前言
数据集使用之前需要对标注文件进行解析，故此记录。
代码实现
1. APA数据集解析
# 20240612: parse jsonfile to labeled image.
import argparse
import json
import os
import os.path as osp
import cv2 as cv
import numpy as np
jsonfilename = "freespace_3Dbox_APA.json"
imagepath = "image_APA"
polygon = {'Chock block', 'Convex Mirror', 'Crosswalk', 'Curb', 'Dashed line', 'Directional arrow', 'Fire Extinguisher', 'Fire hydrant', 'Floating edge', 'Free space', 'Motor vehicle', 'Non-motorized vehicle', 'Other Obstacles', 'Parking line', 'Parking number', 'Pedestrian', 'Pylon', 'Solid line', 'Speed bump', 'Steel pipe', 'Traffic cone', 'Wheel lock', 'background'}
bbox2d = {'Chock block 2D', 'Convex Mirror 2D', 'Fire Extinguisher 2D', 'Fire hydrant 2D', 'Motor vehicle 2D', 'Non-motorized vehicle 2D', 'Other Obstacles 2D', 'Pedestrian 2D', 'Steel pipe 2D', 'Traffic cone 2D', 'Tyre', 'Wheel lock 2D'}
bbox3d = {'Motor vehicle 3D'}
colors = {# BGR
'Chock block' : (251, 56, 56),
'Chock block 2D' : (247,136, 58),
'Convex Mirror' : ( 48,132, 48),
'Convex Mirror 2D' : ( 56,148,228),
'Crosswalk' : (243, 27,243),
'Curb' : ( 22,220,220),
'Dashed line' : (255,192,203),
'Directional arrow' : ( 0, 0,128),
'Fire Extinguisher' : ( 0,255, 0),
'Fire Extinguisher 2D' : (255,255, 0),
'Fire hydrant' : (247, 72,249),
'Fire hydrant 2D' : (103,198,209),
'Floating edge' : ( 88,139, 70),
'Free space' : (172,227, 65),
'Motor vehicle' : ( 76,213,174),
'Motor vehicle 2D' : ( 50, 83, 83),
'Motor vehicle 3D' : (159,156, 73),
'Non-motorized vehicle' : ( 10,239,189),
'Non-motorized vehicle 2D' : (183, 68,121),
'Other Obstacles' : (193, 52,240),
'Other Obstacles 2D' : ( 53,223, 54),
'Parking line' : ( 4,178,106),
'Parking number' : (104, 28,162),
'Pedestrian' : ( 8,197,115),
'Pedestrian 2D' : ( 61,205,183),
'Pylon' : ( 5,129,227),
'Solid line' : (239, 18, 44),
'Speed bump' : (107,251,229),
'Steel pipe' : (101, 49,243),
'Steel pipe 2D' : ( 26,130, 36),
'Traffic cone' : ( 54,175,167),
'Traffic cone 2D' : ( 39,219,102),
'Tyre' : (142, 16,198),
'Wheel lock' : (174,143,124),
'Wheel lock 2D' : (229,208,251),
'background' : ( 0, 0, 0)
}
def parse_data_polygon(tagtype, datastr):
# print('datastr: ', datastr)
if isinstance(datastr, str):
data = eval(datastr)
assert isinstance(data, list)
# print('data: ', data)
pts = []
# list - list - float
# print('data len: ', len(data))
# print('data[-1] len: ', len(data[-1]))
# print('data: ', data)
# print('data type: ', type(data))
if len(data)==4 and isinstance(data[0], float):
print("BBOX2D ANNOtation ERRORRRRRR, error tagtype is: ", tagtype)
else:
for onedata in data:
if len(onedata) == 1:
break
pts.append(list(map(int, onedata[1:]))) # [x,y]
return pts
def parse_data_bbox2d(datastr):
# print('datastr: ', datastr)
if isinstance(datastr, str):
data = eval(datastr)
assert isinstance(data, list)
# print('data: ', data)
box = list(map(int, data)) # [x, y, w, h]
return box
def parse_json(path, dataset):
i = 0
for imgdata in dataset:
