def get_sample(self, ptr):
im_name = self.im_names[ptr]
im_path = osp.join(self.im_dir, im_name)
im = plt.imread(im_path)
im, _ = self.pre_process_im(im)
id = parse_im_name(self.im_names[ptr], 'id')
cam = parse_im_name(self.im_names[ptr], 'cam')
# denoting whether the im is from query, gallery, or multi query set
mark = self.marks[ptr]
return im, id, cam, im_name, mark
def split_train_test_set(img_dir, save_pkl_dir):
train_im_names = []
test_im_names = []
gallery_im_names = []
query_im_names = []
query_ids = []
file_list = os.listdir(img_dir)
file_list.sort()
test_id = random.sample(range(total_id_num), test_id_num)
test_id.sort()
# get the id of training/testing in the form of 'XXXX'
test_ids = ['{0:08}'.format(i) for i in test_id]
train_ids = ['{0:08}'.format(i) for i in range(total_id_num) if i not in test_id]
cam_ids = ['{0:04}'.format(i) for i in range(cam_num)]
# generate training set and test set
for tr in train_ids:
for f in file_list:
if tr == '{0:08}'.format(parse_im_name(f, 'id')):
train_im_names.append(f)
for te in test_ids:
for f in file_list:
if te == '{0:08}'.format(parse_im_name(f, 'id')):
test_im_names.append(f)
# generate query in test set
item = 0
while item < test_id_num:
i_1, i_2 = random.sample(test_im_names, 2)
if parse_im_name(i_1, 'id') == parse_im_name(i_2, 'id')\
and parse_im_name(i_1, 'id') not in query_ids:
if parse_im_name(i_1, 'cam') != parse_im_name(i_2, 'cam'):
item += 1
query_im_names.append(i_1)
query_im_names.append(i_2)
query_ids.append(parse_im_name(i_1, 'id'))
else:
continue
else:
continue
# generate gallery in test set
gallery_im_names = [f for f in test_im_names if f not in query_im_names]
train_im_names.sort()
gallery_im_names.sort()
query_im_names.sort()
partitions = {'gallery_im_names': np.asarray(gallery_im_names),
'query_im_names': np.asarray(query_im_names),
'train_im_names': np.asarray(train_im_names)}
partitions = {'detected': partitions,
'labeled': partitions}
save_pickle(partitions, save_pkl_dir)
print("Spliting operation has been done!")
def __init__(
self,
im_dir=None,
im_names=None,
ids2labels=None,
ids_per_batch=None,
ims_per_id=None,
**kwargs):
# The im dir of all images
self.im_dir = im_dir
self.im_names = im_names
self.ids2labels = ids2labels
self.ids_per_batch = ids_per_batch
self.ims_per_id = ims_per_id
im_ids = [parse_im_name(name, 'id') for name in im_names]
self.ids_to_im_inds = defaultdict(list)
for ind, id in enumerate(im_ids):
self.ids_to_im_inds[id].append(ind)
self.ids = self.ids_to_im_inds.keys()
super(TrainSet, self).__init__(
dataset_size=len(self.ids),
batch_size=ids_per_batch,
**kwargs)
def move_ims(ori_im_paths, new_im_dir, parse_im_name, new_im_name_tmpl,
new_start_id):
"""Rename and move images to new directory."""
ids = [parse_im_name(osp.basename(p), 'id') for p in ori_im_paths]
cams = [parse_im_name(osp.basename(p), 'cam') for p in ori_im_paths]
unique_ids = list(set(ids))
unique_ids.sort()
id_mapping = dict(
zip(unique_ids, range(new_start_id, new_start_id + len(unique_ids))))
new_im_names = []
cnt = defaultdict(int)
for im_path, id, cam in zip(ori_im_paths, ids, cams):
new_id = id_mapping[id]
cnt[(new_id, cam)] += 1
new_im_name = new_im_name_tmpl.format(new_id, cam,
cnt[(new_id, cam)] - 1)
shutil.copy(im_path, ospj(new_im_dir, new_im_name))
new_im_names.append(new_im_name)
return new_im_names, id_mapping
def move_ims(
ori_im_paths,
new_im_dir,
parse_im_name,
new_im_name_tmpl,
new_start_id):
"""Rename and move images to new directory."""
