├── .gitignore
├── Kitti
├── 2011_09_26
│ ├── 2011_09_26_drive_0002_sync
│ │ └── tracklet_labels.xml
│ ├── 2011_09_26_drive_0005_sync
│ │ └── tracklet_labels.xml
│ ├── 2011_09_26_drive_0009_sync
│ │ └── tracklet_labels.xml
│ ├── 2011_09_26_drive_0011_sync
│ │ └── tracklet_labels.xml
│ ├── 2011_09_26_drive_0013_sync
│ │ └── tracklet_labels.xml
│ ├── 2011_09_26_drive_0014_sync
│ │ └── tracklet_labels.xml
│ ├── 2011_09_26_drive_0017_sync
│ │ └── tracklet_labels.xml
│ ├── 2011_09_26_drive_0018_sync
│ │ └── tracklet_labels.xml
│ ├── 2011_09_26_drive_0019_sync
│ │ └── tracklet_labels.xml
│ ├── 2011_09_26_drive_0020_sync
│ │ └── tracklet_labels.xml
│ ├── 2011_09_26_drive_0022_sync
│ │ └── tracklet_labels.xml
│ ├── 2011_09_26_drive_0023_sync
│ │ └── tracklet_labels.xml
│ ├── 2011_09_26_drive_0027_sync
│ │ └── tracklet_labels.xml
│ ├── 2011_09_26_drive_0028_sync
│ │ └── tracklet_labels.xml
│ ├── 2011_09_26_drive_0029_sync
│ │ └── tracklet_labels.xml
│ ├── 2011_09_26_drive_0032_sync
│ │ └── tracklet_labels.xml
│ ├── 2011_09_26_drive_0035_sync
│ │ └── tracklet_labels.xml
│ ├── 2011_09_26_drive_0036_sync
│ │ └── tracklet_labels.xml
│ ├── 2011_09_26_drive_0039_sync
│ │ └── tracklet_labels.xml
│ ├── 2011_09_26_drive_0046_sync
│ │ └── tracklet_labels.xml
│ ├── 2011_09_26_drive_0048_sync
│ │ └── tracklet_labels.xml
│ ├── 2011_09_26_drive_0051_sync
│ │ └── tracklet_labels.xml
│ ├── 2011_09_26_drive_0052_sync
│ │ └── tracklet_labels.xml
│ ├── 2011_09_26_drive_0056_sync
│ │ └── tracklet_labels.xml
│ ├── 2011_09_26_drive_0057_sync
│ │ └── tracklet_labels.xml
│ ├── 2011_09_26_drive_0059_sync
│ │ └── tracklet_labels.xml
│ ├── 2011_09_26_drive_0060_sync
│ │ └── tracklet_labels.xml
│ ├── 2011_09_26_drive_0061_sync
│ │ └── tracklet_labels.xml
│ ├── 2011_09_26_drive_0064_sync
│ │ └── tracklet_labels.xml
│ ├── 2011_09_26_drive_0070_sync
│ │ └── tracklet_labels.xml
│ ├── 2011_09_26_drive_0079_sync
│ │ └── tracklet_labels.xml
│ ├── 2011_09_26_drive_0084_sync
│ │ └── tracklet_labels.xml
│ ├── 2011_09_26_drive_0086_sync
│ │ └── tracklet_labels.xml
│ ├── 2011_09_26_drive_0087_sync
│ │ └── tracklet_labels.xml
│ └── 2011_09_26_drive_0091_sync
│ │ └── tracklet_labels.xml
├── npydata
│ └── 2011_09_26_drive_0091_sync_0000000000.npy
└── npylabel
│ └── 2011_09_26_drive_0091_sync_0000000000.npy
├── README.md
├── Test_data
├── .ipynb_checkpoints
│ └── Untitled-checkpoint.ipynb
├── Kitti
│ └── 2011_09_26
│ │ └── check.txt
├── gene_data.py
├── generator.py
├── parseTrackletXML.py
├── parseTrackletXML.pyc
├── path_gen.py
└── path_generator.py
└── path_generate_for_train_and_valid.ipynb
/.gitignore:
--------------------------------------------------------------------------------
1 | *.npy
2 | *.txt
3 | *.bin
4 | *.png
5 | *.jpg
6 | *.xml
--------------------------------------------------------------------------------
/Kitti/2011_09_26/2011_09_26_drive_0002_sync/tracklet_labels.xml:
--------------------------------------------------------------------------------
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/Kitti/npydata/2011_09_26_drive_0091_sync_0000000000.npy:
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https://raw.githubusercontent.com/ywangeq/instance_dataset_gen/5fc2edb2e82ac7cbaf978a60ddcd2f8fab290cb2/Kitti/npydata/2011_09_26_drive_0091_sync_0000000000.npy
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/Kitti/npylabel/2011_09_26_drive_0091_sync_0000000000.npy:
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https://raw.githubusercontent.com/ywangeq/instance_dataset_gen/5fc2edb2e82ac7cbaf978a60ddcd2f8fab290cb2/Kitti/npylabel/2011_09_26_drive_0091_sync_0000000000.npy
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/README.md:
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1 | ## A python version is under the Test_data file
2 |
3 | ### Step1
4 | download the raw data (2011_09_26_drive_0002_sync....) in the Test_data/2011_09_26
5 |
6 | ## Step2
7 | run 'python generator.py' to get the each label
8 | ## Step3
9 | run ' python path_genertor.py' to get the test.txt which contains the prepared data
10 | ## Step4
