WaterFlowCountersRecognition.py

import json
import os
import collections

import datasets


_CITATION = """\
@SIA86{huggingface:dataset,
title = {WaterFlowCountersRecognition dataset},
author={SIA86},
year={2023}
}
"""

_DESCRIPTION = """\
This dataset is designed to detect digital data from water flow counters photos.
"""
_HOMEPAGE = "https://github.com/SIA86/WaterFlowRecognition"

_REGION_NAME = ['value_a', 'value_b', 'serial']

_REGION_ROTETION = ['0', '90', '180', '270']




class WaterFlowCounterConfig(datasets.BuilderConfig):
    """Builder Config for WaterFlowCounter"""

    def __init__(self, data_url, metadata_urls, **kwargs):
        """BuilderConfig for WaterFlowCounter.
        Args:
          data_url: `string`, url to download the photos.
          metadata_urls: instance segmentation regions and description 
          **kwargs: keyword arguments forwarded to super.
        """
        super(WaterFlowCounterConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
        self.data_url = data_url
        self.metadata_urls = metadata_urls


class WaterFlowCounter(datasets.GeneratorBasedBuilder):
    """WaterFlowCounter Images dataset"""

    BUILDER_CONFIGS = [
        WaterFlowCounterConfig(
            name="WFCR_full",
            description="Full dataset which contains coordinates and names of regions and information about rotation",
            data_url={
                "train": "https://huggingface.co/datasets/SIA86/WaterFlowCountersRecognition/blob/main/data/train_photos.zip",
                "test": "https://huggingface.co/datasets/SIA86/WaterFlowCountersRecognition/blob/main/data/test_photos.zip",
            }
            metadata_url = "https://huggingface.co/datasets/SIA86/WaterFlowCountersRecognition/blob/main/WaterFlowCounter.json"
        )
    ]

    def _info(self):
        features = datasets.Features(
            {
                
                "image": datasets.Image(),
                "regions": datasets.Sequence(
                    {                        
                        "all_points_x": datasets.Sequence(datasets.Value("int64")),
                        "all_points_y": datasets.Sequence(datasets.Value("int64")),                                                                      
                        "name": datasets.ClassLabel(names=_REGION_NAME, num_classes=3),
                        "rotated": datasets.ClassLabel(names=_REGION_ROTETION, num_classes=4)
                    }         
                )
            }
        )

        return datasets.DatasetInfo(
            features=features,
            homepage=_HOMEPAGE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        data_files = dl_manager.download_and_extract(self.config.data_url)
        metadata_files = dl_manager.download_and_extract(self.config.metadata_url)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "folder_dir": data_files["train"],
                    "metadata_path": metadata_files,
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "folder_dir": data_files["test"],
                    "metadata_path": metadata_files,
                },
            )
        ]

    def generate_examples(self, folder_dir, metadata_path):
        name_to_id = {}
        rotation_to_id = {}

        for indx, name in enumerate(_REGION_NAME):
            name_to_id[name] = indx

        for indx, name in enumerate(_REGION_ROTETION):
            rotation_to_id[name] = indx

        
        with open(metadata_path, "r") as f:
            annotations = json.load(f)

        for file in os.listdir(folder_dir):
            filepath = os.path.join(folder_dir, file)

            with open(filepath, "rb") as f:
                image_bytes = f.read()

            all_x = []
            all_y = []
            names = []
            
            for el in annotations['_via_img_metadata']:
                if annotations['_via_img_metadata'][el]['filename'] == file:
                    for region in annotations['_via_img_metadata'][el]['regions']:
                        all_x.append(region['shape_attributes']['all_points_x'])
                        all_y.append(region['shape_attributes']['all_points_y'])
                        names.append(name_to_id[list(region['region_attributes']['name'].keys())[0]])
                        rotated = rotation_to_id[list(region['region_attributes']['rotated'].keys())[0]]

                    yield idx, {       
                        "image": {"path": filepath, "bytes": image_bytes},
                        "regions": {
                            "all_points_x": all_x,
                            "all_points_y": all_y,                                                                      
                            "name":names,
                            "rotated": rotated
                        }
                    }
                    idx += 1