├── .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: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 3 6 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1180 | 0 1181 | 1 1182 | -1 1183 | 1184 | 1185 | 20.442686235604135 1186 | 4.3207183822004227 1187 | -1.741753399892559 1188 | 0 1189 | 0 1190 | -6.1205246979637025 1191 | 1 1192 | 2 1193 | 0 1194 | 0 1195 | 3 1196 | 1 1197 | 0 1198 | 1 1199 | -1 1200 | 1201 | 1202 | 20.154805230463698 1203 | 3.97813791642659 1204 | -1.7389473023632716 1205 | 0 1206 | 0 1207 | -6.1341846423123325 1208 | 1 1209 | 2 1210 | 0 1211 | 0 1212 | 3 1213 | 0 1214 | 0 1215 | 1 1216 | -1 1217 | 1218 | 1219 | 19.86692422532326 1220 | 3.6355574506527577 1221 | -1.7361412048339844 1222 | 0 1223 | 0 1224 | -6.1478445866609626 1225 | 2 1226 | 2 1227 | 0 1228 | 0 1229 | 3 1230 | 1 1231 | 0 1232 | 1 1233 | -1 1234 | 1235 | 1236 | 1 1237 | 1238 | 1239 | 1240 | 1241 | -------------------------------------------------------------------------------- /Kitti/npydata/2011_09_26_drive_0091_sync_0000000000.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ywangeq/instance_dataset_gen/5fc2edb2e82ac7cbaf978a60ddcd2f8fab290cb2/Kitti/npydata/2011_09_26_drive_0091_sync_0000000000.npy -------------------------------------------------------------------------------- /Kitti/npylabel/2011_09_26_drive_0091_sync_0000000000.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ywangeq/instance_dataset_gen/5fc2edb2e82ac7cbaf978a60ddcd2f8fab290cb2/Kitti/npylabel/2011_09_26_drive_0091_sync_0000000000.npy -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 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 -------------------------------------------------------------------------------- /Test_data/Kitti/2011_09_26/check.txt: -------------------------------------------------------------------------------- 1 | here -------------------------------------------------------------------------------- /Test_data/gene_data.py: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /Test_data/parseTrackletXML.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ywangeq/instance_dataset_gen/5fc2edb2e82ac7cbaf978a60ddcd2f8fab290cb2/Test_data/parseTrackletXML.pyc -------------------------------------------------------------------------------- /Test_data/path_gen.py: -------------------------------------------------------------------------------- 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 | -------------------------------------------------------------------------------- /Test_data/path_generator.py: -------------------------------------------------------------------------------- 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() --------------------------------------------------------------------------------