11 | run 'python gene_data.py' the Kitti/npydata will store all the dataset,
12 | I concat the data[:,:,:4] and the mask [:,:,5] in the one file. So each file in the Killt/npydata will have the shape 64 * 512 *6
13 |
14 | ### Attention
15 | When you try to use the Step4, you need to change the index in the line 400:
16 | id_name = para.name[46:67] # 2011_09_26_drive_000
17 | id_root = para.name[79:89] # 00000001
18 | To get the right name depend on you root
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/Test_data/Kitti/2011_09_26/check.txt:
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1 | here
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/Test_data/gene_data.py:
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1 | import sys
2 | import os
3 | import numpy as np
4 | import cv2
5 | import glob
6 | import math
7 | import matplotlib.pyplot as plt
8 | import scipy.misc
9 | def filter_camera_angle(places):
10 | """Filter camera angles for KiTTI Datasets"""
11 | #bool_in = np.logical_and((places[:, 1] < places[:, 0] - 0.27), (-places[:, 1] < places[:, 0] - 0.27))
12 | bool_in = np.logical_and((places[:, 1] < places[:, 0]), (-places[:, 1] < places[:, 0]))
13 | return places[bool_in]
14 | def load_pc_from_bin(path):
15 | obj = np.fromfile(path,dtype=np.float32).reshape(-1,4)
16 | return obj
17 | def read_mask(SIZE,label,data,location,rotate,method=''):
18 | """
19 | [x-w/2.,y-l/2.,z],
20 | [x+w/2.,y-l/2.,z],
21 | [x-w/2.,y+l/2.,z],
22 | [x-w/2.,y-l/2.,z+h],
23 | [x-w/2.,y+l/2.,z+h],
24 | [x+w/2.,y+l/2.,z],
25 | [x+w/2.,y-l/2.,z+h],
26 | [x+w/2.,y+l/2.,z+h],
27 | :param points:
28 | :param data: the pcd point cloud data
29 | :return :
30 | data : x y z intensity(now we don't have) distance mask
31 | """
32 |
33 | assert label.shape[0]== location.shape[0],'the shape are not same'
34 | bboxs = []
35 | Alignment_data=[]
36 | mask=[]
37 | num_count = 0
38 | for sz,rotation,place,index in zip(SIZE,rotate,location,label):
39 | h,w,l = sz
40 | x,y,z,=place
41 |
42 | bbox = np.array([
43 | [ w / 2., -l / 2., 0],
44 | [ w / 2., -l / 2., 0],
45 | [ -w / 2., +l / 2., 0],
46 | [ -w / 2., -l / 2., +h],
47 | [ -w / 2., +l / 2., +h],
48 | [ w / 2., +l / 2., 0],
49 | [ w / 2., -l / 2., h],
50 | [ w / 2., +l / 2., h],
51 | ])
52 | Data = data.copy()
53 |
54 | bboxs.append(bbox)
55 | alig_data = Data[:, 0:3]
56 | alig_data -= np.array([x, y, z])
57 | rotation=np.pi/2 -rotation
58 |
59 | rotate_matrix = np.array([
60 | [np.cos(-rotation), -np.sin(-rotation), 0],
61 | [np.sin(-rotation), np.cos(-rotation), 0],
62 | [0, 0, 1]
63 | ])
64 | alig_data = np.dot(alig_data, rotate_matrix)
65 | # please check the data type and rewrite the shape function
66 | append_zero = np.zeros((alig_data.shape[0], 3))
67 | alig_data =np.hstack((alig_data, append_zero))
68 |
69 | x_min = -w/2.
70 | x_max = w/2.
71 | y_min = -l/2.
72 | y_max = l/2.
73 | z_min = 0
74 | z_max = h
75 | num_count+=1
76 | for xyz in alig_data:
77 | #print 'compare',x_min,xyz[0],x_max
78 | # data is from the pcd with may dont have the intensity
79 | xyz[3] = xyz[2]/(np.sqrt((xyz[0]*xyz[0]+xyz[1]*xyz[1]+xyz[2]*xyz[2])))
80 | xyz[4] = np.sqrt((xyz[0]*xyz[0]+xyz[1]*xyz[1]+xyz[2]*xyz[2]))
81 |
82 | if (x_min20 :
96 | a=np.append(index,id_1)
97 | #print('a',a)
98 | mask.append(a)
99 | if index==1:
100 | id_2 = np.argwhere(alig_data[:,5]==2)
101 | if len(id_2)>12 :
102 | b=np.append(index,id_2)
103 | mask.append(b)
104 | if index==2:
105 |
106 | id_3 = np.argwhere(alig_data[:,5]==3)
107 | if len(id_3)>12 :
108 | c=np.append(index,id_3)
109 | mask.append(c)
110 | MASK = np.array(mask)
111 | return MASK
112 |
113 |
114 | def point_mask(mask_index,points):
115 | append_zero = np.zeros((points.shape[0], 2))
116 | points = np.hstack((points, append_zero))
117 | # n_dim = mask_index.shape[0]
118 | # append_zero_0 = np.zeros((points.shape[0], 1))
119 | # points = np.hstack((points, append_zero_0))
120 |
121 | # for xyz in points:
122 |
123 | # xyz[4] = np.sqrt((xyz[0] * xyz[0] + xyz[1] * xyz[1] + xyz[2] * xyz[2]))
124 | # append_zero = np.zeros((points.shape[0], n_dim))
