Merge branch 'develop' into new_ppdet1

csrcs/fastdeploy/vision.h

@@ -17,6 +17,7 @@
 #ifdef ENABLE_VISION
 #include "fastdeploy/vision/biubug6/retinaface.h"
 #include "fastdeploy/vision/deepcam/yolov5face.h"
+#include "fastdeploy/vision/deepinsight/scrfd.h"
 #include "fastdeploy/vision/linzaer/ultraface.h"
 #include "fastdeploy/vision/megvii/yolox.h"
 #include "fastdeploy/vision/meituan/yolov6.h"

csrcs/fastdeploy/vision/deepinsight/deepinsight_pybind.cc

@@ -0,0 +1,47 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "fastdeploy/pybind/main.h"
+
+namespace fastdeploy {
+void BindDeepinsight(pybind11::module& m) {
+  auto deepinsight_module = m.def_submodule(
+      "deepinsight", "https://github.com/deepinsight");
+  pybind11::class_<vision::deepinsight::SCRFD, FastDeployModel>(
+      deepinsight_module, "SCRFD")
+      .def(pybind11::init<std::string, std::string, RuntimeOption, Frontend>())
+      .def("predict",
+           [](vision::deepinsight::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_readwrite("size", &vision::deepinsight::SCRFD::size)
+      .def_readwrite("padding_value",
+                     &vision::deepinsight::SCRFD::padding_value)
+      .def_readwrite("is_mini_pad", &vision::deepinsight::SCRFD::is_mini_pad)
+      .def_readwrite("is_no_pad", &vision::deepinsight::SCRFD::is_no_pad)
+      .def_readwrite("is_scale_up", &vision::deepinsight::SCRFD::is_scale_up)
+      .def_readwrite("stride", &vision::deepinsight::SCRFD::stride)
+      .def_readwrite("use_kps", &vision::deepinsight::SCRFD::use_kps)
+      .def_readwrite("max_nms", &vision::deepinsight::SCRFD::max_nms)
+      .def_readwrite("downsample_strides",
+                     &vision::deepinsight::SCRFD::downsample_strides)
+      .def_readwrite("num_anchors", &vision::deepinsight::SCRFD::num_anchors)
+      .def_readwrite("landmarks_per_face",
+                     &vision::deepinsight::SCRFD::landmarks_per_face);
+}
+}  // namespace fastdeploy

