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import warnings
import gradio as gr
import numpy as np
import requests
# import spaces
import torch
from PIL import Image
from transformers import Sam3Model, Sam3Processor
warnings.filterwarnings("ignore")
# Global model and processor
device = "cuda" if torch.cuda.is_available() else "cpu"
model = Sam3Model.from_pretrained(
"facebook/sam3", torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
).to(device)
processor = Sam3Processor.from_pretrained("facebook/sam3")
# @spaces.GPU()
def segment(image: Image.Image, text: str, threshold: float, mask_threshold: float):
"""
Perform promptable concept segmentation using SAM3.
Returns format compatible with gr.AnnotatedImage: (image, [(mask, label), ...])
"""
if image is None:
return None, "❌ Please upload an image."
if not text.strip():
return (image, []), "❌ Please enter a text prompt."
try:
inputs = processor(images=image, text=text.strip(), return_tensors="pt").to(device)
for key in inputs:
if inputs[key].dtype == torch.float32:
inputs[key] = inputs[key].to(model.dtype)
with torch.no_grad():
outputs = model(**inputs)
results = processor.post_process_instance_segmentation(
outputs,
threshold=threshold,
mask_threshold=mask_threshold,
target_sizes=inputs.get("original_sizes").tolist(),
)[0]
n_masks = len(results["masks"])
if n_masks == 0:
return (image, []), f"❌ No objects found matching '{text}' (try adjusting thresholds)."
# Format for AnnotatedImage: list of (mask, label) tuples
# mask should be numpy array with values 0-1 (float) matching image dimensions
annotations = []
for i, (mask, score) in enumerate(zip(results["masks"], results["scores"])):
# Convert binary mask to float numpy array (0-1 range)
mask_np = mask.cpu().numpy().astype(np.float32)
label = f"{text} #{i + 1} ({score:.2f})"
annotations.append((mask_np, label))
scores_text = ", ".join([f"{s:.2f}" for s in results["scores"].cpu().numpy()[:5]])
info = f"✅ Found **{n_masks}** objects matching **'{text}'**\nConfidence scores: {scores_text}{'...' if n_masks > 5 else ''}"
# Return tuple: (base_image, list_of_annotations)
return (image, annotations), info
except Exception as e:
return (image, []), f"❌ Error during segmentation: {str(e)}"
def clear_all():
"""Clear all inputs and outputs"""
return None, "", None, 0.5, 0.5, "📝 Enter a prompt and click **Segment** to start."
def segment_example(image_path: str, prompt: str):
"""Handle example clicks"""
if image_path.startswith("http"):
image = Image.open(requests.get(image_path, stream=True).raw).convert("RGB")
else:
image = Image.open(image_path).convert("RGB")
return segment(image, prompt, 0.5, 0.5)
# Gradio Interface
with gr.Blocks(
theme=gr.themes.Soft(),
title="SAM3 - Promptable Concept Segmentation",
css=".gradio-container {max-width: 1400px !important;}",
) as demo:
gr.Markdown(
"""
# SAM3 - Promptable Concept Segmentation (PCS)
**SAM3** performs zero-shot instance segmentation using natural language prompts.
Upload an image, enter a text prompt (e.g., "person", "car", "dog"), and get segmentation masks.
Built with [anycoder](https://huggingface.co/spaces/akhaliq/anycoder)
"""
)
gr.Markdown("### Inputs")
with gr.Row(variant="panel"):
image_input = gr.Image(
label="Input Image",
type="pil",
height=400,
)
# AnnotatedImage expects: (base_image, [(mask, label), ...])
image_output = gr.AnnotatedImage(
label="Output (Segmented Image)",
height=400,
show_legend=True,
)
with gr.Row():
text_input = gr.Textbox(label="Text Prompt", placeholder="e.g., person, ear, cat, bicycle...", scale=3)
clear_btn = gr.Button("🔍 Clear", size="sm", variant="secondary")
with gr.Row():
thresh_slider = gr.Slider(
minimum=0.0,
maximum=1.0,
value=0.5,
step=0.01,
label="Detection Threshold",
info="Higher = fewer detections",
)
mask_thresh_slider = gr.Slider(
minimum=0.0, maximum=1.0, value=0.5, step=0.01, label="Mask Threshold", info="Higher = sharper masks"
)
info_output = gr.Markdown(value="📝 Enter a prompt and click **Segment** to start.", label="Info / Results")
segment_btn = gr.Button("🎯 Segment", variant="primary", size="lg")
gr.Examples(
examples=[
["http://images.cocodataset.org/val2017/000000077595.jpg", "cat"],
],
inputs=[image_input, text_input],
outputs=[image_output, info_output],
fn=segment_example,
cache_examples=False,
)
clear_btn.click(
fn=clear_all, outputs=[image_input, text_input, image_output, thresh_slider, mask_thresh_slider, info_output]
)
segment_btn.click(
fn=segment,
inputs=[image_input, text_input, thresh_slider, mask_thresh_slider],
outputs=[image_output, info_output],
)
gr.Markdown(
"""
### Notes
- **Model**: [facebook/sam3](https://huggingface.co/facebook/sam3)
- Click on segments in the output to see labels
- GPU recommended for faster inference
"""
)
if __name__ == "__main__":
demo.launch(server_name="0.0.0.0", server_port=7860, share=False, debug=True)
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