|
|
|
|
|
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: |
|
detector_kwargs = {'conf': 0.5, 'iou': 0.5, 'half': False, 'verbose': False} |
|
|
|
results: Results = detector.predict(image, **detector_kwargs)[0] |
|
out_im = results.plot() |
|
|
|
return out_im |
|
|
|
|
|
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) |
|
|
