Source code for appliedchemlabwork_tayra.D3._finder

# SPDX-FileCopyrightText: 2026-present Tayra Sakurai <tayra_sakurai@icloud.com>
#
# SPDX-License-Identifier: AGPL-3.0-or-later
import cv2
from pathlib import Path
from ultralytics import YOLO
from ultralytics.engine.results import Results
import numpy as np
import polars as pl
import matplotlib.pyplot as plt
import matplotlib as mpl




def find_boxes(
    filepath: str,
    save_to: str,
    fsave: str = './runs/detect/predict',
):
    """Find bands as bounding boxes.

    Parameters
    ----------
    filepath : str
        The path to the image file containing the DNA electrophoresis bands.
    save_to : str
        The directory to save the data.
    fsave : str, optional
        The directory to save the result.

    Returns
    -------
    data : List of Results
        The list of the results.

    Raises
    ------
    FileNotFoundError
        The image file was not found.
    """
    model = YOLO("./trained-model/weights/best.pt")
    img = cv2.imread(filepath)
    mplimg = plt.imread(filepath)
    plt.imshow(mplimg)
    plt.show()
    if img is None:
        raise FileNotFoundError('The requested file was not found.')
    results: list[Results] = model(img, save=True, save_dir=fsave, conf=0.05)
    for i, result in enumerate(results):
        df = result.to_df()
        print(df)
        print(df[:, 'box'][0])
        df = df.with_columns(
            pl.col('box').struct.with_fields(
                x=pl.mean_horizontal(
                    pl.field('x1'),
                    pl.field('x2')
                ),
                y=pl.mean_horizontal(
                    pl.field('y1'),
                    pl.field('y2')
                )
            )
        )
        print(df.unnest("box"))
        df.unnest('box').write_csv(f'{save_to}/result-{i}.csv', include_bom=True)
    return results