Spaces:
Runtime error
Runtime error
File size: 9,216 Bytes
c2ea21f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 |
from model.floorplan import *
from model.box_utils import *
from model.model import Model
import os
from model.utils import *
import Houseweb.views as vw
import numpy as np
import time
import math
import matlab.engine
global adjust,indxlist
adjust=False
def get_data(fp):
batch = list(fp.get_test_data())
batch[0] = batch[0].unsqueeze(0).cuda()
batch[1] = batch[1].cuda()
batch[2] = batch[2].cuda()
batch[3] = batch[3].cuda()
batch[4] = batch[4].cuda()
return batch
def test(model,fp):
with torch.no_grad():
batch = get_data(fp)
boundary,inside_box,rooms,attrs,triples = batch
model_out = model(
rooms,
triples,
boundary,
obj_to_img = None,
attributes = attrs,
boxes_gt= None,
generate = True,
refine = True,
relative = True,
inside_box=inside_box
)
boxes_pred, gene_layout, boxes_refine= model_out
boxes_pred = boxes_pred.detach()
boxes_pred = centers_to_extents(boxes_pred)
boxes_refine = boxes_refine.detach()
boxes_refine = centers_to_extents(boxes_refine)
gene_layout = gene_layout*boundary[:,:1]
gene_preds = torch.argmax(gene_layout.softmax(1).detach(),dim=1)
return boxes_pred.squeeze().cpu().numpy(),gene_preds.squeeze().cpu().double().numpy(),boxes_refine.squeeze().cpu().numpy()
def load_model():
model = Model()
model.cuda(0)
model.load_state_dict(
torch.load('./model/model.pth', map_location={'cuda:0': 'cuda:0'}))
model.eval()
return model
def get_userinfo(userRoomID,adptRoomID):
start = time.clock()
global model
test_index = vw.testNameList.index(userRoomID.split(".")[0])
test_data = vw.test_data[test_index]
# boundary
Boundary = test_data.boundary
boundary=[[float(x),float(y),float(z),float(k)] for x,y,z,k in list(Boundary)]
test_fp =FloorPlan(test_data)
train_index = vw.trainNameList.index(adptRoomID.split(".")[0])
train_data = vw.train_data[train_index]
train_fp =FloorPlan(train_data,train=True)
fp_end = test_fp.adapt_graph(train_fp)
fp_end.adjust_graph()
return fp_end
def get_userinfo_adjust(userRoomID,adptRoomID,NewGraph):
global adjust,indxlist
test_index = vw.testNameList.index(userRoomID.split(".")[0])
test_data = vw.test_data[test_index]
# boundary
Boundary = test_data.boundary
boundary=[[float(x),float(y),float(z),float(k)] for x,y,z,k in list(Boundary)]
test_fp =FloorPlan(test_data)
train_index = vw.trainNameList.index(adptRoomID.split(".")[0])
train_data = vw.train_data[train_index]
train_fp =FloorPlan(train_data,train=True)
fp_end = test_fp.adapt_graph(train_fp)
fp_end.adjust_graph()
newNode = NewGraph[0]
newEdge = NewGraph[1]
oldNode = NewGraph[2]
temp = []
for newindx, newrmname, newx, newy,scalesize in newNode:
for type, oldrmname, oldx, oldy, oldindx in oldNode:
if (int(newindx) == oldindx):
tmp=int(newindx), (newx - oldx), ( newy- oldy),float(scalesize)
temp.append(tmp)
newbox=[]
print(adjust)
if adjust==True:
oldbox = []
for i in range(len(vw.boxes_pred)):
indxtmp=[vw.boxes_pred[i][0],vw.boxes_pred[i][1],vw.boxes_pred[i][2],vw.boxes_pred[i][3],vw.boxes_pred[i][0]]
oldbox.append(indxtmp)
if adjust==False:
indxlist=[]
oldbox=fp_end.data.box.tolist()
for i in range(len(oldbox)):
indxlist.append([oldbox[i][4]])
indxlist=np.array(indxlist)
adjust=True
oldbox=fp_end.data.box.tolist()
# print("oldbox",oldbox)
# print(oldbox,"oldbox")
X=0
Y=0
for i in range(len(oldbox)):
X= X+(oldbox[i][2]-oldbox[i][0])
Y= Y+(oldbox[i][3]-oldbox[i][1])
x_ave=(X/len(oldbox))/2
y_ave=(Y/len(oldbox))/2
index_mapping = {}
# The room that already exists
# Move: Just by the distance
for newindx, tempx, tempy,scalesize in temp:
index_mapping[newindx] = len(newbox)
tmpbox=[]
scalesize = int(scalesize)
if scalesize<1:
scale = math.sqrt(scalesize)
scalex = (oldbox[newindx][2] - oldbox[newindx][0]) * (1 - scale) / 2
scaley = (oldbox[newindx][3] - oldbox[newindx][1]) * (1 - scale) / 2
tmpbox = [(oldbox[newindx][0] + tempx) + scalex, (oldbox[newindx][1] + tempy)+scaley,
