小组做的一个基于mediapipe的手势识别

前言

在基于mediapipe的基础上实现对手势的识别

一、mediapipe是什么？

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。可以看看这篇文章

二、使用步骤

1.引入库

import cv2 as cv
import numpy as np
import mediapipe as mp
from numpy import linalg

2.定义手势函数

# 手指检测

def finger_stretch_detect(point1, point2, point3):
    result = 0
    # 计算向量的L2范数
    dist1 = np.linalg.norm((point2 - point1), ord=2)
    dist2 = np.linalg.norm((point3 - point1), ord=2)
    if dist2 > dist1:
        result = 1

    return result


# 检测手势
def detect_hands_gesture(result):
    if (result[0] == 1) and (result[1] == 0) and (result[2] == 0) and (result[3] == 0) and (result[4] == 0):
        gesture = "good"
    elif (result[0] == 0) and (result[1] == 1) and (result[2] == 0) and (result[3] == 0) and (result[4] == 0):
        gesture = "one"
    elif (result[0] == 0) and (result[1] == 0) and (result[2] == 1) and (result[3] == 0) and (result[4] == 0):
        gesture = "what?"
    elif (result[0] == 0) and (result[1] == 1) and (result[2] == 1) and (result[3] == 0) and (result[4] == 0):
        gesture = "two"
    elif (result[0] == 0) and (result[1] == 1) and (result[2] == 1) and (result[3] == 1) and (result[4] == 0):
        gesture = "three"
    elif (result[0] == 0) and (result[1] == 1) and (result[2] == 1) and (result[3] == 1) and (result[4] == 1):
        gesture = "four"
    elif (result[0] == 1) and (result[1] == 1) and (result[2] == 1) and (result[3] == 1) and (result[4] == 1):
        gesture = "five"
    elif (result[0] == 1) and (result[1] == 0) and (result[2] == 0) and (result[3] == 0) and (result[4] == 1):
        gesture = "six"
    elif (result[0] == 0) and (result[1] == 0) and (result[2] == 1) and (result[3] == 1) and (result[4] == 1):
        gesture = "OK"
    elif (result[0] == 0) and (result[1] == 0) and (result[2] == 0) and (result[3] == 0) and (result[4] == 0):
        gesture = "stone"
    else:
        gesture = "not in detect range..."

    return gesture

三.函数调用与机器视觉判断

def detect():

    cap = cv.VideoCapture(0)
    # 加载手部检测函数
    mpHands = mp.solutions.hands
    hands = mpHands.Hands()
    # 加载绘制函数，并设置手部关键点和连接线的形状、颜色
    mpDraw = mp.solutions.drawing_utils
    handLmsStyle = mpDraw.DrawingSpec(color=(0, 0, 255), thickness=int(5))
    handConStyle = mpDraw.DrawingSpec(color=(0, 255, 0), thickness=int(10))

    figure = np.zeros(5)
    landmark = np.empty((21, 2))

    if not cap.isOpened():
        print("Can not open camera.")
        exit()

    while True:
        ret, frame = cap.read()
        if not ret:
            print("Can not receive frame. Exiting...")
            break


        frame_RGB = cv.cvtColor(frame, cv.COLOR_BGR2RGB)
        result = hands.process(frame_RGB)
        # 读取视频图像的高和宽
        frame_height = frame.shape[0]
        frame_width = frame.shape[1]

        # print(result.multi_hand_landmarks)
        # 如果检测到手
        if result.multi_hand_landmarks:
            # 为每个手绘制关键点和连接线
            for i, handLms in enumerate(result.multi_hand_landmarks):
                mpDraw.draw_landmarks(frame,
                                      handLms,
                                      mpHands.HAND_CONNECTIONS,
                                      landmark_drawing_spec=handLmsStyle,
                                      connection_drawing_spec=handConStyle)

                for j, lm in enumerate(handLms.landmark):
                    xPos = int(lm.x * frame_width)
                    yPos = int(lm.y * frame_height)
                    landmark_ = [xPos, yPos]
                    landmark[j, :] = landmark_

                # 通过判断手指尖与手指根部到0位置点的距离判断手指是否伸开(拇指检测到17点的距离)
                for k in range(5):
                    if k == 0:
                        figure_ = finger_stretch_detect(landmark[17], landmark[4 * k + 2], landmark[4 * k + 4])
                    else:
                        figure_ = finger_stretch_detect(landmark[0], landmark[4 * k + 2], landmark[4 * k + 4])

                    figure[k] = figure_
                print(figure, '\n')

                gesture_result = detect_hands_gesture(figure)
                cv.putText(frame, f"{gesture_result}", (30, 60 * (i + 1)), cv.FONT_HERSHEY_COMPLEX, 2, (255, 255, 0), 5)

        cv.imshow('frame', frame)
        if cv.waitKey(1) == ord('q'):
            break

    cap.release()
    cv.destroyAllWindows()


if __name__ == '__main__':
    detect()

结果展示：

定义了one-six数字，以及石头，未定义

（根据你手指弯曲状态判定为0或1，再判定手势代表的意义）

总结

全部代码都放在这了，有需要的小伙伴直接复制就可以了