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lpw.py
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lpw.py
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# encoding: utf-8
import os
from glob import glob
from collections import defaultdict
import copy
import random
from fastreid.data.datasets import DATASET_REGISTRY
from fastreid.data.datasets.bases import ImageDataset
__all__ = ['LPW', ]
@DATASET_REGISTRY.register()
class LPW(ImageDataset):
"""LPW
"""
dataset_dir = "pep_256x128/data_slim"
dataset_name = "LPW"
def __init__(self, root='datasets', **kwargs):
self.root = root
self.train_path = os.path.join(self.root, self.dataset_dir)
required_files = [self.train_path]
self.check_before_run(required_files)
#train = self.process_train(self.train_path)
train, query, gallery =self.process_train(self.train_path)
super().__init__(train, query, gallery, **kwargs)
def process_train(self, train_path):
train = []
query = []
gallery = []
# scen 1 for test, scene 2 and scene 3 for training
# In scene1 , persons in view 2 will be the probe and in other two views will be the gallery
scene = 'scen2'
pid_list_2 = []
cam_list2 = os.listdir(os.path.join(train_path, scene))
for cam in cam_list2:
pid_list = os.listdir(os.path.join(train_path, scene, cam))
pid_list_2.extend(pid_list)
pid2_set = set(pid_list_2)
pid2label_2 = {pid: label for label, pid in enumerate(pid2_set, start=0)}
num_pids_scene2 = len(list(pid2label_2.values())) #1751
assert num_pids_scene2 == 1751
# print(min(list(pid2label_2.values())))
# print(max(list(pid2label_2.values())))
scene = 'scen3'
pid_list_3 = []
cam_list3 = os.listdir(os.path.join(train_path, scene))
for cam in cam_list3:
pid_list = os.listdir(os.path.join(train_path, scene, cam))
pid_list_3.extend(pid_list)
pid3_set = set(pid_list_3)
pid2label_3 = {pid: label+num_pids_scene2 for label, pid in enumerate(pid3_set)}
num_pids_scene3 = len(list(pid2label_3.values()))
#print(num_pids_scene3)
assert num_pids_scene3 == 224
# print(min(list(pid2label_3.values())))
# print(max(list(pid2label_3.values())))
scene = 'scen2'
cam_list = os.listdir(os.path.join(train_path, scene))
for cam in cam_list:
camid = self.dataset_name + "_" + cam[4]
pid_list = os.listdir(os.path.join(train_path, scene, cam))
for pid_dir in pid_list:
img_paths = glob(os.path.join(train_path, scene, cam, pid_dir, "*.jpg"))
for img_path in img_paths:
label = pid2label_2[pid_dir]
pid = self.dataset_name + "_" + str(label)
train.append((img_path, pid, camid))
scene = 'scen3'
cam_list = os.listdir(os.path.join(train_path, scene))
for cam in cam_list:
camid = self.dataset_name + "_" + cam[4]
pid_list = os.listdir(os.path.join(train_path, scene, cam))
for pid_dir in pid_list:
img_paths = glob(os.path.join(train_path, scene, cam, pid_dir, "*.jpg"))
for img_path in img_paths:
label = pid2label_3[pid_dir]
pid = self.dataset_name + "_" + str(label)
train.append((img_path, pid, camid))
scene = 'scen1'
pid_list_1 = []
cam_list1 = os.listdir(os.path.join(train_path, scene))
for cam in cam_list1:
pid_list = os.listdir(os.path.join(train_path, scene, cam))
pid_list_1.extend(pid_list)
pid1_set = set(pid_list_1)
pid2label_1 = {pid: label+num_pids_scene2+num_pids_scene3 for label, pid in enumerate(pid1_set, start=0)}
num_pids_scene1 = len(list(pid2label_1.values()))
assert (num_pids_scene1==756)
scene = 'scen1'
cam_list = os.listdir(os.path.join(train_path, scene))
for cam in cam_list:
camid = int(cam[4])
if cam=='view2':
pid_list = os.listdir(os.path.join(train_path, scene, cam))
for pid_dir in pid_list:
img_paths = glob(os.path.join(train_path, scene, cam, pid_dir, "*.jpg"))
for img_path in img_paths:
label = pid2label_1[pid_dir]
query.append((img_path, label, camid))
else:
pid_list = os.listdir(os.path.join(train_path, scene, cam))
for pid_dir in pid_list:
img_paths = glob(os.path.join(train_path, scene, cam, pid_dir, "*.jpg"))
for img_path in img_paths:
label = pid2label_1[pid_dir]
gallery.append((img_path, label, camid))
return train, query, gallery
'''
#random choose 50% ids as test
def prepare_split(self, train_path, split_path):
if not os.path.exists(split_path):
print('Creating splits ...')
