117 lines
3.6 KiB
Python
117 lines
3.6 KiB
Python
from model import DocScanner
|
|
from seg import U2NETP
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
import numpy as np
|
|
import cv2
|
|
import os
|
|
from PIL import Image
|
|
import argparse
|
|
|
|
import warnings
|
|
warnings.filterwarnings('ignore')
|
|
|
|
|
|
class Net(nn.Module):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.msk = U2NETP(3, 1)
|
|
self.bm = DocScanner() # 矫正
|
|
|
|
def forward(self, x):
|
|
msk, _1,_2,_3,_4,_5,_6 = self.msk(x)
|
|
msk = (msk > 0.5).float()
|
|
x = msk * x
|
|
|
|
bm = self.bm(x, iters=12, test_mode=True)
|
|
bm = (2 * (bm / 286.8) - 1) * 0.99
|
|
|
|
return bm
|
|
|
|
|
|
def reload_seg_model(model, path=""):
|
|
if not bool(path):
|
|
return model
|
|
else:
|
|
model_dict = model.state_dict()
|
|
pretrained_dict = torch.load(path, map_location='cuda:0')
|
|
pretrained_dict = {k[6:]: v for k, v in pretrained_dict.items() if k[6:] in model_dict}
|
|
model_dict.update(pretrained_dict)
|
|
model.load_state_dict(model_dict)
|
|
|
|
return model
|
|
|
|
|
|
def reload_rec_model(model, path=""):
|
|
if not bool(path):
|
|
return model
|
|
else:
|
|
model_dict = model.state_dict()
|
|
pretrained_dict = torch.load(path, map_location='cuda:0')
|
|
pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
|
|
model_dict.update(pretrained_dict)
|
|
model.load_state_dict(model_dict)
|
|
|
|
return model
|
|
|
|
|
|
def rec(seg_model_path, rec_model_path, distorrted_path, save_path):
|
|
# distorted images list
|
|
img_list = os.listdir(distorrted_path)
|
|
|
|
# creat save path for rectified images
|
|
if not os.path.exists(save_path):
|
|
os.makedirs(save_path)
|
|
|
|
# net init
|
|
net = Net().cuda()
|
|
# reload seg model
|
|
reload_seg_model(net.msk, seg_model_path)
|
|
# reload rec model
|
|
reload_rec_model(net.bm, rec_model_path)
|
|
|
|
net.eval()
|
|
|
|
for img_path in img_list:
|
|
name = img_path.split('.')[-2] # image name
|
|
img_path = distorrted_path + img_path # image path
|
|
|
|
im_ori = np.array(Image.open(img_path))[:, :, :3] / 255.
|
|
h, w, _ = im_ori.shape
|
|
im = cv2.resize(im_ori, (288, 288))
|
|
im = im.transpose(2, 0, 1)
|
|
im = torch.from_numpy(im).float().unsqueeze(0)
|
|
|
|
with torch.no_grad():
|
|
bm = net(im.cuda())
|
|
bm = bm.cpu()
|
|
|
|
# save rectified image
|
|
bm0 = cv2.resize(bm[0, 0].numpy(), (w, h)) # x flow
|
|
bm1 = cv2.resize(bm[0, 1].numpy(), (w, h)) # y flow
|
|
bm0 = cv2.blur(bm0, (3, 3))
|
|
bm1 = cv2.blur(bm1, (3, 3))
|
|
lbl = torch.from_numpy(np.stack([bm0, bm1], axis=2)).unsqueeze(0) # h * w * 2
|
|
out = F.grid_sample(torch.from_numpy(im_ori).permute(2, 0, 1).unsqueeze(0).float(), lbl, align_corners=True)
|
|
cv2.imwrite(save_path + name + '_rec' + '.png', (((out[0]*255).permute(1, 2, 0).numpy())[:,:,::-1]).astype(np.uint8))
|
|
|
|
|
|
def main():
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('--seg_model_path', default='./model_pretrained/seg.pth')
|
|
parser.add_argument('--rec_model_path', default='./model_pretrained/DocScanner-L.pth')
|
|
parser.add_argument('--distorrted_path', default='./distorted/')
|
|
parser.add_argument('--rectified_path', default='./rectified/')
|
|
opt = parser.parse_args()
|
|
|
|
rec(seg_model_path=opt.seg_model_path,
|
|
rec_model_path=opt.rec_model_path,
|
|
distorrted_path=opt.distorrted_path,
|
|
save_path=opt.rectified_path)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|