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datasets.py
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from __future__ import print_function, absolute_import
import glob
import random
import os
import re
import sys
import urllib
import tarfile
import zipfile
import os.path as osp
from scipy.io import loadmat
import numpy as np
import h5py
from scipy.misc import imsave
import random
from time import time
import torch
import numpy as np
from torch.utils.data import Dataset
from PIL import Image
import torchvision.transforms as transforms
class SYSU_triplet_dataset(Dataset):
def __init__(self, data_folder = 'SYSU-MM01', transforms_list=None, mode='train', search_mode='all'):
if mode == 'train':
self.id_file = 'train_id.txt'
elif mode == 'val':
self.id_file = 'val_id.txt'
else:
self.id_file = 'test_id.txt'
if search_mode == 'all':
self.rgb_cameras = ['cam1','cam2','cam4','cam5']
self.ir_cameras = ['cam3','cam6']
elif search_mode == 'indoor':
self.rgb_cameras = ['cam1','cam2']
self.ir_cameras = ['cam3','cam6']
file_path = os.path.join(data_folder,'exp',self.id_file)
with open(file_path, 'r') as file:
self.ids = file.read().splitlines()
self.ids = [int(y) for y in self.ids[0].split(',')]
self.ids.sort()
self.id_dict = {}
for index, id in enumerate(self.ids):
#print(index,id)
self.id_dict[id] = index
self.ids = ["%04d" % x for x in self.ids]
self.transform = transforms.Compose(transforms_list)
self.files_rgb = {}
self.files_ir = {}
for id in sorted(self.ids):
self.files_rgb[id] = []
self.files_ir[id] = []
for cam in self.rgb_cameras:
img_dir = os.path.join(data_folder,cam,id)
if os.path.isdir(img_dir):
self.files_rgb[id].extend(sorted([img_dir+'/'+i for i in os.listdir(img_dir)]))
for cam in self.ir_cameras:
img_dir = os.path.join(data_folder,cam,id)
if os.path.isdir(img_dir):
self.files_ir[id].extend(sorted([img_dir+'/'+i for i in os.listdir(img_dir)]))
self.all_files = []
#self.rgb_files = []
#self.ir_files = []
for id in sorted(self.ids):
self.all_files.extend(self.files_rgb[id])
#self.rgb_files.extend(self.files_rgb[id])
self.all_files.extend(self.files_rgb[id])
#self.ir_files.extend(self.files_ir[id])
def __getitem__(self, index):
anchor_file = self.all_files[index]
'''
anchor_cam = anchor_file.split('/')[1]
if anchor_cam in self.ir_cameras:
target_files = self.files_rgb
modality = torch.tensor([1,0]).float()
else:
target_files = self.files_ir
modality = torch.tensor([0,1]).float()
'''
anchor_id = anchor_file.split('/')[2]
anchor_rgb = np.random.choice(self.files_rgb[anchor_id])
positive_rgb = np.random.choice([x for x in self.files_rgb[anchor_id] if x != anchor_rgb])
negative_id = np.random.choice([id for id in self.ids if id != anchor_id])
negative_rgb = np.random.choice(self.files_rgb[negative_id])
anchor_ir = np.random.choice(self.files_ir[anchor_id])
positive_ir = np.random.choice([x for x in self.files_ir[anchor_id] if x != anchor_ir])
negative_id = np.random.choice([id for id in self.ids if id != anchor_id])
negative_ir = np.random.choice(self.files_ir[negative_id])
anchor_label = np.array(self.id_dict[int(anchor_id)])
#print(anchor_file, positive_file, negative_file, anchor_id)
anchor_rgb = Image.open(anchor_rgb)
positive_rgb = Image.open(positive_rgb)
negative_rgb = Image.open(negative_rgb)
anchor_ir = Image.open(anchor_ir)
positive_ir = Image.open(positive_ir)
negative_ir = Image.open(negative_ir)
if self.transform is not None:
anchor_rgb = self.transform(anchor_rgb)
positive_rgb = self.transform(positive_rgb)
negative_rgb = self.transform(negative_rgb)
anchor_ir = self.transform(anchor_ir)
positive_ir = self.transform(positive_ir)
negative_ir = self.transform(negative_ir)
modality_rgb = torch.tensor([1,0]).float()
modality_ir = torch.tensor([0,1]).float()
return anchor_rgb, positive_rgb, negative_rgb, anchor_ir, positive_ir, negative_ir, anchor_label, modality_rgb, modality_ir
def __len__(self):
return len(self.all_files)
class SYSU_eval_datasets(object):
def __init__(self, dataset_dir = 'SYSU-MM01', search_mode='all' , data_split='val', **kwargs):
self.data_folder = dataset_dir
self.train_id_file = 'train_id.txt'
self.val_id_file = 'val_id.txt'
self.test_id_file = 'test_id.txt'
