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import cv2
import numpy as np
import mediapipe as mp
import gradio as gr
import tempfile
import os
mp_drawing = mp.solutions.drawing_utils
mp_pose = mp.solutions.pose
# 棒人間を描画する関数
def draw_pose_landmarks(image, results):
annotated_image = image.copy()
if results.pose_landmarks:
mp_drawing.draw_landmarks(
annotated_image,
results.pose_landmarks,
mp_pose.POSE_CONNECTIONS,
landmark_drawing_spec=mp_drawing.DrawingSpec(color=(0,255,0), thickness=2, circle_radius=2),
connection_drawing_spec=mp_drawing.DrawingSpec(color=(255,0,0), thickness=2)
)
return annotated_image
# メイン処理：動画→ポーズ認識→棒人間→新動画出力
def process_video(video_file):
with tempfile.NamedTemporaryFile(delete=False, suffix='.mp4') as temp_output:
output_path = temp_output.name
cap = cv2.VideoCapture(video_file)
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = cap.get(cv2.CAP_PROP_FPS)
out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (width, height))
with mp_pose.Pose(static_image_mode=False, min_detection_confidence=0.5, min_tracking_confidence=0.5) as pose:
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
# RGBに変換
image_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
results = pose.process(image_rgb)
# 棒人間描画
annotated_frame = draw_pose_landmarks(frame, results)
out.write(annotated_frame)
cap.release()
out.release()
return output_path
# Gradioインターフェース
interface = gr.Interface(
fn=process_video,
inputs=gr.Video(label="動画をアップロードしてください"),
outputs=gr.Video(label="ポーズ認識後の動画"),
title="MediaPipeによる棒人間ポーズ認識",
description="アップロードした動画に対してMediaPipeでポーズ検出し、棒人間を描画します。"
)
if __name__ == "__main__":
interface.launch()
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