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app.py

@@ -27,7 +27,7 @@ def _encode_mask(mask_bool: np.ndarray) -> str:
 
 
 @spaces.GPU(duration=120)
-def api_panoptic(image, concepts, conf):
+def api_panoptic(image, concepts, conf, mask_threshold=0.5):
     if image is None:
         return {"error": "no image provided"}
     image = image.convert("RGB")
@@ -43,7 +43,7 @@ def api_panoptic(image, concepts, conf):
         target_sizes = (inputs["original_sizes"].tolist()
                         if "original_sizes" in inputs else [[H, W]])
         res = processor.post_process_instance_segmentation(
-            outputs, threshold=float(conf), mask_threshold=0.5,
+            outputs, threshold=float(conf), mask_threshold=float(mask_threshold),
             target_sizes=target_sizes)[0]
         # NOTE (verify on live Space): expected keys masks/scores/boxes.
         masks, scores = res["masks"], res["scores"]
@@ -69,8 +69,9 @@ with gr.Blocks(title="SAM3 Panoptic") as demo:
         out = gr.JSON(label="Detections")
     txt = gr.Textbox(label="Concepts (comma-separated)",
                      value="person, car, road, sky, building, tree")
-    conf = gr.Slider(0.0, 1.0, value=0.4, step=0.05, label="Confidence")
-    gr.Button("Segment").click(api_panoptic, [inp, txt, conf], out,
+    conf = gr.Slider(0.0, 1.0, value=0.4, step=0.05, label="Confidence (lower = more detail)")
+    mthr = gr.Slider(0.05, 0.95, value=0.5, step=0.05, label="Mask threshold")
+    gr.Button("Segment").click(api_panoptic, [inp, txt, conf, mthr], out,
                                api_name="api_panoptic")
 
 if __name__ == "__main__":