159 lines
7.1 KiB
Python
159 lines
7.1 KiB
Python
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import torch.nn as nn
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import torch
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import torch.nn.functional as F
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from runner.logger import get_logger
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from runner.registry import EVALUATOR
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import json
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import os
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import subprocess
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from shutil import rmtree
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import cv2
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import numpy as np
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def check():
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import subprocess
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import sys
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FNULL = open(os.devnull, 'w')
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result = subprocess.call(
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'./runner/evaluator/culane/lane_evaluation/evaluate', stdout=FNULL, stderr=FNULL)
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if result > 1:
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print('There is something wrong with evaluate tool, please compile it.')
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sys.exit()
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def read_helper(path):
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lines = open(path, 'r').readlines()[1:]
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lines = ' '.join(lines)
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values = lines.split(' ')[1::2]
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keys = lines.split(' ')[0::2]
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keys = [key[:-1] for key in keys]
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res = {k : v for k,v in zip(keys,values)}
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return res
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def call_culane_eval(data_dir, output_path='./output'):
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if data_dir[-1] != '/':
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data_dir = data_dir + '/'
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detect_dir=os.path.join(output_path, 'lines')+'/'
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w_lane=30
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iou=0.5; # Set iou to 0.3 or 0.5
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im_w=1640
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im_h=590
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frame=1
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list0 = os.path.join(data_dir,'list/test_split/test0_normal.txt')
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list1 = os.path.join(data_dir,'list/test_split/test1_crowd.txt')
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list2 = os.path.join(data_dir,'list/test_split/test2_hlight.txt')
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list3 = os.path.join(data_dir,'list/test_split/test3_shadow.txt')
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list4 = os.path.join(data_dir,'list/test_split/test4_noline.txt')
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list5 = os.path.join(data_dir,'list/test_split/test5_arrow.txt')
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list6 = os.path.join(data_dir,'list/test_split/test6_curve.txt')
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list7 = os.path.join(data_dir,'list/test_split/test7_cross.txt')
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list8 = os.path.join(data_dir,'list/test_split/test8_night.txt')
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if not os.path.exists(os.path.join(output_path,'txt')):
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os.mkdir(os.path.join(output_path,'txt'))
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out0 = os.path.join(output_path,'txt','out0_normal.txt')
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out1 = os.path.join(output_path,'txt','out1_crowd.txt')
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out2 = os.path.join(output_path,'txt','out2_hlight.txt')
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out3 = os.path.join(output_path,'txt','out3_shadow.txt')
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out4 = os.path.join(output_path,'txt','out4_noline.txt')
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out5 = os.path.join(output_path,'txt','out5_arrow.txt')
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out6 = os.path.join(output_path,'txt','out6_curve.txt')
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out7 = os.path.join(output_path,'txt','out7_cross.txt')
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out8 = os.path.join(output_path,'txt','out8_night.txt')
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eval_cmd = './runner/evaluator/culane/lane_evaluation/evaluate'
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os.system('%s -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(eval_cmd,data_dir,detect_dir,data_dir,list0,w_lane,iou,im_w,im_h,frame,out0))
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os.system('%s -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(eval_cmd,data_dir,detect_dir,data_dir,list1,w_lane,iou,im_w,im_h,frame,out1))
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os.system('%s -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(eval_cmd,data_dir,detect_dir,data_dir,list2,w_lane,iou,im_w,im_h,frame,out2))
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os.system('%s -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(eval_cmd,data_dir,detect_dir,data_dir,list3,w_lane,iou,im_w,im_h,frame,out3))
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os.system('%s -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(eval_cmd,data_dir,detect_dir,data_dir,list4,w_lane,iou,im_w,im_h,frame,out4))
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os.system('%s -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(eval_cmd,data_dir,detect_dir,data_dir,list5,w_lane,iou,im_w,im_h,frame,out5))
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os.system('%s -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(eval_cmd,data_dir,detect_dir,data_dir,list6,w_lane,iou,im_w,im_h,frame,out6))
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os.system('%s -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(eval_cmd,data_dir,detect_dir,data_dir,list7,w_lane,iou,im_w,im_h,frame,out7))
