🛠Lite.Ai.ToolKit: A lite C++ toolkit of awesome AI models, such as Object Detection, Face Detection, Face Recognition, Segmentation, Matting, etc. See Model Zoo and ONNX Hub, MNN Hub, TNN Hub, NCNN Hub.

News 👇👇

Most of my time now is focused on LLM/VLM Inference. Please check 📖Awesome-LLM-Inference , 📖Awesome-SD-Distributed-Inference and 📖CUDA-Learn-Notes for more details.

Features 👏👋

• Simply and User friendly. Simply and Consistent syntax like lite::cv::Type::Class, see examples.
• Minimum Dependencies. Only OpenCV and ONNXRuntime are required by default, see build.
• Many Models Supported. 300+ C++ implementations and 500+ weights 👉 Supported-Matrix.

Build 👇👇

Download prebuilt lite.ai.toolkit library from tag/v0.2.0, or just build it from source:

git clone --depth=1 https://github.com/DefTruth/lite.ai.toolkit.git  # latest
cd lite.ai.toolkit && sh ./build.sh # >= 0.2.0, support Linux only, tested on Ubuntu 20.04.6 LTS

Quick Start 🌟🌟

Example0: Object Detection using YOLOv5. Download model from Model-Zoo².

#include "lite/lite.h"

int main(int argc, char *argv[]) {
  std::string onnx_path = "yolov5s.onnx";
  std::string test_img_path = "test_yolov5.jpg";
  std::string save_img_path = "test_results.jpg";

  auto *yolov5 = new lite::cv::detection::YoloV5(onnx_path); 
  std::vector<lite::types::Boxf> detected_boxes;
  cv::Mat img_bgr = cv::imread(test_img_path);
  yolov5->detect(img_bgr, detected_boxes);

  lite::utils::draw_boxes_inplace(img_bgr, detected_boxes);
  cv::imwrite(save_img_path, img_bgr);  
  delete yolov5;
  return 0;
}

You can download the prebuilt lite.ai.tooklit library and test resources from tag/v0.2.0.

export LITE_AI_TAG_URL=https://github.com/DefTruth/lite.ai.toolkit/releases/download/v0.2.0
wget ${LITE_AI_TAG_URL}/lite-ort1.17.1+ocv4.9.0+ffmpeg4.2.2-linux-x86_64.tgz
wget ${LITE_AI_TAG_URL}/yolov5s.onnx && wget ${LITE_AI_TAG_URL}/test_yolov5.jpg

Quick Setup 👀

To quickly setup lite.ai.toolkit, you can follow the CMakeLists.txt listed as belows. 👇👀

set(lite.ai.toolkit_DIR YOUR-PATH-TO-LITE-INSTALL)
find_package(lite.ai.toolkit REQUIRED PATHS ${lite.ai.toolkit_DIR})
add_executable(lite_yolov5 test_lite_yolov5.cpp)
target_link_libraries(lite_yolov5 ${lite.ai.toolkit_LIBS})

Mixed with MNN or ONNXRuntime 👇👇

The goal of lite.ai.toolkit is not to abstract on top of MNN and ONNXRuntime. So, you can use lite.ai.toolkit mixed with MNN(-DENABLE_MNN=ON, default OFF) or ONNXRuntime(-DENABLE_ONNXRUNTIME=ON, default ON). The lite.ai.toolkit installation package contains complete MNN and ONNXRuntime. The workflow may looks like:

#include "lite/lite.h"
// 0. use yolov5 from lite.ai.toolkit to detect objs.
auto *yolov5 = new lite::cv::detection::YoloV5(onnx_path);
// 1. use OnnxRuntime or MNN to implement your own classfier.
interpreter = std::shared_ptr<MNN::Interpreter>(MNN::Interpreter::createFromFile(mnn_path));
// or: session = new Ort::Session(ort_env, onnx_path, session_options);
classfier = interpreter->createSession(schedule_config);
// 2. then, classify the detected objs use your own classfier ...

The included headers of MNN and ONNXRuntime can be found at mnn_config.h and ort_config.h.

Supported Models Matrix

• / = not supported now.
• ✅ = known work and official supported now.
• ✔️ = known work, but unofficial supported now.
• ❔ = in my plan, but not coming soon, maybe a few months later.

🔑️ More Examples！Click here!

## Examples. More examples can be found at [examples](https://github.com/DefTruth/lite.ai.toolkit/tree/main/examples/lite/cv).

