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data_utils.py
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data_utils.py
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import random
import time
import cv2
import sys
sys.path.append('.')
sys.path.append('..')
import numpy as np
import glob
import os
# from skimage.io import imread
from plyfile import PlyData
from PIL import Image, ImageFile
from lib.utils.config import cfg
from lib.utils.extend_utils.extend_utils import farthest_point_sampling
from lib.utils.base_utils import read_pickle, save_pickle, Projector, PoseTransformer, read_pose, ModelAligner
from scipy.misc import imread,imsave
from lib.utils.draw_utils import write_points, pts_to_img_pts, img_pts_to_pts_img
def read_rgb_np(rgb_path):
ImageFile.LOAD_TRUNCATED_IMAGES = True
img = Image.open(rgb_path).convert('RGB')
img = np.array(img,np.uint8)
return img
def read_mask_np(mask_path):
mask = Image.open(mask_path)
mask_seg = np.array(mask).astype(np.int32)
return mask_seg
class LineModModelDB(object):
'''
LineModModelDB is used for managing the mesh of each model
'''
corners_3d = {}
models = {}
diameters = {}
centers_3d = {}
farthest_3d = {'8': {}, '4': {}, '12': {}, '16': {}, '20': {}}
small_bbox_corners={}
def __init__(self):
self.ply_pattern = os.path.join(cfg.LINEMOD, '{}/{}.ply')
self.diameter_pattern = os.path.join(cfg.LINEMOD_ORIG,'{}/distance.txt')
self.farthest_pattern = os.path.join(cfg.LINEMOD,'{}/farthest{}.txt')
def get_corners_3d(self, class_type):
if class_type in self.corners_3d:
return self.corners_3d[class_type]
corner_pth=os.path.join(cfg.LINEMOD, class_type, 'corners.txt')
if os.path.exists(corner_pth):
self.corners_3d[class_type]=np.loadtxt(corner_pth)
return self.corners_3d[class_type]
ply_path = self.ply_pattern.format(class_type, class_type)
ply = PlyData.read(ply_path)
data = ply.elements[0].data
x = data['x']
min_x, max_x = np.min(x), np.max(x)
y = data['y']
min_y, max_y = np.min(y), np.max(y)
z = data['z']
min_z, max_z = np.min(z), np.max(z)
corners_3d = np.array([
[min_x, min_y, min_z],
[min_x, min_y, max_z],
[min_x, max_y, min_z],
[min_x, max_y, max_z],
[max_x, min_y, min_z],
[max_x, min_y, max_z],
[max_x, max_y, min_z],
[max_x, max_y, max_z],
])
self.corners_3d[class_type] = corners_3d
np.savetxt(corner_pth,corners_3d)
return corners_3d
def get_small_bbox(self, class_type):
if class_type in self.small_bbox_corners:
return self.small_bbox_corners[class_type]
corners=self.get_corners_3d(class_type)
center=np.mean(corners,0)
small_bbox_corners=(corners-center[None,:])*2.0/3.0+center[None,:]
self.small_bbox_corners[class_type]=small_bbox_corners
return small_bbox_corners
def get_ply_model(self, class_type):
if class_type in self.models:
return self.models[class_type]
ply = PlyData.read(self.ply_pattern.format(class_type, class_type))
data = ply.elements[0].data
x = data['x']
y = data['y']
z = data['z']
model = np.stack([x, y, z], axis=-1)
self.models[class_type] = model
return model
def get_diameter(self, class_type):
if class_type in self.diameters:
return self.diameters[class_type]
diameter_path = self.diameter_pattern.format(class_type)
diameter = np.loadtxt(diameter_path) / 100.
self.diameters[class_type] = diameter
return diameter
def get_centers_3d(self, class_type):
if class_type in self.centers_3d:
return self.centers_3d[class_type]
c3d=self.get_corners_3d(class_type)
self.centers_3d[class_type]=(np.max(c3d,0)+np.min(c3d,0))/2
return self.centers_3d[class_type]
def get_farthest_3d(self,class_type,num=8):
if class_type in self.farthest_3d['{}'.format(num)]:
return self.farthest_3d['{}'.format(num)][class_type]
if num==8:
farthest_path = self.farthest_pattern.format(class_type,'')
else:
farthest_path = self.farthest_pattern.format(class_type,num)
farthest_pts = np.loadtxt(farthest_path)
self.farthest_3d['{}'.format(num)][class_type] = farthest_pts
return farthest_pts
@staticmethod
def compute_farthest_surface_point_3d():
for cls in cfg.linemod_cls_names:
pts=np.loadtxt(os.path.join(cfg.LINEMOD, cls,'dense_pts.txt'))[:,:3]
spts=farthest_point_sampling(pts,8,True)
write_points(os.path.join(cfg.LINEMOD, cls, 'farthest.txt'.format(cls)),spts)
@staticmethod
def compute_farthest_surface_point_3d_num(num):
for cls in cfg.linemod_cls_names:
pts=np.loadtxt(os.path.join(cfg.LINEMOD, cls,'dense_pts.txt'))[:,:3]
spts=farthest_point_sampling(pts,num,True)
write_points(os.path.join(cfg.LINEMOD, cls, 'farthest{}.txt'.format(num)),spts)
def get_ply_mesh(self,class_type):
ply = PlyData.read(self.ply_pattern.format(class_type, class_type))
vert = np.asarray([ply['vertex'].data['x'],ply['vertex'].data['y'],ply['vertex'].data['z']]).transpose()
vert_id = [id for id in ply['face'].data['vertex_indices']]
vert_id = np.asarray(vert_id,np.int64)
return vert, vert_id
class LineModImageDB(object):
'''
rgb_pth relative path to cfg.LINEMOD
dpt_pth relative path to cfg.LINEMOD
RT np.float32 [3,4]
cls_typ 'cat' ...
