帮我写一个fer2013人脸表情识别的代码
时间: 2024-05-12 07:16:59 浏览: 119
人脸表情识别代码
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好的,我可以给您提供一个基于深度学习的fer2013人脸表情识别的代码。请确认您已经准备好了相关的项目环境以及所需的Python库。
首先,我们需要导入所需的库:
```python
import tensorflow as tf
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
```
接着,我们需要加载FER2013数据集并进行相关的预处理。这里我们使用FER2013的csv文件,如果您的数据集是图片,则需要使用相应的图片处理方法。
```python
data = pd.read_csv('fer2013.csv')
emotions = ['Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral']
num_classes = len(emotions)
# 将像素数据转换为图像格式
def process_pixels(pixels):
image_data = np.asarray([int(num) for num in pixels.split()])
image = np.reshape(image_data, (48, 48))
return image.astype(np.uint8)
# 预处理FER2013数据集
def preprocess_data(data):
# 将像素数据转换为图像格式
data['Image'] = data['pixels'].apply(process_pixels)
# 获取图像和情感标签数据
X = np.asarray([image for image in data['Image']])
y = pd.get_dummies(data['emotion']).to_numpy()
# 将图像数据归一化
X = X / 255.0
# 将数据划分为训练集、验证集和测试集
num_samples = len(X)
train_samples = round(num_samples * 0.6)
valid_samples = round(num_samples * 0.2)
X_train = X[0:train_samples]
y_train = y[0:train_samples]
X_valid = X[train_samples:train_samples+valid_samples]
y_valid = y[train_samples:train_samples+valid_samples]
X_test = X[train_samples+valid_samples:]
y_test = y[train_samples+valid_samples:]
return (X_train, y_train), (X_valid, y_valid), (X_test, y_test)
(X_train, y_train), (X_valid, y_valid), (X_test, y_test) = preprocess_data(data)
```
接下来,我们使用CNN模型进行FER2013表情识别。
```python
model = tf.keras.models.Sequential([
tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(48, 48, 1)),
tf.keras.layers.MaxPooling2D((2, 2)),
tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
tf.keras.layers.MaxPooling2D((2, 2)),
tf.keras.layers.Conv2D(128, (3, 3), activation='relu'),
tf.keras.layers.MaxPooling2D((2, 2)),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dropout(0.5),
tf.keras.layers.Dense(num_classes, activation='softmax')
])
opt = tf.keras.optimizers.Adam(lr=0.0001)
model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=50, batch_size=64, validation_data=(X_valid, y_valid), verbose=1)
```
最后我们可以使用测试集进行模型的测试,查看模型预测效果。
```python
test_loss, test_acc = model.evaluate(X_test, y_test, verbose=0)
print('Test Accuracy:', test_acc)
predictions = model.predict(X_test)
predictions_labels = np.argmax(predictions, axis=1)
fig, axes = plt.subplots(4, 4, figsize=(8, 8))
fig.subplots_adjust(hspace=0.7, wspace=0.7)
for i, ax in enumerate(axes.flat):
ax.imshow(X_test[i].reshape(48, 48), cmap='gray')
ax.set_xticks([])
ax.set_yticks([])
ax.set_xlabel(emotions[predictions_labels[i]])
plt.show()
```
希望这份代码可以帮助您完成FER2013人脸表情识别的任务。
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