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import gradio as gr
import cv2
from insightface.app import FaceAnalysis
import torch
app = FaceAnalysis(name="buffalo_l", providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
app.prepare(ctx_id=0, det_size=(640, 640))
def calculate(photo):
image = cv2.imread(photo)
faces = app.get(image)
image_draw = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
faceid_embeds = torch.from_numpy(faces[0].normed_embedding).unsqueeze(0)
image_draw = app.draw_on(image_draw, faces)
return image_draw
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
face_photo = gr.Image(label="Photo", type="filepath")
greet_btn = gr.Button("Calculate")
with gr.Column():
output_image = gr.Image(label="Output")
output = gr.JSON()
greet_btn.click(fn=calculate, inputs=face_photo, outputs=output_image, api_name="calculate_face_embedding")
if __name__ == "__main__":
demo.launch() |