# one image anno data.
result = imgdata['result']
i = i + 1
# if not result or (i>1):
if not result:
# break
continue
file_obj = imgdata['file_obj']
imgname = osp.split(file_obj)[-1]
imgdata= cv.imread(osp.join(path, imagepath, imgname))
# print("file_obj: ", file_obj)
print("imgname: ", imgname)
sgname = imgname.replace('.png', '_sg.png')
odname = imgname.replace('.png', '_od.png')
sgfile = osp.join(path, 'sg', sgname)
odfile = osp.join(path, 'od', odname)
# sgimg = cv.imread(osp.join(path, imagepath, imgname))
sgimg = np.zeros_like(imgdata)
odimg = cv.imread(osp.join(path, imagepath, imgname))
mask = np.zeros_like(imgdata)
maskname = imgname.replace('.png', '_mask.png')
maskfile = osp.join(path, 'mask', maskname)
# print("result type: ", type(result))
# print("result len: ", len(result))
# print("result[0]: ", result[0])
# draw Free space
for res in result:
tagtype = res['tagtype']
# print("tagtype--------------------------------------------------------------: \n\n", tagtype)
if tagtype != 'Free space':
continue
# Free space
points= parse_data_polygon(tagtype, res['data'])
# plot polygon in seg image
points_np = np.array(points, np.int32)
if points:
cv.fillPoly(mask, [points_np], colors[tagtype])
for res in result:
tagtype = res['tagtype']
if tagtype == 'Free space':
continue
if tagtype == 'Motor vehicle 3D':
# print("tagtype--------------------------------------------------------------: ", tagtype)
points= parse_data_polygon(tagtype, res['data'])
# plot bbox3d in od image
points_np = np.array(points, np.int32)
if points:
cv.polylines(odimg, [points_np], True, colors[tagtype])
elif tagtype == 'Floating edge':
# print("tagtype--------------------------------------------------------------: ", tagtype)
points= parse_data_polygon(tagtype, res['data'])
# plot floating edge in seg image
points_np = np.array(points, np.int32)
if points:
cv.polylines(mask, [points_np], False, colors[tagtype], 5)
elif tagtype in polygon:
# print("tagtype--------------------------------------------------------------: ", tagtype)
points= parse_data_polygon(tagtype, res['data'])
# plot polygon in seg image
points_np = np.array(points, np.int32)
# print('points_np: ', points_np)
if points:
cv.fillPoly(mask, [points_np], colors[tagtype])
elif tagtype in bbox2d:
x, y, w, h = parse_data_bbox2d(res['data']) # Rect(xywh)
# rect = cv.Rect(x, y, w, h)
# plot bbox2d in od image
cv.rectangle(odimg, (x,y), (x+w, y+h), colors[tagtype]) # (leftup, rightdown)
# cv.rectangle(odimg, rect, color) #
cv.addWeighted(imgdata, 0.7, mask, 0.3, 0, sgimg)
# save seg and od image.
cv.imwrite(sgfile, sgimg)
cv.imwrite(odfile, odimg)
cv.imwrite(maskfile, mask)
def json2annoimg(path):
jsonfile=os.path.join(path, jsonfilename)
dataset=json.load(open(jsonfile, 'r'))
num=len(dataset)
parse_json(path, dataset)
if __name__ == "__main__":
path = os.path.dirname(os.path.realpath(__file__))
json2annoimg(path)
View Code
数据集特点：标注文件中含有语义分割、目标检测的类别，语义分割一般使用polygon标注，目标检测使用box标注，悬空边使用brokenline标注，解析标注文件的目的是将标注的信息绘制到图像。
参考
完
标签：python,tagtype,2D,print,points,np,解析,data,cv From： https://www.cnblogs.com/happyamyhope/p/18252570