二手车网站模板/网站排名软件

Hi，大家好，我是半亩花海。在当今科技飞速发展的时代，我们身边充斥着各种智能设备，然而，如何更便捷地与这些设备进行交互却是一个不断被探索的课题。本文将主要介绍一个基于 OpenCV 的手势识别项目，通过手势来控制电脑屏幕亮度和音量大小，为用户提供了一种全新的交互方式。

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目录

一、代码拆解

1. 导入必要库

2. 初始化手部关键点

3. 数据格式转换

4. 画手势关键点

5. 手势状态缓冲处理

6. 画直线

7. 屏幕亮度和音量控制

8. 初始化摄像头和手部关键点识别器

9. Pygame 界面初始化和事件监听

二、实战演示

1. 亮度——light

2. 音量——voice

3. 菜单——menu

三、完整代码

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一、代码拆解

1. 导入必要库

在开始介绍项目的实现细节之前，我们首先需要导入项目所需的必要库。这些库包括：

• OpenCV：用于处理图像和视频数据。
• Mediapipe：提供了对手部关键点的识别和跟踪功能。
• Pygame：用于创建图形界面和显示摄像头捕获的图像。
• WMI：用于调节电脑屏幕亮度。
• pycaw：用于控制电脑的音量。

# 导入必要库
import math
import sys
import numpy as np
import cv2
import pygame
import wmi
import mediapipe as mp
from ctypes import cast, POINTER
from comtypes import CLSCTX_ALL
from pycaw.pycaw import AudioUtilities, IAudioEndpointVolume
import warnings  # 忽略警告
warnings.filterwarnings("ignore")

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2. 初始化手部关键点

首先创建一个 HandKeyPoint 类，用于初始化手部关键点检测器，并提供对图像进行处理的方法。

# 手部关键点类
class HandKeyPoint:def __init__(self,static_image_mode=False,max_num_hands=2,model_complexity=1,min_detection_confidence=0.5,min_tracking_confidence=0.5):# 手部识别apiself.mp_hands = mp.solutions.hands# 获取手部识别类self.hands = self.mp_hands.Hands(static_image_mode=static_image_mode,max_num_hands=max_num_hands,model_complexity=model_complexity,min_detection_confidence=min_detection_confidence,min_tracking_confidence=min_tracking_confidence)def process(self, image):# 将BGR转换为RGBimg = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)# 识别图像中的手势，并返回结果results = self.hands.process(img)# numpy格式的数据np_arr = landmarks_to_numpy(results)return results, np_arr

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3. 数据格式转换

将手部关键点的检测结果（将 landmarks 格式的数据）转换为 numpy 数组，以便后续的处理和分析。

# 将landmarks格式的数据转换为numpy格式的数据
def landmarks_to_numpy(results):"""将landmarks格式的数据转换为numpy格式的数据numpy shape:(2, 21, 3):param results::return:"""shape = (2, 21, 3)landmarks = results.multi_hand_landmarksif landmarks is None:# 没有检测到手return np.zeros(shape)elif len(landmarks) == 1:# 检测出一只手，先判断是左手还是右手label = results.multi_handedness[0].classification[0].labelhand = landmarks[0]# print(label)if label == "Left":return np.array([np.array([[hand.landmark[i].x, hand.landmark[i].y, hand.landmark[i].z] for i in range(21)]),np.zeros((21, 3))])else:return np.array([np.zeros((21, 3)),np.array([[hand.landmark[i].x, hand.landmark[i].y, hand.landmark[i].z] for i in range(21)])])elif len(landmarks) == 2:# print(results.multi_handedness)lh_idx = 0rh_idx = 0for idx, hand_type in enumerate(results.multi_handedness):label = hand_type.classification[0].labelif label == 'Left':lh_idx = idxif label == 'Right':rh_idx = idxlh = np.array([[landmarks[lh_idx].landmark[i].x, landmarks[lh_idx].landmark[i].y, landmarks[lh_idx].landmark[i].z] for iin range(21)])rh = np.array([[landmarks[rh_idx].landmark[i].x, landmarks[rh_idx].landmark[i].y, landmarks[rh_idx].landmark[i].z] for iin range(21)])return np.array([lh, rh])else:return np.zeros((2, 21, 3))

