annotate preprocessing.py @ 4:7db48c618bbe draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools commit 743c8acf1ea4e4b1e718743d3772b7e592646611

import argparse
import os

from sklearn.model_selection import train_test_split


def get_basename(f):
    return os.path.splitext(os.path.basename(f))[0]


def pair_files(images_dir, labels_dir):

    img_files = [f for f in os.listdir(images_dir)]
    lbl_files = [f for f in os.listdir(labels_dir)]

    image_dict = {get_basename(f): f for f in img_files}
    label_dict = {get_basename(f): f for f in lbl_files}

    keys = sorted(set(image_dict) & set(label_dict))

    return [(image_dict[k], label_dict[k]) for k in keys]


def copy_file(src, dst):
    with open(src, 'rb') as fsrc, open(dst, 'wb') as fdst:
        while True:
            chunk = fsrc.read(8192)
            if not chunk:
                break
            fdst.write(chunk)


def copy_pairs(pairs, image_src, label_src, image_dst, label_dst):
    os.makedirs(image_dst, exist_ok=True)
    os.makedirs(label_dst, exist_ok=True)
    for img, lbl in pairs:
        copy_file(os.path.join(image_src, img), os.path.join(image_dst, img))
        copy_file(os.path.join(label_src, lbl), os.path.join(label_dst, lbl))


def write_yolo_yaml(output_dir):

    yolo_yaml_path = os.path.join(output_dir, "yolo.yml")
    with open(yolo_yaml_path, 'w') as f:
        f.write(f"path: {output_dir}\n")
        f.write("train: train\n")
        f.write("val: valid\n")
        f.write("test: test\n")
        f.write("\n")
        f.write("names: ['dataset']\n")


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("-i", "--images", required=True)
    parser.add_argument("-y", "--labels", required=True)
    parser.add_argument("-o", "--output", required=True)
    parser.add_argument("-p", "--train_percent", type=int, default=70)
    args = parser.parse_args()

    all_pairs = pair_files(args.images, args.labels)
    train_size = args.train_percent / 100.0
    val_test_size = 1.0 - train_size

    train_pairs, val_test_pairs = train_test_split(all_pairs, test_size=val_test_size, random_state=42)
    val_pairs, test_pairs = train_test_split(val_test_pairs, test_size=0.5, random_state=42)

    copy_pairs(train_pairs, args.images, args.labels, os.path.join(args.output, "train/images"), os.path.join(args.output, "train/labels"))
    copy_pairs(val_pairs, args.images, args.labels, os.path.join(args.output, "valid/images"), os.path.join(args.output, "valid/labels"))
    copy_pairs(test_pairs, args.images, args.labels, os.path.join(args.output, "test/images"), os.path.join(args.output, "test/labels"))

    write_yolo_yaml(args.output)


if __name__ == "__main__":
    main()