├── .gitignore ├── 010b0600-ae1f7be5.jpg ├── BDD100kDbInspector.py ├── CULaneDbInspector.py ├── CaltechLanesDbInspector.py ├── LICENSE ├── README.md ├── TuSimpleDbInspector.py ├── VPGNetDbInspector.py ├── example.json └── screenshot ├── 1.PNG ├── 2.PNG ├── 3.PNG └── 4.PNG /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # 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having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | -------------------------------------------------------------------------------- /010b0600-ae1f7be5.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/eeyrw/LaneDatasetInspector/4acc1a40fa940a6393fa76cbdea2aeedaedc7355/010b0600-ae1f7be5.jpg -------------------------------------------------------------------------------- /BDD100kDbInspector.py: -------------------------------------------------------------------------------- 1 | import scipy.io 2 | import numpy as np 3 | import cv2 4 | import os 5 | from tqdm import tqdm 6 | import json 7 | import pickle 8 | from itertools import groupby 9 | 10 | BDD100kRootDir = r'H:\BDD100K' 11 | 12 | trainJsonFilePath = os.path.join( 13 | BDD100kRootDir, r'labels\bdd100k_labels_images_train.json') 14 | trainPickleFilePath = os.path.join( 15 | BDD100kRootDir, r'labels\bdd100k_labels_images_train.pkl') 16 | trainLaneOnlyPickleFilePath = os.path.join( 17 | BDD100kRootDir, r'labels\bdd100k_labels_images_train_lane_only.pkl') 18 | 19 | 20 | def getRandomColour(): 21 | return tuple(np.random.randint(0, 255, dtype=np.uint8, size=3)) 22 | 23 | 24 | test = True 25 | 26 | if not test: 27 | if not os.path.isfile(trainLaneOnlyPickleFilePath): 28 | if not os.path.isfile(trainPickleFilePath): 29 | with open(trainJsonFilePath, 'r') as f: 30 | data = json.load(f) 31 | with open(trainPickleFilePath, 'wb') as f: 32 | pickle.dump(data, f) 33 | else: 34 | with open(trainPickleFilePath, 'rb') as f: 35 | # The protocol version used is detected automatically, so we do not 36 | # have to specify it. 37 | data = pickle.load(f) 38 | for entry in data: 39 | entry['labels'] = list(filter( 40 | lambda x: x['category'] == 'lane', entry['labels'])) 41 | laneData.append(entry) 42 | 43 | with open(trainLaneOnlyPickleFilePath, 'wb') as f: 44 | pickle.dump(laneData, f) 45 | else: 46 | with open(trainLaneOnlyPickleFilePath, 'rb') as f: 47 | laneData = pickle.load(f) 48 | 49 | # 写入 JSON 数据 50 | with open('example.json', 'w') as f: 51 | json.dump(laneData[np.random.randint(0, 60000)], f, indent=2) 52 | 53 | 54 | with open('example.json', 'r') as f: 55 | entry = json.load(f) 56 | 57 | imagePath = entry['name'] 58 | img_bgr = cv2.imread(imagePath) 59 | labelImage = np.zeros(img_bgr.shape, np.uint8) 60 | 61 | for laneItem in entry['labels']: 62 | curveVerticesDescStr = laneItem['poly2d'][0]['types'] 63 | curveVertices = np.array(laneItem['poly2d'][0]['vertices'], dtype=np.int) 64 | for vertex1, vertex2 in zip(curveVertices[:-1], curveVertices[1:]): 65 | color = getRandomColour() 66 | cv2.line(labelImage, tuple(vertex1), 67 | tuple(vertex2), (int(color[0]),int(color[1]),int(color[2])), 3) 68 | 69 | for node,nodeType in zip(curveVertices,curveVerticesDescStr): 70 | if nodeType=='L': 71 | cv2.circle(labelImage,tuple(node),5,(255,0,0),-1) 72 | else: 73 | cv2.circle(labelImage,tuple(node),5,(0,255,0),-1) 74 | 75 | res = cv2.addWeighted(img_bgr, 0.7, labelImage, 1, 0.4) 76 | cv2.imwrite('dsfsf.png', res) 77 | # cv2.imshow('CULane Dataset Quick Inspector', res) 78 | # k = cv2.waitKey(1) & 0xff 79 | # if k == 27: 80 | # cv2.destroyWindow('CULane Dataset Quick Inspector') 81 | 82 | # trainIndexFilePath = os.path.join(CULaneRootDir, r'list\train_gt.txt') 83 | 84 | # trainFilePairList = [] 85 | 86 | # with open(trainIndexFilePath, 'r') as f: 87 | # for line in f.readlines(): 88 | # imagePath, segImagePath, lane0, lane1, lane2, lane4 = line.split() 89 | # trainFilePairList.append( 90 | # (os.path.join(CULaneRootDir, imagePath[1:]), os.path.join(CULaneRootDir, segImagePath[1:]), lane0, lane1, lane2, lane4)) 91 | 92 | # colorMapMat = np.zeros((5, 3), dtype=np.uint8) 93 | 94 | # for i in range(0, 5): 95 | # if i != 0: 96 | # colorMapMat[i] = np.random.randint(0, 255, dtype=np.uint8, size=3) 97 | 98 | # for imageFile, segFile, _, _, _, _ in tqdm(trainFilePairList): 99 | # img_bgr = cv2.imread(imageFile) 100 | # seg = cv2.imread(segFile,cv2.IMREAD_UNCHANGED) 101 | # # seg = np.max(seg,axis=2) 102 | # segImage = colorMapMat[seg] 103 | 104 | # res = cv2.addWeighted(img_bgr, 0.7, segImage, 0.7, 0.4) 105 | # cv2.imshow('CULane Dataset Quick Inspector', res) 106 | # k = cv2.waitKey(1) & 0xff 107 | # if k == 27: 108 | # break 109 | # cv2.destroyWindow('CULane Dataset Quick Inspector') 110 | -------------------------------------------------------------------------------- /CULaneDbInspector.py: -------------------------------------------------------------------------------- 1 | import scipy.io 2 | import numpy as np 3 | import cv2 4 | import os 5 | from tqdm import tqdm 6 | import argparse 7 | 8 | 9 | def walkThroughDataset(dataSetDir): 10 | trainIndexFilePath = os.path.join(dataSetDir, r'list\train_gt.txt') 11 | 12 | trainFilePairList = [] 13 | 14 | with open(trainIndexFilePath, 'r') as f: 15 | for line in f.readlines(): 16 | imagePath, segImagePath, lane0, lane1, lane2, lane4 = line.split() 17 | trainFilePairList.append( 18 | (os.path.join(dataSetDir, imagePath[1:]), os.path.join(dataSetDir, segImagePath[1:]), lane0, lane1, lane2, lane4)) 19 | 20 | colorMapMat = np.zeros((5, 3), dtype=np.uint8) 21 | 22 | for i in range(0, 5): 23 | if i != 0: 24 | colorMapMat[i] = np.random.randint(0, 255, dtype=np.uint8, size=3) 25 | 26 | for imageFile, segFile, _, _, _, _ in tqdm(trainFilePairList): 27 | img_bgr = cv2.imread(imageFile) 28 | seg = cv2.imread(segFile,cv2.IMREAD_UNCHANGED) 29 | segImage = colorMapMat[seg] 30 | 31 | res = cv2.addWeighted(img_bgr, 0.7, segImage, 0.7, 0.4) 32 | cv2.imshow('CULane Dataset Quick Inspector', res) 33 | k = cv2.waitKey(1) & 0xff 34 | if k == 27: 35 | break 36 | cv2.destroyWindow('CULane Dataset Quick Inspector') 37 | 38 | 39 | def parse_args(): 40 | parser = argparse.ArgumentParser( 41 | description='CULane Dataset Quick Inspector') 42 | parser.add_argument('--rootDir', type=str, default=r'E:\CULane', 43 | help='root directory (default: E:\\CULane)') 44 | args = parser.parse_args() 45 | return args 46 | 47 | if __name__ == '__main__': 48 | args = parse_args() 49 | walkThroughDataset(args.rootDir) 50 | -------------------------------------------------------------------------------- /CaltechLanesDbInspector.py: -------------------------------------------------------------------------------- 1 | import scipy.io as sio 2 | import numpy as np 3 | import cv2 4 | import os 5 | import argparse 6 | 7 | 8 | def walkThroughDataset(dataSetDir): 9 | scene_list = ['cordova1', 'cordova2', 'washington1', 'washington2'] 10 | labels_file_name = 'labels.ccvl' 11 | 12 | subtype_colors = {'bw': (255, 0, 0), 'dy': (0, 255, 0), 'sw': (0, 0, 255)} 13 | for scene_name in scene_list: 14 | scene_data_path = os.path.join(dataSetDir, scene_name) 15 | labels_path = os.path.join(scene_data_path, labels_file_name) 16 | 17 | label_data = sio.loadmat(labels_path, simplify_cells=True) 18 | frames_data = label_data['ld']['frames'] 19 | for frame_data in frames_data: 20 | frame_name = frame_data['frame'] 21 | img_path = os.path.join(scene_data_path, frame_name) 22 | img = cv2.imread(img_path) 23 | label_data = frame_data['labels'] if isinstance( 24 | frame_data['labels'], list) else [frame_data['labels']] 25 | 26 | for label in label_data: 27 | if len(label) != 0: 28 | subtype = label['subtype'] 29 | type = label['type'] 30 | if type == 'spline': 31 | points = label['points'] 32 | points_new = get_lane_points( 33 | spline=points, interval=0.02) 34 | 35 | for point in points_new: 36 | coord = (int(point[0]), int(point[1])) 37 | cv2.circle( 38 | img, coord, 4, subtype_colors[subtype], thickness=-1, lineType=8) 39 | 40 | cv2.imshow('Caltech Lanes Dataset Quick Inspector', img) 41 | k = cv2.waitKey(1) & 0xff 42 | if k == 27: 43 | break 44 | 45 | 46 | def get_lane_points(spline, interval=0.05): 47 | """ 48 | Function to evaluate a Bezier spline with the specified degree. 49 | Algorithm taken from the matlab code: https://github.com/mohamedadaly/caltechcv-image-labeler/blob/master/ccvEvalBezSpline.m 50 | 51 | Parameters 52 | ---------- 53 | spline - input 3 or 4 points in a matrix 3x2 or 4x2 [xs, ys] 54 | interval - [0.05] the interval to use for evaluation 55 | 56 | Returns 57 | ------- 58 | output points nx2 [xs; ys] 59 | 60 | """ 61 | 62 | degree = spline.shape[0] - 1 63 | n = int(np.floor(1/interval) + 1) 64 | if degree == 2: 65 | M = np.array([[1, -2, 1], [-2, 2, 0], [1, 0, 0]]) 66 | abcd = np.matmul(M, spline) 67 | a = abcd[0] 68 | b = abcd[1] 69 | c = abcd[2] 70 | 71 | P = c 72 | dP = b*interval + a*interval**2 73 | ddP = 2*a*interval**2 74 | 75 | outPoints = np.zeros((n, spline.shape[1])) 76 | outPoints[0, :] = P 77 | for i in range(1, n): 78 | # calculate new point 79 | P = P + dP 80 | # update steps 81 | dP = dP + ddP 82 | # store in output array 83 | outPoints[i, :] = P 84 | elif degree == 3: 85 | M = np.array([[-1, 3, -3, 1], [3, -6, 3, 0], 86 | [-3, 3, 0, 0], [1, 0, 0, 0]]) 87 | abcd = np.matmul(M, spline) 88 | a = abcd[0] 89 | b = abcd[1] 90 | c = abcd[2] 91 | d = abcd[3] 92 | 93 | P = d 94 | dP = c*interval + b * interval**2 + a*interval**3 95 | ddP = 2*b*interval**2 + 6*a*interval**3 96 | dddP = 6*a*interval**3 97 | 98 | outPoints = np.zeros((n, spline.shape[1])) 99 | outPoints[0, :] = P 100 | for i in range(1, n): 101 | # calculate new point 102 | P = P + dP 103 | # update steps 104 | dP = dP + ddP 105 | ddP = ddP + dddP 106 | # store in output array 107 | outPoints[i, :] = P 