csrcs/fastdeploy/vision/deepinsight/scrfd.cc

@@ -0,0 +1,363 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "fastdeploy/vision/deepinsight/scrfd.h"
+#include "fastdeploy/utils/perf.h"
+#include "fastdeploy/vision/utils/utils.h"
+
+namespace fastdeploy {
+
+namespace vision {
+
+namespace deepinsight {
+
+void SCRFD::LetterBox(Mat* mat, const std::vector<int>& size,
+                      const std::vector<float>& color, bool _auto,
+                      bool scale_fill, bool scale_up, int stride) {
+  float scale =
+      std::min(size[1] * 1.0 / mat->Height(), size[0] * 1.0 / mat->Width());
+  if (!scale_up) {
+    scale = std::min(scale, 1.0f);
+  }
+
+  int resize_h = int(round(mat->Height() * scale));
+  int resize_w = int(round(mat->Width() * scale));
+
+  int pad_w = size[0] - resize_w;
+  int pad_h = size[1] - resize_h;
+  if (_auto) {
+    pad_h = pad_h % stride;
+    pad_w = pad_w % stride;
+  } else if (scale_fill) {
+    pad_h = 0;
+    pad_w = 0;
+    resize_h = size[1];
+    resize_w = size[0];
+  }
+  if (resize_h != mat->Height() || resize_w != mat->Width()) {
+    Resize::Run(mat, resize_w, resize_h);
+  }
+  if (pad_h > 0 || pad_w > 0) {
+    float half_h = pad_h * 1.0 / 2;
+    int top = int(round(half_h - 0.1));
+    int bottom = int(round(half_h + 0.1));
+    float half_w = pad_w * 1.0 / 2;
+    int left = int(round(half_w - 0.1));
+    int right = int(round(half_w + 0.1));
+    Pad::Run(mat, top, bottom, left, right, color);
+  }
+}
+
+SCRFD::SCRFD(const std::string& model_file, const std::string& params_file,
+             const RuntimeOption& custom_option, const Frontend& model_format) {
+  if (model_format == Frontend::ONNX) {
+    valid_cpu_backends = {Backend::ORT};  // 指定可用的CPU后端
+    valid_gpu_backends = {Backend::ORT, Backend::TRT};  // 指定可用的GPU后端
+  } else {
+    valid_cpu_backends = {Backend::PDINFER, Backend::ORT};
+    valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT};
+  }
+  runtime_option = custom_option;
+  runtime_option.model_format = model_format;
+  runtime_option.model_file = model_file;
+  runtime_option.params_file = params_file;
+  initialized = Initialize();
+}
+
+bool SCRFD::Initialize() {
+  // parameters for preprocess
+  use_kps = true;
+  size = {640, 640};
+  padding_value = {0.0, 0.0, 0.0};
+  is_mini_pad = false;
+  is_no_pad = false;
+  is_scale_up = false;
+  stride = 32;
+  downsample_strides = {8, 16, 32};
+  num_anchors = 2;
+  landmarks_per_face = 5;
+  center_points_is_update_ = false;
+  max_nms = 30000;
+  // num_outputs = use_kps ? 9 : 6;
+  if (!InitRuntime()) {
+    FDERROR << "Failed to initialize fastdeploy backend." << std::endl;
+    return false;
+  }
+  // Check if the input shape is dynamic after Runtime already initialized,
+  // Note that, We need to force is_mini_pad 'false' to keep static
+  // shape after padding (LetterBox) when the is_dynamic_shape is 'false'.
+  is_dynamic_input_ = false;
+  auto shape = InputInfoOfRuntime(0).shape;
+  for (int i = 0; i < shape.size(); ++i) {
+    // if height or width is dynamic
+    if (i >= 2 && shape[i] <= 0) {
+      is_dynamic_input_ = true;
+      break;
+    }
+  }
+  if (!is_dynamic_input_) {
+    is_mini_pad = false;
+  }
+
+  return true;
+}
+
+bool SCRFD::Preprocess(Mat* mat, FDTensor* output,
+                       std::map<std::string, std::array<float, 2>>* im_info) {
+  float ratio = std::min(size[1] * 1.0f / static_cast<float>(mat->Height()),
+                         size[0] * 1.0f / static_cast<float>(mat->Width()));
+  if (ratio != 1.0) {
+    int interp = cv::INTER_AREA;
+    if (ratio > 1.0) {
+      interp = cv::INTER_LINEAR;
+    }
+    int resize_h = int(mat->Height() * ratio);
+    int resize_w = int(mat->Width() * ratio);
+    Resize::Run(mat, resize_w, resize_h, -1, -1, interp);
+  }
+  // scrfd's preprocess steps
+  // 1. letterbox
+  // 2. BGR->RGB
+  // 3. HWC->CHW
+  SCRFD::LetterBox(mat, size, padding_value, is_mini_pad, is_no_pad,
+                   is_scale_up, stride);
+
+  BGR2RGB::Run(mat);
+  // Normalize::Run(mat, std::vector<float>(mat->Channels(), 0.0),
+  //                std::vector<float>(mat->Channels(), 1.0));
+  // Compute `result = mat * alpha + beta` directly by channel
+  // Original Repo/tools/scrfd.py: cv2.dnn.blobFromImage(img, 1.0/128,
+  // input_size, (127.5, 127.5, 127.5), swapRB=True)
+  std::vector<float> alpha = {1.f / 128.f, 1.f / 128.f, 1.f / 128.f};
+  std::vector<float> beta = {-127.5f / 128.f, -127.5f / 128.f, -127.5f / 128.f};
+  Convert::Run(mat, alpha, beta);
+  // Record output shape of preprocessed image
+  (*im_info)["output_shape"] = {static_cast<float>(mat->Height()),
+                                static_cast<float>(mat->Width())};
+  HWC2CHW::Run(mat);
+  Cast::Run(mat, "float");
+  mat->ShareWithTensor(output);
+  output->shape.insert(output->shape.begin(), 1);  // reshape to n, h, w, c
+  return true;
+}
+
+void SCRFD::GeneratePoints() {
+  if (center_points_is_update_ && !is_dynamic_input_) {
+    return;
+  }
+  // 8, 16, 32
+  for (auto local_stride : downsample_strides) {
+    unsigned int num_grid_w = size[0] / local_stride;
+    unsigned int num_grid_h = size[1] / local_stride;
+    // y
+    for (unsigned int i = 0; i < num_grid_h; ++i) {
+      // x
+      for (unsigned int j = 0; j < num_grid_w; ++j) {
+        // num_anchors, col major
+        for (unsigned int k = 0; k < num_anchors; ++k) {
+          SCRFDPoint point;
+          point.cx = static_cast<float>(j);
+          point.cy = static_cast<float>(i);
+          center_points_[local_stride].push_back(point);
+        }
+      }
+    }
+  }
+
+  center_points_is_update_ = 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) {
+  // number of downsample_strides
+  int fmc = downsample_strides.size();
+  // scrfd has 6,9,10,15 output tensors
+  FDASSERT((infer_result.size() == 9 || infer_result.size() == 6 ||
+            infer_result.size() == 10 || infer_result.size() == 15),
+           "The default number of output tensor must be 6, 9, 10, or 15 "
+           "according to scrfd.");
+  FDASSERT((fmc == 3 || fmc == 5), "The fmc must be 3 or 5");
+  FDASSERT((infer_result.at(0).shape[0] == 1), "Only support batch =1 now.");
+  for (int i = 0; i < fmc; ++i) {
+    if (infer_result.at(i).dtype != FDDataType::FP32) {
+      FDERROR << "Only support post process with float32 data." << std::endl;
+      return false;
+    }
+  }
