reid-strong-baseline/data/datasets/bases.py

96 lines
3.8 KiB
Python

# encoding: utf-8
"""
@author: sherlock
@contact: sherlockliao01@gmail.com
"""
import numpy as np
class BaseDataset(object):
"""
Base class of reid dataset
"""
def get_imagedata_info(self, data):
pids, cams = [], []
for _, pid, camid in data:
pids += [pid]
cams += [camid]
pids = set(pids)
cams = set(cams)
num_pids = len(pids)
num_cams = len(cams)
num_imgs = len(data)
return num_pids, num_imgs, num_cams
def get_videodata_info(self, data, return_tracklet_stats=False):
pids, cams, tracklet_stats = [], [], []
for img_paths, pid, camid in data:
pids += [pid]
cams += [camid]
tracklet_stats += [len(img_paths)]
pids = set(pids)
cams = set(cams)
num_pids = len(pids)
num_cams = len(cams)
num_tracklets = len(data)
if return_tracklet_stats:
return num_pids, num_tracklets, num_cams, tracklet_stats
return num_pids, num_tracklets, num_cams
def print_dataset_statistics(self):
raise NotImplementedError
class BaseImageDataset(BaseDataset):
"""
Base class of image reid dataset
"""
def print_dataset_statistics(self, train, query, gallery):
num_train_pids, num_train_imgs, num_train_cams = self.get_imagedata_info(train)
num_query_pids, num_query_imgs, num_query_cams = self.get_imagedata_info(query)
num_gallery_pids, num_gallery_imgs, num_gallery_cams = self.get_imagedata_info(gallery)
print("Dataset statistics:")
print(" ----------------------------------------")
print(" subset | # ids | # images | # cameras")
print(" ----------------------------------------")
print(" train | {:5d} | {:8d} | {:9d}".format(num_train_pids, num_train_imgs, num_train_cams))
print(" query | {:5d} | {:8d} | {:9d}".format(num_query_pids, num_query_imgs, num_query_cams))
print(" gallery | {:5d} | {:8d} | {:9d}".format(num_gallery_pids, num_gallery_imgs, num_gallery_cams))
print(" ----------------------------------------")
class BaseVideoDataset(BaseDataset):
"""
Base class of video reid dataset
"""
def print_dataset_statistics(self, train, query, gallery):
num_train_pids, num_train_tracklets, num_train_cams, train_tracklet_stats = \
self.get_videodata_info(train, return_tracklet_stats=True)
num_query_pids, num_query_tracklets, num_query_cams, query_tracklet_stats = \
self.get_videodata_info(query, return_tracklet_stats=True)
num_gallery_pids, num_gallery_tracklets, num_gallery_cams, gallery_tracklet_stats = \
self.get_videodata_info(gallery, return_tracklet_stats=True)
tracklet_stats = train_tracklet_stats + query_tracklet_stats + gallery_tracklet_stats
min_num = np.min(tracklet_stats)
max_num = np.max(tracklet_stats)
avg_num = np.mean(tracklet_stats)
print("Dataset statistics:")
print(" -------------------------------------------")
print(" subset | # ids | # tracklets | # cameras")
print(" -------------------------------------------")
print(" train | {:5d} | {:11d} | {:9d}".format(num_train_pids, num_train_tracklets, num_train_cams))
print(" query | {:5d} | {:11d} | {:9d}".format(num_query_pids, num_query_tracklets, num_query_cams))
print(" gallery | {:5d} | {:11d} | {:9d}".format(num_gallery_pids, num_gallery_tracklets, num_gallery_cams))
print(" -------------------------------------------")
print(" number of images per tracklet: {} ~ {}, average {:.2f}".format(min_num, max_num, avg_num))
print(" -------------------------------------------")