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#宅家#AI毽子操辅助陪练器

云天 云天 2022-05-29 19:39:52
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听说有不少小伙伴宅家抗疫

偷偷长胖了?

如今刘畊宏的“毽子操”突然席卷全网

《本草纲目》再度翻红

小伙伴们纷纷化身

“刘畊宏女孩”“刘畊宏男孩”

AI能让我们的锻炼更有趣,我今天制作一个“毽子操陪练AI辅助器”,让我们一起燃烧卡路里!!!

步骤1 步骤1
硬件
材料清单 材料清单
1x
LattePanda 拿铁熊猫 Win10 开发板 4GB/64GB
1x
高低音调节数字蓝牙功放板(50W*2)
1x
7英寸 1024 x 600分辨率LattePanda
1x
USB摄像头
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步骤2 步骤2
Mediapipe

该解决方案采用两步检测器-跟踪器 ML 管道,在我们的MediaPipe HandsMediaPipe Face Mesh解决方案中被证明是有效的。使用检测器,管道首先在帧内定位人/姿势感兴趣区域 (ROI)。跟踪器随后使用 ROI 裁剪帧作为输入来预测 ROI 内的姿势标志和分割掩码。请注意,对于视频用例,仅在需要时调用检测器,即,对于第一帧以及当跟踪器无法再识别前一帧中的身体姿势存在时。对于其他帧,管道只是从前一帧的姿势标志中导出 ROI。

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步骤3 步骤3
mind+安装Mediapipe库

在Mind+Python模式下“库管理”中使用“PIP模式”安装“Mediapipe”

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步骤4 步骤4
测试手部坐标点
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代码 代码
	                    					"""
Pose Module
By: Computer Vision Zone
Website: https://www.computervision.zone/
"""
import cv2
import mediapipe as mp
import math


class PoseDetector:
    """
    Estimates Pose points of a human body using the mediapipe library.
    """

    def __init__(self, mode=False, smooth=True,
                 detectionCon=0.5, trackCon=0.5):
        """
        :param mode: In static mode, detection is done on each image: slower
        :param upBody: Upper boy only flag
        :param smooth: Smoothness Flag
        :param detectionCon: Minimum Detection Confidence Threshold
        :param trackCon: Minimum Tracking Confidence Threshold
        """

        self.mode = mode
        self.smooth = smooth
        self.detectionCon = detectionCon
        self.trackCon = trackCon

        self.mpDraw = mp.solutions.drawing_utils
        self.mpPose = mp.solutions.pose
        self.pose = self.mpPose.Pose(static_image_mode=self.mode,
                                     smooth_landmarks=self.smooth,
                                     min_detection_confidence=self.detectionCon,
                                     min_tracking_confidence=self.trackCon)

    def findPose(self, img, draw=True):
        """
        Find the pose landmarks in an Image of BGR color space.
        :param img: Image to find the pose in.
        :param draw: Flag to draw the output on the image.
        :return: Image with or without drawings
        """
        imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        self.results = self.pose.process(imgRGB)
        if self.results.pose_landmarks:
            if draw:
                self.mpDraw.draw_landmarks(img, self.results.pose_landmarks,
                                           self.mpPose.POSE_CONNECTIONS)
        return img

    def findPosition(self, img, draw=True, bboxWithHands=False):
        self.lmList = []
        self.bboxInfo = {}
        if self.results.pose_landmarks:
            for id, lm in enumerate(self.results.pose_landmarks.landmark):
                h, w, c = img.shape
                cx, cy, cz = int(lm.x * w), int(lm.y * h), int(lm.z * w)
                self.lmList.append([id, cx, cy, cz])