+  int total_num_boxes = 0;
+  // compute the reserve space.
+  for (int f = 0; f < fmc; ++f) {
+    total_num_boxes += infer_result.at(f).shape[1];
+  };
+  GeneratePoints();
+  result->Clear();
+  // scale the boxes to the origin image shape
+  auto iter_out = im_info.find("output_shape");
+  auto iter_ipt = im_info.find("input_shape");
+  FDASSERT(iter_out != im_info.end() && iter_ipt != im_info.end(),
+           "Cannot find input_shape or output_shape from im_info.");
+  float out_h = iter_out->second[0];
+  float out_w = iter_out->second[1];
+  float ipt_h = iter_ipt->second[0];
+  float ipt_w = iter_ipt->second[1];
+  float scale = std::min(out_h / ipt_h, out_w / ipt_w);
+  float pad_h = (out_h - ipt_h * scale) / 2.0f;
+  float pad_w = (out_w - ipt_w * scale) / 2.0f;
+  if (is_mini_pad) {
+    // 和 LetterBox中_auto=true的处理逻辑对应
+    pad_h = static_cast<float>(static_cast<int>(pad_h) % stride);
+    pad_w = static_cast<float>(static_cast<int>(pad_w) % stride);
+  }
+  // must be setup landmarks_per_face before reserve
+  result->landmarks_per_face = landmarks_per_face;
+  result->Reserve(total_num_boxes);
+  unsigned int count = 0;
+  // loop each stride
+  for (int f = 0; f < fmc; ++f) {
+    float* score_ptr = static_cast<float*>(infer_result.at(f).Data());
+    float* bbox_ptr = static_cast<float*>(infer_result.at(f + fmc).Data());
+    const unsigned int num_points = infer_result.at(f).shape[1];
+    int current_stride = downsample_strides[f];
+    auto& stride_points = center_points_[current_stride];
+    // loop each anchor
+    for (unsigned int i = 0; i < num_points; ++i) {
+      const float cls_conf = score_ptr[i];
+      if (cls_conf < conf_threshold) continue;  // filter
+      auto& point = stride_points.at(i);
+      const float cx = point.cx;  // cx
+      const float cy = point.cy;  // cy
+      // bbox
+      const float* offsets = bbox_ptr + i * 4;
+      float l = offsets[0];  // left
+      float t = offsets[1];  // top
+      float r = offsets[2];  // right
+      float b = offsets[3];  // bottom
+
+      float x1 =
+          ((cx - l) * static_cast<float>(current_stride) - static_cast<float>(pad_w)) / scale;  // cx - l x1
+      float y1 =
+          ((cy - t) * static_cast<float>(current_stride) - static_cast<float>(pad_h)) / scale;  // cy - t y1
+      float x2 =
+          ((cx + r) * static_cast<float>(current_stride) - static_cast<float>(pad_w)) / scale;  // cx + r x2
+      float y2 =
+          ((cy + b) * static_cast<float>(current_stride) - static_cast<float>(pad_h)) / scale;  // cy + b y2
+      result->boxes.emplace_back(std::array<float, 4>{x1, y1, x2, y2});
+      result->scores.push_back(cls_conf);
+      if (use_kps) {
+        float* landmarks_ptr =
+            static_cast<float*>(infer_result.at(f + 2 * fmc).Data());
+        // landmarks
+        const float* kps_offsets = landmarks_ptr + i * (landmarks_per_face * 2);
+        for (unsigned int j = 0; j < landmarks_per_face * 2; j += 2) {
+          float kps_l = kps_offsets[j];
+          float kps_t = kps_offsets[j + 1];
+          float kps_x = ((cx + kps_l) * static_cast<float>(current_stride) - static_cast<float>(pad_w)) /
+                        scale;  // cx + l x
+          float kps_y = ((cy + kps_t) * static_cast<float>(current_stride) - static_cast<float>(pad_h)) /
+                        scale;  // cy + t y
+          result->landmarks.emplace_back(std::array<float, 2>{kps_x, kps_y});
+        }
+      }
+      count += 1;  // limit boxes for nms.
+      if (count > max_nms) {
+        break;
+      }
+    }
+  }
+
+  // fetch original image shape
+  FDASSERT((iter_ipt != im_info.end()),
+           "Cannot find input_shape from im_info.");
+
+  if (result->boxes.size() == 0) {
+    return true;
+  }
+
+  utils::NMS(result, nms_iou_threshold);
+
+  // scale and clip box
+  for (size_t i = 0; i < result->boxes.size(); ++i) {
+    result->boxes[i][0] = std::max(result->boxes[i][0], 0.0f);
+    result->boxes[i][1] = std::max(result->boxes[i][1], 0.0f);
+    result->boxes[i][2] = std::max(result->boxes[i][2], 0.0f);
+    result->boxes[i][3] = std::max(result->boxes[i][3], 0.0f);
+    result->boxes[i][0] = std::min(result->boxes[i][0], ipt_w - 1.0f);
+    result->boxes[i][1] = std::min(result->boxes[i][1], ipt_h - 1.0f);
+    result->boxes[i][2] = std::min(result->boxes[i][2], ipt_w - 1.0f);
+    result->boxes[i][3] = std::min(result->boxes[i][3], ipt_h - 1.0f);
+  }
+  // scale and clip landmarks
+  for (size_t i = 0; i < result->landmarks.size(); ++i) {
+    result->landmarks[i][0] = std::max(result->landmarks[i][0], 0.0f);
+    result->landmarks[i][1] = std::max(result->landmarks[i][1], 0.0f);
+    result->landmarks[i][0] = std::min(result->landmarks[i][0], ipt_w - 1.0f);
+    result->landmarks[i][1] = std::min(result->landmarks[i][1], ipt_h - 1.0f);
+  }
+  return true;
+}
+
+bool SCRFD::Predict(cv::Mat* im, FaceDetectionResult* result,
+                    float conf_threshold, float nms_iou_threshold) {
+#ifdef FASTDEPLOY_DEBUG
+  TIMERECORD_START(0)
+#endif
+  Mat mat(*im);
+  std::vector<FDTensor> input_tensors(1);
+
+  std::map<std::string, std::array<float, 2>> im_info;
+
+  // Record the shape of image and the shape of preprocessed image
+  im_info["input_shape"] = {static_cast<float>(mat.Height()),
+                            static_cast<float>(mat.Width())};
+  im_info["output_shape"] = {static_cast<float>(mat.Height()),
+                             static_cast<float>(mat.Width())};
+
+  if (!Preprocess(&mat, &input_tensors[0], &im_info)) {
+    FDERROR << "Failed to preprocess input image." << std::endl;
+    return false;
+  }
+
+#ifdef FASTDEPLOY_DEBUG
+  TIMERECORD_END(0, "Preprocess")
+  TIMERECORD_START(1)
+#endif
+
+  input_tensors[0].name = InputInfoOfRuntime(0).name;
+  std::vector<FDTensor> output_tensors;
+  if (!Infer(input_tensors, &output_tensors)) {
+    FDERROR << "Failed to inference." << std::endl;
+    return false;
+  }
+#ifdef FASTDEPLOY_DEBUG
+  TIMERECORD_END(1, "Inference")
+  TIMERECORD_START(2)
+#endif
+
+  if (!Postprocess(output_tensors, result, im_info, conf_threshold,
+                   nms_iou_threshold)) {
+    FDERROR << "Failed to post process." << std::endl;
+    return false;
+  }
+
+#ifdef FASTDEPLOY_DEBUG
+  TIMERECORD_END(2, "Postprocess")
+#endif
+  return true;
+}
+
+}  // namespace deepinsight
+}  // namespace vision
+}  // namespace fastdeploy

