使用Python实现深度学习模型：人脸识别与人脸表情分析

pip install tensorflow opencv-python dlib matplotlib

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

# 下载并解压人脸数据集
url = "https://example.com/face_dataset.zip"
data_dir = tf.keras.utils.get_file('face_dataset', origin=url, extract=True)

# 定义图像加载和预处理函数
def load_image(path):
    image = cv2.imread(path)
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    image = cv2.resize(image, (128, 128))
    return image

def preprocess_image(image):
    image = image / 255.0
    return image

# 示例：加载和预处理图像
image_path = os.path.join(data_dir, 'face_dataset/001.jpg')
image = load_image(image_path)
processed_image = preprocess_image(image)
print(f"Original image shape: {image.shape}")
print(f"Processed image shape: {processed_image.shape}")

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense

# 构建人脸识别模型
model = Sequential([
    Conv2D(32, (3, 3), activation='relu', input_shape=(128, 128, 3)),
    MaxPooling2D((2, 2)),
    Conv2D(64, (3, 3), activation='relu'),
    MaxPooling2D((2, 2)),
    Conv2D(128, (3, 3), activation='relu'),
    MaxPooling2D((2, 2)),
    Flatten(),
    Dense(128, activation='relu'),
    Dense(10, activation='softmax')  # 假设有10个人脸类别
])

# 编译模型
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

# 查看模型结构
model.summary()

from tensorflow.keras.utils import Sequence

class ImageDataGenerator(Sequence):
    def __init__(self, image_paths, labels, batch_size=32):
        self.image_paths = image_paths
        self.labels = labels
        self.batch_size = batch_size

    def __len__(self):
        return len(self.image_paths) // self.batch_size

    def __getitem__(self, idx):
        batch_x = self.image_paths[idx * self.batch_size:(idx + 1) * self.batch_size]
        batch_y = self.labels[idx * self.batch_size:(idx + 1) * self.batch_size]
        images = [preprocess_image(load_image(path)) for path in batch_x]
        return np.array(images), np.array(batch_y)

# 示例：创建数据生成器
image_paths = [os.path.join(data_dir, f'face_dataset/{i:03d}.jpg') for i in range(1, 101)]
labels = [i // 10 for i in range(100)]  # 假设每10张图像属于一个类别
train_generator = ImageDataGenerator(image_paths, labels)

# 训练模型
model.fit(train_generator, epochs=10)

# 构建人脸表情分析模型
expression_model = Sequential([
    Conv2D(32, (3, 3), activation='relu', input_shape=(128, 128, 3)),
    MaxPooling2D((2, 2)),
    Conv2D(64, (3, 3), activation='relu'),
    MaxPooling2D((2, 2)),
    Conv2D(128, (3, 3), activation='relu'),
    MaxPooling2D((2, 2)),
    Flatten(),
    Dense(128, activation='relu'),
    Dense(7, activation='softmax')  # 假设有7种表情类别
])

# 编译模型
expression_model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

# 查看模型结构
expression_model.summary()

# 示例：创建表情数据生成器
expression_image_paths = [os.path.join(data_dir, f'expression_dataset/{i:03d}.jpg') for i in range(1, 701)]
expression_labels = [i // 100 for i in range(700)]  # 假设每100张图像属于一个表情类别
expression_train_generator = ImageDataGenerator(expression_image_paths, expression_labels)

# 训练表情分析模型
expression_model.fit(expression_train_generator, epochs=10)
AI 生成的代码。仔细查看和使用。 有关常见问题解答的详细信息.
步骤七：评估模型
我们可以使用测试数据评估模型的性能。以下是评估模型的代码：

Python

# 示例：评估人脸识别模型
test_image_path = os.path.join(data_dir, 'face_dataset/101.jpg')
test_image = preprocess_image(load_image(test_image_path))
test_label = 10  # 假设测试图像的标签为10

# 预测人脸类别
predicted_label = np.argmax(model.predict(np.expand_dims(test_image, axis=0)))
print(f"Predicted label: {predicted_label}, True label: {test_label}")

# 示例：评估表情分析模型
test_expression_image_path = os.path.join(data_dir, 'expression_dataset/701.jpg')
test_expression_image = preprocess_image(load_image(test_expression_image_path))
test_expression_label = 7  # 假设测试图像的表情标签为7

# 预测表情类别
predicted_expression_label = np.argmax(expression_model.predict(np.expand_dims(test_expression_image, axis=0)))
print(f"Predicted expression label: {predicted_expression_label}, True expression label: {test_expression_label}")