rnd_typ 'real' or 'render'
corner np.float32 [8,2]
'''
def __init__(self, cls_name, render_num=10000, fuse_num=10000, ms_num=10000,
has_render_set=True, has_fuse_set=True):
self.cls_name=cls_name
# some dirs for processing
os.path.join(cfg.LINEMOD,'posedb','{}_render.pkl'.format(cls_name))
self.linemod_dir=cfg.LINEMOD
self.render_dir='renders/{}'.format(cls_name)
self.rgb_dir='{}/JPEGImages'.format(cls_name)
self.mask_dir='{}/mask'.format(cls_name)
self.rt_dir=os.path.join(cfg.DATA_DIR,'LINEMOD_ORIG',cls_name,'data')
self.render_num=render_num
self.test_fn='{}/test.txt'.format(cls_name)
self.train_fn='{}/train.txt'.format(cls_name)
self.val_fn='{}/val.txt'.format(cls_name)
if has_render_set:
self.render_pkl=os.path.join(self.linemod_dir,'posedb','{}_render.pkl'.format(cls_name))
# prepare dataset
if os.path.exists(self.render_pkl):
# read cached
self.render_set=read_pickle(self.render_pkl)
else:
# process render set
self.render_set=self.collect_render_set_info(self.render_pkl,self.render_dir)
else:
self.render_set=[]
self.real_pkl=os.path.join(self.linemod_dir,'posedb','{}_real.pkl'.format(cls_name))
if os.path.exists(self.real_pkl):
# read cached
self.real_set=read_pickle(self.real_pkl)
else:
# process real set
self.real_set=self.collect_real_set_info()
# prepare train test split
self.train_real_set=[]
self.test_real_set=[]
self.val_real_set=[]
self.collect_train_val_test_info()
self.fuse_set=[]
self.fuse_dir='fuse'
self.fuse_num=fuse_num
self.cls_idx=cfg.linemod_cls_names.index(cls_name)
if has_fuse_set:
self.fuse_pkl=os.path.join(cfg.LINEMOD,'posedb','{}_fuse.pkl'.format(cls_name))
# prepare dataset
if os.path.exists(self.fuse_pkl):
# read cached
self.fuse_set=read_pickle(self.fuse_pkl)
else:
# process render set
self.fuse_set=self.collect_fuse_info()
else:
self.fuse_set=[]
def collect_render_set_info(self,pkl_file,render_dir,format='jpg'):
database=[]
projector=Projector()
modeldb=LineModModelDB()
for k in range(self.render_num):
data={}
data['rgb_pth']=os.path.join(render_dir,'{}.{}'.format(k,format))
data['dpt_pth']=os.path.join(render_dir,'{}_depth.png'.format(k))
data['RT']=read_pickle(os.path.join(self.linemod_dir,render_dir,'{}_RT.pkl'.format(k)))['RT']
data['cls_typ']=self.cls_name
data['rnd_typ']='render'
data['corners']=projector.project(modeldb.get_corners_3d(self.cls_name),data['RT'],'blender')
data['farthest']=projector.project(modeldb.get_farthest_3d(self.cls_name),data['RT'],'blender')
data['center']=projector.project(modeldb.get_centers_3d(self.cls_name)[None,:],data['RT'],'blender')
for num in [4,12,16,20]:
data['farthest{}'.format(num)]=projector.project(modeldb.get_farthest_3d(self.cls_name,num),data['RT'],'blender')
data['small_bbox'] = projector.project(modeldb.get_small_bbox(self.cls_name), data['RT'], 'blender')
axis_direct=np.concatenate([np.identity(3), np.zeros([3, 1])], 1).astype(np.float32)
data['van_pts']=projector.project_h(axis_direct, data['RT'], 'blender')
database.append(data)
save_pickle(database,pkl_file)
return database
def collect_real_set_info(self):
database=[]
projector=Projector()
modeldb=LineModModelDB()
img_num=len(os.listdir(os.path.join(self.linemod_dir,self.rgb_dir)))
for k in range(img_num):
data={}
data['rgb_pth']=os.path.join(self.rgb_dir, '{:06}.jpg'.format(k))
data['dpt_pth']=os.path.join(self.mask_dir, '{:04}.png'.format(k))
pose=read_pose(os.path.join(self.rt_dir, 'rot{}.rot'.format(k)),
os.path.join(self.rt_dir, 'tra{}.tra'.format(k)))
pose_transformer = PoseTransformer(class_type=self.cls_name)
data['RT'] = pose_transformer.orig_pose_to_blender_pose(pose).astype(np.float32)
data['cls_typ']=self.cls_name
data['rnd_typ']='real'
data['corners']=projector.project(modeldb.get_corners_3d(self.cls_name),data['RT'],'linemod')
data['farthest']=projector.project(modeldb.get_farthest_3d(self.cls_name),data['RT'],'linemod')
for num in [4,12,16,20]:
data['farthest{}'.format(num)]=projector.project(modeldb.get_farthest_3d(self.cls_name,num),data['RT'],'linemod')
data['center']=projector.project(modeldb.get_centers_3d(self.cls_name)[None, :],data['RT'],'linemod')