csrcs/fastdeploy/vision/deepinsight/scrfd.h

@@ -0,0 +1,122 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#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 deepinsight {
+
+class FASTDEPLOY_DECL SCRFD : public FastDeployModel {
+ public:
+  // 当model_format为ONNX时，无需指定params_file
+  // 当model_format为Paddle时，则需同时指定model_file & params_file
+  SCRFD(const std::string& model_file, const std::string& params_file = "",
+        const RuntimeOption& custom_option = RuntimeOption(),
+        const Frontend& model_format = Frontend::ONNX);
+
+  // 定义模型的名称
+  std::string ModelName() const { return "deepinsight/scrfd"; }
+
+  // 模型预测接口，即用户调用的接口
+  // im 为用户的输入数据，目前对于CV均定义为cv::Mat
+  // result 为模型预测的输出结构体
+  // conf_threshold 为后处理的参数
+  // nms_iou_threshold 为后处理的参数
+  virtual bool Predict(cv::Mat* im, FaceDetectionResult* result,
+                       float conf_threshold = 0.25f,
+                       float nms_iou_threshold = 0.4f);
+
+  // 以下为模型在预测时的一些参数，基本是前后处理所需
+  // 用户在创建模型后，可根据模型的要求，以及自己的需求
+  // 对参数进行修改
+  // tuple of (width, height), default (640, 640)
+  std::vector<int> size;
+  // downsample strides (namely, steps) for SCRFD to
+  // generate anchors, will take (8,16,32) as default values.
+  // padding value, size should be same with Channels
+  std::vector<float> padding_value;
+  // only pad to the minimum rectange which height and width is times of stride
+  bool is_mini_pad;
+  // while is_mini_pad = false and is_no_pad = true, will resize the image to
+  // the set size
+  bool is_no_pad;
+  // if is_scale_up is false, the input image only can be zoom out, the maximum
+  // resize scale cannot exceed 1.0
+  bool is_scale_up;
+  // padding stride, for is_mini_pad
+  int stride;
+  // for offseting the boxes by classes when using NMS
+  std::vector<int> downsample_strides;
+  // landmarks_per_face, default 5 in SCRFD
+  int landmarks_per_face;
+  // are the outputs of onnx file with key points features or not
+  bool use_kps;
+  // the upperbond number of boxes processed by nms.
+  int max_nms;
+  // number anchors of each stride
+  unsigned int num_anchors;
+
+ private:
+  // 初始化函数，包括初始化后端，以及其它模型推理需要涉及的操作
+  bool Initialize();
+
+  // 输入图像预处理操作
+  // Mat为FastDeploy定义的数据结构
+  // FDTensor为预处理后的Tensor数据，传给后端进行推理
+  // im_info为预处理过程保存的数据，在后处理中需要用到
+  bool Preprocess(Mat* mat, FDTensor* output,
+                  std::map<std::string, std::array<float, 2>>* im_info);
+
+  // 后端推理结果后处理，输出给用户
+  // infer_result 为后端推理后的输出Tensor
+  // result 为模型预测的结果
+  // im_info 为预处理记录的信息，后处理用于还原box
+  // conf_threshold 后处理时过滤box的置信度阈值
+  // nms_iou_threshold 后处理时NMS设定的iou阈值
+  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);
+
+  void GeneratePoints();
+
+  // 对图片进行LetterBox处理
+  // mat 为读取到的原图
+  // size 为输入模型的图像尺寸
+  void LetterBox(Mat* mat, const std::vector<int>& size,
+                 const std::vector<float>& color, bool _auto,
+                 bool scale_fill = false, bool scale_up = true,
+                 int stride = 32);
+
+  bool is_dynamic_input_;
+
+  bool center_points_is_update_;
+
+  typedef struct {
+    float cx;
+    float cy;
+  } SCRFDPoint;
+
+  std::unordered_map<int, std::vector<SCRFDPoint>> center_points_;
+};
+}  // namespace deepinsight
+}  // namespace vision
+}  // namespace fastdeploy

csrcs/fastdeploy/vision/ppogg/yolov5lite.cc

@@ -118,6 +118,21 @@ bool YOLOv5Lite::Initialize() {
     FDERROR << "Failed to initialize fastdeploy backend." << std::endl;
     return false;
   }
+  // Check if the input shape is dynamic after Runtime already initialized,
+  // Note that, We need to force is_mini_pad 'false' to keep static
+  // shape after padding (LetterBox) when the is_dynamic_shape is 'false'.
+  is_dynamic_input_ = false;
+  auto shape = InputInfoOfRuntime(0).shape;
+  for (int i = 0; i < shape.size(); ++i) {
+    // if height or width is dynamic
+    if (i >= 2 && shape[i] <= 0) {
+      is_dynamic_input_ = true;
+      break;
+    }
+  }
+  if (!is_dynamic_input_) {
+    is_mini_pad = false;
+  }
   return true;
 }
 

csrcs/fastdeploy/vision/ppogg/yolov5lite.h

@@ -126,6 +126,13 @@ class FASTDEPLOY_DECL YOLOv5Lite : public FastDeployModel {
   void GenerateAnchors(const std::vector<int>& size,
                        const std::vector<int>& downsample_strides,
                        std::vector<Anchor>* anchors, const int num_anchors = 3);
+
+  // whether to inference with dynamic shape (e.g ONNX export with dynamic shape
+  // or not.)
+  // while is_dynamic_shape if 'false', is_mini_pad will force 'false'. This
+  // value will
+  // auto check by fastdeploy after the internal Runtime already initialized.
+  bool is_dynamic_input_;
 };
 }  // namespace ppogg
 }  // namespace vision

csrcs/fastdeploy/vision/vision_pybind.cc

@@ -28,6 +28,7 @@ void BindRangiLyu(pybind11::module& m);
 void BindLinzaer(pybind11::module& m);
 void BindBiubug6(pybind11::module& m);
 void BindPpogg(pybind11::module& m);
+void BindDeepinsight(pybind11::module& m);
 #ifdef ENABLE_VISION_VISUALIZE
 void BindVisualize(pybind11::module& m);
 #endif
@@ -75,6 +76,7 @@ void BindVision(pybind11::module& m) {
   BindLinzaer(m);
   BindBiubug6(m);
   BindPpogg(m);
+  BindDeepinsight(m);
 #ifdef ENABLE_VISION_VISUALIZE
   BindVisualize(m);
 #endif

csrcs/fastdeploy/vision/wongkinyiu/scaledyolov4.cc

@@ -89,6 +89,21 @@ bool ScaledYOLOv4::Initialize() {
     FDERROR << "Failed to initialize fastdeploy backend." << std::endl;
     return false;
   }
+  // Check if the input shape is dynamic after Runtime already initialized,
+  // Note that, We need to force is_mini_pad 'false' to keep static
+  // shape after padding (LetterBox) when the is_dynamic_shape is 'false'.
+  is_dynamic_input_ = false;
+  auto shape = InputInfoOfRuntime(0).shape;
+  for (int i = 0; i < shape.size(); ++i) {
+    // if height or width is dynamic
+    if (i >= 2 && shape[i] <= 0) {
+      is_dynamic_input_ = true;
+      break;
+    }
+  }
+  if (!is_dynamic_input_) {
+    is_mini_pad = false;
+  }
   return true;
 }
 

