52 lines
1.7 KiB
Python
52 lines
1.7 KiB
Python
|
import os
|
||
|
|
import argparse
|
||
|
|
import numpy as np
|
||
|
|
import pandas as pd
|
||
|
|
from PIL import Image
|
||
|
|
import tqdm
|
||
|
|
|
||
|
|
|
||
|
|
def getLane(probmap, pts, cfg = None):
|
||
|
|
thr = 0.3
|
||
|
|
coordinate = np.zeros(pts)
|
||
|
|
cut_height = 0
|
||
|
|
if cfg.cut_height:
|
||
|
|
cut_height = cfg.cut_height
|
||
|
|
for i in range(pts):
|
||
|
|
line = probmap[round(cfg.img_height-i*20/(590-cut_height)*cfg.img_height)-1]
|
||
|
|
if np.max(line)/255 > thr:
|
||
|
|
coordinate[i] = np.argmax(line)+1
|
||
|
|
if np.sum(coordinate > 0) < 2:
|
||
|
|
coordinate = np.zeros(pts)
|
||
|
|
return coordinate
|
||
|
|
|
||
|
|
|
||
|
|
def prob2lines(prob_dir, out_dir, list_file, cfg = None):
|
||
|
|
lists = pd.read_csv(list_file, sep=' ', header=None,
|
||
|
|
names=('img', 'probmap', 'label1', 'label2', 'label3', 'label4'))
|
||
|
|
pts = 18
|
||
|
|
|
||
|
|
for k, im in enumerate(lists['img'], 1):
|
||
|
|
existPath = prob_dir + im[:-4] + '.exist.txt'
|
||
|
|
outname = out_dir + im[:-4] + '.lines.txt'
|
||
|
|
prefix = '/'.join(outname.split('/')[:-1])
|
||
|
|
if not os.path.exists(prefix):
|
||
|
|
os.makedirs(prefix)
|
||
|
|
f = open(outname, 'w')
|
||
|
|
|
||
|
|
labels = list(pd.read_csv(existPath, sep=' ', header=None).iloc[0])
|
||
|
|
coordinates = np.zeros((4, pts))
|
||
|
|
for i in range(4):
|
||
|
|
if labels[i] == 1:
|
||
|
|
probfile = prob_dir + im[:-4] + '_{0}_avg.png'.format(i+1)
|
||
|
|
probmap = np.array(Image.open(probfile))
|
||
|
|
coordinates[i] = getLane(probmap, pts, cfg)
|
||
|
|
|
||
|
|
if np.sum(coordinates[i] > 0) > 1:
|
||
|
|
for idx, value in enumerate(coordinates[i]):
|
||
|
|
if value > 0:
|
||
|
|
f.write('%d %d ' % (
|
||
|
|
round(value*1640/cfg.img_width)-1, round(590-idx*20)-1))
|
||
|
|
f.write('\n')
|
||
|
|
f.close()
|