init.

Author: sundyCoder

.gitignore

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+.DS_Store
+.ipynb_checkpoints
+node_modules
+/.bazelrc
+/.tf_configure.bazelrc
+/bazel-*
+/bazel_pip
+/tools/python_bin_path.sh
+/tools/git/gen
+/pip_test
+/_python_build
+*.pyc
+__pycache__
+*.swp
+*.db
+*.opendb
+.vscode/
+cmake_build/
+.idea/**
+/build/vc14/Debug
+/build/vc14/Release
+/build/vc14/x64
+/tensorflow/core/util/version_info.cc
+/tensorflow/contrib/cmake/build
+/app/classifier
+/app/trainner
+/app/dataGen
+/bin
+/extra/others
+/extra/tensorflow-r1.4/lib

README.md

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+
+## DEye
+
+### 1. Introduction
+
+Defect Eye is an open source software library based on tensorflow1.4, which focus on surface defect inspection. The application area cover the full range of yield applications within the manufacturing environment, including incoming process tool qualification, wafer qualification, glass surface qualification, reticle qualification, research and development, and tool, process and line monitoring. Patterned and unpatterned wafer defect inspection and qualification tools find particles and pattern defects on the front surface, back surface and edge of the wafer, allowing engineers to detect and monitor critical yield excursions. Also, It can be used for medical image inpsection, including Lung PET/CT,breast MRI, CT Colongraphy, Digital Chest X-ray images.
+
+![DEye](https://i.imgur.com/YfiOMJf.png)
+
+### 2. Usage
+
+Compiled tensorflow-r1.4 GPU version using CMake,VisualStudio 2015, CUDA8.0, cudnn6.0.
+
+- tensorflow.dll, tensorflow.lib, libprotobuf.lib
+
+- Download Address: 链接：https://pan.baidu.com/s/1o9tv1n8 密码：ekec
+
+### DEye使用教程
+
+### 准备工作
+
+- 安装VisualStudio Community 2015 安装NVIDIA CUDA 8.0
+
+- git clone https://github.com/sundyCoder/DEye
+
+- 下载tensorflow.dll,放置在DEye/bin目录下
+
+- 下载tensorflow.lib、libprotobuf.lib,放置在DEye/extra/tensorflow-r1.4/lib/目录下
+
+- 下载inception_v3_2016_08_28_frozen.pb，放置在DEye/data目录下
+
+- 打开DEye/build/vc14目录下的visual studio Solution 'tensorflow_classification', 将编译选项为 'Release' 'x64'
+
+### How to use DEye
+
+- Install VisualStudio Community2015 Install NVIDIA CUDA 8.0
+
+- git clone https://github.com/sundyCoder/DEye
+
+- Download tensorflow.dll, place it under DEye/bin
+
+- Download tensorflow.lib and libprotobuf.lib, place theme under DEye/extra/tensorflow-r1.4/
+
+- Download inception_v3_2016_08_28_frozen.pb, place it under DEye/data
+
+- Open Visual Studio Solution "DEye.sln" which should be under DEye/build/vc14，Solution configurations option choose "Release", Soluton Platform option choose "x64".
+
+## 3. License
+Apache License 2.0

