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#!/usr/bin/env python
import pathlib
import os
import cv2
import gradio as gr
import huggingface_hub
import numpy as np
import functools
from ultralytics import YOLO
from ultralytics.yolo.engine.results import Results
TITLE = 'Age and Gender Estimation with Transformers from Face and Body Images in the Wild'
DESCRIPTION = 'This is an official demo for https://github.com/...'
HF_TOKEN = os.getenv('HF_TOKEN')
def load_model():
path = huggingface_hub.hf_hub_download('iitolstykh/demo_yolov8_detector',
'yolov8x_person_face.pt',
use_auth_token=HF_TOKEN)
yolo = YOLO(path)
yolo.fuse()
return yolo
def detect(image: np.ndarray, detector: YOLO) -> np.ndarray:
# input is rgb image, output must be rgb too
detector_kwargs = {'conf': 0.5, 'iou': 0.5, 'half': False, 'verbose': False}
image = image[:, :, ::-1] # RGB -> BGR
results: Results = detector.predict(image, **detector_kwargs)[0]
out_im = results.plot()
return out_im[:, :, ::-1] # BGR -> RGB
detector = load_model()
image_dir = pathlib.Path('images')
examples = [[path.as_posix()] for path in sorted(image_dir.glob('*.jpg'))]
func = functools.partial(detect, detector=detector)
gr.Interface(
fn=func,
inputs=gr.Image(label='Input', type='numpy'),
outputs=gr.Image(label='Output', type='numpy'),
examples=examples,
examples_per_page=30,
title=TITLE,
description=DESCRIPTION,
).launch(show_api=False)
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