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from model import DocGeoNet
from seg import U2NETP
import torch
import torch.nn as nn
import torch.nn.functional as F
import skimage.io as io
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, opt):
super(Net, self).__init__()
self.msk = U2NETP(3, 1)
self.DocTr = DocGeoNet()
def forward(self, x):
msk, _1,_2,_3,_4,_5,_6 = self.msk(x)
msk = (msk > 0.5).float()
x = msk * x
_, _, bm = self.DocTr(x)
bm = (2 * (bm / 255.) - 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='cpu')
print(len(pretrained_dict.keys()))
pretrained_dict = {k[6:]: v for k, v in pretrained_dict.items() if k[6:] in model_dict}
print(len(pretrained_dict.keys()))
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='cpu')
print(len(pretrained_dict.keys()))
pretrained_dict = {k[7:]: v for k, v in pretrained_dict.items() if k[7:] in model_dict}
print(len(pretrained_dict.keys()))
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, opt):
print(torch.__version__)
# distorted images list
img_list = sorted(os.listdir(distorrted_path))
# creat save path for rectified images
if not os.path.exists(save_path):
os.makedirs(save_path)
net = Net(opt)#.cuda()
print(get_parameter_number(net))
# reload rec model
reload_rec_model(net.DocTr, rec_model_path)
reload_seg_model(net.msk, opt.seg_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. # read image 0-255 to 0-1
h, w, _ = im_ori.shape
im = cv2.resize(im_ori, (256, 256))
im = im.transpose(2, 0, 1)
im = torch.from_numpy(im).float().unsqueeze(0)
with torch.no_grad():
bm = net(im)
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 get_parameter_number(net):
total_num = sum(p.numel() for p in net.parameters())
trainable_num = sum(p.numel() for p in net.parameters() if p.requires_grad)
return {'Total': total_num, 'Trainable': trainable_num}
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--seg_model_path', default='./model_pretrained/preprocess.pth')
parser.add_argument('--rec_model_path', default='./model_pretrained/DocGeoNet.pth')
parser.add_argument('--distorrted_path', default='./distorted/')
parser.add_argument('--save_path', default='./rec/')
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.save_path,
opt=opt)
if __name__ == "__main__":
main()
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