csrcs/fastdeploy/vision/wongkinyiu/scaledyolov4.h

@@ -90,6 +90,13 @@ class FASTDEPLOY_DECL ScaledYOLOv4 : public FastDeployModel {
                  const std::vector<float>& color, bool _auto,
                  bool scale_fill = false, bool scale_up = true,
                  int stride = 32);
+
+  // whether to inference with dynamic shape (e.g ONNX export with dynamic shape
+  // or not.)
+  // while is_dynamic_shape if 'false', is_mini_pad will force 'false'. This
+  // value will
+  // auto check by fastdeploy after the internal Runtime already initialized.
+  bool is_dynamic_input_;
 };
 }  // namespace wongkinyiu
 }  // namespace vision

csrcs/fastdeploy/vision/wongkinyiu/yolor.cc

@@ -87,6 +87,21 @@ bool YOLOR::Initialize() {
     FDERROR << "Failed to initialize fastdeploy backend." << std::endl;
     return false;
   }
+  // Check if the input shape is dynamic after Runtime already initialized,
+  // Note that, We need to force is_mini_pad 'false' to keep static
+  // shape after padding (LetterBox) when the is_dynamic_shape is 'false'.
+  is_dynamic_input_ = false;
+  auto shape = InputInfoOfRuntime(0).shape;
+  for (int i = 0; i < shape.size(); ++i) {
+    // if height or width is dynamic
+    if (i >= 2 && shape[i] <= 0) {
+      is_dynamic_input_ = true;
+      break;
+    }
+  }
+  if (!is_dynamic_input_) {
+    is_mini_pad = false;
+  }
   return true;
 }
 
@@ -176,7 +191,7 @@ bool YOLOR::Postprocess(
   float pad_h = (out_h - ipt_h * scale) / 2.0f;
   float pad_w = (out_w - ipt_w * scale) / 2.0f;
   if (is_mini_pad) {
-    // 和 LetterBox中_auto=true的处理逻辑对应 
+    // 和 LetterBox中_auto=true的处理逻辑对应
     pad_h = static_cast<float>(static_cast<int>(pad_h) % stride);
     pad_w = static_cast<float>(static_cast<int>(pad_w) % stride);
   }

csrcs/fastdeploy/vision/wongkinyiu/yolor.h

@@ -89,6 +89,13 @@ class FASTDEPLOY_DECL YOLOR : public FastDeployModel {
                  const std::vector<float>& color, bool _auto,
                  bool scale_fill = false, bool scale_up = true,
                  int stride = 32);
+
+  // whether to inference with dynamic shape (e.g ONNX export with dynamic shape
+  // or not.)
+  // while is_dynamic_shape if 'false', is_mini_pad will force 'false'. This
+  // value will
+  // auto check by fastdeploy after the internal Runtime already initialized.
+  bool is_dynamic_input_;
 };
 }  // namespace wongkinyiu
 }  // namespace vision

csrcs/fastdeploy/vision/wongkinyiu/yolov7.cc

@@ -88,6 +88,21 @@ bool YOLOv7::Initialize() {
     FDERROR << "Failed to initialize fastdeploy backend." << std::endl;
     return false;
   }
+  // Check if the input shape is dynamic after Runtime already initialized,
+  // Note that, We need to force is_mini_pad 'false' to keep static
+  // shape after padding (LetterBox) when the is_dynamic_shape is 'false'.
+  is_dynamic_input_ = false;
+  auto shape = InputInfoOfRuntime(0).shape;
+  for (int i = 0; i < shape.size(); ++i) {
+    // if height or width is dynamic
+    if (i >= 2 && shape[i] <= 0) {
+      is_dynamic_input_ = true;
+      break;
+    }
+  }
+  if (!is_dynamic_input_) {
+    is_mini_pad = false;
+  }
   return true;
 }
 
@@ -177,7 +192,7 @@ bool YOLOv7::Postprocess(
   float pad_h = (out_h - ipt_h * scale) / 2.0f;
   float pad_w = (out_w - ipt_w * scale) / 2.0f;
   if (is_mini_pad) {
-    // 和 LetterBox中_auto=true的处理逻辑对应 
+    // 和 LetterBox中_auto=true的处理逻辑对应
     pad_h = static_cast<float>(static_cast<int>(pad_h) % stride);
     pad_w = static_cast<float>(static_cast<int>(pad_w) % stride);
   }

csrcs/fastdeploy/vision/wongkinyiu/yolov7.h

@@ -89,6 +89,13 @@ class FASTDEPLOY_DECL YOLOv7 : public FastDeployModel {
                  const std::vector<float>& color, bool _auto,
                  bool scale_fill = false, bool scale_up = true,
                  int stride = 32);
+
+  // whether to inference with dynamic shape (e.g ONNX export with dynamic shape
+  // or not.)
+  // while is_dynamic_shape if 'false', is_mini_pad will force 'false'. This
+  // value will
+  // auto check by fastdeploy after the internal Runtime already initialized.
+  bool is_dynamic_input_;
 };
 }  // namespace wongkinyiu
 }  // namespace vision

examples/vision/deepinsight_scrfd.cc

@@ -0,0 +1,51 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "fastdeploy/vision.h"
+int main() {
+  namespace vis = fastdeploy::vision;
+
+  std::string model_file = "../resources/models/SCRFD.onnx";
+  std::string img_path = "../resources/images/test_face_det.jpg";
+  std::string vis_path = "../resources/outputs/deepsight_scrfd_vis_result.jpg";
+
+  auto model = vis::deepinsight::SCRFD(model_file);
+  model.size = {640, 640};  // (width, height)
+  if (!model.Initialized()) {
+    std::cerr << "Init Failed! Model: " << model_file << std::endl;
+    return -1;
+  } else {
+    std::cout << "Init Done! Model:" << model_file << std::endl;
+  }
+  model.EnableDebug();
+
+  cv::Mat im = cv::imread(img_path);
+  cv::Mat vis_im = im.clone();
+  vis::FaceDetectionResult res;
+  if (!model.Predict(&im, &res, 0.3f, 0.3f)) {
+    std::cerr << "Prediction Failed." << std::endl;
+    return -1;
+  } else {
+    std::cout << "Prediction Done!" << std::endl;
+  }
+
+  // 输出预测框结果
+  std::cout << res.Str() << std::endl;
+
+  // 可视化预测结果
+  vis::Visualize::VisFaceDetection(&vis_im, res, 2, 0.3f);
+  cv::imwrite(vis_path, vis_im);
+  std::cout << "Detect Done! Saved: " << vis_path << std::endl;
+  return 0;
+}

