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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 | |
| DESCRIPTION = """ | |
| # Age and Gender Estimation with Transformers from Face and Body Images in the Wild | |
| 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 | |
| def clear(): | |
| return None, 0.4, 0.7, "Use persons and faces", None | |
| 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) | |
| with gr.Blocks(css='style.css') as demo: | |
| gr.Markdown(DESCRIPTION) | |
| with gr.Row(): | |
| with gr.Column(): | |
| image = gr.Image(label='Input', type='numpy') | |
| score_threshold = gr.Slider(0, 1, value=0.4, step=0.05, label='Detector Score Threshold') | |
| iou_threshold = gr.Slider(0, 1, value=0.7, step=0.05, label='NMS Iou Threshold') | |
| mode = 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") | |
| with gr.Row(): | |
| clear_button = gr.Button("Clear") | |
| with gr.Column(): | |
| run_button = gr.Button("Submit") | |
| with gr.Column(): | |
| result = gr.Image(label='Output', type='numpy') | |
| inputs = [image, score_threshold, iou_threshold, mode] | |
| gr.Examples(examples=examples, | |
| inputs=inputs, | |
| outputs=result, | |
| fn=func, | |
| cache_examples=False) | |
| run_button.click(fn=func, inputs=inputs, outputs=result, api_name='predict') | |
| clear_button.click(fn=clear, inputs=None, outputs=[image, score_threshold, iou_threshold, mode, result]) | |
| demo.queue(max_size=15).launch() | |