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4. 画手势关键点

# 画手势关键点
def draw_landmark(img, results):if results.multi_hand_landmarks:for hand_landmark in results.multi_hand_landmarks:mp.solutions.drawing_utils.draw_landmarks(img,hand_landmark,mp.solutions.hands.HAND_CONNECTIONS,mp.solutions.drawing_styles.get_default_hand_landmarks_style(),mp.solutions.drawing_styles.get_default_hand_connections_style())return img

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5. 手势状态缓冲处理

为了平滑处理手势状态的变化，我们实现了一个 Buffer 类，用于缓存手势状态的变化，并提供了添加正例和负例的方法。

# 缓冲区类
class Buffer:def __init__(self, volume=20):self.__positive = 0self.state = Falseself.__negative = 0self.__volume = volumeself.__count = 0def add_positive(self):self.__count += 1if self.__positive >= self.__volume:# 如果正例个数大于容量，将状态定为Trueself.state = Trueself.__negative = 0self.__count = 0else:self.__positive += 1if self.__count > self.__volume:# 如果大于容量次操作后还没有确定状态self.__positive = 0self.__count = 0def add_negative(self):self.__count += 1if self.__negative >= self.__volume:# 如果负例个数大于容量，将状态定为Falseself.state = Falseself.__positive = 0else:self.__negative += 1if self.__count > self.__volume:# 如果大于容量次操作后还没有确定状态self.__positive = 0self.__count = 0# print(f"pos:{self.__positive} neg:{self.__negative} count:{self.__count}")def clear(self):self.__positive = 0self.state = Falseself.__negative = 0self.__count = 0

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6. 画直线

# 画线函数
def draw_line(frame, p1, p2, color=(255, 127, 0), thickness=3):"""画一条直线:param p1::param p2::return:"""return cv2.line(frame, (int(p1[0] * CAM_W), int(p1[1] * CAM_H)), (int(p2[0] * CAM_W), int(p2[1] * CAM_H)), color,thickness)

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7. 屏幕亮度和音量控制

# 控制屏幕亮度
def screen_change(percent):  # percent/2即为亮度百分比SCREEN = wmi.WMI(namespace='root/WMI')a = SCREEN.WmiMonitorBrightnessMethods()[0]a.WmiSetBrightness(Brightness=percent, Timeout=500)# 初始化音量控制
def init_voice():devices = AudioUtilities.GetSpeakers()interface = devices.Activate(IAudioEndpointVolume._iid_, CLSCTX_ALL, None)volume = cast(interface, POINTER(IAudioEndpointVolume))volume.SetMute(0, None)volume_range = volume.GetVolumeRange()min_volume = volume_range[0]max_volume = volume_range[1]return (min_volume, max_volume), volume

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8. 初始化摄像头和手部关键点识别器

在项目的初始化阶段，我们需要加载摄像头实例和手部关键点识别实例，以便后续对手势进行识别和处理。

# 加载摄像头实例
cap = cv2.VideoCapture(0)
CAM_W = 640
CAM_H = 480
CAM_SCALE = CAM_W / CAM_H# 加载手部关键点识别实例
hand = HandKeyPoint()

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9. Pygame 界面初始化和事件监听

为了展示手势控制效果，并提供交互界面，我们使用了 Pygame 库。在初始化阶段，我们创建了一个窗口，并设置了标题。同时，我们实现了事件监听功能，以便在需要时退出程序。

具体来说，我们使用 Pygame 创建了一个窗口，并将摄像头捕获的图像显示在窗口中。同时，我们利用 Pygame 的事件监听功能，监听用户的键盘事件，例如按下"q"键时退出程序。这样，用户就可以通过手势控制屏幕亮度和音量大小，同时在 Pygame 窗口中观察手势识别效果。

# 初始化pygame
pygame.init()
# 设置窗口全屏
screen = pygame.display.set_mode((800, 600))
pygame.display.set_caption("virtual_control_screen")
# 获取当前窗口大小
window_size = list(screen.get_size())# 主循环
while True:
······# 事件监听 若按q则退出程序for event in pygame.event.get():if event.type == pygame.KEYDOWN:if event.key == pygame.K_q:sys.exit(0)

