├── models ├── face_landmark.tflite ├── iris_landmark.tflite └── face_detection_short.tflite ├── .gitignore ├── src ├── CMakeLists.txt ├── DetectionPostProcess.hpp ├── FaceLandmark.hpp ├── FaceLandmark.cpp ├── DetectionPostProcess.cpp ├── demo.cpp ├── FaceDetection.hpp ├── FaceDetection.cpp ├── IrisLandmark.hpp ├── IrisLandmark.cpp ├── ModelLoader.hpp └── ModelLoader.cpp ├── CMakeLists.txt ├── README.md └── LICENSE /models/face_landmark.tflite: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pntt3011/mediapipe_face_iris_cpp/HEAD/models/face_landmark.tflite -------------------------------------------------------------------------------- /models/iris_landmark.tflite: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pntt3011/mediapipe_face_iris_cpp/HEAD/models/iris_landmark.tflite -------------------------------------------------------------------------------- /models/face_detection_short.tflite: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pntt3011/mediapipe_face_iris_cpp/HEAD/models/face_detection_short.tflite -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # VS Code config folder 2 | .vscode/ 3 | 4 | # CMake build folder 5 | build/ 6 | 7 | # CMake test folder 8 | Testing/ 9 | 10 | # Prerequisites 11 | *.d 12 | 13 | # Compiled Object files 14 | *.slo 15 | *.lo 16 | *.o 17 | *.obj 18 | 19 | # Precompiled Headers 20 | *.gch 21 | *.pch 22 | 23 | # Compiled Dynamic libraries 24 | *.so 25 | *.dylib 26 | *.dll 27 | 28 | # Fortran module files 29 | *.mod 30 | *.smod 31 | 32 | # Compiled Static libraries 33 | *.lai 34 | *.la 35 | *.a 36 | *.lib 37 | 38 | # Executables 39 | *.exe 40 | *.out 41 | *.app 42 | -------------------------------------------------------------------------------- /src/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | target_sources(${APP_NAME} 2 | PRIVATE 3 | ${CMAKE_CURRENT_SOURCE_DIR}/demo.cpp 4 | ${CMAKE_CURRENT_SOURCE_DIR}/ModelLoader.cpp 5 | ${CMAKE_CURRENT_SOURCE_DIR}/ModelLoader.hpp 6 | ${CMAKE_CURRENT_SOURCE_DIR}/DetectionPostProcess.cpp 7 | ${CMAKE_CURRENT_SOURCE_DIR}/DetectionPostProcess.hpp 8 | ${CMAKE_CURRENT_SOURCE_DIR}/IrisLandmark.cpp 9 | ${CMAKE_CURRENT_SOURCE_DIR}/IrisLandmark.hpp 10 | ${CMAKE_CURRENT_SOURCE_DIR}/FaceLandmark.cpp 11 | ${CMAKE_CURRENT_SOURCE_DIR}/FaceLandmark.hpp 12 | ${CMAKE_CURRENT_SOURCE_DIR}/FaceDetection.cpp 13 | ${CMAKE_CURRENT_SOURCE_DIR}/FaceDetection.hpp 14 | ) -------------------------------------------------------------------------------- /CMakeLists.txt: -------------------------------------------------------------------------------- 1 | cmake_minimum_required(VERSION 3.16..3.21) 2 | 3 | # Set app name 4 | set(APP_NAME FaceMeshCpp) 5 | 6 | # Set 3rd party path 7 | set(TFLite_PATH "C:/tensorflowlite") 8 | set(TFLite_INCLUDE_DIRS "${TFLite_PATH}/include") 9 | set(TFLite_LIBS "${TFLite_PATH}/libs/windows_x86_64/libtensorflowlite.so.if.lib") 10 | 11 | # Set project 12 | project(${APP_NAME}) 13 | 14 | # Make executable app. 15 | add_executable(${APP_NAME}) 16 | 17 | # Add source file 18 | add_subdirectory(src) 19 | 20 | # Find opengl libraries 21 | find_package(OpenCV REQUIRED) 22 | 23 | # Add include path 24 | target_include_directories(${APP_NAME} 25 | PRIVATE ${OpenCV_INCLUDE_DIRS} 26 | PRIVATE ${TFLite_INCLUDE_DIRS}) 27 | 28 | # Link libraries to app. 29 | target_link_libraries(${APP_NAME} 30 | PRIVATE ${OpenCV_LIBS} 31 | PRIVATE ${TFLite_LIBS} 32 | ) 33 | 34 | # Build in multi-process. 35 | target_compile_options(${APP_NAME} 36 | PRIVATE /MP) -------------------------------------------------------------------------------- /src/DetectionPostProcess.hpp: -------------------------------------------------------------------------------- 1 | #ifndef DETECTIONPOSTPROCESS_H 2 | #define DETECTIONPOSTPROCESS_H 3 | 4 | #include 5 | #include 6 | #include 7 | #include 8 | #include "opencv2/core.hpp" 9 | 10 | #define CLASS_ID 0 11 | #define MIN_THRESHOLD 0.75f 12 | #define DETECTION_SIZE 128 13 | #define NUM_BOXES 896 14 | #define NUM_COORD 16 15 | #define NUM_SIZES 2 16 | 17 | namespace my { 18 | 19 | struct AnchorOptions { 20 | // 2 x 16 x 16 and 6 x 8 x 8 --> 896 21 | const int sizes[NUM_SIZES] = {16, 8}; 22 | const int numLayers[NUM_SIZES] = {2, 6}; 23 | 24 | // The offset for the center of anchors. 25 | const float offsetX = 0.5f; 26 | const float offsetY = 0.5f; 27 | }; 28 | 29 | 30 | struct Detection { 31 | cv::Rect2f roi; 32 | float score; 33 | int classId; 34 | 35 | Detection() : score(), classId(-1), roi() {} 36 | Detection(float score, int classId, cv::Rect2f roi) : 37 | score(score), classId(classId), roi(roi) {} 38 | ~Detection() = default; 39 | }; 40 | 41 | /* 42 | A helper class converts the output from Mediapipe Face Detection to Face box. 43 | */ 44 | class DetectionPostProcess { 45 | public: 46 | DetectionPostProcess(); 47 | ~DetectionPostProcess() = default; 48 | Detection getHighestScoreDetection 49 | (const std::vector& rawBoxes, const std::vector& scores) const; 50 | 51 | private: 52 | cv::Rect2f decodeBox(const std::vector& rawBoxes, int index) const; 53 | 54 | private: 55 | std::vector m_anchors; 56 | }; 57 | } 58 | 59 | #endif // DETECTIONPOSTPROCESS_H -------------------------------------------------------------------------------- /src/FaceLandmark.hpp: -------------------------------------------------------------------------------- 1 | #ifndef FACELANDMARK_H 2 | #define FACELANDMARK_H 3 | 4 | #include "FaceDetection.hpp" 5 | 6 | namespace my { 7 | 8 | /* 9 | A model wrapper to use Mediapipe Face Detector. 