Upload app.py

app.py

@@ -11,7 +11,16 @@ from transformers import pipeline
 pipe = pipeline(task="depth-estimation", model="depth-anything/Depth-Anything-V2-Small-hf")
 
 
-def apply_depth_aware_blur(image):  # Removed blur parameters
+def apply_depth_aware_blur_inverse(
+    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)
@@ -24,36 +33,65 @@ def apply_depth_aware_blur(image):  # Removed blur parameters
     # Normalize the depth map
     normalized_depth_map = (depth - np.min(depth)) / (np.max(depth) - np.min(depth))
 
-    blurred_image = np.copy(np.array(original_image))
+    # Create masks (Inverted Logic)
+    foreground_mask = (normalized_depth_map >= foreground_threshold).astype(np.uint8) * 255
+    midground_mask = ((normalized_depth_map < foreground_threshold) & (normalized_depth_map >= background_threshold)).astype(np.uint8) * 255
+    background_mask = (normalized_depth_map < background_threshold).astype(np.uint8) * 255
 
-    # Calculate blur kernel size based on depth
-    max_kernel_size = 35  # Maximum blur kernel size
-    for y in range(512):
-        for x in range(512):
-            blur_amount = normalized_depth_map[y, x]
-            kernel_size = int(blur_amount * max_kernel_size)
-            # Ensure kernel size is odd and at least 1
-            kernel_size = max(1, kernel_size)
-            if kernel_size % 2 == 0:
-                kernel_size += 1
-
-            if kernel_size > 1 and kernel_size <= max_kernel_size:
-                blurred_image[y:y + 1, x:x + 1] = cv2.GaussianBlur(
-                    np.array(original_image)[y:y + 1, x:x + 1], (kernel_size, kernel_size), 10
-                )
-            else:
-                blurred_image[y:y + 1, x:x + 1] = np.array(original_image)[y:y + 1, x:x + 1] #Keep original pixel
+    blurred_image = np.copy(np.array(original_image))
 
+    # Apply blur, ensuring kernel size is valid (Inverted Blur)
+    if foreground_blur > 0 and foreground_blur % 2 == 1:
+        blurred_image = np.where(
+            (foreground_mask[..., None] == 255),
+            cv2.GaussianBlur(blurred_image, (foreground_blur, foreground_blur), 10),
+            blurred_image,
+        )
+    if midground_blur > 0 and midground_blur % 2 == 1:
+        blurred_image = np.where(
+            (midground_mask[..., None] == 255),
+            cv2.GaussianBlur(blurred_image, (midground_blur, midground_blur), 8),
+            blurred_image,
+        )
+    if background_blur > 0 and background_blur % 2 == 1:
+        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,
+    35,  # foreground_blur
+    7,  # midground_blur
+    15,  # background_blur (default)
+    0.6,  # foreground_threshold (default)
+    0.6,  # midground_lower (default)
+    0.2,  # midground_upper (default)
+    0.2,  # background_threshold (default)
+]
+
 iface = gr.Interface(
-    fn=apply_depth_aware_blur,
-    inputs=gr.Image(label="Input Image"),
+    fn=apply_depth_aware_blur_inverse,  # Changed function name
+    inputs=[
+        gr.Image(label="Input Image"),
+        gr.Slider(1, 51, step=2, label="Foreground Blur Kernel Size"),
+        gr.Slider(1, 51, step=2, label="Midground Blur Kernel Size"),
+        gr.Slider(1, 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-Proportional Lens Blur App",
-    description="Apply blur to an image where the blur intensity is proportional to the estimated depth. Farther objects are more blurred, closer objects are sharper.",
+    title="Inverse Depth-Aware Lens Blur App",  # Changed title
+    description="Apply inverse depth-based blur to uploaded images using Depth Anything V2. Closer objects are blurred, farther objects are sharper.",  # Changed description
+    examples=[example_inputs],  # Provide example inputs
 )
 
 if __name__ == "__main__":