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// Example code for how to: | |
// - load an image using OpenCV | |
// - convert it to a fdeep::tensor3 | |
// - use it as input for a forward pass on an image classification model | |
// - print the class number | |
// compile with: | |
// g++ -std=c++14 -O3 opencv_example.cpp -lopencv_core -lopencv_imgproc -lopencv_imgcodecs -o opencv_example | |
#include <fdeep/fdeep.hpp> | |
#include <opencv2/opencv.hpp> | |
int main() | |
{ | |
const cv::Mat image = cv::imread("image.jpg"); | |
cv::cvtColor(image, image, cv::COLOR_BGR2RGB); | |
assert(image.isContinuous()); | |
const auto model = fdeep::load_model("model.json"); | |
// Use the correct scaling, i.e. low and high. | |
const auto input = fdeep::tensor5_from_bytes(image.ptr(), | |
static_cast<std::size_t>(image.rows), | |
static_cast<std::size_t>(image.cols), | |
static_cast<std::size_t>(image.channels()), | |
0.0f, 1.0f); | |
const auto result = model.predict_class({input}); | |
std::cout << result << std::endl; | |
} |
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Hi Dobias,
Thanks for this snippet. This is similar to what I am looking for. Although, I have a different question, I was not able to get OpenCV working in my environment with Keras. Can you point me any documentation that would help?