108 | else: 109 | raise NotImplementedError( 110 | f"Supports Bezier spline with degrees 2 or 3, but got {degree}") 111 | 112 | return outPoints 113 | 114 | 115 | def parse_args(): 116 | parser = argparse.ArgumentParser( 117 | description='Caltech Lanes Dataset Quick Inspector') 118 | parser.add_argument('--rootDir', type=str, default='/media/caltech-lanes', 119 | help='root directory (default: /media/caltech-lanes)') 120 | args = parser.parse_args() 121 | return args 122 | 123 | 124 | if __name__ == '__main__': 125 | args = parse_args() 126 | walkThroughDataset(args.rootDir) 127 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # LaneDatasetInspector 2 | There are some simple python code for walking through the popular lane detection dataset. The usage is pretty simple: 3 | 4 | ``` 5 | python CULaneDbInspector.py --rootDir DATA_SET_DIR 6 | python TuSimpleDbInspector.py --rootDir DATA_SET_DIR 7 | python VPGNetDbInspector.py --rootDir DATA_SET_DIR 8 | python CaltechLanesDbInspector.py --rootDir DATA_SET_DIR 9 | ``` 10 | 11 | ![1](screenshot/1.PNG) 12 | ![2](screenshot/2.PNG) 13 | ![3](screenshot/3.PNG) 14 | ![3](screenshot/4.PNG) 15 | 16 | PS: BDD100kDbInspector.py is under construction. 17 | 18 | Following python lib should be installed: 19 | 20 | scipy 21 | 22 | numpy 23 | 24 | cv2 25 | 26 | tqdm 27 | 28 | # Contribution from other GitHub users: 29 | 30 | The implementation of visualizer for caltech-lanes (CaltechLanesDbInspector.py) is done by github user [sandeepnmenon](https://github.com/sandeepnmenon). 31 | -------------------------------------------------------------------------------- /TuSimpleDbInspector.py: -------------------------------------------------------------------------------- 1 | import scipy.io 2 | import numpy as np 3 | import cv2 4 | import os 5 | from tqdm import tqdm 6 | import json 7 | import argparse 8 | 9 | 10 | def getRandomColour(): 11 | return tuple(np.random.randint(0, 255, dtype=np.uint8, size=3)) 12 | 13 | 14 | def walkThroughDataset(dataSetDir): 15 | TuSimpleRootDir = dataSetDir 16 | 17 | trainJsonFilePath = os.path.join( 18 | TuSimpleRootDir, r'train_set\label_data_0531.json') 19 | 20 | dataList = [] 21 | with open(trainJsonFilePath, 'r') as f: 22 | for line in f: 23 | data = json.loads(line) 24 | dataList.append(data) 25 | 26 | colorMapMat = np.zeros((6, 3), dtype=np.uint8) 27 | 28 | for i in range(0, 6): 29 | colorMapMat[i] = np.random.randint(0, 255, dtype=np.uint8, size=3) 30 | 31 | for dataEntry in tqdm(dataList): 32 | 33 | clipDir = os.path.dirname(dataEntry['raw_file']) 34 | twentyFrames = [os.path.join(clipDir, '%d.jpg' % x) 35 | for x in range(1, 21)] 36 | for frame in twentyFrames: 37 | img_bgr = cv2.imread(os.path.join( 38 | TuSimpleRootDir, 'train_set', frame)) 39 | segImage = np.zeros_like(img_bgr) 40 | 41 | for laneIndex, laneXPoints in enumerate(dataEntry['lanes'], 0): 42 | laneLabelColor = colorMapMat[laneIndex] 43 | 44 | curveVertices = list(filter(lambda xyPair: xyPair[0] > 0, zip( 45 | laneXPoints, dataEntry['h_samples']))) 46 | 47 | for vertex1, vertex2 in zip(curveVertices[:-1], curveVertices[1:]): 48 | color = getRandomColour() 49 | cv2.line(segImage, tuple(vertex1), 50 | tuple(vertex2), (int(colorMapMat[5][0]), int(colorMapMat[5][1]), int(colorMapMat[5][2])), 