#### Example0: Object Detection using [YOLOv5](https://github.com/ultralytics/yolov5). Download model from Model-Zoo[²](#lite.ai.toolkit-2). ```c++ #include "lite/lite.h" static void test_default() { std::string onnx_path = "../../../examples/hub/onnx/cv/yolov5s.onnx"; std::string test_img_path = "../../../examples/lite/resources/test_lite_yolov5_1.jpg"; std::string save_img_path = "../../../examples/logs/test_lite_yolov5_1.jpg"; auto *yolov5 = new lite::cv::detection::YoloV5(onnx_path); std::vector detected_boxes; cv::Mat img_bgr = cv::imread(test_img_path); yolov5->detect(img_bgr, detected_boxes); lite::utils::draw_boxes_inplace(img_bgr, detected_boxes); cv::imwrite(save_img_path, img_bgr); delete yolov5; } ``` The output is:

Or you can use Newest 🔥🔥 ! YOLO series's detector [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) or [YoloR](https://github.com/WongKinYiu/yolor). They got the similar results. More classes for general object detection (80 classes, COCO). ```c++ auto *detector = new lite::cv::detection::YoloX(onnx_path); // Newest YOLO detector !!! 2021-07 auto *detector = new lite::cv::detection::YoloV4(onnx_path); auto *detector = new lite::cv::detection::YoloV3(onnx_path); auto *detector = new lite::cv::detection::TinyYoloV3(onnx_path); auto *detector = new lite::cv::detection::SSD(onnx_path); auto *detector = new lite::cv::detection::YoloV5(onnx_path); auto *detector = new lite::cv::detection::YoloR(onnx_path); // Newest YOLO detector !!! 2021-05 auto *detector = new lite::cv::detection::TinyYoloV4VOC(onnx_path); auto *detector = new lite::cv::detection::TinyYoloV4COCO(onnx_path); auto *detector = new lite::cv::detection::ScaledYoloV4(onnx_path); auto *detector = new lite::cv::detection::EfficientDet(onnx_path); auto *detector = new lite::cv::detection::EfficientDetD7(onnx_path); auto *detector = new lite::cv::detection::EfficientDetD8(onnx_path); auto *detector = new lite::cv::detection::YOLOP(onnx_path); auto *detector = new lite::cv::detection::NanoDet(onnx_path); // Super fast and tiny! auto *detector = new lite::cv::detection::NanoDetPlus(onnx_path); // Super fast and tiny! 2021/12/25 auto *detector = new lite::cv::detection::NanoDetEfficientNetLite(onnx_path); // Super fast and tiny! auto *detector = new lite::cv::detection::YoloV5_V_6_0(onnx_path); auto *detector = new lite::cv::detection::YoloV5_V_6_1(onnx_path); auto *detector = new lite::cv::detection::YoloX_V_0_1_1(onnx_path); // Newest YOLO detector !!! 2021-07 auto *detector = new lite::cv::detection::YOLOv6(onnx_path); // Newest 2022 YOLO detector !!! ``` ****

#### Example1: Video Matting using [RobustVideoMatting2021🔥🔥🔥](https://github.com/PeterL1n/RobustVideoMatting). Download model from Model-Zoo[²](#lite.ai.toolkit-2). ```c++ #include "lite/lite.h" static void test_default() { std::string onnx_path = "../../../examples/hub/onnx/cv/rvm_mobilenetv3_fp32.onnx"; std::string video_path = "../../../examples/lite/resources/test_lite_rvm_0.mp4"; std::string output_path = "../../../examples/logs/test_lite_rvm_0.mp4"; std::string background_path = "../../../examples/lite/resources/test_lite_matting_bgr.jpg"; auto *rvm = new lite::cv::matting::RobustVideoMatting(onnx_path, 16); // 16 threads std::vector contents; // 1. video matting. cv::Mat background = cv::imread(background_path); rvm->detect_video(video_path, output_path, contents, false, 0.4f, 20, true, true, background); delete rvm; } ``` The output is:

