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tsv2csv.py
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import csv, os
real_img_path_t = '../MESAD/mesad-real/train/images/'
real_imgs_t = os.listdir(real_img_path_t)
phantom_img_path_t = '../MESAD/mesad-phantom/train/images/'
phantom_imgs_t = os.listdir(phantom_img_path_t)
real_anno_path_t = '../MESAD/mesad-real/train/annotations/'
real_annos_t = os.listdir(real_anno_path_t)
phantom_anno_path_t = '../MESAD/mesad-phantom/train/annotations/'
phantom_annos_t = os.listdir(phantom_anno_path_t)
real_img_path_v = '../MESAD/mesad-real/val/images/'
real_imgs_v = os.listdir(real_img_path_v)
phantom_img_path_v = '../MESAD/mesad-phantom/val/images/'
phantom_imgs_v = os.listdir(phantom_img_path_v)
real_anno_path_v = '../MESAD/mesad-real/val/annotations/'
real_annos_v = os.listdir(real_anno_path_v)
phantom_anno_path_v = '../MESAD/mesad-phantom/val/annotations/'
phantom_annos_v = os.listdir(phantom_anno_path_v)
real_train = csv.writer(open('real_train.csv', 'w', newline=''))
real_val = csv.writer(open('real_val.csv', 'w', newline=''))
phantom_train = csv.writer(open('phantom_train.csv', 'w', newline=''))
phantom_val = csv.writer(open('phantom_val.csv', 'w', newline=''))
for ri in real_imgs_t:
label = []
l_0, l_1 = [], []
for ra in real_annos_t:
if ri.split('.')[0] == ra.split('.')[0]:
label.insert(0, ra) if 'labels' in ra else label.insert(1, ra)
f0 = open(real_anno_path_t + label[0], 'r', newline='')
f1 = open(real_anno_path_t + label[1], 'r', newline='')
for i in f0:
l_0.append(i.rstrip('\n').split('\t'))
for i in f1:
l_1.append(i.rstrip('\n').split('\t'))
for j in range(len(l_0)):
real_train.writerow([ri.split('.')[0]] + l_1[j] + l_0[j])
f0.close()
f1.close()
for ri in real_imgs_v:
label = []
l_0, l_1 = [], []
for ra in real_annos_v:
if ri.split('.')[0] == ra.split('.')[0]:
label.insert(0, ra) if 'labels' in ra else label.insert(1, ra)
f0 = open(real_anno_path_v + label[0], 'r', newline='')
f1 = open(real_anno_path_v + label[1], 'r', newline='')
for i in f0:
l_0.append(i.rstrip('\n').split('\t'))
for i in f1:
l_1.append(i.rstrip('\n').split('\t'))
for j in range(len(l_0)):
real_val.writerow([ri.split('.')[0]] + l_1[j] + l_0[j])
f0.close()
f1.close()
for pi in phantom_imgs_t:
label = []
l_0, l_1 = [], []
for pa in phantom_annos_t:
if pi.split('.')[0] == pa.split('.')[0]:
label.insert(0, pa) if 'labels' in pa else label.insert(1, pa)
f0 = open(phantom_anno_path_t + label[0], 'r', newline='')
f1 = open(phantom_anno_path_t + label[1], 'r', newline='')
for i in f0:
l_0.append(i.rstrip('\n').split('\t'))
for i in f1:
l_1.append(i.rstrip('\n').split('\t'))
for j in range(len(l_0)):
phantom_train.writerow([pi.split('.')[0]] + l_1[j] + l_0[j])
f0.close()
f1.close()
for pi in phantom_imgs_v:
label = []
l_0, l_1 = [], []
for pa in phantom_annos_v:
if pi.split('.')[0] == pa.split('.')[0]:
label.insert(0, pa) if 'labels' in pa else label.insert(1, pa)
f0 = open(phantom_anno_path_v + label[0], 'r', newline='')
f1 = open(phantom_anno_path_v + label[1], 'r', newline='')
for i in f0:
l_0.append(i.rstrip('\n').split('\t'))
for i in f1:
l_1.append(i.rstrip('\n').split('\t'))
for j in range(len(l_0)):
phantom_val.writerow([pi.split('.')[0]] + l_1[j] + l_0[j])
f0.close()
f1.close()