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二、实战演示

1. 亮度——light

如果 20 < angle < 90，那么“light ready”即手势控制亮度。

2. 音量——voice

如果 -20 > angle > -50，那么“voice ready”即手势控制音量。

3. 菜单——menu

上述两种情况除外，那么处于“menu”状态即进入菜单。

通过演示可以发现，食指与大拇指在屏幕中的距离越远，亮度越高（音量越大），反之越小，实现了通过手势对亮度和音量的控制。

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三、完整代码

#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
@Project : virtual
@File    : virtual_control.py
@IDE     : PyCharm
@Author  : 半亩花海
@Date    : 2024:02:06 18:01
"""
# 导入模块
import math
import sys
import numpy as np
import cv2
import pygame
import wmi
import mediapipe as mp
from ctypes import cast, POINTER
from comtypes import CLSCTX_ALL
from pycaw.pycaw import AudioUtilities, IAudioEndpointVolume
import warnings  # 忽略警告
warnings.filterwarnings("ignore")# 手部关键点类
class HandKeyPoint:def __init__(self,static_image_mode=False,max_num_hands=2,model_complexity=1,min_detection_confidence=0.5,min_tracking_confidence=0.5):# 手部识别apiself.mp_hands = mp.solutions.hands# 获取手部识别类self.hands = self.mp_hands.Hands(static_image_mode=static_image_mode,max_num_hands=max_num_hands,model_complexity=model_complexity,min_detection_confidence=min_detection_confidence,min_tracking_confidence=min_tracking_confidence)def process(self, image):# 将BGR转换为RGBimg = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)# 识别图像中的手势，并返回结果results = self.hands.process(img)# numpy格式的数据np_arr = landmarks_to_numpy(results)return results, np_arr# 将landmarks格式的数据转换为numpy格式的数据
def landmarks_to_numpy(results):"""将landmarks格式的数据转换为numpy格式的数据numpy shape:(2, 21, 3):param results::return:"""shape = (2, 21, 3)landmarks = results.multi_hand_landmarksif landmarks is None:# 没有检测到手return np.zeros(shape)elif len(landmarks) == 1:# 检测出一只手，先判断是左手还是右手label = results.multi_handedness[0].classification[0].labelhand = landmarks[0]# print(label)if label == "Left":return np.array([np.array([[hand.landmark[i].x, hand.landmark[i].y, hand.landmark[i].z] for i in range(21)]),np.zeros((21, 3))])else:return np.array([np.zeros((21, 3)),np.array([[hand.landmark[i].x, hand.landmark[i].y, hand.landmark[i].z] for i in range(21)])])elif len(landmarks) == 2:# print(results.multi_handedness)lh_idx = 0rh_idx = 0for idx, hand_type in enumerate(results.multi_handedness):label = hand_type.classification[0].labelif label == 'Left':lh_idx = idxif label == 'Right':rh_idx = idxlh = np.array([[landmarks[lh_idx].landmark[i].x, landmarks[lh_idx].landmark[i].y, landmarks[lh_idx].landmark[i].z] for iin range(21)])rh = np.array([[landmarks[rh_idx].landmark[i].x, landmarks[rh_idx].landmark[i].y, landmarks[rh_idx].landmark[i].z] for iin range(21)])return np.array([lh, rh])else:return np.zeros((2, 21, 3))# 画手势关键点
def draw_landmark(img, results):if results.multi_hand_landmarks:for hand_landmark in results.multi_hand_landmarks:mp.solutions.drawing_utils.draw_landmarks(img,hand_landmark,mp.solutions.hands.HAND_CONNECTIONS,mp.solutions.drawing_styles.get_default_hand_landmarks_style(),mp.solutions.drawing_styles.get_default_hand_connections_style())return img# 缓冲区类