10 | It also includes the detection phase. 11 | This class is non-copyable. 12 | */ 13 | class FaceLandmark : public my::FaceDetection { 14 | public: 15 | /* 16 | Users MUST provide the FOLDER contain BOTH the face_detection_short.tflite 17 | and face_landmark.tflite, 18 | */ 19 | FaceLandmark(std::string modelPath); 20 | virtual ~FaceLandmark() = default; 21 | 22 | /* 23 | Override function from ModelLoader 24 | */ 25 | virtual void runInference(); 26 | 27 | /* 28 | Get a landmark from output (index must be in range 0-467) 29 | The position is relative to the input image at InputTensor(0) 30 | */ 31 | virtual cv::Point getFaceLandmarkAt(int index) const; 32 | 33 | /* 34 | Get all landmarks from output. 35 | The positions is relative to the input image at InputTensor(0) 36 | */ 37 | virtual std::vector getAllFaceLandmarks() const; 38 | 39 | /* 40 | Get all landmarks from output, which is a vector of length 468 * 3 * 4 (although the first 468 * 3 are enough). 41 | (Note: index does not matter, it always load from OutputTensor(0)) 42 | Each landmark is represented by x, y, z(depth), which are raw outputs from Mediapipe Face Landmark model. 43 | If you want to get relative position to input image, use getAllFaceLandmarks() or getFaceLandmarkAt() 44 | */ 45 | virtual std::vector loadOutput(int index = 0) const; 46 | 47 | 48 | private: 49 | my::ModelLoader m_landmarkModel; 50 | 51 | }; 52 | } 53 | 54 | #endif // FACELANDMARK_H -------------------------------------------------------------------------------- /src/FaceLandmark.cpp: -------------------------------------------------------------------------------- 1 | #include "FaceLandmark.hpp" 2 | #include 3 | 4 | 5 | #define FACE_LANDMARKS 468 6 | /* 7 | Helper function 8 | */ 9 | bool __isIndexValid(int idx) { 10 | if (idx < 0 || idx >= FACE_LANDMARKS) { 11 | std::cerr << "Index " << idx << " is out of range (" \ 12 | << FACE_LANDMARKS << ")." << std::endl; 13 | return false; 14 | } 15 | return true; 16 | } 17 | 18 | 19 | my::FaceLandmark::FaceLandmark(std::string modelPath): 20 | FaceDetection(modelPath), 21 | m_landmarkModel(modelPath + std::string("/face_landmark.tflite")) 22 | {} 23 | 24 | 25 | void my::FaceLandmark::runInference() { 26 | FaceDetection::runInference(); 27 | auto roi = FaceDetection::getFaceRoi(); 28 | if (roi.empty()) return; 29 | 30 | auto face = FaceDetection::cropFrame(roi); 31 | m_landmarkModel.loadImageToInput(face); 32 | m_landmarkModel.runInference(); 33 | } 34 | 35 | 36 | cv::Point my::FaceLandmark::getFaceLandmarkAt(int index) const { 37 | if (__isIndexValid(index)) { 38 | auto roi = FaceDetection::getFaceRoi(); 39 | 40 | float _x = m_landmarkModel.getOutputData()[index * 3]; 41 | float _y = m_landmarkModel.getOutputData()[index * 3 + 1]; 42 | 43 | int x = (int)(_x / m_landmarkModel.getInputShape()[2] * roi.width) + roi.x; 44 | int y = (int)(_y / m_landmarkModel.getInputShape()[1] * roi.height) + roi.y; 45 | 46 | return cv::Point(x,y); 47 | } 48 | return cv::Point(); 49 | } 50 | 51 | 52 | std::vector my::FaceLandmark::getAllFaceLandmarks() const { 53 | if (FaceDetection::getFaceRoi().empty()) 54 | return std::vector(); 55 | 56 | std::vector landmarks(FACE_LANDMARKS); 57 | for (int i = 0; i < FACE_LANDMARKS; ++i) { 58 | landmarks[i] = getFaceLandmarkAt(i); 59 | } 60 | return landmarks; 61 | } 62 | 63 | 64 | std::vector my::FaceLandmark::loadOutput(int index) const { 65 | return m_landmarkModel.loadOutput(); 66 | } -------------------------------------------------------------------------------- /src/DetectionPostProcess.cpp: -------------------------------------------------------------------------------- 1 | #include "DetectionPostProcess.hpp" 2 | #include 3 | 4 | /* 5 | Helper function 6 | */ 7 | std::vector generateAnchors(my::AnchorOptions options) { 8 | std::vector anchors; 9 | for (int i = 0; i < NUM_SIZES; ++i) { 10 | auto size = options.sizes[i]; 11 | auto numLayer = options.numLayers[i]; 12 | 13 | for (auto y = 0; y < size; ++y) { 14 | for (auto x = 0; x < size; ++x) { 15 | float x_center = (x + options.offsetX) * 1.f / size; 16 | float y_center = (y + options.offsetY) * 1.f / size; 17 | float w = 1.f; 18 | float h = 1.f; 19 | anchors.insert(anchors.end(), numLayer, cv::Rect2f(x_center - w/2.f, y_center - h/2.f, w, h)); 20 | } 21 | } 22 | } 23 | return anchors; 24 | } 25 | 26 | 27 | my::DetectionPostProcess::DetectionPostProcess() : 28 | m_anchors(generateAnchors(AnchorOptions())) {} 29 | 30 | 31 | cv::Rect2f my::DetectionPostProcess::decodeBox 32 | (const std::vector& rawBoxes, int index) const { 33 | auto anchor = m_anchors[index]; 34 | auto center = (anchor.tl() + anchor.br()) * 0.5; 35 | 36 | auto boxOffset = index * NUM_COORD; 37 | float cx = rawBoxes[boxOffset]; 38 | float cy = rawBoxes[boxOffset + 1]; 39 | float