├── .gitignore
├── .gitmodules
├── .readme
    ├── example_003.jpg
    └── example_004.jpg
├── data
    └── 33823288584_1d21cf0a26_k.jpg
├── src
    ├── example_001.cpp
    ├── example_002.cpp
    ├── example_003.cpp
    ├── example_004.py
    └── example_004.cpp
├── CMakeLists.txt
└── README.md


/.gitignore:
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1 | /build/
2 | /devel/
3 | 


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/.gitmodules:
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1 | [submodule "pybind11"]
2 | 	path = pybind11
3 | 	url = https://github.com/pybind/pybind11.git
4 | 


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/.readme/example_003.jpg:
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https://raw.githubusercontent.com/wkentaro/call-python-from-cpp/HEAD/.readme/example_003.jpg


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/.readme/example_004.jpg:
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https://raw.githubusercontent.com/wkentaro/call-python-from-cpp/HEAD/.readme/example_004.jpg


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/data/33823288584_1d21cf0a26_k.jpg:
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https://raw.githubusercontent.com/wkentaro/call-python-from-cpp/HEAD/data/33823288584_1d21cf0a26_k.jpg


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/src/example_001.cpp:
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1 | #include <pybind11/embed.h> // everything needed for embedding
2 | namespace py = pybind11;
3 | 
4 | int main() {
5 |     py::scoped_interpreter guard{}; // start the interpreter and keep it alive
6 | 
7 |     py::print("Hello, World!"); // use the Python API
8 | }
9 | 


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/CMakeLists.txt:
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 1 | cmake_minimum_required(VERSION 2.8 FATAL_ERROR)
 2 | project(python_from_cpp)
 3 | 
 4 | find_package(OpenCV REQUIRED)
 5 | 
 6 | add_subdirectory(pybind11)
 7 | 
 8 | add_executable(example_001 src/example_001.cpp)
 9 | target_link_libraries(example_001 PRIVATE pybind11::embed)
10 | add_executable(example_002 src/example_002.cpp)
11 | target_link_libraries(example_002 PRIVATE pybind11::embed)
12 | add_executable(example_003 src/example_003.cpp)
13 | target_link_libraries(example_003 PRIVATE pybind11::embed)
14 | add_executable(example_004 src/example_004.cpp)
15 | target_link_libraries(example_004 PRIVATE pybind11::embed ${OpenCV_LIBS})
16 | 


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/src/example_002.cpp:
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 1 | #include <iostream>
 2 | #include <pybind11/embed.h> // everything needed for embedding
 3 | namespace py = pybind11;
 4 | 
 5 | int main(int argc, char** argv)
 6 | {
 7 |   py::scoped_interpreter guard{}; // start the interpreter and keep it alive
 8 | 
 9 |   py::object sys = py::module::import("sys");
10 |   std::string sys_version = sys.attr("version").cast<std::string>();
11 |   std::cout << "sys version: " << sys_version << std::endl;
12 | 
13 |   py::object scipy = py::module::import("scipy");
14 |   std::string scipy_version = scipy.attr("__version__").cast<std::string>();
15 |   std::cout << "scipy version: " << scipy_version << std::endl;
16 | 
17 |   return 0;
18 | }
19 | 


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/src/example_003.cpp:
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 1 | #include <iostream>
 2 | #include <pybind11/embed.h>
 3 | #include <pybind11/numpy.h>
 4 | namespace py = pybind11;
 5 | 
 6 | int main() {
 7 |     py::scoped_interpreter guard{};
 8 | 
 9 |     py::object scipy_misc = py::module::import("scipy.misc");
10 |     py::object plt = py::module::import("matplotlib.pyplot");
11 | 
12 |     py::array_t<uint8_t> img = scipy_misc.attr("face")()
13 |       .cast<py::array_t<uint8_t> >();
14 |     std::cout << "shape: " << img.shape()[0] << " " << img.shape()[1] << " " << img.shape()[2] << std::endl;
15 |     std::cout << "ndim: " << img.ndim() << std::endl;
16 | 
17 |     py::object img_py = py::cast<py::object>(img);
18 |     std::cout << "==> Saved to: image.jpg" << std::endl;
19 |     plt.attr("imsave")("image.jpg", img_py);
20 | }
21 | 


