Mediapipe实战——导出身体节点坐标并用TensorFlow搭建LSTM网络来训练自己的手势检测模型再部署到树莓派4B-Toy模板网

这篇具有很好参考价值的文章主要介绍了Mediapipe实战——导出身体节点坐标并用TensorFlow搭建LSTM网络来训练自己的手势检测模型再部署到树莓派4B。希望对大家有所帮助。如果存在错误或未考虑完全的地方，请大家不吝赐教，您也可以点击"举报违法"按钮提交疑问。

一、前言
在YouTube上看到up主——Nicholas Renotte的相关教程，觉得非常有用。使用他的方法，我训练了能够检测四种手势的模型，在这里和大家分享一下。
附上该up主的视频链接Sign Language Detection using ACTION RECOGNITION with Python | LSTM Deep Learning Model

视频的代码链接https://github.com/nicknochnack/ActionDetectionforSignLanguage
我的系列文章一：Mediapipe入门——搭建姿态检测模型并实时输出人体关节点3d坐标
我的系列文章二：Mediapipe姿态估计——用坐标计算手指关节弯曲角度并实时标注

我使用的环境
Pycharm2021
mediapipe0.8.9
tensorflow2.3.0
openCV4.5.4
个人认为版本影响不大，可以跟我不一致，但tensorflow最好2.0以上

二、使用mediapipe搭建姿态估计模型并打开摄像头采集坐标数据集
源代码中，up主进行了很好地封装，代码稍长，接下来我只挑重要的部分说一下，完整的代码请看文末（代码中的中文注释是我添加的，英文的是原作者的）。
首先是处理视频流的函数。

def mediapipe_detection(image, model):
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) #  BGR 转 RGB
    image.flags.writeable = False                  # Image is no longer writeable
    results = model.process(image)                 # 对视频流处理，返回坐标
    image.flags.writeable = True                   # Image is now writeable
    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) #  RGB 转 BGR
    return image, results

然后是在人体上渲染节点的函数。

def draw_styled_landmarks(image, results):
    mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS,
                             mp_drawing.DrawingSpec(color=(80,22,10), thickness=2, circle_radius=4),
                             mp_drawing.DrawingSpec(color=(80,44,121), thickness=2, circle_radius=2)
                             )
  	......
    #剩下还有，不一一放上来了，完整请看文末

这两个功能比较简单，如果想了解如何用mediapipe搭建姿态检测模型，请看我的系列文章一。
然后是比较重要的提取坐标的函数，将process返回的坐标提取出来，并转换为numpy矩阵。为了训练手势模型，我使用了姿势坐标33个、左右手坐标各21个。原作者还使用了脸部坐标一起训练，个人没这个需求，将相关代码注释了。

def extract_keypoints(results):
	#姿势坐标33个，np.zeros(33*4)是因为除x,y,z外，还有置信度visibility，以下类似
    pose = np.array([[res.x, res.y, res.z, res.visibility] for res in results.pose_landmarks.landmark]).flatten() if results.pose_landmarks else np.zeros(33*4)
    #mediapipe面网多达468个节点，这里我不用，注释掉
    #face = np.array([[res.x, res.y, res.z] for res in results.face_landmarks.landmark]).flatten() if results.face_landmarks else np.zeros(468*3)
    #左手坐标21个
    lh = np.array([[res.x, res.y, res.z] for res in results.left_hand_landmarks.landmark]).flatten() if results.left_hand_landmarks else np.zeros(21*3)
    #右手坐标21个
    rh = np.array([[res.x, res.y, res.z] for res in results.right_hand_landmarks.landmark]).flatten() if results.right_hand_landmarks else np.zeros(21*3)
    return np.concatenate([pose, lh, rh])
    #如果要使用脸部坐标训练，列表更换为[pose, face, lh, rh]