(oldbox[newindx][2] + tempx) - scalex, (oldbox[newindx][3] + tempy) - scaley, oldbox[newindx][4]]
if scalesize == 1:
tmpbox = [(oldbox[newindx][0] + tempx) , (oldbox[newindx][1] + tempy) ,(oldbox[newindx][2] + tempx), (oldbox[newindx][3] + tempy), oldbox[newindx][4]]
if scalesize>1:
scale=math.sqrt(scalesize)
scalex = (oldbox[newindx][2] - oldbox[newindx][0]) * ( scale-1) / 2
scaley = (oldbox[newindx][3] - oldbox[newindx][1]) * (scale-1) / 2
tmpbox = [(oldbox[newindx][0] + tempx) - scalex, (oldbox[newindx][1] + tempy) - scaley,
(oldbox[newindx][2] + tempx) + scalex, (oldbox[newindx][3] + tempy) + scaley, oldbox[newindx][4]]
newbox.append(tmpbox)
# The room just added
# Move: The room node with the average size of the existing room
for newindx, newrmname, newx, newy,scalesize in newNode:
if int(newindx)>(len(oldbox)-1):
scalesize=int(scalesize)
index_mapping[int(newindx)] = (len(newbox))
tmpbox=[]
if scalesize < 1:
scale = math.sqrt(scalesize)
scalex = x_ave * (1 - scale) / 2
scaley = y_ave* (1 - scale) / 2
tmpbox = [(newx-x_ave) +scalex,(newy-y_ave) +scaley,(newx+x_ave)-scalex,(newy+y_ave)-scaley,vocab['object_name_to_idx'][newrmname]]
if scalesize == 1:
tmpbox = [(newx - x_ave), (newy - y_ave), (newx + x_ave), (newy + y_ave),vocab['object_name_to_idx'][newrmname]]
if scalesize > 1:
scale = math.sqrt(scalesize)
scalex = x_ave * (scale - 1) / 2
scaley = y_ave * (scale - 1) / 2
tmpbox = [(newx-x_ave) - scalex, (newy-y_ave) - scaley,(newx+x_ave) + scalex, (newy+y_ave) + scaley,vocab['object_name_to_idx'][newrmname]]
print(scalesize)
newbox.append(tmpbox)
fp_end.data.box=np.array(newbox)
adjust_Edge=[]
for u, v in newEdge:
tmp=[index_mapping[int(u)],index_mapping[int(v)], 0]
adjust_Edge.append(tmp)
fp_end.data.edge=np.array(adjust_Edge)
rNode = fp_end.get_rooms(tensor=False)
rEdge = fp_end.get_triples(tensor=False)[:, [0, 2, 1]]
Edge = [[float(u), float(v), float(type2)] for u, v, type2 in rEdge]
s=time.clock()
boxes_pred, gene_layout, boxes_refeine = test(vw.model, fp_end)
e=time.clock()
print(' model test time: %s Seconds' % (e - s))
boxes_pred = boxes_pred * 255
fp_end.data.gene = gene_layout
rBox = boxes_pred[:]
Box = [[float(x), float(y), float(z), float(k)] for x, y, z, k in rBox]
boundary_mat = matlab.double(boundary)
rNode_mat = matlab.double(rNode.tolist())
print("rNode.tolist()",rNode.tolist())
Edge_mat = matlab.double(Edge)
Box_mat=matlab.double(Box)
fp_end.data.boundary =np.array(boundary)
fp_end.data.rType =np.array(rNode).astype(int)
fp_end.data.refineBox=np.array(Box)
fp_end.data.rEdge=np.array(Edge)
gene_mat=matlab.double(np.array(fp_end.data.gene).tolist())
startcom= time.clock()
box_refine = vw.engview.align_fp(boundary_mat, Box_mat, rNode_mat,Edge_mat,matlab.double(fp_end.data.gene.astype(float).copy().tolist()) ,18,False, nargout=3)
endcom = time.clock()
print(' matlab.compute time: %s Seconds' % (endcom - startcom))
box_out=box_refine[0]
box_order=box_refine[1]
rBoundary=box_refine[2]
fp_end.data.newBox = np.array(box_out)
fp_end.data.order = np.array(box_order)
fp_end.data.rBoundary = [np.array(rb) for rb in rBoundary]
return fp_end,box_out,box_order, gene_layout, boxes_refeine
def get_userinfo_net(userRoomID,adptRoomID):
global model
test_index = vw.testNameList.index(userRoomID.split(".")[0])
test_data = vw.test_data[test_index]
# boundary
Boundary = test_data.boundary
boundary = [[float(x), float(y), float(z), float(k)] for x, y, z, k in list(Boundary)]
test_fp = FloorPlan(test_data)
train_index = vw.trainNameList.index(adptRoomID.split(".")[0])
train_data = vw.train_data[train_index]
train_fp = FloorPlan(train_data, train=True)
fp_end = test_fp.adapt_graph(train_fp)
fp_end.adjust_graph()
boxes_pred, gene_layout, boxes_refeine = test(model, fp_end)
boxes_pred=boxes_pred*255
for i in range(len(boxes_pred)):
for j in range(len(boxes_pred[i])):
boxes_pred[i][j]=float(boxes_pred[i][j])
return fp_end,boxes_pred, gene_layout, boxes_refeine
if __name__ == "__main__":
pass
|