            # Bounding Box
            ad = abs(self.lmList[12][1] - self.lmList[11][1]) // 2
            if bboxWithHands:
                x1 = self.lmList[16][1] - ad
                x2 = self.lmList[15][1] + ad
            else:
                x1 = self.lmList[12][1] - ad
                x2 = self.lmList[11][1] + ad

            y2 = self.lmList[29][2] + ad
            y1 = self.lmList[1][2] - ad
            bbox = (x1, y1, x2 - x1, y2 - y1)
            cx, cy = bbox[0] + (bbox[2] // 2), \
                     bbox[1] + bbox[3] // 2

            self.bboxInfo = {"bbox": bbox, "center": (cx, cy)}

            if draw:
                cv2.rectangle(img, bbox, (255, 0, 255), 3)
                cv2.circle(img, (cx, cy), 5, (255, 0, 0), cv2.FILLED)

        return self.lmList, self.bboxInfo

    def findAngle(self, img, p1, p2, p3, draw=True):
        """
        Finds angle between three points. Inputs index values of landmarks
        instead of the actual points.
        :param img: Image to draw output on.
        :param p1: Point1 - Index of Landmark 1.
        :param p2: Point2 - Index of Landmark 2.
        :param p3: Point3 - Index of Landmark 3.
        :param draw:  Flag to draw the output on the image.
        :return:
        """

        # Get the landmarks
        x1, y1 = self.lmList[p1][1:]
        x2, y2 = self.lmList[p2][1:]
        x3, y3 = self.lmList[p3][1:]

        # Calculate the Angle
        angle = math.degrees(math.atan2(y3 - y2, x3 - x2) -
                             math.atan2(y1 - y2, x1 - x2))
        if angle < 0:
            angle += 360

        # Draw
        if draw:
            cv2.line(img, (x1, y1), (x2, y2), (255, 255, 255), 3)
            cv2.line(img, (x3, y3), (x2, y2), (255, 255, 255), 3)
            cv2.circle(img, (x1, y1), 10, (0, 0, 255), cv2.FILLED)
            cv2.circle(img, (x1, y1), 15, (0, 0, 255), 2)
            cv2.circle(img, (x2, y2), 10, (0, 0, 255), cv2.FILLED)
            cv2.circle(img, (x2, y2), 15, (0, 0, 255), 2)
            cv2.circle(img, (x3, y3), 10, (0, 0, 255), cv2.FILLED)
            cv2.circle(img, (x3, y3), 15, (0, 0, 255), 2)
            cv2.putText(img, str(int(angle)), (x2 - 50, y2 + 50),
                        cv2.FONT_HERSHEY_PLAIN, 2, (0, 0, 255), 2)
        return angle

    def findDistance(self, p1, p2,img):

        x1, y1 = self.lmList[p1][1:3]
        x2, y2 = self.lmList[p2][1:3]
        cx, cy = (x1 + x2) // 2, (y1 + y2) // 2

        
        length = math.hypot(x2 - x1, y2 - y1)
        if length<50:
           cv2.circle(img, ((x1+x2)//2,(y1+y2)//2),int(length), (255, 255, 255), 10)
        return length,img

    def angleCheck(self, myAngle, targetAngle, addOn=20):
        return targetAngle - addOn < myAngle < targetAngle + addOn


def main():
    cap = cv2.VideoCapture(0)
    detector = PoseDetector()
    while True:
        success, img = cap.read()
        img = detector.findPose(img)
        lmList, bboxInfo = detector.findPosition(img, bboxWithHands=False)
        if bboxInfo:
            center = bboxInfo["center"]
            cv2.circle(img, center, 5, (255, 0, 255), cv2.FILLED)
            length,img=detector.findDistance(16,15,img)
            print(lmList[16][1:3])
        cv2.imshow("Image", img)
        cv2.waitKey(1)


if __name__ == "__main__":
    main()

	                    				
步骤5 步骤5
使用比例表达两点距离

因人像在摄像头中会出现近大远小的现象,所以只使用固定值来表达人体姿态中两坐标点的距离不合适。所以我采用两坐标点距离和两肩中点与两髋中点距离的比例,做为判断距离状态。

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如:右手(16)到右膝(26)距离与两肩中点(C1)与两髋中点(C2)距离,做比值。