fastdeploy/vision/__init__.py

@@ -27,3 +27,4 @@ from . import rangilyu
 from . import linzaer
 from . import biubug6
 from . import ppogg
+from . import deepinsight

fastdeploy/vision/deepinsight/__init__.py

@@ -0,0 +1,158 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+import logging
+from ... import FastDeployModel, Frontend
+from ... import fastdeploy_main as C
+
+
+class SCRFD(FastDeployModel):
+    def __init__(self,
+                 model_file,
+                 params_file="",
+                 runtime_option=None,
+                 model_format=Frontend.ONNX):
+        # 调用基函数进行backend_option的初始化
+        # 初始化后的option保存在self._runtime_option
+        super(SCRFD, self).__init__(runtime_option)
+
+        self._model = C.vision.deepinsight.SCRFD(
+            model_file, params_file, self._runtime_option, model_format)
+        # 通过self.initialized判断整个模型的初始化是否成功
+        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)
+
+    # 一些跟SCRFD模型有关的属性封装
+    # 多数是预处理相关，可通过修改如model.size = [640, 640]改变预处理时resize的大小（前提是模型支持）
+    @property
+    def size(self):
+        return self._model.size
+
+    @property
+    def padding_value(self):
+        return self._model.padding_value
+
+    @property
+    def is_no_pad(self):
+        return self._model.is_no_pad
+
+    @property
+    def is_mini_pad(self):
+        return self._model.is_mini_pad
+
+    @property
+    def is_scale_up(self):
+        return self._model.is_scale_up
+
+    @property
+    def stride(self):
+        return self._model.stride
+
+    @property
+    def downsample_strides(self):
+        return self._model.downsample_strides
+
+    @property
+    def landmarks_per_face(self):
+        return self._model.landmarks_per_face
+
+    @property
+    def use_kps(self):
+        return self._model.use_kps
+
+    @property
+    def max_nms(self):
+        return self._model.max_nms
+
+    @property
+    def num_anchors(self):
+        return self._model.num_anchors
+
+    @size.setter
+    def size(self, wh):
+        assert isinstance(wh, [list, tuple]),\
+            "The value to set `size` must be type of tuple or list."
+        assert len(wh) == 2,\
+            "The value to set `size` must contatins 2 elements means [width, height], but now it contains {} elements.".format(
+            len(wh))
+        self._model.size = wh
+
+    @padding_value.setter
+    def padding_value(self, value):
+        assert isinstance(
+            value,
+            list), "The value to set `padding_value` must be type of list."
+        self._model.padding_value = value
+
+    @is_no_pad.setter
+    def is_no_pad(self, value):
+        assert isinstance(
+            value, bool), "The value to set `is_no_pad` must be type of bool."
+        self._model.is_no_pad = value
+
+    @is_mini_pad.setter
+    def is_mini_pad(self, value):
+        assert isinstance(
+            value,
+            bool), "The value to set `is_mini_pad` must be type of bool."
+        self._model.is_mini_pad = value
+
+    @is_scale_up.setter
+    def is_scale_up(self, value):
+        assert isinstance(
+            value,
+            bool), "The value to set `is_scale_up` must be type of bool."
+        self._model.is_scale_up = value
+
+    @stride.setter
+    def stride(self, value):
+        assert isinstance(
+            value, int), "The value to set `stride` must be type of int."
+        self._model.stride = value
+
+    @downsample_strides.setter
+    def downsample_strides(self, value):
+        assert isinstance(
+            value,
+            list), "The value to set `downsample_strides` must be type of list."
+        self._model.downsample_strides = value
+
+    @landmarks_per_face.setter
+    def landmarks_per_face(self, value):
+        assert isinstance(
+            value,
+            int), "The value to set `landmarks_per_face` must be type of int."
+        self._model.landmarks_per_face = value
+
+    @use_kps.setter
+    def use_kps(self, value):
+        assert isinstance(
+            value, bool), "The value to set `use_kps` must be type of bool."
+        self._model.use_kps = value
+
+    @max_nms.setter
+    def max_nms(self, value):
+        assert isinstance(
+            value, int), "The value to set `max_nms` must be type of int."
+        self._model.max_nms = value
+
+    @num_anchors.setter
+    def num_anchors(self, value):
+        assert isinstance(
+            value, int), "The value to set `num_anchors` must be type of int."
+        self._model.num_anchors = value