w = rawBoxes[boxOffset + 2]; 40 | float h = rawBoxes[boxOffset + 3]; 41 | 42 | cx = cx / DETECTION_SIZE * anchor.width + center.x; 43 | cy = cy / DETECTION_SIZE * anchor.height + center.y; 44 | w = w / DETECTION_SIZE * anchor.width; 45 | h = h / DETECTION_SIZE * anchor.height; 46 | 47 | return cv::Rect2f(cx - w/2, cy - h/2, w, h); 48 | } 49 | 50 | 51 | my::Detection my::DetectionPostProcess::getHighestScoreDetection 52 | (const std::vector& rawBoxes, const std::vector& scores) const { 53 | my::Detection detection; 54 | for (int i = 0; i < NUM_BOXES; i++) { 55 | if (scores[i] > std::max(MIN_THRESHOLD, detection.score)) { 56 | auto data = decodeBox(rawBoxes, i); 57 | detection = my::Detection(scores[i], CLASS_ID, data); 58 | } 59 | } 60 | return detection; 61 | } -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Face + Iris Landmarks Real-time Detection in C++ (OpenCV + Tensorflow Lite) 2 | 3 | ## (Note: This guide is for Windows OS, but the code should work fine on other OS, too) 4 | 5 | This project runs on Mediapipe TFLite models without using Mediapipe framework. It can run at **90+ FPS** on **CPU**. 6 | I perform the test on an AMD Ryzen 7 3700U Pro and the app takes about 5% CPU while running. 7 | For more information: 8 | * Face detection: https://google.github.io/mediapipe/solutions/face_detection.html 9 | * Face landmarks: https://google.github.io/mediapipe/solutions/face_mesh.html 10 | * Iris landmarks: https://google.github.io/mediapipe/solutions/iris.html 11 | 12 | ## :warning: Why not using GPU ? 13 | Because Tensorflow Lite only supports GPU delegate for Android and IOS. 14 | For more information: https://www.tensorflow.org/lite/performance/gpu 15 | 16 | ## :computer: Requirements: 17 | 18 | ### Hardware: Windows 10 64-bit 19 | 20 | ### Visual Studio 2019 21 | 22 | ### CMake >= 3.16 23 | You can follow instructions at https://www.40tude.fr/compile-cpp-code-with-vscode-cmake-nmake/ 24 | 25 | ### OpenCV (for Demo) 26 |
27 | How to install (Windows 64-bit) 28 | 29 | 1. Download and install pre-built binaries at https://sourceforge.net/projects/opencvlibrary/files/4.5.3/opencv-4.5.3-vc14_vc15.exe/download 30 | 2. Add `/build/x64/vc15/bin` and `/build/x64/vc15/lib` to PATH. 31 |
32 | Since the prebuilt OPENCV libraries do not contain the 32-bit version, you will have to manually build it using cmake. 33 | https://docs.opencv.org/master/d3/d52/tutorial_windows_install.html 34 | 35 | ### Tensorflow Lite 36 |
37 | How to use pre-built library 38 | 39 | 1. Download and extract tensorflowlite.zip from https://github.com/shigure3011/mediapipe_face_iris_cpp/releases 40 | 2. Change `TFLite_PATH` in CMakeLists.txt 41 | 3. Add `TFLite_LIBS` to PATH 42 | 43 |
44 | 45 | ## :key: How to use: 46 | 1. Clone this repo and go to FaceMeshCpp folder 47 | 2. Run `cmake -S . -B build` 48 | 3. Run `cmake --build build --config Release --target FaceMeshCpp` 49 | 4. Now it will build an `.exe` at `~/build/Release`. Make sure to copy `model` folder to `~/build/Release/` before running. 50 | -------------------------------------------------------------------------------- /src/demo.cpp: -------------------------------------------------------------------------------- 1 | #include "IrisLandmark.hpp" 2 | 3 | #include 4 | #include 5 | 6 | #define SHOW_FPS (1) 7 | 8 | #if SHOW_FPS 9 | #include 10 | #endif 11 | 12 | 13 | int main(int argc, char* argv[]) { 14 | 15 | my::IrisLandmark irisLandmarker("./models"); 16 | cv::VideoCapture cap(0); 17 | 18 | bool success = cap.isOpened(); 19 | if (success == false) 20 | { 21 | std::cerr << "Cannot open the camera." << std::endl; 22 | return 1; 23 | } 24 | 25 | #if SHOW_FPS 26 | float sum = 0; 27 | int count = 0; 28 | #endif 29 | 30 | while (success) 31 | { 32 | cv::Mat rframe, frame; 33 | success = cap.read(rframe); // read a new frame from video 34 | 35 | if (success == false) 36 | break; 37 | 38 | cv::flip(rframe, rframe, 1); 39 | 40 | #if SHOW_FPS 41 | auto start = std::chrono::high_resolution_clock::now(); 42 | #endif 43 | 44 | irisLandmarker.loadImageToInput(rframe); 45 | irisLandmarker.runInference(); 46 | 47 | for (auto landmark: irisLandmarker.getAllFaceLandmarks()) { 48 | cv::circle(rframe, landmark, 2, cv::Scalar(0, 255, 0), -1); 49 | } 50 | 51 | for (auto landmark: irisLandmarker.getAllEyeLandmarks(true, true)) { 52 | cv::circle(rframe, landmark, 2, cv::Scalar(0, 0, 255), -1); 53 | } 54 | 55 | for (auto landmark: irisLandmarker.getAllEyeLandmarks(false, true)) { 56 | cv::circle(rframe, landmark, 2, cv::Scalar(0, 0, 255), -1); 57 | } 58 | 59 | #if SHOW_FPS 60 | auto stop = std::chrono::high_resolution_clock::now(); 61 | auto duration = std::chrono::duration_cast(stop - start); 62 | float inferenceTime = duration.count() / 1e3; 63 | sum += inferenceTime; 64 | count += 1; 65 | int fps = (int) 1e3/ inferenceTime; 66 | 67 | cv::putText(rframe, std::to_string(fps), cv::Point(20, 70), cv::FONT_HERSHEY_PLAIN, 3, cv::Scalar(0, 196, 255), 2); 68 | #endif 69 | 70 | cv::imshow("Face detector", rframe); 71 | 72 | if (cv::waitKey(10) == 27) 73 | break; 74 | } 75 | 76 | #if SHOW_FPS 77 | std::cout << "Average inference time: " << sum / count << "ms " << std::endl; 78 | #endif 79 | 80 | cap.release(); 81 | cv::destroyAllWindows(); 82 | return 0; 83 | } -------------------------------------------------------------------------------- /src/FaceDetection.hpp: -------------------------------------------------------------------------------- 1 | #ifndef FACEDETECTION_H 2 | #define FACEDETECTION_H 3 | 4 | #include "ModelLoader.hpp" 5 | #include "DetectionPostProcess.hpp" 6 | 7 | namespace my { 8 | 9 | /* 10 | A model wrapper to use Mediapipe Face Detector. 