├── .gitignore
├── ofxaddons_thumbnail.png
├── libs
    └── INSTALL.md
├── README.md
├── src
    ├── ofxGFNet.h
    └── ofxGFNet.cpp
├── addon_config.mk
└── LICENSE


/.gitignore:
--------------------------------------------------------------------------------
1 | .DS_Store
2 | *.so
3 | .idea
4 | /libs/google
5 | /libs/tensorflow
6 | __MACOSX
7 | 


--------------------------------------------------------------------------------
/ofxaddons_thumbnail.png:
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https://raw.githubusercontent.com/gilbertfrancois/ofxGFTensorflow/HEAD/ofxaddons_thumbnail.png


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/libs/INSTALL.md:
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1 | # Libs folder
2 | 
3 | - Download the precompiled Tensorflow C++ libraries for your operating system from the [release](https://github.com/gilbertfrancois/ofxGFTensorflow/releases) page.
4 | 
5 | - Unzip the archive in the `${OPENFRAMEWORKS_DIR}/ofxGFTensorflow` folder
6 | 
7 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # ofxGFTensorflow
 2 | 
 3 | 
 4 | 
 5 | ## About
 6 | 
 7 | This addon allows you to run Tensorflow models in your own openFrameworks projects, inspired by the OpenCV DNN module. The goal is to have a simple interface for loading a model and feeding data to the network. The addon library should work in any project, not just in a openFramework project. See remarks below how to modify it.
 8 | 
 9 | There is another excellent project ofxMSATensorflow by Memo Akten. Please have a look there as well and see what addon suits you best. 
10 | 
11 | 
12 | 
13 | ## Dependencies
14 | 
15 | - ofxOpenCV
16 | - ofxCv
17 | - openFrameworks v0.10.0 (untested on lower versions)
18 | 
19 | 
20 | 
21 | ## Installation
22 | 
23 | - Go to the [release](https://github.com/gilbertfrancois/ofxGFTensorflow/releases) page and download a stable version of the source _or_ type  `git clone https://github.com/gilbertfrancois/ofxGFTensorflow.git` to install this repo inside your `${openframeworks_dir}/addons` folder.
24 | - Download the precompiled Tensorflow C++ libraries for your operating system from the [release](https://github.com/gilbertfrancois/ofxGFTensorflow/releases) page and unzip the archive with headers and libraries.
25 | - **[Optional]** Copy the file `./libs/tensorflow/lib/{linux64 | osx}/libtensorflow_cc.so` to `/usr/local/lib` to make your programs work in runtime.
26 | 


--------------------------------------------------------------------------------
/src/ofxGFNet.h:
--------------------------------------------------------------------------------
 1 | //
 2 | // Created by G.F. Duivesteijn on 08/10/2018.
 3 | //
 4 | 
 5 | #ifndef HYDRA_GFNET_H
 6 | #define HYDRA_GFNET_H
 7 | 
 8 | #include <set>
 9 | #include <algorithm>
10 | #include <iostream>
11 | #include <sstream>
12 | #include <iterator>
13 | #include <numeric>
14 | #include <vector>
15 | #include <opencv2/opencv.hpp>
16 | #include "tensorflow/core/public/session.h"
17 | #include "tensorflow/core/platform/env.h"
18 | #include "tensorflow/core/framework/tensor.h"
19 | #include "tensorflow/cc/client/client_session.h"
20 | #include "tensorflow/cc/ops/standard_ops.h"
21 | 
22 | 
23 | namespace gf {
24 | namespace dnn {
25 | 
26 | 
27 | class Net {
28 | 
29 | private:
30 |     tensorflow::Session *session;
31 | 
32 |     cv::Mat preprocess(const cv::Mat &src, const double scale, const cv::Size &size, const cv::Scalar &mean, bool swapRB, int ddepth);
33 | 
34 | public:
35 | 
36 |     Net();
37 | 
38 |     virtual ~Net();
39 | 
40 |     void readNet(const std::string &graph_file_name);
41 | 
42 |     tensorflow::Tensor tensorFromCvImageFast(cv::Mat image, const double scale, const cv::Size size, const int channels, const cv::Scalar &mean, bool swapRB, bool crop, int ddepth);
43 | 
44 |     tensorflow::Tensor tensorFromCvImagesFast(std::vector<cv::Mat> images, const double scale, const cv::Size size, const int channels, const cv::Scalar &mean, bool swapRB, bool crop, int ddepth);
45 | 
46 |     tensorflow::Tensor tensorFromCvImage(cv::Mat image, const double scale_, const cv::Size size_, const int channels_, const cv::Scalar &mean, bool swapRB, bool crop, int ddepth);
47 | 
48 |     tensorflow::Tensor tensorFromCvImages(std::vector<cv::Mat> images, const double scale, cv::Size size, const int channels, const cv::Scalar &mean, bool swapRB, bool crop, int ddepth);
49 | 
50 |     std::vector<tensorflow::Tensor> forward(tensorflow::Tensor input_tensor, const std::string &input_layer_name, const std::string &output_layer_name);
51 | 
52 |     std::vector<tensorflow::Tensor> forward(tensorflow::Tensor input_tensor, const std::vector<std::pair<std::string, tensorflow::Tensor>> feed_dict, const std::string &output_layer_name);
53 | 
54 | };
55 | 
56 | }
57 | }
58 | 
59 | #endif //HYDRA_GFNET_H
60 | 


