.

app.py

@@ -57,7 +57,7 @@ cdl_color_map = [{'value': 1, 'label': 'Natural vegetation', 'rgb': (233,255,190
                  {'value': 12, 'label': 'Sorghum', 'rgb':(255,158,15), 'qtt': 0},
                  {'value': 13, 'label': 'Other', 'rgb':(0,175,77), 'qtt': 0}]
 
-def apply_color_map(rgb, color_map=cdl_color_map, map_resume=map_resume):
+def apply_color_map(rgb, color_map=cdl_color_map):
     
     rgb_mapped = rgb.copy()
     
@@ -180,7 +180,7 @@ def process_rgb(input, mask, indexes):
 
     return rgb
 
-def inference_on_file(target_image, model, custom_test_pipeline, map_resume):
+def inference_on_file(target_image, model, custom_test_pipeline):
 
     target_image = target_image.name
     time_taken=-1
@@ -214,7 +214,7 @@ def inference_on_file(target_image, model, custom_test_pipeline, map_resume):
     output = np.vstack([output[None], output[None], output[None]]).astype(np.uint8)
     
     
-    output=apply_color_map(output,map_resume).transpose((1,2,0))
+    output=apply_color_map(output).transpose((1,2,0))
         
     return rgb1,rgb2,rgb3,output
 
@@ -245,7 +245,7 @@ model = init_segmentor(config, ckpt, device='cpu')
 custom_test_pipeline=process_test_pipeline(model.cfg.data.test.pipeline, None)
 
 
-func = partial(inference_on_file, model=model, custom_test_pipeline=custom_test_pipeline, map_resume=map_resume)
+func = partial(inference_on_file, model=model, custom_test_pipeline=custom_test_pipeline)
 print("")
 bar_data = pd.DataFrame(map_resume)