init

inference.py

@@ -0,0 +1,116 @@
+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()