ids = [parse_im_name(osp.basename(p), 'id') for p in ori_im_paths]
cams = [parse_im_name(osp.basename(p), 'cam') for p in ori_im_paths]
unique_ids = list(set(ids))
unique_ids.sort()
id_mapping = dict(
zip(unique_ids, range(new_start_id, new_start_id + len(unique_ids))))
new_im_names = []
cnt = defaultdict(int)
for im_path, id, cam in zip(ori_im_paths, ids, cams):
new_id = id_mapping[id]
cnt[(new_id, cam)] += 1
new_im_name = new_im_name_tmpl.format(new_id, cam, cnt[(new_id, cam)] - 1)
shutil.copy(im_path, ospj(new_im_dir, new_im_name))
new_im_names.append(new_im_name)
return new_im_names, id_mapping
def __init__(self, im_dir=None, im_names=None, ids2labels=None,
ids_per_batch=None, ims_per_id=None, **kwargs):
# The im dir of all images
self.im_dir = im_dir
self.im_names = im_names
self.ids2labels = ids2labels
self.ids_per_batch = ids_per_batch
self.ims_per_id = ims_per_id
im_ids = [parse_im_name(name, 'id') for name in im_names]
self.ids_to_im_inds = defaultdict(list)
for ind, id in enumerate(im_ids):
self.ids_to_im_inds[id].append(ind)
self.ids = self.ids_to_im_inds.keys()
super(TrainSet, self).__init__(
dataset_size=len(self.ids),
batch_size=ids_per_batch,
**kwargs)
def transform(zip_file, train_test_partition_file, save_dir=None):
"""Save images and partition the train/val/test set.
"""
print("Extracting zip file")
root = osp.dirname(osp.abspath(zip_file))
if save_dir is None:
save_dir = root
may_make_dir(save_dir)
with ZipFile(zip_file) as z:
z.extractall(path=save_dir)
print("Extracting zip file done")
mat_file = osp.join(save_dir, osp.basename(zip_file)[:-4], 'cuhk-03.mat')
save_images(mat_file, save_dir, new_im_name_tmpl)
if osp.exists(train_test_partition_file):
train_test_partition = load_pickle(train_test_partition_file)
else:
raise RuntimeError('Train/test partition file should be provided.')
for im_type in ['detected', 'labeled']:
trainval_im_names = train_test_partition[im_type]['train_im_names']
trainval_ids = list(
set([parse_im_name(n, 'id') for n in trainval_im_names]))
# Sort ids, so that id-to-label mapping remains the same when running
# the code on different machines.
trainval_ids.sort()
trainval_ids2labels = dict(zip(trainval_ids, range(len(trainval_ids))))
train_val_partition = \
partition_train_val_set(trainval_im_names, parse_im_name, num_val_ids=100)
train_im_names = train_val_partition['train_im_names']
train_ids = list(
set([
parse_im_name(n, 'id')
for n in train_val_partition['train_im_names']
]))
# Sort ids, so that id-to-label mapping remains the same when running
# the code on different machines.
train_ids.sort()
train_ids2labels = dict(zip(train_ids, range(len(train_ids))))
# A mark is used to denote whether the image is from
# query (mark == 0), or
# gallery (mark == 1), or
# multi query (mark == 2) set
val_marks = [0, ] * len(train_val_partition['val_query_im_names']) \
+ [1, ] * len(train_val_partition['val_gallery_im_names'])
val_im_names = list(train_val_partition['val_query_im_names']) \
+ list(train_val_partition['val_gallery_im_names'])
test_im_names = list(train_test_partition[im_type]['query_im_names']) \
+ list(train_test_partition[im_type]['gallery_im_names'])
test_marks = [0, ] * len(train_test_partition[im_type]['query_im_names']) \
+ [1, ] * len(
train_test_partition[im_type]['gallery_im_names'])
partitions = {
'trainval_im_names': trainval_im_names,
'trainval_ids2labels': trainval_ids2labels,
'train_im_names': train_im_names,
'train_ids2labels': train_ids2labels,
'val_im_names': val_im_names,
'val_marks': val_marks,
'test_im_names': test_im_names,
'test_marks': test_marks
}
partition_file = osp.join(save_dir, im_type, 'partitions.pkl')
save_pickle(partitions, partition_file)
print('Partition file for "{}" saved to {}'.format(
im_type, partition_file))
def create_dataset(
name='market1501',
part='trainval',
**kwargs):
assert name in ['market1501', 'cuhk03', 'duke', 'combined'], \
"Unsupported Dataset {}".format(name)
assert part in ['trainval', 'train', 'val', 'test'], \
"Unsupported Dataset Part {}".format(part)
########################################
# Specify Directory and Partition File #
########################################
if name == 'market1501':
im_dir = ospeu('~/Dataset/market1501/images')
partition_file = ospeu('~/Dataset/market1501/partitions.pkl')
elif name == 'cuhk03':
im_type = ['detected', 'labeled'][0]
im_dir = ospeu(ospj('~/Dataset/cuhk03', im_type, 'images'))
partition_file = ospeu(ospj('~/Dataset/cuhk03', im_type, 'partitions.pkl'))
elif name == 'duke':
im_dir = ospeu('~/Dataset/duke/images')
partition_file = ospeu('~/Dataset/duke/partitions.pkl')
elif name == 'combined':
assert part in ['trainval'], \
"Only trainval part of the combined dataset is available now."
