def __init__(self, args, transform, dtype):
self.transform = transform
self.loader = default_loader
data_path = args.datadir
if dtype == 'test':
data_path += '/gallery'
else:
data_path += '/query'
self.imgs = [path for path in list_pictures(data_path)]
def test(self):
self.model.eval()
qf = self.extract_feature(self.query_loader).numpy()
gf = self.extract_feature(self.test_loader).numpy()
print('query shape:', qf.shape)
print('gallery shape:', gf.shape)
# 查看提取的图像特征是否归一化
# print(qf[0])
# sum_qf = []
# sum_gf = []
# for i in range(len(qf)):
# sum_qf.append(np.sum(qf[i] ** 2))
# for i in range(len(gf)):
# sum_gf.append(np.sum(gf[i] ** 2))
#
# print('sum_qf length:', len(sum_qf))
# print('sum_gf length:', len(sum_gf))
# 计算距离
dist = cdist(qf, gf)
print(dist.shape)
# 结果保存为CSV文件
res_df = pd.DataFrame(dist)
data_path = args.datadir
gallery_data_path = data_path + '/test_images'
query_filename = 'query_test_image_name.txt'
query_file_txt = os.path.join(data_path, query_filename)
query_file = open(query_file_txt, 'r')
query_inx_list = [
path.rsplit('_', 1)[1].split('.')[0] for path in query_file
]
gallery_inx_list = [
path.rsplit('_', 1)[1].split('.')[0]
for path in list_pictures(gallery_data_path)
]
res_df.index = query_inx_list
res_df.columns = gallery_inx_list
res_dict_dictlist = {}
for i in res_df.index:
res_dict_i = pd.DataFrame(res_df.loc[i]).sort_values(
by=[i], axis=0, ascending=True).to_dict()
res_dict_dictlist.update(res_dict_i)
saveCSV('PRCV2020_preid_result.csv', res_dict_dictlist)
def __init__(self, args, transform, dtype):
self.transform = transform
self.loader = default_loader
data_path = args.datadir
if dtype == 'train':
data_path += '/bounding_box_train'
elif dtype == 'test':
data_path += '/bounding_box_test'
else:
data_path += '/query'
self.imgs = [path for path in list_pictures(data_path) if self.id(path) != -1]
self._id2label = {_id: idx for idx, _id in enumerate(self.unique_ids)}
def __init__(self, dataset_root, transform, split='train'):
if not split in ['train', 'gallery', 'query']:
raise Exception('Invalid dataset split.')
self.transform = transform
self.loader = default_loader
self.split = split
if split == 'train':
data_path = ospj(dataset_root, 'bounding_box_train')
elif split == 'gallery':
data_path = ospj(dataset_root, 'bounding_box_test')
elif split == 'query':
data_path = ospj(dataset_root, 'query')
self.imgs = [path for path in list_pictures(data_path)]
if split == 'train':
self._id2label = {_id: idx for idx, _id in enumerate(self.unique_ids)}
def __init__(self, args, transform, dtype):
self.transform = transform
self.loader = default_loader
print('reading data !!!!!!')
data_path = args.datadir
if dtype == 'query':
query_filename = 'query_test_image_name.txt'
query_file_txt = os.path.join(data_path, query_filename)
query_file = open(query_file_txt, 'r')
data_path += '/query_images'
self.imgs = [
os.path.join(data_path, path.strip()) for path in query_file
]
elif dtype == 'gallery':
data_path += '/test_images'
self.imgs = [path
for path in list_pictures(data_path)] # img path list
print(dtype + ' images num:', len(self.imgs))