11 | This class is non-copyable. 12 | */ 13 | class FaceDetection : public my::ModelLoader { 14 | public: 15 | /* 16 | Users MUST provide the FOLDER contain face_detection_short.tflite, NOT THE FILE itself. 17 | */ 18 | FaceDetection(std::string modelPath); 19 | virtual ~FaceDetection() = default; 20 | 21 | /* 22 | Get access to original input image 23 | */ 24 | cv::Mat getOriginalImage() const; 25 | 26 | /* 27 | Get the regressor result (first output tensor). 28 | */ 29 | std::vector getFaceRegressor() const; 30 | 31 | /* 32 | Get the classificator result (second output tensor). 33 | */ 34 | std::vector getFaceClassificator() const; 35 | 36 | /* 37 | Get the position of the HIGHEST CONFIDENT face 38 | (Note: the position is relative to the image passed to InputTensor(0)) 39 | */ 40 | virtual cv::Rect getFaceRoi() const; 41 | 42 | /* 43 | Override function from ModelLoader. 44 | (Note: index does not matter, the model always load to InputTensor(0)) 45 | */ 46 | virtual void loadImageToInput(const cv::Mat& inputImage, int index = 0); 47 | 48 | /* 49 | Override function from ModelLoader. 50 | Can only run when all input tensors have been loaded. 51 | */ 52 | virtual void runInference(); 53 | 54 | /* 55 | Crop input frame at roi (padding if need) 56 | */ 57 | cv::Mat cropFrame(const cv::Rect& roi) const; 58 | 59 | 60 | private: 61 | /* 62 | Override function from ModelLoader. 63 | This class can only load image to input. 64 | */ 65 | using ModelLoader::loadBytesToInput; 66 | 67 | /* 68 | Convert Detection box back to original size 69 | */ 70 | cv::Rect calculateRoiFromDetection(const Detection& detection) const; 71 | 72 | 73 | private: 74 | /* 75 | Help getting Region of Interest from model outputs 76 | */ 77 | DetectionPostProcess m_postProcessor; 78 | 79 | /* 80 | Save some informations 81 | */ 82 | cv::Mat m_originImage; 83 | cv::Rect m_roi; 84 | }; 85 | } 86 | #endif // FACEDETECTION_H -------------------------------------------------------------------------------- /src/FaceDetection.cpp: -------------------------------------------------------------------------------- 1 | #include "FaceDetection.hpp" 2 | 3 | 4 | my::FaceDetection::FaceDetection(std::string modelDir) : 5 | my::ModelLoader(modelDir + std::string("/face_detection_short.tflite")) 6 | {} 7 | 8 | 9 | void my::FaceDetection::loadImageToInput(const cv::Mat& in, int index) { 10 | m_originImage = in; 11 | ModelLoader::loadImageToInput(in); 12 | } 13 | 14 | 15 | void my::FaceDetection::runInference() { 16 | ModelLoader::runInference(); 17 | 18 | auto regressor = getFaceRegressor(); 19 | auto classificator = getFaceClassificator(); 20 | auto detection = m_postProcessor.getHighestScoreDetection(regressor, classificator); 21 | 22 | if (detection.classId != -1) { 23 | /* 24 | The detection is still in local shape [0..1] 25 | */ 26 | m_roi = calculateRoiFromDetection(detection); 27 | } 28 | else { 29 | m_roi = cv::Rect(); 30 | } 31 | } 32 | 33 | 34 | cv::Mat my::FaceDetection::getOriginalImage() const { 35 | return m_originImage; 36 | } 37 | 38 | 39 | std::vector my::FaceDetection::getFaceRegressor() const { 40 | return ModelLoader::loadOutput(0); 41 | } 42 | 43 | 44 | std::vector my::FaceDetection::getFaceClassificator() const { 45 | return ModelLoader::loadOutput(1); 46 | } 47 | 48 | 49 | cv::Rect my::FaceDetection::getFaceRoi() const { 50 | return m_roi; 51 | } 52 | 53 | 54 | cv::Mat my::FaceDetection::cropFrame(const cv::Rect& roi) const { 55 | cv::Mat frame = getOriginalImage(); 56 | cv::Size originalSize(roi.size()); 57 | 58 | cv::Point offsetStart(0, 0); 59 | cv::Point offsetEnd(roi.width, roi.height); 60 | 61 | /* 62 | Padding the frame with 0 if the Roi is out of frame size. 63 | */ 64 | auto pt1 = roi.tl(); 65 | auto pt2 = roi.br(); 66 | 67 | if (pt1.x < 0) { 68 | offsetStart.x -= pt1.x; pt1.x = 0; 69 | } 70 | if (pt1.y < 0) { 71 | offsetStart.y -= pt1.y; pt1.y = 0; 72 | } 73 | if (pt2.x >= frame.cols) { 74 | offsetEnd.x -= pt2.x - frame.cols + 1; 75 | pt2.x = frame.cols - 1; 76 | } 77 | if (pt2.y >= frame.rows) { 78 | offsetEnd.y -= pt2.y - frame.rows + 1; 79 | pt2.y = frame.rows - 1; 80 | } 81 | 82 | cv::Mat face(originalSize, CV_8UC3, cv::Scalar(0)); 83 | frame(cv::Rect(pt1, pt2)).copyTo(face(cv::Rect(offsetStart, offsetEnd))); 84 | return face; 85 | } 86 | 87 | //-------------------Private methods start here------------------- 88 | 89 | cv::Rect my::FaceDetection::calculateRoiFromDetection(const Detection& detection) const { 90 | int origWidth = m_originImage.size().width; 91 | int origHeight = m_originImage.size().height; 92 | 93 | auto center = (detection.roi.tl() + detection.roi.br()) * 