3D视觉——4.手势识别(Gesture Recognition)入门——使用MediaPipe含单帧(Singel Frame)和实时视频(Real-Time Video)

企业开发 2023-04-08 18:12:27 阅读次数: 0

上一话3D视觉——3.人体姿态估计(Pose Estimation) 算法对比 即 效果展示——MediaPipe与OpenPosehttps://blog.csdn.net/XiaoyYidiaodiao/article/details/125571632?spm=1001.2014.3001.5502这一章 讲述 使用MediaPipe的手势识别

单帧手势识别代码

重点简单代码讲解

1.solutions.hands

import mediapipe as mp
mp_hands = mp.solutions.hands

mediapipe手势模块(.solutions.hands)将手分成21个点(0-20)如下图1. ，可通过判断手势的角度，来识别是什么手势。8号关键点很重要，因为做HCI（人机交互）都是以8号关键点为要素。

2.mp_hand.Hands()

static_image_mode 表示 静态图像还是连续帧视频；

max_num_hands 表示 最多识别多少只手，手越多识别越慢；

min_detection_confidence 表示 检测置信度阈值；

min_tracking_confidence 表示 各帧之间跟踪置信度阈值；

hands = mp_hands.Hands(static_image_mode=True,
                           max_num_hands=2,
                           min_detection_confidence=0.5,
                           min_tracking_confidence=0.5)

3.mp.solutions.drawing_utils

绘图

draw = mp.solutions.drawing_utils
draw.draw_landmarks(img, hand, mp_hands.HAND_CONNECTIONS)

完整代码

import cv2
import mediapipe as mp
import matplotlib.pyplot as plt

if __name__ == '__main__':
    mp_hands = mp.solutions.hands
    hands = mp_hands.Hands(static_image_mode=True,
                           max_num_hands=2,
                           min_detection_confidence=0.5,
                           min_tracking_confidence=0.5)

    draw = mp.solutions.drawing_utils
    img = cv2.imread("3.jpg")

    # Flip Horizontal
    img = cv2.flip(img, 1)

    # BGR to RGB
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

    results = hands.process(img)
    
    # detected hands
    if results.multi_hand_landmarks:
        for hand_idx, _ in enumerate(results.multi_hand_landmarks):
            hand = results.multi_hand_landmarks[hand_idx]
            draw.draw_landmarks(img, hand, mp_hands.HAND_CONNECTIONS)
    plt.imshow(img)
    plt.show()

运行结果

输出结果解析

import cv2
import mediapipe as mp
import matplotlib.pyplot as plt
if __name__ == "__main__":
    mp_hands = mp.solutions.hands
    hands = mp_hands.Hands(static_image_mode=False,
                           max_num_hands=2,
                           min_detection_confidence=0.5,
                           min_tracking_confidence=0.5)
    draw = mp.soultions.drawing_utils
    img = cv2.imread("3.jpg")
    img = cv2.filp(img, 1)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    results = hands.process(img)
    print(results.multi_handedness)

显示置信度与左右手

print(results.multi_handedness)
# results.multi_handedness

结果如下，其中：label 表示 左右手；socre 表示 置信度。

[classification {
  index: 0
  score: 0.9686367511749268
  label: "Left"
}
, classification {
  index: 1
  score: 0.9293265342712402
  label: "Right"
}
]

Process finished with exit code 0

调用索引为1的置信度或者标签(左右手)

print(results.multi_handedness[1].classification[0].label)
# results.multi_handedness[1].classification[0].label

print(results.multi_handedness[1].classification[0].score)
# results.multi_handedness[1].classification[0].scor

