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Zero
File size: 6,228 Bytes
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import torch
import numpy as np
from torch.utils.data.dataloader import default_collate
from scalelsd.base.csrc import _C
class HAFMencoder(object):
def __init__(self, dis_th = 10, ang_th = 0):
self.dis_th = dis_th
self.ang_th = ang_th
def __call__(self,annotations):
targets = []
metas = []
batch_size = annotations['batch_size']
stride = annotations['stride']
for batch_id in range(batch_size):
junctions = annotations['junctions'][batch_id].clone()[:,[1,0]]/float(stride)
width = annotations['width']//stride
height = annotations['height']//stride
edge_indices = annotations['line_map'][batch_id].triu().nonzero()
t, m = self.encoding_single_image(junctions,edge_indices,height,width)
targets.append(t)
metas.append(m)
return default_collate(targets),metas
def adjacent_matrix(self, n, edges, device):
mat = torch.zeros(n+1,n+1,dtype=torch.bool,device=device)
if edges.size(0)>0:
mat[edges[:,0], edges[:,1]] += True
mat[edges[:,1], edges[:,0]] += True
return mat
def lines2hafm(self, lines, height, width):
device = lines.device
if lines.shape[0] == 0:
hafm_ang = torch.zeros((3,height,width),device=device)
hafm_dis = torch.zeros((1,height,width),device=device)
hafm_mask = torch.zeros((1,height,width),device=device)
return torch.zeros((3,height,width),device=device), torch.zeros((1,height,width),device=device), torch.zeros((1,height,width),device=device)
lmap, _, _ = _C.encodels(lines,height,width,height,width,lines.size(0))
dismap = torch.sqrt(lmap[0]**2+lmap[1]**2)[None]
def _normalize(inp):
mag = torch.sqrt(inp[0]*inp[0]+inp[1]*inp[1])
return inp/(mag+1e-6)
md_map = _normalize(lmap[:2])
st_map = _normalize(lmap[2:4])
ed_map = _normalize(lmap[4:])
st_map = lmap[2:4]
ed_map = lmap[4:]
md_ = md_map.reshape(2,-1).t()
st_ = st_map.reshape(2,-1).t()
ed_ = ed_map.reshape(2,-1).t()
Rt = torch.cat(
(torch.cat((md_[:,None,None,0],md_[:,None,None,1]),dim=2),
torch.cat((-md_[:,None,None,1], md_[:,None,None,0]),dim=2)),dim=1)
R = torch.cat(
(torch.cat((md_[:,None,None,0], -md_[:,None,None,1]),dim=2),
torch.cat((md_[:,None,None,1], md_[:,None,None,0]),dim=2)),dim=1)
#Rtst_ = torch.matmul(Rt, st_[:,:,None]).squeeze(-1).t()
#Rted_ = torch.matmul(Rt, ed_[:,:,None]).squeeze(-1).t()
Rtst_ = torch.bmm(Rt, st_[:,:,None]).squeeze(-1).t()
Rted_ = torch.bmm(Rt, ed_[:,:,None]).squeeze(-1).t()
swap_mask = (Rtst_[1]<0)*(Rted_[1]>0)
pos_ = Rtst_.clone()
neg_ = Rted_.clone()
temp = pos_[:,swap_mask]
pos_[:,swap_mask] = neg_[:,swap_mask]
neg_[:,swap_mask] = temp
pos_[0] = pos_[0].clamp(min=1e-9)
pos_[1] = pos_[1].clamp(min=1e-9)
neg_[0] = neg_[0].clamp(min=1e-9)
neg_[1] = neg_[1].clamp(max=-1e-9)
mask = (dismap.view(-1)<=self.dis_th).float()
pos_map = pos_.reshape(-1,height,width)
neg_map = neg_.reshape(-1,height,width)
md_angle = torch.atan2(md_map[1], md_map[0])
pos_angle = torch.atan2(pos_map[1],pos_map[0])
neg_angle = torch.atan2(neg_map[1],neg_map[0])
mask *= (pos_angle.reshape(-1)>self.ang_th*np.pi/2.0)
mask *= (neg_angle.reshape(-1)<-self.ang_th*np.pi/2.0)
pos_angle_n = pos_angle/(np.pi/2)
neg_angle_n = -neg_angle/(np.pi/2)
md_angle_n = md_angle/(np.pi*2) + 0.5
mask = mask.reshape(height,width)
hafm_ang = torch.cat((md_angle_n[None],pos_angle_n[None],neg_angle_n[None],),dim=0)
hafm_dis = dismap.clamp(max=self.dis_th)/self.dis_th
mask = mask[None]
return hafm_ang, hafm_dis, mask
def encoding_single_image(self, junctions, edge_indices, height, width):
device = junctions.device
# jmap = torch.zeros((height,width),device=device)
# joff = torch.zeros((2,height,width),device=device,dtype=torch.float32)
jmap = np.zeros((height,width),dtype=np.float32)
joff = np.zeros((2,height,width),dtype=np.float32)
dx, dy = np.meshgrid(np.arange(width), np.arange(height))
# gaussian = np.exp(-(dx**2+dy**2)/2.0/2.0**2)
if junctions.shape[0] > 0:
junctions_np = junctions.cpu().numpy()
xint, yint = junctions_np[:,0].astype(np.int32), junctions_np[:,1].astype(np.int32)
off_x = junctions_np[:,0] - np.floor(junctions_np[:,0]) - 0.5
off_y = junctions_np[:,1] - np.floor(junctions_np[:,1]) - 0.5
jmap[yint,xint] = 1#= jmap[yint,xint] + 1
joff[0,yint,xint] = off_x
joff[1,yint,xint] = off_y
lines = junctions[edge_indices].reshape(-1,4)
pos_mat = self.adjacent_matrix(junctions.size(0), edge_indices, device)
labels = torch.ones((lines.shape[0],),device=device)
else:
lines = torch.empty((0,4),device=device)
pos_mat = None
labels = None
# for _x,_y in junctions.cpu().numpy():
# _map = np.exp(-((dx-_x)**2+(dy-_y)**2)/2.0/8.0**2)
# _map /= _map.max()
# jmap = np.maximum(jmap,_map)
# import matplotlib.pyplot as plt
# import pdb; pdb.set_trace()
jmap = torch.from_numpy(jmap).to(device)
joff = torch.from_numpy(joff).to(device)
hafm_ang, hafm_dis, hafm_mask = self.lines2hafm(lines,height,width)
target = {
'jloc': jmap[None],
'joff': joff,
'md': hafm_ang,
'dis': hafm_dis,
'mask': hafm_mask
}
meta = {
'junc': junctions,
'lines': lines,
'Lpos': pos_mat,
'lpre': lines,
'lpre_label': labels
}
return target, meta
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