怎么用python代码实现图片去摩尔纹

import mathimport paddleimport paddle.nn as nnimport paddle.nn.functional as F# import pywtfrom paddle.nn import Linear, Dropout, ReLUfrom paddle.nn import Conv2D, MaxPool2Dclass mcnn(nn.Layer):    def __init__(self, num_classes=1000):        super(mcnn, self).__init__()        self.num_classes = num_classes        self._conv1_LL = Conv2D(3,32,7,stride=2,padding=1,)              # self.bn1_LL = nn.BatchNorm2D(128)        self._conv1_LH = Conv2D(3,32,7,stride=2,padding=1,)          # self.bn1_LH = nn.BatchNorm2D(256)        self._conv1_HL = Conv2D(3,32,7,stride=2,padding=1,)        # self.bn1_HL = nn.BatchNorm2D(512)        self._conv1_HH = Conv2D(3,32,7,stride=2,padding=1,)        # self.bn1_HH = nn.BatchNorm2D(256)        self.pool_1_LL = nn.MaxPool2D(kernel_size=2,stride=2, padding=0)        self.pool_1_LH = nn.MaxPool2D(kernel_size=2,stride=2, padding=0)        self.pool_1_HL = nn.MaxPool2D(kernel_size=2,stride=2, padding=0)        self.pool_1_HH = nn.MaxPool2D(kernel_size=2,stride=2, padding=0)        self._conv2 = Conv2D(32,16,3,stride=2,padding=1,)        self.pool_2 = nn.MaxPool2D(kernel_size=2,stride=2, padding=0)        self.dropout2 = Dropout(p=0.5)        self._conv3 = Conv2D(16,32,3,stride=2,padding=1,)        self.pool_3 = nn.MaxPool2D(kernel_size=2,stride=2, padding=0)        self._conv4 = Conv2D(32,32,3,stride=2,padding=1,)        self.pool_4 = nn.MaxPool2D(kernel_size=2,stride=2, padding=0)        self.dropout4 = Dropout(p=0.5)        # self.bn1_HH = nn.BatchNorm1D(256)        self._fc1 = Linear(in_features=64,out_features=num_classes)        self.dropout5 = Dropout(p=0.5)        self._fc2 = Linear(in_features=2,out_features=num_classes)    def forward(self, inputs1, inputs2, inputs3, inputs4):        x1_LL = self._conv1_LL(inputs1)        x1_LL = F.relu(x1_LL)        x1_LH = self._conv1_LH(inputs2)        x1_LH = F.relu(x1_LH)        x1_HL = self._conv1_HL(inputs3)        x1_HL = F.relu(x1_HL)        x1_HH = self._conv1_HH(inputs4)        x1_HH = F.relu(x1_HH)        pool_x1_LL = self.pool_1_LL(x1_LL)        pool_x1_LH = self.pool_1_LH(x1_LH)        pool_x1_HL = self.pool_1_HL(x1_HL)        pool_x1_HH = self.pool_1_HH(x1_HH)        temp = paddle.maximum(pool_x1_LH, pool_x1_HL)        avg_LH_HL_HH = paddle.maximum(temp, pool_x1_HH)        inp_merged = paddle.multiply(pool_x1_LL, avg_LH_HL_HH)        x2 = self._conv2(inp_merged)        x2 = F.relu(x2)        x2 = self.pool_2(x2)        x2 = self.dropout2(x2)        x3 = self._conv3(x2)        x3 = F.relu(x3)        x3 = self.pool_3(x3)        x4 = self._conv4(x3)        x4 = F.relu(x4)        x4 = self.pool_4(x4)        x4 = self.dropout4(x4)        x4 = paddle.flatten(x4, start_axis=1, stop_axis=-1)        x5 = self._fc1(x4)        x5 = self.dropout5(x5)        out = self._fc2(x5)        return outmodel_res = mcnn(num_classes=2)paddle.summary(model_res,[(1,3,512,384),(1,3,512,384),(1,3,512,384),(1,3,512,384)])