33个姿势节点如下所示。
mediapipe训练自己的数据集,mediapipe学习,机器学习,人工智能,计算机视觉,tensorflow,lstm
21个手部节点如下所示。

现在使用os库在同一目录下新建文件夹存放等下要采集的数据集。

DATA_PATH = os.path.join('MP_Data')

接下来比较重要了。我将训练的四个手势是“666”，“大拇指”、“比心”、“剪刀手”。每个动作将采集30次，每次采集30帧（这些可以改）

actions = np.array(['666', 'thumbs_up', 'finger_heart','scissor_hand'])#你要训练的手势名称，即动作标签label
# Thirty videos worth of data
no_sequences = 30#采集30次
# Videos are going to be 30 frames in length
sequence_length = 30#30帧
#关于这个for循环，会在MP_data文件下建立四个文件夹（对应四个动作），每个文件夹又包含30个子文件夹，
#每个子文件夹包含30个.npy文件，都是每次采集坐标信息时保存的
for action in actions:
    for sequence in range(no_sequences):
        try:
            os.makedirs(os.path.join(DATA_PATH, action, str(sequence)))
        except:
            pass

然后运行这部分程序开始采集数据集（完整代码请看文末）。采集前都会有提示，原作者做得很好。
mediapipe训练自己的数据集,mediapipe学习,机器学习,人工智能,计算机视觉,tensorflow,lstm
就这样慢慢采集，大概几分钟，采集完会自动结束程序。
三、使用Tensorflow搭建LSTM网络进行训练，然后保存模型
有了数据集，开始搭建网络训练。关于长短期记忆网络LSTM，请看官网的介绍

#同样，这里只是部分代码，详细请看文末
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense

model = Sequential()
#关于input_shape，原作者的网络是(30,1662),1662=33*4 + 468*2 + 21*3 + 21*3，而我不需要面网坐标，故只有258
model.add(LSTM(64, return_sequences=True, activation='relu', input_shape=(30,258)))
model.add(LSTM(128, return_sequences=True, activation='relu'))
model.add(LSTM(64, return_sequences=False, activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(actions.shape[0], activation='softmax'))
model.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=['categorical_accuracy'])
model.fit(X_train, y_train, epochs=2000, callbacks=[tb_callback])
model.summary()
model.save('action.h5')#要保存的模型名称，保存在当前目录

tensorflow的使用还是比较简单的，如果看不懂，请看TensorFlow中文官网。训练结果如图。
mediapipe训练自己的数据集,mediapipe学习,机器学习,人工智能,计算机视觉,tensorflow,lstm
虽有2000个epochs，但即使是CPU下训练速度也很快。最后在同一目录下得到了我们的权重文件action.h5，接下来就可以实际使用训练好的模型了。
四、使用训练好的模型进行实际检测
看效果图吧，当识别到对应手势，相应标签的框框颜色条会变长，这代表分类到这一手势的概率。同时运行端也会输出此刻检测到手势类别。这部分代码与上文的代码大体类似，请看文末吧。总的来说，手势基本上都能识别正确，响应速度也很快。
mediapipe训练自己的数据集,mediapipe学习,机器学习,人工智能,计算机视觉,tensorflow,lstm
最后我将该模型部署到了树莓派上，虽然运行起来有点慢，但还是很成功的。部署的话，就是注意相关库都要安装，然后代码和权重文件拖过去运行就好了，没什么难点。

五、总结
借助该up主的代码，可以简便的训练自己的手势识别模型，准确率也高。不过要注意的是，当使用训练好的模型进行实际检测时，所做动作务必和采集数据集时的动作保持一致。这是因为，代码中使用的mediapipe坐标会随你离摄像头的距离变化而变化。所以同样的手势动作，只要你离摄像头的距离或角度变了，识别准确率就会大大下降，这是我多次实践得出的结论。使用自己的模型时，所做动作务必和采集数据集时的动作保持一致！