class Buffer:def __init__(self, volume=20):self.__positive = 0self.state = Falseself.__negative = 0self.__volume = volumeself.__count = 0def add_positive(self):self.__count += 1if self.__positive >= self.__volume:# 如果正例个数大于容量，将状态定为Trueself.state = Trueself.__negative = 0self.__count = 0else:self.__positive += 1if self.__count > self.__volume:# 如果大于容量次操作后还没有确定状态self.__positive = 0self.__count = 0def add_negative(self):self.__count += 1if self.__negative >= self.__volume:# 如果负例个数大于容量，将状态定为Falseself.state = Falseself.__positive = 0else:self.__negative += 1if self.__count > self.__volume:# 如果大于容量次操作后还没有确定状态self.__positive = 0self.__count = 0# print(f"pos:{self.__positive} neg:{self.__negative} count:{self.__count}")def clear(self):self.__positive = 0self.state = Falseself.__negative = 0self.__count = 0# 画线函数
def draw_line(frame, p1, p2, color=(255, 127, 0), thickness=3):"""画一条直线:param p1::param p2::return:"""return cv2.line(frame, (int(p1[0] * CAM_W), int(p1[1] * CAM_H)), (int(p2[0] * CAM_W), int(p2[1] * CAM_H)), color,thickness)# 控制屏幕亮度
def screen_change(percent):  # percent/2即为亮度百分比SCREEN = wmi.WMI(namespace='root/WMI')a = SCREEN.WmiMonitorBrightnessMethods()[0]a.WmiSetBrightness(Brightness=percent, Timeout=500)# 初始化音量控制
def init_voice():devices = AudioUtilities.GetSpeakers()interface = devices.Activate(IAudioEndpointVolume._iid_, CLSCTX_ALL, None)volume = cast(interface, POINTER(IAudioEndpointVolume))volume.SetMute(0, None)volume_range = volume.GetVolumeRange()min_volume = volume_range[0]max_volume = volume_range[1]return (min_volume, max_volume), volume# 加载摄像头实例
cap = cv2.VideoCapture(0)
CAM_W = 640
CAM_H = 480
CAM_SCALE = CAM_W / CAM_H# 加载手部关键点识别实例
hand = HandKeyPoint()# 初始化pygame
pygame.init()
# 设置窗口全屏
screen = pygame.display.set_mode((800, 600))
pygame.display.set_caption("virtual_control_screen")
# 获取当前窗口大小
window_size = list(screen.get_size())# 设置缓冲区
buffer_light = Buffer(10)
buffer_voice = Buffer(10)last_y = 0
last_2_y = 1
last_2_x = 0# 初始化声音控制
voice_range, volume = init_voice()# 设置亮度条参数
bright_bar_length = 300
bright_bar_height = 20
bright_bar_x = 50
bright_bar_y = 100# 设置音量条参数
vol_bar_length = 300
vol_bar_height = 20
vol_bar_x = 50
vol_bar_y = 50# 主循环 每次循环就是对每帧的处理
while True:img_menu = Nonelh_index = -1# 读取摄像头画面success, frame = cap.read()# 将opencv中图片格式的BGR转换为常规的RGBframe = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)# 镜面反转frame = cv2.flip(frame, 1)# 处理图像res, arr = hand.process(frame)frame = draw_landmark(frame, res)scale = math.hypot((arr[0, 7, 0] - arr[0, 8, 0]),(arr[0, 7, 1] - arr[0, 8, 1]),(arr[0, 7, 2] - arr[0, 8, 2]))# 计算tan值tan = (arr[0, 0, 1] - arr[0, 12, 1]) / (arr[0, 0, 0] - arr[0, 12, 0])# 计算角度angle = np.arctan(tan) * 180 / np.pi# print(angle)if 20 < angle < 90:path = 'resources/menu/light.png'buffer_light.add_positive()buffer_voice.add_negative()# 显示亮度条和亮度刻度值show_brightness = Trueshow_volume = Falseelif -20 > angle > -50:path = 'resources/menu/voice.png'buffer_voice.add_positive()buffer_light.add_negative()# 显示音量条和音量刻度值show_brightness = Falseshow_volume = Trueelse:path = 'resources/menu/menu.png'buffer_light.add_negative()buffer_voice.add_negative()# 不显示刻度值和百分比show_brightness = Falseshow_volume = False# 计算拇指与食指之间的距离dis = math.hypot(int((arr[1, 4, 0] - arr[1, 8, 0]) * CAM_W), int((arr[1, 4, 1] - arr[1, 8, 1]) * CAM_H))# 右手映射时的缩放尺度s = math.hypot((arr[1, 5, 0] - arr[1, 9, 0]), (arr[1, 5, 1] - arr[1, 9, 1]), (arr[1, 5, 2] - arr[1, 9, 2]))# 调节亮度if buffer_light.state:frame = cv2.putText(frame, 'light ready', (10, 35), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 127, 0))frame = draw_line(frame, arr[1, 4], arr[1, 8], thickness=5, color=(255, 188, 66))if dis != 0:# 线性插值，可以理解为将一个区间中的一个值映射到另一区间内light = np.interp(dis, [int(500 * s), int(3000 * s)], (0, 100))# 调节亮度screen_change(light)# 调节声音elif buffer_voice.state:frame = cv2.putText(frame, 'voice ready', (10, 35), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 127, 0))frame = draw_line(frame, arr[1, 4], arr[1, 8], thickness=5, color=(132, 134, 248))if dis != 0:vol = np.interp(dis, [int(500 * s), int(3000 * s)], voice_range)# 调节音量volume.SetMasterVolumeLevel(vol, None)# 将图片改为与窗口一样的大小frame = cv2.resize(frame, (int(window_size[1] * CAM_SCALE), window_size[1]))frame = cv2.transpose(frame)# 渲染图片frame = pygame.surfarray.make_surface(frame)screen.blit(frame, (int(0.5 * (CAM_W - CAM_H * CAM_SCALE)), 0))img_menu = pygame.image.load(path).convert_alpha()img_w, img_h = img_menu.get_size()img_menu = pygame.transform.scale(img_menu, (int(img_w * scale * 5), int(img_h * scale * 5)))x = (arr[0][9][0] + arr[0][13][0] + arr[0][0][0]) / 3y = (arr[0][9][1] + arr[0][13][1] + arr[0][0][1]) / 3x = int(x * window_size[0] - window_size[0] * scale * 3.5)y = int(y * window_size[1] - window_size[1] * scale * 12)# print(x, y)screen.blit(img_menu, (x, y))# 绘制音量条和亮度条的外框if show_volume:pygame.draw.rect(screen, (255, 255, 255), (vol_bar_x, vol_bar_y, vol_bar_length, vol_bar_height), 3)elif show_brightness:pygame.draw.rect(screen, (255, 255, 255), (bright_bar_x, bright_bar_y, bright_bar_length, bright_bar_height),3)# 计算当前音量和亮度在条上的位置和大小，并绘制已填充的条if show_volume:vol = volume.GetMasterVolumeLevel()vol_range = voice_range[1] - voice_range[0]vol_bar_fill_length = int((vol - voice_range[0]) / vol_range * vol_bar_length)pygame.draw.rect(screen, (0, 255, 0), (vol_bar_x, vol_bar_y, vol_bar_fill_length, vol_bar_height))# 显示音量刻度值和当前音量大小vol_text = f"Volume: {int((vol - voice_range[0]) / vol_range * 100)}%"vol_text_surface = pygame.font.SysFont(None, 24).render(vol_text, True, (255, 255, 255))screen.blit(vol_text_surface, (vol_bar_x + vol_bar_length + 10, vol_bar_y))elif show_brightness:brightness = wmi.WMI(namespace='root/WMI').WmiMonitorBrightness()[0].CurrentBrightnessbright_bar_fill_length = int(brightness / 100 * bright_bar_length)pygame.draw.rect(screen, (255, 255, 0), (bright_bar_x, bright_bar_y, bright_bar_fill_length, bright_bar_height))# 显示亮度刻度值和当前亮度大小bright_text = f"Brightness: {brightness}%"bright_text_surface = pygame.font.SysFont(None, 24).render(bright_text, True, (255, 255, 255))screen.blit(bright_text_surface, (bright_bar_x + bright_bar_length + 10, bright_bar_y))pygame.display.flip()# 事件监听 若按q则退出程序for event in pygame.event.get():if event.type == pygame.KEYDOWN:if event.key == pygame.K_q:sys.exit(0)