Upload app.py

app.py

@@ -10,50 +10,92 @@ from transformers import pipeline
 # Load the depth estimation pipeline
 pipe = pipeline(task="depth-estimation", model="depth-anything/Depth-Anything-V2-Small-hf")
 
-def apply_depth_aware_blur(image, foreground_blur, midground_blur, background_blur, foreground_threshold, midground_lower, midground_upper, background_threshold):
+
+def apply_depth_aware_blur(
+    image,
+    foreground_blur,
+    midground_blur,
+    background_blur,
+    foreground_threshold,
+    midground_lower,
+    midground_upper,
+    background_threshold,
+):
     original_image = Image.fromarray(image).convert("RGB")
     original_image = original_image.resize((512, 512))
     image_np = np.array(original_image)
 
     # Inference
     depth = pipe(original_image)["depth"]
-    depth = np.array(depth) # Convert to numpy array
-    depth = cv2.resize(depth, (512, 512), interpolation=cv2.INTER_CUBIC) # Resize depth map
-    
+    depth = np.array(depth)  # Convert to numpy array
+    depth = cv2.resize(depth, (512, 512), interpolation=cv2.INTER_CUBIC)  # Resize depth map
+
     # Normalize the depth map
     normalized_depth_map = (depth - np.min(depth)) / (np.max(depth) - np.min(depth))
 
     # Create masks
     foreground_mask = (normalized_depth_map < foreground_threshold).astype(np.uint8) * 255
-    midground_mask = ((normalized_depth_map >= midground_lower) & (normalized_depth_map < midground_upper)).astype(np.uint8) * 255
-    background_mask = (normalized_depth_map >= background_threshold).astype(np.uint8) * 255
+    midground_mask = (
+        (normalized_depth_map >= midground_lower)
+        & (normalized_depth_map < midground_upper)
+    ).astype(np.uint8) * 255
+    background_mask = (normalized_depth_map >= background_threshold).astype(
+        np.uint8
+    ) * 255
 
     blurred_image = np.copy(np.array(original_image))
 
     if foreground_blur > 0:
-        blurred_image = np.where((foreground_mask[..., None] == 255), cv2.GaussianBlur(blurred_image, (foreground_blur, foreground_blur), 10), blurred_image)
+        blurred_image = np.where(
+            (foreground_mask[..., None] == 255),
+            cv2.GaussianBlur(blurred_image, (foreground_blur, foreground_blur), 10),
+            blurred_image,
+        )
     if midground_blur > 0:
-        blurred_image = np.where((midground_mask[..., None] == 255), cv2.GaussianBlur(blurred_image, (midground_blur, midground_blur), 8), blurred_image)
+        blurred_image = np.where(
+            (midground_mask[..., None] == 255),
+            cv2.GaussianBlur(blurred_image, (midground_blur, midground_blur), 8),
+            blurred_image,
+        )
     if background_blur > 0:
-        blurred_image = np.where((background_mask[..., None] == 255), cv2.GaussianBlur(blurred_image, (background_blur, background_blur), 20), blurred_image)
+        blurred_image = np.where(
+            (background_mask[..., None] == 255),
+            cv2.GaussianBlur(blurred_image, (background_blur, background_blur), 20),
+            blurred_image,
+        )
 
     return Image.fromarray(blurred_image.astype(np.uint8))
 
+
+# Example input values (including defaults)
+example_image = np.zeros((512, 512, 3), dtype=np.uint8)  # Placeholder for an image
+example_inputs = [
+    example_image,
+    0,  # foreground_blur
+    0,  # midground_blur
+    35,  # background_blur (default)
+    0.2,  # foreground_threshold (default)
+    0.2,  # midground_lower (default)
+    0.6,  # midground_upper (default)
+    0.6,  # background_threshold (default)
+]
+
 iface = gr.Interface(
     fn=apply_depth_aware_blur,
     inputs=[
         gr.Image(label="Input Image"),
-        gr.Slider(0, 51, step=2, label="Foreground Blur Kernel Size", default=0),
-        gr.Slider(0, 51, step=2, label="Midground Blur Kernel Size", default=0),
-        gr.Slider(0, 51, step=2, label="Background Blur Kernel Size", default=35),
-        gr.Slider(0, 1, label="Foreground Threshold", default=0.2),
-        gr.Slider(0, 1, label="Midground Lower Threshold", default=0.2),
-        gr.Slider(0, 1, label="Midground Upper Threshold", default=0.6),
-        gr.Slider(0, 1, label="Background Threshold", default=0.6)
+        gr.Slider(0, 51, step=2, label="Foreground Blur Kernel Size"),
+        gr.Slider(0, 51, step=2, label="Midground Blur Kernel Size"),
+        gr.Slider(0, 51, step=2, label="Background Blur Kernel Size"),
+        gr.Slider(0, 1, label="Foreground Threshold"),
+        gr.Slider(0, 1, label="Midground Lower Threshold"),
+        gr.Slider(0, 1, label="Midground Upper Threshold"),
+        gr.Slider(0, 1, label="Background Threshold"),
     ],
     outputs=gr.Image(label="Blurred Image"),
     title="Depth-Aware Lens Blur App",
     description="Apply depth-based blur to uploaded images using Depth Anything V2. Adjust blur intensity for foreground, midground, and background.",
+    examples=[example_inputs],  # Provide example inputs
 )
 
 if __name__ == "__main__":