app/example/convertMnist.cpp

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+#include <iostream>  
+#include <fstream>  
+#include "opencv2/core/core.hpp"  
+#include "opencv2/highgui/highgui.hpp"  
+#include "opencv2/imgproc/imgproc.hpp"  
+#include <cstdio>
+using namespace std;
+
+int ReverseInt(int i)
+{
+	unsigned char ch1, ch2, ch3, ch4;
+	ch1 = i & 255;
+	ch2 = (i >> 8) & 255;
+	ch3 = (i >> 16) & 255;
+	ch4 = (i >> 24) & 255;
+	return((int)ch1 << 24) + ((int)ch2 << 16) + ((int)ch3 << 8) + ch4;
+}
+void read_Mnist(string filename, vector<cv::Mat> &vec)
+{
+	ifstream file(filename, ios::binary);
+	if (file.is_open()) {
+		int magic_number = 0;
+		int number_of_images = 0;
+		int n_rows = 0;
+		int n_cols = 0;
+		file.read((char*)&magic_number, sizeof(magic_number));
+		magic_number = ReverseInt(magic_number);
+		file.read((char*)&number_of_images, sizeof(number_of_images));
+		number_of_images = ReverseInt(number_of_images);
+		file.read((char*)&n_rows, sizeof(n_rows));
+		n_rows = ReverseInt(n_rows);
+		file.read((char*)&n_cols, sizeof(n_cols));
+		n_cols = ReverseInt(n_cols);
+		for (int i = 0; i < number_of_images; ++i) {
+			cv::Mat tp = cv::Mat::zeros(n_rows, n_cols, CV_8UC1);
+			for (int r = 0; r < n_rows; ++r) {
+				for (int c = 0; c < n_cols; ++c) {
+					unsigned char temp = 0;
+					file.read((char*)&temp, sizeof(temp));
+					tp.at<uchar>(r, c) = (int)temp;
+				}
+			}
+			vec.push_back(tp);
+		}
+	}
+}
+void read_Mnist_Label(string filename, vector<int> &vec)
+{
+	ifstream file(filename, ios::in | ios::binary);
+	if (file.is_open()) {
+		int magic_number = 0;
+		int number_of_images = 0;
+		int n_rows = 0;
+		int n_cols = 0;
+		file.read((char*)&magic_number, sizeof(magic_number));
+		magic_number = ReverseInt(magic_number);
+		file.read((char*)&number_of_images, sizeof(number_of_images));
+		number_of_images = ReverseInt(number_of_images);
+		for (int i = 0; i < number_of_images; ++i) {
+			unsigned char temp = 0;
+			file.read((char*)&temp, sizeof(temp));
+			vec[i] = (int)temp;
+		}
+	}
+}
+string GetImageName(int number, int arr[])
+{
+	string str1, str2;
+	for (int i = 0; i < 10; i++) {
+		if (number == i) {
+			arr[i]++;
+			char ch1[10];
+			sprintf(ch1, "%d", arr[i]);
+			str1 = std::string(ch1);
+			if (arr[i] < 10) {
+				str1 = "0000" + str1;
+			}
+			else if (arr[i] < 100) {
+				str1 = "000" + str1;
+			}
+			else if (arr[i] < 1000) {
+				str1 = "00" + str1;
+			}
+			else if (arr[i] < 10000) {
+				str1 = "0" + str1;
+			}
+			break;
+		}
+	}
+	char ch2[10];
+	sprintf(ch2, "%d", number);
+	str2 = std::string(ch2);
+	str2 = str2 + "_" + str1;
+
+	return str2;
+}
+
+
+int main1(int argc, char *argv[])
+{
+	//reference: http://eric-yuan.me/cpp-read-mnist/  
+	//test images and test labels  
+	//read MNIST image into OpenCV Mat vector  
+	FILE* fpTrain = fopen("examples/trainLabel.txt", "w+");
+	FILE* fpTest = fopen("examples/testLabel.txt", "w+");
+	if (argc < 6) {
+		std::cout << "please input parameters!" << std::endl;
+		return 0;
+	}
+	string filename_test_images = argv[1];
+	int number_of_test_images = 10000;
+	vector<cv::Mat> vec_test_images;
+	read_Mnist(filename_test_images, vec_test_images);
+
+	//read MNIST label into int vector  
+	string filename_test_labels = argv[2];
+	vector<int> vec_test_labels(number_of_test_images);
+	read_Mnist_Label(filename_test_labels, vec_test_labels);
+
+	if (vec_test_images.size() != vec_test_labels.size()) {
+		cout << "parse MNIST test file error" << endl;
+		return -1;
+	}
+	//save test images  
+	int count_digits[10];
+	for (int i = 0; i < 10; i++)
+		count_digits[i] = 0;
+	string save_test_images_path = argv[3];
+
+	for (unsigned int i = 0; i < vec_test_images.size(); i++) {
+		int number = vec_test_labels[i];
+		string image_name = GetImageName(number, count_digits);
+		image_name = save_test_images_path + image_name + ".jpg";
+//		cv::imwrite(image_name, vec_test_images[i]);
+
+	    string str;
+//		image_name = image_name.substr(14,11);
+		str = image_name + " " + std::to_string(number);
+		int length = str.length();
+		fwrite(str.c_str(), length, 1, fpTest);
+		fputc('\n', fpTest);
+	}
+	//train images and train labels  
+	//read MNIST image into OpenCV Mat vector  
+	string filename_train_images = argv[4];
+	int number_of_train_images = 60000;
+	vector<cv::Mat> vec_train_images;
+	read_Mnist(filename_train_images, vec_train_images);
+
+	//read MNIST label into int vector  
+	string filename_train_labels = argv[5];
+	vector<int> vec_train_labels(number_of_train_images);
+	read_Mnist_Label(filename_train_labels, vec_train_labels);
+
+	if (vec_train_images.size() != vec_train_labels.size()) {
+		cout << "parse MNIST train file error" << endl;
+		return -1;
+	}
+	//save train images  
+	for (int i = 0; i < 10; i++)
+		count_digits[i] = 0;
+	string save_train_images_path = argv[6];
+
+	for (size_t i = 0; i < vec_train_images.size(); i++) {
+		int number = vec_train_labels[i];
+		string image_name = GetImageName(number, count_digits);
+		image_name = save_train_images_path + image_name + ".jpg";
+//		cv::imwrite(image_name, vec_train_images[i]);
+
+	    string str;
+//		image_name = image_name.substr(15, 11);
+		str = image_name + " " + std::to_string(number);
+		int length = str.length();
+		fwrite(str.c_str(), length, 1, fpTrain);
+		fputc('\n', fpTrain);
+	}
+	fclose(fpTest);
+	fclose(fpTrain);
+	return 0;
+}