More classes for matting (image matting, video matting, trimap/mask-free, trimap/mask-based) ```c++ auto *matting = new lite::cv::matting::RobustVideoMatting:(onnx_path); // WACV 2022. auto *matting = new lite::cv::matting::MGMatting(onnx_path); // CVPR 2021 auto *matting = new lite::cv::matting::MODNet(onnx_path); // AAAI 2022 auto *matting = new lite::cv::matting::MODNetDyn(onnx_path); // AAAI 2022 Dynamic Shape Inference. auto *matting = new lite::cv::matting::BackgroundMattingV2(onnx_path); // CVPR 2020 auto *matting = new lite::cv::matting::BackgroundMattingV2Dyn(onnx_path); // CVPR 2020 Dynamic Shape Inference. auto *matting = new lite::cv::matting::MobileHumanMatting(onnx_path); // 3Mb only !!! ``` ****

#### Example2: 1000 Facial Landmarks Detection using [FaceLandmarks1000](https://github.com/Single430/FaceLandmark1000). Download model from Model-Zoo[²](#lite.ai.toolkit-2). ```c++ #include "lite/lite.h" static void test_default() { std::string onnx_path = "../../../examples/hub/onnx/cv/FaceLandmark1000.onnx"; std::string test_img_path = "../../../examples/lite/resources/test_lite_face_landmarks_0.png"; std::string save_img_path = "../../../examples/logs/test_lite_face_landmarks_1000.jpg"; auto *face_landmarks_1000 = new lite::cv::face::align::FaceLandmark1000(onnx_path); lite::types::Landmarks landmarks; cv::Mat img_bgr = cv::imread(test_img_path); face_landmarks_1000->detect(img_bgr, landmarks); lite::utils::draw_landmarks_inplace(img_bgr, landmarks); cv::imwrite(save_img_path, img_bgr); delete face_landmarks_1000; } ``` The output is:

More classes for face alignment (68 points, 98 points, 106 points, 1000 points) ```c++ auto *align = new lite::cv::face::align::PFLD(onnx_path); // 106 landmarks, 1.0Mb only! auto *align = new lite::cv::face::align::PFLD98(onnx_path); // 98 landmarks, 4.8Mb only! auto *align = new lite::cv::face::align::PFLD68(onnx_path); // 68 landmarks, 2.8Mb only! auto *align = new lite::cv::face::align::MobileNetV268(onnx_path); // 68 landmarks, 9.4Mb only! auto *align = new lite::cv::face::align::MobileNetV2SE68(onnx_path); // 68 landmarks, 11Mb only! auto *align = new lite::cv::face::align::FaceLandmark1000(onnx_path); // 1000 landmarks, 2.0Mb only! auto *align = new lite::cv::face::align::PIPNet98(onnx_path); // 98 landmarks, CVPR2021! auto *align = new lite::cv::face::align::PIPNet68(onnx_path); // 68 landmarks, CVPR2021! auto *align = new lite::cv::face::align::PIPNet29(onnx_path); // 29 landmarks, CVPR2021! auto *align = new lite::cv::face::align::PIPNet19(onnx_path); // 19 landmarks, CVPR2021! ``` ****

#### Example3: Colorization using [colorization](https://github.com/richzhang/colorization). Download model from Model-Zoo[²](#lite.ai.toolkit-2). ```c++ #include "lite/lite.h" static void test_default() { std::string onnx_path = "../../../examples/hub/onnx/cv/eccv16-colorizer.onnx"; std::string test_img_path = "../../../examples/lite/resources/test_lite_colorizer_1.jpg"; std::string save_img_path = "../../../examples/logs/test_lite_eccv16_colorizer_1.jpg"; auto *colorizer = new lite::cv::colorization::Colorizer(onnx_path); cv::Mat img_bgr = cv::imread(test_img_path); lite::types::ColorizeContent colorize_content; colorizer->detect(img_bgr, colorize_content); if (colorize_content.flag) cv::imwrite(save_img_path, colorize_content.mat); delete colorizer; } ``` The output is:

More classes for colorization (gray to rgb) ```c++ auto *colorizer = new lite::cv::colorization::Colorizer(onnx_path); ``` ****

