Update app.py

app.py

@@ -10,6 +10,7 @@ from transformers import AutoModelForImageSegmentation, pipeline
 # Global Setup and Model Loading
 # ----------------------------
 
+# Set device (GPU if available, else CPU)
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
 # Load the segmentation model (RMBG-2.0)
@@ -20,7 +21,7 @@ segmentation_model = AutoModelForImageSegmentation.from_pretrained(
 segmentation_model.to(device)
 segmentation_model.eval()
 
-# Transformation for segmentation (resizes to 512 for the model input)
+# Define the image transformation for segmentation (resize to 512x512, then normalize)
 image_size = (512, 512)
 segmentation_transform = transforms.Compose([
     transforms.Resize(image_size),
@@ -35,120 +36,119 @@ depth_pipeline = pipeline("depth-estimation", model="depth-anything/Depth-Anythi
 # Processing Functions
 # ----------------------------
 
-def segment_and_blur_background(input_image: Image.Image, blur_strength: int = 15, threshold: float = 0.5) -> Image.Image:
+def segment_and_blur_background(input_image: Image.Image, blur_radius: int = 15, threshold: float = 0.5) -> Image.Image:
     """
-    Applies segmentation using the RMBG-2.0 model and composites the original image with
-    a Gaussian-blurred background based on an adjustable mask sensitivity threshold.
+    Uses the RMBG-2.0 segmentation model to create a binary mask,
+    then composites a Gaussian-blurred background with the sharp foreground.
+    The segmentation threshold is adjustable.
     """
+    # Ensure the image is in RGB and get its original dimensions
     image = input_image.convert("RGB")
     orig_width, orig_height = image.size
 
-    # Preprocess image for segmentation (resize only for model inference)
+    # Preprocess image for segmentation
     input_tensor = segmentation_transform(image).unsqueeze(0).to(device)
     
+    # Run inference on the segmentation model
     with torch.no_grad():
         preds = segmentation_model(input_tensor)[-1].sigmoid().cpu()
     pred = preds[0].squeeze()
     
-    # Create binary mask with adjustable threshold (mask sensitivity)
+    # Create a binary mask using the adjustable threshold
     binary_mask = (pred > threshold).float()
     mask_pil = transforms.ToPILImage()(binary_mask).convert("L")
+    # Convert grayscale mask to pure binary (0 or 255)
     mask_pil = mask_pil.point(lambda p: 255 if p > 128 else 0)
+    # Resize mask back to the original image dimensions
     mask_pil = mask_pil.resize((orig_width, orig_height), resample=Image.BILINEAR)
     
-    blurred_image = image.filter(ImageFilter.GaussianBlur(blur_strength))
+    # Apply Gaussian blur to the entire image for background
+    blurred_image = image.filter(ImageFilter.GaussianBlur(blur_radius))
+    # Composite the original image (foreground) with the blurred background using the mask
     final_image = Image.composite(image, blurred_image, mask_pil)
     return final_image
 
 def depth_based_lens_blur(input_image: Image.Image, max_blur: float = 2, num_bands: int = 40, invert_depth: bool = False) -> Image.Image:
     """
-    Applies a depth-based blur effect using a depth map produced by Depth-Anything.
-    The effect simulates a lens blur where the max_blur parameter controls the maximum blur.
-    This function uses the original input image size.
+    Applies a depth-based blur effect using a depth map from Depth-Anything.
+    The max_blur parameter (controlled by a slider) sets the highest blur intensity.
     """
-    # Use the original image for depth estimation (no resizing)
-    image_original = input_image.convert("RGB")
+    # Resize the input image to 512x512 for the depth estimation model
+    image_resized = input_image.resize((512, 512))
     
-    # Obtain depth map using the pipeline (assumes model accepts variable sizes)
-    results = depth_pipeline(image_original)
+    # Run depth estimation to obtain the depth map (as a PIL image)
+    results = depth_pipeline(image_resized)
     depth_map_image = results['depth']
     
+    # Convert the depth map to a NumPy array and normalize to [0, 1]
     depth_array = np.array(depth_map_image, dtype=np.float32)
     d_min, d_max = depth_array.min(), depth_array.max()
     depth_norm = (depth_array - d_min) / (d_max - d_min + 1e-8)
     if invert_depth:
         depth_norm = 1.0 - depth_norm
 
-    orig_rgba = image_original.convert("RGBA")
+    # Convert the resized image to RGBA for compositing
+    orig_rgba = image_resized.convert("RGBA")
     final_image = orig_rgba.copy()
     