125 | # points = np.hstack((points, append_zero))
126 | # #print(n_dim)
127 | # for dim in range(n_dim):
128 | # #print('index',dim)
129 | # ins_mask = mask_index[dim]
130 | # label = ins_mask[0]
131 | # #print('class_id',label)
132 | # if label == 0:
133 | # for id in ins_mask[1:]:
134 | # points[id][5+dim]= label + 1
135 | # if label == 1:
136 | # for id in ins_mask[1:]:
137 | # points[id][5+dim] = label + 1
138 | # if label == 2:
139 | # for id in ins_mask[1:]:
140 | # points[id][5+dim] = label + 1
141 | for xyz in points:
142 |
143 | xyz[4] = np.sqrt((xyz[0] * xyz[0] + xyz[1] * xyz[1] + xyz[2] * xyz[2]))
144 |
145 |
146 | for index in mask_index:
147 | label = index[0]
148 | # print label
149 | if label == 0:
150 | for id in index[1:]:
151 | points[id][5] = label + 1
152 | if label == 1:
153 | for id in index[1:]:
154 | points[id][5] = label + 1
155 | if label == 2:
156 | for id in index[1:]:
157 | points[id][5] = label + 1
158 |
159 | return points
160 |
161 | def get_dir(root_path):
162 | bounding_box =[]
163 | for line in open(root_path):
164 | line =line.strip('\n')
165 | data_root,label_root = line.split(' ')
166 | #para = open(label_root,'r')
167 | with open(label_root,'r') as para:
168 | labels = para.read().strip('\n').split('\n')
169 | print (labels)
170 | for label in labels:
171 | label =label.split(' ')
172 | id = label[0]
173 | if id == ('Car'):
174 | label[0] = 0
175 | bounding_box.append(label[0:7])
176 | if id ==('Pedestrian'):
177 | label[0]=1
178 | bounding_box.append(label[0:7])
179 | if id ==('Cyclist'):
180 | label[0]=2
181 | bounding_box.append(label[0:7])
182 |
183 | print (bounding_box)
184 | if bounding_box:
185 | data = np.array(bounding_box, dtype=np.float32)
186 | index = np.array(data[:,0], dtype=np.int8)
187 | return data[:, 4:7], data[:, 1:4], index
188 |
189 |
190 | def read_calib_file(calib_path):
191 | data={}
192 | with open(calib_path,'r') as f:
193 | for line in f.readline():
194 | if not line or line == '\n':
195 | continue
196 | key,value = line.split(':',1)
197 | try:
198 | data[key] = np.array([float(x) for x in value.split()])
199 | except ValueError:
200 | return data
201 | def get_boxcorners(places,size,rotate):
202 | """assume the point x y z is the bottom center"""
203 | corners = []
204 | #print 'test',places,size
205 | for place, sz, rotation in zip(places, size, rotate):
206 | x, y, z = place
207 | h, w, l = sz
208 | #r_z = rotation
209 | #print x,y,z
210 | #print h,w,l
211 |
212 | corner = np.array([
213 | [x - l / 2., y - w / 2., z],
214 | [x + l / 2., y - w / 2., z],
215 | [x - l / 2., y + w / 2., z],
216 | [x - l / 2., y - w / 2., z + h],
217 | [x - l / 2., y + w / 2., z + h],
218 | [x + l / 2., y + w / 2., z],
219 | [x + l / 2., y - w / 2., z + h],
220 | [x + l / 2., y + w / 2., z + h],
221 | ])
222 | corner -= np.array([x, y, z])
223 |
224 | rotation=np.pi/2 -rotation
225 |
226 | rotate_matrix = np.array([
227 | [np.cos(rotation), -np.sin(rotation), 0.],
228 | [np.sin(rotation), np.cos(rotation), 0.],
229 | [0., 0., 1.]
230 | ])
231 | a = np.dot(corner, rotate_matrix.transpose())
232 |
233 | a += np.array([x, y, z])
234 | corners.append(a)
235 | return np.array(corners)
236 | def get_boxcorners(places,size,rotate):
237 | """assume the point x y z is the bottom center"""
238 | corners = []
239 | #print 'test',places,size
240 | for place, sz, rotation in zip(places, size, rotate):
241 | x, y, z = place
242 | h, w, l = sz
243 | #r_z = rotation
244 | #print x,y,z
245 | #print h,w,l
246 |
247 | corner = np.array([
248 | [x - l / 2., y - w / 2., z],
249 | [x + l / 2., y - w / 2., z],
250 | [x - l / 2., y + w / 2., z],
251 | [x - l / 2., y - w / 2., z + h],
252 | [x - l / 2., y + w / 2., z + h],
253 | [x + l / 2., y + w / 2., z],
254 | [x + l / 2., y - w / 2., z + h],
255 | [x + l / 2., y + w / 2., z + h],
256 | ])
257 | corner -= np.array([x, y, z])
258 |
259 | rotation=np.pi/2 -rotation
260 |
261 | rotate_matrix = np.array([
262 | [np.cos(rotation), -np.sin(rotation), 0.],
263 | [np.sin(rotation), np.cos(rotation), 0.],
264 | [0., 0., 1.]
