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| #ALL as a whole | |
| import os | |
| import gradio as gr | |
| from deepface import DeepFace | |
| import matplotlib.pyplot as plt | |
| model_name = 'ArcFace' #VGG-Face, Facenet, OpenFace, DeepFace, DeepID, Dlib, ArcFace or Ensemble | |
| def get_deepface_verify(img1_path, img2_path, model_name): | |
| img1_detect= DeepFace.detectFace(img1_path) | |
| img2_detect= DeepFace.detectFace(img2_path) | |
| result = DeepFace.verify(img1_path=img1_path,img2_path=img2_path,model_name = model_name) | |
| return result | |
| title = "DeepFace" | |
| description = "Deepface is a lightweight face recognition and facial attribute analysis (age, gender, emotion and race) framework for python. It is a hybrid face recognition framework wrapping state-of-the-art models: VGG-Face, Google FaceNet, OpenFace, Facebook DeepFace, DeepID, ArcFace and Dlib." | |
| examples=[["10Jan_1.jpeg"],["10Jan_2.jpeg"]] | |
| facial_attribute_demo = gr.Interface(get_deepface_verify, inputs = ["image","image"],outputs="json",title=title, | |
| description=description,enable_queue=True,examples=[["10Jan_1.jpeg"]],cache_examples=False) | |
| #########################3 | |
| from Deepface_analyze import facial_attribute_demo | |
| facial_attribute_demo.launch(debug=True) | |
| #################### | |
| demo = gr.TabbedInterface([facial_attribute_demo , facial_attribute_demo], ["Deepface-Verify","Deepface-analyze"]) | |
| if __name__ == "__main__": | |
| demo.launch() | |