0.5f; 94 | center.x *= origWidth; 95 | center.y *= origHeight; 96 | 97 | auto w = detection.roi.width * origWidth * 1.5f; 98 | auto h = detection.roi.height * origHeight * 2.f; 99 | 100 | return cv::Rect((int)center.x - w/2, (int)center.y - h/2, (int)w, (int)h); 101 | } -------------------------------------------------------------------------------- /src/IrisLandmark.hpp: -------------------------------------------------------------------------------- 1 | #ifndef IRISLANDMARK_H 2 | #define IRISLANDMARK_H 3 | 4 | #include "FaceLandmark.hpp" 5 | #include 6 | 7 | namespace my { 8 | /* 9 | const std::bitset<468> eyeAnnotations("000000010000000000000011111111110100000000\ 10 | 000000000000001000000000000000000000000000000000000000000000000000001111000111110\ 11 | 000001010010000000001110000000111011111010000000001000000000000000110000000000000\ 12 | 000000000000000001111110111111000000000111110100111111111101000000000000000000000\ 13 | 010000000000000000000000000000000000000000000000000000111100011111000000101001000\ 14 | 000000111000001110111110100000001000000000000001100000000000000000000000000111111\ 15 | 011111100000000011111"); 16 | */ 17 | 18 | /* 19 | A model wrapper to use Mediapipe Iris Landmark. 20 | It includes the face detection and face landmark phases. 21 | This class is non-copyable. 22 | */ 23 | class IrisLandmark: public my::FaceLandmark { 24 | public: 25 | /* 26 | Users MUST provide the FOLDER contain ALL the face_detection_short.tflite, 27 | face_landmark.tflite and iris_landmark.tflite 28 | */ 29 | IrisLandmark(std::string modelPath); 30 | virtual ~IrisLandmark() = default; 31 | 32 | /* 33 | Override function from FaceLandmark 34 | */ 35 | virtual void runInference(); 36 | 37 | /* 38 | Get an eye/iris landmark from output. 39 | If isIris == true: index must be in range 0-4 40 | else index must be in range 0-70 41 | The position is relative to the input image at InputTensor(0) 42 | */ 43 | virtual cv::Point getEyeLandmarkAt(int index, bool isLeftEye, bool isIris) const; 44 | 45 | /* 46 | Get all eye/iris landmarks from output. 47 | The positions is relative to the input image at InputTensor(0) 48 | */ 49 | virtual std::vector getAllEyeLandmarks(bool isLeftEye, bool isIris) const; 50 | 51 | /* 52 | Get all landmarks from output (index = 0: Eye landmarks, index != 0: Iris landmarks) 53 | Each landmark is represented by x, y, z(depth), which are raw outputs from Mediapipe Iris Landmark model. 54 | If you want to get relative position to input image, use getAllIrisLandmarks() or getAllIrisLandmark() 55 | */ 56 | virtual std::vector loadOutput(int index = 0, bool isLeftEye = true) const; 57 | 58 | /* 59 | Get eye Roi relative to input image at InputTensor(0) 60 | */ 61 | cv::Rect getEyeRoi(bool isLeftEye) const; 62 | 63 | 64 | private: 65 | /* 66 | Calculate Eye Roi from the first and last EyeLower2 landmark 67 | */ 68 | cv::Rect calculateEyeRoi(cv::Point leftMoft, cv::Point rightMost) const; 69 | 70 | /* 71 | Run inference on each eye (for multithread) 72 | */ 73 | void runEyeInference(bool isLeftEye); 74 | 75 | 76 | private: 77 | ModelLoader m_leftIrisLandmarker; 78 | ModelLoader m_rightIrisLandmarker; 79 | 80 | cv::Rect m_leftEyeRoi; 81 | cv::Rect m_rightEyeRoi; 82 | }; 83 | } 84 | #endif // IRISLANDMARK_H -------------------------------------------------------------------------------- /src/IrisLandmark.cpp: -------------------------------------------------------------------------------- 1 | #include "IrisLandmark.hpp" 2 | #include 3 | #include 4 | 5 | #define EYE_LANDMARKS 71 6 | #define IRIS_LANDMARKS 5 7 | 8 | /* 9 | Helper functions 10 | */ 11 | bool __isEyeIndexValid(int idx) { 12 | if (idx < 0 || idx >= EYE_LANDMARKS) { 13 | std::cerr << "Index " << idx << " is out of range (" \ 14 | << EYE_LANDMARKS << ")." << std::endl; 15 | return false; 16 | } 17 | return true; 18 | } 19 | 20 | 21 | bool __isIrisIndexValid(int idx) { 22 | if (idx < 0 || idx >= IRIS_LANDMARKS) { 23 | std::cerr << "Index " << idx << " is out of range (" \ 24 | << IRIS_LANDMARKS << ")." << std::endl; 25 | return false; 26 | } 27 | return true; 28 | } 29 | 30 | 31 | my::IrisLandmark::IrisLandmark(std::string modelPath): 32 | FaceLandmark(modelPath), 33 | m_leftIrisLandmarker(modelPath + std::string("/iris_landmark.tflite")), 34 | m_rightIrisLandmarker(modelPath + std::string("/iris_landmark.tflite")) 35 | {} 36 | 37 | 38 | void my::IrisLandmark::runInference() { 39 | FaceLandmark::runInference(); 40 | auto roi = FaceDetection::getFaceRoi(); 41 | if (roi.empty()) return; 42 | 43 | std::thread t([this]() {this->runEyeInference(true);}); 44 | runEyeInference(false); 45 | t.join(); 46 | } 47 | 48 | 49 | cv::Point my::IrisLandmark::getEyeLandmarkAt(int index, bool isLeftEye, bool isIris) const { 50 | if (__isEyeIndexValid(index)) { 51 | auto model = isLeftEye ? &m_leftIrisLandmarker: &m_rightIrisLandmarker; 52 | auto eyeRoi = isLeftEye ? m_leftEyeRoi: m_rightEyeRoi; 53 | 54 | float _x = model->getOutputData(isIris)[index * 3]; 55 | float _y = model->getOutputData(isIris)[index * 3 + 1]; 56 | 57 | int x = (int)(_x / model->getInputShape()[2] * eyeRoi.width) + eyeRoi.x; 