--------------------------------------------------------------------------------
/addon_config.mk:
--------------------------------------------------------------------------------
 1 | # All variables and this file are optional, if they are not present the PG and the
 2 | # makefiles will try to parse the correct values from the file system.
 3 | #
 4 | # Variables that specify exclusions can use % as a wildcard to specify that anything in
 5 | # that position will match. A partial path can also be specified to, for example, exclude
 6 | # a whole folder from the parsed paths from the file system
 7 | #
 8 | # Variables can be specified using = or +=
 9 | # = will clear the contents of that variable both specified from the file or the ones parsed
10 | # from the file system
11 | # += will add the values to the previous ones in the file or the ones parsed from the file
12 | # system
13 | #
14 | # The PG can be used to detect errors in this file, just create a new project with this addon
15 | # and the PG will write to the console the kind of error and in which line it is
16 | 
17 | meta:
18 | 	ADDON_NAME = ofxGFTensorflow
19 | 	ADDON_DESCRIPTION = openFrameworks addon for running pre-trained Tensorflow models, using the Tensorflow C++ library.
20 | 	ADDON_AUTHOR = Gilbert Francois Duivesteijn
21 | 	ADDON_TAGS = "deep learning" "machine learning" "numerical computation" "artificial intelligence" "tensorflow"
22 | 	ADDON_URL = https://github.com/gilbertfrancois/ofxGFTensorflow
23 | 
24 | common:
25 | 	# dependencies with other addons, a list of them separated by spaces
26 | 	# or use += in several lines
27 | 	ADDON_DEPENDENCIES  = ofxCv
28 | 	ADDON_DEPENDENCIES += ofxOpenCv
29 | 
30 | 	ADDON_INCLUDES = src
31 | 	ADDON_INCLUDES += libs/tensorflow/include
32 | 	ADDON_INCLUDES += libs/tensorflow/include/external/nsync
33 | 	ADDON_INCLUDES += libs/protobuf/include
34 | 	ADDON_INCLUDES += libs/google/include
35 | 
36 | 	ADDON_SOURCES_EXCLUDE = libs/%
37 | 
38 | linux64:
39 | 	ADDON_LDFLAGS = ${OF_ROOT}/addons/ofxGFTensorflow/libs/tensorflow/lib/linux64/libtensorflow_cc.so
40 | #	ADDON_LDFLAGS += -Wl,-rpath=${OF_ROOT}/addons/ofxGFTensorflow/libs/tensorflow/lib/linux64/lib/linux
41 | 
42 | linux:
43 | 	ADDON_LDFLAGS = ${OF_ROOT}/addons/ofxGFTensorflow/libs/lib/linux/libtensorflow_cc.so
44 | 	ADDON_LDFLAGS += -Wl,-rpath=${OF_ROOT}/addons/ofxGFTensorflow/libs/lib/linux
45 | 
46 | 
47 | linuxarmv6l:
48 | 
49 | linuxarmv7l:
50 | 
51 | msys2:
52 | 
53 | android/armeabi:
54 | 
55 | android/armeabi-v7a:
56 | 
57 | ios:
58 | 
59 | osx:
60 |     ADDON_LIBS = ${OF_ROOT}/addons/ofxGFTensorflow/libs/lib/osx/libtensorflow_cc.so
61 | 	#ADDON_LDFLAGS = -Xlinker -rpath -Xlinker @executable_path
62 | 


