Calling TensorFlow Python Program from C++
This Demo will show how to call an pre-trained imagenet model to predict picture in C++.
├─ prediction.cpp % C++ file
├─ vgg_model.py % TensorFlow vgg model
├─ makefile % Compile file
├─ little_demo % An simple Demo
└─ test_pic/ % Test pictures
├─ cat.jpeg
├─ puzzle.jpeg
└─ tiger.jpeg
The python script
Firstly, we need an python script which let us to load VGG model & use it to do predict op.
You can test the script vgg_model.py by using the following cmd:
(deeplearning) bg@bg-cgi:~/Desktop/C_python$ python3 vgg_model.py
Using TensorFlow backend.
...
...
...
Please input picture file to predict: test_pic/cat.jpeg
Predicted: [('n02124075', 'Egyptian_cat', 0.93183666)]
It will download weight file vgg19_weights_tf_dim_ordering_tf_kernels.h5 and imagenet_class_index.json to ~/.keras/models the first time.
Compile
use Makefile
Using make all to Compile your cpp file.
Make sure your python's version is correct.
bg@bg-cgi:~/.keras/models$ locate Python.h
/usr/include/python2.7/Python.h
/usr/include/python3.5m/Python.h
so I use the following cmd to compile
g++ -std=c++11 -Wall -O3 prediction.cpp -lpython3.5m -L/usr/include/python3.5m/ -I/usr/include/python3.5m/ -o pred_test
use Cmake
I just write a CMakeLists.txt, which can help you to compile your cpp code.
So just use the following cmd:
mkdir build && cd build
cmake ../
make
cd ../
Then you will see a executable file classifier, just use ./classifier to run the program.
Usage
Makefile
make all to compile, then ./pred_test to run c++ program.
Cmake
use ./classifier to run the program after building.
Then you can see it works:
===========> START CALL PYTHON SCRIPT <===========
===========> 1st CALL <===========
Please input picture file to predict: huhu
file not exist!
===========> 2nd CALL <===========
Please input picture file to predict: test_pic/cat.jpeg
Predicted: [('n02124075', 'Egyptian_cat', 0.93183666)]
===========> 3rd CALL <===========
Please input picture file to predict: test_pic/tiger.jpeg
Predicted: [('n02129604', 'tiger', 0.82598984)]
===========> 4th CALL <===========
Please input picture file to predict: test_pic/puzzle.jpeg
Predicted: [('n03598930', 'jigsaw_puzzle', 0.99813461)]
===========> CALLING FINISHED <===========
(deeplearning) bg@bg-cgi:~/Desktop/C_python$
Feel free to contact me if you have any question.