| 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 |
|
|