结果如下

Right

0.9293265342712402

Process finished with exit code 0

关键点坐标

所有手指的关键点坐标都显示出来

print(results.multi_hand_landmarks)
# results.multi_hand_landmarks

结果如下，其中这个z轴不是真正的归一化或者真实距离，是与第0号关键点（手腕根部）相对的单位，所以这个在另外一个方面这也算是2.5D

[landmark {
  x: 0.2627016007900238
  y: 0.6694213151931763
  z: 5.427047540251806e-07
}
landmark {
  x: 0.33990585803985596
  y: 0.6192424297332764
  z: -0.03650109842419624
}
landmark {
  x: 0.393616259098053
  y: 0.5356684923171997
  z: -0.052688632160425186
}
landmark {
  x: 0.43515193462371826
  y: 0.46728551387786865
  z: -0.06730890274047852
}
landmark {
  x: 0.47741779685020447
  y: 0.4358704090118408
  z: -0.08190854638814926
}
landmark {
  x: 0.3136638104915619
  y: 0.3786224126815796
  z: -0.031268805265426636
}
landmark {
  x: 0.32385849952697754
  y: 0.2627469599246979
  z: -0.05158748850226402
}
landmark {
  x: 0.32598018646240234
  y: 0.19216321408748627
  z: -0.06765355914831161
}
landmark {
  x: 0.32443925738334656
  y: 0.13277988135814667
  z: -0.08096525818109512
}
landmark {
  x: 0.2579415738582611
  y: 0.37382692098617554
  z: -0.032878294587135315
}
landmark {
  x: 0.2418910712003708
  y: 0.24533957242965698
  z: -0.052028004080057144
}
landmark {
  x: 0.2304030954837799
  y: 0.16545917093753815
  z: -0.06916359812021255
}
landmark {
  x: 0.2175263613462448
  y: 0.10054746270179749
  z: -0.08164216578006744
}
landmark {
  x: 0.2122785747051239
  y: 0.4006640613079071
  z: -0.03896263241767883
}
landmark {
  x: 0.16991624236106873
  y: 0.29440340399742126
  z: -0.06328895688056946
}
landmark {
  x: 0.14282439649105072
  y: 0.22940337657928467
  z: -0.0850684642791748
}
landmark {
  x: 0.12077423930168152
  y: 0.17327186465263367
  z: -0.09951525926589966
}
landmark {
  x: 0.17542198300361633
  y: 0.45321160554885864
  z: -0.04842161759734154
}
landmark {
  x: 0.11945687234401703
  y: 0.3971719741821289
  z: -0.07581596076488495
}
landmark {
  x: 0.08262123167514801
  y: 0.364059180021286
  z: -0.0922597199678421
}
landmark {
  x: 0.05292154848575592
  y: 0.3294071555137634
  z: -0.10204877704381943
}
, landmark {
  x: 0.7470765113830566
  y: 0.6501559019088745
  z: 5.611524898085918e-07
}
landmark {
  x: 0.6678823232650757
  y: 0.5958800911903381
  z: -0.02732565440237522
}
landmark {
  x: 0.6151978969573975
  y: 0.5073886513710022
  z: -0.04038363695144653
}
landmark {
  x: 0.5764827728271484
  y: 0.4352443814277649
  z: -0.05264268442988396
}
landmark {
  x: 0.5317870378494263
  y: 0.39630216360092163
  z: -0.0660330131649971
}
landmark {
  x: 0.6896227598190308
  y: 0.36289966106414795
  z: -0.0206490196287632
}
landmark {
  x: 0.6775739192962646
  y: 0.24553297460079193
  z: -0.040212471038103104
}
landmark {
  x: 0.6752403974533081
  y: 0.1725052148103714
  z: -0.0579310804605484
}
landmark {
  x: 0.6765506267547607
  y: 0.10879574716091156
  z: -0.07280831784009933
}
landmark {
  x: 0.7441056370735168
  y: 0.35811278223991394
  z: -0.027878733351826668
}
landmark {
  x: 0.7618186473846436
  y: 0.22912392020225525
  z: -0.04549985006451607
}
landmark {
  x: 0.7751038670539856
  y: 0.14770638942718506
  z: -0.06489630043506622
}
landmark {
  x: 0.788221538066864
  y: 0.07831943035125732
  z: -0.08037586510181427
}
landmark {
  x: 0.7893437743186951
  y: 0.38241785764694214
  z: -0.03979477286338806
}
landmark {
  x: 0.8274303674697876
  y: 0.27563461661338806
  z: -0.06700978428125381
}
landmark {
  x: 0.8543692827224731
  y: 0.20750769972801208
  z: -0.09000862389802933
}
landmark {
  x: 0.877074122428894
  y: 0.14460964500904083
  z: -0.10605450719594955
}
landmark {
  x: 0.8275361061096191
  y: 0.4312361180782318
  z: -0.05377437546849251
}
landmark {
  x: 0.879731297492981
  y: 0.3712681233882904
  z: -0.08328921347856522
}
landmark {
  x: 0.9155224561691284
  y: 0.33470967411994934
  z: -0.09889543056488037
}
landmark {
  x: 0.9464077949523926
  y: 0.297884464263916
  z: -0.109173983335495
}
]

Process finished with exit code 0

统计手的数量

print(len(results.multi_hand_landmarks))
# len(results.multi_hand_landmarks)

结果如下

2

Process finished with exit code 0

获取索引为1的第3号关键点的坐标

print(results.multi_hand_landmarks[1].landmark[3])
# results.multi_hand_landmarks[1].landmark[3]