58 | int y = (int)(_y / model->getInputShape()[1] * eyeRoi.height) + eyeRoi.y; 59 | 60 | return cv::Point(x,y); 61 | } 62 | return cv::Point(); 63 | } 64 | 65 | 66 | std::vector my::IrisLandmark::getAllEyeLandmarks(bool isLeftEye, bool isIris) const { 67 | if (my::FaceDetection::getFaceRoi().empty()) 68 | return std::vector(); 69 | 70 | int n = isIris ? IRIS_LANDMARKS : EYE_LANDMARKS; 71 | 72 | std::vector landmarks(n); 73 | for (int i = 0; i < n; ++i) { 74 | landmarks[i] = getEyeLandmarkAt(i, isLeftEye, isIris); 75 | } 76 | return landmarks; 77 | } 78 | 79 | 80 | std::vector my::IrisLandmark::loadOutput(int index, bool isLeftEye) const { 81 | auto model = isLeftEye ? &m_leftIrisLandmarker: &m_rightIrisLandmarker; 82 | return model->loadOutput(); 83 | } 84 | 85 | 86 | cv::Rect my::IrisLandmark::getEyeRoi(bool isLeftEye) const { 87 | return isLeftEye ? m_leftEyeRoi: m_rightEyeRoi; 88 | } 89 | 90 | //-------------------Private methods start here------------------- 91 | 92 | cv::Rect my::IrisLandmark::calculateEyeRoi(cv::Point leftMoft, cv::Point rightMost) const{ 93 | int cx = (leftMoft.x + rightMost.x) / 2; 94 | int cy = (leftMoft.y + rightMost.y) / 2; 95 | 96 | int w = std::abs(leftMoft.x - rightMost.x); 97 | int h = std::abs(leftMoft.y - rightMost.y); 98 | w = h = std::max(w, h); 99 | 100 | return cv::Rect(cx - w/2, cy - h/2, w, h); 101 | } 102 | 103 | 104 | void my::IrisLandmark::runEyeInference(bool isLeftEye) { 105 | int idx1 = isLeftEye ? 446 : 244; 106 | int idx2 = isLeftEye ? 464 : 226; 107 | 108 | auto roi = isLeftEye ? &m_leftEyeRoi: &m_rightEyeRoi; 109 | auto model = isLeftEye ? &m_leftIrisLandmarker: &m_rightIrisLandmarker; 110 | 111 | auto pt1 = FaceLandmark::getFaceLandmarkAt(idx1); 112 | auto pt2 = FaceLandmark::getFaceLandmarkAt(idx2); 113 | 114 | *roi = calculateEyeRoi(pt1, pt2); 115 | auto eyePatch = FaceDetection::cropFrame(*roi); 116 | 117 | model->loadImageToInput(eyePatch); 118 | model->runInference(); 119 | } -------------------------------------------------------------------------------- /src/ModelLoader.hpp: -------------------------------------------------------------------------------- 1 | #ifndef MODELLOADER_H 2 | #define MODELLOADER_H 3 | 4 | #include 5 | #include 6 | #include 7 | 8 | #include "opencv2/core.hpp" 9 | #include "opencv2/imgproc.hpp" 10 | #include "tensorflow/lite/interpreter.h" 11 | #include "tensorflow/lite/model.h" 12 | 13 | namespace my { 14 | 15 | template 16 | using Matrix = std::vector>; 17 | 18 | /* 19 | A tensor wrapper to save information of tflite tensors. 20 | Attributes: 21 | data: a float pointer to tensor data 22 | bytes: size of data in bytes 23 | dims: shape of data tensor 24 | */ 25 | struct TensorWrapper { 26 | float* data; 27 | size_t bytes; 28 | std::vector dims; 29 | 30 | TensorWrapper(float* t_data, size_t t_bytes, int* t_dims, int t_dimSize): 31 | data(t_data), bytes(t_bytes), dims(t_dims, t_dims + t_dimSize) {} 32 | }; 33 | 34 | /* 35 | A model wrapper to simplify the procedure of using tflite's models. 36 | This class is non-copyable. 37 | */ 38 | class ModelLoader { 39 | public: 40 | /* 41 | Constructor from a .tflite file 42 | Parameters: 43 | modelPath: path to .tflite 44 | */ 45 | ModelLoader(std::string modelPath); 46 | ModelLoader(const ModelLoader& other) = delete; 47 | ModelLoader& operator=(const ModelLoader& other) = delete; 48 | virtual ~ModelLoader() = default; 49 | 50 | /* 51 | Get shape of input tensor at index. 52 | (Note: A model can have multiple inputs) 53 | Parameters: 54 | index: index of input tensor 55 | */ 56 | std::vector getInputShape(int index = 0) const; 57 | 58 | /* 59 | Get the pointer to the data of input tensor at index. 60 | (Note: A model can have multiple inputs) 61 | Parameters: 62 | index: index of input tensor 63 | */ 64 | float* getInputData(int index = 0) const; 65 | 66 | /* 67 | Get size in bytes of input tensor at index. 68 | (Note: A model can have multiple inputs) 69 | Parameters: 70 | index: index of input tensor 71 | */ 72 | size_t getInputSize(int index = 0) const; 73 | 74 | /* 75 | Get number of inputs needed to run inference. 76 | */ 77 | int getNumberOfInputs() const; 78 | 79 | /* 80 | Get shape of output tensor at index. 81 | (Note: A model can have multiple outputs) 82 | Parameters: 83 | index: index of output tensor 84 | */ 85 | std::vector getOutputShape(int index = 0) const; 86 | 87 | /* 88 | Get the pointer to the data of output tensor at index. 89 | (Note: A model can have multiple outputs) 90 | Parameters: 91 | index: index of output tensor 92 | */ 93 | float* getOutputData(int index = 0) const; 94 | 95 | /* 96 | Get size in bytes of output tensor at index. 97 | (Note: A model can have multiple outputs) 98 | Parameters: 99 | index: index of output tensor 100 | */ 101 | size_t getOutputSize(int index = 0) const; 102 | 103 | /* 104 | Get number of outputs from inference. 105 | */ 106 | int getNumberOfOutputs() const; 107 | 108 | /* 109 | Load image (BGR format) to model at index 110 | (Note: Only support image of type CV_8UC3 and CV_8UC4) 111 | */ 112 | virtual void loadImageToInput(const cv::Mat& inputImage, int index = 0); 113 | 114 | /* 115 | Load byte data to model at index 116 | */ 117 | virtual void loadBytesToInput(const void* data, int index = 0); 118 | 119 | /* 120 | Run inference on the inputs. 