im_dir = ospeu('~/Dataset/market1501_cuhk03_duke/trainval_images')
partition_file = ospeu('~/Dataset/market1501_cuhk03_duke/partitions.pkl')
##################
# Create Dataset #
##################
# Use standard Market1501 CMC settings for all datasets here.
cmc_kwargs = dict(separate_camera_set=False,
single_gallery_shot=False,
first_match_break=True)
partitions = load_pickle(partition_file)
im_names = partitions['{}_im_names'.format(part)]
if part == 'trainval':
ids2labels = partitions['trainval_ids2labels']
ret_set = TrainSet(
im_dir=im_dir,
im_names=im_names,
ids2labels=ids2labels,
**kwargs)
elif part == 'train':
ids2labels = partitions['train_ids2labels']
ret_set = TrainSet(
im_dir=im_dir,
im_names=im_names,
ids2labels=ids2labels,
**kwargs)
elif part == 'val':
marks = partitions['val_marks']
kwargs.update(cmc_kwargs)
ret_set = TestSet(
im_dir=im_dir,
im_names=im_names,
marks=marks,
**kwargs)
elif part == 'test':
marks = partitions['test_marks']
kwargs.update(cmc_kwargs)
ret_set = TestSet(
im_dir=im_dir,
im_names=im_names,
marks=marks,
**kwargs)
if part in ['trainval', 'train']:
num_ids = len(ids2labels)
elif part in ['val', 'test']:
ids = [parse_im_name(n, 'id') for n in im_names]
num_ids = len(list(set(ids)))
num_query = np.sum(np.array(marks) == 0)
num_gallery = np.sum(np.array(marks) == 1)
num_multi_query = np.sum(np.array(marks) == 2)
# Print dataset information
print('-' * 40)
print('{} {} set'.format(name, part))
print('-' * 40)
print('NO. Images: {}'.format(len(im_names)))
print('NO. IDs: {}'.format(num_ids))
try:
print('NO. Query Images: {}'.format(num_query))
print('NO. Gallery Images: {}'.format(num_gallery))
print('NO. Multi-query Images: {}'.format(num_multi_query))
except:
pass
print('-' * 40)
return ret_set
def create_dataset(name='market1501', part='trainval', **kwargs):
assert name in ['market1501', 'cuhk03', 'duke', 'combined'], \
"Unsupported Dataset {}".format(name)
assert part in ['trainval', 'train', 'val', 'test'], \
"Unsupported Dataset Part {}".format(part)
########################################
# Specify Directory and Partition File #
########################################
if name == 'market1501':
im_dir = ospeu('~/Dataset/market1501/images')
partition_file = ospeu('~/Dataset/market1501/partitions.pkl')
elif name == 'cuhk03':
im_type = ['detected', 'labeled'][0]
im_dir = ospeu(ospj('~/Dataset/cuhk03', im_type, 'images'))
partition_file = ospeu(
ospj('~/Dataset/cuhk03', im_type, 'partitions.pkl'))
elif name == 'duke':
im_dir = ospeu('~/Dataset/duke/images')
partition_file = ospeu('~/Dataset/duke/partitions.pkl')
elif name == 'combined':
assert part in ['trainval'], \
"Only trainval part of the combined dataset is available now."
