基于Opencv的虚拟拖拽项目

预备知识

勾股定理
在这里插入图片描述跟随移动算法
手势识别图解

项目源代码

"""
演示一个简单的虚拟拖拽
步骤：
1、opencv 读取视频流
2、在视频图像上画一个方块
3、通过mediapipe库获取手指关节坐标
4、判断手指是否在方块上
5、是，方块跟着移动
6、完善：通过食指和中指指尖距离确定是否激活移动
7、完善：画面显示FPS等信息
"""
import cv2
import math


#导入mediapipe的相关模块
# 导入mediapipe：https://google.github.io/mediapipe/solutions/hands
import mediapipe as mp
mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
mp_hands = mp.solutions.hands

hands = mp_hands.Hands(
    model_complexity=0,
    min_detection_confidence=0.5,
    min_tracking_confidence=0.5)


#读取视频流
cap = cv2.VideoCapture(0) #调用摄像头

#获取画面的长度和宽度
width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
print(f"width = {width}\n")
print(f"height = {height}")
#方块初始化数组
x = 100
y = 100
w = 200
h = 200

on_square = False
square_color = (0,255,0)


while True:
    ret,frame = cap.read()#读取视频帧

    if ret:

        #镜像
        frame = cv2.flip(frame,1)
        frame.flags.writeable = False
        frame = cv2.cvtColor(frame,cv2.COLOR_BGR2RGB)

        #识别
        results = hands.process(frame)

        frame.flags.writeable = True
        frame = cv2.cvtColor(frame,cv2.COLOR_RGB2BGR)

        #如果有结果的话
        if results.multi_hand_landmarks:

            #遍历双手
            for hand_landmarks in results.multi_hand_landmarks:
                mp_drawing.draw_landmarks(
                    frame,
                    hand_landmarks,
                    mp_hands.HAND_CONNECTIONS,
                    mp_drawing_styles.get_default_hand_landmarks_style(),
                    mp_drawing_styles.get_default_hand_connections_style()
                )
            # 使用这两句看一下里面到底是什么？
            # print(type(hand_landmarks))
            # print(hand_landmarks)
            # exit()

            #21个关键点的x、y坐标列表
            x_list = []
            y_list = []
            for landmarks in hand_landmarks.landmark:
                x_list.append(landmarks.x)
                y_list.append(landmarks.y)


            #输出一下长度
            # print(len(x_list))

            # 获取食指指尖坐标，坐标位置查看：https://google.github.io/mediapipe/solutions/hands
            index_finger_x = int(x_list[8] * width)
            index_finger_y = int(y_list[8] * height)

            # 获取中指坐标
            middle_finger_x = int(x_list[12] * width)
            middle_finger_y = int(y_list[12] * height)

           # 计算两指距离
            # finger_distance =math.sqrt( (middle_finger_x - index_finger_x)**2 + (middle_finger_y-index_finger_y)**2)
            finger_distance = math.hypot((middle_finger_x - index_finger_x),(middle_finger_y - index_finger_y))

            # 看一下距离
            # print(finger_distance)

            #把食指画出来
            cv2.circle(frame,(index_finger_x,index_finger_y),20,(0,0,255),-1)

            #判断食指指尖在不在方块上
            if finger_distance < 60:

                # X坐标范围 Y坐标范围
                if (index_finger_x > x and index_finger_x < (x+w)) and (index_finger_y > y and index_finger_y < (y+h)):

                    if on_square == False:
                        print('在')
                        L1 = index_finger_x - x
                        L2 = index_finger_y - y
                        square_color = (255,0,255)
                        on_square = True
                else:
                    print("不在")

            else:
                #解除
                on_square = False
                square_color = (0,255,0)


            #更新坐标
            if on_square:
                x = index_finger_x - L1
                y = index_finger_y - L2

        #在视频中画一个方块
        # cv2.rectangle(frame,(x,y),(x+w,y+h),square_color,-1)

        #半透明处理
        overlay = frame.copy()
        cv2.rectangle(frame, (x, y), (x + w, y + h), square_color, -1)
        frame = cv2.addWeighted(overlay, 0.5, frame, 1 - 0.5, 0)

        #展示画面
        cv2.imshow("demo",frame)


        #退出条件
        if cv2.waitKey(10) & 0xFF == ord('q'):
            break