265 | ])
266 | a = np.dot(corner, rotate_matrix.transpose())
267 |
268 | a += np.array([x, y, z])
269 | corners.append(a)
270 | return np.array(corners)
271 | def creat_label_64_512(points,num_height,num_width,v_fov=(-45,45)):
272 | #assert points.shape[1] == 9,'x,y,z,itensity,distance,mask'
273 | #x=points[:,0]
274 | #y=points[:,1]
275 | #z=points[:,2]
276 | #dis =points[:,4]
277 |
278 | append_zero = np.zeros((points.shape[0], 2))
279 | alig_data = np.hstack((points, append_zero))
280 | len_=alig_data.shape[1]
281 | for p in alig_data:
282 |
283 | r = np.sqrt(p[0] * p[0] + p[1] * p[1]) # p[0] x
284 | p[6] = np.arcsin(p[2] / p[4]) # p[1] y
285 | p[7] = np.arcsin(p[1] / r) # fie
286 | # p[2] z p[4] dis
287 |
288 | theta =alig_data[:,6]
289 | fie =alig_data[:,7]
290 | t_a=theta.max()
291 | t_i= theta.min()
292 | f_a=fie.max()
293 | f_i=fie.min()
294 | t_range = (t_a - t_i)
295 | f_range = (f_a - f_i)
296 |
297 | resolution_h = t_range / (num_height-1)
298 | resolution_w = (f_range)/ (num_width-1)
299 |
300 | x_min = (f_i)/resolution_w
301 | y_min = (t_i)/resolution_h
302 | #print 'shift',y_min,x_min
303 | #print 'angle',theta,fie
304 | #print 'range',t_range,f_range
305 | #print'resl', resolution_h,resolution_w
306 |
307 |
308 | append_64 = np.zeros((num_height,num_width,6))
309 | # len(mask_isntance) = len_ -2 -5(x,y,z,itensity,dis)
310 | for p in alig_data:
311 | #print 'check',t_i,p[7],t_a
312 | index_h=(p[6]/resolution_h-y_min)
313 | index_w=(p[7]/resolution_w-x_min)
314 | shitf_h =-round(index_h-num_height)
315 | shitf_w = -round(index_w-num_width)
316 | append_64[int(shitf_h) - 1, int(shitf_w) - 1, 0] = p[0] # x
317 | append_64[int(shitf_h) - 1, int(shitf_w) - 1, 1] = p[1] # y
318 | append_64[int(shitf_h) - 1, int(shitf_w) - 1, 2] = p[2] # z
319 | append_64[int(shitf_h) - 1, int(shitf_w) - 1, 3] = p[3] # in
320 | append_64[int(shitf_h) - 1, int(shitf_w) - 1, 4] = p[4] # dis
321 | append_64[int(shitf_h) - 1, int(shitf_w) - 1, 5] = p[5] # mask
322 | return append_64
323 |
324 | def check_filter_points(points):
325 | points=filter_camera_angle(points)
326 | return points
327 | def process(root_path,calib_path,dataformat='bin',method='',type=''):
328 | p = []
329 | pc = None
330 | bounding_boxes = None
331 | places = None
332 | size = None
333 | for line in open(root_path):
334 | line = line.strip('\n')
335 | raw_data,label_root = line.split(' ')
336 |
337 | if dataformat == 'bin':
338 | pc = load_pc_from_bin(raw_data)
339 |
340 | if label_root:
341 | with open(label_root,'r') as para:
342 | # print('root',label_root)
343 | labels = para.read().strip('\n').split('\n')
344 |
345 | bounding_box=[]
346 | for label in labels:
347 | label = label.split(' ')
348 | id = label[0]
349 | if id == ('Car') or id ==('Van'):
350 | label[0] = 0
351 | a = np.append(label[0],label[8:15])
352 | bounding_box.append(a)# need to change followed kitti label format
353 | if id == ('Pedestrian') :
354 | label[0] = 1
355 | print('get')
356 | a = np.append(label[0], label[8:15])
357 | bounding_box.append(a)
358 |
359 | if id =='Cyclist':
360 | label[0] = 2
361 | a = np.append(label[0], label[8:15])
362 | bounding_box.append(a)
363 | if bounding_box:
364 | data = np.array(bounding_box,dtype=np.float32)
365 | index = np.array(data[:, 0], dtype=np.int8)
366 | places = data[:,4:7]
367 |
368 | size = data[:,1:4]
369 | rotate = data[:,7] # depend on kitti
370 | #print(data)
371 | if calib_path:
372 | if type=='kitti_obj':
373 | calib = read_calib_file(calib_path)
374 | pro_j_velo=proj_to_velo(calib)[:,:3]
375 | places = np.dot(places,pro_j_velo.transpose())[:,:3]
376 | #places[:,0] +=0.27
377 | if type =='kitti_raw':
378 | #Calib=read_raw_calib_file(calib_path)
379 | #proj_raw_data = proj_raw(Calib)[:,:3]
380 | #places = np.dot(places, proj_raw_data.transpose())[:, :3]
381 | continue
382 |
383 |
384 | pc = filter_camera_angle(pc)
385 | shape = pc.shape
386 | #corners = get_boxcorners(places,size,rotate)
387 | #print('cor',corners)
388 | Pc_ = pc.copy()
389 | print('size',size)
390 | Mask = read_mask(size,index,Pc_,places,rotate)
391 | print('mask',Mask.shape)
392 |
393 | Mask_point = point_mask(Mask,pc)
394 | #print('mask',Mask_point.shape)
395 |
396 | store_npy =creat_label_64_512(Mask_point, 64, 512, v_fov=(-45, 45))
397 | #print (store_npy.shape) #0 1 2 3 4 5 6 7 ------
398 | #x y z i d ob_1 ob_2 ob_3
399 |
400 | id_name = para.name[46:67]
401 | id_root = para.name[79:89]
402 | print(para.name,id_name,id_root)
403 |
404 | file_name = id_name+'_'+id_root
405 | root = 'Kitti'
406 | raw_root = '/data'
407 | label_root = '/npylabel'
408 | npy ='/npydata'
