add fashion

springkim · Aug 2, 2018 · 0c1f81e · 0c1f81e
1 parent 333bd8b
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diff --git a/OpenIMDS_TEST/.vs/OpenIMDS_TEST/v14/.suo b/OpenIMDS_TEST/.vs/OpenIMDS_TEST/v14/.suo
diff --git a/OpenIMDS_TEST/test.cpp b/OpenIMDS_TEST/test.cpp
@@ -1,15 +1,29 @@
 #include"../classification/mnist.h"
+#include"../classification/cifar10.h"
 #include<opencv2/opencv.hpp>
-
-int main(){
-	IMDSImage train=GetMnistTrainData();
-
+void CifarTest() {
+	IMDSImage train = GetCifar10TrainData();
 	IMDSImage mini_batch = GetRandomMiniBatch(train, 64);
-
-	for(int i=0;i<mini_batch.n;i++){
-		cv::Mat img(mini_batch.h, mini_batch.w,CV_32FC1, mini_batch.image[i]);
-		cv::imshow("image",img);
+	for (int i = 0; i<mini_batch.n; i++) {
+		cv::Mat img(mini_batch.h, mini_batch.w, CV_32FC3, mini_batch.image[i]);
+		img.convertTo(img, CV_8UC3);
+		cv::resize(img, img, cv::Size(500, 500));
+		cv::imshow("image", img);
 		cv::waitKey();
 	}
+}
+void FashionTest() {
+	IMDSImage train = GetFashionTrainData();
+	IMDSImage mini_batch = GetRandomMiniBatch(train, 64);
+	for (int i = 0; i<mini_batch.n; i++) {
+		cv::Mat img(mini_batch.h, mini_batch.w, CV_32FC1, mini_batch.image[i]);
+		img.convertTo(img, CV_8UC1);
+		cv::resize(img, img, cv::Size(500, 500));
+		cv::imshow("image", img);
+		cv::waitKey();
+	}
+}
+int main(){
+	FashionTest();
 	return 0;
 }
diff --git a/README.md b/README.md
@@ -5,13 +5,13 @@ OpenIMDS
 
 This simple header only C library can read popular classification datasets.
 
-1.	Classification datasets --------------------------
+## 1.	Classification datasets
 
 We are using `IMDSImage` type for each datasets. It has width,height,the number of images,images and labels only.
 
-#### MNIST
+### MNIST(mnist.h)
 
-<img src="https://i.imgur.com/9BcVOYQ.png" width="128">
+<img src="https://i.imgur.com/9BcVOYQ.png" width="256">
 
 The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image.
 
