更换文档检测模型

paddle_detection/deploy/lite/src/config_parser.cc

@@ -0,0 +1,32 @@
+//   Copyright (c) 2021 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 "include/config_parser.h"
+
+namespace PaddleDetection {
+
+void load_jsonf(std::string jsonfile, Json::Value &jsondata) {
+  std::ifstream ifs;
+  ifs.open(jsonfile);
+
+  Json::CharReaderBuilder builder;
+  builder["collectComments"] = true;
+  JSONCPP_STRING errs;
+  if (!parseFromStream(builder, ifs, &jsondata, &errs)) {
+    std::cout << errs << std::endl;
+    return;
+  }
+}
+
+}  // namespace PaddleDetection

paddle_detection/deploy/lite/src/keypoint_detector.cc

@@ -0,0 +1,224 @@
+//   Copyright (c) 2021 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 <sstream>
+// for setprecision
+#include <chrono>
+#include <iomanip>
+#include "include/keypoint_detector.h"
+
+namespace PaddleDetection {
+
+// Load Model and create model predictor
+void KeyPointDetector::LoadModel(std::string model_file, int num_theads) {
+  MobileConfig config;
+  config.set_threads(num_theads);
+  config.set_model_from_file(model_file + "/model.nb");
+  config.set_power_mode(LITE_POWER_HIGH);
+
+  predictor_ = std::move(CreatePaddlePredictor<MobileConfig>(config));
+}
+
+// Visualiztion MaskDetector results
+cv::Mat VisualizeKptsResult(const cv::Mat& img,
+                            const std::vector<KeyPointResult>& results,
+                            const std::vector<int>& colormap,
+                            float threshold) {
+  const int edge[][2] = {{0, 1},
+                         {0, 2},
+                         {1, 3},
+                         {2, 4},
+                         {3, 5},
+                         {4, 6},
+                         {5, 7},
+                         {6, 8},
+                         {7, 9},
+                         {8, 10},
+                         {5, 11},
+                         {6, 12},
+                         {11, 13},
+                         {12, 14},
+                         {13, 15},
+                         {14, 16},
+                         {11, 12}};
+  cv::Mat vis_img = img.clone();
+  for (int batchid = 0; batchid < results.size(); batchid++) {
+    for (int i = 0; i < results[batchid].num_joints; i++) {
+      if (results[batchid].keypoints[i * 3] > threshold) {
+        int x_coord = int(results[batchid].keypoints[i * 3 + 1]);
+        int y_coord = int(results[batchid].keypoints[i * 3 + 2]);
+        cv::circle(vis_img,
+                   cv::Point2d(x_coord, y_coord),
+                   1,
+                   cv::Scalar(0, 0, 255),
+                   2);
+      }
+    }
+    for (int i = 0; i < results[batchid].num_joints; i++) {
+      if (results[batchid].keypoints[edge[i][0] * 3] > threshold &&
+          results[batchid].keypoints[edge[i][1] * 3] > threshold) {
+        int x_start = int(results[batchid].keypoints[edge[i][0] * 3 + 1]);
+        int y_start = int(results[batchid].keypoints[edge[i][0] * 3 + 2]);
+        int x_end = int(results[batchid].keypoints[edge[i][1] * 3 + 1]);
+        int y_end = int(results[batchid].keypoints[edge[i][1] * 3 + 2]);
+        cv::line(vis_img,
+                 cv::Point2d(x_start, y_start),
+                 cv::Point2d(x_end, y_end),
+                 colormap[i],
+                 1);
+      }
+    }
+  }
+  return vis_img;
+}
+
+void KeyPointDetector::Preprocess(const cv::Mat& ori_im) {