结果如下

x: 0.5764827728271484
y: 0.4352443814277649
z: -0.05264268442988396


Process finished with exit code 0

以上的x,y坐标是根据高和宽归一化之后的结果，将其转化为图像中坐标结果

    height, width, _ = img.shape
    print("height:{},width:{}".format(height, width))
    cx = results.multi_hand_landmarks[1].landmark[3].x * width
    cy = results.multi_hand_landmarks[1].landmark[3].y * height
    print("cx:{}, cy:{}".format(cx, cy))

结果如下

height:512,width:720
cx:415.0675964355469, cy:222.84512329101562

Process finished with exit code 0

关键点之间的连接

print(mp_hands.HAND_CONNECTIONS)
# mp_hands.HAND_CONNECTIONS

结果如下

frozenset({(3, 4), 
           (0, 5), 
           (17, 18), 
           (0, 17), 
           (13, 14), 
           (13, 17), 
           (18, 19), 
           (5, 6), 
           (5, 9), 
           (14, 15), 
           (0, 1), 
           (9, 10), 
           (1, 2), 
           (9, 13), 
           (10, 11), 
           (19, 20), 
           (6, 7), 
           (15, 16), 
           (2, 3), 
           (11, 12), 
           (7, 8)})

Process finished with exit code 0

显示左右手信息

    if results.multi_hand_landmarks:
        hand_info = ""
        for h_idx, hand in enumerate(results.multi_hand_landmarks):
            hand_info += str(h_idx) + ":" + str(results.multi_handedness[h_idx].classification[0].label) + ", "
        print(hand_info)

结果如下

0:Left, 1:Right, 

Process finished with exit code 0

单帧手势识别代码优化

完整代码

import cv2
import mediapipe as mp
import matplotlib.pyplot as plt

if __name__ == '__main__':
    mp_hands = mp.solutions.hands
    hands = mp_hands.Hands(static_image_mode=True,
                           max_num_hands=2,
                           min_detection_confidence=0.5,
                           min_tracking_confidence=0.5)

    draw = mp.solutions.drawing_utils
    img = cv2.imread("3.jpg")
    img = cv2.flip(img, 1)
    height, width, _ = img.shape
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    results = hands.process(img)
    if results.multi_hand_landmarks:
        handness_str = ''
        index_finger_tip_str = ''
        for h_idx, hand in enumerate(results.multi_hand_landmarks):
            # get 21 key points of hand
            hand_info = results.multi_hand_landmarks[h_idx]

            # log left and right hands info
            draw.draw_landmarks(img, hand_info, mp_hands.HAND_CONNECTIONS)

            temp_handness = results.multi_handedness[h_idx].classification[0].label
            handness_str += "{}:{} ".format(h_idx, temp_handness)

            cz0 = hand_info.landmark[0].z

            for idx, keypoint in enumerate(hand_info.landmark):
                cx = int(keypoint.x * width)
                cy = int(keypoint.y * height)
                cz = keypoint.z
                depth_z = cz0 - cz

                # radius about depth_z
                radius = int(6 * (1 + depth_z))

                if idx == 0:
                    img = cv2.circle(img, (cx, cy), radius * 2, (0, 0, 255), -1)
                elif idx == 8:
                    img = cv2.circle(img, (cx, cy), radius * 2, (193, 182, 255), -1)
                    index_finger_tip_str += '{}:{:.2f} '.format(h_idx, depth_z)
                elif idx in [1, 5, 9, 13, 17]:
                    img = cv2.circle(img, (cx, cy), radius, (16, 144, 247), -1)
                elif idx in [2, 6, 10, 14, 18]:
                    img = cv2.circle(img, (cx, cy), radius, (1, 240, 255), -1)
                elif idx in [3, 7, 11, 15, 19]:
                    img = cv2.circle(img, (cx, cy), radius, (140, 47, 240), -1)
                elif idx in [4, 12, 16, 20]:
                    img = cv2.circle(img, (cx, cy), radius, (223, 155, 60), -1)

    img = cv2.putText(img, handness_str, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 0, 0), 2)
    img = cv2.putText(img, index_finger_tip_str, (10, 80), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 0, 255), 2)
    plt.imshow(img)
    plt.show()

