OpenCV 是一个用于计算机视觉应用程序的库。在 OpenCV 的帮助下，我们可以构建大量实时运行更好的应用程序。主要用于图像和视频处理。
可以在此处获取有关 OpenCV 的更多信息 (https://opencv.org/)
除了 OpenCV，我们将使用 MediaPipe 库。
1.MediaPipe简介
MediaPipe是一个主要用于构建音频、视频或任何时间序列数据的框架。在 MediaPipe 框架的帮助下，我们可以为不同的媒体处理功能构建管道。
MediaPipe 的一些主要应用。
多手追踪
人脸检测
对象检测和跟踪
Objection：3D 对象检测和跟踪
AutoFlip：自动视频裁剪管道等。
MediaPipe 使用单次手掌检测模型，一旦完成，它会对检测到的手部区域中的 21 个 3D 手掌坐标执行精确的关键点定位。
MediaPipe 管道使用多个模型，例如，从完整图像返回定向手边界框的手掌检测模型。裁剪后的图像区域被馈送到由手掌检测器定义的手部标志模型，并返回高保真 3D 手部关键点。
现在让我们实现手部跟踪模型。
安装所需的模块
–> pip install opencv-python
–> pip install mediapipe
注意：这里的python版本尽量在3.8以上，不然会报各种错误！！
首先，让我们检查网络摄像头的工作情况。
import cv2import mediapipe as mpimport timecap = cv2.VideoCapture(0)mpHands = mp.solutions.handshands = mpHands.Hands(static_image_mode=False, max_num_hands=2, min_detection_confidence=0.5, min_tracking_confidence=0.5)mpDraw = mp.solutions.drawing_utilspTime = 0cTime = 0while True: success, img = cap.read() img = cv2.flip(img, 1) imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) results = hands.process(imgRGB) #print(results.multi_hand_landmarks) if results.multi_hand_landmarks: for handLms in results.multi_hand_landmarks: for id, lm in enumerate(handLms.landmark): #print(id,lm) h, w, c = img.shape cx, cy = int(lm.x *w), int(lm.y*h) #if id ==0: cv2.circle(img, (cx,cy), 3, (255,0,255), cv2.FILLED) mpDraw.draw_landmarks(img, handLms, mpHands.HAND_CONNECTIONS) cTime = time.time() fps = 1/(cTime-pTime) pTime = cTime cv2.putText(img,str(int(fps)), (10,70), cv2.FONT_HERSHEY_PLAIN, 3, (255,0,255), 3) cv2.imshow("Image", img) cv2.waitKey(1)结果见图示：
2.mediapipe实现手掌拖拽屏幕中的方块（转载于恩培大佬）
效果如示：
源码如下：
"""功能：手势虚拟拖拽1、使用OpenCV读取摄像头视频流；2、识别手掌关键点像素坐标；3、根据食指和中指指尖的坐标，利用勾股定理计算距离，当距离较小且都落在矩形内，则触发拖拽（矩形变色）；4、矩形跟着手指动；5、两指放开，则矩形停止移动"""# 导入OpenCVimport cv2# 导入mediapipeimport mediapipe as mp# 导入其他依赖包import timeimport math# 方块管理类class SquareManager: def __init__(self, rect_width): # 方框长度 self.rect_width = rect_width # 方块list self.square_count = 0 self.rect_left_x_list = [] self.rect_left_y_list = [] self.alpha_list = [] # 中指与矩形左上角点的距离 self.L1 = 0 self.L2 = 0 # 激活移动模式 self.drag_active = False # 激活的方块ID self.active_index = -1 # 创建一个方块，但是没有显示 def create(self,rect_left_x,rect_left_y,alpha=0.4): self.rect_left_x_list.append(rect_left_x) self.rect_left_y_list.append(rect_left_y) self.alpha_list.append(alpha) self.square_count+=1 # 更新位置 def display(self,class_obj): for i in range(0,self.square_count): x= self.rect_left_x_list[i] y= self.rect_left_y_list[i] alpha = self.alpha_list[i] overlay = class_obj.image.copy() if(i == self.active_index): cv2.rectangle(overlay,(x,y),(x+self.rect_width,y+self.rect_width),(255, 0, 255),-1) else: cv2.rectangle(overlay,(x,y),(x+self.rect_width,y+self.rect_width),(255, 0, 0),-1) # Following line overlays transparent rectangle over the self.image class_obj.image = cv2.addWeighted(overlay, alpha, class_obj.image, 1 - alpha, 0) # 判断落在哪个方块上，返回方块的ID def checkOverlay(self,check_x,check_y): for i in range(0,self.square_count): x= self.rect_left_x_list[i] y= self.rect_left_y_list[i] if (x < check_x < (x+self.rect_width) ) and ( y < check_y 100): # 取消激活 squareManager.drag_active =False squareManager.active_index = -1 elif (line_len<100) and (squareManager.checkOverlay(between_finger_tip[0],between_finger_tip[1]) != -1 )and( squareManager.drag_active == False): # 激活 squareManager.drag_active =True # 计算距离 squareManager.setLen(between_finger_tip[0],between_finger_tip[1]) # 显示方块，传入本实例，主要为了半透明的处理 squareManager.display(self) # 显示距离 cv2.putText(self.image, "Distance:"+str(rect_percent_text), (10, 120),cv2.FONT_HERSHEY_PLAIN, 3, (255, 0, 0), 3) # 显示当前激活 cv2.putText(self.image, "Active:"+( "None" if squareManager.active_index == -1 else str(squareManager.active_index)), (10, 170),cv2.FONT_HERSHEY_PLAIN, 3, (255, 0, 0), 3) # 显示刷新率FPS cTime = time.time() fps_text = 1/(cTime-fpsTime) fpsTime = cTime cv2.putText(self.image, "FPS: " + str(int(fps_text)), (10, 70), cv2.FONT_HERSHEY_PLAIN, 3, (255, 0, 0), 3) # 显示画面 # self.image = cv2.resize(self.image, (resize_w//2, resize_h//2)) cv2.imshow('virtual drag and drop', self.image) if cv2.waitKey(5) & 0xFF == 27: break cap.release()# 开始程序control = HandControlVolume()control.recognize()3.mediapipe实现手势控制音量大小（转载于恩培大佬）
