Update cropped image functionality and revert back inference

app.py

@@ -204,7 +204,7 @@ hypar["model"] = ISNetDIS()
  # Build Model
 net = build_model(hypar, device)
 
-'''
+
 def inference(image):
     image_path = image
     
@@ -213,10 +213,14 @@ def inference(image):
      
     # Process the original mask with smoothing and denoising
     processed_mask = smooth_and_denoise(original_mask)
+    cropped_processed_mask = crop_signature(image_path, processed_mask, 64)
 
     # Convert processed mask to PIL image
     pil_processed_mask = Image.fromarray((processed_mask * 255).astype(np.uint8)).convert('L')
     pil_original_mask = Image.fromarray(original_mask).convert('L')
+
+    im_dark_cropped = Image.new('RGB', cropped_signature_image.size, (0, 0, 0))
+    im_dark_cropped.putalpha(cropped_processed_mask)
     
     im_rgb = Image.open(image).convert("RGB")
     im_dark = Image.new('RGB', im_rgb.size, (0, 0, 0))
@@ -227,43 +231,7 @@ def inference(image):
     im_rgba.putalpha(pil_original_mask)
     im_dark.putalpha(pil_processed_mask)
     
-    return [cropped_signature_image, processed_mask, im_dark]
-'''
-
-def inference(image):
-    image_path = image
-    
-    # Load and predict the mask for the original image
-    image_tensor, orig_size = load_image(image_path, hypar) 
-    original_mask = predict(net, image_tensor, orig_size, hypar, device)
-    
-    # Crop the signature from the original image
-    cropped_signature_image = crop_signature(image_path, original_mask, 64)
-    
-    # Resize the cropped image to the expected input size for the model
-    resized_cropped_image = cropped_signature_image.resize(hypar["input_size"], Image.BILINEAR)
-    
-    # Convert the resized cropped image to a tensor for model input
-    resized_cropped_image_tensor, _ = load_image(resized_cropped_image, hypar)
-
-    # Predict the mask for the resized cropped signature image
-    cropped_mask = predict(net, resized_cropped_image_tensor, orig_size, hypar, device)
-    
-    # Resize the mask to match the resized cropped signature image dimensions
-    pil_cropped_mask = Image.fromarray(cropped_mask).convert('L')
-    pil_cropped_mask = pil_cropped_mask.resize(resized_cropped_image.size, Image.BILINEAR)
-    
-    # Apply the refined mask to the resized cropped signature image
-    im_rgba_cropped = resized_cropped_image.copy()
-    im_rgba_cropped.putalpha(pil_cropped_mask)
-    
-    # Create a dark image with the same size as the resized cropped signature image
-    im_dark_cropped = Image.new('RGB', im_rgba_cropped.size, (0, 0, 0))
-    im_dark_cropped.putalpha(pil_cropped_mask)
-    
-    # The outputs are now based on the resized cropped image
-    return [im_rgba_cropped, pil_cropped_mask, im_dark_cropped]
-
+    return [cropped_signature_image, processed_mask, im_dark_cropped]
 
 title = "Mysign.id - Signature Background removal based on DIS"
 description = "ML Model based on ECCV2022/dis-background-removal specifically made for removing background from signatures."