460 lines
20 KiB
Python
460 lines
20 KiB
Python
import streamlit as st
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import cv2
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import time
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import os
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import numpy as np
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import torch
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from rtmlib import Body, draw_skeleton
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from audio_player import AudioPlayer
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from pose_analyzer import PoseSimilarityAnalyzer
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from config import REALSENSE_AVAILABLE
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if REALSENSE_AVAILABLE:
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import pyrealsense2 as rs
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class MotionComparisonApp:
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"""Main application class for motion comparison."""
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def __init__(self):
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self.body_detector = None
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self.is_running = False
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self.standard_video_path = None
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self.webcam_cap = None
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self.standard_cap = None
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self.similarity_analyzer = PoseSimilarityAnalyzer()
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self.frame_counter = 0
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self.audio_player = AudioPlayer()
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self.display_settings = {'resolution_mode': 'high', 'target_width': 960, 'target_height': 720}
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self.realsense_pipeline = None
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self.is_realsense_active = False
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self.last_error_time = 0
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self.error_count = 0
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if 'comparison_state' not in st.session_state:
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st.session_state.comparison_state = {'is_running': False, 'should_stop': False, 'should_restart': False}
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def get_display_resolution(self):
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modes = {'high': (1280, 800), 'medium': (960, 720), 'low': (640, 480)}
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mode = self.display_settings.get('resolution_mode', 'medium')
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return modes.get(mode, (960, 720))
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def initialize_detector(self):
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if self.body_detector is None:
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try:
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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self.body_detector = Body(mode='lightweight', to_openpose=True, backend='onnxruntime', device=device)
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st.success(f"Keypoint detector initialized on device: {device}")
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return True
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except Exception as e:
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st.error(f"Detector initialization failed: {e}")
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return False
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return True
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def initialize_camera(self):
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if REALSENSE_AVAILABLE:
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try:
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self.realsense_pipeline = rs.pipeline()
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config = rs.config()
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width, height = self.get_display_resolution()
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config.enable_stream(rs.stream.color, width, height, rs.format.bgr8, 30)
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profile = self.realsense_pipeline.start(config)
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device = profile.get_device().get_info(rs.camera_info.name)
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st.success(f"✅ RealSense摄像头初始化成功: {device} ({width}x{height})")
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self.is_realsense_active = True
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return True
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except Exception as e:
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st.warning(f"RealSense初始化失败: {e}. 切换到USB摄像头.")
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return self._initialize_webcam()
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else:
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return self._initialize_webcam()
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def _initialize_webcam(self):
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try:
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self.webcam_cap = cv2.VideoCapture(0)
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if self.webcam_cap.isOpened():
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width, height = self.get_display_resolution()
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self.webcam_cap.set(cv2.CAP_PROP_FRAME_WIDTH, width)
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self.webcam_cap.set(cv2.CAP_PROP_FRAME_HEIGHT, height)
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self.webcam_cap.set(cv2.CAP_PROP_FPS, 30)
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actual_w = int(self.webcam_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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actual_h = int(self.webcam_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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st.success(f"✅ USB摄像头初始化成功 ({actual_w}x{actual_h})")
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return True
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else:
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st.error("❌ 无法打开USB摄像头")
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return False
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except Exception as e:
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st.error(f"❌ USB摄像头初始化失败: {e}")
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return False
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def read_camera_frame(self):
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if self.is_realsense_active and self.realsense_pipeline:
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try:
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frames = self.realsense_pipeline.wait_for_frames(timeout_ms=1000)
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color_frame = frames.get_color_frame()
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if not color_frame: return False, None
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return True, np.asanyarray(color_frame.get_data())
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except Exception:
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return False, None
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elif self.webcam_cap and self.webcam_cap.isOpened():
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return self.webcam_cap.read()
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return False, None
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def get_camera_preview_frame(self):
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ret, frame = self.read_camera_frame()
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if not ret or frame is None: return None
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frame = cv2.flip(frame, 1)
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if self.body_detector:
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try:
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keypoints, scores = self.body_detector(frame)
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frame = draw_skeleton(frame.copy(), keypoints, scores, openpose_skeleton=True, kpt_thr=0.43)
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except Exception: pass
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return cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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def cleanup(self):
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"""Cleans up all resources."""
