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High-level Python API for Rootscope.
from rootscope import predict_tif, predict_folder
df = predict_tif("image.tif", out_dir="results/", gpu=True)
df = predict_folder("my_tifs/", out_dir="results/", gpu=True)
Both return a pandas DataFrame of per-cell predictions and also write
per-image CSVs + labeled overlay PNGs into ``out_dir``.
"""
from pathlib import Path
import pandas as pd
from . import predict as _predict
from .weights import resolve_cnn_weights, resolve_model_dir
def _load(model_dir=None, cnn_weights=None):
mdir = resolve_model_dir(model_dir)
cnn = resolve_cnn_weights(cnn_weights)
models_dict, scalers, feature_cols, le = _predict.load_models(str(mdir))
if not models_dict:
raise RuntimeError(f"No usable models found in {mdir}.")
return models_dict, scalers, feature_cols, le, cnn
def predict_tif(
tif,
out_dir="results",
gpu=True,
model_dir=None,
cnn_weights=None,
um_per_px=1.0,
max_rounds=10,
label_cells=False,
):
"""Segment + predict cell types for a single TIFF. Returns a DataFrame."""
models_dict, scalers, feature_cols, le, cnn = _load(model_dir, cnn_weights)
df = _predict.predict_single_tif(
str(tif), models_dict, scalers, feature_cols, le,
um_per_px=um_per_px, gpu=gpu, out_dir=str(out_dir),
max_rounds=max_rounds, cnn_weights=str(cnn) if cnn else None,
label_cells=label_cells,
)
return df
def predict_folder(
tif_dir,
out_dir="results",
gpu=True,
model_dir=None,
cnn_weights=None,
um_per_px=1.0,
max_rounds=10,
label_cells=False,
pattern="*.tif",
):
"""Segment + predict for every TIFF in a folder. Returns a combined
DataFrame and writes ``all_predictions.csv`` into ``out_dir``."""
models_dict, scalers, feature_cols, le, cnn = _load(model_dir, cnn_weights)
tif_paths = sorted(Path(tif_dir).glob(pattern))
if not tif_paths:
raise FileNotFoundError(f"No files matching {pattern} in {tif_dir}")
tables = []
for tp in tif_paths:
try:
df = _predict.predict_single_tif(
str(tp), models_dict, scalers, feature_cols, le,
um_per_px=um_per_px, gpu=gpu, out_dir=str(out_dir),
max_rounds=max_rounds, cnn_weights=str(cnn) if cnn else None,
label_cells=label_cells,
)
if df is not None:
tables.append(df)
except Exception as e: # noqa: BLE001
print(f" FAILED on {tp.name}: {e}")
if not tables:
return None
combined = pd.concat(tables, ignore_index=True)
out = Path(out_dir)
out.mkdir(parents=True, exist_ok=True)
combined.to_csv(out / "all_predictions.csv", index=False)
return combined
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