[深さ学習]手話動作学習モデルの構築(LSTM)

データセット

:npyファイルはデータを生成します.

学習LSTM(train.ipynb)

actions = [
    'a',
    'b',
    'c'
]

data = np.concatenate([
    np.load('dataset/seq_a_1650133564.npy'),
    np.load('dataset/seq_b_1650133564.npy'),
    np.load('dataset/seq_c_1650133564.npy')
], axis=0)

data.shape

x_data = data[:, :, :-1]
labels = data[:, 0, -1]

print(x_data.shape)
print(labels.shape)

from tensorflow.keras.utils import to_categorical

y_data = to_categorical(labels, num_classes=len(actions))
y_data.shape

ここでちょっと待ってください。

分類データとは?

分類符号化、一次熱符号化とは何ですか?

from sklearn.model_selection import train_test_split

x_data = x_data.astype(np.float32)
y_data = y_data.astype(np.float32)

x_train, x_val, y_train, y_val = train_test_split(x_data, y_data, test_size=0.1, random_state=2021)

print(x_train.shape, y_train.shape)
print(x_val.shape, y_val.shape)

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense

model = Sequential([
    LSTM(64, activation='relu', input_shape=x_train.shape[1:3]), # node 개수 64개
    Dense(32, activation='relu'), # node 개수 32개
    Dense(len(actions), activation='softmax')
])

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['acc']) # loss='categorical_crossentropy -> 3개의 action 중 어떤 건지 모델에게 추론하게 함
model.summary()

from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau

history = model.fit(
    x_train,
    y_train,
    validation_data=(x_val, y_val),
    epochs=200,
    callbacks=[
        ModelCheckpoint('models/model.h5', monitor='val_acc', verbose=1, save_best_only=True, mode='auto'),
        ReduceLROnPlateau(monitor='val_acc', factor=0.5, patience=50, verbose=1, mode='auto')
    ]
)

の勉強をしましたが、参考資料によると正確率は100%で、私の正確率は約67%しかありません.私は不思議に思って、もう一度行います.

で約93%であった.

ここでちょっと待ってください。

epochとは?

One Epoch is when an ENTIRE dataset is passed forward and backward through the neural network only ONCE

import matplotlib.pyplot as plt

fig, loss_ax = plt.subplots(figsize=(16, 10))
acc_ax = loss_ax.twinx()

loss_ax.plot(history.history['loss'], 'y', label='train loss')
loss_ax.plot(history.history['val_loss'], 'r', label='val loss')
loss_ax.set_xlabel('epoch')
loss_ax.set_ylabel('loss')
loss_ax.legend(loc='upper left')

acc_ax.plot(history.history['acc'], 'b', label='train acc')
acc_ax.plot(history.history['val_acc'], 'g', label='val acc')
acc_ax.set_ylabel('accuracy')
acc_ax.legend(loc='upper left')

plt.show()

Test (test.py)

import cv2
import mediapipe as mp
import numpy as np
from tensorflow.keras.models import load_model

actions = ['a', 'b', 'c']
seq_length = 30

model = load_model('models/model.h5')

# MediaPipe hands model
mp_hands = mp.solutions.hands
mp_drawing = mp.solutions.drawing_utils
hands = mp_hands.Hands(
    max_num_hands=1,
    min_detection_confidence=0.5,
    min_tracking_confidence=0.5)

cap = cv2.VideoCapture(0)

# w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
# h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
# fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
# out = cv2.VideoWriter('input.mp4', fourcc, cap.get(cv2.CAP_PROP_FPS), (w, h))
# out2 = cv2.VideoWriter('output.mp4', fourcc, cap.get(cv2.CAP_PROP_FPS), (w, h))

seq = []
action_seq = []

while cap.isOpened():
    ret, img = cap.read()
    img0 = img.copy()

    img = cv2.flip(img, 1)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    result = hands.process(img)
    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)

    if result.multi_hand_landmarks is not None:
        for res in result.multi_hand_landmarks:
            joint = np.zeros((21, 4))
            for j, lm in enumerate(res.landmark):
                joint[j] = [lm.x, lm.y, lm.z, lm.visibility]

            # Compute angles between joints
            v1 = joint[[0,1,2,3,0,5,6,7,0,9,10,11,0,13,14,15,0,17,18,19], :3] # Parent joint
            v2 = joint[[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20], :3] # Child joint
            v = v2 - v1 # [20, 3]
            # Normalize v
            v = v / np.linalg.norm(v, axis=1)[:, np.newaxis]

            # Get angle using arcos of dot product
            angle = np.arccos(np.einsum('nt,nt->n',
                v[[0,1,2,4,5,6,8,9,10,12,13,14,16,17,18],:], 
                v[[1,2,3,5,6,7,9,10,11,13,14,15,17,18,19],:])) # [15,]

            angle = np.degrees(angle) # Convert radian to degree

            d = np.concatenate([joint.flatten(), angle])

            seq.append(d)

            mp_drawing.draw_landmarks(img, res, mp_hands.HAND_CONNECTIONS)

            if len(seq) < seq_length:
                continue

            input_data = np.expand_dims(np.array(seq[-seq_length:], dtype=np.float32), axis=0)

            y_pred = model.predict(input_data).squeeze()

            i_pred = int(np.argmax(y_pred))
            conf = y_pred[i_pred]

            if conf < 0.9:
                continue

            action = actions[i_pred]
            action_seq.append(action)

            if len(action_seq) < 3:
                continue

            this_action = '?'
            if action_seq[-1] == action_seq[-2] == action_seq[-3]:
                this_action = action

            cv2.putText(img, f'{this_action.upper()}', org=(int(res.landmark[0].x * img.shape[1]), int(res.landmark[0].y * img.shape[0] + 20)), fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=1, color=(255, 255, 255), thickness=2)

    # out.write(img0)
    # out2.write(img)
    cv2.imshow('img', img)
    if cv2.waitKey(1) == ord('q'):
        break

Traceback (most recent call last):
  File "c:/Users/JH/Documents/VSCodeSonsuProjects/GESTURE_RECOGNITION/test.py", line 9, in <module>
    model = load_model('model.h5')
  File "C:\Users\JH\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\keras\saving\save.py", line 211, in load_model  
    loader_impl.parse_saved_model(filepath)
  File "C:\Users\JH\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\saved_model\loader_impl.py", line 111, in parse_saved_model
    raise IOError("SavedModel file does not exist at: %s/{%s|%s}" %
OSError: SavedModel file does not exist at: model.h5/{saved_model.pbtxt|saved_model.pb}

学習モデルから始めて、もう一度やってみることにしました。

今回の精度は約96%だった.

グラフはさっきのグラフとは全然違います.
しかし、なぜ学習が100%の精度に達しなかったのか、グラフの形がそうだったのか知りたい.

Trouble Shooting

OSError: SavedModel file does not exist at: model.h5/{saved_model.pbtxt|saved_model.pb}

import tensorflow as tf

model = tf.keras.models.load_model('model.h5')
tf.saved_model.save('model')

model = tf.keras.Model(...)

NotImplementedError: When subclassing the `Model` class, you should implement a `call` method.

def call(self, inputs, *args, **kwargs):
       return self.model(inputs)

def call(self, inputs):
        self.total.assign_add(tf.reduce_sum(inputs, axis=0))
        return self.total

  import tensorflow as tf
  class MyModel(tf.keras.Model):
    def __init__(self):
      super().__init__()
      self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu)
      self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax)
    def call(self, inputs):
      x = self.dense1(inputs)
      return self.dense2(x)
  model = MyModel()

リファレンス