#### Example4: Face Recognition using [ArcFace](https://github.com/deepinsight/insightface/tree/master/recognition/arcface_torch). Download model from Model-Zoo[²](#lite.ai.toolkit-2). ```c++ #include "lite/lite.h" static void test_default() { std::string onnx_path = "../../../examples/hub/onnx/cv/ms1mv3_arcface_r100.onnx"; std::string test_img_path0 = "../../../examples/lite/resources/test_lite_faceid_0.png"; std::string test_img_path1 = "../../../examples/lite/resources/test_lite_faceid_1.png"; std::string test_img_path2 = "../../../examples/lite/resources/test_lite_faceid_2.png"; auto *glint_arcface = new lite::cv::faceid::GlintArcFace(onnx_path); lite::types::FaceContent face_content0, face_content1, face_content2; cv::Mat img_bgr0 = cv::imread(test_img_path0); cv::Mat img_bgr1 = cv::imread(test_img_path1); cv::Mat img_bgr2 = cv::imread(test_img_path2); glint_arcface->detect(img_bgr0, face_content0); glint_arcface->detect(img_bgr1, face_content1); glint_arcface->detect(img_bgr2, face_content2); if (face_content0.flag && face_content1.flag && face_content2.flag) { float sim01 = lite::utils::math::cosine_similarity( face_content0.embedding, face_content1.embedding); float sim02 = lite::utils::math::cosine_similarity( face_content0.embedding, face_content2.embedding); std::cout << "Detected Sim01: " << sim << " Sim02: " << sim02 << std::endl; } delete glint_arcface; } ``` The output is:

> Detected Sim01: 0.721159 Sim02: -0.0626267 More classes for face recognition (face id vector extract) ```c++ auto *recognition = new lite::cv::faceid::GlintCosFace(onnx_path); // DeepGlint(insightface) auto *recognition = new lite::cv::faceid::GlintArcFace(onnx_path); // DeepGlint(insightface) auto *recognition = new lite::cv::faceid::GlintPartialFC(onnx_path); // DeepGlint(insightface) auto *recognition = new lite::cv::faceid::FaceNet(onnx_path); auto *recognition = new lite::cv::faceid::FocalArcFace(onnx_path); auto *recognition = new lite::cv::faceid::FocalAsiaArcFace(onnx_path); auto *recognition = new lite::cv::faceid::TencentCurricularFace(onnx_path); // Tencent(TFace) auto *recognition = new lite::cv::faceid::TencentCifpFace(onnx_path); // Tencent(TFace) auto *recognition = new lite::cv::faceid::CenterLossFace(onnx_path); auto *recognition = new lite::cv::faceid::SphereFace(onnx_path); auto *recognition = new lite::cv::faceid::PoseRobustFace(onnx_path); auto *recognition = new lite::cv::faceid::NaivePoseRobustFace(onnx_path); auto *recognition = new lite::cv::faceid::MobileFaceNet(onnx_path); // 3.8Mb only ! auto *recognition = new lite::cv::faceid::CavaGhostArcFace(onnx_path); auto *recognition = new lite::cv::faceid::CavaCombinedFace(onnx_path); auto *recognition = new lite::cv::faceid::MobileSEFocalFace(onnx_path); // 4.5Mb only ! ``` ****

#### Example5: Face Detection using [SCRFD 2021](https://github.com/deepinsight/insightface/blob/master/detection/scrfd/). Download model from Model-Zoo[²](#lite.ai.toolkit-2). ```c++ #include "lite/lite.h" static void test_default() { std::string onnx_path = "../../../examples/hub/onnx/cv/scrfd_2.5g_bnkps_shape640x640.onnx"; std::string test_img_path = "../../../examples/lite/resources/test_lite_face_detector.jpg"; std::string save_img_path = "../../../examples/logs/test_lite_scrfd.jpg"; auto *scrfd = new lite::cv::face::detect::SCRFD(onnx_path); std::vector detected_boxes; cv::Mat img_bgr = cv::imread(test_img_path); scrfd->detect(img_bgr, detected_boxes); lite::utils::draw_boxes_with_landmarks_inplace(img_bgr, detected_boxes); cv::imwrite(save_img_path, img_bgr); delete scrfd; } ``` The output is:

More classes for face detection (super fast face detection) ```c++ auto *detector = new lite::face::detect::UltraFace(onnx_path); // 1.1Mb only ! auto *detector = new lite::face::detect::FaceBoxes(onnx_path); // 3.8Mb only ! auto *detector = new lite::face::detect::FaceBoxesv2(onnx_path); // 4.0Mb only ! auto *detector = new lite::face::detect::RetinaFace(onnx_path); // 1.6Mb only ! CVPR2020 auto *detector = new lite::face::detect::SCRFD(onnx_path); // 2.5Mb only ! CVPR2021, Super fast and accurate!! auto *detector = new lite::face::detect::YOLO5Face(onnx_path); // 2021, Super fast and accurate!! auto *detector = new lite::face::detect::YOLOv5BlazeFace(onnx_path); // 2021, Super fast and accurate!! ``` ****