file_path_list = ['scen1', 'scen2', 'scen3']
pid_dict = defaultdict(list)
for scene in file_path_list:
cam_list = os.listdir(os.path.join(train_path, scene))
for cam in cam_list:
camid = self.dataset_name + "_" + cam
pid_list = os.listdir(os.path.join(train_path, scene, cam))
for pid_dir in pid_list:
pid = scene + "-" + pid_dir
img_path = glob(os.path.join(train_path, scene, cam, pid_dir, "*.jpg"))
pid_dict[pid].extend(img_path)
pids = list(pid_dict.keys())
num_pids = len(pids)
assert num_pids == 2731, 'There should be 2731 identities, ' \
'but got {}, please check the data'.format(num_pids)
num_train_pids = int(num_pids * 0.5)
splits = []
for _ in range(10):
# randomly choose num_train_pids train IDs and the rest for test IDs
pids_copy = copy.deepcopy(pids)
random.shuffle(pids_copy)
train_pids = pids_copy[:num_train_pids]
test_pids = pids_copy[num_train_pids:]
#print(test_pids)
train = []
query = []
gallery = []
pid_int = set()
for pid_str in pids:
if int(pid_str.split('-')[0][4]) == 1:
pid_int.add(int(pid_str.split('-')[1])+ 3000)
elif int(pid_str.split('-')[0][4]) == 2:
pid_int.add(int(pid_str.split('-')[1]) + 6001)
elif int(pid_str.split('-')[0][4]) == 3:
pid_int.add(int(pid_str.split('-')[1]) + 9002)
pid2label = {pid: label for label, pid in enumerate(pid_int)}
# for train IDs, all images are used in the train set.
for pid_ in train_pids:
if int(pid_.split('-')[0][4]) == 1:
pid = (int(pid_.split('-')[1]) + 3000)
elif int(pid_.split('-')[0][4]) == 2:
pid = (int(pid_.split('-')[1]) + 6001)
elif int(pid_.split('-')[0][4]) == 3:
pid = (int(pid_.split('-')[1]) + 9002)
newpid = pid2label[pid]
newpid = self.dataset_name + "_" + str(newpid)
img_paths = pid_dict[pid_]
for img_path in img_paths:
#print(img_path)
#/data/hby0728/All_ReID_Datasets/clear_all_datasets/pep_256x128/data_slim/scen1/view1/pid/*.jpg
cam = img_path.split('/')[8][4] #view1-4
camid = self.dataset_name + '_' + cam
train.append([img_path, newpid, camid])
# for each test ID, choose 2 images, one for query and others for gallery
for pid_ in test_pids:
img_names = pid_dict[pid_]
selected_img_paths = random.sample(img_names, 1)
if int(pid_.split('-')[0][4]) == 1:
pid = (int(pid_.split('-')[1]) + 3000)
elif int(pid_.split('-')[0][4]) == 2:
pid = (int(pid_.split('-')[1]) + 6001)
elif int(pid_.split('-')[0][4]) == 3:
pid = (int(pid_.split('-')[1]) + 9002)
newpid = pid2label[pid]
# first image for query
camid = int(selected_img_paths[0].split('/')[8][4])
query.append([selected_img_paths[0], newpid, camid])
# other images for gallery
# camid = int(selected_img_paths[1].split('/')[8][4])
# gallery.append([selected_img_paths[1], pid, camid])
for img_path in img_names:
if img_path is not selected_img_paths[0]:
camid = int(img_path.split('/')[8][4])
gallery.append([img_path, newpid, camid])
split = {'train': train, 'query': query, 'gallery': gallery}
splits.append(split)
print('Totally {} splits are created'.format(len(splits)))
self.write_json(splits, split_path)
print('Split file is saved to {}'.format(split_path))
def process_data(self, train_path):
split_id = 0
split_path = os.path.join(train_path, 'splits.json')
self.prepare_split(train_path, split_path)
splits = self.read_json(split_path)
split = splits[split_id]
return split['train'], split['query'], split['gallery']
def read_json(self, fpath):
import json
"""Reads json file from a path."""
with open(fpath, 'r') as f:
obj = json.load(f)
return obj
def write_json(self, obj, fpath):
import json
"""Writes to a json file."""
self.mkdir_if_missing(os.path.dirname(fpath))
with open(fpath, 'w') as f:
json.dump(obj, f, indent=4, separators=(',', ': '))
def mkdir_if_missing(self, dirname):
import errno
"""Creates dirname if it is missing."""
if not os.path.exists(dirname):
try:
os.makedirs(dirname)
except OSError as e:
if e.errno != errno.EEXIST:
raise
'''