六、所有代码
如果你想复现我的模型，你不需要改动任何代码；如果想扩大数据集，请修改no_sequences 和sequence_length；如果想训练别的动作或增加动作数目，请修改actions列表和colors列表（增加或减少动作数目就要修改）；想训练面网坐标，增加表情识别，请取消相应注释。如果有其他不懂的，可以在评论区问我。
首先是采集数据集的代码

import cv2
import numpy as np
import os
import mediapipe as mp

mp_holistic = mp.solutions.holistic # Holistic model
mp_drawing = mp.solutions.drawing_utils # Drawing utilities

def mediapipe_detection(image, model):
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # COLOR CONVERSION BGR 2 RGB
    image.flags.writeable = False                  # Image is no longer writeable
    results = model.process(image)                 # Make prediction
    image.flags.writeable = True                   # Image is now writeable
    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) # COLOR COVERSION RGB 2 BGR
    return image, results

def draw_styled_landmarks(image, results):
    """
    要训练脸部坐标就取消注释
    # Draw face connections
    mp_drawing.draw_landmarks(image, results.face_landmarks, mp_holistic.FACEMESH_CONTOURS,
                             mp_drawing.DrawingSpec(color=(80,110,10), thickness=1, circle_radius=1),
                             mp_drawing.DrawingSpec(color=(80,256,121), thickness=1, circle_radius=1)
                             )
    """
    # Draw pose connections
    mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS,
                             mp_drawing.DrawingSpec(color=(80,22,10), thickness=2, circle_radius=4),
                             mp_drawing.DrawingSpec(color=(80,44,121), thickness=2, circle_radius=2)
                             )
    # Draw left hand connections
    mp_drawing.draw_landmarks(image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS,
                             mp_drawing.DrawingSpec(color=(121,22,76), thickness=2, circle_radius=4),
                             mp_drawing.DrawingSpec(color=(121,44,250), thickness=2, circle_radius=2)
                             )
    # Draw right hand connections
    mp_drawing.draw_landmarks(image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS,
                             mp_drawing.DrawingSpec(color=(245,117,66), thickness=2, circle_radius=4),
                             mp_drawing.DrawingSpec(color=(245,66,230), thickness=2, circle_radius=2)
                             )

def extract_keypoints(results):
    pose = np.array([[res.x, res.y, res.z, res.visibility] for res in results.pose_landmarks.landmark]).flatten() if results.pose_landmarks else np.zeros(33*4)
    #face = np.array([[res.x, res.y, res.z] for res in results.face_landmarks.landmark]).flatten() if results.face_landmarks else np.zeros(468*3)
    lh = np.array([[res.x, res.y, res.z] for res in results.left_hand_landmarks.landmark]).flatten() if results.left_hand_landmarks else np.zeros(21*3)
    rh = np.array([[res.x, res.y, res.z] for res in results.right_hand_landmarks.landmark]).flatten() if results.right_hand_landmarks else np.zeros(21*3)
    return np.concatenate([pose, lh, rh])

# Path for exported data, numpy arrays
DATA_PATH = os.path.join('MP_Data')

# Actions that we try to detect
actions = np.array(['666', 'thumbs_up', 'finger_heart','scissor_hand'])
# Thirty videos worth of data
no_sequences = 30
# Videos are going to be 30 frames in length
sequence_length = 30
for action in actions:
    for sequence in range(no_sequences):
        try:
            os.makedirs(os.path.join(DATA_PATH, action, str(sequence)))
        except:
            pass