409 | if os.path.exists(root+npy) is False:
410 | os.makedirs(root+npy)
411 | os.makedirs(root+label_root)
412 | else:
413 | #assert xyz_in_dis.shape[2] == 5
414 | np.save(root+npy+'/'+file_name,store_npy)
415 | #scipy.misc.imsave(root+raw_root+'/'+file_name+'.jpg',xyz_in_dis)
416 | # np.save(root+label_root+'/'+file_name,mask)
417 | root ='test.txt' # txt file with store the object file which you want to make
418 | calib = 'Kitti/2011_09_26/calib_velo_to_cam.txt' # root of cal file
419 | process(root,calib)
--------------------------------------------------------------------------------
/Test_data/generator.py:
--------------------------------------------------------------------------------
1 | import parseTrackletXML as xmlParser
2 | import os
3 | import numpy as np
4 |
5 | root = os.getcwd()
6 |
7 | raw_data = root+'/Kitti/2011_09_26'
8 | def method(dir_path,label_path,way):
9 | data = xmlParser.parseXML(dir_path)
10 | for IT,it in enumerate(data):
11 | h,w,l = it.size
12 | label = it.objectType
13 | for translation, rotation, state, occlusion, truncation, amtOcclusion, amtBorders, absoluteFrameNumber in it:
14 | id = absoluteFrameNumber
15 | print( id)
16 | x,y,z =translation
17 | id= '%03d'% id
18 | print( id)
19 |
20 | path = label_path+'/0000000'+str(id)+'.txt'
21 | #print path
22 | if way=='dir':
23 | if os.path.exists(path):
24 | print ('pass')
25 | else:
26 | os.mknod(path)
27 | else:
28 | file=open(path,'a')
29 | print (label)
30 | file.write(label)
31 | file.write(' ')
32 |
33 | file.write('1')
34 | file.write(' ')
35 |
36 | file.write('2')
37 | file.write(' ')
38 |
39 | file.write('3')
40 | file.write(' ')
41 |
42 | file.write('4')
43 | file.write(' ')
44 |
45 | file.write('5')
46 | file.write(' ')
47 |
48 | file.write('6')
49 | file.write(' ')
50 |
51 | file.write('7')
52 | file.write(' ')
53 |
54 | file.write(str(h))
55 | file.write(' ')
56 | file.write(str(w))
57 | file.write(' ')
58 |
59 | file.write(str(l))
60 | file.write(' ')
61 |
62 | file.write(str(x))
63 | file.write(' ')
64 |
65 | file.write(str(y))
66 | file.write(' ')
67 |
68 | file.write(str(z))
69 | file.write(' ')
70 |
71 | file.write(str(rotation[2]))
72 | file.write('\n')
73 | file.close()
74 | def make_dir(path,way):
75 | for path in os.listdir(path):
76 | if path[-4:] == 'sync':
77 | Data_path = raw_data+'/'+path
78 | #for dir_ in os.listdir(Data_path):
79 | # if dir_ == 'velodyne_points':
80 | #lidar = Data_path +'/'+velodyne_points+'/data'
81 | laber = raw_data+'/'+path+'/label'
82 | xml_path = Data_path +'/tracklet_labels.xml'
83 | print('data',xml_path,laber)
84 |
85 | if os.path.exists(laber):
86 | print('the file has been made')
87 | else:
88 | os.makedirs(laber)
89 | #for bin_lidar in os.listdir(lidar):
90 | method(xml_path,laber,way)
91 | make_dir(raw_data,'a')
--------------------------------------------------------------------------------
/Test_data/parseTrackletXML.py:
--------------------------------------------------------------------------------
1 | """
2 | parse XML files containing tracklet info for kitti data base (raw data section)
3 | (http://cvlibs.net/datasets/kitti/raw_data.php)
4 |
5 | No guarantees that this code is correct, usage is at your own risk!
6 |
7 | created by Christian Herdtweck, Max Planck Institute for Biological Cybernetics
8 | (christian.herdtweck@tuebingen.mpg.de)
9 |
10 | requires numpy!
11 |
12 | example usage:
13 | import parseTrackletXML as xmlParser
14 | kittiDir = '/path/to/kitti/data'
15 | drive = '2011_09_26_drive_0001'
16 | xmlParser.example(kittiDir, drive)
17 | or simply on command line:
18 | python parseTrackletXML.py
19 | """
20 |
21 | # Version History:
22 | # 4/7/12 Christian Herdtweck: seems to work with a few random test xml tracklet files;
23 | # converts file contents to ElementTree and then to list of Tracklet objects;
24 | # Tracklet objects have str and iter functions
25 | # 5/7/12 ch: added constants for state, occlusion, truncation and added consistency checks
26 | # 30/1/14 ch: create example function from example code
27 |
28 | from sys import argv as cmdLineArgs
29 | from xml.etree.ElementTree import ElementTree
30 | import numpy as np
31 | import itertools
32 | from warnings import warn
33 |
34 | STATE_UNSET = 0
35 | STATE_INTERP = 1
36 | STATE_LABELED = 2
37 | stateFromText = {'0':STATE_UNSET, '1':STATE_INTERP, '2':STATE_LABELED}
38 |
39 | OCC_UNSET = 255 # -1 as uint8
40 | OCC_VISIBLE = 0
41 | OCC_PARTLY = 1
42 | OCC_FULLY = 2
43 | occFromText = {'-1':OCC_UNSET, '0':OCC_VISIBLE, '1':OCC_PARTLY, '2':OCC_FULLY}
44 |
45 | TRUNC_UNSET = 255 # -1 as uint8, but in xml files the value '99' is used!
