Update app.py

app.py

@@ -72,59 +72,59 @@ def reload_segmodel(model, path=""):
         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)
-    if not os.path.exists(opt.isave_path):  # create save path
-        os.mkdir(opt.isave_path)
+# 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)
+#     if not os.path.exists(opt.isave_path):  # create save path
+#         os.mkdir(opt.isave_path)
     
-    GeoTr_Seg_model = GeoTr_Seg()#.cuda()
-    # reload segmentation model
-    reload_segmodel(GeoTr_Seg_model.msk, opt.Seg_path)
-    # reload geometric unwarping model
-    reload_model(GeoTr_Seg_model.GeoTr, opt.GeoTr_path)
+#     GeoTr_Seg_model = GeoTr_Seg()#.cuda()
+#     # reload segmentation model
+#     reload_segmodel(GeoTr_Seg_model.msk, opt.Seg_path)
+#     # reload geometric unwarping model
+#     reload_model(GeoTr_Seg_model.GeoTr, opt.GeoTr_path)
     
-    IllTr_model = IllTr()#.cuda()
-    # reload illumination rectification model
-    reload_model(IllTr_model, opt.IllTr_path)
+#     IllTr_model = IllTr()#.cuda()
+#     # reload illumination rectification model
+#     reload_model(IllTr_model, opt.IllTr_path)
     
-    # To eval mode
-    GeoTr_Seg_model.eval()
-    IllTr_model.eval()
+#     # To eval mode
+#     GeoTr_Seg_model.eval()
+#     IllTr_model.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)
+#     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 = GeoTr_Seg_model(im)
-            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
+#         with torch.no_grad():
+#             # geometric unwarping
+#             bm = GeoTr_Seg_model(im)
+#             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
+#             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
             
-            # illumination rectification
-            if opt.ill_rec:
-                ill_savep = opt.isave_path + name + '_ill' + '.png'
-                rec_ill(IllTr_model, img_geo, saveRecPath=ill_savep)
+#             # illumination rectification
+#             if opt.ill_rec:
+#                 ill_savep = opt.isave_path + name + '_ill' + '.png'
+#                 rec_ill(IllTr_model, img_geo, saveRecPath=ill_savep)
         
-        print('Done: ', img_path)
+#         print('Done: ', img_path)