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os.system('%s -a %s -d %s -i %s -l %s -w %s -t %s -c %s -r %s -f %s -o %s'%(eval_cmd,data_dir,detect_dir,data_dir,list8,w_lane,iou,im_w,im_h,frame,out8))
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res_all = {}
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res_all['normal'] = read_helper(out0)
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res_all['crowd']= read_helper(out1)
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res_all['night']= read_helper(out8)
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res_all['noline'] = read_helper(out4)
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res_all['shadow'] = read_helper(out3)
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res_all['arrow']= read_helper(out5)
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res_all['hlight'] = read_helper(out2)
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res_all['curve']= read_helper(out6)
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res_all['cross']= read_helper(out7)
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return res_all
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@EVALUATOR.register_module
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class CULane(nn.Module):
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def __init__(self, cfg):
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super(CULane, self).__init__()
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# Firstly, check the evaluation tool
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check()
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self.cfg = cfg
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self.blur = torch.nn.Conv2d(
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5, 5, 9, padding=4, bias=False, groups=5).cuda()
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torch.nn.init.constant_(self.blur.weight, 1 / 81)
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self.logger = get_logger('resa')
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self.out_dir = os.path.join(self.cfg.work_dir, 'lines')
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if cfg.view:
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self.view_dir = os.path.join(self.cfg.work_dir, 'vis')
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def evaluate(self, dataset, output, batch):
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seg, exists = output['seg'], output['exist']
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predictmaps = F.softmax(seg, dim=1).cpu().numpy()
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exists = exists.cpu().numpy()
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batch_size = seg.size(0)
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img_name = batch['meta']['img_name']
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img_path = batch['meta']['full_img_path']
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for i in range(batch_size):
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coords = dataset.probmap2lane(predictmaps[i], exists[i])
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outname = self.out_dir + img_name[i][:-4] + '.lines.txt'
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outdir = os.path.dirname(outname)
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if not os.path.exists(outdir):
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os.makedirs(outdir)
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f = open(outname, 'w')
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for coord in coords:
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for x, y in coord:
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if x < 0 and y < 0:
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continue
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f.write('%d %d ' % (x, y))
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f.write('\n')
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f.close()
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if self.cfg.view:
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img = cv2.imread(img_path[i]).astype(np.float32)
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dataset.view(img, coords, self.view_dir+img_name[i])
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def summarize(self):
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self.logger.info('summarize result...')
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eval_list_path = os.path.join(
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self.cfg.dataset_path, "list", self.cfg.dataset.val.data_list)
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#prob2lines(self.prob_dir, self.out_dir, eval_list_path, self.cfg)
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res = call_culane_eval(self.cfg.dataset_path, output_path=self.cfg.work_dir)
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TP,FP,FN = 0,0,0
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out_str = 'Copypaste: '
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for k, v in res.items():
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val = float(v['Fmeasure']) if 'nan' not in v['Fmeasure'] else 0
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val_tp, val_fp, val_fn = int(v['tp']), int(v['fp']), int(v['fn'])
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val_p, val_r, val_f1 = float(v['precision']), float(v['recall']), float(v['Fmeasure'])
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TP += val_tp
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FP += val_fp
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FN += val_fn
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self.logger.info(k + ': ' + str(v))
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out_str += k
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for metric, value in v.items():
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out_str += ' ' + str(value).rstrip('\n')
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out_str += ' '
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P = TP * 1.0 / (TP + FP + 1e-9)
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R = TP * 1.0 / (TP + FN + 1e-9)
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F = 2*P*R/(P + R + 1e-9)
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overall_result_str = ('Overall Precision: %f Recall: %f F1: %f' % (P, R, F))
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self.logger.info(overall_result_str)
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out_str = out_str + overall_result_str
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self.logger.info(out_str)
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# delete the tmp output
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rmtree(self.out_dir)
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