#### Example6: Object Segmentation using [DeepLabV3ResNet101](https://pytorch.org/hub/pytorch_vision_deeplabv3_resnet101/). Download model from Model-Zoo[²](#lite.ai.toolkit-2). ```c++ #include "lite/lite.h" static void test_default() { std::string onnx_path = "../../../examples/hub/onnx/cv/deeplabv3_resnet101_coco.onnx"; std::string test_img_path = "../../../examples/lite/resources/test_lite_deeplabv3_resnet101.png"; std::string save_img_path = "../../../examples/logs/test_lite_deeplabv3_resnet101.jpg"; auto *deeplabv3_resnet101 = new lite::cv::segmentation::DeepLabV3ResNet101(onnx_path, 16); // 16 threads lite::types::SegmentContent content; cv::Mat img_bgr = cv::imread(test_img_path); deeplabv3_resnet101->detect(img_bgr, content); if (content.flag) { cv::Mat out_img; cv::addWeighted(img_bgr, 0.2, content.color_mat, 0.8, 0., out_img); cv::imwrite(save_img_path, out_img); if (!content.names_map.empty()) { for (auto it = content.names_map.begin(); it != content.names_map.end(); ++it) { std::cout << it->first << " Name: " << it->second << std::endl; } } } delete deeplabv3_resnet101; } ``` The output is:

More classes for object segmentation (general objects segmentation) ```c++ auto *segment = new lite::cv::segmentation::FCNResNet101(onnx_path); auto *segment = new lite::cv::segmentation::DeepLabV3ResNet101(onnx_path); ``` ****

#### Example7: Age Estimation using [SSRNet](https://github.com/oukohou/SSR_Net_Pytorch) . Download model from Model-Zoo[²](#lite.ai.toolkit-2). ```c++ #include "lite/lite.h" static void test_default() { std::string onnx_path = "../../../examples/hub/onnx/cv/ssrnet.onnx"; std::string test_img_path = "../../../examples/lite/resources/test_lite_ssrnet.jpg"; std::string save_img_path = "../../../examples/logs/test_lite_ssrnet.jpg"; auto *ssrnet = new lite::cv::face::attr::SSRNet(onnx_path); lite::types::Age age; cv::Mat img_bgr = cv::imread(test_img_path); ssrnet->detect(img_bgr, age); lite::utils::draw_age_inplace(img_bgr, age); cv::imwrite(save_img_path, img_bgr); delete ssrnet; } ``` The output is:

More classes for face attributes analysis (age, gender, emotion) ```c++ auto *attribute = new lite::cv::face::attr::AgeGoogleNet(onnx_path); auto *attribute = new lite::cv::face::attr::GenderGoogleNet(onnx_path); auto *attribute = new lite::cv::face::attr::EmotionFerPlus(onnx_path); auto *attribute = new lite::cv::face::attr::VGG16Age(onnx_path); auto *attribute = new lite::cv::face::attr::VGG16Gender(onnx_path); auto *attribute = new lite::cv::face::attr::EfficientEmotion7(onnx_path); // 7 emotions, 15Mb only! auto *attribute = new lite::cv::face::attr::EfficientEmotion8(onnx_path); // 8 emotions, 15Mb only! auto *attribute = new lite::cv::face::attr::MobileEmotion7(onnx_path); // 7 emotions, 13Mb only! auto *attribute = new lite::cv::face::attr::ReXNetEmotion7(onnx_path); // 7 emotions auto *attribute = new lite::cv::face::attr::SSRNet(onnx_path); // age estimation, 190kb only!!! ``` ****

#### Example8: 1000 Classes Classification using [DenseNet](https://pytorch.org/hub/pytorch_vision_densenet/). Download model from Model-Zoo[²](#lite.ai.toolkit-2). ```c++ #include "lite/lite.h" static void test_default() { std::string onnx_path = "../../../examples/hub/onnx/cv/densenet121.onnx"; std::string test_img_path = "../../../examples/lite/resources/test_lite_densenet.jpg"; auto *densenet = new lite::cv::classification::DenseNet(onnx_path); lite::types::ImageNetContent content; cv::Mat img_bgr = cv::imread(test_img_path); densenet->detect(img_bgr, content); if (content.flag) { const unsigned int top_k = content.scores.size(); if (top_k > 0) { for (unsigned int i = 0; i < top_k; ++i) std::cout << i + 1 << ": " << content.labels.at(i) << ": " << content.texts.at(i) << ": " << content.scores.at(i) << std::endl; } } delete densenet; } ``` The output is:

More classes for image classification (1000 classes) ```c++ auto *classifier = new lite::cv::classification::EfficientNetLite4(onnx_path); auto *classifier = new lite::cv::classification::ShuffleNetV2(onnx_path); // 8.7Mb only! auto *classifier = new lite::cv::classification::GhostNet(onnx_path); auto *classifier = new lite::cv::classification::HdrDNet(onnx_path); auto *classifier = new lite::cv::classification::IBNNet(onnx_path); auto *classifier = new lite::cv::classification::MobileNetV2(onnx_path); // 13Mb only! auto *classifier = new lite::cv::classification::ResNet(onnx_path); auto *classifier = new lite::cv::classification::ResNeXt(onnx_path); ``` ****

#### Example9: Head Pose Estimation using [FSANet](https://github.com/omasaht/headpose-fsanet-pytorch). Download model from Model-Zoo[²](#lite.ai.toolkit-2). ```c++ #include "lite/lite.h" static void test_default() { std::string onnx_path = "../../../examples/hub/onnx/cv/fsanet-var.onnx"; std::string test_img_path = "../../../examples/lite/resources/test_lite_fsanet.jpg"; std::string save_img_path = "../../../examples/logs/test_lite_fsanet.jpg"; auto *fsanet = new lite::cv::face::pose::FSANet(onnx_path); cv::Mat img_bgr = cv::imread(test_img_path); lite::types::EulerAngles euler_angles; fsanet->detect(img_bgr, euler_angles); if (euler_angles.flag) { lite::utils::draw_axis_inplace(img_bgr, euler_angles); cv::imwrite(save_img_path, img_bgr); std::cout << "yaw:" << euler_angles.yaw << " pitch:" << euler_angles.pitch << " row:" << euler_angles.roll << std::endl; } delete fsanet; } ``` The output is:

More classes for head pose estimation (euler angle, yaw, pitch, roll) ```c++ auto *pose = new lite::cv::face::pose::FSANet(onnx_path); // 1.2Mb only! ``` ****

#### Example10: Style Transfer using [FastStyleTransfer](https://github.com/onnx/models/tree/master/vision/style_transfer/fast_neural_style). Download model from Model-Zoo[²](#lite.ai.toolkit-2). ```c++ #include "lite/lite.h" static void test_default() { std::string onnx_path = "../../../examples/hub/onnx/cv/style-candy-8.onnx"; std::string test_img_path = "../../../examples/lite/resources/test_lite_fast_style_transfer.jpg"; std::string save_img_path = "../../../examples/logs/test_lite_fast_style_transfer_candy.jpg"; auto *fast_style_transfer = new lite::cv::style::FastStyleTransfer(onnx_path); lite::types::StyleContent style_content; cv::Mat img_bgr = cv::imread(test_img_path); fast_style_transfer->detect(img_bgr, style_content); if (style_content.flag) cv::imwrite(save_img_path, style_content.mat); delete fast_style_transfer; } ``` The output is:

More classes for style transfer (neural style transfer, others) ```c++ auto *transfer = new lite::cv::style::FastStyleTransfer(onnx_path); // 6.4Mb only ``` **** #### Example11: Human Head Segmentation using [HeadSeg](https://github.com/minivision-ai/photo2cartoon). Download model from Model-Zoo[²](#lite.ai.toolkit-2). ```c++ #include "lite/lite.h" static void test_default() { std::string onnx_path = "../../../examples/hub/onnx/cv/minivision_head_seg.onnx"; std::string test_img_path = "../../../examples/lite/resources/test_lite_head_seg.png"; std::string save_img_path = "../../../examples/logs/test_lite_head_seg.jpg"; auto *head_seg = new lite::cv::segmentation::HeadSeg(onnx_path, 4); // 4 threads lite::types::HeadSegContent content; cv::Mat img_bgr = cv::imread(test_img_path); head_seg->detect(img_bgr, content); if (content.flag) cv::imwrite(save_img_path, content.mask * 255.f); delete head_seg; } ``` The output is:

More classes for human segmentation (head, portrait, hair, others) ```c++ auto *segment = new lite::cv::segmentation::HeadSeg(onnx_path); // 31Mb auto *segment = new lite::cv::segmentation::FastPortraitSeg(onnx_path); // <= 400Kb !!! auto *segment = new lite::cv::segmentation::PortraitSegSINet(onnx_path); // <= 380Kb !!! auto *segment = new lite::cv::segmentation::PortraitSegExtremeC3Net(onnx_path); // <= 180Kb !!! Extreme Tiny !!! auto *segment = new lite::cv::segmentation::FaceHairSeg(onnx_path); // 18M auto *segment = new lite::cv::segmentation::HairSeg(onnx_path); // 18M auto *segment = new lite::cv::segmentation::MobileHairSeg(onnx_path); // 14M ``` **** #### Example12: Photo transfer to Cartoon [Photo2Cartoon](https://github.com/minivision-ai/photo2cartoon). Download model from Model-Zoo[²](#lite.ai.toolkit-2). ```c++ #include "lite/lite.h" static void test_default() { std::string head_seg_onnx_path = "../../../examples/hub/onnx/cv/minivision_head_seg.onnx"; std::string cartoon_onnx_path = "../../../examples/hub/onnx/cv/minivision_female_photo2cartoon.onnx"; std::string test_img_path = "../../../examples/lite/resources/test_lite_female_photo2cartoon.jpg"; std::string save_mask_path = "../../../examples/logs/test_lite_female_photo2cartoon_seg.jpg"; std::string save_cartoon_path = "../../../examples/logs/test_lite_female_photo2cartoon_cartoon.jpg"; auto *head_seg = new lite::cv::segmentation::HeadSeg(head_seg_onnx_path, 4); // 4 threads auto *female_photo2cartoon = new lite::cv::style::FemalePhoto2Cartoon(cartoon_onnx_path, 4); // 4 threads lite::types::HeadSegContent head_seg_content; cv::Mat img_bgr = cv::imread(test_img_path); head_seg->detect(img_bgr, head_seg_content); if (head_seg_content.flag && !head_seg_content.mask.empty()) { cv::imwrite(save_mask_path, head_seg_content.mask * 255.f); // Female Photo2Cartoon Style Transfer lite::types::FemalePhoto2CartoonContent female_cartoon_content; female_photo2cartoon->detect(img_bgr, head_seg_content.mask, female_cartoon_content); if (female_cartoon_content.flag && !female_cartoon_content.cartoon.empty()) cv::imwrite(save_cartoon_path, female_cartoon_content.cartoon); } delete head_seg; delete female_photo2cartoon; } ``` The output is:

More classes for photo style transfer. ```c++ auto *transfer = new lite::cv::style::FemalePhoto2Cartoon(onnx_path); ``` **** #### Example13: Face Parsing using [FaceParsing](https://github.com/zllrunning/face-parsing.PyTorch). Download model from Model-Zoo[²](#lite.ai.toolkit-2). ```c++ #include "lite/lite.h" static void test_default() { std::string onnx_path = "../../../examples/hub/onnx/cv/face_parsing_512x512.onnx"; std::string test_img_path = "../../../examples/lite/resources/test_lite_face_parsing.png"; std::string save_img_path = "../../../examples/logs/test_lite_face_parsing_bisenet.jpg"; auto *face_parsing_bisenet = new lite::cv::segmentation::FaceParsingBiSeNet(onnx_path, 8); // 8 threads lite::types::FaceParsingContent content; cv::Mat img_bgr = cv::imread(test_img_path); face_parsing_bisenet->detect(img_bgr, content); if (content.flag && !content.merge.empty()) cv::imwrite(save_img_path, content.merge); delete face_parsing_bisenet; } ``` The output is:

More classes for face parsing (hair, eyes, nose, mouth, others) ```c++ auto *segment = new lite::cv::segmentation::FaceParsingBiSeNet(onnx_path); // 50Mb auto *segment = new lite::cv::segmentation::FaceParsingBiSeNetDyn(onnx_path); // Dynamic Shape Inference. ```

Citations 🎉🎉

@misc{lite.ai.toolkit@2021,
  title={lite.ai.toolkit: A lite C++ toolkit of awesome AI models.},
  url={https://github.com/DefTruth/lite.ai.toolkit},
  note={Open-source software available at https://github.com/DefTruth/lite.ai.toolkit},
  author={DefTruth, wangzijian1010 etc},
  year={2021}
}