data['small_bbox'] = projector.project(modeldb.get_small_bbox(self.cls_name), data['RT'], 'linemod')
axis_direct=np.concatenate([np.identity(3), np.zeros([3, 1])], 1).astype(np.float32)
data['van_pts']=projector.project_h(axis_direct, data['RT'], 'linemod')
database.append(data)
save_pickle(database,self.real_pkl)
return database
def collect_train_val_test_info(self):
with open(os.path.join(self.linemod_dir,self.test_fn),'r') as f:
test_fns=[line.strip().split('/')[-1] for line in f.readlines()]
with open(os.path.join(self.linemod_dir,self.train_fn),'r') as f:
train_fns=[line.strip().split('/')[-1] for line in f.readlines()]
with open(os.path.join(self.linemod_dir,self.val_fn),'r') as f:
val_fns=[line.strip().split('/')[-1] for line in f.readlines()]
for data in self.real_set:
if data['rgb_pth'].split('/')[-1] in test_fns:
if data['rgb_pth'].split('/')[-1] in val_fns:
self.val_real_set.append(data)
else:
self.test_real_set.append(data)
if data['rgb_pth'].split('/')[-1] in train_fns:
self.train_real_set.append(data)
def collect_fuse_info(self):
database=[]
modeldb=LineModModelDB()
projector=Projector()
for k in range(self.fuse_num):
data=dict()
data['rgb_pth']=os.path.join(self.fuse_dir, '{}_rgb.jpg'.format(k))
data['dpt_pth']=os.path.join(self.fuse_dir, '{}_mask.png'.format(k))
# if too few foreground pts then continue
mask=imread(os.path.join(self.linemod_dir,data['dpt_pth']))
if np.sum(mask==(cfg.linemod_cls_names.index(self.cls_name)+1))<400: continue
data['cls_typ']=self.cls_name
data['rnd_typ']='fuse'
begins,poses=read_pickle(os.path.join(self.linemod_dir,self.fuse_dir,'{}_info.pkl'.format(k)))
data['RT'] = poses[self.cls_idx]
K=projector.intrinsic_matrix['linemod'].copy()
K[0,2]+=begins[self.cls_idx,1]
K[1,2]+=begins[self.cls_idx,0]
data['K']=K
data['corners']=projector.project_K(modeldb.get_corners_3d(self.cls_name),data['RT'],K)
data['center']=projector.project_K(modeldb.get_centers_3d(self.cls_name),data['RT'],K)
data['farthest']=projector.project_K(modeldb.get_farthest_3d(self.cls_name),data['RT'],K)
for num in [4,12,16,20]:
data['farthest{}'.format(num)]=projector.project_K(modeldb.get_farthest_3d(self.cls_name,num),data['RT'],K)
data['small_bbox'] = projector.project_K(modeldb.get_small_bbox(self.cls_name), data['RT'], K)
database.append(data)
save_pickle(database,self.fuse_pkl)
return database
def collect_ms_info(self):
database=[]
projector=Projector()
model_db=LineModModelDB()
for k in range(self.ms_num):
data=dict()
data['rgb_pth']=os.path.join(self.ms_dir, '{}.jpg'.format(k))
data['dpt_pth']=os.path.join(self.ms_dir, '{}_{}_mask.png'.format(k,self.cls_name))
# if too few foreground pts then continue
mask=imread(os.path.join(self.linemod_dir,data['dpt_pth']))
if np.sum(mask)<5: continue
data['RT'] = read_pickle(os.path.join(self.linemod_dir, self.ms_dir, '{}_{}_RT.pkl'.format(self.cls_name,k)))['RT']
data['cls_typ']=self.cls_name
data['rnd_typ']='render_multi'
data['corners']=projector.project(model_db.get_corners_3d(self.cls_name),data['RT'],'blender')
data['farthest']=projector.project(model_db.get_farthest_3d(self.cls_name),data['RT'],'blender')
for num in [4,12,16,20]:
data['farthest{}'.format(num)]=projector.project(modeldb.get_farthest_3d(self.cls_name,num),data['RT'],'blender')
data['center']=projector.project(model_db.get_centers_3d(self.cls_name)[None,:],data['RT'],'blender')
data['small_bbox'] = projector.project(modeldb.get_small_bbox(self.cls_name), data['RT'], 'blender')
axis_direct=np.concatenate([np.identity(3), np.zeros([3, 1])], 1).astype(np.float32)
data['van_pts']=projector.project_h(axis_direct, data['RT'], 'blender')
database.append(data)
save_pickle(database,self.ms_pkl)
return database
def collect_printer_info(self):
pdb=PrinterModelDB(self.cls_name)
database=[]
modeldb=LineModModelDB()
for k in range(pdb.image_num):
data={}
data['rgb_pth']=pdb.image_pattern.format(k+1)
data['dpt_pth']=pdb.mask_pattern.format(k+1)
data['RT']=pdb.aligned_poses[k]
data['K']=pdb.K[self.cls_name]