im_dir = ospeu('~/Dataset/market1501_cuhk03_duke/trainval_images')
partition_file = ospeu(
'~/Dataset/market1501_cuhk03_duke/partitions.pkl')
##################
# Create Dataset #
##################
# Use standard Market1501 CMC settings for all datasets here.
cmc_kwargs = dict(separate_camera_set=False,
single_gallery_shot=False,
first_match_break=True)
partitions = load_pickle(partition_file)
im_names = partitions['{}_im_names'.format(part)]
if part == 'trainval':
ids2labels = partitions['trainval_ids2labels']
ret_set = TrainSet(im_dir=im_dir,
im_names=im_names,
ids2labels=ids2labels,
**kwargs)
elif part == 'train':
ids2labels = partitions['train_ids2labels']
ret_set = TrainSet(im_dir=im_dir,
im_names=im_names,
ids2labels=ids2labels,
**kwargs)
elif part == 'val':
marks = partitions['val_marks']
kwargs.update(cmc_kwargs)
ret_set = TestSet(im_dir=im_dir,
im_names=im_names,
marks=marks,
**kwargs)
elif part == 'test':
marks = partitions['test_marks']
kwargs.update(cmc_kwargs)
ret_set = TestSet(im_dir=im_dir,
im_names=im_names,
marks=marks,
**kwargs)
if part in ['trainval', 'train']:
num_ids = len(ids2labels)
elif part in ['val', 'test']:
ids = [parse_im_name(n, 'id') for n in im_names]
num_ids = len(list(set(ids)))
num_query = np.sum(np.array(marks) == 0)
num_gallery = np.sum(np.array(marks) == 1)
num_multi_query = np.sum(np.array(marks) == 2)
# Print dataset information
print('-' * 40)
print('{} {} set'.format(name, part))
print('-' * 40)
print('NO. Images: {}'.format(len(im_names)))
print('NO. IDs: {}'.format(num_ids))
try:
print('NO. Query Images: {}'.format(num_query))
print('NO. Gallery Images: {}'.format(num_gallery))
print('NO. Multi-query Images: {}'.format(num_multi_query))
except:
pass
print('-' * 40)
return ret_set
def transform(zip_file, train_test_partition_file, save_dir=None):
"""Save images and partition the train/val/test set.
"""
print("Extracting zip file")
root = osp.dirname(osp.abspath(zip_file))
if save_dir is None:
save_dir = root
may_make_dir(save_dir)
with ZipFile(zip_file) as z:
z.extractall(path=save_dir)
print("Extracting zip file done")
mat_file = osp.join(save_dir, osp.basename(zip_file)[:-4], 'cuhk-03.mat')
save_images(mat_file, save_dir, new_im_name_tmpl)
if osp.exists(train_test_partition_file):
train_test_partition = load_pickle(train_test_partition_file)
else:
raise RuntimeError('Train/test partition file should be provided.')
for im_type in ['detected', 'labeled']:
trainval_im_names = train_test_partition[im_type]['train_im_names']
trainval_ids = list(set([parse_im_name(n, 'id')
for n in trainval_im_names]))
# Sort ids, so that id-to-label mapping remains the same when running
# the code on different machines.
trainval_ids.sort()
trainval_ids2labels = dict(zip(trainval_ids, range(len(trainval_ids))))
train_val_partition = \
partition_train_val_set(trainval_im_names, parse_im_name, num_val_ids=100)
train_im_names = train_val_partition['train_im_names']
train_ids = list(set([parse_im_name(n, 'id')
for n in train_val_partition['train_im_names']]))
# Sort ids, so that id-to-label mapping remains the same when running
# the code on different machines.
train_ids.sort()
train_ids2labels = dict(zip(train_ids, range(len(train_ids))))
# A mark is used to denote whether the image is from
# query (mark == 0), or
# gallery (mark == 1), or
# multi query (mark == 2) set
val_marks = [0, ] * len(train_val_partition['val_query_im_names']) \
+ [1, ] * len(train_val_partition['val_gallery_im_names'])
val_im_names = list(train_val_partition['val_query_im_names']) \
+ list(train_val_partition['val_gallery_im_names'])
test_im_names = list(train_test_partition[im_type]['query_im_names']) \
+ list(train_test_partition[im_type]['gallery_im_names'])
test_marks = [0, ] * len(train_test_partition[im_type]['query_im_names']) \
+ [1, ] * len(
train_test_partition[im_type]['gallery_im_names'])
partitions = {'trainval_im_names': trainval_im_names,
'trainval_ids2labels': trainval_ids2labels,
'train_im_names': train_im_names,
'train_ids2labels': train_ids2labels,
'val_im_names': val_im_names,
'val_marks': val_marks,
'test_im_names': test_im_names,
'test_marks': test_marks}
partition_file = osp.join(save_dir, im_type, 'partitions.pkl')
save_pickle(partitions, partition_file)
print('Partition file for "{}" saved to {}'.format(im_type, partition_file))
