CIFAR是一個用于普通物體識別的數(shù)據(jù)集。CIFAR數(shù)據(jù)集分為兩種：CIFAR-10和CIFAR-100。The CIFAR-10 and CIFAR-100 are labeled subsets of the 80 million tiny images dataset. They were collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton.

CIFAR-10由60000張大小為32*32的三通道彩色圖像組成，被分為10類，分別為airplane、automobile、bird、cat、deer、dog、frog、horse、ship、truck。每類由6000張圖像。其中50000張圖像用來訓練，10000張圖像用來測試。數(shù)據(jù)集分為5個訓練塊和1個測試塊,每個塊包含10000張圖像.訓練集每類包含5000張圖像,測試集每類包含1000張圖像.

CIFAR-100由60000張大小為32*32的三通道彩色圖像組成，分為20個大類，每個大類又包含5個小類，總共100個小類。每個小類包含600張圖像，其中500張用于訓練，100張用于測試。

從https://www.cs.toronto.edu/~kriz/cifar.html?下載CIFAR C版本的二進制數(shù)據(jù)：

(1)、CIFAR-10：下載cifar-10-binary.tar.gz，解壓縮，共8個文件，batches.meta.txt中存放10個種類名，data_batch_1.bin… data_batch_5.bin、test_batch.bin共6個文件，每個文件中存放10000張圖像數(shù)據(jù)。

(2)、CIFAR-100：下載cifar-100-binary.tar.gz，解壓縮，共5個文件，coarse_label_names.txt中存放20個大類名，fine_label_names.txt中存放100個小類名，train.bin中存放50000張訓練圖像，test.bin中存放10000張測試圖像。