--------------------------------------------------------------------------------
/src/ofxGFNet.cpp:
--------------------------------------------------------------------------------
  1 | //
  2 | // Created by G.F. Duivesteijn on 08/10/2018.
  3 | //
  4 | 
  5 | #include "ofxGFNet.h"
  6 | 
  7 | 
  8 | gf::dnn::Net::Net() {
  9 | 
 10 | }
 11 | 
 12 | 
 13 | gf::dnn::Net::~Net() {
 14 |     if (session != nullptr) {
 15 |         session->Close();
 16 |     }
 17 |     delete session;
 18 | }
 19 | 
 20 | 
 21 | void gf::dnn::Net::readNet(const std::string &graph_file_name) {
 22 | 
 23 |     tensorflow::GraphDef graph_def;
 24 |     tensorflow::Status status;
 25 |     status = ReadBinaryProto(tensorflow::Env::Default(), graph_file_name, &graph_def);
 26 |     if (!status.ok()) {
 27 |         std::cout << status.ToString() << std::endl;
 28 |     }
 29 |     status = tensorflow::NewSession(tensorflow::SessionOptions(), &session);
 30 |     if (!status.ok()) {
 31 |         std::cout << status.ToString() << std::endl;
 32 |     }
 33 |     status = session->Create(graph_def);
 34 |     if (!status.ok()) {
 35 |         std::cout << status.ToString() << std::endl;
 36 |     }
 37 | }
 38 | 
 39 | 
 40 | tensorflow::Tensor gf::dnn::Net::tensorFromCvImageFast(cv::Mat image, const double scale, const cv::Size size,
 41 |         const int channels, const cv::Scalar &mean, bool swapRB, bool crop, int ddepth) {
 42 | 
 43 |     const auto n_elements_per_image = size.height * size.width * channels;
 44 |     tensorflow::TensorShape shape = tensorflow::TensorShape({1, size.height, size.width, channels});
 45 |     tensorflow::Tensor _tensor(tensorflow::DT_FLOAT, shape);
 46 |     auto _tensor_ptr = _tensor.flat<float>().data();
 47 |     image = preprocess(image, scale, size, mean, swapRB, ddepth);
 48 |     auto _image_ptr = (float *) image.data;
 49 |     std::memcpy(_tensor_ptr, _image_ptr, n_elements_per_image * sizeof(float));
 50 |     return _tensor;
 51 | }
 52 | 
 53 | 
 54 | tensorflow::Tensor gf::dnn::Net::tensorFromCvImagesFast(std::vector<cv::Mat> images, const double scale,
 55 |         const cv::Size size, const int channels, const cv::Scalar &mean, bool swapRB, bool crop, int ddepth) {
 56 | 
 57 |     const auto n_elements_per_image = size.height * size.width * channels;
 58 |     tensorflow::TensorShape _tensor_shape = tensorflow::TensorShape({static_cast<int64>(images.size()), size.height, size.width, channels});
 59 |     tensorflow::Tensor _tensor(tensorflow::DT_FLOAT, _tensor_shape);
 60 |     auto _tensor_ptr = _tensor.flat<float>().data();
 61 |     for (int i = 0; i < images.size(); i++) {
 62 |         images[i] = preprocess(images[i], scale, size, mean, swapRB, ddepth);
 63 |         auto _image_ptr = (float *) images[i].data;
 64 |         std::memcpy(_tensor_ptr + i * n_elements_per_image, _image_ptr, n_elements_per_image * sizeof(float));
 65 |     }
 66 |     return _tensor;
 67 | }
 68 | 
 69 | 
 70 | tensorflow::Tensor gf::dnn::Net::tensorFromCvImage(cv::Mat image, const double scale, const cv::Size size,
 71 |         const int channels, const cv::Scalar &mean, bool swapRB, bool crop, int ddepth) {
 72 | 
 73 |     std::vector<cv::Mat> images(1, image);
