#pragma once
#include <unordered_map>
#include "fastdeploy/fastdeploy_model.h"
#include "fastdeploy/vision/common/processors/transform.h"
#include "fastdeploy/vision/common/result.h"
namespace fastdeploy {
namespace vision {
namespace facedet {
class FASTDEPLOY_DECL SCRFD : public FastDeployModel {
 public:
  SCRFD(const std::string& model_file, const std::string& params_file = "",
        const RuntimeOption& custom_option = RuntimeOption(), const ModelFormat& model_format = ModelFormat::ONNX);
  std::string ModelName() const { return "scrfd"; }
  virtual bool Predict(cv::Mat* im, FaceDetectionResult* result, float conf_threshold = 0.25f, float nms_iou_threshold = 0.4f);
 private:
  bool Initialize();
  bool Preprocess(Mat* mat, FDTensor* output, std::map<std::string, std::array<float, 2>>* im_info);
  bool Postprocess(std::vector<FDTensor>& infer_result, FaceDetectionResult* result, const std::map<std::string, std::array<float, 2>>& im_info, float conf_threshold, float nms_iou_threshold);
};
}  // namespace facedet
}  // namespace vision
}  // namespace fastdeploy   
     

   
     
    
      #include "fastdeploy/vision/facedet/contrib/scrfd.h"
#include "fastdeploy/utils/perf.h"
#include "fastdeploy/vision/utils/utils.h"
namespace fastdeploy {
namespace vision {
namespace facedet {
bool SCRFD::Preprocess(Mat* mat, FDTensor* output, std::map<std::string, std::array<float, 2>>* im_info) {
  return true;
}

bool SCRFD::Postprocess(std::vector<FDTensor>& infer_result, FaceDetectionResult* result, const std::map<std::string, std::array<float, 2>>& im_info, float conf_threshold, float nms_iou_threshold) {
  return true;
}

bool SCRFD::Predict(cv::Mat* im, FaceDetectionResult* result, float conf_threshold, float nms_iou_threshold) {
  return true;
}
}  // namespace facedet
}  // namespace vision
}  // namespace fastdeploy   
     

   
     
    
      git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy
mkdir build && cd build
cmake ..  -DENABLE_ORT_BACKEND=ON \
          -DENABLE_RKNPU2_BACKEND=ON \
          -DENABLE_VISION=ON \
          -DRKNN2_TARGET_SOC=RK3588 \
          -DCMAKE_INSTALL_PREFIX=${PWD}/fastdeploy-0.0.3
make -j8
make install   
     

   
     
    
      #include <iostream>
#include <string>
#include "fastdeploy/vision.h"
void RKNPU2Infer(const std::string& model_dir, const std::string& image_file) {
  auto model = fastdeploy::vision::facedet::SCRFD(model_file, params_file, option, format);
  model.Initialized();
  model.DisableNormalize();
  model.DisablePermute();
  auto im = cv::imread(image_file);
  fastdeploy::vision::FaceDetectionResult res;
  model.Predict(&im, &res)
  auto vis_im = fastdeploy::vision::VisFaceDetection(im, res);
  cv::imwrite("infer_rknn.jpg", vis_im);
  std::cout << "Visualized result saved in ./infer_rknn.jpg" << std::endl;
}

int main(int argc, char* argv[]) {
  if (argc < 3) {
    std::cout
        << "Usage: infer_demo path/to/model_dir path/to/image run_option, "
           "e.g ./infer_model ./picodet_model_dir ./test.jpeg"
        << std::endl;
    return -1;
  }

  RKNPU2Infer(argv[1], argv[2]);
  return 0;
}
   
     

   
     
    
      CMAKE_MINIMUM_REQUIRED(VERSION 3.10)
project(rknpu_test)
set(CMAKE_CXX_STANDARD 14)
# 指定下载解压后的fastdeploy库路径
set(FASTDEPLOY_INSTALL_DIR "thirdpartys/fastdeploy-0.7.0")
include(${FASTDEPLOY_INSTALL_DIR}/FastDeployConfig.cmake)
include_directories(${FastDeploy_INCLUDE_DIRS})
add_executable(rknpu_test infer.cc)
target_link_libraries(rknpu_test ${FastDeploy_LIBS})
   
     

   
     
    
      #include "fastdeploy/pybind/main.h"
namespace fastdeploy {
void BindSCRFD(pybind11::module& m) {
  // Bind SCRFD
  pybind11::class_<vision::facedet::SCRFD, FastDeployModel>(m, "SCRFD")
      .def(pybind11::init<std::string, std::string, RuntimeOption,
                          ModelFormat>())
      .def("predict",
           [](vision::facedet::SCRFD& self, pybind11::array& data,
              float conf_threshold, float nms_iou_threshold) {
             auto mat = PyArrayToCvMat(data);
             vision::FaceDetectionResult res;
             self.Predict(&mat, &res, conf_threshold, nms_iou_threshold);
             return res;
           })
      .def("disable_normalize",&vision::facedet::SCRFD::DisableNormalize)
      .def("disable_permute",&vision::facedet::SCRFD::DisablePermute);
}
}  // namespace fastdeploy   
     

   
     
    
      #include "fastdeploy/pybind/main.h"
namespace fastdeploy {
void BindSCRFD(pybind11::module& m);
void BindFaceDet(pybind11::module& m) {
  auto facedet_module = m.def_submodule("facedet", "Face detection models.");
  BindSCRFD(facedet_module);
}
}
   
     

   
     
    
      from __future__ import absolute_import
import logging
from .... import FastDeployModel, ModelFormat
from .... import c_lib_wrap as C
class SCRFD(FastDeployModel):
    def __init__(self,
                 model_file,
                 params_file="",
                 runtime_option=None,
                 model_format=ModelFormat.ONNX):
        super(SCRFD, self).__init__(runtime_option)

        self._model = C.vision.facedet.SCRFD(model_file, params_file, self._runtime_option, model_format)
        assert self.initialized, "SCRFD initialize failed."

    def predict(self, input_image, conf_threshold=0.7, nms_iou_threshold=0.3):
        return self._model.predict(input_image, conf_threshold, nms_iou_threshold)
   
     

   
     
    
      cd FastDeploy
cd python
export ENABLE_ORT_BACKEND=ON
export ENABLE_RKNPU2_BACKEND=ON
export ENABLE_VISION=ON
export RKNN2_TARGET_SOC=RK3588
python3 setup.py build
python3 setup.py bdist_wheel
cd dist
pip3 install fastdeploy_python-0.0.0-cp39-cp39-linux_aarch64.whl   
     

   
     
    
      import fastdeploy as fd
import cv2
import os
def parse_arguments():
    import argparse
    import ast
    parser = argparse.ArgumentParser()
    parser.add_argument("--model_file", required=True, help="Path of FaceDet model.")
    parser.add_argument("--image", type=str, required=True, help="Path of test image file.")
    return parser.parse_args()
def build_option(args):
    option = fd.RuntimeOption()
    option.use_rknpu2()
    return option
args = parse_arguments()
# 配置runtime，加载模型
runtime_option = build_option(args)
model_file = args.model_file
params_file = ""
model = fd.vision.facedet.SCRFD(model_file, params_file, runtime_option=runtime_option, model_format=fd.ModelFormat.RKNN)
model.disable_normalize()
model.disable_permute()
# 预测图片分割结果
im = cv2.imread(args.image)
result = model.predict(im)
print(result)
# 可视化结果
vis_im = fd.vision.vis_face_detection(im, result)
cv2.imwrite("visualized_result.jpg", vis_im)
print("Visualized result save in ./visualized_result.jpg")