Chengfang Lu
e72bc061c5
Major changes: - New frontend (platform/web/): Vite + React 18 + TypeScript + Tailwind - 4-module navigation: 数据送标 / 模型管理 / 车队管理 / 系统管理 - Data catalog with charts (DMS/ADAS/Lane 3-tab view) - Quality review workflow (标注质检): Good/Fine/Bad scoring with auto-advance - Audit enhancements: batch operations, rejection categories, Feishu notifications - Operation audit log (操作日志) - World model simulation studio (仿真工坊) - Dataset version management with snapshots and diff - ADAS 7-class dataset integration (138K images organized + compressed) - User management with Feishu integration and pagination - CRUD/search/filter on all pages, card layout redesign - PIL-optimized image overlay rendering - Auto-snapshot on build, in_review workflow stage - Removed embedded algorithm code (now in workspace)
161 lines
5.9 KiB
Python
Executable File
161 lines
5.9 KiB
Python
Executable File
import os
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import cv2
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import tqdm
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import numpy as np
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import pdb
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import json, argparse
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def calc_k(line):
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'''
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Calculate the direction of lanes
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'''
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line_x = line[::2]
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line_y = line[1::2]
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length = np.sqrt((line_x[0]-line_x[-1])**2 + (line_y[0]-line_y[-1])**2)
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if length < 90:
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return -10 # if the lane is too short, it will be skipped
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p = np.polyfit(line_x, line_y,deg = 1)
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rad = np.arctan(p[0])
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return rad
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def draw(im, line, idx, show=False):
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'''
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Generate the segmentation label according to json annotation
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'''
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line_x = line[::2]
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line_y = line[1::2]
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pt0 = (int(line_x[0]),int(line_y[0]))
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if show:
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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)
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idx = idx * 60
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for i in range(len(line_x)-1):
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cv2.line(im,pt0,(int(line_x[i+1]),int(line_y[i+1])),(idx,),thickness=16)
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pt0 = (int(line_x[i+1]),int(line_y[i+1]))
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def get_tusimple_list(root, label_list):
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'''
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Get all the files' names from the json annotation
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'''
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label_json_all = []
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for l in label_list:
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l = os.path.join(root,l)
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label_json = [json.loads(line) for line in open(l).readlines()]
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label_json_all += label_json
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names = [l['raw_file'] for l in label_json_all]
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h_samples = [np.array(l['h_samples']) for l in label_json_all]
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lanes = [np.array(l['lanes']) for l in label_json_all]
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line_txt = []
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for i in range(len(lanes)):
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line_txt_i = []
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for j in range(len(lanes[i])):
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if np.all(lanes[i][j] == -2):
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continue
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valid = lanes[i][j] != -2
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line_txt_tmp = [None]*(len(h_samples[i][valid])+len(lanes[i][j][valid]))
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line_txt_tmp[::2] = list(map(str,lanes[i][j][valid]))
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line_txt_tmp[1::2] = list(map(str,h_samples[i][valid]))
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line_txt_i.append(line_txt_tmp)
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line_txt.append(line_txt_i)
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return names,line_txt
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def generate_segmentation_and_train_list(root, line_txt, names):
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"""
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The lane annotations of the Tusimple dataset is not strictly in order, so we need to find out the correct lane order for segmentation.
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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.
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"""
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print(root, line_txt[0], names[0])
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train_gt_fp = open(os.path.join(root, 'train_gt.txt'), 'w')
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for i in tqdm.tqdm(range(len(line_txt))):
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tmp_line = line_txt[i]
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lines = []
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for j in range(len(tmp_line)):
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lines.append(list(map(float,tmp_line[j])))
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ks = np.array([calc_k(line) for line in lines]) # get the direction of each lane
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k_neg = ks[ks<0].copy()
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k_pos = ks[ks>0].copy()
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k_neg = k_neg[k_neg != -10] # -10 means the lane is too short and is discarded
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k_pos = k_pos[k_pos != -10]
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k_neg.sort()
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k_pos.sort()
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label_path = names[i][:-3]+'png'
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label = np.zeros((720,1280),dtype=np.uint8)
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bin_label = [0,0,0,0]
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if len(k_neg) == 1: # for only one lane in the left
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which_lane = np.where(ks == k_neg[0])[0][0]
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draw(label,lines[which_lane],2)
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bin_label[1] = 1
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elif len(k_neg) == 2: # for two lanes in the left
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which_lane = np.where(ks == k_neg[1])[0][0]
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draw(label,lines[which_lane],1)
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which_lane = np.where(ks == k_neg[0])[0][0]
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draw(label,lines[which_lane],2)
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bin_label[0] = 1
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bin_label[1] = 1
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elif len(k_neg) > 2: # for more than two lanes in the left,
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which_lane = np.where(ks == k_neg[1])[0][0] # we only choose the two lanes that are closest to the center
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draw(label,lines[which_lane],1)
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which_lane = np.where(ks == k_neg[0])[0][0]
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draw(label,lines[which_lane],2)
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bin_label[0] = 1
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bin_label[1] = 1
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if len(k_pos) == 1: # For the lanes in the right, the same logical is adopted.
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which_lane = np.where(ks == k_pos[0])[0][0]
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draw(label,lines[which_lane],3)
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bin_label[2] = 1
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elif len(k_pos) == 2:
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which_lane = np.where(ks == k_pos[1])[0][0]
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draw(label,lines[which_lane],3)
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which_lane = np.where(ks == k_pos[0])[0][0]
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draw(label,lines[which_lane],4)
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bin_label[2] = 1
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bin_label[3] = 1
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elif len(k_pos) > 2:
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which_lane = np.where(ks == k_pos[-1])[0][0]
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draw(label,lines[which_lane],3)
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which_lane = np.where(ks == k_pos[-2])[0][0]
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draw(label,lines[which_lane],4)
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bin_label[2] = 1
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bin_label[3] = 1
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cv2.imwrite(os.path.join(root,label_path),label)
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train_gt_fp.write(names[i] + ' ' + label_path + ' '+' '.join(list(map(str,bin_label))) + '\n')
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train_gt_fp.close()
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def get_args():
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parser = argparse.ArgumentParser()
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parser.add_argument('--root', required=True, help='The root of the Tusimple dataset')
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return parser
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if __name__ == "__main__":
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args = get_args().parse_args()
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# training set
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names, line_txt = get_tusimple_list(args.root, ['label_data_0601.json', 'label_data_0531.json', 'label_data_0313.json'])
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# generate segmentation and training list for training
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generate_segmentation_and_train_list(args.root, line_txt, names)
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# testing set
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names,line_txt = get_tusimple_list(args.root, ['test_tasks_0627.json'])
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# generate testing set for testing
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with open(os.path.join(args.root, 'test.txt'), 'w') as fp:
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for name in names:
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fp.write(name + '\n')
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