Python MediaPipe视觉应用：戒指随手指移动

前几天看见一位博主用mediapipe写的拖动方块，于是我就有了写一个这个 代码 的想法，我原本要实现的功能是一只手给另一只手带上，但是随着我对里面函数的探索，我发现有点难实现，因为他results.multi_handedness里面可以知道左右手，但是点位在hand_landmarks.landmark这个里面，hand_landmarks.landmark不管你是左手还是右手，只要是手就往里面加点。于是戴戒指这个功能我就分成了两部分，右手拇指和食指达到一定距离后可以移动，左手的中指三个点位分别穿过戒指后，戒指就随左手中指的10号点位移动。

手的点 位图 ：

这个 程序 目前只可以做到戒指的一维移动，也就是说戒指还只是图片，他只能保持原有的状态直线运动，当手竖起来的时候戒指的形状和手就不匹配了，这就需要通过其他算法完成了，我目前的水平还做不到。

这是戒指图片：

由于我还不会怎么弄动图，就只有放几张 截图 了

import cv2import timeimport mathimport numpy as npimport mediapipe as mp mp_drawing = mp.solutions.drawing_utilsmp_drawing_styles = mp.solutions.drawing_stylesmp_hands = mp.solutions.handshands =  mp_hands.Hands(    static_image_mode=True,    max_num_hands=2,    min_detection_confidence=0.5)cap = cv2.VideoCapture(0)width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) img=cv2.imread("./ring.png")ring_h,ring_w,ring_ch=img.shapeL1 = 0L2 = 0x=100y=100on_square = Falsemid_12=Falsemid_11=Falsemid_10=Falsewhile True:    rec,frame = cap.read()    frame = cv2.flip(frame,1)    frame.flags.writeable = False    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)    results = hands.process(frame)    frame.flags.writeable = True    frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)    if results.multi_hand_landmarks:        for hand_landmarks in results.multi_hand_landmarks:            if results.multi_handedness:                for hand in results.multi_handedness: #如果是右手                    if hand.classification[0].label=='Right':# mp_drawing.draw_landmarks( # 显示手的点位# frame,# hand_landmarks,# mp_hands.HAND_CONNECTIONS,# mp_drawing_styles.get_default_hand_landmarks_style(),# mp_drawing_styles.get_default_hand_connections_style())                        x_list = []                        y_list = []                        for landmark in hand_landmarks.landmark:                            x_list.append(landmark.x)                            y_list.append(landmark.y)                        index_finger_x, index_finger_y = int(x_list[8] * width),int(y_list[8] * height)                        thumb_finger_x,thumb_finger_y = int(x_list[4] * width), int(y_list[4] * height)                        finger_distance = math.hypot((thumb_finger_x - index_finger_x), (thumb_finger_y - index_finger_y))                        if finger_distance < 100 :                            if (index_finger_x > x-10 and index_finger_x < (x + ring_h+10)) and (                                    index_finger_y > y-10 and index_finger_y < (y + ring_w+10)):                                if on_square == False:                                    L1 = index_finger_x - x                                    L2 = index_finger_y - y                                    on_square = True                        else:                            on_square = False                        if on_square:                            x = index_finger_x - L1                            y = index_finger_y - L2                    if hand.classification[0].label=='Left': #如果识别为左手，处理左手中指的行为                        x_list = []                        y_list = []                        for landmark in hand_landmarks.landmark:                            x_list.append(landmark.x)                            y_list.append(landmark.y)                                                mid_finger_12_x,mid_finger_12_y=int(x_list[12]*width),int(y_list[12]*width)                        mid_finger_11_x,mid_finger_11_y=int(x_list[11]*width),int(y_list[11]*width)                        mid_finger_10_x,mid_finger_10_y=int(x_list[10]*width),int(y_list[10]*width)# if(mid_finger_12_y<y+ring_w and mid_finger_12_y>y and not mid_10):# mid_12=True# if(mid_finger_11_y<y+ring_w and mid_finger_11_y>y and mid_12 and mid_finger_12_x>x):# mid_11=True# if(mid_finger_10_y<y+ring_w and mid_finger_10_y>y and mid_11):# mid_10=True# if mid_10: #跟随中指10点位移动#这里可以先注释了省去戴的过程                         x=mid_finger_10_x                        y=mid_finger_10_y-130    overlay = frame.copy()    for i in range(ring_h):#这里是将原图片中接近白色的颜色筛除，只剩余戒指颜色        for j in range(ring_w):            px=img[i,j]            src2_beta=1               if(px[0],px[1],px[2])>=(245,245,245):                continue                color=(int(px[0]),int(px[1]),int(px[2]))            cv2.rectangle(frame, (x+i, y+j), (x+i,y+j),color,-1)    frame = cv2.addWeighted(overlay, 0.5, frame, src2_beta, 0)    cv2.imshow('frame',frame)    if cv2.waitKey(1) & 0xFF == ord('q'):        break    cap.release()cv2.destroyAllWindows()   

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