Update app.py

app.py

@@ -14,13 +14,14 @@ from transformers import AutoModelForImageSegmentation, pipeline
 # Set device (GPU if available, else CPU)
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
-# Retrieve Hugging Face token from environment variable
+# Retrieve the Hugging Face access token from environment variables.
+# In your Space, you will set this in the Secrets panel.
 hf_token = os.environ.get("HF_ACCESS_TOKEN")
 if hf_token is None:
-    print("Warning: HF_ACCESS_TOKEN is not set. Access to gated models may fail.")
+    print("Warning: HF_ACCESS_TOKEN environment variable is not set. Model access might fail.")
 
 # Load the segmentation model (RMBG-2.0)
-# Ensure you have access to this gated repo and that your token is valid.
+# Make sure that you have been granted access to this gated model.
 segmentation_model = AutoModelForImageSegmentation.from_pretrained(
     'briaai/RMBG-2.0',
     trust_remote_code=True,
@@ -29,7 +30,7 @@ segmentation_model = AutoModelForImageSegmentation.from_pretrained(
 segmentation_model.to(device)
 segmentation_model.eval()
 
-# Define the image transformation for segmentation (resize to 512x512, then normalize)
+# Define the transformation for segmentation (resize to 512x512, convert to tensor, and normalize)
 image_size = (512, 512)
 segmentation_transform = transforms.Compose([
     transforms.Resize(image_size),
@@ -50,11 +51,11 @@ def segment_and_blur_background(input_image: Image.Image, blur_radius: int = 15,
     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
+    # Ensure the image is in RGB and record original dimensions
     image = input_image.convert("RGB")
     orig_width, orig_height = image.size
 
-    # Preprocess image for segmentation
+    # Preprocess the image for segmentation
     input_tensor = segmentation_transform(image).unsqueeze(0).to(device)
     
     # Run inference on the segmentation model
@@ -65,75 +66,63 @@ def segment_and_blur_background(input_image: Image.Image, blur_radius: int = 15,
     # 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)
     
-    # Apply Gaussian blur to the entire image for background
+    # Apply Gaussian blur to create the background
     blurred_image = image.filter(ImageFilter.GaussianBlur(blur_radius))
-    # Composite the original image (foreground) with the blurred background using the mask
+    # Composite the 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 from Depth-Anything.
-    The max_blur parameter (controlled by a slider) sets the highest blur intensity.
+    The blur intensity is controlled by the max_blur parameter.
     """
-    # Resize the input image to 512x512 for the depth estimation model
+    # Resize input image to 512x512 for the depth estimation model
     image_resized = input_image.resize((512, 512))
     
-    # Run depth estimation to obtain the depth map (as a PIL image)
+    # Obtain the depth map using the depth estimation pipeline
     results = depth_pipeline(image_resized)
     depth_map_image = results['depth']
     
-    # Convert the depth map to a NumPy array and normalize to [0, 1]
+    # Normalize the depth map 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
 
-    # Convert the resized image to RGBA for compositing
+    # Convert the resized image to RGBA for proper compositing
     orig_rgba = image_resized.convert("RGBA")
     final_image = orig_rgba.copy()
     
-    # Divide the normalized depth range into bands and apply variable blur
+    # Divide the 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, threshold: float, blur_intensity: float) -> Image.Image:
     """
-    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.
+    Dispatches the image processing based on the chosen effect:
+      - 'Gaussian Blur Background' uses segmentation with adjustable threshold and blur radius.
+      - 'Depth-based Lens Blur' uses a depth-based approach with adjustable blur intensity.
     """
     if effect == "Gaussian Blur Background":
-        # 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":
-        # 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