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import os |
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import shlex |
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import subprocess |
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if os.getenv('SYSTEM') == 'spaces': |
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GITHUB_TOKEN = os.getenv('GITHUB_TOKEN') |
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GITHUB_USER = os.getenv('GITHUB_USER') |
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git_repo = f"https://{GITHUB_TOKEN}@github.com/{GITHUB_USER}/xnet_demo.git" |
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subprocess.call(shlex.split(f'pip install git+{git_repo}')) |
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import pathlib |
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import os |
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import gradio as gr |
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import huggingface_hub |
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import numpy as np |
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import functools |
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from dataclasses import dataclass |
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from xnet.predictor import Predictor |
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@dataclass |
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class Cfg: |
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detector_weights: str |
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checkpoint: str |
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device: str = "cpu" |
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with_persons: bool = True |
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disable_faces: bool = False |
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draw: bool = True |
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DESCRIPTION = """ |
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# Age and Gender Estimation with Transformers from Face and Body Images in the Wild |
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This is an official demo. |
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""" |
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HF_TOKEN = os.getenv('HF_TOKEN') |
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def load_models(): |
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detector_path = huggingface_hub.hf_hub_download('iitolstykh/demo_yolov8_detector', |
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'yolov8x_person_face.pt', |
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use_auth_token=HF_TOKEN) |
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age_gender_path = huggingface_hub.hf_hub_download('iitolstykh/demo_xnet_volo_cross', |
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'checkpoint-377.pth.tar', |
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use_auth_token=HF_TOKEN) |
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predictor_cfg = Cfg(detector_path, age_gender_path) |
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predictor = Predictor(predictor_cfg) |
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return predictor |
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def detect( |
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image: np.ndarray, |
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score_threshold: float, |
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iou_threshold: float, |
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mode: str, |
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predictor: Predictor |
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) -> np.ndarray: |
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predictor.detector.detector_kwargs['conf'] = score_threshold |
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predictor.detector.detector_kwargs['iou'] = iou_threshold |
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if mode == "Use persons and faces": |
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use_persons = True |
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disable_faces = False |
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elif mode == "Use persons only": |
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use_persons = True |
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disable_faces = True |
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elif mode == "Use faces only": |
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use_persons = False |
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disable_faces = False |
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predictor.age_gender_model.meta.use_persons = use_persons |
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predictor.age_gender_model.meta.disable_faces = disable_faces |
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image = image[:, :, ::-1] |
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detected_objects, out_im = predictor.recognize(image) |
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return out_im[:, :, ::-1] |
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def clear(): |
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return None, 0.4, 0.7, "Use persons and faces", None |
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predictor = load_models() |
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image_dir = pathlib.Path('images') |
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examples = [[path.as_posix(), 0.4, 0.7, "Use persons and faces"] for path in sorted(image_dir.glob('*.jpg'))] |
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func = functools.partial(detect, predictor=predictor) |
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with gr.Blocks( |
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theme=gr.themes.Default(), |
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css="style.css" |
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) as demo: |
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gr.Markdown(DESCRIPTION) |
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with gr.Row(): |
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with gr.Column(): |
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image = gr.Image(label='Input', type='numpy') |
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score_threshold = gr.Slider(0, 1, value=0.4, step=0.05, label='Detector Score Threshold') |
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iou_threshold = gr.Slider(0, 1, value=0.7, step=0.05, label='NMS Iou Threshold') |
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mode = gr.Radio(["Use persons and faces", "Use persons only", "Use faces only"], |
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value="Use persons and faces", |
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label="Inference mode", |
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info="What to use for gender and age recognition") |
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with gr.Row(): |
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clear_button = gr.Button("Clear") |
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with gr.Column(): |
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run_button = gr.Button("Submit", variant="primary") |
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with gr.Column(): |
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result = gr.Image(label='Output', type='numpy') |
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inputs = [image, score_threshold, iou_threshold, mode] |
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gr.Examples(examples=examples, |
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inputs=inputs, |
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outputs=result, |
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fn=func, |
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cache_examples=False) |
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run_button.click(fn=func, inputs=inputs, outputs=result, api_name='predict') |
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clear_button.click(fn=clear, inputs=None, outputs=[image, score_threshold, iou_threshold, mode, result]) |
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demo.queue(max_size=15).launch() |
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