data['cls_typ']=self.cls_name
data['rnd_typ']='printer'
data['corners']=Projector.project_K(modeldb.get_corners_3d(self.cls_name),data['RT'],pdb.K[self.cls_name])
data['farthest']=Projector.project_K(modeldb.get_farthest_3d(self.cls_name),data['RT'],pdb.K[self.cls_name])
for num in [4,12,16,20]:
data['farthest{}'.format(num)]=Projector.project_K(modeldb.get_farthest_3d(self.cls_name,num),data['RT'],pdb.K[self.cls_name])
data['center']=Projector.project_K(modeldb.get_centers_3d(self.cls_name)[None, :],data['RT'],pdb.K[self.cls_name])
database.append(data)
save_pickle(database,self.printer_pkl)
return database
@staticmethod
def split_val_set():
image_dbs=[LineModImageDB(cls,has_ms_set=False,has_fuse_set=False) for cls in cfg.linemod_cls_names]
for db in image_dbs:
random.shuffle(db.test_real_set)
with open(os.path.join(db.linemod_dir,db.cls_name,'val.txt'),'w') as f:
for k in range(len(db.test_real_set)//2):
f.write('LINEMOD/'+db.test_real_set[k]['rgb_pth']+'\n')
@staticmethod
def crop_instance(rgb,msk,size=256):
h,w,_=rgb.shape
hs,ws=np.nonzero(msk)
hmin,hmax=np.min(hs),np.max(hs)
wmin,wmax=np.min(ws),np.max(ws)
hlen=hmax-hmin
wlen=wmax-wmin
type=np.random.random()
if type<=0.4:
# truncate x
truncated_ratio=np.random.uniform(0.4,0.6)
x_pt=int(wlen*truncated_ratio)+wmin
if np.random.random()<0.5:
wbeg = x_pt - size
wend = x_pt
else:
wbeg = x_pt
wend = x_pt + size
if hlen<size:
hbeg = np.random.randint(hmax-size,hmin)
hend = hbeg + size
else:
hbeg = np.random.randint(hmax-size,hmin+size)
hend = hbeg + size
elif 0.4<type<=0.8:
# truncate y
truncated_ratio=np.random.uniform(0.4,0.6)
y_pt=int(hlen*truncated_ratio)+hmin
if np.random.random()<0.5:
hbeg = y_pt - size
hend = y_pt
else:
hbeg = y_pt
hend = y_pt + size
if wlen<size:
wbeg = np.random.randint(wmax-size,wmin)
wend = wbeg + size
else:
wbeg = np.random.randint(wmax-size,wmin+size)
wend = wbeg + size
else:
# truncate x and y
truncated_ratio=np.random.uniform(0.4,0.6)
y_pt=int(hlen*truncated_ratio)+hmin
if np.random.random()<0.5:
hbeg = y_pt - size
hend = y_pt
else:
hbeg = y_pt
hend = y_pt + size
truncated_ratio=np.random.uniform(0.4,0.6)
x_pt=int(wlen*truncated_ratio)+wmin
if np.random.random()<0.5:
wbeg = x_pt - size
wend = x_pt
else:
wbeg = x_pt
wend = x_pt + size
hpad_bfr=0 if hbeg>=0 else 0-hbeg
hpad_aft=0 if hend<=h else hend-h
wpad_bfr=0 if wbeg>=0 else 0-wbeg
wpad_aft=0 if wend<=w else wend-w
hbeg=hbeg if hbeg>=0 else 0
hend=hend if hend<=h else h
wbeg=wbeg if wbeg>=0 else 0
wend=wend if wend<=w else w
rgb_new=np.pad(rgb[hbeg:hend,wbeg:wend],((hpad_bfr,hpad_aft),(wpad_bfr,wpad_aft),(0,0)),mode='constant')
msk_new=np.pad(msk[hbeg:hend,wbeg:wend],((hpad_bfr,hpad_aft),(wpad_bfr,wpad_aft)),mode='constant')
return rgb_new,msk_new,-hbeg+hpad_bfr,-wbeg+wpad_bfr
@staticmethod
def make_truncated_linemod_dataset():
for cls_name in cfg.linemod_cls_names:
print(cls_name)
linemod_dir=cfg.LINEMOD
rgb_dir='{}/JPEGImages'.format(cls_name)
mask_dir='{}/mask'.format(cls_name)
rt_dir=os.path.join(cfg.DATA_DIR,'LINEMOD_ORIG',cls_name,'data')
if not os.path.exists(os.path.join(linemod_dir,'truncated',cls_name)):
os.mkdir(os.path.join(linemod_dir,'truncated',cls_name))
projector=Projector()
img_num=len(os.listdir(os.path.join(linemod_dir,rgb_dir)))
print(img_num)
for k in range(img_num):
rgb=imread(os.path.join(linemod_dir, rgb_dir, '{:06}.jpg'.format(k)))
msk=imread(os.path.join(linemod_dir, mask_dir, '{:04}.png'.format(k)))
msk=(np.sum(msk,2)>0).astype(np.uint8)
before=np.sum(msk)
count=0
while True:
rgb_new,msk_new,hbeg,wbeg=LineModImageDB.crop_instance(rgb,msk,256)
after=np.sum(msk_new)
count+=1
if after/before>=0.2 or count>50:
rgb,msk=rgb_new, msk_new
break
imsave(os.path.join(linemod_dir,'truncated',cls_name,'{:06}_rgb.jpg'.format(k)),rgb)
imsave(os.path.join(linemod_dir,'truncated',cls_name,'{:04}_msk.png'.format(k)),msk)
pose=read_pose(os.path.join(rt_dir, 'rot{}.rot'.format(k)),
os.path.join(rt_dir, 'tra{}.tra'.format(k)))
pose_transformer = PoseTransformer(class_type=cls_name)
pose = pose_transformer.orig_pose_to_blender_pose(pose).astype(np.float32)
