Python實戰(zhàn)之手勢識別控制電腦音量

更新時間：2023年05月04日 16:06:50 作者：Python數(shù)據(jù)挖掘

這篇文章主要為大家詳細介紹了一個Python OpenCV的實戰(zhàn)小項目——手勢識別控制電腦音量，文中的示例代碼講解詳細，感興趣的小伙伴可以了解一下

一、環(huán)境配置

這個項目需要的環(huán)境比較簡單，主要就是opencv和mediapipe庫

import cv2
import mediapipe as mp
from ctypes import cast, POINTER
from comtypes import CLSCTX_ALL
from pycaw.pycaw import AudioUtilities, IAudioEndpointVolume
import pyautogui

缺庫的話直接：

pip install -i https://pypi.tuna.tsinghua.edu.cn/simple  庫名稱

二、代碼介紹

1）初始化mediapipe庫

self.mp_drawing = mp.solutions.drawing_utils
self.mp_drawing_styles = mp.solutions.drawing_styles
self.mp_hands = mp.solutions.hands

2）獲取電腦音量范圍

devices = AudioUtilities.GetSpeakers()
interface = devices.Activate(IAudioEndpointVolume._iid_, CLSCTX_ALL, None)
self.volume = cast(interface, POINTER(IAudioEndpointVolume))
self.volume.SetMute(0, None)
self.volume_range = self.volume.GetVolumeRange()

3）利用OpenCV讀取攝像頭視頻流進行顯示

cap = cv2.VideoCapture(0)
resize_w = 640
resize_h = 480
while cap.isOpened():
    success, image = cap.read()
    image = cv2.resize(image, (resize_w, resize_h))

4）識別手掌，獲取手掌關鍵點坐標

# 判斷是否有手掌
if results.multi_hand_landmarks:
    # 遍歷每個手掌
    for hand_landmarks in results.multi_hand_landmarks:
        # 在畫面標注手指
        # 解析手指，存入各個手指坐標
        landmark_list = []
        for landmark_id, finger_axis in enumerate(
                hand_landmarks.landmark):
            landmark_list.append([
                landmark_id, finger_axis.x, finger_axis.y,
                finger_axis.z
            ])
        if landmark_list:
            # 獲取大拇指指尖坐標
            thumb_finger_tip = landmark_list[4]
            thumb_finger_tip_x = math.ceil(thumb_finger_tip[1] * resize_w)
            thumb_finger_tip_y = math.ceil(thumb_finger_tip[2] * resize_h)
            # 獲取食指指尖坐標
            index_finger_tip = landmark_list[8]
            index_finger_tip_x = math.ceil(index_finger_tip[1] * resize_w)
            index_finger_tip_y = math.ceil(index_finger_tip[2] * resize_h)
            # 獲取中指尖坐標
            middle_finger_tip = landmark_list[12]
            middle_finger_tip_x = math.ceil(middle_finger_tip[1] * resize_w)
            middle_finger_tip_y = math.ceil(middle_finger_tip[2] * resize_h)
            # 中指與食指中間點
            middle_index_finger_middle_point = (middle_finger_tip_x + index_finger_tip_x) // 2, (
                        middle_finger_tip_y + index_finger_tip_y) // 2
            # print(thumb_finger_tip_x)
            middle_finger_point = (middle_finger_tip_x, middle_finger_tip_y)
            index_finger_point = (index_finger_tip_x, index_finger_tip_y)
            # 畫指尖2點
            image = cv2.circle(image, middle_finger_point, 10, (255, 0, 255), -1)
            image = cv2.circle(image, index_finger_point, 10, (255, 0, 255), -1)
            image = cv2.circle(image,  middle_index_finger_middle_point, 10, (255, 0, 255), -1)
            # 畫2點連線
            image1 = cv2.line(image, middle_finger_point, index_finger_point, (255, 0, 255), 5)
            # 勾股定理計算長度
            middle_index_line_len = math.hypot((middle_finger_tip_x - index_finger_tip_x),
                                      (middle_finger_tip_y - index_finger_tip_y))

5）將拇指與食指距離與電腦音量進行關聯(lián)

# 當食指中指距離大于65像素允許調(diào)音量
if middle_index_line_len < 65.0:
    # 拇指與食指中間點
    finger_middle_point = (thumb_finger_tip_x + index_finger_tip_x) // 2, (
                thumb_finger_tip_y + index_finger_tip_y) // 2
    # print(thumb_finger_tip_x)
    thumb_finger_point = (thumb_finger_tip_x, thumb_finger_tip_y)
    index_finger_point = (index_finger_tip_x, index_finger_tip_y)
    # 畫2點連線
    image = cv2.line(image, thumb_finger_point, index_finger_point, (255, 0, 255), 5)
    # 勾股定理計算長度
    line_len = math.hypot((index_finger_tip_x - thumb_finger_tip_x),
                          (index_finger_tip_y - thumb_finger_tip_y))
    # 獲取電腦最大最小音量
    min_volume = self.volume_range[0]
    max_volume = self.volume_range[1]
    # 將指尖長度映射到音量上
    vol = np.interp(line_len, [50, 300], [min_volume, max_volume])
    # 將指尖長度映射到矩形顯示上
    rect_height = np.interp(line_len, [50, 300], [0, 200])
    rect_percent_text = np.interp(line_len, [50, 300], [0, 100])
    # 設置電腦音量
    self.volume.SetMasterVolumeLevel(vol, None)
#鎖定調(diào)音量，進行鼠標控制
else:                             
    for id, lm in enumerate(hand_landmarks.landmark):
        # print(id,lm)
        h, w, c = image.shape
        cx, cy = int(lm.x * w), int(lm.y * h)
        # id=手部關鍵點
        if id == 0:
            if cx > dot[0] and cx < dot[2] and cy > dot[1] and cy < dot[3]:
                x0 = ((cx-dot[0])/(dot[2]-dot[0]))*1920
                y0 = ((cy-dot[1])/(dot[3]-dot[1]))*1080
                pyautogui.moveTo(x0, y0, duration=0.02)
        # print(thumb_finger_tip_x)
        thumb_finger_point = (thumb_finger_tip_x, thumb_finger_tip_y)
        index_finger_point = (index_finger_tip_x, index_finger_tip_y)
        # 畫指尖2點
        image = cv2.circle(image, thumb_finger_point, 10, (255, 0, 255), -1)
        image = cv2.circle(image, index_finger_point, 10, (255, 0, 255), -1)
        image = cv2.circle(image, finger_middle_point, 10, (255, 0, 255), -1)
        # 畫2點連線
        image = cv2.line(image, thumb_finger_point, index_finger_point, (255, 0, 255), 5)
        # 勾股定理計算長度
        line_len = math.hypot((index_finger_tip_x - thumb_finger_tip_x),
                              (index_finger_tip_y - thumb_finger_tip_y))
        # 操作
        # 左鍵雙擊   
        if line_len < 20:
            pyautogui.doubleClick()
            ms_d = 0