46 | TRUNC_IN_IMAGE = 0
47 | TRUNC_TRUNCATED = 1
48 | TRUNC_OUT_IMAGE = 2
49 | TRUNC_BEHIND_IMAGE = 3
50 | truncFromText = {'99':TRUNC_UNSET, '0':TRUNC_IN_IMAGE, '1':TRUNC_TRUNCATED, \
51 | '2':TRUNC_OUT_IMAGE, '3': TRUNC_BEHIND_IMAGE}
52 |
53 |
54 | class Tracklet(object):
55 | """
56 | representation an annotated object track
57 |
58 | Tracklets are created in function parseXML and can most conveniently used as follows:
59 |
60 | for trackletObj in parseXML(trackletFile):
61 | for translation, rotation, state, occlusion, truncation, amtOcclusion, amtBorders, absoluteFrameNumber in trackletObj:
62 | ... your code here ...
63 | #end: for all frames
64 | #end: for all tracklets
65 |
66 | absoluteFrameNumber is in range [firstFrame, firstFrame+nFrames[
67 | amtOcclusion and amtBorders could be None
68 |
69 | You can of course also directly access the fields objType (string), size (len-3 ndarray), firstFrame/nFrames (int),
70 | trans/rots (nFrames x 3 float ndarrays), states/truncs (len-nFrames uint8 ndarrays), occs (nFrames x 2 uint8 ndarray),
71 | and for some tracklets amtOccs (nFrames x 2 float ndarray) and amtBorders (nFrames x 3 float ndarray). The last two
72 | can be None if the xml file did not include these fields in poses
73 | """
74 |
75 | objectType = None
76 | size = None # len-3 float array: (height, width, length)
77 | firstFrame = None
78 | trans = None # n x 3 float array (x,y,z)
79 | rots = None # n x 3 float array (x,y,z)
80 | states = None # len-n uint8 array of states
81 | occs = None # n x 2 uint8 array (occlusion, occlusion_kf)
82 | truncs = None # len-n uint8 array of truncation
83 | amtOccs = None # None or (n x 2) float array (amt_occlusion, amt_occlusion_kf)
84 | amtBorders = None # None (n x 3) float array (amt_border_l / _r / _kf)
85 | nFrames = None
86 |
87 | def __init__(self):
88 | r""" create Tracklet with no info set """
89 | self.size = np.nan*np.ones(3, dtype=float)
90 |
91 | def __str__(self):
92 | r""" return human-readable string representation of tracklet object
93 |
94 | called implicitly in
95 | print trackletObj
96 | or in
97 | text = str(trackletObj)
98 | """
99 | return '[Tracklet over {0} frames for {1}]'.format(self.nFrames, self.objectType)
100 |
101 | def __iter__(self):
102 | r""" returns an iterator that yields tuple of all the available data for each frame
103 |
104 | called whenever code iterates over a tracklet object, e.g. in
105 | for translation, rotation, state, occlusion, truncation, amtOcclusion, amtBorders, absoluteFrameNumber in trackletObj:
106 | ...do something ...
107 | or
108 | trackDataIter = iter(trackletObj)
109 | """
110 | if self.amtOccs is None:
111 | return itertools.izip(self.trans, self.rots, self.states, self.occs, self.truncs, \
112 | itertools.repeat(None), itertools.repeat(None), xrange(self.firstFrame, self.firstFrame+self.nFrames))
113 | else:
114 | return itertools.izip(self.trans, self.rots, self.states, self.occs, self.truncs, \
115 | self.amtOccs, self.amtBorders, xrange(self.firstFrame, self.firstFrame+self.nFrames))
116 | #end: class Tracklet
117 |
118 |
119 | def parseXML(trackletFile):
120 | r""" parse tracklet xml file and convert results to list of Tracklet objects
121 |
122 | :param trackletFile: name of a tracklet xml file
123 | :returns: list of Tracklet objects read from xml file
124 | """
125 |
126 | # convert tracklet XML data to a tree structure
127 | eTree = ElementTree()
128 | print ('parsing tracklet file', trackletFile)
129 | with open(trackletFile) as f:
130 | eTree.parse(f)
131 |
132 | # now convert output to list of Tracklet objects
133 | trackletsElem = eTree.find('tracklets')
134 | tracklets = []
135 | trackletIdx = 0
136 | nTracklets = None
137 | for trackletElem in trackletsElem:
138 | #print 'track:', trackletElem.tag
139 | if trackletElem.tag == 'count':
140 | nTracklets = int(trackletElem.text)
141 | print ('file contains', nTracklets, 'tracklets')
142 | elif trackletElem.tag == 'item_version':
143 | pass
144 | elif trackletElem.tag == 'item':
145 | #print 'tracklet {0} of {1}'.format(trackletIdx, nTracklets)
146 | # a tracklet
147 | newTrack = Tracklet()
148 | isFinished = False
149 | hasAmt = False
150 | frameIdx = None
151 | for info in trackletElem:
152 | #print 'trackInfo:', info.tag
153 | if isFinished:
154 | raise ValueError('more info on element after finished!')
155 | if info.tag == 'objectType':
156 | newTrack.objectType = info.text
157 | elif info.tag == 'h':
158 | newTrack.size[0] = float(info.text)
159 | elif info.tag == 'w':
160 | newTrack.size[1] = float(info.text)
161 | elif info.tag == 'l':
162 | newTrack.size[2] = float(info.text)
163 | elif info.tag == 'first_frame':
164 | newTrack.firstFrame = int(info.text)
165 | elif info.tag == 'poses':
166 | # this info is the possibly long list of poses
167 | for pose in info:
168 | #print 'trackInfoPose:', pose.tag
169 | if pose.tag == 'count': # this should come before the others
170 | if newTrack.nFrames is not None:
171 | raise ValueError('there are several pose lists for a single track!')
172 | elif frameIdx is not None:
173 | raise ValueError('?!')