K=projector.intrinsic_matrix['linemod'].copy()
K[0,2]+=wbeg
K[1,2]+=hbeg
save_pickle([pose,K],os.path.join(linemod_dir,'truncated',cls_name,'{:06}_info.pkl'.format(k)))
if k%500==0: print(k)
class SpecialDuckDataset(object):
def __init__(self):
pass
@staticmethod
def get_dataset(num=10):
dataset=[]
projector=Projector()
modeldb=LineModModelDB()
for k in range(num):
data={}
data['rgb_pth']='special/duck/{}.jpg'.format(k)
data['dpt_pth']='special/duck/{}_depth.png'.format(k)
data['RT']=read_pickle(os.path.join(cfg.LINEMOD,'special/duck/{}_RT.pkl'.format(k)))['RT']
data['center']=projector.project(modeldb.get_centers_3d('duck'),data['RT'],'blender')
data['rnd_typ']='render'
dataset.append(data)
return dataset
class OcclusionLineModImageDB(object):
def __init__(self,cls_name):
self.cls_name=cls_name
# some dirs for processing
self.linemod_dir=cfg.OCCLUSION_LINEMOD
self.rgb_dir='RGB-D/rgb_noseg'
self.mask_dir='masks/{}'.format(cls_name)
self.rt_dir=os.path.join(self.linemod_dir,'poses/{}{}'.format(cls_name[0].upper(),cls_name[1:]))
self.real_pkl=os.path.join(self.linemod_dir,'posedb','{}_real.pkl'.format(cls_name))
if os.path.exists(self.real_pkl):
# read cached
self.real_set=read_pickle(self.real_pkl)
else:
# process real set
self.real_set=self.collect_real_set_info()
self.test_real_set=[]
self.train_real_set=[]
self.get_train_test_split()
def get_train_test_split(self):
test_fns=[]
with open(os.path.join(cfg.LINEMOD,self.cls_name,'test_occlusion.txt'),'r') as f:
for line in f.readlines():
test_id=int(line.strip().split('/')[-1].split('.')[0])
test_fns.append('color_{:05}.png'.format(test_id))
# print(len(self.real_set),len(test_fns))
for data in self.real_set:
fn=data['rgb_pth'].split('/')[-1]
if fn in test_fns:
self.test_real_set.append(data)
else:
self.train_real_set.append(data)
def collect_real_set_info(self):
database=[]
projector=Projector()
modeldb=LineModModelDB()
transformer=PoseTransformer(class_type=self.cls_name)
img_num=len(os.listdir(os.path.join(self.linemod_dir,self.rgb_dir)))
print(img_num)
for k in range(img_num):
data={}
data['rgb_pth']=os.path.join(self.rgb_dir,'color_{:05}.png'.format(k))
data['dpt_pth']=os.path.join(self.mask_dir,'{}.png'.format(k))
pose=self.read_pose(os.path.join(self.rt_dir,'info_{:05}.txt'.format(k)))
if len(pose)==0:
# os.system('cp {} ./{:05}.png'.format(os.path.join(cfg.OCCLUSION_LINEMOD,data['rgb_pth']),k))
continue
data['RT']=transformer.occlusion_pose_to_blender_pose(pose)
data['cls_typ']=self.cls_name
data['rnd_typ']='real'
data['corners']=projector.project(modeldb.get_corners_3d(self.cls_name),data['RT'],'linemod')
data['farthest']=projector.project(modeldb.get_farthest_3d(self.cls_name),data['RT'],'linemod')
for num in [4,12,16,20]:
data['farthest{}'.format(num)]=projector.project(modeldb.get_farthest_3d(self.cls_name,num),data['RT'],'linemod')
data['center']=projector.project(modeldb.get_centers_3d(self.cls_name)[None,:],data['RT'],'linemod')
data['small_bbox'] = projector.project(modeldb.get_small_bbox(self.cls_name), data['RT'], 'linemod')
axis_direct=np.concatenate([np.identity(3), np.zeros([3, 1])], 1).astype(np.float32)
data['van_pts']=projector.project_h(axis_direct, data['RT'], 'blender')
database.append(data)
save_pickle(database,self.real_pkl)
return database
def get_test_val_split(self):
with open(os.path.join(self.linemod_dir,'{}_val.txt'.format(self.cls_name)),'r') as f:
val_fns=[line.strip() for line in f.readlines()]
for data in self.real_set:
if data['rgb_pth'] in val_fns:
self.val_real_set.append(data)
else:
self.test_real_set.append(data)
@staticmethod
def read_pose(pose_path):
with open(pose_path) as pose_info:
lines = [line[:-1] for line in pose_info.readlines()]
if 'rotation:' not in lines:
return np.array([])
row = lines.index('rotation:') + 1
rotation = np.loadtxt(lines[row:row + 3])
translation = np.loadtxt(lines[row + 4:row + 5])
return np.concatenate([rotation, np.reshape(translation, newshape=[3, 1])], axis=-1)
@staticmethod
def split_val_set():