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/src/example_004.py:
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 1 | #!/usr/bin/env python
 2 | 
 3 | import chainer
 4 | from chainercv.datasets import coco_instance_segmentation_label_names
 5 | from chainercv.links import MaskRCNNFPNResNet50
 6 | import imgviz
 7 | import numpy as np
 8 | 
 9 | 
10 | def main():
11 |     gpu = 0
12 |     image_file = '../data/33823288584_1d21cf0a26_k.jpg'
13 | 
14 |     chainer.config.cv_resize_backend = 'cv2'
15 | 
16 |     label_names = coco_instance_segmentation_label_names
17 |     model = MaskRCNNFPNResNet50(
18 |         n_fg_class=len(label_names),
19 |         pretrained_model='coco',
20 |     )
21 | 
22 |     chainer.cuda.get_device_from_id(gpu).use()
23 |     model.to_gpu()
24 | 
25 |     img = imgviz.io.imread(image_file)
26 |     img_input = img.transpose(2, 0, 1).astype(np.float32)
27 | 
28 |     masks, labels, scores = model.predict([img_input])
29 |     masks = masks[0]
30 |     labels = labels[0]
31 |     scores = scores[0]
32 |     class_ids = labels + 1
33 |     captions = [
34 |         '{:s}: {:.2%}'.format(label_names[c - 1], s)
35 |         for c, s in zip(class_ids, scores)
36 |     ]
37 |     viz = imgviz.instances2rgb(
38 |         image=img,
39 |         labels=class_ids,
40 |         masks=masks,
41 |         captions=captions,
42 |         font_size=15,
43 |     )
44 | 
45 |     imgviz.io.pyglet_imshow(viz)
46 |     imgviz.io.pyglet_run()
47 | 
48 | 
49 | if __name__ == '__main__':
50 |     main()
51 | 


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/README.md:
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 1 | # call-python-from-cpp
 2 | 
 3 | Example Code of Calling Python from C++,
 4 | following the instruction
 5 | <a href="https://pybind11.readthedocs.io/en/stable/advanced/embedding.html"><i>Embedding the Interpreter</i></a>.
 6 | 
 7 | ## Installation
 8 | 
 9 | ```bash
10 | # for example_001 - example_003
11 | sudo python3 -m pip install numpy
12 | sudo python3 -m pip install scipy
13 | 
14 | # for example_004
15 | sudo python3 -m pip install opencv-python
16 | sudo python3 -m pip install chainer
17 | sudo python3 -m pip install chainercv
18 | sudo python3 -m pip install imgviz
19 | sudo python3 -m pip install pyglet
20 | sudo python3 -m pip install cupy-cuda101     # CUDA10.1, cupy-cuda92 for 9.2
21 | ```
22 | 
23 | ```bash
24 | mkdir build
25 | cd build
26 | 
27 | cmake ..
28 | make
29 | ```
30 | 
31 | 
32 | ## Examples
33 | 
34 | ### Hello World! ([src/example_001.cpp](src/example_001.cpp))
35 | 
36 | ```bash
37 | $ cd build
38 | $ ./example_001
39 | Hello, World!
40 | ```
41 | 
42 | ### Import SciPy ([src/example_002.cpp](src/example_002.cpp))
43 | 
44 | ```bash
45 | $ cd build
46 | $ ./example_002
47 | sys version: 3.5.2 (default, Nov 12 2018, 13:43:14) 
48 | [GCC 5.4.0 20160609]
49 | scipy version: 1.3.0
50 | ```
51 | 
52 | ### Communicate Numpy Array ([src/example_003.cpp](src/example_003.cpp))
53 | 
54 | ```bash
55 | $ cd build
56 | $ ./example_003
57 | shape: 768 1024 3
58 | ndim: 3
59 | ==> Saved to: image.jpg
60 | ```
61 | 
62 | <img src=".readme/example_003.jpg" height="300px"><br/>`image.jpg`
63 | 
64 | ### Run Mask R-CNN ([src/example_004.cpp](src/example_004.cpp))
65 | 
66 | ```bash
67 | $ cd build
68 | $ ./example_004
69 | ndim: 3
70 | shape: (600, 956, 3, )
71 | ```
72 | 
73 | <img src=".readme/example_004.jpg" height="300px"><br/>`cv::imshow(...)`
74 | 


