inference переписан под CLI, удаление не рантайм файлов

inference.py

@@ -9,8 +9,8 @@ import cv2
 import os
 from PIL import Image
 import argparse
-
 import warnings
+
 warnings.filterwarnings('ignore')
 
 
@@ -18,98 +18,80 @@ class Net(nn.Module):
     def __init__(self):
         super(Net, self).__init__()
         self.msk = U2NETP(3, 1)
-        self.bm = DocScanner()  # 矫正
+        self.bm = DocScanner()
 
     def forward(self, x):
-        msk, _1,_2,_3,_4,_5,_6 = self.msk(x)
+        msk, *_ = 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')
+def load_model(model, path, strip_prefix=False):
+    """Универсальная загрузка весов"""
+    if not path or not os.path.exists(path):
+        raise FileNotFoundError(f"Model not found: {path}")
+    
+    model_dict = model.state_dict()
+    pretrained_dict = torch.load(path, map_location='cuda:0')
+    
+    if strip_prefix:
+        # Для seg модели ключи имеют префикс 'module.' (6 символов)
         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))
+        
+    model_dict.update(pretrained_dict)
+    model.load_state_dict(model_dict)
+    return model
 
 
 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/')
+    parser = argparse.ArgumentParser(description='DocScanner Single File Inference')
+    parser.add_argument('-i', '--input', required=True, help='Input image path')
+    parser.add_argument('-o', '--output', required=True, help='Output image path')
+    parser.add_argument('--seg_model', default='./model_pretrained/seg.pth')
+    parser.add_argument('--rec_model', default='./model_pretrained/DocScanner-L.pth')
     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)
+    # 1. Init & Load Models
+    net = Net().cuda()
+    net.msk = load_model(net.msk, opt.seg_model, strip_prefix=True)
+    net.bm = load_model(net.bm, opt.rec_model, strip_prefix=False)
+    net.eval()
+
+    # 2. Preprocess
+    im_ori = np.array(Image.open(opt.input))[:, :, :3] / 255.0
+    h, w, _ = im_ori.shape
+    
+    im = cv2.resize(im_ori, (288, 288))
+    im = torch.from_numpy(im.transpose(2, 0, 1)).float().unsqueeze(0)
+
+    # 3. Inference
+    with torch.no_grad():
+        bm = net(im.cuda()).cpu()
+
+    # 4. Postprocess & Warp
+    bm0 = cv2.blur(cv2.resize(bm[0, 0].numpy(), (w, h)), (3, 3))
+    bm1 = cv2.blur(cv2.resize(bm[0, 1].numpy(), (w, h)), (3, 3))
+    
+    lbl = torch.from_numpy(np.stack([bm0, bm1], axis=2)).unsqueeze(0)
+    inp_tensor = torch.from_numpy(im_ori).permute(2, 0, 1).unsqueeze(0).float()
+    
+    out = F.grid_sample(inp_tensor, lbl, align_corners=True)
+    
+    # 5. Save
+    result = ((out[0] * 255).permute(1, 2, 0).numpy()[:, :, ::-1]).astype(np.uint8)
+    
+    out_dir = os.path.dirname(opt.output)
+    if out_dir and not os.path.exists(out_dir):
+        os.makedirs(out_dir)
+        
+    cv2.imwrite(opt.output, result)
+    print(f"[OK] Saved: {opt.output}")
 
 
 if __name__ == "__main__":