image_dbs=[OcclusionLineModImageDB(cls) for cls in cfg.occ_linemod_cls_names]
for db in image_dbs:
random.shuffle(db.real_set)
with open(os.path.join(db.linemod_dir,'{}_val.txt'.format(db.cls_name)),'w') as f:
for k in range(len(db.real_set)//2):
f.write(db.real_set[k]['rgb_pth']+'\n')
class TruncatedLineModImageDB(object):
def __init__(self,cls_name):
self.cls_name=cls_name
# some dirs for processing
self.linemod_dir=cfg.LINEMOD
self.pkl=os.path.join(self.linemod_dir,'posedb','{}_truncated.pkl'.format(cls_name))
if os.path.exists(self.pkl):
# read cached
self.set=read_pickle(self.pkl)
else:
# process real set
self.set=self.collect_truncated_set_info()
def collect_truncated_set_info(self):
database=[]
projector=Projector()
modeldb=LineModModelDB()
img_num=len(os.listdir(os.path.join(self.linemod_dir,self.cls_name,'JPEGImages')))
for k in range(img_num):
data={}
data['rgb_pth']=os.path.join('truncated',self.cls_name,'{:06}_rgb.jpg'.format(k))
data['dpt_pth']=os.path.join('truncated',self.cls_name,'{:04}_msk.png'.format(k))
pose,K=read_pickle(os.path.join(self.linemod_dir,'truncated',self.cls_name,'{:06}_info.pkl'.format(k)))
data['RT']=pose
data['cls_typ']=self.cls_name
data['rnd_typ']='truncated'
data['corners']=projector.project_K(modeldb.get_corners_3d(self.cls_name),data['RT'],K)
data['farthest']=projector.project_K(modeldb.get_farthest_3d(self.cls_name),data['RT'],K)
for num in [4,12,16,20]:
data['farthest{}'.format(num)]=projector.project_K(modeldb.get_farthest_3d(self.cls_name,num),data['RT'],K)
data['small_bbox'] = projector.project_K(modeldb.get_small_bbox(self.cls_name), data['RT'], K)
data['center']=projector.project_K(modeldb.get_centers_3d(self.cls_name)[None,:],data['RT'],K)
# axis_direct=np.concatenate([np.identity(3), np.zeros([3, 1])], 1).astype(np.float32)
# data['van_pts']=projector.project_h(axis_direct, data['RT'], K)
data['K']=K
database.append(data)
save_pickle(database,self.pkl)
return database
class OcclusionLineModDB(LineModModelDB):
class_type_to_number = {
'ape': '001',
'can': '004',
'cat': '005',
'driller': '006',
'duck': '007',
'eggbox': '008',
'glue': '009',
'holepuncher': '010',
'benchvise': '011'
}
translation_transforms = {}
blender_models = {}
def __init__(self):
super(OcclusionLineModDB, self).__init__()
from lib.utils.render_utils import OpenGLRenderer
self.class_type = None
self.xyz_pattern = os.path.join(cfg.OCCLUSION_LINEMOD,'models/{}/{}.xyz')
self.rgb_pattern = os.path.join(cfg.OCCLUSION_LINEMOD,'RGB-D/rgb_noseg/color_{:05}.png')
self.pose_pattern = os.path.join(cfg.OCCLUSION_LINEMOD,'poses/{}/info_{:05}.txt')
self.rgb_dir_path = os.path.join(cfg.OCCLUSION_LINEMOD,'RGB-D/rgb_noseg')
self.mask_dir_pattern = os.path.join(cfg.OCCLUSION_LINEMOD,'masks/{}')
self.mask_pattern = os.path.join(self.mask_dir_pattern, '{}.png')
self.opengl_renderer = OpenGLRenderer()
@staticmethod
def load_ply_model(model_path):
ply = PlyData.read(model_path)
data = ply.elements[0].data
x = data['x']
y = data['y']
z = data['z']
return np.stack([x, y, z], axis=-1)
@staticmethod
def read_pose(pose_path):
with open(pose_path) as pose_info:
lines = [line[:-1] for line in pose_info.readlines()]
if 'rotation:' not in lines:
return np.array([])
row = lines.index('rotation:') + 1
rotation = np.loadtxt(lines[row:row + 3])
translation = np.loadtxt(lines[row + 4:row + 5])
return np.concatenate([rotation, np.reshape(translation, newshape=[3, 1])], axis=-1)
def get_blender_model(self):
if self.class_type in self.blender_models:
return self.blender_models[self.class_type]
blender_model = self.load_ply_model(self.ply_pattern.format(self.class_type, self.class_type))
self.blender_models[self.class_type] = blender_model
return blender_model
def get_translation_transform(self):
if self.class_type in self.translation_transforms:
return self.translation_transforms[self.class_type]
model = self.get_blender_model()
xyz = np.loadtxt(self.xyz_pattern.format(self.class_type.title(), self.class_type_to_number[self.class_type]))
rotation = np.array([[0., 0., 1.],
[1., 0., 0.],
[0., 1., 0.]])