这次就不用那个莫兰迪色了，朋友说我用了那个莫兰迪色系还不如本来的颜色明显。

运行程序

视频手势识别

1.摄像头拍摄手势识别

简化版代码

import cv2
import mediapipe as mp

mp_hands = mp.solutions.hands
hands = mp_hands.Hands(static_image_mode=False,
                       max_num_hands=2,
                       min_tracking_confidence=0.5,
                       min_detection_confidence=0.5)
draw = mp.solutions.drawing_utils


def process_frame(img):
    img = cv2.flip(img, 1)
    height, width, _ = img.shape
    results = hands.process(img)
    if results.multi_hand_landmarks:
        for h_idx, hand in enumerate(results.multi_hand_landmarks):
            draw.draw_landmarks(img, hand, mp_hands.HAND_CONNECTIONS)

    return img


if __name__ == '__main__':
    cap = cv2.VideoCapture()
    cap.open(0)
    while cap.isOpened():
        success, frame = cap.read()
        if not success:
            raise ValueError("Error")
        frame = process_frame(frame)
        cv2.imshow("hand", frame)
        if cv2.waitKey(1) in [ord('q'), 27]:
            break
    cap.release()
    cv2.destroyAllWindows()

优化版代码

import cv2
import mediapipe as mp

mp_hands = mp.solutions.hands
hands = mp_hands.Hands(static_image_mode=False,
                       max_num_hands=2,
                       min_tracking_confidence=0.5,
                       min_detection_confidence=0.5)
draw = mp.solutions.drawing_utils


def process_frame(img):
    img = cv2.flip(img, 1)
    height, width, _ = img.shape
    results = hands.process(img)

    handness_str = ''
    index_finger_tip_str = ''
    if results.multi_hand_landmarks:
        for h_idx, hand in enumerate(results.multi_hand_landmarks):
            draw.draw_landmarks(img, hand, mp_hands.HAND_CONNECTIONS)
            # get 21 key points of hand
            hand_info = results.multi_hand_landmarks[h_idx]

            # log left and right hands info
            temp_handness = results.multi_handedness[h_idx].classification[0].label
            handness_str += "{}:{} ".format(h_idx, temp_handness)

            cz0 = hand_info.landmark[0].z
            for idx, keypoint in enumerate(hand_info.landmark):
                cx = int(keypoint.x * width)
                cy = int(keypoint.y * height)
                cz = keypoint.z
                depth_z = cz0 - cz

                # radius about depth_z
                radius = int(6 * (1 + depth_z))

                if idx == 0:
                    img = cv2.circle(img, (cx, cy), radius * 2, (0, 0, 255), -1)
                elif idx == 8:
                    img = cv2.circle(img, (cx, cy), radius * 2, (193, 182, 255), -1)
                    index_finger_tip_str += '{}:{:.2f} '.format(h_idx, depth_z)
                elif idx in [1, 5, 9, 13, 17]:
                    img = cv2.circle(img, (cx, cy), radius, (16, 144, 247), -1)
                elif idx in [2, 6, 10, 14, 18]:
                    img = cv2.circle(img, (cx, cy), radius, (1, 240, 255), -1)
                elif idx in [3, 7, 11, 15, 19]:
                    img = cv2.circle(img, (cx, cy), radius, (140, 47, 240), -1)
                elif idx in [4, 12, 16, 20]:
                    img = cv2.circle(img, (cx, cy), radius, (223, 155, 60), -1)
    img = cv2.putText(img, handness_str, (25, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 0, 0), 2)
    img = cv2.putText(img, index_finger_tip_str, (25, 100), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 0, 255), 2)

    return img


if __name__ == '__main__':
    cap = cv2.VideoCapture()
    cap.open(0)
    while cap.isOpened():
        success, frame = cap.read()
        if not success:
            raise ValueError("Error")
        frame = process_frame(frame)
        cv2.imshow("hand", frame)
        if cv2.waitKey(1) in [ord('q'), 27]:
            break
    cap.release()
    cv2.destroyAllWindows()

此代码能正常运行，不展示运行结果！

2.视频实时手势识别

简化版完整代码

import cv2
from tqdm import tqdm
import mediapipe as mp

mp_hands = mp.solutions.hands
hands = mp_hands.Hands(static_image_mode=False,
                       max_num_hands=2,
                       min_tracking_confidence=0.5,
                       min_detection_confidence=0.5)
draw = mp.solutions.drawing_utils


def process_frame(img):
    img = cv2.flip(img, 1)
    height, width, _ = img.shape
    results = hands.process(img)
    if results.multi_hand_landmarks:
        for h_idx, hand in enumerate(results.multi_hand_landmarks):
            draw.draw_landmarks(img, hand, mp_hands.HAND_CONNECTIONS)

