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| import json | |
| import operator | |
| import gradio as gr | |
| import torchvision | |
| from typing import Tuple, Dict | |
| from facetorch import FaceAnalyzer | |
| from facetorch.datastruct import ImageData | |
| from omegaconf import OmegaConf | |
| from torch.nn.functional import cosine_similarity | |
| cfg = OmegaConf.load("config.merged.yml") | |
| analyzer = FaceAnalyzer(cfg.analyzer) | |
| def get_sim_dict_str(response: ImageData, pred_name: str = "verify", index: int = 0)-> str: | |
| base_emb = response.faces[index].preds[pred_name].logits | |
| sim_dict = {face.indx: cosine_similarity(base_emb, face.preds[pred_name].logits, dim=0).item() for face in response.faces} | |
| sim_dict_sort = dict(sorted(sim_dict.items(), key=operator.itemgetter(1),reverse=True)) | |
| sim_dict_sort_str = str(sim_dict_sort) | |
| return sim_dict_sort_str | |
| def inference(path_image: str) -> Tuple: | |
| response = analyzer.run( | |
| path_image=path_image, | |
| batch_size=cfg.batch_size, | |
| fix_img_size=cfg.fix_img_size, | |
| return_img_data=cfg.return_img_data, | |
| include_tensors=cfg.include_tensors, | |
| path_output=None, | |
| ) | |
| pil_image = torchvision.transforms.functional.to_pil_image(response.img) | |
| fer_dict_str = str({face.indx: face.preds["fer"].label for face in response.faces}) | |
| deepfake_dict_str = str({face.indx: face.preds["deepfake"].label for face in response.faces}) | |
| response_str = str(response) | |
| sim_dict_str_embed = get_sim_dict_str(response, pred_name="embed", index=0) | |
| sim_dict_str_verify = get_sim_dict_str(response, pred_name="verify", index=0) | |
| out_tuple = (pil_image, fer_dict_str, deepfake_dict_str, sim_dict_str_embed, sim_dict_str_verify, response_str) | |
| return out_tuple | |
| title = "facetorch-app" | |
| description = "Demo of facetorch, a Python library that uses pre-trained deep neural networks for face detection, representation learning, verification, expression recognition, deepfake detection, and 3D alignment. Try selecting one of the example images or upload your own. The quality of different models varies. Use responsibly." | |
| article = "<p style='text-align: center'><a href='https://github.com/tomas-gajarsky/facetorch' target='_blank'>facetorch GitHub repository</a></p><a href='https://pypi.org/project/facetorch/'><img src='https://img.shields.io/pypi/v/facetorch' alt='PyPI'></a><a href='https://anaconda.org/conda-forge/facetorch'><img src='https://img.shields.io/conda/vn/conda-forge/facetorch' alt='Conda (channel only)'></a><a href='https://raw.githubusercontent.com/tomas-gajarsky/facetorch/main/LICENSE'><img src='https://img.shields.io/pypi/l/facetorch' alt='PyPI - License'></a><p><img src='https://raw.githubusercontent.com/tomas-gajarsky/facetorch/main/data/facetorch-logo-64.png' alt='' title='facetorch logo'></p>" | |
| demo=gr.Interface( | |
| inference, | |
| [gr.inputs.Image(label="Input", type="filepath")], | |
| [gr.outputs.Image(type="pil", label="Face Detection and 3D Landmarks"), | |
| gr.outputs.Textbox(label="Facial Expression Recognition"), | |
| gr.outputs.Textbox(label="DeepFake Detection"), | |
| gr.outputs.Textbox(label="Cosine similarity of Face Representation Embeddings"), | |
| gr.outputs.Textbox(label="Cosine similarity of Face Verification Embeddings"), | |
| gr.outputs.Textbox(label="Response")], | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=[["./test5.jpg"], ["./test.jpg"], ["./test4.jpg"], ["./test2.jpg"], ["./test8.jpg"], ["./test6.jpg"], ["./test3.jpg"]], | |
| ) | |
| demo.launch(server_name="0.0.0.0", server_port=7860, debug=True) | |