代码 代码
	                    					"""
Pose Module
By: Computer Vision Zone
Website: https://www.computervision.zone/
"""
import cv2
import mediapipe as mp
import math


class PoseDetector:
    """
    Estimates Pose points of a human body using the mediapipe library.
    """

    def __init__(self, mode=False, smooth=True,
                 detectionCon=0.5, trackCon=0.5):
        """
        :param mode: In static mode, detection is done on each image: slower
        :param upBody: Upper boy only flag
        :param smooth: Smoothness Flag
        :param detectionCon: Minimum Detection Confidence Threshold
        :param trackCon: Minimum Tracking Confidence Threshold
        """

        self.mode = mode
        self.smooth = smooth
        self.detectionCon = detectionCon
        self.trackCon = trackCon

        self.mpDraw = mp.solutions.drawing_utils
        self.mpPose = mp.solutions.pose
        self.pose = self.mpPose.Pose(static_image_mode=self.mode,
                                     smooth_landmarks=self.smooth,
                                     min_detection_confidence=self.detectionCon,
                                     min_tracking_confidence=self.trackCon)

    def findPose(self, img, draw=True):
        """
        Find the pose landmarks in an Image of BGR color space.
        :param img: Image to find the pose in.
        :param draw: Flag to draw the output on the image.
        :return: Image with or without drawings
        """
        imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        self.results = self.pose.process(imgRGB)
        if self.results.pose_landmarks:
            if draw:
                self.mpDraw.draw_landmarks(img, self.results.pose_landmarks,
                                           self.mpPose.POSE_CONNECTIONS)
        return img

    def findPosition(self, img, draw=True, bboxWithHands=False):
        self.lmList = []
        self.bboxInfo = {}
        if self.results.pose_landmarks:
            for id, lm in enumerate(self.results.pose_landmarks.landmark):
                h, w, c = img.shape
                cx, cy, cz = int(lm.x * w), int(lm.y * h), int(lm.z * w)
                self.lmList.append([id, cx, cy, cz])

            # Bounding Box
            ad = abs(self.lmList[12][1] - self.lmList[11][1]) // 2
            if bboxWithHands:
                x1 = self.lmList[16][1] - ad
                x2 = self.lmList[15][1] + ad
            else:
                x1 = self.lmList[12][1] - ad
                x2 = self.lmList[11][1] + ad

            y2 = self.lmList[29][2] + ad
            y1 = self.lmList[1][2] - ad
            bbox = (x1, y1, x2 - x1, y2 - y1)
            cx, cy = bbox[0] + (bbox[2] // 2), \
                     bbox[1] + bbox[3] // 2

            self.bboxInfo = {"bbox": bbox, "center": (cx, cy)}

            if draw:
                cv2.rectangle(img, bbox, (255, 0, 255), 3)
                cv2.circle(img, (cx, cy), 5, (255, 0, 0), cv2.FILLED)

        return self.lmList, self.bboxInfo

    def findAngle(self, img, p1, p2, p3, draw=True):
        """
        Finds angle between three points. Inputs index values of landmarks
        instead of the actual points.
        :param img: Image to draw output on.
        :param p1: Point1 - Index of Landmark 1.
        :param p2: Point2 - Index of Landmark 2.
        :param p3: Point3 - Index of Landmark 3.
        :param draw:  Flag to draw the output on the image.
        :return:
        """

        # Get the landmarks
        x1, y1 = self.lmList[p1][1:]
        x2, y2 = self.lmList[p2][1:]
        x3, y3 = self.lmList[p3][1:]

        # Calculate the Angle
        angle = math.degrees(math.atan2(y3 - y2, x3 - x2) -
                             math.atan2(y1 - y2, x1 - x2))
        if angle < 0:
            angle += 360

        # Draw
        if draw:
            cv2.line(img, (x1, y1), (x2, y2), (255, 255, 255), 3)
            cv2.line(img, (x3, y3), (x2, y2), (255, 255, 255), 3)
            cv2.circle(img, (x1, y1), 10, (0, 0, 255), cv2.FILLED)
            cv2.circle(img, (x1, y1), 15, (0, 0, 255), 2)
            cv2.circle(img, (x2, y2), 10, (0, 0, 255), cv2.FILLED)
            cv2.circle(img, (x2, y2), 15, (0, 0, 255), 2)
            cv2.circle(img, (x3, y3), 10, (0, 0, 255), cv2.FILLED)
            cv2.circle(img, (x3, y3), 15, (0, 0, 255), 2)
            cv2.putText(img, str(int(angle)), (x2 - 50, y2 + 50),
                        cv2.FONT_HERSHEY_PLAIN, 2, (0, 0, 255), 2)
        return angle

