设计高准确度的人像自动分割方法的代码

以下是一个基于U-Net网络的人像自动分割方法的代码示例，供参考：

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.layers import *
from tensorflow.keras.models import Model

# 数据预处理
def preprocess(img, mask):
    # 对图像进行裁剪和缩放操作
    img = tf.image.resize_with_crop_or_pad(img, 256, 256)
    mask = tf.image.resize_with_crop_or_pad(mask, 256, 256)
    # 将图像和标记图转换为tensor类型
    img = tf.cast(img, tf.float32) / 255.0
    mask = tf.cast(mask, tf.float32) / 255.0
    return img, mask

# 数据读取
def load_data(img_path, mask_path):
    img = tf.io.read_file(img_path)
    img = tf.image.decode_jpeg(img, channels=3)
    mask = tf.io.read_file(mask_path)
    mask = tf.image.decode_png(mask, channels=1)
    img, mask = preprocess(img, mask)
    return img, mask

# 构建U-Net网络结构
def build_unet(input_shape):
    inputs = Input(input_shape)
    conv1 = Conv2D(64, 3, activation='relu', padding='same')(inputs)
    conv1 = Conv2D(64, 3, activation='relu', padding='same')(conv1)
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)

    conv2 = Conv2D(128, 3, activation='relu', padding='same')(pool1)
    conv2 = Conv2D(128, 3, activation='relu', padding='same')(conv2)
    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)

    conv3 = Conv2D(256, 3, activation='relu', padding='same')(pool2)
    conv3 = Conv2D(256, 3, activation='relu', padding='same')(conv3)
    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)

    conv4 = Conv2D(512, 3, activation='relu', padding='same')(pool3)
    conv4 = Conv2D(512, 3, activation='relu', padding='same')(conv4)
    drop4 = Dropout(0.5)(conv4)
    pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)

    conv5 = Conv2D(1024, 3, activation='relu', padding='same')(pool4)
    conv5 = Conv2D(1024, 3, activation='relu', padding='same')(conv5)
    drop5 = Dropout(0.5)(conv5)

    up6 = Conv2D(512, 2, activation='relu', padding='same')(UpSampling2D(size=(2, 2))(drop5))
    merge6 = concatenate([drop4, up6], axis=3)
    conv6 = Conv2D(512, 3, activation='relu', padding='same')(merge6)
    conv6 = Conv2D(512, 3, activation='relu', padding='same')(conv6)

    up7 = Conv2D(256, 2, activation='relu', padding='same')(UpSampling2D(size=(2, 2))(conv6))
    merge7 = concatenate([conv3, up7], axis=3)
    conv7 = Conv2D(256, 3, activation='relu', padding='same')(merge7)
    conv7 = Conv2D(256, 3, activation='relu', padding='same')(conv7)

    up8 = Conv2D(128, 2, activation='relu', padding='same')(UpSampling2D(size=(2, 2))(conv7))
    merge8 = concatenate([conv2, up8], axis=3)
    conv8 = Conv2D(128, 3, activation='relu', padding='same')(merge8)
    conv8 = Conv2D(128, 3, activation='relu', padding='same')(conv8)

    up9 = Conv2D(64, 2, activation='relu', padding='same')(UpSampling2D(size=(2, 2))(conv8))
    merge9 = concatenate([conv1, up9], axis=3)
    conv9 = Conv2D(64, 3, activation='relu', padding='same')(merge9)
    conv9 = Conv2D(64, 3, activation='relu', padding='same')(conv9)

    outputs = Conv2D(1, 1, activation='sigmoid')(conv9)

    model = Model(inputs, outputs)
    return model

# 计算Dice系数
def dice_coef(y_true, y_pred, smooth=1.0):
    y_true_f = tf.reshape(y_true, [-1])
    y_pred_f = tf.reshape(y_pred, [-1])
    intersection = tf.reduce_sum(y_true_f * y_pred_f)
    coef = (2.0 * intersection + smooth) / (tf.reduce_sum(y_true_f) + tf.reduce_sum(y_pred_f) + smooth)
    return coef

# 计算平均Dice系数
def dice_coef_loss(y_true, y_pred):
    return 1.0 - dice_coef(y_true, y_pred)

# 加载数据集
def load_dataset(img_paths, mask_paths):
    img_list = []
    mask_list = []
    for i in range(len(img_paths)):
        img_path = img_paths[i]
        mask_path = mask_paths[i]
        img, mask = load_data(img_path, mask_path)
        img_list.append(img)
        mask_list.append(mask)
    return tf.data.Dataset.from_tensor_slices((img_list, mask_list))

# 训练模型
def train():
    # 加载训练集和验证集数据
    train_img_paths = ["./training/{}.png".format(i) for i in range(1, 1701)]
    train_mask_paths = ["./training/{}_matte.png".format(i) for i in range(1, 1701)]
    val_img_paths = ["./training/{}.png".format(i) for i in range(1701, 3401)]
    val_mask_paths = ["./training/{}_matte.png".format(i) for i in range(1701, 3401)]
    train_dataset = load_dataset(train_img_paths, train_mask_paths).batch(8)
    val_dataset = load_dataset(val_img_paths, val_mask_paths).batch(8)

    # 构建模型
    model = build_unet((256, 256, 3))
    model.compile(optimizer='adam', loss=dice_coef_loss, metrics=[dice_coef])

    # 训练模型
    history = model.fit(train_dataset, epochs=20, validation_data=val_dataset)

    # 可视化训练过程
    plt.plot(history.history['loss'], label='train loss')
    plt.plot(history.history['val_loss'], label='val loss')
    plt.legend()
    plt.show()

# 测试模型
def test():
    # 加载测试集数据
    test_img_paths = ["./testing/{}.png".format(i) for i in range(1, 301)]
    test_mask_paths = ["./testing/{}_matte.png".format(i) for i in range(1, 301)]
    test_dataset = load_dataset(test_img_paths, test_mask_paths).batch(8)

    # 加载模型
    model = tf.keras.models.load_model('model.h5', custom_objects={'dice_coef_loss': dice_coef_loss, 'dice_coef': dice_coef})

    # 测试模型
    results = model.predict(test_dataset)
    dices = []
    for i in range(len(results)):
        dice = dice_coef(test_dataset[i][1], results[i])
        dices.append(dice)
    print("Average Dice coefficient:", np.mean(dices))

    # 可视化测试结果
    for i in range(5):
        img, mask = test_dataset[i]
        result = model.predict(tf.expand_dims(img, axis=0))[0]
        plt.figure(figsize=(10, 10))
        plt.subplot(131)
        plt.imshow(img)
        plt.title("Input")
        plt.subplot(132)
        plt.imshow(mask[:, :, 0])
        plt.title("Ground truth")
        plt.subplot(133)
        plt.imshow(result[:, :, 0])
        plt.title("Prediction")
        plt.show()

# 训练和保存模型
train()
model.save('model.h5')

# 测试模型
test()

在该代码中，使用了TensorFlow 2.0版本进行开发，包含了数据预处理、U-Net网络结构的构建、Dice系数的计算、模型训练和测试等功能。需要注意的是，由于训练数据和测试数据需要分别存储在training和testing文件夹中，因此需要使用相对路径进行文件读取操作。同时，在测试过程中，需要自行拍摄一张人像照片，并进行测试。