Download resources

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

Write test code

#include"lite/lite.h"intmain(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 =newlite::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;return0;}

Setup CMakeLists.txt

cmake_minimum_required(VERSION 3.10)project(lite_yolov5)set(CMAKE_CXX_STANDARD 17)set(lite.ai.toolkit_DIR YOUR-PATH-TO-LITE-INSTALL)find_package(lite.ai.toolkit REQUIREDPATHS ${lite.ai.toolkit_DIR})if (lite.ai.toolkit_Found)    message(STATUS"lite.ai.toolkit_INCLUDE_DIRS: ${lite.ai.toolkit_INCLUDE_DIRS}")    message(STATUS"        lite.ai.toolkit_LIBS: ${lite.ai.toolkit_LIBS}")    message(STATUS"   lite.ai.toolkit_LIBS_DIRS: ${lite.ai.toolkit_LIBS_DIRS}")endif()add_executable(lite_yolov5 test_lite_yolov5.cpp)target_link_libraries(lite_yolov5 ${lite.ai.toolkit_LIBS})

Build example

mkdir build&&cd build&& cmake ..&& make -j1

export LD_LIBRARY_PATH=YOUR-PATH-TO-LITE-INSTALL/lib:$LD_LIBRARY_PATHexport LD_LIBRARY_PATH=YOUR-PATH-TO-LITE-INSTALL/third_party/opencv/lib:$LD_LIBRARY_PATHexport LD_LIBRARY_PATH=YOUR-PATH-TO-LITE-INSTALL/third_party/onnxruntime/lib:$LD_LIBRARY_PATHexport LD_LIBRARY_PATH=YOUR-PATH-TO-LITE-INSTALL/third_party/MNN/lib:$LD_LIBRARY_PATH# if -DENABLE_MNN=ON

Run binary:

cp ../yolov5s.onnx ../test_yolov.jpg../lite_yolov5

LITEORT_DEBUG LogId: ../examples/hub/onnx/cv/yolov5s.onnx=============== Input-Dims ==============Name: imagesDims: 1Dims: 3Dims: 640Dims: 640=============== Output-Dims ==============Output: 0 Name: pred Dim: 0 :1Output: 0 Name: pred Dim: 1 :25200Output: 0 Name: pred Dim: 2 :85Output: 1 Name: output2 Dim: 0 :1......Output: 3 Name: output4 Dim: 1 :3Output: 3 Name: output4 Dim: 2 :20Output: 3 Name: output4 Dim: 3 :20Output: 3 Name: output4 Dim: 4 :85========================================detected num_anchors: 25200generate_bboxes num: 48

Model Zoo.

  docker pull qyjdefdocker/lite.ai.toolkit-onnx-hub:v0.1.22.01.08# (28G)  docker pull qyjdefdocker/lite.ai.toolkit-mnn-hub:v0.1.22.01.08# (11G)  docker pull qyjdefdocker/lite.ai.toolkit-ncnn-hub:v0.1.22.01.08# (9G)  docker pull qyjdefdocker/lite.ai.toolkit-tnn-hub:v0.1.22.01.08# (11G)  docker pull qyjdefdocker/lite.ai.toolkit-onnx-hub:v0.1.22.02.02# (400M) + YOLO5Face  docker pull qyjdefdocker/lite.ai.toolkit-mnn-hub:v0.1.22.02.02# (213M) + YOLO5Face  docker pull qyjdefdocker/lite.ai.toolkit-ncnn-hub:v0.1.22.02.02# (197M) + YOLO5Face  docker pull qyjdefdocker/lite.ai.toolkit-tnn-hub:v0.1.22.02.02# (217M) + YOLO5Face

🔑️ How to download Model Zoo from Docker Hub?

Model Hubs

🔑️ More Examples.

Example0: Object Detection usingYOLOv5. Download model from Model-Zoo².

#include"lite/lite.h"staticvoidtest_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 =newlite::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;}

auto *detector =new lite::cv::detection::YoloX(onnx_path);// Newest YOLO detector !!! 2021-07auto *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 !!! 2021-05auto *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! 2021/12/25auto *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 !!! 2021-07auto *detector =new lite::cv::detection::YOLOv6(onnx_path);// Newest 2022 YOLO detector !!!

Example1: Video Matting usingRobustVideoMatting2021🔥🔥🔥. Download model from Model-Zoo².