+    # Divide the normalized depth range into bands and apply variable blur
     band_edges = np.linspace(0, 1, num_bands + 1)
     for i in range(num_bands):
         band_min = band_edges[i]
         band_max = band_edges[i + 1]
+        # Use the midpoint of the band to determine the blur strength.
         mid = (band_min + band_max) / 2.0
         blur_radius_band = (1 - mid) * max_blur
         
+        # Create a blurred version of the image for this band.
         blurred_version = orig_rgba.filter(ImageFilter.GaussianBlur(blur_radius_band))
+        
+        # Create a mask for pixels whose normalized depth falls within this band.
         band_mask = ((depth_norm >= band_min) & (depth_norm < band_max)).astype(np.uint8) * 255
         band_mask_pil = Image.fromarray(band_mask, mode="L")
+        
+        # Composite the blurred version with the current final image using the band mask.
         final_image = Image.composite(blurred_version, final_image, band_mask_pil)
     
+    # Return the final composited image as RGB.
     return final_image.convert("RGB")
 
-def process_image(input_image: Image.Image, effect: str, mask_sensitivity: float, blur_strength: float) -> Image.Image:
+def process_image(input_image: Image.Image, effect: str, threshold: float, blur_intensity: float) -> Image.Image:
     """
-    Applies the selected effect:
-      - "Gaussian Blur Background": uses segmentation with adjustable mask sensitivity and blur strength.
+    Dispatch function to apply the selected effect:
+      - "Gaussian Blur Background": uses segmentation with an adjustable threshold and blur radius.
       - "Depth-based Lens Blur": applies depth-based blur with an adjustable maximum blur.
+    The threshold slider is used only for the segmentation effect.
+    The blur_intensity slider controls the blur strength in both effects.
     """
     if effect == "Gaussian Blur Background":
-        return segment_and_blur_background(input_image, blur_strength=int(blur_strength), threshold=mask_sensitivity)
+        # For segmentation, use the threshold and blur_intensity (as blur_radius)
+        return segment_and_blur_background(input_image, blur_radius=int(blur_intensity), threshold=threshold)
     elif effect == "Depth-based Lens Blur":
-        return depth_based_lens_blur(input_image, max_blur=blur_strength)
+        # For depth-based blur, use the blur_intensity as the max blur value.
+        return depth_based_lens_blur(input_image, max_blur=blur_intensity)
     else:
         return input_image
 
 # ----------------------------
-# Gradio Blocks Layout
+# Gradio Interface
 # ----------------------------
 
-with gr.Blocks(title="Interactive Blur Effects Demo") as demo:
-    gr.Markdown(
-        """
-        # Interactive Blur Effects Demo
-        Upload an image and choose an effect below.  
-        For **Gaussian Blur Background**, adjust the mask sensitivity (controls segmentation threshold) 
-        and blur strength (controls Gaussian blur radius).  
-        For **Depth-based Lens Blur**, the blur strength slider sets the maximum blur intensity.
-        """
-    )
-    
-    with gr.Row():
-        with gr.Column():
-            input_image = gr.Image(type="pil", label="Input Image")
-            effect_choice = gr.Radio(
-                choices=["Gaussian Blur Background", "Depth-based Lens Blur"],
-                label="Select Effect",
-                value="Gaussian Blur Background"
-            )
-            mask_sensitivity_slider = gr.Slider(
-                minimum=0.0, maximum=1.0, value=0.5, step=0.01,
-                label="Mask Sensitivity (for segmentation)"
-            )
-            blur_strength_slider = gr.Slider(
-                minimum=0, maximum=30, value=15, step=1,
-                label="Blur Strength"
-            )
-            run_button = gr.Button("Apply Effect")
-        with gr.Column():
-            output_image = gr.Image(type="pil", label="Output Image")
-    
-    run_button.click(
-        fn=process_image,
-        inputs=[input_image, effect_choice, mask_sensitivity_slider, blur_strength_slider],
-        outputs=output_image
+iface = gr.Interface(
+    fn=process_image,
+    inputs=[
+        gr.Image(type="pil", label="Input Image"),
+        gr.Radio(choices=["Gaussian Blur Background", "Depth-based Lens Blur"], label="Select Effect"),
+        gr.Slider(0.0, 1.0, value=0.5, label="Segmentation Threshold (for Gaussian Blur)"),
+        gr.Slider(0, 30, value=15, step=1, label="Blur Intensity (for both effects)")
+    ],
+    outputs=gr.Image(type="pil", label="Output Image"),
+    title="Interactive Blur Effects Demo",
+    description=(
+        "Upload an image and choose an effect. For 'Gaussian Blur Background', adjust the segmentation threshold and blur intensity. "
+        "For 'Depth-based Lens Blur', the blur intensity slider sets the maximum blur based on depth."
     )
+)
 
 if __name__ == "__main__":
-    demo.launch()
+    iface.launch()