import os from xml.etree import ElementTree as ET import datasets _CITATION = """\ @InProceedings{huggingface:dataset, title = {medical-staff-people-tracking}, author = {TrainingDataPro}, year = {2023} } """ _DESCRIPTION = """\ The dataset contains a collection of frames extracted from videos captured within a **hospital environment**. The **bounding boxes** are drawn around the **doctors, nurses, and other people** who appear in the video footage. The dataset can be used for **computer vision in healthcare settings** and *the development of systems that monitor medical staff activities, patient flow, analyze wait times, and assess the efficiency of hospital processes*. """ _NAME = "medical-staff-people-tracking" _HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}" _LICENSE = "" _DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/" _LABELS = ["nurse", "doctor", "other_people"] class MedicalStaffPeopleTracking(datasets.GeneratorBasedBuilder): BUILDER_CONFIGS = [ datasets.BuilderConfig(name="video_01", data_dir=f"{_DATA}video_01.zip"), datasets.BuilderConfig(name="video_02", data_dir=f"{_DATA}video_02.zip"), ] DEFAULT_CONFIG_NAME = "video_01" def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "id": datasets.Value("int32"), "name": datasets.Value("string"), "image": datasets.Image(), "mask": datasets.Image(), "shapes": datasets.Sequence( { "track_id": datasets.Value("uint32"), "label": datasets.ClassLabel( num_classes=len(_LABELS), names=_LABELS, ), "type": datasets.Value("string"), "points": datasets.Sequence( datasets.Sequence( datasets.Value("float"), ), ), "rotation": datasets.Value("float"), "occluded": datasets.Value("uint8"), "attributes": datasets.Sequence( { "name": datasets.Value("string"), "text": datasets.Value("string"), } ), } ), } ), supervised_keys=None, homepage=_HOMEPAGE, citation=_CITATION, ) def _split_generators(self, dl_manager): data = dl_manager.download_and_extract(self.config.data_dir) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "data": data, }, ), ] @staticmethod def extract_shapes_from_tracks( root: ET.Element, file: str, index: int ) -> ET.Element: img = ET.Element("image") img.set("name", file) img.set("id", str(index)) for track in root.iter("track"): shape = track.find(f".//*[@frame='{index}']") if not (shape is None): shape.set("label", track.get("label")) shape.set("track_id", track.get("id")) img.append(shape) return img @staticmethod def parse_shape(shape: ET.Element) -> dict: label = shape.get("label") track_id = shape.get("track_id") shape_type = shape.tag rotation = shape.get("rotation", 0.0) occluded = shape.get("occluded", 0) points = None if shape_type == "points": points = tuple(map(float, shape.get("points").split(","))) elif shape_type == "box": points = [ (float(shape.get("xtl")), float(shape.get("ytl"))), (float(shape.get("xbr")), float(shape.get("ybr"))), ] elif shape_type == "polygon": points = [ tuple(map(float, point.split(","))) for point in shape.get("points").split(";") ] attributes = [] for attr in shape: attr_name = attr.get("name") attr_text = attr.text attributes.append({"name": attr_name, "text": attr_text}) shape_data = { "label": label, "track_id": track_id, "type": shape_type, "points": points, "rotation": rotation, "occluded": occluded, "attributes": attributes, } return shape_data def _generate_examples(self, data): tree = ET.parse(f"{data}/annotations.xml") root = tree.getroot() for idx, file in enumerate(sorted(os.listdir(f"{data}/images"))): img = self.extract_shapes_from_tracks(root, file, idx) image_id = img.get("id") name = img.get("name") shapes = [self.parse_shape(shape) for shape in img] print(shapes) yield idx, { "id": image_id, "name": name, "image": f"{data}/images/{file}", "mask": f"{data}/boxes/{file}", "shapes": shapes, }