 74 |     return tensorFromCvImages(images, scale, size, channels, mean, swapRB, crop, ddepth);
 75 | }
 76 | 
 77 | 
 78 | tensorflow::Tensor gf::dnn::Net::tensorFromCvImages(std::vector<cv::Mat> images, const double scale, cv::Size size,
 79 |         const int channels, const cv::Scalar &mean, bool swapRB, bool crop, int ddepth) {
 80 | 
 81 |     for (int i = 0; i < images.size(); i++) {
 82 |         images[i] = preprocess(images[i], scale, size, mean, swapRB, ddepth);
 83 |     }
 84 |     tensorflow::TensorShape shape = tensorflow::TensorShape({static_cast<int64>(images.size()), size.height, size.width,
 85 |             channels});
 86 |     tensorflow::Tensor input_tensor(tensorflow::DT_FLOAT, shape);
 87 |     auto input_tensor_mapped = input_tensor.tensor<float, 4>();
 88 |     int N = 0;
 89 |     for (auto &image: images) {
 90 |         const int _rows = image.rows;
 91 |         const int _cols = image.cols;
 92 |         const int _channels = image.channels();
 93 |         const float *data = (float *) image.data;
 94 |         for (int H = 0; H < _rows; ++H) {
 95 |             for (int W = 0; W < _cols; ++W) {
 96 |                 for (int C = 0; C < _channels; ++C) {
 97 |                     input_tensor_mapped(N, H, W, C) = *(data + (H * _cols + W) * _channels + C);
 98 |                 }
 99 |             }
100 |         }
101 |         N++;
102 |     }
103 |     return input_tensor;
104 | }
105 | 
106 | 
107 | std::vector<tensorflow::Tensor> gf::dnn::Net::forward(const tensorflow::Tensor input_tensor,
108 | 
109 |         const std::string &input_layer_name, const std::string &output_layer_name) {
110 |     const std::vector<std::pair<std::string, tensorflow::Tensor>> feed_dict = {{input_layer_name, input_tensor}};
111 |     return forward(input_tensor, feed_dict, output_layer_name);
112 | }
113 | 
114 | 
115 | std::vector<tensorflow::Tensor> gf::dnn::Net::forward(const tensorflow::Tensor input_tensor,
116 |         const std::vector<std::pair<std::string, tensorflow::Tensor>> feed_dict, const std::string &output_layer_name) {
117 | 
118 |     tensorflow::Status status;
119 |     std::vector<tensorflow::Tensor> outputs;
120 |     status = session->Run(feed_dict, {output_layer_name}, {}, &outputs);
121 |     if (!status.ok()) {
122 |         std::cout << status.ToString() << std::endl;
123 |     }
124 |     return outputs;
125 | }
126 | 
127 | 
128 | cv::Mat gf::dnn::Net::preprocess(const cv::Mat &src, const double scale, const cv::Size &size,
129 |         const cv::Scalar &mean, bool swapRB, int ddepth) {
130 | 
131 |     cv::Mat dst = src.clone();
132 |     cv::Size _image_size = dst.size();
133 |     if (size != _image_size) {
134 |         resize(dst, dst, size, 0, 0, cv::INTER_LINEAR);
135 |     }
136 |     if (dst.depth() == CV_8U && ddepth == CV_32F)
137 |         dst.convertTo(dst, CV_32F);
138 |     cv::Scalar _mean = mean;
139 |     if (swapRB) {
140 |         std::swap(_mean[0], _mean[2]);
141 |         cvtColor(dst, dst, cv::COLOR_BGR2RGB);
142 |     }
143 |     dst -= _mean;
144 |     dst *= scale;
145 |     return dst;
146 | }
147 | 


--------------------------------------------------------------------------------
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