+  // Clone the image : keep the original mat for postprocess
+  cv::Mat im = ori_im.clone();
+  cv::cvtColor(im, im, cv::COLOR_BGR2RGB);
+  preprocessor_.Run(&im, &inputs_);
+}
+
+void KeyPointDetector::Postprocess(std::vector<float>& output,
+                                   std::vector<int64_t>& output_shape,
+                                   std::vector<int64_t>& idxout,
+                                   std::vector<int64_t>& idx_shape,
+                                   std::vector<KeyPointResult>* result,
+                                   std::vector<std::vector<float>>& center_bs,
+                                   std::vector<std::vector<float>>& scale_bs) {
+  std::vector<float> preds(output_shape[1] * 3, 0);
+
+  for (int batchid = 0; batchid < output_shape[0]; batchid++) {
+    get_final_preds(output,
+                    output_shape,
+                    idxout,
+                    idx_shape,
+                    center_bs[batchid],
+                    scale_bs[batchid],
+                    preds,
+                    batchid,
+                    this->use_dark());
+    KeyPointResult result_item;
+    result_item.num_joints = output_shape[1];
+    result_item.keypoints.clear();
+    for (int i = 0; i < output_shape[1]; i++) {
+      result_item.keypoints.emplace_back(preds[i * 3]);
+      result_item.keypoints.emplace_back(preds[i * 3 + 1]);
+      result_item.keypoints.emplace_back(preds[i * 3 + 2]);
+    }
+    result->push_back(result_item);
+  }
+}
+
+void KeyPointDetector::Predict(const std::vector<cv::Mat> imgs,
+                               std::vector<std::vector<float>>& center_bs,
+                               std::vector<std::vector<float>>& scale_bs,
+                               const int warmup,
+                               const int repeats,
+                               std::vector<KeyPointResult>* result,
+                               std::vector<double>* times) {
+  auto preprocess_start = std::chrono::steady_clock::now();
+  int batch_size = imgs.size();
+
+  // in_data_batch
+  std::vector<float> in_data_all;
+
+  // Preprocess image
+  for (int bs_idx = 0; bs_idx < batch_size; bs_idx++) {
+    cv::Mat im = imgs.at(bs_idx);
+    Preprocess(im);
+
+    // TODO: reduce cost time
+    in_data_all.insert(
+        in_data_all.end(), inputs_.im_data_.begin(), inputs_.im_data_.end());
+  }
+
+  // Prepare input tensor
+
+  auto input_names = predictor_->GetInputNames();
+  for (const auto& tensor_name : input_names) {
+    auto in_tensor = predictor_->GetInputByName(tensor_name);
+    if (tensor_name == "image") {
+      int rh = inputs_.in_net_shape_[0];
+      int rw = inputs_.in_net_shape_[1];
+      in_tensor->Resize({batch_size, 3, rh, rw});
+      auto* inptr = in_tensor->mutable_data<float>();
+      std::copy_n(in_data_all.data(), in_data_all.size(), inptr);
+    }
+  }
+
+  auto preprocess_end = std::chrono::steady_clock::now();
+  std::vector<int64_t> output_shape, idx_shape;
+  // Run predictor
+  // warmup
+  for (int i = 0; i < warmup; i++) {
+    predictor_->Run();
+    // Get output tensor
+    auto output_names = predictor_->GetOutputNames();
+    auto out_tensor = predictor_->GetTensor(output_names[0]);
+    auto idx_tensor = predictor_->GetTensor(output_names[1]);
+  }
+
+  auto inference_start = std::chrono::steady_clock::now();
