fix bugs

app.py

@@ -131,35 +131,32 @@ def inference_on_file(target_image, model, custom_test_pipeline):
 
     # output_image = target_image.replace('.tif', '_pred.tif')
     time_taken=-1
-    try:
-        st = time.time()
-        print('Running inference...')
-        result = inference_segmentor(model, target_image, custom_test_pipeline)
-        print("Output has shape: " + str(result[0].shape))
-        
-        ##### get metadata mask
-        mask = open_tiff(target_image)
-        # rgb = mask[[2, 1, 0], :, :].transpose((1,2,0))
-        rgb = mask[[5, 3, 2], :, :].transpose((1,2,0))
-        meta = get_meta(target_image)
-        mask = np.where(mask == meta['nodata'], 1, 0)
-        mask = np.max(mask, axis=0)[None]
-        
-        result[0] = np.where(mask == 1, -1, result[0])
-        
-        ##### Save file to disk
-        meta["count"] = 1
-        meta["dtype"] = "int16"
-        meta["compress"] = "lzw"
-        meta["nodata"] = -1
-        print('Saving output...')
-        # write_tiff(result[0], output_image, meta)
-        et = time.time()
-        time_taken = np.round(et - st, 1)
-        print(f'Inference completed in {str(time_taken)} seconds')
-        
-    except:
-        print(f'Error on image {target_image} \nContinue to next input')
+    
+    st = time.time()
+    print('Running inference...')
+    result = inference_segmentor(model, target_image, custom_test_pipeline)
+    print("Output has shape: " + str(result[0].shape))
+
+    ##### get metadata mask
+    mask = open_tiff(target_image)
+    # rgb = mask[[2, 1, 0], :, :].transpose((1,2,0))
+    rgb = mask[[5, 3, 2], :, :].transpose((1,2,0))
+    meta = get_meta(target_image)
+    mask = np.where(mask == meta['nodata'], 1, 0)
+    mask = np.max(mask, axis=0)[None]
+
+    result[0] = np.where(mask == 1, -1, result[0])
+
+    ##### Save file to disk
+    meta["count"] = 1
+    meta["dtype"] = "int16"
+    meta["compress"] = "lzw"
+    meta["nodata"] = -1
+    print('Saving output...')
+    # write_tiff(result[0], output_image, meta)
+    et = time.time()
+    time_taken = np.round(et - st, 1)
+    print(f'Inference completed in {str(time_taken)} seconds')
         
     return rgb, result[0][0]*255