Update saliency_gradio.py

saliency_gradio.py

@@ -51,18 +51,26 @@ def postprocess_output(output_tensor, vertical_padding, horizontal_padding, orig
     output_tensor = tf.image.resize(output_tensor, original_shape)
     return output_tensor.numpy().squeeze()
 
-def process_image(input_image):
+def process_image(input_image, threshold=0.5):
     input_image = np.array(input_image, dtype=np.float32)
     original_shape = input_image.shape[:2]
     target_shape = get_target_shape(original_shape)
+
     input_tensor, vertical_padding, horizontal_padding = preprocess_input(input_image, target_shape)
     output_tensor = model(input_tensor)["output"]
     saliency_gray = postprocess_output(output_tensor, vertical_padding, horizontal_padding, original_shape)
+
     total_saliency = np.sum(saliency_gray)
+    above_threshold_saliency = np.sum(saliency_gray[saliency_gray > threshold])
+    ratio = above_threshold_saliency / total_saliency if total_saliency > 0 else 0.0
+
     saliency_rgb = plt.cm.inferno(saliency_gray)[..., :3]
     alpha = 0.9
     blended_image = alpha * saliency_rgb + (1 - alpha) * input_image / 255
-    return blended_image, f"Total grayscale saliency: {total_saliency:.2f}"
+
+    summary_text = f"{ratio:.4f}"
+
+    return blended_image, summary_text
 
 def predict_single(image):
     return process_image(image)