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if self.standard_cap: self.standard_cap.release()
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if self.webcam_cap: self.webcam_cap.release()
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if self.is_realsense_active and self.realsense_pipeline: self.realsense_pipeline.stop()
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self.audio_player.cleanup()
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self.is_running = False
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st.session_state.comparison_state['is_running'] = False
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def show_final_statistics(self):
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"""Displays final statistics after the comparison ends."""
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history = self.similarity_analyzer.similarity_history
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if not history: return
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final_avg = sum(history) / len(history)
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level, color = ("非常棒! 👏", "success") if final_avg >= 80 else \
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("整体不错! 👍", "info") if final_avg >= 60 else \
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("需要改进! 💪", "warning")
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st.success("🎉 比较完成!")
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st.markdown(f"**整体表现**: :{color}[{level}]")
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col1, col2, col3 = st.columns(3)
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col1.metric("平均相似度", f"{final_avg:.1f}%")
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col2.metric("最高相似度", f"{max(history):.1f}%")
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col3.metric("最低相似度", f"{min(history):.1f}%")
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if final_avg < 60:
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with st.expander("💡 改善建议"):
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st.markdown("- 确保您的全身在摄像头画面中清晰可见\n"
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"- 尽量匹配标准视频的节奏和动作幅度\n"
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"- 确保光线充足且稳定")
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def start_comparison(self, video_path):
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"""The main loop for comparing motion."""
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# Setup and initialization... (abbreviated for clarity, logic is the same as original)
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self.is_running = True
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st.session_state.comparison_state.update({'is_running': True, 'should_stop': False, 'should_restart': False})
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self.standard_video_path = video_path
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self.frame_counter = 0
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self.similarity_analyzer.reset()
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audio_loaded = self.audio_player.load_audio(video_path)
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if audio_loaded: st.success("✅ 音频加载成功")
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else: st.info("ℹ️ 将不会播放音频")
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self.standard_cap = cv2.VideoCapture(video_path)
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if not self.standard_cap.isOpened():
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st.error("无法打开标准视频文件")
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return
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if not self.is_realsense_active and (not self.webcam_cap or not self.webcam_cap.isOpened()):
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if not self.initialize_camera(): return
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# Get video info and setup variables
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total_frames = int(self.standard_cap.get(cv2.CAP_PROP_FRAME_COUNT))
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video_fps = self.standard_cap.get(cv2.CAP_PROP_FPS)
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frame_delay = 1.0 / video_fps if video_fps > 0 else 1.0 / 30.0
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target_width, target_height = self.get_display_resolution()
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# UI Placeholders
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st.markdown("### 📺 视频比较")
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video_col1, video_col2 = st.columns(2, gap="small")
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standard_placeholder = video_col1.empty()
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webcam_placeholder = video_col2.empty()
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with video_col1:
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st.markdown("#### 🎯 标准动作视频")
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with video_col2:
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camera_type = "RealSense摄像头" if self.is_realsense_active else "USB摄像头"
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st.markdown(f"#### 📹 {camera_type}实时影像")
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# 创建控制按钮区域（紧凑布局）
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st.markdown("---")
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control_container = st.container()
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with control_container:
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control_col1, control_col2, control_col3, control_col4 = st.columns([1, 1, 1, 1])
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with control_col1:
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stop_button = st.button("⏹️ 停止", use_container_width=True, key="stop_comparison")
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with control_col2:
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restart_button = st.button("🔄 重新开始", use_container_width=True, key="restart_comparison")
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with control_col3:
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if audio_loaded:
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if st.button("🔊 音频状态", use_container_width=True, key="audio_status"):
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st.info(f"音频: {'播放中' if self.audio_player.is_playing else '已停止'}")
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with control_col4:
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# 分辨率切换按钮
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if st.button("📐 切换分辨率", use_container_width=True, key="resolution_toggle"):
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modes = ['high', 'medium', 'low']
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current_idx = modes.index(self.display_settings.get('resolution_mode', 'high'))
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next_idx = (current_idx + 1) % len(modes)
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self.display_settings['resolution_mode'] = modes[next_idx]
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st.info(f"分辨率模式: {modes[next_idx]}")
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# Similarity UI
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st.markdown("---")
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st.markdown("### 📊 动作相似度分析")
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sim_col1, sim_col2, sim_col3 = st.columns([1, 1, 2])
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similarity_score_placeholder = sim_col1.empty()
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avg_score_placeholder = sim_col2.empty()