    def findDistance(self, p1, p2,img):
        x1, y1 = self.lmList[12][1:3]
        x2, y2 = self.lmList[11][1:3]
        cx1, cy1 = (x1 + x2) // 2, (y1 + y2) // 2
        x1, y1 = self.lmList[23][1:3]
        x2, y2 = self.lmList[24][1:3]
        cx2, cy2 = (x1 + x2) // 2, (y1 + y2) // 2
        length1 = math.hypot(cx2 - cx1, cy2 - cy1)
        print(length1) 
        x1, y1 = self.lmList[p1][1:3]
        x2, y2 = self.lmList[p2][1:3]
        cx, cy = (x1 + x2) // 2, (y1 + y2) // 2

        
        length2 = math.hypot(x2 - x1, y2 - y1)
        if length2/length1<0.3:
           cv2.circle(img, ((x1+x2)//2,(y1+y2)//2),int(length2), (255, 255, 255), 10)
        return length2,img

    def angleCheck(self, myAngle, targetAngle, addOn=20):
        return targetAngle - addOn < myAngle < targetAngle + addOn


def main():
    cap = cv2.VideoCapture(0)
    detector = PoseDetector()
    while True:
        success, img = cap.read()
        img = detector.findPose(img)
        lmList, bboxInfo = detector.findPosition(img, bboxWithHands=False)
        if bboxInfo:
            center = bboxInfo["center"]
            cv2.circle(img, center, 5, (255, 0, 255), cv2.FILLED)
            length,img=detector.findDistance(16,15,img)
            print(lmList[16][1:3])
        cv2.imshow("Image", img)
        cv2.waitKey(1)


if __name__ == "__main__":
    main()

	                    				

如果length2/length1<0.5,认为手碰到膝盖或脚踝。

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步骤6 步骤6
加声音特效,完成程序编写
代码 代码
	                    					
import cv2
import mediapipe as mp
import math
import time
import pygame


pygame.init()
pygame.mixer.init()
sound1 = pygame.mixer.Sound("chimes.wav")

class PoseDetector:
    """
    Estimates Pose points of a human body using the mediapipe library.
    """

    def __init__(self, mode=False, smooth=True,
                 detectionCon=0.5, trackCon=0.8):
        """
        :param mode: In static mode, detection is done on each image: slower
        :param upBody: Upper boy only flag
        :param smooth: Smoothness Flag
        :param detectionCon: Minimum Detection Confidence Threshold
        :param trackCon: Minimum Tracking Confidence Threshold
        """
        self.ptime=0
        self.mode = mode
        self.smooth = smooth
        self.detectionCon = detectionCon
        self.trackCon = trackCon
        self.i=0
        self.mpDraw = mp.solutions.drawing_utils
        self.mpPose = mp.solutions.pose
        self.pose = self.mpPose.Pose(static_image_mode=self.mode,
                                     smooth_landmarks=self.smooth,
                                     min_detection_confidence=self.detectionCon,
                                     min_tracking_confidence=self.trackCon)

    def findPose(self, img, draw=True):
        """
        Find the pose landmarks in an Image of BGR color space.
        :param img: Image to find the pose in.
        :param draw: Flag to draw the output on the image.
        :return: Image with or without drawings
        """
        imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        self.results = self.pose.process(imgRGB)
        if self.results.pose_landmarks:
            if draw:
                self.mpDraw.draw_landmarks(img, self.results.pose_landmarks,
                                           self.mpPose.POSE_CONNECTIONS)
        return img

    def findPosition(self, img, draw=True, bboxWithHands=False):
        self.lmList = []
        self.bboxInfo = {}
        
        if self.results.pose_landmarks:
            for id, lm in enumerate(self.results.pose_landmarks.landmark):
                h, w, c = img.shape
                cx, cy, cz = int(lm.x * w), int(lm.y * h), int(lm.z * w)
                self.lmList.append([id, cx, cy, cz])

