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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" | |
| description = "Demo of facetorch, a Python library that can detect faces and analyze facial features using deep neural networks. The goal is to gather open-sourced face analysis models from the community and optimize them for performance using TorchScript. Try selecting one of the example images or upload your own." | |
| article = "<p style='text-align: center'><a href='https://github.com/tomas-gajarsky/facetorch' target='_blank'>facetorch GitHub repository</a></p>" | |
| demo=gr.Interface( | |
| inference, | |
| [gr.inputs.Image(label="Input", type="filepath")], | |
| [gr.outputs.Image(type="pil", label="Output"), | |
| gr.outputs.Textbox(label="Facial Expression Recognition"), | |
| gr.outputs.Textbox(label="DeepFake Detection"), | |
| gr.outputs.Textbox(label="Cosine similarity on Face Representation Embeddings"), | |
| gr.outputs.Textbox(label="Cosine similarity on 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) | |