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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 | |
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) | |