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#!/usr/bin/env python
import os
import shlex
import subprocess
if os.getenv('SYSTEM') == 'spaces':
GITHUB_TOKEN = os.getenv('GITHUB_TOKEN')
GITHUB_USER = os.getenv('GITHUB_USER')
git_repo = f"https://{GITHUB_TOKEN}@github.com/{GITHUB_USER}/xnet_demo.git"
subprocess.call(shlex.split(f'pip install git+{git_repo}'))
import pathlib
import os
import gradio as gr
import huggingface_hub
import numpy as np
import functools
from dataclasses import dataclass
from xnet.predictor import Predictor
@dataclass
class Cfg:
detector_weights: str
checkpoint: str
device: str = "cpu"
with_persons: bool = True
disable_faces: bool = False
draw: bool = True
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_models():
detector_path = huggingface_hub.hf_hub_download('iitolstykh/demo_yolov8_detector',
'yolov8x_person_face.pt',
use_auth_token=HF_TOKEN)
age_gender_path = huggingface_hub.hf_hub_download('iitolstykh/demo_xnet_volo_cross',
'checkpoint-377.pth.tar',
use_auth_token=HF_TOKEN)
predictor_cfg = Cfg(detector_path, age_gender_path)
predictor = Predictor(predictor_cfg)
return predictor
def detect(
image: np.ndarray,
score_threshold: float,
iou_threshold: float,
mode: str,
predictor: Predictor
) -> np.ndarray:
# input is rgb image, output must be rgb too
predictor.detector.detector_kwargs['conf'] = score_threshold
predictor.detector.detector_kwargs['iou'] = iou_threshold
if mode == "Use persons and faces":
use_persons = True
disable_faces = False
elif mode == "Use persons only":
use_persons = True
disable_faces = True
elif mode == "Use faces only":
use_persons = False
disable_faces = False
predictor.age_gender_model.meta.use_persons = use_persons
predictor.age_gender_model.meta.disable_faces = disable_faces
image = image[:, :, ::-1] # RGB -> BGR
detected_objects, out_im = predictor.recognize(image)
return out_im[:, :, ::-1] # BGR -> RGB
predictor = load_models()
image_dir = pathlib.Path('images')
examples = [[path.as_posix(), 0.4, 0.7, "Use persons and faces"] for path in sorted(image_dir.glob('*.jpg'))]
func = functools.partial(detect, predictor=predictor)
gr.Interface(
fn=func,
inputs=[
gr.Image(label='Input', type='numpy'),
gr.Slider(0, 1, value=0.4, step=0.05, label='Score Threshold'),
gr.Slider(0, 1, value=0.7, step=0.05, label='Iou Threshold'),
gr.Radio(["Use persons and faces", "Use persons only", "Use faces only"],
value="Use persons and faces",
label="Inference mode",
info="What to use for gender and age recognition"),
],
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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