espcn-inference-using-cpp's Introduction
# cpp_project-inference-espcn C++ implementation of ESPCN algorithm described in [1]. This project was done for the final project of the "ECE 596C" course. The code is written from scratch and the only library used other than C++ STD is OpenCV. There is no limitation on the input size however the upscaling is used for small size images. For the 500*500 image the algorithm takes 2 minutes to produce the output image. The "Nueral_net" template class is a general class to define CNNs in Keras software library manner. ESPCN was defined using the Nueral_net class. For running the code make do the following steps: 1. go to the $TOP_DIR of the code, the $TOP_DIR denote the directory containing this README file. And let $INSTALL_DIR denote the directory into which this software is to be installed. 2. Go to the $TOP_DIR: cd $TOP_DIR 3. To build and install the software, use the commands: cmake -H. -Btmp_cmake -DCMAKE_INSTALL_PREFIX=$INSTALL_DIR cmake --build tmp_cmake --clean-first --target install * * This command might need the root privilege after running the above commands, an executable file will be generated in the $INSTALL_DIR called "upcale_image" Now you can use the following command to upscale your images with the factor of Three! Go to the $INSTALL_DIR and run the followling command. `cat {path to jpg image} | ./upscale_image "path to network's weights" > {upscaled_image.jpg} ` \ The above command read the input image from the standard input stream and write the image to the standard output stream which is stored in the "upscaled_image.jpg"` References [1] Shi, W., Caballero, J., Huszár, F., Totz, J., Aitken, A., Bishop, R., Rueckert, D. and Wang, Z. (2016). Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network. Available at: https://arxiv.org/abs/1609.05158 \
espcn-inference-using-cpp's People
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