DocTr-Plus/inference.py at master · fh2019ustc/DocTr-Plus · GitHub

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
from GeoTr import GeoTr

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 glob
import os
from PIL import Image
import argparse
import warnings
warnings.filterwarnings('ignore')


class GeoTrP(nn.Module):
    def __init__(self):
        super(GeoTrP, self).__init__()
        self.GeoTr = GeoTr()

    def forward(self, x):
        bm = self.GeoTr(x)
        bm = (2 * (bm / 286.8) - 1) * 0.99

        return bm


def reload_model(model, path=""):
    if not bool(path):
        return model
    else:
        model_dict = model.state_dict()
        pretrained_dict = torch.load(path, map_location='cuda:0')
        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(opt):
    # print(torch.__version__) # 1.5.1
    img_list = os.listdir(opt.distorrted_path)  # distorted images list

    if not os.path.exists(opt.gsave_path):  # create save path
        os.mkdir(opt.gsave_path)

    GeoTrP = GeoTrP().cuda()
    # reload geometric unwarping model
    reload_model(GeoTrP.GeoTr, opt.GeoTr_path)

    # To eval mode
    GeoTrP.eval()

    for img_path in img_list:
        name = img_path.split('.')[-2]  # image name

        img_path = opt.distorrted_path + img_path  # read image and to tensor
        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():
            # geometric unwarping
            bm = GeoTrP(im.cuda())
            bm = bm.cpu()
            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)
            img_geo = ((out[0]*255).permute(1, 2, 0).numpy())[:,:,::-1].astype(np.uint8)
            cv2.imwrite(opt.gsave_path + name + '_geo' + '.png', img_geo)  # save

        print('Done: ', img_path)


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument('--distorrted_path',  default='./distorted/')
    parser.add_argument('--gsave_path',  default='./rectified/')
    parser.add_argument('--GeoTr_path',  default='./model_pretrained/DocTrP.pth')

    opt = parser.parse_args()

    rec(opt)


if __name__ == '__main__':
    main()