| import json | |
| import os | |
| from PIL import Image | |
| 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_url, **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_url = metadata_url | |
| 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": "data/train_photos.zip", | |
| "test": "data/test_photos.zip", | |
| }, | |
| metadata_url={ | |
| 'full': "data/WaterFlowCounter.json" | |
| } | |
| ) | |
| ] | |
| def _info(self): | |
| features = datasets.Features( | |
| { | |
| "image_id": datasets.Value("int64"), | |
| "image": datasets.Image(), | |
| "width": datasets.Value("int32"), | |
| "height": datasets.Value("int32"), | |
| "objects": datasets.Sequence( | |
| { | |
| "id": datasets.Value("int64"), | |
| "area": datasets.Value("int64"), | |
| "bbox": datasets.Sequence(datasets.Value("float32"), length=4), | |
| "category": datasets.ClassLabel(names=_REGION_NAME), | |
| } | |
| ), | |
| } | |
| ) | |
| 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) | |
| meta_file = dl_manager.download(self.config.metadata_url) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "folder_dir": data_files["train"], | |
| "metadata_path": meta_file['full'] | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| gen_kwargs={ | |
| "folder_dir": data_files["test"], | |
| "metadata_path": meta_file['full'] | |
| }, | |
| ) | |
| ] | |
| 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", encoding='utf-8') as f: | |
| annotations = json.load(f) | |
| idx = 0 | |
| id = 0 | |
| for file in os.listdir(folder_dir): | |
| filepath = os.path.join(folder_dir, file) | |
| with open(filepath, "rb") as f: | |
| image_bytes = f.read() | |
| image = Image.open(filepath) | |
| width, height = image.size | |
| all_bbox = [] | |
| all_area = [] | |
| all_segmentation = [] | |
| names = [] | |
| rotated = [] | |
| ids = [] | |
| for el in annotations['_via_img_metadata']: | |
| if annotations['_via_img_metadata'][el]['filename'] == file: | |
| for region in annotations['_via_img_metadata'][el]['regions']: | |
| ids.append(id) | |
| id += 1 | |
| all_x = region['shape_attributes']['all_points_x'] | |
| all_y = region['shape_attributes']['all_points_y'] | |
| x_min = min(all_x) | |
| y_min = min(all_y) | |
| x_max = max(all_x) | |
| y_max = max(all_y) | |
| p_width = x_max - x_min | |
| p_height = y_max - y_min | |
| bbox = [x_min, y_min, p_width, p_height ] | |
| area = p_width * p_height | |
| segmentation = list(zip(all_x, all_y)) | |
| all_bbox.append(bbox) | |
| all_area.append(area) | |
| all_segmentation.append(segmentation) | |
| for name in list(region['region_attributes']['name'].keys()): | |
| names.append(name_to_id[name]) | |
| if len(names) > 3: | |
| names = names[:-3] | |
| ''' | |
| try: | |
| for rot in list(region['region_attributes']['rotated'].keys()): | |
| rotated.append(rotation_to_id[rot]) | |
| except: | |
| rotated.append(int(region['region_attributes']['rotated'])) | |
| ''' | |
| yield idx, { | |
| "image_id": idx, | |
| "image": {"path": filepath, "bytes": image_bytes}, | |
| "width": width, | |
| "height": height, | |
| "objects": { | |
| "id": ids, | |
| "area": all_area, | |
| "bbox": all_bbox, | |
| "category":names, | |
| } | |
| } | |
| idx += 1 | |