+  for (int i = 0; i < repeats; i++) {
+    predictor_->Run();
+    // Get output tensor
+    auto output_names = predictor_->GetOutputNames();
+    auto out_tensor = predictor_->GetTensor(output_names[0]);
+    output_shape = out_tensor->shape();
+    // Calculate output length
+    int output_size = 1;
+    for (int j = 0; j < output_shape.size(); ++j) {
+      output_size *= output_shape[j];
+    }
+    if (output_size < 6) {
+      std::cerr << "[WARNING] No object detected." << std::endl;
+    }
+    output_data_.resize(output_size);
+    std::copy_n(
+        out_tensor->mutable_data<float>(), output_size, output_data_.data());
+
+    auto idx_tensor = predictor_->GetTensor(output_names[1]);
+    idx_shape = idx_tensor->shape();
+    // Calculate output length
+    output_size = 1;
+    for (int j = 0; j < idx_shape.size(); ++j) {
+      output_size *= idx_shape[j];
+    }
+    idx_data_.resize(output_size);
+    std::copy_n(
+        idx_tensor->mutable_data<int64_t>(), output_size, idx_data_.data());
+  }
+  auto inference_end = std::chrono::steady_clock::now();
+  auto postprocess_start = std::chrono::steady_clock::now();
+  // Postprocessing result
+  Postprocess(output_data_,
+              output_shape,
+              idx_data_,
+              idx_shape,
+              result,
+              center_bs,
+              scale_bs);
+  auto postprocess_end = std::chrono::steady_clock::now();
+
+  std::chrono::duration<float> preprocess_diff =
+      preprocess_end - preprocess_start;
+  times->push_back(double(preprocess_diff.count() * 1000));
+  std::chrono::duration<float> inference_diff = inference_end - inference_start;
+  times->push_back(double(inference_diff.count() / repeats * 1000));
+  std::chrono::duration<float> postprocess_diff =
+      postprocess_end - postprocess_start;
+  times->push_back(double(postprocess_diff.count() * 1000));
+}
+
+}  // namespace PaddleDetection

paddle_detection/deploy/lite/src/keypoint_postprocess.cc

@@ -0,0 +1,231 @@
+//   Copyright (c) 2021 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 "include/keypoint_postprocess.h"
+#define PI 3.1415926535
+#define HALF_CIRCLE_DEGREE 180
+
+cv::Point2f get_3rd_point(cv::Point2f& a, cv::Point2f& b) {
+  cv::Point2f direct{a.x - b.x, a.y - b.y};
+  return cv::Point2f(a.x - direct.y, a.y + direct.x);
+}
+
+std::vector<float> get_dir(float src_point_x,
+                           float src_point_y,
+                           float rot_rad) {
+  float sn = sin(rot_rad);
+  float cs = cos(rot_rad);
+  std::vector<float> src_result{0.0, 0.0};
+  src_result[0] = src_point_x * cs - src_point_y * sn;
+  src_result[1] = src_point_x * sn + src_point_y * cs;
+  return src_result;
+}
+
+void affine_tranform(
+    float pt_x, float pt_y, cv::Mat& trans, std::vector<float>& preds, int p) {
+  double new1[3] = {pt_x, pt_y, 1.0};
+  cv::Mat new_pt(3, 1, trans.type(), new1);
+  cv::Mat w = trans * new_pt;
+  preds[p * 3 + 1] = static_cast<float>(w.at<double>(0, 0));
+  preds[p * 3 + 2] = static_cast<float>(w.at<double>(1, 0));
+}
+
+void get_affine_transform(std::vector<float>& center,
+                          std::vector<float>& scale,
+                          float rot,
+                          std::vector<int>& output_size,
+                          cv::Mat& trans,
+                          int inv) {
+  float src_w = scale[0];