if search_mode == 'all':
self.rgb_cameras = ['cam1','cam2','cam4','cam5']
self.ir_cameras = ['cam3','cam6']
elif search_mode == 'indoor':
self.rgb_cameras = ['cam1','cam2']
self.ir_cameras = ['cam3','cam6']
if data_split == 'train':
self.id_file = self.train_id_file
elif data_split == 'val':
self.id_file = self.val_id_file
elif data_split == 'test':
self.id_file = self.test_id_file
random.seed(time)
query, num_query_pids, num_query_imgs = self._process_query_images(id_file = self.id_file, relabel=False)
gallery, num_gallery_pids, num_gallery_imgs = self._process_gallery_images(id_file = self.id_file, relabel=False)
num_total_pids = num_query_pids
num_total_imgs = num_query_imgs + num_gallery_imgs
print("Dataset statistics:")
print(" ------------------------------")
print(" subset | # ids | # images")
print(" ------------------------------")
print(" query | {:5d} | {:8d}".format(num_query_pids, num_query_imgs))
print(" gallery | {:5d} | {:8d}".format(num_gallery_pids, num_gallery_imgs))
print(" ------------------------------")
print(" total | {:5d} | {:8d}".format(num_total_pids, num_total_imgs))
print(" ------------------------------")
self.query = query
self.gallery = gallery
self.num_query_pids = num_query_pids
self.num_gallery_pids = num_gallery_pids
def _process_query_images(self, id_file, relabel=False):
file_path = os.path.join(self.data_folder,'exp',id_file)
files_rgb = []
files_ir = []
with open(file_path, 'r') as file:
ids = file.read().splitlines()
ids = [int(y) for y in ids[0].split(',')]
ids = ["%04d" % x for x in ids]
for id in sorted(ids):
for cam in self.rgb_cameras:
img_dir = os.path.join(self.data_folder,cam,id)
if os.path.isdir(img_dir):
new_files = sorted([img_dir+'/'+i for i in os.listdir(img_dir)])
files_rgb.append(random.choice(new_files))#files_rgb.extend(new_files)
for cam in self.ir_cameras:
img_dir = os.path.join(self.data_folder,cam,id)
if os.path.isdir(img_dir):
new_files = sorted([img_dir+'/'+i for i in os.listdir(img_dir)])
files_ir.append(random.choice(new_files))#files_ir.extend(new_files)
pid_container = set()
for img_path in files_ir:
camid, pid = int(img_path.split('/')[1].split('cam')[1]), int(img_path.split('/')[2])
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 files_ir:
#print(img_path)
camid, pid = int(img_path.split('/')[1].split('cam')[1]), int(img_path.split('/')[2])
if pid == -1: continue # junk images are just ignored
if relabel: pid = pid2label[pid]
dataset.append((img_path, pid, camid))
#print("query done")
#input()
num_pids = len(pid_container)
num_imgs = len(dataset)
return dataset, num_pids, num_imgs
def _process_gallery_images(self, id_file, relabel=False):
file_path = os.path.join(self.data_folder,'exp',id_file)
files_rgb = []
files_ir = []
with open(file_path, 'r') as file:
ids = file.read().splitlines()
ids = [int(y) for y in ids[0].split(',')]
ids = ["%04d" % x for x in ids]
for id in sorted(ids):
for cam in self.rgb_cameras:
img_dir = os.path.join(self.data_folder,cam,id)
if os.path.isdir(img_dir):
new_files = sorted([img_dir+'/'+i for i in os.listdir(img_dir)])
files_rgb.extend(new_files)
for cam in self.ir_cameras:
img_dir = os.path.join(self.data_folder,cam,id)
if os.path.isdir(img_dir):
new_files = sorted([img_dir+'/'+i for i in os.listdir(img_dir)])
files_ir.extend(new_files)
pid_container = set()
for img_path in files_rgb:
camid, pid = int(img_path.split('/')[1].split('cam')[1]), int(img_path.split('/')[2])
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 files_rgb:
#print(img_path)
camid, pid = int(img_path.split('/')[1].split('cam')[1]), int(img_path.split('/')[2])
if pid == -1: continue # junk images are just ignored
if relabel: pid = pid2label[pid]
dataset.append((img_path, pid, camid))
num_pids = len(pid_container)
num_imgs = len(dataset)
return dataset, num_pids, num_imgs
class Image_dataset(Dataset):
"""Image Person ReID Dataset"""
def __init__(self, dataset, transform=None):
self.dataset = dataset
self.transform = transform
def __len__(self):
return len(self.dataset)
def __getitem__(self, index):
img_path, pid, camid = self.dataset[index]
img = Image.open(img_path)
if self.transform is not None:
img = self.transform(img)
return img, pid, camid