121 | Can only run when all input tensors have been loaded. 122 | */ 123 | virtual void runInference(); 124 | 125 | /* 126 | A vector contains output data at index. 127 | Its shape is flattened from getOutputShape(index) 128 | */ 129 | virtual std::vector loadOutput(int index = 0) const; 130 | 131 | 132 | private: 133 | /* 134 | Constructor helper functions 135 | */ 136 | void loadModel(const char* modelPath); 137 | void buildInterpreter(int numThreads = -1); 138 | void allocateTensors(); 139 | void fillInputTensors(); 140 | void fillOutputTensors(); 141 | 142 | /* 143 | Check if index is valid for input and output tensor 144 | */ 145 | bool isIndexValid(int index, const char c = 'i') const; 146 | 147 | /* 148 | Check if all inputs have been loaded 149 | */ 150 | bool isAllInputsLoaded() const; 151 | 152 | /* 153 | Process input loads before run inference 154 | */ 155 | void inputChecker(); 156 | 157 | /* 158 | Convert image to float and resize to getInputShape(idx) 159 | */ 160 | cv::Mat preprocessImage(const cv::Mat& in, int idx) const; 161 | 162 | /* 163 | Convert image of type CV_8UC3 or CV_8UC4 to RGB format 164 | */ 165 | cv::Mat convertToRGB(const cv::Mat& in) const; 166 | 167 | 168 | private: 169 | /* 170 | Information of input tensors 171 | */ 172 | std::vector m_inputs; 173 | 174 | /* 175 | Information of output tensors 176 | */ 177 | std::vector m_outputs; 178 | 179 | /* 180 | TFLite core 181 | */ 182 | std::unique_ptr m_model; 183 | 184 | /* 185 | TFLite core 186 | */ 187 | std::unique_ptr m_interpreter; 188 | 189 | /* 190 | Tracking inputs loaded 191 | */ 192 | std::vector m_inputLoads; 193 | }; 194 | }; 195 | 196 | #endif // MODELLOADER_H -------------------------------------------------------------------------------- /src/ModelLoader.cpp: -------------------------------------------------------------------------------- 1 | #include "ModelLoader.hpp" 2 | 3 | #include 4 | 5 | #include "tensorflow/lite/builtin_op_data.h" 6 | #include "tensorflow/lite/kernels/register.h" 7 | 8 | #define INPUT_NORM_MEAN 127.5f 9 | #define INPUT_NORM_STD 127.5f 10 | 11 | 12 | my::ModelLoader::ModelLoader(std::string modelPath) { 13 | loadModel(modelPath.c_str()); 14 | buildInterpreter(); 15 | allocateTensors(); 16 | fillInputTensors(); 17 | fillOutputTensors(); 18 | 19 | m_inputLoads.resize(getNumberOfInputs(), false); 20 | } 21 | 22 | 23 | std::vector my::ModelLoader::getInputShape(int index) const { 24 | if (isIndexValid(index, 'i')) 25 | return m_inputs[index].dims; 26 | 27 | return std::vector(); 28 | } 29 | 30 | 31 | float* my::ModelLoader::getInputData(int index) const { 32 | if (isIndexValid(index, 'i')) 33 | return m_inputs[index].data; 34 | 35 | return nullptr; 36 | } 37 | 38 | 39 | size_t my::ModelLoader::getInputSize(int index) const { 40 | if (isIndexValid(index, 'i')) 41 | return m_inputs[index].bytes; 42 | 43 | return 0; 44 | } 45 | 46 | 47 | int my::ModelLoader::getNumberOfInputs() const { 48 | return m_inputs.size(); 49 | } 50 | 51 | 52 | std::vector my::ModelLoader::getOutputShape(int index) const { 53 | if (isIndexValid(index, 'o')) 54 | return m_outputs[index].dims; 55 | 56 | return std::vector(); 57 | } 58 | 59 | 60 | float* my::ModelLoader::getOutputData(int index) const { 61 | if (isIndexValid(index, 'o')) 62 | return m_outputs[index].data; 63 | 64 | return nullptr; 65 | } 66 | 67 | 68 | size_t my::ModelLoader::getOutputSize(int index) const { 69 | if (isIndexValid(index, 'o')) 70 | return m_outputs[index].bytes; 71 | 72 | return 0; 73 | } 74 | 75 | 76 | int my::ModelLoader::getNumberOfOutputs() const { 77 | return m_outputs.size(); 78 | } 79 | 80 | 81 | void my::ModelLoader::loadImageToInput(const cv::Mat& inputImage, int idx) { 82 | if (isIndexValid(idx, 'i')) { 83 | cv::Mat resizedImage = preprocessImage(inputImage, idx); // Need optimize 84 | loadBytesToInput(resizedImage.data, idx); 85 | } 86 | } 87 | 88 | 89 | void my::ModelLoader::loadBytesToInput(const void* data, int idx) { 90 | if (isIndexValid(idx, 'i')) { 91 | memcpy(m_inputs[idx].data, data, m_inputs[idx].bytes); 92 | m_inputLoads[idx] = true; 93 | } 94 | } 95 | 96 | 97 | void my::ModelLoader::runInference() { 98 | inputChecker(); 99 | m_interpreter->Invoke(); // Tflite inference 100 | } 101 | 102 | 103 | std::vector my::ModelLoader::loadOutput(int index) const { 104 | if (isIndexValid(index, 'o')) { 105 | int sizeInByte = m_outputs[index].bytes; 106 | int sizeInFloat = sizeInByte / sizeof(float); 107 | 108 | std::vector inference(sizeInFloat); 109 | memcpy(&(inference[0]), m_outputs[index].data, sizeInByte); 110 | 111 | return inference; 112 | } 113 | return std::vector(); 114 | } 115 | 116 | 117 | //-------------------Private methods start here------------------- 118 | 119 | void my::ModelLoader::loadModel(const char* modelPath) { 120 | m_model = tflite::FlatBufferModel::BuildFromFile(modelPath); 121 | if (m_model == nullptr) { 122 | std::cerr << "Fail to build FlatBufferModel