+  float dst_w = static_cast<float>(output_size[0]);
+  float dst_h = static_cast<float>(output_size[1]);
+  float rot_rad = rot * PI / HALF_CIRCLE_DEGREE;
+  std::vector<float> src_dir = get_dir(-0.5 * src_w, 0, rot_rad);
+  std::vector<float> dst_dir{static_cast<float>(-0.5) * dst_w, 0.0};
+  cv::Point2f srcPoint2f[3], dstPoint2f[3];
+  srcPoint2f[0] = cv::Point2f(center[0], center[1]);
+  srcPoint2f[1] = cv::Point2f(center[0] + src_dir[0], center[1] + src_dir[1]);
+  srcPoint2f[2] = get_3rd_point(srcPoint2f[0], srcPoint2f[1]);
+
+  dstPoint2f[0] = cv::Point2f(dst_w * 0.5, dst_h * 0.5);
+  dstPoint2f[1] =
+      cv::Point2f(dst_w * 0.5 + dst_dir[0], dst_h * 0.5 + dst_dir[1]);
+  dstPoint2f[2] = get_3rd_point(dstPoint2f[0], dstPoint2f[1]);
+  if (inv == 0) {
+    trans = cv::getAffineTransform(srcPoint2f, dstPoint2f);
+  } else {
+    trans = cv::getAffineTransform(dstPoint2f, srcPoint2f);
+  }
+}
+
+void transform_preds(std::vector<float>& coords,
+                     std::vector<float>& center,
+                     std::vector<float>& scale,
+                     std::vector<int>& output_size,
+                     std::vector<int64_t>& dim,
+                     std::vector<float>& target_coords,
+                     bool affine=false) {
+  if (affine) {
+    cv::Mat trans(2, 3, CV_64FC1);
+    get_affine_transform(center, scale, 0, output_size, trans, 1);
+    for (int p = 0; p < dim[1]; ++p) {
+      affine_tranform(
+          coords[p * 2], coords[p * 2 + 1], trans, target_coords, p);
+    }
+  } else {
+    float heat_w = static_cast<float>(output_size[0]);
+    float heat_h = static_cast<float>(output_size[1]);
+    float x_scale = scale[0] / heat_w;
+    float y_scale = scale[1] / heat_h;
+    float offset_x = center[0] - scale[0] / 2.;
+    float offset_y = center[1] - scale[1] / 2.;
+    for (int i = 0; i < dim[1]; i++) {
+      target_coords[i * 3 + 1] = x_scale * coords[i * 2] + offset_x;
+      target_coords[i * 3 + 2] = y_scale * coords[i * 2 + 1] + offset_y;
+    }
+  }
+}
+
+// only for batchsize == 1
+void get_max_preds(std::vector<float>& heatmap,
+                   std::vector<int>& dim,
+                   std::vector<float>& preds,
+                   std::vector<float>& maxvals,
+                   int batchid,
+                   int joint_idx) {
+  int num_joints = dim[1];
+  int width = dim[3];
+  std::vector<int> idx;
+  idx.resize(num_joints * 2);
+
+  for (int j = 0; j < dim[1]; j++) {
+    float* index = &(
+        heatmap[batchid * num_joints * dim[2] * dim[3] + j * dim[2] * dim[3]]);
+    float* end = index + dim[2] * dim[3];
+    float* max_dis = std::max_element(index, end);
+    auto max_id = std::distance(index, max_dis);
+    maxvals[j] = *max_dis;
+    if (*max_dis > 0) {
+      preds[j * 2] = static_cast<float>(max_id % width);
+      preds[j * 2 + 1] = static_cast<float>(max_id / width);
+    }
+  }
+}
+
+
+void dark_parse(std::vector<float>& heatmap,
+                std::vector<int64_t>& dim,
+                std::vector<float>& coords,
+                int px, 
+                int py, 
+                int index,
+                int ch){
+  /*DARK postpocessing, Zhang et al. Distribution-Aware Coordinate
+  Representation for Human Pose Estimation (CVPR 2020).
+  1) offset = - hassian.inv() * derivative
+  2) dx = (heatmap[x+1] - heatmap[x-1])/2.
+  3) dxx = (dx[x+1] - dx[x-1])/2.
