Update app.py

app.py

@@ -73,63 +73,6 @@ 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)
-    
-#     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)
-    
-#     # 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)
-        
-#         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
-            
-#             # 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)
-        
-
-
-
 
 
 def process_image(input_image):
@@ -169,6 +112,14 @@ def process_image(input_image):
         else:
             return Image.fromarray(img_geo)
 
+    
+    desired_width = 200
+    scale_ratio = desired_width / output_image.width
+    desired_height = int(output_image.height * scale_ratio)
+    output_image_resized = output_image.resize((desired_width, desired_height), Image.ANTIALIAS)
+
+    return output_image_resized
+
 # Define Gradio interface
 input_image = gr.inputs.Image()
 output_image = gr.outputs.Image(type='pil')