2) 51 | 52 | for node in curveVertices: 53 | cv2.circle(segImage, tuple(node), 5, (int(laneLabelColor[0]), int( 54 | laneLabelColor[1]), int(laneLabelColor[2])), -1) 55 | 56 | res = cv2.addWeighted(img_bgr, 1, segImage, 0.7, 0.4) 57 | cv2.imshow('TuSimple Dataset Quick Inspector', res) 58 | k = cv2.waitKey(1) & 0xff 59 | if k == 27: 60 | break 61 | cv2.destroyWindow('TuSimple Dataset Quick Inspector') 62 | 63 | 64 | def parse_args(): 65 | parser = argparse.ArgumentParser( 66 | description='TuSimple Dataset Quick Inspector') 67 | parser.add_argument('--rootDir', type=str, default=r'E:\tusimple', 68 | help='root directory (default: E:\\tusimple)') 69 | args = parser.parse_args() 70 | return args 71 | 72 | 73 | if __name__ == '__main__': 74 | args = parse_args() 75 | walkThroughDataset(args.rootDir) 76 | -------------------------------------------------------------------------------- /VPGNetDbInspector.py: -------------------------------------------------------------------------------- 1 | import scipy.io 2 | import numpy as np 3 | import cv2 4 | import os 5 | from tqdm import tqdm 6 | import argparse 7 | 8 | 9 | def walkThroughDataset(dataSetDir): 10 | markClass = [ 11 | ### Lane and road markings (4th channel) ### 12 | 'background', 13 | 'lane_solid_white', 14 | 'lane_broken_white', 15 | 'lane_double_white', 16 | 'lane_solid_yellow', 17 | 'lane_broken_yellow', 18 | 'lane_double_yellow', 19 | 'lane_broken_blue', 20 | 'lane_slow', 21 | 'stop_line', 22 | 'arrow_left', 23 | 'arrow_right', 24 | 'arrow_go_straight', 25 | 'arrow_u_turn', 26 | 'speed_bump', 27 | 'crossWalk', 28 | 'safety_zone', 29 | 'other_road_markings'] 30 | 31 | vpClass = [ 32 | ### Vanishing Points (5th channel) ### 33 | 'background', 34 | 'easy', 35 | 'hard', 36 | ] 37 | 38 | # Get the list of all files in directory tree at given path 39 | listOfFiles = list() 40 | for (dirpath, dirnames, filenames) in os.walk(dataSetDir): 41 | listOfFiles += [os.path.join(dirpath, file) for file in filenames] 42 | 43 | colorMapMat = np.zeros((18, 3), dtype=np.uint8) 44 | laneColor = np.array([0, 255, 0], dtype=np.uint8) 45 | vpColorMapMat = np.array([[0, 255, 0], [0, 0, 255]], dtype=np.uint8) 46 | for i in range(0, len(markClass)): 47 | if i != 0: 48 | colorMapMat[i] = np.random.randint(0, 255, dtype=np.uint8, size=3) 49 | 50 | for imageFile in tqdm(listOfFiles): 51 | data = scipy.io.loadmat(imageFile) 52 | rgb_seg_vp = data['rgb_seg_vp'] 53 | rgb = rgb_seg_vp[:, :, 0: 3] 54 | seg = rgb_seg_vp[:, :, 3] 55 | vp = rgb_seg_vp[:, :, 4] 56 | img_bgr = rgb[:, :, :: -1] 57 | segImage = colorMapMat[seg] 58 | x = np.nonzero(vp)[1] 59 | y = np.nonzero(vp)[0] 60 | if x.size > 0 and y.size > 0: 61 | vpPoint = (x[0], y[0]) 62 | vpLevel = vp[(y, x)] 63 | else: 64 | vpPoint = (-1, -1) 65 | 66 | if vpPoint[0] > 1 and vpPoint[1] > 1: 67 | c = vpColorMapMat[vpLevel-1][0].tolist() 68 | cv2.circle(segImage, vpPoint, 15, (c[0], c[1], 69 | c[2]), thickness=-1, lineType=8) 70 | 71 | res = cv2.addWeighted(img_bgr, 1, segImage, 0.7, 0) 72 | cv2.imshow('VPGNet Dataset Quick Inspector', res) 73 | k = cv2.waitKey(1) & 0xff 74 | if k == 27: 75 | break 76 | cv2.destroyWindow('VPGNet Dataset Quick Inspector') 77 | 78 | 79 | def parse_args(): 80 | parser = argparse.ArgumentParser( 81 | description='VPGNet Dataset Quick Inspector') 82 | parser.add_argument('--rootDir', type=str, default=r'D:\VPGNet-DB-5ch', 83 | help='root directory (default: D:\\VPGNet-DB-5ch)') 84 | args = parser.parse_args() 85 | return args 86 | 87 | 88 | if __name__ == '__main__': 89 | args = parse_args() 90 | walkThroughDataset(args.rootDir) 91 | -------------------------------------------------------------------------------- /example.json: -------------------------------------------------------------------------------- 1 | { 2 | "name": "010b0600-ae1f7be5.jpg", 3 | "attributes": { 4 | "weather": "snowy", 5 | "scene": "undefined", 6 | "timeofday": "daytime" 7 | }, 8 | "timestamp": 10000, 9 | "labels": [ 10 | { 11 | "category": "lane", 12 | "attributes": { 13 | "laneDirection": "parallel", 14 | "laneStyle": "solid", 15 | "laneType": "road curb" 16 | }, 17 | "manualShape": true, 18 | "manualAttributes": true, 19 | "poly2d": [ 20 | { 21 | "vertices": [ 22 | [ 23 | 1137.292164, 24 | 455.332694 25 | ], 26 | [ 27 | 0, 28 | 414.789372 29 | ] 30 | ], 31 | "types": "LL", 32 | "closed": false 33 | } 34 | ], 35 | "id": 10674 36 | }, 37 | { 38 | "category": "lane", 39 | "attributes": { 40 | "laneDirection": "parallel", 41 | "laneStyle": "solid", 42 | "laneType": "road curb" 43 | }, 44 | "manualShape": true, 45 | "manualAttributes": true, 46 | "poly2d": [ 47 | { 48 | "vertices": [ 49 | [ 50 | 1025.018348, 51 | 544.735917 52 | ], 53 | [ 54 | 1014.622624, 55 | 559.28993 56 | ], 57 | [ 58 | 1013.583052, 59 | 568.646082 60 | ], 61 | [ 62 | 1028.137066, 63 | 581.120951 64 | ] 65 | ], 66 | "types": "CCCL", 67 | "closed": false 68 | } 69 | ], 70 | "id": 10675 71 | }, 72 | { 73 | "category": "lane", 74 | "attributes": { 75 | "laneDirection": "vertical", 76 | "laneStyle": "solid", 77 | "laneType": "single white" 78 | }, 79 | "manualShape": true, 80 | "manualAttributes": true, 81 | "poly2d": [ 82 | { 83 | "vertices": [ 84 | [ 85 | 191.281315, 86 | 614.387266 87 | ], 88 | [ 89 | 451.174406, 90 | 607.110259 91 | ], 92 | [ 93 | 715.225785, 94 | 586.318812 95 | ], 96 | [ 97 | 969.921013, 98 | 592.556245 99 | ] 100 | ], 101 | "types": "CCCL", 102 | "closed": false 103 | } 104 | ], 105 | "id": 10676 106 | }, 107 | { 108 | "category": "lane", 109 | "attributes": { 110 | "laneDirection": "vertical", 111 | "laneStyle": "solid", 112 | "laneType": "single white" 113 | }, 114 | "manualShape": true, 115 | "manualAttributes": true, 116 | "poly2d": [ 117 | { 118 | "vertices": [ 119 | [ 120 | 942.892132, 121 | 602.95197 122 | ], 123 | [ 124 | 751.610817, 125 | 605.031114 126 | ], 127 | [ 128 | 589.091058, 129 | 610.861815 130 | ], 131 | [ 132 | 405.433221, 133 | 622.703845 134 | ] 135 | ], 136 | "types": "CCCL", 137 | "closed": false 138 | } 139 | ], 140 | "id": 10677 141 | }, 142 | { 143 | "category": "lane", 144 | "attributes": { 145 | "laneDirection": "parallel", 146 | "laneStyle": "solid", 147 | "laneType": "single white" 148 | }, 149 | "manualShape": true, 150 | "manualAttributes": true, 151 | "poly2d": [ 152 | { 153 | "vertices": [ 154 | [ 155 | 0, 156 | 463.649273 157 | ], 158 | [ 159 | 471.272846, 160 | 466.150316 161 | ] 162 | ], 163 | "types": "LL", 164 | 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