174 | newTrack.nFrames = int(pose.text)
175 | newTrack.trans = np.nan * np.ones((newTrack.nFrames, 3), dtype=float)
176 | newTrack.rots = np.nan * np.ones((newTrack.nFrames, 3), dtype=float)
177 | newTrack.states = np.nan * np.ones(newTrack.nFrames, dtype='uint8')
178 | newTrack.occs = np.nan * np.ones((newTrack.nFrames, 2), dtype='uint8')
179 | newTrack.truncs = np.nan * np.ones(newTrack.nFrames, dtype='uint8')
180 | newTrack.amtOccs = np.nan * np.ones((newTrack.nFrames, 2), dtype=float)
181 | newTrack.amtBorders = np.nan * np.ones((newTrack.nFrames, 3), dtype=float)
182 | frameIdx = 0
183 | elif pose.tag == 'item_version':
184 | pass
185 | elif pose.tag == 'item':
186 | # pose in one frame
187 | if frameIdx is None:
188 | raise ValueError('pose item came before number of poses!')
189 | for poseInfo in pose:
190 | #print 'trackInfoPoseInfo:', poseInfo.tag
191 | if poseInfo.tag == 'tx':
192 | newTrack.trans[frameIdx, 0] = float(poseInfo.text)
193 | elif poseInfo.tag == 'ty':
194 | newTrack.trans[frameIdx, 1] = float(poseInfo.text)
195 | elif poseInfo.tag == 'tz':
196 | newTrack.trans[frameIdx, 2] = float(poseInfo.text)
197 | elif poseInfo.tag == 'rx':
198 | newTrack.rots[frameIdx, 0] = float(poseInfo.text)
199 | elif poseInfo.tag == 'ry':
200 | newTrack.rots[frameIdx, 1] = float(poseInfo.text)
201 | elif poseInfo.tag == 'rz':
202 | newTrack.rots[frameIdx, 2] = float(poseInfo.text)
203 | elif poseInfo.tag == 'state':
204 | newTrack.states[frameIdx] = stateFromText[poseInfo.text]
205 | elif poseInfo.tag == 'occlusion':
206 | newTrack.occs[frameIdx, 0] = occFromText[poseInfo.text]
207 | elif poseInfo.tag == 'occlusion_kf':
208 | newTrack.occs[frameIdx, 1] = occFromText[poseInfo.text]
209 | elif poseInfo.tag == 'truncation':
210 | newTrack.truncs[frameIdx] = truncFromText[poseInfo.text]
211 | elif poseInfo.tag == 'amt_occlusion':
212 | newTrack.amtOccs[frameIdx,0] = float(poseInfo.text)
213 | hasAmt = True
214 | elif poseInfo.tag == 'amt_occlusion_kf':
215 | newTrack.amtOccs[frameIdx,1] = float(poseInfo.text)
216 | hasAmt = True
217 | elif poseInfo.tag == 'amt_border_l':
218 | newTrack.amtBorders[frameIdx,0] = float(poseInfo.text)
219 | hasAmt = True
220 | elif poseInfo.tag == 'amt_border_r':
221 | newTrack.amtBorders[frameIdx,1] = float(poseInfo.text)
222 | hasAmt = True
223 | elif poseInfo.tag == 'amt_border_kf':
224 | newTrack.amtBorders[frameIdx,2] = float(poseInfo.text)
225 | hasAmt = True
226 | else:
227 | raise ValueError('unexpected tag in poses item: {0}!'.format(poseInfo.tag))
228 | frameIdx += 1
229 | else:
230 | raise ValueError('unexpected pose info: {0}!'.format(pose.tag))
231 | elif info.tag == 'finished':
232 | isFinished = True
233 | else:
234 | raise ValueError('unexpected tag in tracklets: {0}!'.format(info.tag))
235 | #end: for all fields in current tracklet
236 |
237 | # some final consistency checks on new tracklet
238 | if not isFinished:
239 | warn('tracklet {0} was not finished!'.format(trackletIdx))
240 | if newTrack.nFrames is None:
241 | warn('tracklet {0} contains no information!'.format(trackletIdx))
242 | elif frameIdx != newTrack.nFrames:
243 | warn('tracklet {0} is supposed to have {1} frames, but perser found {1}!'.format(\
244 | trackletIdx, newTrack.nFrames, frameIdx))
245 | if np.abs(newTrack.rots[:,:2]).sum() > 1e-16:
246 | warn('track contains rotation other than yaw!')