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similarity_plot_placeholder = sim_col3.empty()
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# 处理按钮点击
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if stop_button:
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st.session_state.comparison_state['should_stop'] = True
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if restart_button:
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st.session_state.comparison_state['should_restart'] = True
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# 状态显示区域
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status_container = st.container()
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with status_container:
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progress_bar = st.progress(0)
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status_text = st.empty()
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# 主循环
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video_frame_idx = 0
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start_time = time.time()
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current_similarity = 0
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last_plot_update = 0
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# Start Audio
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if audio_loaded: self.audio_player.play()
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while (st.session_state.comparison_state['is_running'] and
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not st.session_state.comparison_state['should_stop']):
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loop_start = time.time()
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self.frame_counter += 1
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# 检查重新开始
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if st.session_state.comparison_state['should_restart']:
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self.standard_cap.set(cv2.CAP_PROP_POS_FRAMES, 0)
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self.similarity_analyzer.reset()
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start_time = time.time()
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video_frame_idx = 0
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if audio_loaded:
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self.audio_player.restart()
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st.session_state.comparison_state['should_restart'] = False
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continue
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# 读取标准视频的当前帧
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ret_standard, standard_frame = self.standard_cap.read()
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if not ret_standard:
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# 视频结束，重新开始
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self.standard_cap.set(cv2.CAP_PROP_POS_FRAMES, 0)
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video_frame_idx = 0
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self.similarity_analyzer.reset()
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start_time = time.time()
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if audio_loaded:
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self.audio_player.restart()
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continue
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# 读取摄像头当前帧
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ret_webcam, webcam_frame = self.read_camera_frame()
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if not ret_webcam or webcam_frame is None:
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self.error_count += 1
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if self.error_count > 100: # 连续错误过多时停止
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st.error("摄像头连接出现问题，停止比较")
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break
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continue
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self.error_count = 0 # 重置错误计数
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# 翻转摄像头画面（镜像效果）
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webcam_frame = cv2.flip(webcam_frame, 1)
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# 调整尺寸使两个视频大小一致（使用更高分辨率）
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standard_frame = cv2.resize(standard_frame, (target_width, target_height))
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webcam_frame = cv2.resize(webcam_frame, (target_width, target_height))
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# 处理关键点检测
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try:
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standard_keypoints, standard_scores = self.body_detector(standard_frame)
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webcam_keypoints, webcam_scores = self.body_detector(webcam_frame)
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except Exception as e:
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# 关键点检测失败时继续显示原始图像
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standard_keypoints, standard_scores = None, None
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webcam_keypoints, webcam_scores = None, None
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# 计算相似度（每5帧计算一次以提高性能）
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if (self.frame_counter % 5 == 0 and
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standard_keypoints is not None and
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webcam_keypoints is not None):
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try:
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# 提取当前标准视频帧和摄像头帧的关节角度
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standard_angles = self.similarity_analyzer.extract_joint_angles(
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standard_keypoints, standard_scores
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)
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webcam_angles = self.similarity_analyzer.extract_joint_angles(
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webcam_keypoints, webcam_scores
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)
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# 计算相似度
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if standard_angles and webcam_angles:
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current_similarity = self.similarity_analyzer.calculate_similarity(
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standard_angles, webcam_angles
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)
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# 添加到历史记录
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timestamp = time.time() - start_time
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self.similarity_analyzer.add_similarity_score(current_similarity, timestamp)
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except Exception as e:
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pass # 忽略相似度计算错误
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# 绘制关键点
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try:
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if standard_keypoints is not None and standard_scores is not None:
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standard_with_keypoints = draw_skeleton(
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standard_frame.copy(),
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standard_keypoints,
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standard_scores,
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openpose_skeleton=True,