@@ -40,10 +40,23 @@ IMDSImage GetMnistValidData(int padding=0,float alpha=1.0F,bool bias=true);`
 IMDSImage train= GetMnistTrainData(0, 1 / 255.0F,true);
 IMDSImage valid= GetMnistValidData(0, 1 / 255.0F,true);
 ```
+### Fashion(mnist.h)
 
-#### Cifar-10
+<img src="https://i.imgur.com/MtTIit7.png" width="256">
 
-<img src="https://i.imgur.com/X7Tv9dQ.png" width="128">
+**[Fashion-MNIST](https://github.com/zalandoresearch/fashion-mnist)** is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. We intend Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits.
+
+```c
+IMDSImage GetFashionTrainData(int padding=0,float alpha=1.0F,bool bias=true)
+```
+
+```c
+IMDSImage GetFashionValidData(int padding=0,float alpha=1.0F,bool bias=true)
+```
+
+### Cifar-10(cifar10.h)
+
+<img src="https://i.imgur.com/X7Tv9dQ.png" width="256">
 
 The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.
 

diff --git a/classification/cifar10.h b/classification/cifar10.h
@@ -91,7 +91,7 @@ static inline IMDSImage __ReadCIFAR10(char* image_file, int padding, float alpha
 	imgs.n = 10000;
 	imgs.label = (int*)calloc(imgs.n, sizeof(int));
 	imgs.image = (float**)calloc(imgs.n, sizeof(float*));
-	char row[3073];
+	unsigned char row[3073];
 	for (int i = 0; i < 10000; i++) {
 		fread(row, 1, 3073, fp);
 		imgs.label[i] = (int)row[0];
@@ -156,11 +156,11 @@ static inline IMDSImage GetCifar10TrainData(int padding _IMDSCPPDV(0), float alp
 	char name_img[MAX_PATH + 1] = { 0 };
 	IMDSImage train;
 	char* url[5] = {
-		"https://www.dropbox.com/s/1u0hpkbbddn3hc1/openimds_cifar10_train_1.bin?dl=1"
-		,"https://www.dropbox.com/s/mxk1zozw42cqk2a/openimds_cifar10_train_2.bin?dl=1"
-		,"https://www.dropbox.com/s/cxg81q891j78wa9/openimds_cifar10_train_3.bin?dl=1"
-		,"https://www.dropbox.com/s/tcnds6l7rba5tn7/openimds_cifar10_train_4.bin?dl=1"
-		,"https://www.dropbox.com/s/u9500s411uocjgg/openimds_cifar10_train_5.bin?dl=1"
+		"https://github.com/springkim/OpenIMDS/releases/download/Cifar10/openimds_cifar10_train_1.bin"
+		,"https://github.com/springkim/OpenIMDS/releases/download/Cifar10/openimds_cifar10_train_2.bin"
+		,"https://github.com/springkim/OpenIMDS/releases/download/Cifar10/openimds_cifar10_train_3.bin"
+		,"https://github.com/springkim/OpenIMDS/releases/download/Cifar10/openimds_cifar10_train_4.bin"
+		,"https://github.com/springkim/OpenIMDS/releases/download/Cifar10/openimds_cifar10_train_5.bin"
 	};
 	train.c = 3;
 	train.w = 32 + padding * 2;
@@ -184,7 +184,7 @@ static inline IMDSImage GetCifar10TrainData(int padding _IMDSCPPDV(0), float alp
 static inline IMDSImage GetCifar10ValidData(int padding _IMDSCPPDV(0), float alpha _IMDSCPPDV(1.0F), bool bias _IMDSCPPDV(true)) {
 	char tmp_path[MAX_PATH + 1] = { 0 };
 	char* name_img = "openimds_cifar10_test.bin";
-	char* url = "https://www.dropbox.com/s/uifxmb6hi0rmvr3/openimds_cifar10_test.bin?dl=1";
+	char* url = "https://github.com/springkim/OpenIMDS/releases/download/Cifar10/openimds_cifar10_test.bin";
 	__DownloadCifar10(tmp_path, url,name_img,30730000);
 	char path_img[MAX_PATH + 1] = { 0 };
 	strcat(strcpy(path_img, tmp_path), name_img);

diff --git a/classification/mnist.h b/classification/mnist.h
@@ -217,8 +217,8 @@ static inline IMDSImage GetMnistTrainData(int padding _IMDSCPPDV(0), float alpha
 	char tmp_path[MAX_PATH + 1] = { 0 };
 	char* name_img = "openimds_mnist_train_image.bin";
 	char* name_lbl = "openimds_mnist_train_label.bin";
-	__DownloadMNIST(tmp_path, "https://www.dropbox.com/s/f56cag59pk58lel/openimds_mnist_train_image.bin?dl=1"
-					, "https://www.dropbox.com/s/r1rgfhshebdhnms/openimds_mnist_train_label.bin?dl=1"
+	__DownloadMNIST(tmp_path, "https://github.com/springkim/OpenIMDS/releases/download/MNIST/openimds_mnist_train_image.bin"
+					, "https://github.com/springkim/OpenIMDS/releases/download/MNIST/openimds_mnist_train_label.bin"
 					, name_img, name_lbl, 47040016,60008);
 	char path_img[MAX_PATH + 1] = { 0 };
 	char path_lbl[MAX_PATH + 1] = { 0 };
@@ -230,13 +230,39 @@ static inline IMDSImage GetMnistValidData(int padding _IMDSCPPDV(0), float alpha
 	char tmp_path[MAX_PATH + 1] = { 0 };
 	char* name_img = "openimds_mnist_valid_image.bin";
 	char* name_lbl = "openimds_mnist_valid_label.bin";
-	__DownloadMNIST(tmp_path, "https://www.dropbox.com/s/zsf4wicmmru34s9/openimds_mnist_valid_image.bin?dl=1"
-					, "https://www.dropbox.com/s/wsj7xetwmwuhxj8/openimds_mnist_valid_label.bin?dl=1"
+	__DownloadMNIST(tmp_path, "https://github.com/springkim/OpenIMDS/releases/download/MNIST/openimds_mnist_valid_image.bin"
+					, "https://github.com/springkim/OpenIMDS/releases/download/MNIST/openimds_mnist_valid_label.bin"
 					, name_img, name_lbl,7840016,10008);
 	char path_img[MAX_PATH + 1] = { 0 };
 	char path_lbl[MAX_PATH + 1] = { 0 };
 	strcat(strcpy(path_img, tmp_path), name_img);
 	strcat(strcpy(path_lbl, tmp_path), name_lbl);
 	return __ReadMNIST(path_img, path_lbl, padding, alpha,bias);
 }
+static inline IMDSImage GetFashionTrainData(int padding _IMDSCPPDV(0), float alpha _IMDSCPPDV(1.0F), bool bias _IMDSCPPDV(true)) {
+	char tmp_path[MAX_PATH + 1] = { 0 };
+	char* name_img = "openimds_fashion_train_image.bin";
+	char* name_lbl = "openimds_fashion_train_label.bin";
+	__DownloadMNIST(tmp_path, "https://github.com/springkim/OpenIMDS/releases/download/fashion/train-images-idx3-ubyte"
+					, "https://github.com/springkim/OpenIMDS/releases/download/fashion/train-labels-idx1-ubyte"
+					, name_img, name_lbl, 47040016, 60008);
+	char path_img[MAX_PATH + 1] = { 0 };
+	char path_lbl[MAX_PATH + 1] = { 0 };
+	strcat(strcpy(path_img, tmp_path), name_img);
+	strcat(strcpy(path_lbl, tmp_path), name_lbl);
+	return __ReadMNIST(path_img, path_lbl, padding, alpha, bias);
+}
+static inline IMDSImage GetFashionValidData(int padding _IMDSCPPDV(0), float alpha _IMDSCPPDV(1.0F), bool bias _IMDSCPPDV(true)) {
+	char tmp_path[MAX_PATH + 1] = { 0 };
+	char* name_img = "openimds_fashion_valid_image.bin";
+	char* name_lbl = "openimds_fashion_valid_label.bin";
+	__DownloadMNIST(tmp_path, "https://github.com/springkim/OpenIMDS/releases/download/fashion/t10k-images-idx3-ubyte"
+					, "https://github.com/springkim/OpenIMDS/releases/download/fashion/t10k-labels-idx1-ubyte"
+					, name_img, name_lbl, 7840016, 10008);
+	char path_img[MAX_PATH + 1] = { 0 };
+	char path_lbl[MAX_PATH + 1] = { 0 };
+	strcat(strcpy(path_img, tmp_path), name_img);
+	strcat(strcpy(path_lbl, tmp_path), name_lbl);
+	return __ReadMNIST(path_img, path_lbl, padding, alpha, bias);
+}
 #endif  //MNIST_7E2_1_1C_MNIST_HPP_INCLUDED