TensorFlow2.0 进行CK＋数据集的处理的代码

以下是使用TensorFlow 2.0处理CK+数据集的示例代码：

import tensorflow as tf
import numpy as np
import os
import cv2

# 设置数据集路径和标签
data_dir = 'CK+48'
labels = ['anger', 'contempt', 'disgust', 'fear', 'happy', 'sadness', 'surprise']

# 定义函数将图像转换为灰度图并调整大小
def preprocess_image(image_path):
    img = cv2.imread(image_path)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    img = cv2.resize(img, (48, 48))
    return img

# 加载图像并将其转换为NumPy数组
def load_data(data_dir):
    images = []
    labels = []
    for label in os.listdir(data_dir):
        for image in os.listdir(os.path.join(data_dir, label)):
            image_path = os.path.join(data_dir, label, image)
            img = preprocess_image(image_path)
            images.append(img)
            labels.append(label)
    return np.array(images), np.array(labels)

# 加载数据集
images, labels = load_data(data_dir)

# 将标签转换为数字编码
label_to_id = {v: k for k, v in enumerate(np.unique(labels))}
id_to_label = {v: k for k, v in label_to_id.items()}
labels = np.array([label_to_id[x] for x in labels])

# 将数据集划分为训练集和测试集
train_images, train_labels = images[:800], labels[:800]
test_images, test_labels = images[800:], labels[800:]

# 将数据集转换为TensorFlow数据集
train_dataset = tf.data.Dataset.from_tensor_slices((train_images, train_labels))
test_dataset = tf.data.Dataset.from_tensor_slices((test_images, test_labels))

# 对数据集进行预处理
def preprocess(image, label):
    image = tf.cast(image, tf.float32)
    image /= 255.0
    return image, label

train_dataset = train_dataset.map(preprocess).shuffle(800).batch(32)
test_dataset = test_dataset.map(preprocess).batch(32)