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kpt_thr=0.43
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)
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else:
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standard_with_keypoints = standard_frame.copy()
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if webcam_keypoints is not None and webcam_scores is not None:
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webcam_with_keypoints = draw_skeleton(
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webcam_frame.copy(),
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webcam_keypoints,
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webcam_scores,
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openpose_skeleton=True,
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kpt_thr=0.43
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)
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else:
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webcam_with_keypoints = webcam_frame.copy()
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except Exception as e:
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# 如果绘制失败，使用原始帧
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standard_with_keypoints = standard_frame.copy()
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webcam_with_keypoints = webcam_frame.copy()
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# 转换颜色空间 (BGR to RGB)
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standard_rgb = cv2.cvtColor(standard_with_keypoints, cv2.COLOR_BGR2RGB)
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webcam_rgb = cv2.cvtColor(webcam_with_keypoints, cv2.COLOR_BGR2RGB)
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# 添加帧信息到图像上
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current_time = time.time() - start_time
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frame_info = f"时间: {current_time:.1f}s | 帧: {video_frame_idx}/{total_frames}"
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audio_info = f" | 音频: {'🔊' if self.audio_player.is_playing else '🔇'}" if audio_loaded else ""
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resolution_info = f" | {target_width}x{target_height}"
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# 显示大尺寸画面
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with video_col1:
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standard_placeholder.image(
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standard_rgb,
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caption=f"标准动作 - {frame_info}{audio_info}{resolution_info}",
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use_container_width=True
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)
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with video_col2:
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webcam_placeholder.image(
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webcam_rgb,
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caption=f"您的动作 - 实时画面{resolution_info}",
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use_container_width=True
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)
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# 显示相似度信息（紧凑显示）
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if len(self.similarity_analyzer.similarity_history) > 0:
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try:
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avg_similarity = sum(self.similarity_analyzer.similarity_history) / len(self.similarity_analyzer.similarity_history)
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# 使用不同颜色显示相似度
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if current_similarity >= 80:
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similarity_color = "🟢"
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level = "优秀"
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elif current_similarity >= 60:
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similarity_color = "🟡"
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level = "良好"
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else:
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similarity_color = "🔴"
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level = "需要改进"
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with sim_col1:
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similarity_score_placeholder.metric(
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"当前相似度",
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f"{similarity_color} {current_similarity:.1f}%",
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delta=f"{level}"
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)
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with sim_col2:
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avg_score_placeholder.metric(
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"平均相似度",
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f"{avg_similarity:.1f}%"
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)
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# 更新相似度图表（每20帧更新一次以提高性能）
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if (len(self.similarity_analyzer.similarity_history) >= 2 and
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self.frame_counter - last_plot_update >= 20):
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try:
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similarity_plot = self.similarity_analyzer.get_similarity_plot()
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if similarity_plot:
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with sim_col3:
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similarity_plot_placeholder.plotly_chart(
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similarity_plot,
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use_container_width=True,
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key=f"similarity_plot_{int(time.time() * 1000)}" # 使用时间戳避免重复ID
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)
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last_plot_update = self.frame_counter
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except Exception as e:
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pass # 忽略图表更新错误
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except Exception as e:
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pass # 忽略显示错误
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# 更新进度和状态（紧凑显示）
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try:
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progress = min(video_frame_idx / total_frames, 1.0) if total_frames > 0 else 0
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progress_bar.progress(progress)
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elapsed_time = time.time() - start_time
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fps_actual = self.frame_counter / elapsed_time if elapsed_time > 0 else 0
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status_text.text(
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f"进度: {video_frame_idx}/{total_frames} | "
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f"实际FPS: {fps_actual:.1f} | "
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f"分辨率: {target_width}x{target_height} | "
|
||
f"模式: {self.display_settings['resolution_mode']}"
|
||
)
|
||
except Exception as e:
|
||
pass # 忽略状态更新错误
|
||
|
||
video_frame_idx += 1
|
||
|
||
# 精确的帧率控制
|
||
loop_elapsed = time.time() - loop_start
|
||
sleep_time = max(0, frame_delay - loop_elapsed)
|
||
if sleep_time > 0:
|
||
time.sleep(sleep_time)
|
||
|
||
# 强制更新UI（每30帧一次）
|
||
if self.frame_counter % 30 == 0:
|
||
st.empty() # 触发UI更新
|
||
|
||
|
||
self.cleanup()
|
||
self.show_final_statistics()
|