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import json
import gradio as gr
import torchvision
from facetorch import FaceAnalyzer
from omegaconf import OmegaConf
cfg = OmegaConf.load("config.merged.yml")
analyzer = FaceAnalyzer(cfg.analyzer)
def inference(path_image):
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)
base_emb = response.faces[0].preds["verify"].logits
sim_dict = {face.indx: cosine_similarity(base_emb, face.preds["verify"].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)
out_tuple = (pil_image, fer_dict_str, deepfake_dict_str, sim_dict_sort_str, 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 TorchScrip. 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 Verification Embeddings"),
gr.outputs.Textbox(label="Response")],
title=title,
description=description,
article=article,
examples=[["./test.jpg"], ["./test2.jpg"], ["./test3.jpg"], ["./test4.jpg"]],
)
demo.launch(server_name="0.0.0.0", server_port=7860, debug=True)
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