tools/dataset_tools/create_dataset.py

import os
from glob import glob
from typing import List, Literal
import shutil
from PIL import Image
import json
import numpy as np
from rich.progress import track
import cv2
from vegseg.datasets import GrassDataset
from sklearn.model_selection import train_test_split
import argparse


def give_color_to_mask(mask: np.ndarray, palette: List[int]) -> Image.Image:
    """
    Convert mask to color image
    Args:
        mask (np.ndarray): numpy array of shape (H, W)
        palette (List[int]): list of RGB values
    return:
      color_mask (Image.Image): PIL Image of shape (H, W)
    """
    im = Image.fromarray(mask).convert("P")
    im.putpalette(palette)
    # exit(0)
    return im


def get_mask_by_json(filename: str) -> np.ndarray:
    """
    Convert json to mask
    Args:
        filename (str): path to json file
    return:
      mask (np.ndarray): numpy array of shape (H, W)
    """

    json_file = json.load(open(filename))
    img_height = json_file["imageHeight"]
    img_width = json_file["imageWidth"]
    mask = np.zeros((img_height, img_width), dtype="int8")
    for shape in json_file["shapes"]:
        label = int(shape["label"])
        label -= 1
        label = max(label, 0)
        points = np.array(shape["points"]).astype(np.int32)
        cv2.fillPoly(mask, [points], label)
    return mask


def json_to_image(json_path, image_path):
    """
    Convert json to image
    Args:
        json_path (str): path to json file
        image_path (str): path to save image
    return: None
    """
    mask = get_mask_by_json(json_path)
    palette_list = GrassDataset.METAINFO["palette"]
    palette = []
    for palette_item in palette_list:
        palette.extend(palette_item)
    color_mask = give_color_to_mask(mask, palette)
    color_mask.save(image_path)


def create_dataset(
    image_paths: List[str],
    ann_paths: List[str],
    phase: Literal["train", "val"],
    output_dir: str,
):
    """
    Args:
        image_paths (List[str]): list of image paths
        ann_paths (List[str]): list of annotation paths
        phase (Literal["train", "val"]): train or val
        output_dir (str): path to save dataset
    Return: 
            None
    """
    for image_path, ann_path in track(
        zip(image_paths, ann_paths),
        description=f"{phase} dataset",
        total=len(image_paths),
    ):
        ann_save_path = os.path.join(
            output_dir,
            "ann_dir",
            phase,
            os.path.basename(ann_path).replace(".json", ".png"),
        )

        # 将image复制到指定路径
        new_image_path = os.path.join(
            output_dir, "img_dir", phase, os.path.basename(image_path)
        )
        shutil.copy(image_path, new_image_path)

        # 将ann保存到指定路径
        json_to_image(ann_path, ann_save_path)


def split_dataset(
    root_path: str,
    output_path: str,
    split_ratio: float = 0.8,
    shuffle: bool = True,
    seed: int = 42,
) -> None:
    """
    Split a dataset into train, test, and validation sets.

    Args:
        root_path (str): Path to the dataset. The dataset should be organized as follows:
            dataset_path/
                image1.tif
                image2.tif
                ...
                imageN.tif
                label1.tif
                label2.tif
                ...
                labelN.tif
        output_path (str): Path to the output directory where the split dataset will be saved.
        split_ratio (float, optional): Ratio of the dataset to be used for training. Defaults to 0.8.
        seed (int, optional): Seed for the random number generator. Defaults to 42.
    """
    image_paths = glob(os.path.join(root_path, "*.tif"))
    ann_paths = [filename.replace("tif", "json") for filename in image_paths]
    assert len(image_paths) == len(
        ann_paths
    ), "Number of images and annotations do not match"
    print(f"images: {len(image_paths)}, annotations: {len(ann_paths)}")

    image_train, image_test, ann_train, ann_test = train_test_split(
        image_paths,
        ann_paths,
        train_size=split_ratio,
        random_state=seed,
        shuffle=shuffle,
    )
    print(f"train: {len(image_train)}, test: {len(image_test)}")

    os.makedirs(os.path.join(output_path, "img_dir", "train"), exist_ok=True)
    os.makedirs(os.path.join(output_path, "img_dir", "val"), exist_ok=True)
    os.makedirs(os.path.join(output_path, "ann_dir", "train"), exist_ok=True)
    os.makedirs(os.path.join(output_path, "ann_dir", "val"), exist_ok=True)

    create_dataset(image_train, ann_train, "train", output_path)
    create_dataset(image_test, ann_test, "val", output_path)


def main():
    args = argparse.ArgumentParser()
    args.add_argument("--root", type=str, default="data/raw_data")
    args.add_argument("--output", type=str, default="data/grass")
    args.add_argument("--split_ratio", type=float, default=0.8)
    args.add_argument("--seed", type=int, default=42)
    args.add_argument("--shuffle", type=bool, default=True)
    args = args.parse_args()

    root: str = args.root
    output_path: str = args.output
    split_ratio: float = args.split_ratio
    seed: int = args.seed
    shuffle: bool = args.shuffle

    split_dataset(
        root_path=root,
        output_path=output_path,
        split_ratio=split_ratio,
        shuffle=shuffle,
        seed=seed,
    )

    print("数据集划分完成")


if __name__ == "__main__":

    # 使用示例 : python src/tools/split_dataset.py --root data/raw_data --output data/grass --split_ratio 0.8 --seed 42 --shuffle True
    main()