--------------------------------------------------------------------------- Layer (type)       Input Shape          Output Shape         Param #    ===========================================================================   Conv2D-1      [[1, 3, 512, 384]]   [1, 32, 254, 190]        4,736        Conv2D-2      [[1, 3, 512, 384]]   [1, 32, 254, 190]        4,736        Conv2D-3      [[1, 3, 512, 384]]   [1, 32, 254, 190]        4,736        Conv2D-4      [[1, 3, 512, 384]]   [1, 32, 254, 190]        4,736       MaxPool2D-1   [[1, 32, 254, 190]]    [1, 32, 127, 95]          0         MaxPool2D-2   [[1, 32, 254, 190]]    [1, 32, 127, 95]          0         MaxPool2D-3   [[1, 32, 254, 190]]    [1, 32, 127, 95]          0         MaxPool2D-4   [[1, 32, 254, 190]]    [1, 32, 127, 95]          0          Conv2D-5      [[1, 32, 127, 95]]    [1, 16, 64, 48]         4,624       MaxPool2D-5    [[1, 16, 64, 48]]     [1, 16, 32, 24]           0          Dropout-1     [[1, 16, 32, 24]]     [1, 16, 32, 24]           0          Conv2D-6      [[1, 16, 32, 24]]     [1, 32, 16, 12]         4,640       MaxPool2D-6    [[1, 32, 16, 12]]      [1, 32, 8, 6]            0          Conv2D-7       [[1, 32, 8, 6]]       [1, 32, 4, 3]          9,248       MaxPool2D-7     [[1, 32, 4, 3]]       [1, 32, 2, 1]            0          Dropout-2      [[1, 32, 2, 1]]       [1, 32, 2, 1]            0          Linear-1          [[1, 64]]              [1, 2]              130         Dropout-3          [[1, 2]]              [1, 2]               0          Linear-2           [[1, 2]]              [1, 2]               6       ===========================================================================Total params: 37,592Trainable params: 37,592Non-trainable params: 0---------------------------------------------------------------------------Input size (MB): 9.00Forward/backward pass size (MB): 59.54Params size (MB): 0.14Estimated Total Size (MB): 68.68---------------------------------------------------------------------------{'total_params': 37592, 'trainable_params': 37592}

!pip install PyWavelets

import numpy as npimport pywtdef splitFreqBands(img, levRows, levCols):    halfRow = int(levRows/2)    halfCol = int(levCols/2)    LL = img[0:halfRow, 0:halfCol]    LH = img[0:halfRow, halfCol:levCols]    HL = img[halfRow:levRows, 0:halfCol]    HH = img[halfRow:levRows, halfCol:levCols]    return LL, LH, HL, HHdef haarDWT1D(data, length):    avg0 = 0.5;    avg1 = 0.5;    dif0 = 0.5;    dif1 = -0.5;    temp = np.empty_like(data)    # temp = temp.astype(float)    temp = temp.astype(np.uint8)    h = int(length/2)    for i in range(h):        k = i*2        temp[i] = data[k] * avg0 + data[k + 1] * avg1;        temp[i + h] = data[k] * dif0 + data[k + 1] * dif1;    data[:] = temp# computes the homography coefficients for PIL.Image.transform using point correspondencesdef fwdHaarDWT2D(img):    img = np.array(img)    levRows = img.shape[0];    levCols = img.shape[1];    # img = img.astype(float)    img = img.astype(np.uint8)    for i in range(levRows):        row = img[i,:]        haarDWT1D(row, levCols)        img[i,:] = row    for j in range(levCols):        col = img[:,j]        haarDWT1D(col, levRows)        img[:,j] = col    return splitFreqBands(img, levRows, levCols)

!cd "data/data188843/" && unzip -q 'total_images.zip'

import os recapture_keys = [ 'ValidationMoire']original_keys = ['ValidationClear']def get_image_label_from_folder_name(folder_name):    """    :param folder_name:    :return:    """    for key in original_keys:        if key in folder_name:            return 'original'    for key in recapture_keys:        if key in folder_name:            return 'recapture'    return 'unclear'label_name2label_id = {    'original': 0,    'recapture': 1,}src_image_dir = "data/data188843/total_images"dst_file = "data/data188843/total_images/train.txt"image_folder = [file for file in os.listdir(src_image_dir)]print(image_folder)image_anno_list = []for folder in image_folder:    label_name = get_image_label_from_folder_name(folder)    # label_id = label_name2label_id.get(label_name, 0)    label_id = label_name2label_id[label_name]    folder_path = os.path.join(src_image_dir, folder)    image_file_list = [file for file in os.listdir(folder_path) if                        file.endswith('.jpg') or file.endswith('.jpeg') or                        file.endswith('.JPG') or file.endswith('.JPEG') or file.endswith('.png')]    for image_file in image_file_list:        # if need_root_dir:        #     image_path = os.path.join(folder_path, image_file)        # else:        image_path = image_file        image_anno_list.append(folder +"/"+image_path +"	"+ str(label_id) + '
')dst_path = os.path.dirname(src_image_dir)if not os.path.exists(dst_path):    os.makedirs(dst_path)with open(dst_file, 'w') as fd:    fd.writelines(image_anno_list)