#include"lite/lite.h"staticvoidtest_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 =newlite::cv::matting::RobustVideoMatting(onnx_path,16);// 16 threads  std::vector<lite::types::MattingContent> 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;}

auto *matting =new lite::cv::matting::RobustVideoMatting:(onnx_path);//  WACV 2022.auto *matting =new lite::cv::matting::MGMatting(onnx_path);// CVPR 2021auto *matting =new lite::cv::matting::MODNet(onnx_path);// AAAI 2022auto *matting =new lite::cv::matting::MODNetDyn(onnx_path);// AAAI 2022 Dynamic Shape Inference.auto *matting =new lite::cv::matting::BackgroundMattingV2(onnx_path);// CVPR 2020auto *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 usingFaceLandmarks1000. Download model from Model-Zoo².

#include"lite/lite.h"staticvoidtest_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 =newlite::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;}

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 usingcolorization. Download model from Model-Zoo².

#include"lite/lite.h"staticvoidtest_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 =newlite::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;}

auto *colorizer =new lite::cv::colorization::Colorizer(onnx_path);

Example4: Face Recognition usingArcFace. Download model from Model-Zoo².

#include"lite/lite.h"staticvoidtest_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 =newlite::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<float>(        face_content0.embedding, face_content1.embedding);float sim02 = lite::utils::math::cosine_similarity<float>(        face_content0.embedding, face_content2.embedding);    std::cout <<"Detected Sim01:" << sim  <<" Sim02:" << sim02 << std::endl;  }delete glint_arcface;}

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 usingSCRFD 2021. Download model from Model-Zoo².

#include"lite/lite.h"staticvoidtest_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 =newlite::cv::face::detect::SCRFD(onnx_path);    std::vector<lite::types::BoxfWithLandmarks> 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;}

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 ! CVPR2020auto *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 usingDeepLabV3ResNet101. Download model from Model-Zoo².

#include"lite/lite.h"staticvoidtest_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 =newlite::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;}

auto *segment =new lite::cv::segmentation::FCNResNet101(onnx_path);auto *segment =new lite::cv::segmentation::DeepLabV3ResNet101(onnx_path);

Example7: Age Estimation usingSSRNet . Download model from Model-Zoo².

#include"lite/lite.h"staticvoidtest_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 =newlite::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;}

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 emotionsauto *attribute =new lite::cv::face::attr::SSRNet(onnx_path);// age estimation, 190kb only!!!

Example8: 1000 Classes Classification usingDenseNet. Download model from Model-Zoo².

#include"lite/lite.h"staticvoidtest_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 =newlite::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)  {constunsignedint top_k = content.scores.size();if (top_k >0)    {for (unsignedint 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;}

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 usingFSANet. Download model from Model-Zoo².

#include"lite/lite.h"staticvoidtest_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 =newlite::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;}

auto *pose =new lite::cv::face::pose::FSANet(onnx_path);// 1.2Mb only!

Example10: Style Transfer usingFastStyleTransfer. Download model from Model-Zoo².

#include"lite/lite.h"staticvoidtest_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 =newlite::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;}

auto *transfer =new lite::cv::style::FastStyleTransfer(onnx_path);// 6.4Mb only

Example11: Human Head Segmentation usingHeadSeg. Download model from Model-Zoo².

#include"lite/lite.h"staticvoidtest_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 =newlite::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;}

auto *segment =new lite::cv::segmentation::HeadSeg(onnx_path);// 31Mbauto *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);// 18Mauto *segment =new lite::cv::segmentation::HairSeg(onnx_path);// 18Mauto *segment =new lite::cv::segmentation::MobileHairSeg(onnx_path);// 14M

Example12: Photo transfer to CartoonPhoto2Cartoon. Download model from Model-Zoo².

#include"lite/lite.h"staticvoidtest_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 =newlite::cv::segmentation::HeadSeg(head_seg_onnx_path,4);// 4 threadsauto *female_photo2cartoon =newlite::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;}

auto *transfer =new lite::cv::style::FemalePhoto2Cartoon(onnx_path);

Example13: Face Parsing usingFaceParsing. Download model from Model-Zoo².

#include"lite/lite.h"staticvoidtest_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 =newlite::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;}

auto *segment =new lite::cv::segmentation::FaceParsingBiSeNet(onnx_path);// 50Mbauto *segment =new lite::cv::segmentation::FaceParsingBiSeNetDyn(onnx_path);// Dynamic Shape Inference.