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92d7cf4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 | """SAM3 open-vocabulary panoptic segmentation API for Hugging Face Spaces."""
from __future__ import annotations
import base64
import io
import os
import traceback
import gradio as gr
import numpy as np
import spaces
import torch
from PIL import Image
from transformers import Sam3Model, Sam3Processor
HF_TOKEN = os.environ.get("HF_TOKEN")
MODEL_ID = "facebook/sam3"
DEVICE = "cuda"
# ZeroGPU emulates CUDA during startup and swaps in a real GPU for @spaces.GPU.
processor = Sam3Processor.from_pretrained(MODEL_ID, token=HF_TOKEN)
model = Sam3Model.from_pretrained(MODEL_ID, token=HF_TOKEN).to(DEVICE)
model.eval()
def _encode_mask(mask_bool: np.ndarray) -> str:
buffer = io.BytesIO()
Image.fromarray(mask_bool.astype(np.uint8) * 255, mode="L").save(
buffer, format="PNG"
)
return base64.b64encode(buffer.getvalue()).decode("ascii")
def _gpu_duration(image, concepts, conf, mask_threshold=0.5) -> int:
del image, conf, mask_threshold
concept_count = len(
[value for value in str(concepts or "").split(",") if value.strip()]
)
return min(120, max(30, concept_count * 20))
@spaces.GPU(duration=_gpu_duration)
def api_panoptic(image, concepts, conf, mask_threshold=0.5):
if image is None:
return {"error": "no image provided", "detections": []}
image = image.convert("RGB")
width, height = image.size
concept_list = [
value.strip() for value in str(concepts or "").split(",") if value.strip()
]
if not concept_list:
return {
"version": "4",
"model": MODEL_ID,
"width": width,
"height": height,
"detections": [],
}
print(
f"SAM3 request: size={width}x{height} concepts={len(concept_list)}",
flush=True,
)
detections = []
try:
for concept in concept_list:
inputs = processor(
images=image, text=concept, return_tensors="pt"
).to(DEVICE)
with torch.inference_mode():
outputs = model(**inputs)
target_sizes = (
inputs["original_sizes"].tolist()
if "original_sizes" in inputs
else [[height, width]]
)
result = processor.post_process_instance_segmentation(
outputs,
threshold=float(conf),
mask_threshold=float(mask_threshold),
target_sizes=target_sizes,
)[0]
masks = result["masks"]
scores = result["scores"]
boxes = result.get("boxes")
for index in range(len(scores)):
mask = masks[index]
mask_array = (
mask.detach().cpu().numpy()
if hasattr(mask, "detach")
else np.asarray(mask)
)
mask_bool = (
mask_array
if mask_array.dtype == bool
else mask_array > 0.5
)
box = (
boxes[index].detach().cpu().numpy().tolist()
if boxes is not None
else [0, 0, 0, 0]
)
detections.append(
{
"label": concept,
"score": float(scores[index]),
"box": box,
"mask_png_b64": _encode_mask(mask_bool),
}
)
except Exception:
traceback.print_exc()
raise
print(f"SAM3 response: detections={len(detections)}", flush=True)
return {
"version": "4",
"model": MODEL_ID,
"width": width,
"height": height,
"detections": detections,
}
with gr.Blocks(title="SAM3 Panoptic") as demo:
gr.Markdown("# SAM3 Panoptic API")
with gr.Row():
input_image = gr.Image(type="pil", label="Image")
output_json = gr.JSON(label="Detections")
concept_text = gr.Textbox(
label="Concepts",
value="person, car, road, building, tree",
)
confidence = gr.Slider(
0.0, 1.0, value=0.4, step=0.05, label="Confidence"
)
mask_threshold = gr.Slider(
0.05, 0.95, value=0.5, step=0.05, label="Mask threshold"
)
gr.Button("Segment").click(
api_panoptic,
[input_image, concept_text, confidence, mask_threshold],
output_json,
api_name="api_panoptic",
)
if __name__ == "__main__":
demo.queue(default_concurrency_limit=1).launch()
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