model_zoo/vision/scrfd/README.md

@@ -0,0 +1,92 @@
+# 编译SCRFD示例
+
+当前支持模型版本为：[SCRFD CID:17cdeab](https://github.com/deepinsight/insightface/tree/17cdeab12a35efcebc2660453a8cbeae96e20950)
+
+本文档说明如何进行[SCRFD](https://github.com/deepinsight/insightface/tree/master/detection/scrfd)的快速部署推理。本目录结构如下
+
+```
+.
+├── cpp
+│   ├── CMakeLists.txt
+│   ├── README.md
+│   └── scrfd.cc
+├── README.md
+└── scrfd.py
+```
+
+## 获取ONNX文件
+
+- 手动获取
+
+  访问[SCRFD](https://github.com/deepinsight/insightface/tree/master/detection/scrfd)官方github库，按照指引下载安装，下载`scrfd.pt` 模型，利用 `tools/scrfd2onnx.py` 得到`onnx`格式文件。
+
+
+
+  ```
+  #下载scrfd模型文件
+  e.g. download from  https://onedrive.live.com/?authkey=%21ABbFJx2JMhNjhNA&id=4A83B6B633B029CC%215542&cid=4A83B6B633B029CC
+
+  # 安装官方库配置环境，此版本导出环境为：
+  - 手动配置环境
+    torch==1.8.0
+    mmcv==1.3.5
+    mmdet==2.7.0
+
+  - 通过docker配置
+    docker pull qyjdefdocker/onnx-scrfd-converter:v0.3
+
+  # 导出onnx格式文件
+  - 手动生成
+    python tools/scrfd2onnx.py configs/scrfd/scrfd_500m.py weights/scrfd_500m.pth --shape 640 --input-img face-xxx.jpg
+
+  - docker
+    docker的onnx目录中已有生成好的onnx文件
+
+
+  # 移动onnx文件到demo目录
+  cp PATH/TO/SCRFD.onnx PATH/TO/model_zoo/vision/scrfd/
+  ```
+
+## 安装FastDeploy
+
+使用如下命令安装FastDeploy，注意到此处安装的是`vision-cpu`，也可根据需求安装`vision-gpu`
+
+```
+# 安装fastdeploy-python工具
+pip install fastdeploy-python
+
+# 安装vision-cpu模块
+fastdeploy install vision-cpu
+```
+## Python部署
+
+执行如下代码即会自动下载测试图片
+```
+python scrfd.py
+```
+
+执行完成后会将可视化结果保存在本地`vis_result.jpg`，同时输出检测结果如下
+```
+FaceDetectionResult: [xmin, ymin, xmax, ymax, score]
+437.670410,194.262772, 478.729828, 244.633911, 0.912465
+418.303650,118.277687, 455.877838, 169.209564, 0.911748
+269.449493,280.810608, 319.466614, 342.681213, 0.908530
+775.553955,237.509979, 814.626526, 286.252350, 0.901296
+565.155945,303.849670, 608.786255, 356.025726, 0.898307
+411.813477,296.117584, 454.560394, 353.151367, 0.889968
+688.620239,153.063812, 728.825195, 204.860321, 0.888146
+686.523071,304.881104, 732.901245, 364.715088, 0.885789
+194.658829,236.657883, 234.194748, 289.099701, 0.881143
+137.273422,286.025787, 183.479523, 344.614441, 0.877399
+289.256775,148.388992, 326.087769, 197.035645, 0.875090
+182.943939,154.105682, 221.422440, 204.460495, 0.871119
+330.301849,207.786499, 367.546692, 260.813232, 0.869559
+659.884216,254.861847, 701.580017, 307.984711, 0.869249
+550.305359,232.336868, 591.702026, 281.101532, 0.866158
+567.473511,127.402367, 604.959839, 175.831696, 0.858938
+```
+
+## 其它文档
+
+- [C++部署](./cpp/README.md)
+- [SCRFD API文档](./api.md)

model_zoo/vision/scrfd/api.md

@@ -0,0 +1,71 @@
+# SCRFD API说明
+
+## Python API
+
+### SCRFD类
+```
+fastdeploy.vision.deepinsight.SCRFD(model_file, params_file=None, runtime_option=None, model_format=fd.Frontend.ONNX)
+```
+SCRFD模型加载和初始化，当model_format为`fd.Frontend.ONNX`时，只需提供model_file，如`SCRFD.onnx`；当model_format为`fd.Frontend.PADDLE`时，则需同时提供model_file和params_file。
+
+**参数**
+
+> * **model_file**(str): 模型文件路径
+> * **params_file**(str): 参数文件路径
+> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
+> * **model_format**(Frontend): 模型格式
+
+#### predict函数
+> ```
+> SCRFD.predict(image_data, conf_threshold=0.25, nms_iou_threshold=0.5)
+> ```
+> 模型预测结口，输入图像直接输出检测结果。
+>
+> **参数**
+>
+> > * **image_data**(np.ndarray): 输入数据，注意需为HWC，BGR格式
+> > * **conf_threshold**(float): 检测框置信度过滤阈值
+> > * **nms_iou_threshold**(float): NMS处理过程中iou阈值
+
+示例代码参考[scrfd.py](./scrfd.py)
+
+
+## C++ API
+
+### SCRFD类
+```
+fastdeploy::vision::deepinsight::SCRFD(
+        const string& model_file,
+        const string& params_file = "",
+        const RuntimeOption& runtime_option = RuntimeOption(),
+        const Frontend& model_format = Frontend::ONNX)
+```
+SCRFD模型加载和初始化，当model_format为`Frontend::ONNX`时，只需提供model_file，如`SCRFD.onnx`；当model_format为`Frontend::PADDLE`时，则需同时提供model_file和params_file。
+
+**参数**
+
+> * **model_file**(str): 模型文件路径
+> * **params_file**(str): 参数文件路径
+> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
+> * **model_format**(Frontend): 模型格式
+
+#### Predict函数
+> ```
+> SCRFD::Predict(cv::Mat* im, FaceDetectionResult* result,
+>                float conf_threshold = 0.25,
+>                float nms_iou_threshold = 0.5)
+> ```
+> 模型预测接口，输入图像直接输出检测结果。
+>
+> **参数**
+>
+> > * **im**: 输入图像，注意需为HWC，BGR格式
+> > * **result**: 检测结果，包括检测框，各个框的置信度
+> > * **conf_threshold**: 检测框置信度过滤阈值
+> > * **nms_iou_threshold**: NMS处理过程中iou阈值
+
+示例代码参考[cpp/scrfd.cc](cpp/scrfd.cc)
+
+## 其它API使用
+
+- [模型部署RuntimeOption配置](../../../docs/api/runtime_option.md)

model_zoo/vision/scrfd/cpp/CMakeLists.txt

@@ -0,0 +1,17 @@
+PROJECT(scrfd_demo C CXX)
+CMAKE_MINIMUM_REQUIRED (VERSION 3.16)
+
+# 在低版本ABI环境中，通过如下代码进行兼容性编译
+# add_definitions(-D_GLIBCXX_USE_CXX11_ABI=0)
+
+# 指定下载解压后的fastdeploy库路径
+set(FASTDEPLOY_INSTALL_DIR ${PROJECT_SOURCE_DIR}/fastdeploy-linux-x64-0.3.0/)
+
+include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
+
+# 添加FastDeploy依赖头文件
+include_directories(${FASTDEPLOY_INCS})
+
+add_executable(scrfd_demo ${PROJECT_SOURCE_DIR}/scrfd.cc)
+# 添加FastDeploy库依赖
+target_link_libraries(scrfd_demo ${FASTDEPLOY_LIBS})