    return img


def out_video(input):
    file = input.split("/")[-1]
    output = "out-" + file
    print("It will start processing video: {}".format(input))
    cap = cv2.VideoCapture(input)
    frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
    frame_size = (cap.get(cv2.CAP_PROP_FRAME_WIDTH), cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
    # create VideoWriter, VideoWriter_fourcc is video decode
    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
    fps = cap.get(cv2.CAP_PROP_FPS)
    out = cv2.VideoWriter(output, fourcc, fps, (int(frame_size[0]), int(frame_size[1])))
    with tqdm(range(frame_count)) as pbar:
        while cap.isOpened:
            success, frame = cap.read()
            if not success:
                break
            frame = process_frame(frame)
            out.write(frame)
            pbar.update(1)
    pbar.close()
    cv2.destroyAllWindows()
    out.release()
    cap.release()
    print("{} finished!".format(output))


if __name__ == '__main__':
    video_dirs = "7.mp4"
    out_video(video_dirs)

运行结果

此视频是我在网上随便找来做实验的 

手势识别

优化版完整代码

import cv2
import time
from tqdm import tqdm
import mediapipe as mp

mp_hands = mp.solutions.hands
hands = mp_hands.Hands(static_image_mode=False,
                       max_num_hands=2,
                       min_tracking_confidence=0.5,
                       min_detection_confidence=0.5)
draw = mp.solutions.drawing_utils


def process_frame(img):
    t0 = time.time()
    img = cv2.flip(img, 1)
    height, width, _ = img.shape
    results = hands.process(img)
    handness_str = ''
    index_finger_tip_str = ''
    if results.multi_hand_landmarks:
        for h_idx, hand in enumerate(results.multi_hand_landmarks):
            draw.draw_landmarks(img, hand, mp_hands.HAND_CONNECTIONS)

            # get 21 key points of hand
            hand_info = results.multi_hand_landmarks[h_idx]

            # log left and right hands info
            temp_handness = results.multi_handedness[h_idx].classification[0].label
            handness_str += "{}:{} ".format(h_idx, temp_handness)

            cz0 = hand_info.landmark[0].z
            for idx, keypoint in enumerate(hand_info.landmark):
                cx = int(keypoint.x * width)
                cy = int(keypoint.y * height)
                cz = keypoint.z
                depth_z = cz0 - cz

                # radius about depth_z
                radius = int(6 * (1 + depth_z))

                if idx == 0:
                    img = cv2.circle(img, (cx, cy), radius * 2, (0, 0, 255), -1)
                elif idx == 8:
                    img = cv2.circle(img, (cx, cy), radius * 2, (193, 182, 255), -1)
                    index_finger_tip_str += '{}:{:.2f} '.format(h_idx, depth_z)
                elif idx in [1, 5, 9, 13, 17]:
                    img = cv2.circle(img, (cx, cy), radius, (16, 144, 247), -1)
                elif idx in [2, 6, 10, 14, 18]:
                    img = cv2.circle(img, (cx, cy), radius, (1, 240, 255), -1)
                elif idx in [3, 7, 11, 15, 19]:
                    img = cv2.circle(img, (cx, cy), radius, (140, 47, 240), -1)
                elif idx in [4, 12, 16, 20]:
                    img = cv2.circle(img, (cx, cy), radius, (223, 155, 60), -1)
        img = cv2.putText(img, handness_str, (25, 100), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 0, 0), 2)
        img = cv2.putText(img, index_finger_tip_str, (25, 150), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 0, 255), 2)
        fps = 1 / (time.time() - t0)
        img = cv2.putText(img, "FPS:" + str(int(fps)), (25, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)

    return img


def out_video(input):
    file = input.split("/")[-1]
    output = "out-optim-" + file
    print("It will start processing video: {}".format(input))
    cap = cv2.VideoCapture(input)
    frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
    frame_size = (cap.get(cv2.CAP_PROP_FRAME_WIDTH), cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
    # create VideoWriter, VideoWriter_fourcc is video decode
    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
    fps = cap.get(cv2.CAP_PROP_FPS)
    out = cv2.VideoWriter(output, fourcc, fps, (int(frame_size[0]), int(frame_size[1])))
    with tqdm(range(frame_count)) as pbar:
        while cap.isOpened:
            success, frame = cap.read()
            if not success:
                break
            frame = process_frame(frame)
            out.write(frame)
            pbar.update(1)
    pbar.close()
    cv2.destroyAllWindows()
    out.release()
    cap.release()
    print("{} finished!".format(output))


if __name__ == '__main__':
    video_dirs = "7.mp4"
    out_video(video_dirs)

运行结果

手势识别优化

未完...

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