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mediapipe_logic.py
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mediapipe_logic.py
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import cv2
import mediapipe as mp
import json
import os
import time
import numpy as np
import vgamepad as vg
# 初始化 MediaPipe 姿势估计模型
mp_pose = mp.solutions.pose
pose = mp_pose.Pose(min_detection_confidence=0.5, min_tracking_confidence=0.5)
# 初始化 OpenCV 视频捕获对象
cap = cv2.VideoCapture(0)
gamepad = vg.VX360Gamepad()
# 全局变量
is_active = True # 标志是否正在录制
output_dir = None # 当前输出文件夹
video_writer = None # 视频写入对象
frame_index = 0 # 帧计数
x_value = 0 # X 轴的初始位置（范围 -32768 到 32767）
y_value = 0 # Y 轴的初始位置（范围 -32768 到 32767）
left_triggers = 0
right_triggers = 0
button_mapping = {
'h': vg.XUSB_BUTTON.XUSB_GAMEPAD_X, # H -> X 按键
'j': vg.XUSB_BUTTON.XUSB_GAMEPAD_Y, # J -> Y 按键
'k': vg.XUSB_BUTTON.XUSB_GAMEPAD_A, # K -> A 按键
'l': vg.XUSB_BUTTON.XUSB_GAMEPAD_B # L -> B 按键
}
def update_axes():
"""更新手柄的 X 和 Y 轴值"""
gamepad.left_joystick(x_value, y_value)
gamepad.update()
def update_triggers():
"""更新手柄的左右摇杆值"""
gamepad.left_trigger(left_triggers)
gamepad.right_trigger(right_triggers)
gamepad.update()
while True:
ret, frame = cap.read()
if not ret:
break
# 将图像从 BGR 转换为 RGB
rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# 进行姿势估计
result = pose.process(rgb_frame)
# 仅绘制关键点和文字用于显示，不影响视频录制
frame_with_text = frame.copy() # 创建一个副本用于显示文字和关键点
if result.pose_landmarks:
# 在副本上绘制关键点
mp.solutions.drawing_utils.draw_landmarks(frame_with_text, result.pose_landmarks, mp_pose.POSE_CONNECTIONS)
if is_active:
# 提取关键点数据
keypoints = []
left_right_threshold = 0.11
head_body_threshold = 0.08
for landmark in result.pose_landmarks.landmark:
keypoints.append({
"x": landmark.x,
"y": landmark.y,
"z": landmark.z,
"visibility": landmark.visibility
})
left_hand_indices = [15, 17, 19, 21]
right_hand_indices = [16, 18, 20, 22]
head_indices = [1,2,3,4,5,6,7,8,9,10]
body_up_inices = [11, 12]
body_down_inices = [23, 24]
left_hand_avg = {key: np.mean([keypoints[i][key] for i in left_hand_indices]) for key in keypoints[0].keys()}
right_hand_avg = {key: np.mean([keypoints[i][key] for i in right_hand_indices]) for key in keypoints[0].keys()}
# head_avg = {key: np.mean([keypoints[i][key] for i in head_indices]) for key in keypoints[0].keys()}
body_up_avg = {key: np.mean([keypoints[i][key] for i in body_up_inices]) for key in keypoints[0].keys()}
body_down_avg = {key: np.mean([keypoints[i][key] for i in body_down_inices]) for key in keypoints[0].keys()}
body_down_avg['z'] = body_down_avg['z'] - 0.11
# if frame_index % 60 == 0:
# print(f'left_hand_avg: {left_hand_avg}')
# print(f'right_hand_avg: {right_hand_avg}')
# # print(f'head_avg: {head_avg}')
# print(f'body_up_avg: {body_up_avg}')
# print(f'body_down_avg: {body_down_avg}')
if (lr_amplitude := abs(left_hand_avg['y'] - right_hand_avg['y'])) > left_right_threshold and \
all(0 <= x <= 1 for x in [left_hand_avg['x'], right_hand_avg['x'], left_hand_avg['y'], right_hand_avg['y']]):
if left_hand_avg['y'] < right_hand_avg['y']:
x_value = min(32767, int(32767 * (lr_amplitude-left_right_threshold+0.03) * 2.1))
print(f"Frame {frame_index}: Left; amplitude: {x_value}, {lr_amplitude}")
update_axes()
else:
x_value = -min(32768, int(32768 * (lr_amplitude-left_right_threshold+0.03) * 2.1))
print(f"Frame {frame_index}: Right; amplitude: {x_value}, {lr_amplitude}")
update_axes()
else:
if x_value > 0:
x_value -= 1000
x_value = max(0, x_value)
if x_value < 0:
x_value += 1000
x_value = min(0, x_value)
update_axes()
if (fb_amplitude := abs(body_up_avg['z'] - body_down_avg['z'])) > head_body_threshold and \
all(0 <= x <= 1 for x in [body_up_avg['x'], body_up_avg['y'], body_down_avg['x'], body_down_avg['y']]):
if body_up_avg['z'] < body_down_avg['z']:
right_triggers = min(32767, int(32767 * (fb_amplitude-head_body_threshold+0.03) * 6.5))
left_triggers = 0
print(f"Frame {frame_index}: Forward; amplitude: {right_triggers}, {fb_amplitude}")
update_triggers()
else:
left_triggers = min(32767, int(32767 * (fb_amplitude-head_body_threshold+0.03) * 13))
right_triggers = 0
print(f"Frame {frame_index}: Backward; amplitude: {left_triggers}, {fb_amplitude}")
update_triggers()
else:
left_triggers -= 1000
right_triggers -= 1000
left_triggers = max(0, left_triggers)
right_triggers = max(0, right_triggers)
update_triggers()
# 在副本上绘制文字（显示录制状态）
cv2.putText(frame_with_text, "Recording: ON" if is_active else "Recording: OFF",
(10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0) if is_active else (0, 0, 255), 2)
# 显示带有标记的窗口
cv2.imshow("MediaPipe Pose Demo", frame_with_text)
# 如果正在录制，将帧写入视频文件（这里写入的是原始视频帧，不包含标记）
if is_active and video_writer:
video_writer.write(frame) # 录制原始画面，不包含标记
# 按键处理
key = cv2.waitKey(1) & 0xFF
if key == ord('r'): # 按 'r' 键开始新录制
if not is_active:
is_active = True
elif key == ord('s'): # 按 's' 键停止录制
if is_active:
is_active = False
elif key == ord('q'): # 按 'q' 键退出程序
break
if is_active:
frame_index += 1 # 增加帧计数
# 释放资源
cap.release()
if video_writer:
video_writer.release()
cv2.destroyAllWindows()