-
Notifications
You must be signed in to change notification settings - Fork 2
/
eval.py
57 lines (54 loc) · 1.98 KB
/
eval.py
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
import os
import cv2
import numpy as np
import torch
import model
ptrimap='./trimap/'
pimgs='./image/'
p_outs='./alpha/'
os.makedirs(p_outs,exist_ok=True)
if __name__ == '__main__':
matmodel = model.AEMatter()
matmodel.load_state_dict(torch.load('./AEMFIX.ckpt',map_location='cpu')['model'])
matmodel=matmodel.cuda()
matmodel.eval()
for idx,file in enumerate(os.listdir(ptrimap)) :
print(idx)
rawimg=pimgs+file
trimap=ptrimap+file
rawimg=cv2.imread(rawimg)
trimap=cv2.imread(trimap,cv2.IMREAD_GRAYSCALE)
trimap_nonp=trimap.copy()
h,w,c=rawimg.shape
nonph,nonpw,_=rawimg.shape
newh= (((h-1)//32)+1)*32
neww= (((w-1)//32)+1)*32
padh=newh-h
padh1=int(padh/2)
padh2=padh-padh1
padw=neww-w
padw1=int(padw/2)
padw2=padw-padw1
rawimg_pad=cv2.copyMakeBorder(rawimg,padh1,padh2,padw1,padw2,cv2.BORDER_REFLECT)
trimap_pad=cv2.copyMakeBorder(trimap,padh1,padh2,padw1,padw2,cv2.BORDER_REFLECT)
h_pad,w_pad,_=rawimg_pad.shape
tritemp = np.zeros([*trimap_pad.shape, 3], np.float32)
tritemp[:, :, 0] = (trimap_pad == 0)
tritemp[:, :, 1] = (trimap_pad == 128)
tritemp[:, :, 2] = (trimap_pad == 255)
tritempimgs=np.transpose(tritemp,(2,0,1))
tritempimgs=tritempimgs[np.newaxis,:,:,:]
img=np.transpose(rawimg_pad,(2,0,1))[np.newaxis,::-1,:,:]
img=np.array(img,np.float32)
img=img/255.
img=torch.from_numpy(img).cuda()
tritempimgs=torch.from_numpy(tritempimgs).cuda()
with torch.no_grad():
pred=matmodel(img,tritempimgs)
pred=pred.detach().cpu().numpy()[0]
pred=pred[:,padh1:padh1+h,padw1:padw1+w]
preda=pred[0:1,]*255
preda=np.transpose(preda,(1,2,0))
preda=preda*(trimap_nonp[:,:,None]==128)+(trimap_nonp[:,:,None]==255)*255
preda=np.array(preda,np.uint8)
cv2.imwrite(p_outs+file,preda)