caviara.py

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# encoding: utf-8
"""
@author:  xingyu liao
@contact: sherlockliao01@gmail.com
"""
 
import os
from scipy.io import loadmat
from glob import glob
 
from fastreid.data.datasets import DATASET_REGISTRY
from fastreid.data.datasets.bases import ImageDataset
import pdb
import random
import numpy as np
 
__all__ = ['CAVIARa',]
 
 
@DATASET_REGISTRY.register()
class CAVIARa(ImageDataset):
    dataset_dir = "CAVIARa"
    dataset_name = "caviara"
 
    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)
 
        super().__init__(train, [], [], **kwargs)
 
    def process_train(self, train_path):
        data = []
 
        img_list = glob(os.path.join(train_path, "*.jpg"))
        for img_path in img_list:
            img_name = img_path.split('/')[-1]
            pid = self.dataset_name + "_" + img_name[:4]
            camid = self.dataset_name + "_cam0"
            data.append([img_path, pid, camid])
 
        return data