model_zoo/vision/scrfd/cpp/README.md

@@ -0,0 +1,76 @@
+# 编译SCRFD示例
+
+当前支持模型版本为：[SCRFD CID:17cdeab](https://github.com/deepinsight/insightface/tree/17cdeab12a35efcebc2660453a8cbeae96e20950)
+
+本文档说明如何进行[SCRFD](https://github.com/deepinsight/insightface/tree/master/detection/scrfd)的快速部署推理。本目录结构如下
+
+## 获取ONNX文件
+
+- 手动获取
+
+  访问[SCRFD](https://github.com/deepinsight/insightface/tree/master/detection/scrfd)官方github库，按照指引下载安装，下载`scrfd.pt` 模型，利用 `tools/scrfd2onnx.py` 得到`onnx`格式文件。
+
+
+  ```
+  #下载scrfd模型文件
+  e.g. download from  https://onedrive.live.com/?authkey=%21ABbFJx2JMhNjhNA&id=4A83B6B633B029CC%215542&cid=4A83B6B633B029CC
+
+  # 安装官方库配置环境，此版本导出环境为：
+  - 手动配置环境
+    torch==1.8.0
+    mmcv==1.3.5
+    mmdet==2.7.0
+
+  - 通过docker配置
+    docker pull qyjdefdocker/onnx-scrfd-converter:v0.3
+
+  # 导出onnx格式文件
+  - 手动生成
+    python tools/scrfd2onnx.py configs/scrfd/scrfd_500m.py weights/scrfd_500m.pth --shape 640 --input-img face-xxx.jpg
+
+  - docker
+    docker的onnx目录中已有生成好的onnx文件
+
+
+## 运行demo
+
+```
+# 下载和解压预测库
+wget https://bj.bcebos.com/paddle2onnx/fastdeploy/fastdeploy-linux-x64-0.0.3.tgz
+tar xvf fastdeploy-linux-x64-0.0.3.tgz
+
+# 编译示例代码
+mkdir build & cd build
+cmake ..
+make -j
+
+# 移动onnx文件到demo目录
+cp PATH/TO/SCRFD.onnx PATH/TO/model_zoo/vision/scrfd/cpp/build/
+
+# 下载图片
+wget https://raw.githubusercontent.com/DefTruth/lite.ai.toolkit/main/examples/lite/resources/test_lite_face_detector_3.jpg
+
+# 执行
+./scrfd_demo
+```
+
+执行完后可视化的结果保存在本地`vis_result.jpg`，同时会将检测框输出在终端，如下所示
+```
+FaceDetectionResult: [xmin, ymin, xmax, ymax, score]
+437.670410,194.262772, 478.729828, 244.633911, 0.912465
+418.303650,118.277687, 455.877838, 169.209564, 0.911748
+269.449493,280.810608, 319.466614, 342.681213, 0.908530
+775.553955,237.509979, 814.626526, 286.252350, 0.901296
+565.155945,303.849670, 608.786255, 356.025726, 0.898307
+411.813477,296.117584, 454.560394, 353.151367, 0.889968
+688.620239,153.063812, 728.825195, 204.860321, 0.888146
+686.523071,304.881104, 732.901245, 364.715088, 0.885789
+194.658829,236.657883, 234.194748, 289.099701, 0.881143
+137.273422,286.025787, 183.479523, 344.614441, 0.877399
+289.256775,148.388992, 326.087769, 197.035645, 0.875090
+182.943939,154.105682, 221.422440, 204.460495, 0.871119
+330.301849,207.786499, 367.546692, 260.813232, 0.869559
+659.884216,254.861847, 701.580017, 307.984711, 0.869249
+550.305359,232.336868, 591.702026, 281.101532, 0.866158
+567.473511,127.402367, 604.959839, 175.831696, 0.858938
+```

model_zoo/vision/scrfd/cpp/scrfd.cc

@@ -0,0 +1,44 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "fastdeploy/vision.h"
+
+int main() {
+  namespace vis = fastdeploy::vision;
+  auto model = vis::deepinsight::SCRFD("SCRFD.onnx");
+  if (!model.Initialized()) {
+    std::cerr << "Init Failed." << std::endl;
+    return -1;
+  }
+  cv::Mat im = cv::imread("test_lite_face_detector_3.jpg");
+  cv::Mat vis_im = im.clone();
+
+  // 如果导入不带有关键点预测的模型，请修改模型参数 use_kps 和 landmarks_per_face，示例如下
+  // model.landmarks_per_face = 0;
+  // model.use_kps = false;
+
+  vis::FaceDetectionResult res;
+  if (!model.Predict(&im, &res)) {
+    std::cerr << "Prediction Failed." << std::endl;
+    return -1;
+  }
+
+  // 输出预测框结果
+  std::cout << res.Str() << std::endl;
+
+  // 可视化预测结果
+  vis::Visualize::VisFaceDetection(&vis_im, res, 2, 0.3f);
+  cv::imwrite("vis_result.jpg", vis_im);
+  return 0;
+}

model_zoo/vision/scrfd/scrfd.py

@@ -0,0 +1,25 @@
+import fastdeploy as fd
+import cv2
+
+# 下载模型和测试图片
+test_jpg_url = "https://raw.githubusercontent.com/DefTruth/lite.ai.toolkit/main/examples/lite/resources/test_lite_face_detector_3.jpg"
+fd.download(test_jpg_url, ".", show_progress=True)
+
+# 加载模型
+model = fd.vision.deepinsight.SCRFD("SCRFD.onnx")
+
+# 如果导入不带有关键点预测的模型，请修改模型参数 use_kps 和 landmarks_per_face，示例如下
+# model.use_kps = False
+# model.landmarks_per_face = 0
+
+# 预测图片
+im = cv2.imread("test_lite_face_detector_3.jpg")
+result = model.predict(im, conf_threshold=0.5, nms_iou_threshold=0.5)
+
+# 可视化结果
+fd.vision.visualize.vis_face_detection(im, result)
+cv2.imwrite("vis_result.jpg", im)
+
+# 输出预测结果
+print(result)
+print(model.runtime_option)