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