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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_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
mp_pose = mp.solutions.pose
TITLE = 'MediaPipe Human Pose Estimation'
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 = ['002.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_complexity: int, enable_segmentation: bool,
min_detection_confidence: float, background_color: str) -> np.ndarray:
with mp_pose.Pose(
static_image_mode=True,
model_complexity=model_complexity,
enable_segmentation=enable_segmentation,
min_detection_confidence=min_detection_confidence) as pose:
results = pose.process(image)
res = image[:, :, ::-1].copy()
if enable_segmentation:
if background_color == 'white':
bg_color = 255
elif background_color == 'black':
bg_color = 0
elif background_color == 'green':
bg_color = (0, 255, 0) # type: ignore
else:
raise ValueError
if results.segmentation_mask is not None:
res[results.segmentation_mask <= 0.1] = bg_color
else:
res[:] = bg_color
mp_drawing.draw_landmarks(res,
results.pose_landmarks,
mp_pose.POSE_CONNECTIONS,
landmark_drawing_spec=mp_drawing_styles.
get_default_pose_landmarks_style())
return res[:, :, ::-1]
model_complexities = list(range(3))
background_colors = ['white', 'black', 'green']
image_paths = load_sample_images()
examples = [[
path.as_posix(), model_complexities[1], True, 0.5, background_colors[0]
] for path in image_paths]
gr.Interface(
fn=run,
inputs=[
gr.Image(label='Input', type='numpy'),
gr.Radio(label='Model Complexity',
choices=model_complexities,
type='index',
value=model_complexities[1]),
gr.Checkbox(default=True, label='Enable Segmentation'),
gr.Slider(label='Minimum Detection Confidence',
minimum=0,
maximum=1,
step=0.05,
value=0.5),
gr.Radio(label='Background Color',
choices=background_colors,
type='value',
value=background_colors[0]),
],
outputs=gr.Image(label='Output', type='numpy'),
examples=examples,
title=TITLE,
description=DESCRIPTION,
).launch(show_api=False)
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