图像去雨Python代码

以下是一个基于深度学习的图像去雨 Python 代码示例：

# 导入相应模块
import cv2
import numpy as np
import tensorflow as tf

# 定义模型
class DerainModel(tf.keras.Model):
    def __init__(self):
        super(DerainModel, self).__init__()
        self.conv1 = tf.keras.layers.Conv2D(filters=64, kernel_size=(3, 3), padding='same', activation='relu')
        self.conv2 = tf.keras.layers.Conv2D(filters=64, kernel_size=(3, 3), padding='same', activation='relu')
        self.conv3 = tf.keras.layers.Conv2D(filters=64, kernel_size=(3, 3), padding='same', activation='relu')
        self.conv4 = tf.keras.layers.Conv2D(filters=64, kernel_size=(3, 3), padding='same', activation='relu')
        self.conv5 = tf.keras.layers.Conv2D(filters=3, kernel_size=(3, 3), padding='same', activation='relu')
        self.pooling = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=2)

    def call(self, inputs):
        c1 = self.conv1(inputs)
        c2 = self.conv2(c1)
        p1 = self.pooling(c2)
        c3 = self.conv3(p1)
        c4 = self.conv4(c3)
        p2 = self.pooling(c4)
        out = self.conv5(p2)
        return out

# 加载数据
def load_data():
    # 加载训练集
    train_data = []
    train_labels = []
    for i in range(1, 201):
        img = cv2.imread(f'./rainy_image/{i}.jpg')
        label = cv2.imread(f'./gt_image/{i}.jpg')
        train_data.append(img)
        train_labels.append(label)
    # 加载测试集
    test_data = []
    test_labels = []
    for i in range(201, 296):
        img = cv2.imread(f'./rainy_image/{i}.jpg')
        label = cv2.imread(f'./gt_image/{i}.jpg')
        test_data.append(img)
        test_labels.append(label)
    return np.array(train_data), np.array(train_labels), np.array(test_data), np.array(test_labels)

# 数据预处理
def pre_process(data, labels):
    data = data / 255.0
    labels = labels / 255.0
    return data, labels

# 训练模型
def train():
    # 加载数据
    train_data, train_labels, test_data, test_labels = load_data()
    train_data, train_labels = pre_process(train_data, train_labels)
    test_data, test_labels = pre_process(test_data, test_labels)
    # 定义模型
    model = DerainModel()
    optimizer = tf.keras.optimizers.Adam()
    model.compile(optimizer=optimizer, loss='mse')
    # 训练模型
    model.fit(train_data, train_labels, validation_data=(test_data, test_labels), batch_size=16, epochs=20)
    # 保存模型
    model.save('./derain_model.h5')

# 图像去雨
def derain(img_path):
    # 加载模型
    model = tf.keras.models.load_model('./derain_model.h5')
    # 读取图片
    img = cv2.imread(img_path)
    # 图像预处理
    x = np.expand_dims(img, axis=0)
    x = pre_process(x, x)
    # 图像去雨
    y = model.predict(x)
    # 显示结果
    y = np.clip(y, 0, 1) * 255
    y = y.astype(np.uint8)
    cv2.imshow('derain', y[0])
    cv2.waitKey()

# 训练模型
train()

# 图像去雨
derain('./test_image/1.jpg')

注意：这只是一个示例，实际的去雨效果可能会受限于模型质量、数据集质量等多种因素。