| 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 = {facial-emotion-recognition-dataset}, | |
| author = {TrainingDataPro}, | |
| year = {2023} | |
| } | |
| """ | |
| _DESCRIPTION = """\ | |
| The dataset consists of images capturing people displaying 7 distinct emotions | |
| (anger, contempt, disgust, fear, happiness, sadness and surprise). | |
| Each image in the dataset represents one of these specific emotions, | |
| enabling researchers and machine learning practitioners to study and develop | |
| models for emotion recognition and analysis. | |
| The images encompass a diverse range of individuals, including different | |
| genders, ethnicities, and age groups*. The dataset aims to provide | |
| a comprehensive representation of human emotions, allowing for a wide range of | |
| use cases. | |
| """ | |
| _NAME = 'facial-emotion-recognition-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 FacialEmotionRecognitionDataset(datasets.GeneratorBasedBuilder): | |
| def _info(self): | |
| return datasets.DatasetInfo(description=_DESCRIPTION, | |
| features=datasets.Features({ | |
| 'set_id': datasets.Value('int32'), | |
| 'neutral': datasets.Image(), | |
| 'anger': datasets.Image(), | |
| 'contempt': datasets.Image(), | |
| 'disgust': datasets.Image(), | |
| "fear": datasets.Image(), | |
| "happy": datasets.Image(), | |
| "sad": datasets.Image(), | |
| "surprised": datasets.Image(), | |
| "age": datasets.Value('int8'), | |
| "gender": datasets.Value('string'), | |
| "country": datasets.Value('string') | |
| }), | |
| supervised_keys=None, | |
| homepage=_HOMEPAGE, | |
| citation=_CITATION, | |
| license=_LICENSE) | |
| def _split_generators(self, dl_manager): | |
| images = dl_manager.download_and_extract(f"{_DATA}images.zip") | |
| annotations = dl_manager.download(f"{_DATA}{_NAME}.csv") | |
| images = dl_manager.iter_files(images) | |
| return [ | |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "images": images, | |
| 'annotations': annotations | |
| }), | |
| ] | |
| def _generate_examples(self, images, annotations): | |
| annotations_df = pd.read_csv(annotations, sep=';') | |
| images = sorted(images) | |
| images = [images[i:i + 8] for i in range(0, len(images), 8)] | |
| for idx, images_set in enumerate(images): | |
| set_id = int(images_set[0].split('/')[2]) | |
| data = {'set_id': set_id} | |
| for file in images_set: | |
| if 'neutral' in file.lower(): | |
| data['neutral'] = file | |
| elif 'anger' in file.lower(): | |
| data['anger'] = file | |
| elif 'contempt' in file.lower(): | |
| data['contempt'] = file | |
| elif 'disgust' in file.lower(): | |
| data['disgust'] = file | |
| elif 'fear' in file.lower(): | |
| data['fear'] = file | |
| elif 'happy' in file.lower(): | |
| data['happy'] = file | |
| elif 'sad' in file.lower(): | |
| data['sad'] = file | |
| elif 'surprised' in file.lower(): | |
| data['surprised'] = file | |
| data['age'] = annotations_df.loc[annotations_df['set_id'] == | |
| set_id]['age'].values[0] | |
| data['gender'] = annotations_df.loc[annotations_df['set_id'] == | |
| set_id]['gender'].values[0] | |
| data['country'] = annotations_df.loc[annotations_df['set_id'] == | |
| set_id]['country'].values[0] | |
| yield idx, data | |