from file: " << modelPath << std::endl; 123 | std::exit(1); 124 | } 125 | } 126 | 127 | 128 | void my::ModelLoader::buildInterpreter(int numThreads) { 129 | tflite::ops::builtin::BuiltinOpResolver resolver; 130 | 131 | if (tflite::InterpreterBuilder(*m_model, resolver)(&m_interpreter) != kTfLiteOk) { 132 | std::cerr << "Failed to build interpreter." << std::endl; 133 | std::exit(1); 134 | } 135 | m_interpreter->SetNumThreads(numThreads); 136 | } 137 | 138 | 139 | void my::ModelLoader::allocateTensors() { 140 | if (m_interpreter->AllocateTensors() != kTfLiteOk) { 141 | std::cerr << "Failed to allocate tensors." << std::endl; 142 | std::exit(1); 143 | } 144 | } 145 | 146 | 147 | void my::ModelLoader::fillInputTensors() { 148 | for (auto input: m_interpreter->inputs()) { 149 | TfLiteTensor* inputTensor = m_interpreter->tensor(input); 150 | TfLiteIntArray* dims = inputTensor->dims; 151 | 152 | m_inputs.push_back({ 153 | inputTensor->data.f, 154 | inputTensor->bytes, 155 | dims->data, 156 | dims->size 157 | }); 158 | } 159 | } 160 | 161 | 162 | void my::ModelLoader::fillOutputTensors() { 163 | for (auto output: m_interpreter->outputs()) { 164 | TfLiteTensor* outputTensor = m_interpreter->tensor(output); 165 | TfLiteIntArray* dims = outputTensor->dims; 166 | 167 | m_outputs.push_back({ 168 | outputTensor->data.f, 169 | outputTensor->bytes, 170 | dims->data, 171 | dims->size 172 | }); 173 | } 174 | } 175 | 176 | 177 | bool my::ModelLoader::isIndexValid(int idx, const char c) const { 178 | int size = 0; 179 | if (c == 'i') 180 | size = m_inputs.size(); 181 | else if (c == 'o') 182 | size = m_outputs.size(); 183 | else 184 | return false; 185 | 186 | if (idx < 0 || idx >= size) { 187 | std::cerr << "Index " << idx << " is out of range (" \ 188 | << size << ")." << std::endl; 189 | return false; 190 | } 191 | return true; 192 | } 193 | 194 | 195 | bool my::ModelLoader::isAllInputsLoaded() const { 196 | return ( 197 | std::find(m_inputLoads.begin(), m_inputLoads.end(), false) 198 | == m_inputLoads.end()); 199 | } 200 | 201 | 202 | void my::ModelLoader::inputChecker() { 203 | if (isAllInputsLoaded() == false) { 204 | std::cerr << "Input "; 205 | for (int i = 0; i < m_inputLoads.size(); ++i) { 206 | if (m_inputLoads[i] == false) { 207 | std::cerr << i << " "; 208 | } 209 | } 210 | std::cerr << "haven't been loaded." << std::endl; 211 | std::exit(1); 212 | } 213 | std::fill(m_inputLoads.begin(), m_inputLoads.end(), false); 214 | } 215 | 216 | 217 | cv::Mat my::ModelLoader::preprocessImage(const cv::Mat& in, int idx) const { 218 | auto out = convertToRGB(in); 219 | 220 | std::vector inputShape = getInputShape(idx); 221 | int H = inputShape[1]; 222 | int W = inputShape[2]; 223 | 224 | cv::Size wantedSize = cv::Size(W, H); 225 | cv::resize(out, out, wantedSize); 226 | 227 | /* 228 | Equivalent to (out - mean)/ std 229 | */ 230 | out.convertTo(out, CV_32FC3, 1 / INPUT_NORM_STD, -INPUT_NORM_MEAN / INPUT_NORM_STD); 231 | return out; 232 | } 233 | 234 | 235 | cv::Mat my::ModelLoader::convertToRGB(const cv::Mat& in) const { 236 | cv::Mat out; 237 | int type = in.type(); 238 | 239 | if (type == CV_8UC3) { 240 | cv::cvtColor(in, out, cv::COLOR_BGR2RGB); 241 | } 242 | else if (type == CV_8UC4) { 243 | cv::cvtColor(in, out, cv::COLOR_BGRA2RGB); 244 | } 245 | else { 246 | std::cerr << "Image of type " << type << " not supported" << std::endl; 247 | std::exit(1); 248 | } 249 | return out; 250 | } -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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No Surrender of Others' Freedom. 541 | 542 | If conditions are imposed on you (whether by court order, agreement or 543 | otherwise) that contradict the conditions of this License, they do not 544 | excuse you from the conditions of this License. If you cannot convey a 545 | covered work so as to satisfy simultaneously your obligations under this 546 | License and any other pertinent obligations, then as a consequence you may 547 | not convey it at all. For example, if you agree to terms that obligate you 548 | to collect a royalty for further conveying from those to whom you convey 549 | the Program, the only way you could satisfy both those terms and this 550 | License would be to refrain entirely from conveying the Program. 551 | 552 | 13. Use with the GNU Affero General Public License. 553 | 554 | Notwithstanding any other provision of this License, you have 555 | permission to link or combine any covered work with a work licensed 556 | under version 3 of the GNU Affero General Public License into a single 557 | combined work, and to convey the resulting work. The terms of this 558 | License will continue to apply to the part which is the covered work, 559 | but the special requirements of the GNU Affero General Public License, 560 | section 13, concerning interaction through a network will apply to the 561 | combination as such. 562 | 563 | 14. Revised Versions of this License. 564 | 565 | The Free Software Foundation may publish revised and/or new versions of 566 | the GNU General Public License from time to time. Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------