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/src/example_004.cpp:
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 1 | #include <iostream>
 2 | #include <pybind11/embed.h>
 3 | #include <pybind11/numpy.h>
 4 | #include <opencv2/opencv.hpp>
 5 | namespace py = pybind11;
 6 | using namespace py::literals;
 7 | 
 8 | int main() {
 9 |     py::scoped_interpreter guard{};
10 | 
11 |     auto locals = py::dict();
12 |     py::exec(R"(
13 |         import chainer
14 |         from chainercv.datasets import coco_instance_segmentation_label_names
15 |         from chainercv.links import MaskRCNNFPNResNet50
16 |         from chainercv import utils
17 |         import imgviz
18 |         import numpy as np
19 | 
20 |         gpu = 0
21 |         image_file = '../data/33823288584_1d21cf0a26_k.jpg'
22 | 
23 |         chainer.config.cv_resize_backend = 'cv2'
24 | 
25 |         label_names = coco_instance_segmentation_label_names
26 |         model = MaskRCNNFPNResNet50(
27 |             n_fg_class=len(label_names),
28 |             pretrained_model='coco',
29 |         )
30 | 
31 |         chainer.cuda.get_device_from_id(gpu).use()
32 |         model.to_gpu()
33 | 
34 |         img = imgviz.io.imread(image_file)
35 |         img_input = img.transpose(2, 0, 1).astype(np.float32)
36 | 
37 |         masks, labels, scores = model.predict([img_input])
38 |         masks = masks[0]
39 |         labels = labels[0]
40 |         scores = scores[0]
41 |         class_ids = labels + 1
42 |         captions = []
43 |         for c, s in zip(class_ids, scores):
44 |             captions.append('{:s}: {:.2%}'.format(label_names[c - 1], s))
45 |         viz = imgviz.instances2rgb(
46 |             image=img,
47 |             labels=class_ids,
48 |             masks=masks,
49 |             captions=captions,
50 |             font_size=15,
51 |         )
52 |     )", py::globals(), locals);
53 | 
54 |     std::string image_file = locals["image_file"].cast<std::string>();
55 |     py::array_t<uint8_t> viz = locals["viz"].cast<py::array_t<uint8_t> >();
56 |     std::cout << "ndim: " << viz.ndim() << std::endl;
57 |     std::cout << "shape: (";
58 |     for (size_t i = 0; i < viz.ndim(); i++) {
59 |       std::cout << viz.shape()[i] << ", ";
60 |     }
61 |     std::cout << ")" << std::endl;
62 | 
63 |     // pybind11::array_t -> cv::Mat
64 |     // TODO(unknown): more efficient implementation
65 |     unsigned int rows = viz.shape(0);
66 |     unsigned int cols = viz.shape(1);
67 |     cv::Mat viz_mat = cv::Mat::zeros(/*rows=*/rows, /*cols=*/cols, CV_8UC3);
68 |     for (size_t i = 0; i < rows; i++) {
69 |       for (size_t j = 0; j < cols; j++) {
70 |         // RGB -> BGR
71 |         viz_mat.at<cv::Vec3b>(/*row=*/i, /*col=*/j) = cv::Vec3b(
72 |           *viz.data(i, j, 2), *viz.data(i, j, 1), *viz.data(i, j, 0)
73 |         );
74 |       }
75 |     }
76 | 
77 |     cv::imshow(image_file, viz_mat);
78 |     cv::waitKey(0);
79 | }
80 | 


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