go

app.py

@@ -347,7 +347,6 @@ def clear_cache():
     torch.cuda.empty_cache()
     return gr.update(value="Cache cleared!")
 
-sam2_mask_tab = create_sam2_mask_interface()
 
 css = """
 .nulgradio-container {

sam2_mask.py

@@ -1,69 +0,0 @@
-# K-I-S-S
-import spaces
-import gradio as gr
-from gradio_image_prompter import ImagePrompter
-from sam2.sam2_image_predictor import SAM2ImagePredictor
-import torch
-import numpy as np
-from PIL import Image as PILImage
-
-# Initialize SAM2 predictor
-MODEL = "facebook/sam2.1-hiera-large"
-DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-@spaces.GPU()
-
-def predict_masks(image, points):
-    """Predict a single mask from the image based on selected points."""
-    image_np = np.array(image)
-    points_list = [[point["x"], point["y"]] for point in points]
-    input_labels = [1] * len(points_list)
-
-    with torch.inference_mode():
-        PREDICTOR.set_image(image_np)
-        masks, _, _ = PREDICTOR.predict(
-            point_coords=points_list, point_labels=input_labels, multimask_output=False
-        )
-
-    # Prepare the overlay image
-    red_mask = np.zeros_like(image_np)
-    if masks and len(masks) > 0:
-        red_mask[:, :, 0] = masks[0].astype(np.uint8) * 255  # Apply the red channel
-        red_mask = PILImage.fromarray(red_mask)
-        original_image = PILImage.fromarray(image_np)
-        blended_image = PILImage.blend(original_image, red_mask, alpha=0.5)
-        return np.array(blended_image)
-    else:
-        return image_np
-
-
-def create_sam2_mask_interface():
-    """Create the Gradio interface for SAM2 mask generation."""
-    def update_mask(prompts):
-        """Update the mask based on the prompts."""
-        image = prompts["image"]
-        points = prompts["points"]
-        return predict_masks(image, points)
-
-    with gr.Blocks() as sam2_mask_tab:
-        gr.Markdown("# Object Segmentation with SAM2")
-        gr.Markdown(
-            """
-            This application utilizes **Segment Anything V2 (SAM2)** to allow you to upload an image and interactively generate a segmentation mask based on multiple points you select on the image.
-            """
-        )
-        with gr.Row():
-            with gr.Column():
-                upload_image_input = ImagePrompter(show_label=False)
-            with gr.Column():
-                image_output = gr.Image(label="Segmented Image", type="pil", height=400)
-        
-        # Define the action triggered by the upload_image_input change
-        upload_image_input.change(
-            fn=update_mask,
-            inputs=[upload_image_input],
-            outputs=[image_output],
-            show_progress=True,
-        )
-    
-    return sam2_mask_tab