added files

app.py

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+import gradio as gr
+import numpy as np
+import pandas as pd
+from csbdeep.utils import normalize
+from skimage.color import rgb2gray
+from skimage.measure import regionprops
+from skimage.morphology import binary_closing
+from skimage.util import img_as_ubyte
+from skimage.measure import shannon_entropy
+from stardist.models import StarDist2D
+from stardist.plot import render_label
+
+
+MODEL_NAMES = [
+    "2D_versatile_fluo",
+    "2D_versatile_he",
+    "2D_paper_dsb2018",
+]
+
+
+_MODEL_CACHE = {}
+
+
+def get_model(name: str) -> StarDist2D:
+    if name not in _MODEL_CACHE:
+        _MODEL_CACHE[name] = StarDist2D.from_pretrained(name)
+    return _MODEL_CACHE[name]
+
+
+def to_gray(image: np.ndarray) -> np.ndarray:
+    if image.ndim == 2:
+        return image
+    return rgb2gray(image)
+
+
+def box_counting_fd(mask: np.ndarray) -> float:
+    if mask.sum() == 0:
+        return 0.0
+    sizes = np.array([2, 4, 8, 16, 32, 64])
+    sizes = sizes[sizes <= min(mask.shape)]
+    counts = []
+    for size in sizes:
+        shape = (
+            int(np.ceil(mask.shape[0] / size)),
+            int(np.ceil(mask.shape[1] / size)),
+        )
+        pad_h = shape[0] * size - mask.shape[0]
+        pad_w = shape[1] * size - mask.shape[1]
+        padded = np.pad(mask, ((0, pad_h), (0, pad_w)), mode="constant")
+        blocks = padded.reshape(shape[0], size, shape[1], size)
+        blocks = blocks.any(axis=(1, 3))
+        counts.append(np.count_nonzero(blocks))
+    counts = np.array(counts)
+    sizes = sizes[counts > 0]
+    counts = counts[counts > 0]
+    if len(counts) < 2:
+        return 0.0
+    coeffs = np.polyfit(np.log(1 / sizes), np.log(counts), 1)
+    return float(coeffs[0])
+
+
+def compute_metrics(
+    labels: np.ndarray, intensity_image: np.ndarray, width_units: float
+):
+    props = regionprops(labels, intensity_image=intensity_image)
+    image_width = labels.shape[1]
+    pixel_size = (width_units / image_width) if image_width > 0 else 0.0
+    rows = []
+    for region in props:
+        area_px = float(region.area)
+        perimeter_px = float(region.perimeter)
+        major_px = float(region.major_axis_length) if region.major_axis_length else 0.0
+        minor_px = float(region.minor_axis_length) if region.minor_axis_length else 0.0
+        area = area_px * (pixel_size**2)
+        perimeter = perimeter_px * pixel_size
+        major = major_px * pixel_size
+        minor = minor_px * pixel_size
+        aspect_ratio = major / minor if minor > 0 else 0.0
+        circularity = (4 * np.pi * area / (perimeter**2)) if perimeter > 0 else 0.0
+        roundness = (4 * area / (np.pi * major**2)) if major > 0 else 0.0
+        region_mask = labels == region.label
+        region_mask = binary_closing(region_mask)
+        entropy_val = float(
+            shannon_entropy(region.intensity_image[region.image], base=2)
+        )
+        fractal_dim = box_counting_fd(region_mask)
+        integrated_density = float(region.intensity_image.sum()) * (pixel_size**2)
+        ecc_rel = float(region.eccentricity * major)
+        rows.append(
+            {
+                "Label": int(region.label),
+                "Area": area,
+                "Perimeter": perimeter,
+                "Aspect ratio": aspect_ratio,
+                "Circularity": circularity,
+                "Roundness": roundness,
+                "Entropy": entropy_val,
+                "Fractal dimension": fractal_dim,
+                "Integrated density": integrated_density,
+                "Eccentricity (rel width)": ecc_rel,
+            }
+        )
+    metrics_df = pd.DataFrame(rows)
+    avg_df = pd.DataFrame()
+    if not metrics_df.empty:
+        numeric_cols = metrics_df.columns.drop("Label")
