Create wrapper.py

wrapper.py

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+from pathlib import Path
+from typing import Dict
+import shutil
+from PIL import Image
+import glob
+import tempfile
+
+from sorghum_pipeline.pipeline import SorghumPipeline
+
+
+def run_pipeline_on_image(input_image_path: str, work_dir: str, save_artifacts: bool = True) -> Dict[str, str]:
+    """
+    Run sorghum pipeline on a single image (no instance segmentation).
+    Returns dict[label -> image_path] for gallery display.
+    """
+
+    work = Path(work_dir)
+    work.mkdir(parents=True, exist_ok=True)
+
+    # Copy input to work dir
+    input_copy = work / Path(input_image_path).name
+    shutil.copy(input_image_path, input_copy)
+
+    # Initialize pipeline with config
+    #  adjust this if you have a YAML config file (e.g., "configs/demo.yaml")
+    pipeline = SorghumPipeline(
+        config_path=str(Path("sorghum_pipeline/config.py")),
+        enable_occlusion_handling=False,
+        enable_instance_integration=False
+    )
+
+    # Run the pipeline (single image, no frames, no SAM2Long)
+    results = pipeline.run(
+        load_all_frames=False,
+        segmentation_only=False,
+        run_instance_segmentation=False,
+        features_frame_only=None
+    )
+
+    # Collect outputs
+    outputs: Dict[str, str] = {}
+
+    # Save original for reference
+    original = work / "original.png"
+    Image.open(input_copy).convert("RGB").save(original)
+    outputs["Original"] = str(original)
+
+    # Gather all PNG files created by OutputManager
+    for f in glob.glob(str(work / "**/*.png"), recursive=True):
+        name = Path(f).stem
+        if name.lower() not in outputs:  # avoid duplicate "Original"
+            outputs[name] = f
+
+    return outputs