xyz = np.dot(xyz, rotation.T)
translation_transform = np.mean(xyz, axis=0) - np.mean(model, axis=0)
self.translation_transforms[self.class_type] = translation_transform
return translation_transform
def occlusion_pose_to_blender_pose(self, pose):
rot, tra = pose[:, :3], pose[:, 3]
rotation = np.array([[0., 1., 0.],
[0., 0., 1.],
[1., 0., 0.]])
rot = np.dot(rot, rotation)
tra[1:] *= -1
translation_transform = np.dot(rot, self.get_translation_transform())
rot[1:] *= -1
translation_transform[1:] *= -1
tra += translation_transform
pose = np.concatenate([rot, np.reshape(tra, newshape=[3, 1])], axis=-1)
return pose
@staticmethod
def read_benchvise_pose(index):
orig_pose_dir_path = os.path.join(cfg.LINEMOD_ORIG, 'benchvise/data')
pose=read_pose(os.path.join(orig_pose_dir_path, 'rot{}.rot'.format(index)),
os.path.join(orig_pose_dir_path, 'tra{}.tra'.format(index)))
pose_transformer = PoseTransformer(class_type='benchvise')
return pose_transformer.orig_pose_to_blender_pose(pose).astype(np.float32)
def read_blender_pose(self, index):
if self.class_type == 'benchvise':
return self.read_benchvise_pose(index)
pose_path = self.pose_pattern.format(self.class_type.title(), index)
pose = self.read_pose(pose_path)
if len(pose) == 0:
return np.array([])
return self.occlusion_pose_to_blender_pose(pose)
def get_mask_of_all_objects(self, index):
""" get the mask of all objects
1. initialize both mask map and depth map
2. update the mask map and depth map for each object by order
2.1 compute the col_row and depth of objects
2.2 for each pixel, if object's depth is shallower than the corresponding one in the depth map, then replace the label in the mask map
"""
mask_map = np.zeros(shape=[480, 640], dtype=np.uint8)
depth_map = 10 * np.ones(shape=[480, 640], dtype=np.float32)
def update(class_type):
self.class_type = class_type
pose = self.read_blender_pose(index)
if len(pose) == 0:
return
depth = self.opengl_renderer.render(class_type, pose, camera_type='linemod')
col_row = np.argwhere(depth != 0)[:, [1, 0]]
depth = depth[col_row[:, 1], col_row[:, 0]]
pixel_depth = depth_map[col_row[:, 1], col_row[:, 0]]
inds = (depth < pixel_depth)
selected_col_row = col_row[inds]
selected_depth = depth[inds]
selected_col_row = 640 * selected_col_row[:, 1] + selected_col_row[:, 0]
inds = np.lexsort([selected_depth, selected_col_row])
selected_col_row = selected_col_row[inds]
selected_depth = selected_depth[inds]
selected_col_row, inds = np.unique(selected_col_row, return_index=True)
selected_depth = selected_depth[inds]
selected_row = selected_col_row // 640
selected_col = selected_col_row % 640
mask_map[selected_row, selected_col] = int(self.class_type_to_number[class_type])
depth_map[selected_row, selected_col] = selected_depth
for class_type in self.class_type_to_number.keys():
update(class_type)
return mask_map
def get_mask(self, index):
""" get the mask for each object
1. get the mask of all objects
2. separate each object's mask from the mask map
"""
mask_path = self.mask_pattern.format('all_objects', index)
mask_map = self.get_mask_of_all_objects(index)
if os.path.exists(mask_path):
mask_map = np.array(Image.open(mask_path))
else:
mask_map = self.get_mask_of_all_objects(index)
Image.fromarray(mask_map).save(mask_path)
for class_type, class_type_num in self.class_type_to_number.items():
mask_path = self.mask_pattern.format(class_type, index)
class_type_num = int(class_type_num)
mask = (mask_map == class_type_num).astype(np.uint8)
Image.fromarray(mask).save(mask_path)
def get_masks(self):
""" get masks for each object in images
1. mkdir for each category
2. get masks for each image
"""
mask_dir_path = self.mask_dir_pattern.format('all_objects')
os.system('mkdir -p {}'.format(mask_dir_path))
for class_type in self.class_type_to_number.keys():
mask_dir_path = self.mask_dir_pattern.format(class_type)
os.system('mkdir -p {}'.format(mask_dir_path))
num_masks = len(os.listdir(self.rgb_dir_path))
for i in range(num_masks):
self.get_mask(i)
class OcclusionLineModDBSyn(OcclusionLineModDB):
def __init__(self):
super(OcclusionLineModDBSyn, self).__init__()
self.pose_pattern = os.path.join(cfg.LINEMOD, 'renders/all_objects/{}_{}_RT.pkl')
self.mask_dir_pattern = os.path.join(cfg.LINEMOD, 'renders/all_objects')
self.mask_pattern = os.path.join(self.mask_dir_pattern, '{}_{}_mask.png')
def read_blender_pose(self, index):
