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#!/usr/bin/env python
from __future__ import annotations
import os
import pathlib
import shlex
import subprocess
import tarfile
if os.environ.get('SYSTEM') == 'spaces':
subprocess.call(shlex.split('pip uninstall -y opencv-python'))
subprocess.call(shlex.split('pip uninstall -y opencv-python-headless'))
subprocess.call(
shlex.split('pip install opencv-python-headless==4.5.5.64'))
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/'
HF_TOKEN = os.getenv('HF_TOKEN')
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=HF_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]
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(
fn=run,
inputs=[
gr.Image(label='Input', type='numpy'),
gr.Radio(label='Model',
choices=model_types,
type='index',
value=model_types[0]),
gr.Slider(label='Minimum Detection Confidence',
minimum=0,
maximum=1,
step=0.05,
value=0.5),
],
outputs=gr.Image(label='Output', type='numpy'),
examples=examples,
title=TITLE,
description=DESCRIPTION,
).launch(show_api=False)
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