效果如示：
源码如下：
"""功能：手势操作电脑音量1、使用OpenCV读取摄像头视频流；2、识别手掌关键点像素坐标；3、根据拇指和食指指尖的坐标，利用勾股定理计算距离；4、将距离等比例转为音量大小，控制电脑音量"""# 导入OpenCVimport cv2# 导入mediapipeimport mediapipe as mp# 导入电脑音量控制模块from ctypes import cast, POINTERfrom comtypes import CLSCTX_ALLfrom pycaw.pycaw import AudioUtilities, IAudioEndpointVolume# 导入其他依赖包import timeimport mathimport numpy as npclass HandControlVolume: def __init__(self): # 初始化medialpipe self.mp_drawing = mp.solutions.drawing_utils self.mp_drawing_styles = mp.solutions.drawing_styles self.mp_hands = mp.solutions.hands # 获取电脑音量范围 devices = AudioUtilities.GetSpeakers() interface = devices.Activate( IAudioEndpointVolume._iid_, CLSCTX_ALL, None) self.volume = cast(interface, POINTER(IAudioEndpointVolume)) self.volume_range = self.volume.GetVolumeRange() # 主函数 def recognize(self): # 计算刷新率 fpsTime = time.time() # OpenCV读取视频流 cap = cv2.VideoCapture(0) # 视频分辨率 resize_w = 640 resize_h = 480 # 画面显示初始化参数 rect_height = 0 rect_percent_text = 0 with self.mp_hands.Hands(min_detection_confidence=0.7, min_tracking_confidence=0.5, max_num_hands=2) as hands: while cap.isOpened(): success, image = cap.read() image = cv2.resize(image, (resize_w, resize_h)) if not success: print("空帧.") continue # 提高性能 image.flags.writeable = False # 转为RGB image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # 镜像 image = cv2.flip(image, 1) # mediapipe模型处理 results = hands.process(image) image.flags.writeable = True image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) # 判断是否有手掌 if results.multi_hand_landmarks: # 遍历每个手掌 for hand_landmarks in results.multi_hand_landmarks: # 在画面标注手指 self.mp_drawing.draw_landmarks( image, hand_landmarks, self.mp_hands.HAND_CONNECTIONS, self.mp_drawing_styles.get_default_hand_landmarks_style(), self.mp_drawing_styles.get_default_hand_connections_style()) # 解析手指，存入各个手指坐标 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: # 获取大拇指指尖坐标 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) # 获取食指指尖坐标 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) # 中间点 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) # 画指尖2点 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) # 画2点连线 image = cv2.line(image,thumb_finger_point,index_finger_point,(255,0,255),5) # 勾股定理计算长度 line_len = math.hypot((index_finger_tip_x-thumb_finger_tip_x),(index_finger_tip_y-thumb_finger_tip_y)) # 获取电脑最大最小音量 min_volume = self.volume_range[0] max_volume = self.volume_range[1] # 将指尖长度映射到音量上 vol = np.interp(line_len,[50,300],[min_volume,max_volume]) # 将指尖长度映射到矩形显示上 rect_height = np.interp(line_len,[50,300],[0,200]) rect_percent_text = np.interp(line_len,[50,300],[0,100]) # 设置电脑音量 self.volume.SetMasterVolumeLevel(vol, None) # 显示矩形 cv2.putText(image, str(math.ceil(rect_percent_text))+"%", (10, 350), cv2.FONT_HERSHEY_PLAIN, 3, (255, 0, 0), 3) image = cv2.rectangle(image,(30,100),(70,300),(255, 0, 0),3) image = cv2.rectangle(image,(30,math.ceil(300-rect_height)),(70,300),(255, 0, 0),-1) # 显示刷新率FPS cTime = time.time() fps_text = 1/(cTime-fpsTime) fpsTime = cTime cv2.putText(image, "FPS: " + str(int(fps_text)), (10, 70), cv2.FONT_HERSHEY_PLAIN, 3, (255, 0, 0), 3) # 显示画面 cv2.imshow('MediaPipe Hands', image) if cv2.waitKey(5) & 0xFF == 27: break cap.release()# 开始程序control = HandControlVolume()control.recognize()
reference：
使用 Python+OpenCV 构建手部跟踪系统 – 知乎 (zhihu.com)
源码来源：恩培大佬
enpeizhao/CVprojects: computer vision projects | 计算机视觉等好玩的AI项目 (github.com)