| import json | |
| from pathlib import Path | |
| import datasets | |
| import numpy as np | |
| import pandas as pd | |
| import PIL.Image | |
| import PIL.ImageOps | |
| _CITATION = """\ | |
| @InProceedings{huggingface:dataset, | |
| title = {body-measurements-dataset}, | |
| author = {TrainingDataPro}, | |
| year = {2023} | |
| } | |
| """ | |
| _DESCRIPTION = """\ | |
| The dataset consists of a compilation of people's photos along with their | |
| corresponding body measurements. It is designed to provide information and | |
| insights into the physical appearances and body characteristics of individuals. | |
| The dataset includes a diverse range of subjects representing different age | |
| groups, genders, and ethnicities. | |
| The photos are captured in a standardized manner, depicting individuals in a | |
| front and side positions. | |
| The images aim to capture the subjects' physical appearance using appropriate | |
| lighting and angles that showcase their body proportions accurately. | |
| The dataset serves various purposes, including: | |
| - research projects | |
| - body measurement analysis | |
| - fashion or apparel industry applications | |
| - fitness and wellness studies | |
| - anthropometric studies for ergonomic design in various fields | |
| """ | |
| _NAME = 'body-measurements-dataset' | |
| _HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}" | |
| _LICENSE = "cc-by-nc-nd-4.0" | |
| _DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/" | |
| class BodyMeasurementsDataset(datasets.GeneratorBasedBuilder): | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features({ | |
| 'front_img': datasets.Image(), | |
| 'selfie_img': datasets.Image(), | |
| 'side_img': datasets.Image(), | |
| "arm_circumference_cm": datasets.Value('string'), | |
| "arm_length_cm": datasets.Value('string'), | |
| "back_build_cm": datasets.Value('string'), | |
| "calf_circumference_cm": datasets.Value('string'), | |
| "chest_circumference_cm": datasets.Value('string'), | |
| "crotch_height_cm": datasets.Value('string'), | |
| "front_build_cm": datasets.Value('string'), | |
| "hips_circumference_cm": datasets.Value('string'), | |
| "leg_length_cm": datasets.Value('string'), | |
| "neck_circumference_cm": datasets.Value('string'), | |
| "neck_pelvis_length_front_cm": datasets.Value('string'), | |
| "neck_waist_length_back_cm": datasets.Value('string'), | |
| "neck_waist_length_front_cm": datasets.Value('string'), | |
| "pelvis_circumference_cm": datasets.Value('string'), | |
| "shoulder_length_cm": datasets.Value('string'), | |
| "shoulder_width_cm": datasets.Value('string'), | |
| "thigh_circumference_cm": datasets.Value('string'), | |
| "under_chest_circumference_cm": datasets.Value('string'), | |
| "upper_arm_length_cm": datasets.Value('string'), | |
| "waist_circumference_cm": datasets.Value('string'), | |
| "height": datasets.Value('string'), | |
| "weight": datasets.Value('string'), | |
| "age": datasets.Value('string'), | |
| "gender": datasets.Value('string'), | |
| "race": datasets.Value('string'), | |
| "profession": datasets.Value('string'), | |
| "arm_circumference": datasets.Image(), | |
| "arm_length": datasets.Image(), | |
| "back_build": datasets.Image(), | |
| "calf_circumference": datasets.Image(), | |
| "chest_circumference": datasets.Image(), | |
| "crotch_height": datasets.Image(), | |
| "front_build": datasets.Image(), | |
| "hips_circumference": datasets.Image(), | |
| "leg_length": datasets.Image(), | |
| "neck_circumference": datasets.Image(), | |
| "neck_pelvis_length_front": datasets.Image(), | |
| "neck_waist_length_back": datasets.Image(), | |
| "neck_waist_length_front": datasets.Image(), | |
| "pelvis_circumference": datasets.Image(), | |
| "shoulder_length": datasets.Image(), | |
| "shoulder_width": datasets.Image(), | |
| "thigh_circumference": datasets.Image(), | |
| "under_chest_circumference": datasets.Image(), | |
| "upper_arm_length": datasets.Image(), | |
| "waist_circumference": datasets.Image() | |
| }), | |
| supervised_keys=None, | |
| homepage=_HOMEPAGE, | |
| citation=_CITATION, | |
| license=_LICENSE) | |
| def _split_generators(self, dl_manager): | |
| files = dl_manager.download_and_extract(f"{_DATA}files.zip") | |
| proofs = dl_manager.download_and_extract(f"{_DATA}proofs.zip") | |
| annotations = dl_manager.download(f"{_DATA}{_NAME}.csv") | |
| files = dl_manager.iter_files(files) | |
| proofs = dl_manager.iter_files(proofs) | |
| return [ | |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "files": files, | |
| 'proofs': proofs, | |
| 'annotations': annotations | |
| }), | |
| ] | |
| def _generate_examples(self, files, proofs, annotations): | |
| files = list(files) | |
| files = [files[i:i + 4] for i in range(0, len(files), 4)] | |
| proofs = list(proofs) | |
| proofs = [proofs[i:i + 20] for i in range(0, len(proofs), 20)] | |
| for idx, (files_dir, proofs_dir) in enumerate(zip(files, proofs)): | |
| data = {} | |
| for file in files_dir: | |
| if 'front_img' in file: | |
| data['front_img'] = file | |
| elif 'selfie_img' in file: | |
| data['selfie_img'] = file | |
| elif 'side_img' in file: | |
| data['side_img'] = file | |
| elif 'measurements' in file: | |
| with open(file) as f: | |
| data.update(json.load(f)) | |
| for proof in proofs_dir: | |
| if "arm_circumference" in proof: | |
| data['arm_circumference'] = proof | |
| elif 'upper_arm_length' in proof: | |
| data['upper_arm_length'] = proof | |
| elif 'arm_length' in proof: | |
| data['arm_length'] = proof | |
| elif 'back_build' in proof: | |
| data['back_build'] = proof | |
| elif 'calf_circumference' in proof: | |
| data['calf_circumference'] = proof | |
| elif 'under_chest_circumference' in proof: | |
| data['under_chest_circumference'] = proof | |
| elif 'chest_circumference' in proof: | |
| data['chest_circumference'] = proof | |
| elif 'crotch_height' in proof: | |
| data['crotch_height'] = proof | |
| elif 'front_build' in proof: | |
| data['front_build'] = proof | |
| elif 'hips_circumference' in proof: | |
| data['hips_circumference'] = proof | |
| elif 'leg_length' in proof: | |
| data['leg_length'] = proof | |
| elif 'neck_circumference' in proof: | |
| data['neck_circumference'] = proof | |
| elif 'neck_pelvis_length_front' in proof: | |
| data['neck_pelvis_length_front'] = proof | |
| elif 'neck_waist_length_back' in proof: | |
| data['neck_waist_length_back'] = proof | |
| elif 'neck_waist_length_front' in proof: | |
| data['neck_waist_length_front'] = proof | |
| elif 'pelvis_circumference' in proof: | |
| data['pelvis_circumference'] = proof | |
| elif 'shoulder_length' in proof: | |
| data['shoulder_length'] = proof | |
| elif 'shoulder_width' in proof: | |
| data['shoulder_width'] = proof | |
| elif 'thigh_circumference' in proof: | |
| data['thigh_circumference'] = proof | |
| elif 'waist_circumference' in proof: | |
| data['waist_circumference'] = proof | |
| yield idx, data | |