cam_a_path):
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raise RuntimeError("'{}' is not available".format(self.cam_a_path))
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if not osp.exists(self.cam_b_path):
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raise RuntimeError("'{}' is not available".format(self.cam_b_path))
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def _prepare_split(self):
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if not osp.exists(self.split_path):
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print("Creating 10 random splits of train ids and test ids")
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cam_a_imgs = sorted(glob.glob(osp.join(self.cam_a_path, '*.bmp')))
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cam_b_imgs = sorted(glob.glob(osp.join(self.cam_b_path, '*.bmp')))
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assert len(cam_a_imgs) == len(cam_b_imgs)
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num_pids = len(cam_a_imgs)
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print("Number of identities: {}".format(num_pids))
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num_train_pids = num_pids // 2
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"""
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In total, there will be 20 splits because each random split creates two
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sub-splits, one using cameraA as query and cameraB as gallery
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while the other using cameraB as query and cameraA as gallery.
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Therefore, results should be averaged over 20 splits (split_id=0~19).
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In practice, a model trained on split_id=0 can be applied to split_id=0&1
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as split_id=0&1 share the same training data (so on and so forth).
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"""
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splits = []
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for _ in range(10):
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order = np.arange(num_pids)
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np.random.shuffle(order)
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train_idxs = order[:num_train_pids]
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test_idxs = order[num_train_pids:]
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assert not bool(set(train_idxs) & set(test_idxs)), "Error: train and test overlap"
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train = []
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for pid, idx in enumerate(train_idxs):
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cam_a_img = cam_a_imgs[idx]
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cam_b_img = cam_b_imgs[idx]
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train.append((cam_a_img, pid, 0))
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train.append((cam_b_img, pid, 1))
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test_a = []
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test_b = []
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for pid, idx in enumerate(test_idxs):
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cam_a_img = cam_a_imgs[idx]
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cam_b_img = cam_b_imgs[idx]
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test_a.append((cam_a_img, pid, 0))
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test_b.append((cam_b_img, pid, 1))
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# use cameraA as query and cameraB as gallery
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split = {'train': train, 'query': test_a, 'gallery': test_b,
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'num_train_pids': num_train_pids,
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'num_query_pids': num_pids - num_train_pids,
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'num_gallery_pids': num_pids - num_train_pids
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}
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splits.append(split)
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# use cameraB as query and cameraA as gallery
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split = {'train': train, 'query': test_b, 'gallery': test_a,
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'num_train_pids': num_train_pids,
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'num_query_pids': num_pids - num_train_pids,
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'num_gallery_pids': num_pids - num_train_pids
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}
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splits.append(split)
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print("Totally {} splits are created".format(len(splits)))
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write_json(splits, self.split_path)
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print("Split file saved to {}".format(self.split_path))
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print("Splits created")
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