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#!/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()
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