CIFAR數(shù)據(jù)集到圖像轉換實現(xiàn)的代碼如下：

static void write_image_cifar(const cv::Mat& bgr, const std::string& image_save_path, const std::vector<int>& label_count, int label_class)
{std::string str = std::to_string(label_count[label_class]);if (label_count[label_class] < 10) {str = "0000" + str;} else if (label_count[label_class] < 100) {str = "000" + str;} else if (label_count[label_class] < 1000) {str = "00" + str;} else if (label_count[label_class] < 10000) {str = "0" + str;} else {fprintf(stderr, "save image name fail\n");return;}str = std::to_string(label_class) + "_" + str + ".png";str = image_save_path + str;cv::imwrite(str, bgr);
}static void read_cifar_10(const std::string& bin_name, const std::string& image_save_path, int image_count, std::vector<int>& label_count)
{int image_width = 32;int image_height = 32;std::ifstream file(bin_name, std::ios::binary);if (file.is_open()) {for (int i = 0; i < image_count; ++i) {cv::Mat red = cv::Mat::zeros(image_height, image_width, CV_8UC1);cv::Mat green = cv::Mat::zeros(image_height, image_width, CV_8UC1);cv::Mat blue = cv::Mat::zeros(image_height, image_width, CV_8UC1);int label_class = 0;file.read((char*)&label_class, 1);label_count[label_class]++;file.read((char*)red.data, 1024);file.read((char*)green.data, 1024);file.read((char*)blue.data, 1024);std::vector<cv::Mat> tmp{ blue, green, red };cv::Mat bgr;cv::merge(tmp, bgr);write_image_cifar(bgr, image_save_path, label_count, label_class);}file.close();}
}int CIFAR10toImage()
{std::string images_path = "E:/GitCode/NN_Test/data/database/CIFAR/CIFAR-10/";// train imagestd::vector<int> label_count(10, 0);for (int i = 1; i <= 5; i++) {std::string bin_name = images_path + "data_batch_" + std::to_string(i) + ".bin";std::string image_save_path = "E:/GitCode/NN_Test/data/tmp/cifar-10_train/";int image_count = 10000;read_cifar_10(bin_name, image_save_path, image_count, label_count);}// test imagestd::fill(&label_count[0], &label_count[0] + 10, 0);std::string bin_name = images_path + "test_batch.bin";std::string image_save_path = "E:/GitCode/NN_Test/data/tmp/cifar-10_test/";int image_count = 10000;read_cifar_10(bin_name, image_save_path, image_count, label_count);// save big imagsimages_path = "E:/GitCode/NN_Test/data/tmp/cifar-10_train/";int width = 32 * 20;int height = 32 * 10;cv::Mat dst(height, width, CV_8UC3);for (int i = 0; i < 10; i++) {for (int j = 1; j <= 20; j++) {int x = (j - 1) * 32;int y = i * 32;cv::Mat part = dst(cv::Rect(x, y, 32, 32));std::string str = std::to_string(j);if (j < 10)str = "0000" + str;elsestr = "000" + str;str = std::to_string(i) + "_" + str + ".png";std::string input_image = images_path + str;cv::Mat src = cv::imread(input_image, 1);if (src.empty()) {fprintf(stderr, "read image error: %s\n", input_image.c_str());return -1;}src.copyTo(part);}}std::string output_image = images_path + "result.png";cv::imwrite(output_image, dst);return 0;
}static void write_image_cifar(const cv::Mat& bgr, const std::string& image_save_path,const std::vector<std::vector<int>>& label_count, int label_class_coarse, int label_class_fine)
{std::string str = std::to_string(label_count[label_class_coarse][label_class_fine]);if (label_count[label_class_coarse][label_class_fine] < 10) {str = "0000" + str;} else if (label_count[label_class_coarse][label_class_fine] < 100) {str = "000" + str;} else if (label_count[label_class_coarse][label_class_fine] < 1000) {str = "00" + str;} else if (label_count[label_class_coarse][label_class_fine] < 10000) {str = "0" + str;} else {fprintf(stderr, "save image name fail\n");return;}str = std::to_string(label_class_coarse) + "_" + std::to_string(label_class_fine) + "_" + str + ".png";str = image_save_path + str;cv::imwrite(str, bgr);
}static void read_cifar_100(const std::string& bin_name, const std::string& image_save_path, int image_count, std::vector<std::vector<int>>& label_count)
{int image_width = 32;int image_height = 32;std::ifstream file(bin_name, std::ios::binary);if (file.is_open()) {for (int i = 0; i < image_count; ++i) {cv::Mat red = cv::Mat::zeros(image_height, image_width, CV_8UC1);cv::Mat green = cv::Mat::zeros(image_height, image_width, CV_8UC1);cv::Mat blue = cv::Mat::zeros(image_height, image_width, CV_8UC1);int label_class_coarse = 0;file.read((char*)&label_class_coarse, 1);int label_class_fine = 0;file.read((char*)&label_class_fine, 1);label_count[label_class_coarse][label_class_fine]++;file.read((char*)red.data, 1024);file.read((char*)green.data, 1024);file.read((char*)blue.data, 1024);std::vector<cv::Mat> tmp{ blue, green, red };cv::Mat bgr;cv::merge(tmp, bgr);write_image_cifar(bgr, image_save_path, label_count, label_class_coarse, label_class_fine);}file.close();}
}int CIFAR100toImage()
{std::string images_path = "E:/GitCode/NN_Test/data/database/CIFAR/CIFAR-100/";// train imagestd::vector<std::vector<int>> label_count;label_count.resize(20);for (int i = 0; i < 20; i++) {label_count[i].resize(100);std::fill(&label_count[i][0], &label_count[i][0] + 100, 0);}std::string bin_name = images_path + "train.bin";std::string image_save_path = "E:/GitCode/NN_Test/data/tmp/cifar-100_train/";int image_count = 50000;read_cifar_100(bin_name, image_save_path, image_count, label_count);// test imagefor (int i = 0; i < 20; i++) {label_count[i].resize(100);std::fill(&label_count[i][0], &label_count[i][0] + 100, 0);}bin_name = images_path + "test.bin";image_save_path = "E:/GitCode/NN_Test/data/tmp/cifar-100_test/";image_count = 10000;read_cifar_100(bin_name, image_save_path, image_count, label_count);// save big imagsimages_path = "E:/GitCode/NN_Test/data/tmp/cifar-100_train/";int width = 32 * 20;int height = 32 * 100;cv::Mat dst(height, width, CV_8UC3);std::vector<std::string> image_names;for (int j = 0; j < 20; j++) {for (int i = 0; i < 100; i++) {std::string str1 = std::to_string(j);std::string str2 = std::to_string(i);std::string str = images_path + str1 + "_" + str2 + "_00001.png";cv::Mat src = cv::imread(str, 1);if (src.data) {for (int t = 1; t < 21; t++) {if (t < 10)str = "0000" + std::to_string(t);elsestr = "000" + std::to_string(t);str = images_path + str1 + "_" + str2 + "_" + str + ".png";image_names.push_back(str);}}}}for (int i = 0; i < 100; i++) {for (int j = 0; j < 20; j++) {int x = j * 32;int y = i * 32;cv::Mat part = dst(cv::Rect(x, y, 32, 32));cv::Mat src = cv::imread(image_names[i * 20 + j], 1);if (src.empty()) {fprintf(stderr, "read image fail: %s\n", image_names[i * 20 + j].c_str());return -1;}src.copyTo(part);}}std::string output_image = images_path + "result.png";cv::imwrite(output_image, dst);cv::Mat src = cv::imread(output_image, 1);if (src.empty()) {fprintf(stderr, "read result image fail: %s\n", output_image.c_str());return -1;}for (int i = 0; i < 4; i++) {cv::Mat dst = src(cv::Rect(0, i * 800, 640, 800));std::string str = images_path + "result_" + std::to_string(i + 1) + ".png";cv::imwrite(str, dst);}return 0;
}

cifar-10轉換的結果如下：

cifar-100轉換的結果如下：

GitHub：https://github.com/fengbingchun/NN_Test

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