feat: script

facial-emotion-recognition-dataset.py

@@ -0,0 +1,183 @@
+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