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import cv2
from huggingface_hub import hf_hub_download
from ultralytics import YOLO
from supervision import Detections
from PIL import Image
import torch
import numpy as np
import gradio as gr
def detect_faces(input_img):
# download model
model_path = hf_hub_download(
repo_id="arnabdhar/YOLOv8-Face-Detection", filename="model.pt"
)
# load model
model = YOLO(model_path)
# 이미지를 YOLO 모델 입력 형식으로 변환
image_cv = cv2.cvtColor(np.array(input_img), cv2.COLOR_RGB2BGR)
# 얼굴 탐지
output = model(input_img)
results = Detections.from_ultralytics(output[0])
# 탐지 결과 그리기
drawn_image = draw_rect_with_conf(image_cv, results)
# OpenCV 이미지를 PIL 이미지로 변환
drawn_image_pil = Image.fromarray(cv2.cvtColor(drawn_image, cv2.COLOR_BGR2RGB))
return drawn_image_pil
def gradio_interface(input_img):
# 얼굴 탐지 함수 호출
detected_img = detect_faces(input_img)
return detected_img
# Gradio 인터페이스 설정
demo = gr.Interface(fn=gradio_interface, inputs=gr.Image(type="pil"), outputs="image")
if __name__ == "__main__":
demo.launch()
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