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feat: initial HSAP platform

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Huaxu Sentinel Active Safety Platform with embedded algorithm code,
Docker Compose setup, and vendored dataset scaffolds for clone-and-run.

Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
Chengfang Lu
2026-05-25 16:59:59 +08:00
commit 7c43b44c57
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algorithms/lane_ufld/code/UFLD/scripts/convert_tusimple.py Executable file
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import os
import cv2
import tqdm
import numpy as np
import pdb
import json, argparse
def calc_k(line):
'''
Calculate the direction of lanes
'''
line_x = line[::2]
line_y = line[1::2]
length = np.sqrt((line_x[0]-line_x[-1])**2 + (line_y[0]-line_y[-1])**2)
if length < 90:
return -10 # if the lane is too short, it will be skipped
p = np.polyfit(line_x, line_y,deg = 1)
rad = np.arctan(p[0])
return rad
def draw(im, line, idx, show=False):
'''
Generate the segmentation label according to json annotation
'''
line_x = line[::2]
line_y = line[1::2]
pt0 = (int(line_x[0]),int(line_y[0]))
if show:
cv2.putText(im,str(idx),(int(line_x[len(line_x) // 2]),int(line_y[len(line_x) // 2]) - 20),cv2.FONT_HERSHEY_SIMPLEX, 1.0, (255, 255, 255), lineType=cv2.LINE_AA)
idx = idx * 60
for i in range(len(line_x)-1):
cv2.line(im,pt0,(int(line_x[i+1]),int(line_y[i+1])),(idx,),thickness=16)
pt0 = (int(line_x[i+1]),int(line_y[i+1]))
def get_tusimple_list(root, label_list):
'''
Get all the files' names from the json annotation
'''
label_json_all = []
for l in label_list:
l = os.path.join(root,l)
label_json = [json.loads(line) for line in open(l).readlines()]
label_json_all += label_json
names = [l['raw_file'] for l in label_json_all]
h_samples = [np.array(l['h_samples']) for l in label_json_all]
lanes = [np.array(l['lanes']) for l in label_json_all]
line_txt = []
for i in range(len(lanes)):
line_txt_i = []
for j in range(len(lanes[i])):
if np.all(lanes[i][j] == -2):
continue
valid = lanes[i][j] != -2
line_txt_tmp = [None]*(len(h_samples[i][valid])+len(lanes[i][j][valid]))
line_txt_tmp[::2] = list(map(str,lanes[i][j][valid]))
line_txt_tmp[1::2] = list(map(str,h_samples[i][valid]))
line_txt_i.append(line_txt_tmp)
line_txt.append(line_txt_i)
return names,line_txt
def generate_segmentation_and_train_list(root, line_txt, names):
"""
The lane annotations of the Tusimple dataset is not strictly in order, so we need to find out the correct lane order for segmentation.
We use the same definition as CULane, in which the four lanes from left to right are represented as 1,2,3,4 in segentation label respectively.
"""
print(root, line_txt[0], names[0])
train_gt_fp = open(os.path.join(root, 'train_gt.txt'), 'w')
for i in tqdm.tqdm(range(len(line_txt))):
tmp_line = line_txt[i]
lines = []
for j in range(len(tmp_line)):
lines.append(list(map(float,tmp_line[j])))
ks = np.array([calc_k(line) for line in lines]) # get the direction of each lane
k_neg = ks[ks<0].copy()
k_pos = ks[ks>0].copy()
k_neg = k_neg[k_neg != -10] # -10 means the lane is too short and is discarded
k_pos = k_pos[k_pos != -10]
k_neg.sort()
k_pos.sort()
label_path = names[i][:-3]+'png'
label = np.zeros((720,1280),dtype=np.uint8)
bin_label = [0,0,0,0]
if len(k_neg) == 1: # for only one lane in the left
which_lane = np.where(ks == k_neg[0])[0][0]
draw(label,lines[which_lane],2)
bin_label[1] = 1
elif len(k_neg) == 2: # for two lanes in the left
which_lane = np.where(ks == k_neg[1])[0][0]
draw(label,lines[which_lane],1)
which_lane = np.where(ks == k_neg[0])[0][0]
draw(label,lines[which_lane],2)
bin_label[0] = 1
bin_label[1] = 1
elif len(k_neg) > 2: # for more than two lanes in the left,
which_lane = np.where(ks == k_neg[1])[0][0] # we only choose the two lanes that are closest to the center
draw(label,lines[which_lane],1)
which_lane = np.where(ks == k_neg[0])[0][0]
draw(label,lines[which_lane],2)
bin_label[0] = 1
bin_label[1] = 1
if len(k_pos) == 1: # For the lanes in the right, the same logical is adopted.
which_lane = np.where(ks == k_pos[0])[0][0]
draw(label,lines[which_lane],3)
bin_label[2] = 1
elif len(k_pos) == 2:
which_lane = np.where(ks == k_pos[1])[0][0]
draw(label,lines[which_lane],3)
which_lane = np.where(ks == k_pos[0])[0][0]
draw(label,lines[which_lane],4)
bin_label[2] = 1
bin_label[3] = 1
elif len(k_pos) > 2:
which_lane = np.where(ks == k_pos[-1])[0][0]
draw(label,lines[which_lane],3)
which_lane = np.where(ks == k_pos[-2])[0][0]
draw(label,lines[which_lane],4)
bin_label[2] = 1
bin_label[3] = 1
cv2.imwrite(os.path.join(root,label_path),label)
train_gt_fp.write(names[i] + ' ' + label_path + ' '+' '.join(list(map(str,bin_label))) + '\n')
train_gt_fp.close()
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--root', required=True, help='The root of the Tusimple dataset')
return parser
if __name__ == "__main__":
args = get_args().parse_args()
# training set
names, line_txt = get_tusimple_list(args.root, ['label_data_0601.json', 'label_data_0531.json', 'label_data_0313.json'])
# generate segmentation and training list for training
generate_segmentation_and_train_list(args.root, line_txt, names)
# testing set
names,line_txt = get_tusimple_list(args.root, ['test_tasks_0627.json'])
# generate testing set for testing
with open(os.path.join(args.root, 'test.txt'), 'w') as fp:
for name in names:
fp.write(name + '\n')