app.py

#!/usr/bin/env python

from __future__ import annotations

import pathlib

import gradio as gr
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/"


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 = sorted(pathlib.Path("images").rglob("*.jpg"))
examples = [[path, model_complexities[1], True, 0.5, background_colors[0]] for path in image_paths]

demo = 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(label="Enable Segmentation", value=True),
        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"),
    examples=examples,
    title=TITLE,
    description=DESCRIPTION,
)

if __name__ == "__main__":
    demo.queue().launch()