247 |
248 | # if amtOccs / amtBorders are not set, set them to None
249 | if not hasAmt:
250 | newTrack.amtOccs = None
251 | newTrack.amtBorders = None
252 |
253 | # add new tracklet to list
254 | tracklets.append(newTrack)
255 | trackletIdx += 1
256 |
257 | else:
258 | raise ValueError('unexpected tracklet info')
259 | #end: for tracklet list items
260 |
261 | print ('loaded', trackletIdx, 'tracklets')
262 |
263 | # final consistency check
264 | if trackletIdx != nTracklets:
265 | warn('according to xml information the file has {0} tracklets, but parser found {1}!'.format(nTracklets, trackletIdx))
266 |
267 | return tracklets
268 | #end: function parseXML
269 |
270 |
271 | def example(kittiDir=None, drive=None):
272 |
273 | from os.path import join, expanduser
274 | import readline # makes raw_input behave more fancy
275 | # from xmlParser import parseXML, TRUNC_IN_IMAGE, TRUNC_TRUNCATED
276 |
277 | DEFAULT_DRIVE = '2011_09_26_drive_0001'
278 | twoPi = 2.*np.pi
279 |
280 | # get dir names
281 | if kittiDir is None:
282 | kittiDir = expanduser(raw_input('please enter kitti base dir (e.g. ~/path/to/kitti): ').strip())
283 | if drive is None:
284 | drive = raw_input('please enter drive name (default {0}): '.format(DEFAULT_DRIVE)).strip()
285 | if len(drive) == 0:
286 | drive = DEFAULT_DRIVE
287 |
288 | # read tracklets from file
289 | myTrackletFile = join(kittiDir, drive, 'tracklet_labels.xml')
290 | tracklets = parseXML(myTrackletFile)
291 |
292 | # loop over tracklets
293 | for iTracklet, tracklet in enumerate(tracklets):
294 | print ('tracklet {0: 3d}: {1}'.format(iTracklet, tracklet))
295 |
296 | # this part is inspired by kitti object development kit matlab code: computeBox3D
297 | h,w,l = tracklet.size
298 | trackletBox = np.array([ # in velodyne coordinates around zero point and without orientation yet\
299 | [-l/2, -l/2, l/2, l/2, -l/2, -l/2, l/2, l/2], \
300 | [ w/2, -w/2, -w/2, w/2, w/2, -w/2, -w/2, w/2], \
301 | [ 0.0, 0.0, 0.0, 0.0, h, h, h, h]])
302 |
303 | # loop over all data in tracklet
304 | for translation, rotation, state, occlusion, truncation, amtOcclusion, amtBorders, absoluteFrameNumber \
305 | in tracklet:
306 |
307 | # determine if object is in the image; otherwise continue
308 | if truncation not in (TRUNC_IN_IMAGE, TRUNC_TRUNCATED):
309 | continue
310 |
311 | # re-create 3D bounding box in velodyne coordinate system
312 | yaw = rotation[2] # other rotations are 0 in all xml files I checked
313 | assert np.abs(rotation[:2]).sum() == 0, 'object rotations other than yaw given!'
314 | rotMat = np.array([\
315 | [np.cos(yaw), -np.sin(yaw), 0.0], \
316 | [np.sin(yaw), np.cos(yaw), 0.0], \
317 | [ 0.0, 0.0, 1.0]])
318 | cornerPosInVelo = np.dot(rotMat, trackletBox) + np.tile(translation, (8,1)).T
319 |
320 | # calc yaw as seen from the camera (i.e. 0 degree = facing away from cam), as opposed to
321 | # car-centered yaw (i.e. 0 degree = same orientation as car).
322 | # makes quite a difference for objects in periphery!
323 | # Result is in [0, 2pi]
324 | x, y, z = translation
325 | yawVisual = ( yaw - np.arctan2(y, x) ) % twoPi
326 |
327 | #end: for all frames in track
328 | #end: for all tracks
329 | #end: function example
330 |
331 | # when somebody runs this file as a script:
332 | # run example if no arg or only 'example' was given as arg
333 | # otherwise run parseXML
334 | if __name__ == "__main__":
335 | # cmdLineArgs[0] is 'parseTrackletXML.py'
336 | if len(cmdLineArgs) < 2:
337 | example()
338 | elif (len(cmdLineArgs) == 2) and (cmdLineArgs[1] == 'example'):
339 | example()
340 | else:
341 | parseXML(*cmdLineArgs[1:])
342 |
343 | # (created using vim - the world's best text editor)
344 |
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/Test_data/parseTrackletXML.pyc:
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https://raw.githubusercontent.com/ywangeq/instance_dataset_gen/5fc2edb2e82ac7cbaf978a60ddcd2f8fab290cb2/Test_data/parseTrackletXML.pyc
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/Test_data/path_gen.py:
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1 | import os
2 | import glob
3 | import random
4 | data = 'Kitti'
5 | data_store = 'npydata'
6 | label_store = 'npylabel'
7 | root =os.getcwd()
8 |
9 | data_path = os.path.join(root,data,data_store)
10 | label_path = os.path.join(root,data,label_store)
11 | print(data_path,label_path)
12 | if not os.path.exists(data_path):
13 | print('no path exist')
14 | os.makedirs(data_path)
15 | if not os.path.exists(label_path):
16 | os.makedirs(label_path)
17 | print('no path exist')
18 |
19 |
20 | train = open('train.txt','w')
21 | val = open('valid.txt','w')
22 |
23 | file_path=os.path.join(data_path,'*.npy')
24 | file_P = glob.glob(file_path)
25 | L=len(file_P)
26 | random.shuffle(file_P)
27 | train_len = int(L*0.8)
28 |
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/Test_data/path_generator.py:
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1 | import os
2 |
3 | root = os.getcwd()
4 |
5 | raw_data = root+'/Kitti/2011_09_26'
6 | type = 'label'
7 | root_txt ='test.txt'
8 | file_object = open(root_txt,'wb')
9 | a =0
10 | for path in os.listdir(raw_data):
11 | if path[-4:] == 'sync':
12 | Data_file = raw_data+'/'+path
13 | print(Data_file)
14 | for file in os.listdir(Data_file):
15 | if file ==type:
16 | lidar_data = Data_file + '/'+ file
17 | for data in os.listdir(lidar_data):
18 | print('data',data)
19 | a= a+1
20 | #print(a)
21 | root_name = lidar_data[:-6] +'/'+'velodyne_points/data/'+data[:-4]+'.bin'
22 | lidar_name = Data_file+'/'+'label/'+data[:-4]+'.txt'
23 | if os.path.exists(root_name) and os.path.exists(lidar_name):
24 | print('root',root_name)
25 | print('lidar',lidar_name)
26 | file_object.write(root_name)
27 | file_object.write(' ')
28 | file_object.write(lidar_name)
29 | file_object.write('\n')
30 | #file_object.close()
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