            # Bounding Box
            ad = abs(self.lmList[12][1] - self.lmList[11][1]) // 2
            if bboxWithHands:
                x1 = self.lmList[16][1] - ad
                x2 = self.lmList[15][1] + ad
            else:
                x1 = self.lmList[12][1] - ad
                x2 = self.lmList[11][1] + ad

            y2 = self.lmList[29][2] + ad
            y1 = self.lmList[1][2] - ad
            bbox = (x1, y1, x2 - x1, y2 - y1)
            cx, cy = bbox[0] + (bbox[2] // 2), \
                     bbox[1] + bbox[3] // 2

            self.bboxInfo = {"bbox": bbox, "center": (cx, cy)}

            if draw:
                cv2.rectangle(img, bbox, (255, 0, 255), 3)
                cv2.circle(img, (cx, cy), 5, (255, 0, 0), cv2.FILLED)

        return self.lmList, self.bboxInfo

    def findAngle(self, img, p1, p2, p3, draw=True):
        # Get the landmarks
        x1, y1 = self.lmList[p1][1:]
        x2, y2 = self.lmList[p2][1:]
        x3, y3 = self.lmList[p3][1:]

        # Calculate the Angle
        angle = math.degrees(math.atan2(y3 - y2, x3 - x2) -
                             math.atan2(y1 - y2, x1 - x2))
        if angle < 0:
            angle += 360

        # Draw
        if draw:
            cv2.line(img, (x1, y1), (x2, y2), (255, 255, 255), 3)
            cv2.line(img, (x3, y3), (x2, y2), (255, 255, 255), 3)
            cv2.circle(img, (x1, y1), 10, (0, 0, 255), cv2.FILLED)
            cv2.circle(img, (x1, y1), 15, (0, 0, 255), 2)
            cv2.circle(img, (x2, y2), 10, (0, 0, 255), cv2.FILLED)
            cv2.circle(img, (x2, y2), 15, (0, 0, 255), 2)
            cv2.circle(img, (x3, y3), 10, (0, 0, 255), cv2.FILLED)
            cv2.circle(img, (x3, y3), 15, (0, 0, 255), 2)
            cv2.putText(img, str(int(angle)), (x2 - 50, y2 + 50),
                        cv2.FONT_HERSHEY_PLAIN, 2, (0, 0, 255), 2)
        return angle

    def findDistance(self, p1, p2,img):
        
        x1, y1 = self.lmList[12][1:3]
        x2, y2 = self.lmList[11][1:3]
        cx1, cy1 = (x1 + x2) // 2, (y1 + y2) // 2
        x1, y1 = self.lmList[23][1:3]
        x2, y2 = self.lmList[24][1:3]
        cx2, cy2 = (x1 + x2) // 2, (y1 + y2) // 2
        length1 = math.hypot(cx2 - cx1, cy2 - cy1)
        
        x1, y1 = self.lmList[p1][1:3]
        x2, y2 = self.lmList[p2][1:3]
        cx, cy = (x1 + x2) // 2, (y1 + y2) // 2

        
        length2 = math.hypot(x2 - x1, y2 - y1)
        
        if length2/length1<0.3:
           cv2.circle(img, ((x1+x2)//2,(y1+y2)//2),int(length2), (255, 255, 255), 10)
           if  time.time()-self.ptime>0.3:
              self.ptime=time.time()
              sound1.play()
              print(length2/length1)
              self.i+=1
              
        return img

    def angleCheck(self, myAngle, targetAngle, addOn=20):
        return targetAngle - addOn < myAngle < targetAngle + addOn


def main():
    cap = cv2.VideoCapture("jianzicao1.mp4")

    detector = PoseDetector()
    post=[25,26,27,28]
    while True:
        success, img = cap.read()
        img=cv2.resize(img,(640,480))
        img = detector.findPose(img)
        lmList, bboxInfo = detector.findPosition(img, bboxWithHands=False)
        if bboxInfo:
            center = bboxInfo["center"]
            cv2.circle(img, center, 5, (255, 0, 255), cv2.FILLED)
            for i in post:
             img=detector.findDistance(16,i,img)
             img=detector.findDistance(15,i,img)
             cv2.putText(img, str(detector.i), (50, 200),cv2.FONT_HERSHEY_PLAIN, 8, (0, 0, 255), 5)
             
        cv2.imshow("Image", img)
        cv2.waitKey(1)

if __name__ == "__main__":
    main()

	                    				

声音文件,在下面附件中。

附件 附件
步骤7 步骤7
使用网络视频进行测试
步骤8 步骤8
实景测试
步骤9 步骤9
投电视(大屏测试)

通过HDMI线,投到电视上。

Makelog作者原创文章,未经授权禁止转载。
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