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#include <iostream>
#include <opencv2/opencv.hpp>
/*#include <opencv2/imgproc.hpp>
#include <opencv2/imgcodecs.hpp>
#include <opencv2/highgui.hpp>*/
#include "tiny_cnn/tiny_cnn.h"
using namespace tiny_cnn;
using namespace tiny_cnn::activation;
using namespace std;
// rescale output to 0-100
template <typename Activation>
double rescale(double x) {
Activation a;
return 100.0 * (x - a.scale().first) / (a.scale().second - a.scale().first);
}
void convert_image(const std::string& imagefilename,
double minv,
double maxv,
int w,
int h,
vec_t& data) {
cv::Mat img = cv::imread(imagefilename);
if (img.data == nullptr) return; // cannot open, or it's not an image
cv::Mat resized;
cv::resize(img, resized, cv::Size(w, h), .0, .0);
data.resize(w*h*resized.channels(), minv);
for(int c = 0; c < resized.channels(); ++c){
for(int y = 0; y < resized.rows; ++y){
for(int x = 0; x < resized.cols; ++x){
data[c * w * h + y*w + x] = resized.data[y*resized.step + x*resized.step + c];
}
}
}
}
template <typename N>
void construct_net(N& nn) {
typedef convolutional_layer<activation::identity> conv;
typedef max_pooling_layer<relu> pool;
const int n_fmaps = 32; ///< number of feature maps for upper layer
const int n_fmaps2 = 64; ///< number of feature maps for lower layer
const int n_fc = 64; ///< number of hidden units in fully-connected layer
nn << conv(32, 32, 5, 3, n_fmaps, padding::same)
<< pool(32, 32, n_fmaps, 2)
<< conv(16, 16, 5, n_fmaps, n_fmaps, padding::same)
<< pool(16, 16, n_fmaps, 2)
<< conv(8, 8, 5, n_fmaps, n_fmaps2, padding::same)
<< pool(8, 8, n_fmaps2, 2)
<< fully_connected_layer<activation::identity>(4 * 4 * n_fmaps2, n_fc)
<< fully_connected_layer<softmax>(n_fc, 10);
}
void recognize(const std::string& dictionary, const std::string& filename) {
network<sequential> nn;
construct_net(nn);
// load nets
ifstream ifs(dictionary.c_str());
ifs >> nn;
// convert imagefile to vec_t
vec_t data;
convert_image(filename, -1.0, 1.0, 32, 32, data);
// recognize
auto res = nn.predict(data);
vector<pair<double, int> > scores;
// sort & print top-3
for (int i = 0; i < 10; i++)
scores.emplace_back(rescale<tan_h>(res[i]), i);
sort(scores.begin(), scores.end(), greater<pair<double, int>>());
for (int i = 0; i < 3; i++)
cout << scores[i].second << "," << scores[i].first << endl;
}
int main(int argc, char** argv) {
if (argc != 2) {
cout << "please specify image file";
return 0;
}
recognize("cifar-weights", argv[1]);
}
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