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
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, )
总结
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。
在本项目中，CNN 仅使用 1 级小波分解进行训练。 可以探索对多级小波分解网络精度的影响。 CNN 模型可以用更多更难的例子和更深的网络进行训练。
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