+        avg_row = {"Metric": "Average"}
+        avg_row.update(metrics_df[numeric_cols].mean().to_dict())
+        avg_df = pd.DataFrame([avg_row])
+    return metrics_df, avg_df
+
+
+def run_inference(image: np.ndarray, model_name: str, width_units: float):
+    if image is None:
+        return None, None, None
+    if width_units <= 0:
+        width_units = 1.0
+    model = get_model(model_name)
+    image_input = image.copy()
+    if model_name == "2D_versatile_fluo":
+        image_input = to_gray(image_input)
+    image_norm = normalize(image_input, 1, 99.8, axis=(0, 1))
+    labels, _ = model.predict_instances(image_norm)
+    overlay = render_label(labels, img=image)
+    if np.issubdtype(overlay.dtype, np.floating):
+        overlay = np.clip(overlay, 0, 1)
+    overlay = img_as_ubyte(overlay)
+    intensity_image = to_gray(image)
+    metrics_df, avg_df = compute_metrics(labels, intensity_image, width_units)
+    return overlay, metrics_df, avg_df
+
+
+with gr.Blocks(title="StarDist 2D Segmentation - HF app by Ram Sevuggan") as demo:
+    gr.Markdown("# StarDist 2D Segmentation - HF app by Ram Sevuggan")
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(label="Input image", type="numpy")
+            model_dropdown = gr.Dropdown(
+                choices=MODEL_NAMES,
+                value="2D_versatile_fluo",
+                label="Model",
+            )
+            width_units = gr.Number(
+                value=1.0,
+                minimum=1e-6,
+                label="Image width (units)",
+                info="Used for eccentricity relative to image width",
+            )
+            run_button = gr.Button("Run")
+        with gr.Column():
+            output_image = gr.Image(label="Overlay", type="numpy")
+    metrics_table = gr.Dataframe(
+        label="Object metrics",
+        interactive=False,
+    )
+    avg_table = gr.Dataframe(
+        label="Average metrics",
+        interactive=False,
+    )
+
+    run_button.click(
+        fn=run_inference,
+        inputs=[input_image, model_dropdown, width_units],
+        outputs=[output_image, metrics_table, avg_table],
+    )
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,91 @@
+absl-py==2.4.0
+aiofiles==24.1.0
+annotated-doc==0.0.4
+annotated-types==0.7.0
+anyio==4.12.1
+astunparse==1.6.3
+brotli==1.2.0
+certifi==2026.2.25
+charset-normalizer==3.4.4
+click==8.3.1
+contourpy==1.3.3
+csbdeep==0.8.2
+cycler==0.12.1
+fastapi==0.135.1
+ffmpy==1.0.0
+filelock==3.25.0
+flatbuffers==25.12.19
+fonttools==4.61.1
+fsspec==2026.2.0
+gast==0.7.0
+google-pasta==0.2.0
+gradio==6.8.0
+gradio-client==2.2.0
+groovy==0.1.2
+grpcio==1.78.0
+h11==0.16.0
+h5py==3.15.1
+hf-xet==1.3.2
+httpcore==1.0.9
+httpx==0.28.1
+huggingface-hub==1.5.0
+idna==3.11
+imageio==2.37.2
+jinja2==3.1.6
+keras==3.13.2
+kiwisolver==1.4.9
+lazy-loader==0.4
+libclang==18.1.1
+llvmlite==0.46.0
+markdown==3.10.2
+markdown-it-py==4.0.0
+markupsafe==3.0.3
+matplotlib==3.10.8
+mdurl==0.1.2
+ml-dtypes==0.5.4
+namex==0.1.0
+networkx==3.6.1
+numba==0.64.0
+numpy==2.4.2
+opt-einsum==3.4.0
+optree==0.19.0
+orjson==3.11.7
+packaging==26.0
+pandas==3.0.1
+pillow==12.1.1
+protobuf==7.34.0
+pydantic==2.12.5
+pydantic-core==2.41.5
+pydub==0.25.1
+pygments==2.19.2
+pyparsing==3.3.2
+python-dateutil==2.9.0.post0
+python-multipart==0.0.22
+pytz==2025.2
+pyyaml==6.0.3
+requests==2.32.5
+rich==14.3.3
+safehttpx==0.1.7
+scikit-image==0.26.0
+scipy==1.17.1
+semantic-version==2.10.0
+setuptools==82.0.0
+shellingham==1.5.4
+six==1.17.0
+stardist==0.9.2
+starlette==0.52.1
+tensorboard==2.20.0
+tensorboard-data-server==0.7.2
+tensorflow==2.20.0
+termcolor==3.3.0
+tifffile==2026.2.24
+tomlkit==0.13.3
+tqdm==4.67.3
+typer==0.24.1
+typing-extensions==4.15.0
+typing-inspection==0.4.2
+urllib3==2.6.3
+uvicorn==0.41.0
+werkzeug==3.1.6
+wheel==0.46.3
+wrapt==2.1.1