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#!/usr/bin/env python
from __future__ import annotations
import argparse
import os
import pathlib
import subprocess
import tarfile
if os.environ.get('SYSTEM') == 'spaces':
subprocess.call('pip uninstall -y opencv-python'.split())
subprocess.call('pip uninstall -y opencv-python-headless'.split())
subprocess.call('pip install opencv-python-headless==4.5.5.64'.split())
import gradio as gr
import huggingface_hub
import mediapipe as mp
import numpy as np
mp_face_detection = mp.solutions.face_detection
mp_drawing = mp.solutions.drawing_utils
TITLE = 'MediaPipe Face Detection'
DESCRIPTION = 'https://google.github.io/mediapipe/'
ARTICLE = '<center><img src="https://visitor-badge.glitch.me/badge?page_id=hysts.mediapipe-face-detection" alt="visitor badge"/></center>'
TOKEN = os.environ['TOKEN']
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument('--theme', type=str)
parser.add_argument('--live', action='store_true')
parser.add_argument('--share', action='store_true')
parser.add_argument('--port', type=int)
parser.add_argument('--disable-queue',
dest='enable_queue',
action='store_false')
parser.add_argument('--allow-flagging', type=str, default='never')
return parser.parse_args()
def load_sample_images() -> list[pathlib.Path]:
image_dir = pathlib.Path('images')
if not image_dir.exists():
image_dir.mkdir()
dataset_repo = 'hysts/input-images'
filenames = ['001.tar', '005.tar']
for name in filenames:
path = huggingface_hub.hf_hub_download(dataset_repo,
name,
repo_type='dataset',
use_auth_token=TOKEN)
with tarfile.open(path) as f:
f.extractall(image_dir.as_posix())
return sorted(image_dir.rglob('*.jpg'))
def run(image: np.ndarray, model_selection: int,
min_detection_confidence: float) -> np.ndarray:
with mp_face_detection.FaceDetection(
model_selection=model_selection,
min_detection_confidence=min_detection_confidence
) as face_detection:
results = face_detection.process(image)
res = image[:, :, ::-1].copy()
if results.detections is not None:
for detection in results.detections:
mp_drawing.draw_detection(res, detection)
return res[:, :, ::-1]
def main():
args = parse_args()
model_types = [
'Short-range model (best for faces within 2 meters)',
'Full-range model (best for faces within 5 meters)',
]
image_paths = load_sample_images()
examples = [[path.as_posix(), model_types[0], 0.5] for path in image_paths]
gr.Interface(
run,
[
gr.inputs.Image(type='numpy', label='Input'),
gr.inputs.Radio(model_types,
type='index',
default=model_types[0],
label='Model'),
gr.inputs.Slider(0,
1,
step=0.05,
default=0.5,
label='Minimum Detection Confidence'),
],
gr.outputs.Image(type='numpy', label='Output'),
examples=examples,
title=TITLE,
description=DESCRIPTION,
article=ARTICLE,
theme=args.theme,
allow_flagging=args.allow_flagging,
live=args.live,
).launch(
enable_queue=args.enable_queue,
server_port=args.port,
share=args.share,
)
if __name__ == '__main__':
main()
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