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Created June 1, 2021 14:36
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What would you like to do?
use Keras Model in C plus plus
#include <iostream>
#include <fdeep/fdeep.hpp>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc.hpp>
#include <filesystem>
#include <exception>
using namespace stdext;
namespace fs = std::filesystem;
int main()
std::string test_image_folder_path = "d:/catdog/"; // path to folder containing test images
const auto mymodel = fdeep::load_model("D:/CatDogNew.json"); // load the converted model
for (const auto& entry : fs::directory_iterator(test_image_folder_path)) {
std::string image_path = entry.path().string(); // get images path one by one
const cv::Mat image = cv::imread(image_path, cv::IMREAD_GRAYSCALE); // read the image in single channel grayscale mode
cv::Mat resizedImg;
cv::resize(image, resizedImg, cv::Size(128, 128)); // resize image to the 128x128 (Model input dimension)
imshow("Display Window", resizedImg); // just show the image
// convert cv::MAT to fdeep::tensor
const auto input = fdeep::tensor_from_bytes(resizedImg.ptr(),
0.0f, 1.0f);
auto result = mymodel.predict({ input }); // predict the image's label and ouput a 1x2 tensor containing each class probability
std::cout << fdeep::show_tensors(result) << std::endl; // print the tensor
catch (const std::exception& e)
std::cout << e.what() << '\n';
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birddevelper commented Aug 14, 2021

is it possible to use this example with image output?



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yes, I checked it but I have still always had the same issue.

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