每日一博 | 使用卷积神经网络实现图片去摩尔纹

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摘要：本项目主要介绍了如何使用卷积神经网络去检测翻拍图片，主要为摩尔纹图片；其主要创新点在于网络结构上，将图片的高低频信息分开处理。

本文分享自华为云社区《图片去摩尔纹简述与代码实现》，作者：李长安。

1前言

当感光元件像素的空间频率与影像中条纹的空间频率接近时，可能产生一种新的波浪形的干扰图案，即所谓的摩尔纹。传感器的网格状纹理构成了一个这样的图案。当图案中的细条状结构与传感器的结构以小角度交叉时，这种效应也会在图像中产生明显的干扰。这种现象在一些细密纹理情况下，比如时尚摄影中的布料上，非常普遍。这种摩尔纹可能通过亮度也可能通过颜色来展现。但是在这里，仅针对在翻拍过程中产生的图像摩尔纹进行处理。

翻拍即从计算机屏幕上捕获图片，或对着屏幕拍摄图片；该方式会在图片上产生摩尔纹现象

论文主要处理思路

对原图作Haar变换得到四个下采样特征图（原图下二采样cA、Horizontal横向高频cH、Vertical纵向高频cV、Diagonal斜向高频cD）
然后分别利用四个独立的CNN对四个下采样特征图卷积池化，提取特征信息
原文随后对三个高频信息卷积池化后的结果的每个channel、每个像素点比对，取max
将上一步得到的结果和cA卷积池化后的结果作笛卡尔积

论文地址

2、网络结构复现

如下图所示，本项目复现了论文的图像去摩尔纹方法，并对数据处理部分进行了修改，并且网络结构上也参考了源码中的结构，对图片产生四个下采样特征图，而不是论文中的三个，具体处理方式大家可以参考一下网络结构。

import math

import paddle

import paddle.nn as nn

import paddle.nn.functional as F

# import pywt

from paddle.nn import Linear, Dropout, ReLU

from paddle.nn import Conv2D, MaxPool2D

class 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 out

model_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: 37,592

Trainable params: 37,592

Non-trainable params: 0

---------------------------------------------------------------------------

Input size (MB): 9.00

Forward/backward pass size (MB): 59.54

Params size (MB): 0.14

Estimated Total Size (MB): 68.68

---------------------------------------------------------------------------

{'total_params': 37592, 'trainable_params': 37592}

3、数据预处理

与源代码不同的是，本项目将图像的小波分解部分集成在了数据读取部分，即改为了线上进行小波分解，而不是源代码中的线下进行小波分解并且保存图片。首先，定义小波分解的函数

!pip install PyWavelets

import numpy as np

import pywt

def 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, HH

def 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 correspondences

def 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 +"t"+ str(label_id) + 'n')

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 paddle

import numpy as np

import pandas as pd

import PIL.Image as Image

from paddle.vision import transforms

# from haar2D import fwdHaarDWT2D

paddle.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Dataset

class 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

4、模型训练

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,

 )