| 12345678910111213141516171819202122232425262728293031323334353637 |
- import os
- import numpy as np
- prefix_dir = 'MOT17/'
- root_dir = 'train/'
- result_csv = 'train_half_annots.csv'
- train_half_set = {2: 301, 4: 526, 5:419, 9:263, 10:328, 11:451, 13:376}
- fout = open(result_csv, 'w')
-
- for data_name in sorted(os.listdir(prefix_dir + root_dir)):
- print(data_name)
- gt_path = os.path.join(prefix_dir, root_dir, data_name, 'gt', 'gt.txt')
- # print(gt_path)
- data_raw = np.loadtxt(gt_path, delimiter=',', dtype='float', usecols=(0,1,2,3,4,5,6,7,8))
-
- data_sort = data_raw[np.lexsort(data_raw[:,::-1].T)]
- visible_raw = data_sort[:,8]
- # print(data_sort)
- # print(data_sort[-1, 0])
- img_num = data_sort[-1, 0]
-
- # print(data_sort.shape[0])
- box_num = data_sort.shape[0]
-
- person_box_num = np.sum(data_sort[:,6] == 1)
- # print(person_box_num)
- # import ipdb; ipdb.set_trace()
- for i in range(box_num):
- c = int(data_sort[i, 6])
- v = visible_raw[i]
- img_index = int(data_sort[i, 0])
- if c == 1 and v > 0.1 and img_index < train_half_set[int(data_name[-2:])]:
- img_index = int(data_sort[i, 0])
- img_name = data_name + '/img1/' + str(img_index).zfill(6) + '.jpg'
- print(root_dir + img_name + ', ' + str(int(data_sort[i, 1])) + ', ' + str(data_sort[i, 2]) + ', ' + str(data_sort[i, 3]) + ', ' + str(data_sort[i, 2] + data_sort[i, 4]) + ', ' + str(data_sort[i, 3] + data_sort[i, 5]) + ', person\n')
- fout.write(root_dir + img_name + ', ' + str(int(data_sort[i, 1])) + ', ' + str(data_sort[i, 2]) + ', ' + str(data_sort[i, 3]) + ', ' + str(data_sort[i, 2] + data_sort[i, 4]) + ', ' + str(data_sort[i, 3] + data_sort[i, 5]) + ', person\n')
-
- fout.close()
|