pose_path = self.pose_pattern.format(self.class_type, index)
return read_pickle(pose_path)['RT']
def get_mask(self, index):
""" get the mask for each object
1. get the mask of all objects
2. separate each object's mask from the mask map
"""
mask_path = self.mask_pattern.format(index, 'all_objects')
if os.path.exists(mask_path):
mask_map = np.array(Image.open(mask_path))
else:
mask_map = self.get_mask_of_all_objects(index)
Image.fromarray(mask_map).save(mask_path)
for class_type, class_type_num in self.class_type_to_number.items():
mask_path = self.mask_pattern.format(index, class_type)
class_type_num = int(class_type_num)
mask = (mask_map == class_type_num).astype(np.uint8)
Image.fromarray(mask).save(mask_path)
def get_masks(self):
""" get masks for each object in images
1. mkdir for each category
2. get masks for each image
"""
mask_dir_path = self.mask_dir_pattern
os.system('mkdir -p {}'.format(mask_dir_path))
for class_type in self.class_type_to_number.keys():
mask_dir_path = self.mask_dir_pattern.format(class_type)
os.system('mkdir -p {}'.format(mask_dir_path))
num_masks = len(glob.glob(os.path.join(mask_dir_path, '*_depth.png')))
for i in range(num_masks):
self.get_mask(i)
print('{} done'.format(i))
class YCBDB(object):
def __init__(self, class_type):
self.class_type = class_type
self.data_dir_path = os.path.join(cfg.YCB, 'data')
self.rgb_pattern = os.path.join(self.data_dir_path, '{:04}/{:06}-color.png')
self.projector = Projector()
def validate_pose(self):
rgb_path = '/home/pengsida/Datasets/YCB/renders/{}/0.jpg'.format(self.class_type)
pose_path = '/home/pengsida/Datasets/YCB/renders/{}/0_RT.pkl'.format(self.class_type)
model_path = '/home/pengsida/Datasets/YCB/models/{}/points.xyz'.format(self.class_type)
img = np.array(Image.open(rgb_path))
pose = read_pickle(pose_path)['RT']
model_3d = np.loadtxt(model_path)
model_2d = self.projector.project(model_3d, pose, 'blender')
import matplotlib.pyplot as plt
plt.imshow(img)
plt.plot(model_2d[:, 0], model_2d[:, 1], 'r.')
plt.show()
@staticmethod
def get_proper_crop_size():
mask_paths = glob.glob('/home/pengsida/Datasets/YCB/renders/003_cracker_box/*_depth.png')
widths = []
heights = []
for mask_path in mask_paths:
mask = np.array(Image.open(mask_path))
row_col = np.argwhere(mask == 1)
min_row, max_row = np.min(row_col[:, 0]), np.max(row_col[:, 0])
min_col, max_col = np.min(row_col[:, 1]), np.max(row_col[:, 1])
width = max_col - min_col
height = max_row - min_row
widths.append(width)
heights.append(height)
widths = np.array(widths)
heights = np.array(heights)
inds = np.lexsort([heights, widths])
print('min width: {}, max width: {}'.format(np.min(widths), np.max(widths)))
print('min height: {}, max height: {}'.format(np.min(heights), np.max(heights)))
print('min size: {}, {}'.format(heights[inds[0]], widths[inds[0]]))
print('max size: {}, {}'.format(heights[inds[-1]], widths[inds[-1]]))
class PrinterModelDB(object):
K={
'cat':np.asarray([[551.701,0.0,325.622],[0.0,552.732,239.996],[0.0,0.0,1.0]],np.float32)
}
def __init__(self,cls_name='cat'):
self.root_dir=os.path.join(cfg.DATA_DIR, '{}_print'.format(cls_name))
self.image_dir=os.path.join(self.root_dir,'images')
self.image_pattern=os.path.join(self.image_dir,'{:06}_color.png')
self.mask_pattern=os.path.join(self.image_dir,'{:06}_mask.png')
self.printer_model_pts= np.loadtxt(os.path.join(self.root_dir, 'point_cloud.txt'))[:, :3]
self.model_pts=LineModModelDB().get_ply_model(cls_name)
self.poses=self.parse_poses()
self.image_num=len(self.poses)
self.aligned_poses=self.align_poses()
# parse pose
def parse_poses(self):
RTs=[]
with open(os.path.join(self.root_dir, 'new_camera_poses_ascii.txt'), 'r') as f:
lines=f.readlines()
pose_num=len(lines)//5
for k in range(pose_num):
cur_lines=[line.replace('\n',' ') for line in lines[k*5+1:k*5+4]]
RT=[]
for line in cur_lines:
for item in line.strip().split(' '):
if len(item)>0:
RT.append(float(item))
RT=np.asarray(RT).reshape([3, 4])
R=RT[:,:3].transpose()
T=-np.dot(R,RT[:,3])
RT=np.concatenate([R,T[:,None]],1)
RTs.append(RT)
return RTs
def validate_original_poses(self):
for k in range(0,self.image_num,20):
rgb=imread(self.image_pattern.format(k+1))
img_pts=Projector.project_K(self.printer_model_pts.copy(), self.poses[k], self.K['cat'])
pts_img=img_pts_to_pts_img(img_pts,484,648)
print(self.poses[k])
rgb[pts_img>0]//=2
rgb[pts_img>0]+=np.asarray([127,0,0],np.uint8)
plt.imshow(rgb)