RDD_2020.py

# TODO: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""

import csv
import json
import os
from typing import List
import datasets
import logging
import xml.etree.ElementTree as ET
import os
from PIL import Image

# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={Shixuan An
},
year={2024}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = ""

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)


# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class RDD2020_Dataset(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""

    VERSION = datasets.Version("1.1.0")

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features({
                "image_id": datasets.Value("string"),
                "country": datasets.Value("string"),
                "type": datasets.Value("string"),
                "image_resolution": datasets.Features({
                    "width": datasets.Value("int32"),
                    "height": datasets.Value("int32"),
                    "depth": datasets.Value("int32"),
                }),
                "image": datasets.Value("string"),
                "image_path": datasets.Value("string"),
                #"pics_array": datasets.Array3D(shape=(None, None, 3), dtype="uint8"),
                "crack_type": datasets.Sequence(datasets.Value("string")),
                "crack_coordinates": datasets.Sequence(datasets.Features({
                    "x_min": datasets.Value("int32"),
                    "x_max": datasets.Value("int32"),
                    "y_min": datasets.Value("int32"),
                    "y_max": datasets.Value("int32"),
                })),
            }),
            homepage='https://data.mendeley.com/datasets/5ty2wb6gvg/1',
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):

        urls_to_download = {
        "train": 'https://huggingface.co/datasets/ShixuanAn/RDD_2020/resolve/main/train.zip',
        "test1": "https://huggingface.co/datasets/ShixuanAn/RDD_2020/resolve/main/test1.zip",
        "test2": "https://huggingface.co/datasets/ShixuanAn/RDD_2020/resolve/main/test2.zip"
        }

        downloaded_files = {
        name: dl_manager.download_and_extract(url)
        for name, url in urls_to_download.items()
        }
       # print( downloaded_files['train'])

     
       # ls /root/.cache/huggingface/datasets/downloads/extracted/da17428e8597064d3819cfa1c73686f8876572f681a1b577a2fe8a68c885806a
        #ls /root/.cache/huggingface/datasets/downloads/extracted/8391428f7cc3295c41551812c745861c0fa9ffe588cccef4cd4f618f92e6333f
       # ls /root/.cache/huggingface/datasets/downloads/extracted/037a5dd551ee4ecb710b7ea376f03a5a3031aa9555aff200c7b758b066b1981d
        # directory_path = '/root/.cache/huggingface/datasets/downloads/extracted/da17428e8597064d3819cfa1c73686f8876572f681a1b577a2fe8a68c885806a'

       # files_and_directories = os.listdir(directory_path)
       # print(files_and_directories)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": os.path.join(downloaded_files["train"], "train"),
                    "split": "train",
                }
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "filepath": os.path.join(downloaded_files["test1"], "test1"),
                    "split": "test1",
                }
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "filepath":os.path.join(downloaded_files["test2"], "test2"),
                    "split": "test2",
                }
            ),
        ]


    def _generate_examples(self, filepath, split):
        
        # Iterate over each country directory
        for country_dir in ['Czech', 'India', 'Japan']:
            images_dir = f"{filepath}/{country_dir}/images"
            
          #  print(os.listdir(filepath))
          # print(os.listdir(filepath))
          #  print(os.listdir(f"{filepath}/{country_dir}"))
            
            annotations_dir = f"{filepath}/{country_dir}/annotations/xmls" if split == "train" else None

            # Iterate over each image in the country's image directory
            for image_file in os.listdir(images_dir):
                if not image_file.endswith('.jpg'):
                    continue

                image_id = f"{image_file.split('.')[0]}"
            
                image_path = os.path.join(images_dir, image_file)
                # Load the image as a PIL Image object
                with Image.open(image_path) as img:
                    img.load()  # This is important to load the image data

                    if annotations_dir:
                        annotation_file = image_id + '.xml'
                        annotation_path = os.path.join(annotations_dir, annotation_file)
                        if not os.path.exists(annotation_path):
                            continue
                        tree = ET.parse(annotation_path)
                        root = tree.getroot()
                        crack_type = []
                        crack_coordinates = []
                        for obj in root.findall('object'):
                            crack_type.append(obj.find('name').text)
                            bndbox = obj.find('bndbox')
                            coordinates = {
                                "x_min": int(bndbox.find('xmin').text),
                                "x_max": int(bndbox.find('xmax').text),
                                "y_min": int(bndbox.find('ymin').text),
                                "y_max": int(bndbox.find('ymax').text),
                            }
                            crack_coordinates.append(coordinates)
                    else:
                        crack_type = []
                        crack_coordinates = []
    
                    # 将 PIL 图像对象添加到生成的字典中
                    yield image_id, {
                        "image_id": image_id,
                        "country": country_dir,
                        "type": split,
                        "image": img,  # PIL 图像对象
                        "crack_type": crack_type,
                        "crack_coordinates": crack_coordinates,
                    }