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utils.py
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utils.py
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import numpy as np
import torch
from transformer.Models import Transformer
import pandas as pd
import argparse
__author__ = "Yudong Zhang"
def readXML(file):
with open(file) as f:
lines = f.readlines()
f.close()
poslist = []
p = 0
for i in range(len(lines)):
if '<particle>' in lines[i]:
posi = []
elif '<detection t=' in lines[i]:
ind1 = lines[i].find('"')
ind2 = lines[i].find('"', ind1 + 1)
t = float(lines[i][ind1 + 1:ind2])
ind1 = lines[i].find('"', ind2 + 1)
ind2 = lines[i].find('"', ind1 + 1)
x = float(lines[i][ind1 + 1:ind2])
ind1 = lines[i].find('"', ind2 + 1)
ind2 = lines[i].find('"', ind1 + 1)
y = float(lines[i][ind1 + 1:ind2])
ind1 = lines[i].find('"', ind2 + 1)
ind2 = lines[i].find('"', ind1 + 1)
z = float(lines[i][ind1 + 1:ind2])
posi.append([x, y, t, z, float(p)])
elif '</particle>' in lines[i]:
p += 1
poslist.append(posi)
return poslist
def find_near(pdcontent,x,y):
pdcontent = pdcontent.drop_duplicates(subset=['pos_x','pos_y'])
pos_column = ['pos_x','pos_y']
other_column = [co for co in pdcontent.columns if co not in pos_column]
reindex_column = pos_column + other_column
pdcontent = pdcontent[reindex_column]
all_posi = pdcontent.values.tolist()
dis_all_posi = []
for thisframepos in all_posi:
dis = (thisframepos[0]-x)**2 +(thisframepos[1]-y)**2
dis_all_posi.append(thisframepos+[dis])
dis_all_posi_np = np.array(dis_all_posi)
a_arg = np.argsort(dis_all_posi_np[:,-1])
sortnp = dis_all_posi_np[a_arg.tolist()]
return sortnp
def load_model(g_opt, device):
checkpoint = torch.load(g_opt['model'])
opt = checkpoint['settings']
transformer = Transformer(
n_passed = opt.len_established,
n_future = opt.len_future,
n_candi = opt.num_cand,
n_position = opt.n_position,
d_k=opt.d_k,
d_v=opt.d_v,
d_model=opt.d_model,
d_word_vec=opt.d_word_vec,
d_inner=opt.d_inner_hid,
n_layers=opt.n_layers,
n_head=opt.n_head,
dropout=opt.dropout).to(device)
transformer.load_state_dict(checkpoint['model'])
print('[Info] Trained model state loaded.')
return transformer
def resultcsv_2xml(xmlfilepath, output_csv_pa, testfilename=None):
result_csv = pd.read_csv(output_csv_pa)
if testfilename:
snr = testfilename.split(' ')[2]
dens = testfilename.split(' ')[-1]
scenario = testfilename.split(' ')[0]
else:
snr=0
dens='none'
scenario='none'
method= '_MoTT'
thrs = 0
t_trackid = list(set(result_csv['trackid']))
# csv to xml
with open(xmlfilepath, "w+") as output:
output.write('<?xml version="1.0" encoding="UTF-8" standalone="no"?>\n')
output.write('<root>\n')
output.write('<TrackContestISBI2012 SNR="' + str(
snr) + '" density="' + dens + '" scenario="' + scenario + \
'" ' + method + '="' + str(thrs) + '">\n')
for trackid in t_trackid:
thistrack = result_csv[result_csv['trackid']==trackid]
if len(thistrack) > 1:
thistrack.sort_values("frame",inplace=True)
thistrack_np = thistrack.values
output.write('<particle>\n')
for pos in thistrack_np:
output.write('<detection t="' + str(int(pos[-1])) +
'" x="' + str(pos[2]) +
'" y="' + str(pos[3]) + '" z="0"/>\n')
output.write('</particle>\n')
output.write('</TrackContestISBI2012>\n')
output.write('</root>\n')
output.close()
if __name__=='__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--movie_name', type=str, default=None)
parser.add_argument('--pred_csvpath', type=str, default='./prediction/20240409_13_45_39_GTxml/test_2024_04_08__14_44_25/test_2024_04_08__14_44_25.csv')
parser.add_argument('--save_xmlpath', type=str, default='./prediction/20240409_13_45_39_GTxml/test_2024_04_08__14_44_25/test_2024_04_08__14_44_25.xml')
opt = parser.parse_args()
resultcsv_2xml(opt.save_xmlpath, opt.pred_csvpath, opt.movie_name)