import paddleimport numpy as npimport pandas as pdimport PIL.Image as Imagefrom paddle.vision import transforms# from haar2D import fwdHaarDWT2Dpaddle.disable_static()# 定义数据预处理data_transforms = transforms.Compose([    transforms.Resize(size=(448,448)),    transforms.ToTensor(), # transpose操作 + (img / 255)    # transforms.Normalize(      # 减均值 除标准差    #     mean=[0.31169346, 0.25506335, 0.12432463],            #     std=[0.34042713, 0.29819837, 0.1375536])    #计算过程：output[channel] = (input[channel] - mean[channel]) / std[channel]])# 构建Datasetclass MyDataset(paddle.io.Dataset):    """    步骤一：继承paddle.io.Dataset类    """    def __init__(self, train_img_list, val_img_list, train_label_list, val_label_list, mode='train', ):        """        步骤二：实现构造函数，定义数据读取方式，划分训练和测试数据集        """        super(MyDataset, self).__init__()        self.img = []        self.label = []        # 借助pandas读csv的库        self.train_images = train_img_list        self.test_images = val_img_list        self.train_label = train_label_list        self.test_label = val_label_list        if mode == 'train':            # 读train_images的数据            for img,la in zip(self.train_images, self.train_label):                self.img.append('/home/aistudio/data/data188843/total_images/'+img)                self.label.append(paddle.to_tensor(int(la), dtype='int64'))        else:            # 读test_images的数据            for img,la in zip(self.test_images, self.test_label):                self.img.append('/home/aistudio/data/data188843/total_images/'+img)                self.label.append(paddle.to_tensor(int(la), dtype='int64'))    def load_img(self, image_path):        # 实际使用时使用Pillow相关库进行图片读取即可，这里我们对数据先做个模拟        image = Image.open(image_path).convert('RGB')        # image = data_transforms(image)        return image    def __getitem__(self, index):        """        步骤三：实现__getitem__方法，定义指定index时如何获取数据，并返回单条数据（训练数据，对应的标签）        """        image = self.load_img(self.img[index])        LL, LH, HL, HH = fwdHaarDWT2D(image)        label = self.label[index]        # print(LL.shape)        # print(LH.shape)        # print(HL.shape)        # print(HH.shape)        LL = data_transforms(LL)        LH = data_transforms(LH)        HL = data_transforms(HL)        HH = data_transforms(HH)        print(type(LL))        print(LL.dtype)        return LL, LH, HL, HH, np.array(label, dtype='int64')    def __len__(self):        """        步骤四：实现__len__方法，返回数据集总数目        """        return len(self.img)image_file_txt = '/home/aistudio/data/data188843/total_images/train.txt'with open(image_file_txt) as fd:    lines = fd.readlines()train_img_list = list()train_label_list = list()for line in lines:    split_list = line.strip().split()    image_name, label_id = split_list    train_img_list.append(image_name)    train_label_list.append(label_id)# print(train_img_list)# print(train_label_list)# 测试定义的数据集train_dataset = MyDataset(mode='train',train_label_list=train_label_list,  train_img_list=train_img_list, val_img_list=train_img_list, val_label_list=train_label_list)# test_dataset = MyDataset(mode='test')# 构建训练集数据加载器train_loader = paddle.io.DataLoader(train_dataset, batch_size=2, shuffle=True)# 构建测试集数据加载器valid_loader = paddle.io.DataLoader(train_dataset, batch_size=2, shuffle=True)print('=============train dataset=============')for LL, LH, HL, HH, label in train_dataset:    print('label: {}'.format(label))    break

model2 = paddle.Model(model_res)model2.prepare(optimizer=paddle.optimizer.Adam(parameters=model2.parameters()),              loss=nn.CrossEntropyLoss(),              metrics=paddle.metric.Accuracy())model2.fit(train_loader,        valid_loader,        epochs=5,        verbose=1,        )