cap = cv2.VideoCapture(0)
# Set mediapipe model
with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:
    # NEW LOOP
    # Loop through actions
    for action in actions:
        # Loop through sequences aka videos
        for sequence in range(no_sequences):
            # Loop through video length aka sequence length
            for frame_num in range(sequence_length):
                # Read feed
                ret, frame = cap.read()
                # Make detections
                image, results = mediapipe_detection(frame, holistic)
                #                 print(results)
                # Draw landmarks
                draw_styled_landmarks(image, results)
                # NEW Apply wait logic
                if frame_num == 0:
                    cv2.putText(image, 'STARTING COLLECTION', (120, 200),
                                cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 4, cv2.LINE_AA)
                    cv2.putText(image, 'Collecting frames for {} Video Number {}'.format(action, sequence), (15, 12),
                                cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1, cv2.LINE_AA)
                    # Show to screen
                    cv2.imshow('OpenCV Feed', image)
                    cv2.waitKey(2000)
                else:
                    cv2.putText(image, 'Collecting frames for {} Video Number {}'.format(action, sequence), (15, 12),
                                cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1, cv2.LINE_AA)
                    # Show to screen
                    cv2.imshow('OpenCV Feed', image)
                # NEW Export keypoints
                keypoints = extract_keypoints(results)
                npy_path = os.path.join(DATA_PATH, action, str(sequence), str(frame_num))
                np.save(npy_path, keypoints)
                # Break gracefully
                if cv2.waitKey(10) & 0xFF == ord('q'):
                    break
cap.release()
cv2.destroyAllWindows()

使用TensorFlow搭建LSTM网络进行训练

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.callbacks import TensorBoard
import numpy as np
import os
from sklearn.model_selection import train_test_split
from tensorflow.keras.utils import to_categorical

log_dir = os.path.join('Logs')
tb_callback = TensorBoard(log_dir=log_dir)

no_sequences = 30
# Videos are going to be 30 frames in length
sequence_length = 30
DATA_PATH = os.path.join('MP_Data')
actions = np.array(['666', 'thumbs_up', 'finger_heart','scissor_hand'])

label_map = {label:num for num, label in enumerate(actions)}
sequences, labels = [], []
for action in actions:
    for sequence in range(no_sequences):
        window = []
        for frame_num in range(sequence_length):
            res = np.load(os.path.join(DATA_PATH, action, str(sequence), "{}.npy".format(frame_num)))
            window.append(res)
        sequences.append(window)
        labels.append(label_map[action])

X = np.array(sequences)
y = to_categorical(labels).astype(int)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.05)

model = Sequential()
model.add(LSTM(64, return_sequences=True, activation='relu', input_shape=(30,258)))
model.add(LSTM(128, return_sequences=True, activation='relu'))
model.add(LSTM(64, return_sequences=False, activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(actions.shape[0], activation='softmax'))
model.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=['categorical_accuracy'])
model.fit(X_train, y_train, epochs=2000, callbacks=[tb_callback])
model.summary()
model.save('action.h5')

使用训练好的模型进行实际检测

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense
import cv2
import numpy as np
import mediapipe as mp

mp_holistic = mp.solutions.holistic # Holistic model
mp_drawing = mp.solutions.drawing_utils # Drawing utilities

sequence = []
sentence = []
threshold = 0.8
actions = np.array(['666', 'thumbs_up', 'finger_heart','scissor_hand'])

def mediapipe_detection(image, model):
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # COLOR CONVERSION BGR 2 RGB
    image.flags.writeable = False                  # Image is no longer writeable
    results = model.process(image)                 # Make prediction
    image.flags.writeable = True                   # Image is now writeable
    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) # COLOR COVERSION RGB 2 BGR
    return image, results

def draw_styled_landmarks(image, results):
    # Draw face connections
    """
    mp_drawing.draw_landmarks(image, results.face_landmarks, mp_holistic.FACEMESH_CONTOURS,
                             mp_drawing.DrawingSpec(color=(80,110,10), thickness=1, circle_radius=1),
                             mp_drawing.DrawingSpec(color=(80,256,121), thickness=1, circle_radius=1)
                             )
    """
    # Draw pose connections
    mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS,
                             mp_drawing.DrawingSpec(color=(80,22,10), thickness=2, circle_radius=4),
                             mp_drawing.DrawingSpec(color=(80,44,121), thickness=2, circle_radius=2)
                             )
    # Draw left hand connections
    mp_drawing.draw_landmarks(image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS,
                             mp_drawing.DrawingSpec(color=(121,22,76), thickness=2, circle_radius=4),
                             mp_drawing.DrawingSpec(color=(121,44,250), thickness=2, circle_radius=2)
                             )
    # Draw right hand connections
    mp_drawing.draw_landmarks(image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS,
                             mp_drawing.DrawingSpec(color=(245,117,66), thickness=2, circle_radius=4),
                             mp_drawing.DrawingSpec(color=(245,66,230), thickness=2, circle_radius=2)
                             )

def extract_keypoints(results):
    pose = np.array([[res.x, res.y, res.z, res.visibility] for res in results.pose_landmarks.landmark]).flatten() if results.pose_landmarks else np.zeros(33*4)
    #face = np.array([[res.x, res.y, res.z] for res in results.face_landmarks.landmark]).flatten() if results.face_landmarks else np.zeros(468*3)
    lh = np.array([[res.x, res.y, res.z] for res in results.left_hand_landmarks.landmark]).flatten() if results.left_hand_landmarks else np.zeros(21*3)
    rh = np.array([[res.x, res.y, res.z] for res in results.right_hand_landmarks.landmark]).flatten() if results.right_hand_landmarks else np.zeros(21*3)
    return np.concatenate([pose, lh, rh])

model = Sequential()
model.add(LSTM(64, return_sequences=True, activation='relu', input_shape=(30,258)))
model.add(LSTM(128, return_sequences=True, activation='relu'))
model.add(LSTM(64, return_sequences=False, activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(actions.shape[0], activation='softmax'))
model.load_weights('action.h5')

colors = [(245, 117, 16), (117, 245, 16), (16, 117, 245),(16, 117, 245)]#四个动作的框框，要增加动作数目，就多加RGB元组

def prob_viz(res, actions, input_frame, colors):
    output_frame = input_frame.copy()
    for num, prob in enumerate(res):
        cv2.rectangle(output_frame, (0, 60 + num * 40), (int(prob * 100), 90 + num * 40), colors[num], -1)
        cv2.putText(output_frame, actions[num], (0, 85 + num * 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2,
                    cv2.LINE_AA)
    return output_frame

cap = cv2.VideoCapture(0)
# Set mediapipe model
with mp_holistic.Holistic(min_detection_confidence=0.5, min_tracking_confidence=0.5) as holistic:
    while cap.isOpened():
        # Read feed
        ret, frame = cap.read()
        # Make detections
        image, results = mediapipe_detection(frame, holistic)
        print(results)
        # Draw landmarks
        draw_styled_landmarks(image, results)
        # 2. Prediction logic
        keypoints = extract_keypoints(results)
        sequence.append(keypoints)
        sequence = sequence[-30:]
        if len(sequence) == 30:
            res = model.predict(np.expand_dims(sequence, axis=0))[0]
            print(actions[np.argmax(res)])
            # 3. Viz logic
            if res[np.argmax(res)] > threshold:
                if len(sentence) > 0:
                    if actions[np.argmax(res)] != sentence[-1]:
                        sentence.append(actions[np.argmax(res)])
                else:
                    sentence.append(actions[np.argmax(res)])
            if len(sentence) > 5:
                sentence = sentence[-5:]
            # Viz probabilities
            image = prob_viz(res, actions, image, colors)
        cv2.rectangle(image, (0, 0), (640, 40), (245, 117, 16), -1)
        cv2.putText(image, ' '.join(sentence), (3, 30),
                    cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)
        # Show to screen
        cv2.imshow('OpenCV Feed', image)
        # Break gracefully
        if cv2.waitKey(10) & 0xFF == ord('q'):
            break
cap.release()
cv2.destroyAllWindows()