+  4) derivative = Mat([dx, dy])
+  5) hassian = Mat([[dxx, dxy], [dxy, dyy]])
+  */
+  std::vector<float>::const_iterator first1 = heatmap.begin() + index;
+  std::vector<float>::const_iterator last1 = heatmap.begin() + index + dim[2] * dim[3];
+  std::vector<float> heatmap_ch(first1, last1);
+  cv::Mat heatmap_mat = cv::Mat(heatmap_ch).reshape(0,dim[2]);
+  heatmap_mat.convertTo(heatmap_mat, CV_32FC1);
+  cv::GaussianBlur(heatmap_mat, heatmap_mat, cv::Size(3, 3), 0, 0);
+  heatmap_mat = heatmap_mat.reshape(1,1);
+  heatmap_ch = std::vector<float>(heatmap_mat.reshape(1,1));
+
+  float epsilon = 1e-10;
+  //sample heatmap to get values in around target location
+  float xy = log(fmax(heatmap_ch[py * dim[3] + px], epsilon));
+  float xr = log(fmax(heatmap_ch[py * dim[3] + px + 1], epsilon));
+  float xl = log(fmax(heatmap_ch[py * dim[3] + px - 1], epsilon));
+
+  float xr2 = log(fmax(heatmap_ch[py * dim[3] + px + 2], epsilon));
+  float xl2 = log(fmax(heatmap_ch[py * dim[3] + px - 2], epsilon));
+  float yu = log(fmax(heatmap_ch[(py + 1) * dim[3] + px], epsilon));
+  float yd = log(fmax(heatmap_ch[(py - 1) * dim[3] + px], epsilon));
+  float yu2 = log(fmax(heatmap_ch[(py + 2) * dim[3] + px], epsilon));
+  float yd2 = log(fmax(heatmap_ch[(py - 2) * dim[3] + px], epsilon));
+  float xryu = log(fmax(heatmap_ch[(py + 1) * dim[3] + px + 1], epsilon));
+  float xryd = log(fmax(heatmap_ch[(py - 1) * dim[3] + px + 1], epsilon));
+  float xlyu = log(fmax(heatmap_ch[(py + 1) * dim[3] + px - 1], epsilon));
+  float xlyd = log(fmax(heatmap_ch[(py - 1) * dim[3] + px - 1], epsilon));
+
+  //compute dx/dy and dxx/dyy with sampled values
+  float dx = 0.5 * (xr - xl);
+  float dy = 0.5 * (yu - yd);
+  float dxx = 0.25 * (xr2 - 2*xy + xl2);
+  float dxy = 0.25 * (xryu - xryd - xlyu + xlyd);
+  float dyy = 0.25 * (yu2 - 2*xy + yd2);
+
+  //finally get offset by derivative and hassian, which combined by dx/dy and dxx/dyy
+  if(dxx * dyy - dxy*dxy != 0){
+    float M[2][2] = {dxx, dxy, dxy, dyy};
+    float D[2] = {dx, dy};
+    cv::Mat hassian(2,2,CV_32F,M);
+    cv::Mat derivative(2,1,CV_32F,D);
+    cv::Mat offset = - hassian.inv() * derivative;
+    coords[ch * 2] += offset.at<float>(0,0);
+    coords[ch * 2 + 1] += offset.at<float>(1,0);
+  }
+}
+
+void get_final_preds(std::vector<float>& heatmap,
+                     std::vector<int64_t>& dim,
+                     std::vector<int64_t>& idxout,
+                     std::vector<int64_t>& idxdim,
+                     std::vector<float>& center,
+                     std::vector<float> scale,
+                     std::vector<float>& preds,
+                     int batchid,
+                     bool DARK) {
+  std::vector<float> coords;
+  coords.resize(dim[1] * 2);
+  int heatmap_height = dim[2];
+  int heatmap_width = dim[3];
+
+  for (int j = 0; j < dim[1]; ++j) {
+    int index = (batchid * dim[1] + j) * dim[2] * dim[3];
+
+    int idx = idxout[batchid * dim[1] + j];
+    preds[j * 3] = heatmap[index + idx];
+    coords[j * 2] = idx % heatmap_width;
+    coords[j * 2 + 1] = idx / heatmap_width;
+
+    int px = int(coords[j * 2] + 0.5);
+    int py = int(coords[j * 2 + 1] + 0.5);
+
+    if(DARK && px > 1 && px < heatmap_width - 2){
+      dark_parse(heatmap, dim, coords, px, py, index, j);
+    }
+    else{
+      if (px > 0 && px < heatmap_width - 1) {
+        float diff_x = heatmap[index + py * dim[3] + px + 1] -
+                      heatmap[index + py * dim[3] + px - 1];
+        coords[j * 2] += diff_x > 0 ? 1 : -1 * 0.25;
+      }
+      if (py > 0 && py < heatmap_height - 1) {
+        float diff_y = heatmap[index + (py + 1) * dim[3] + px] -
+                      heatmap[index + (py - 1) * dim[3] + px];
+        coords[j * 2 + 1] += diff_y > 0 ? 1 : -1 * 0.25;
+      }
+    }
+  }
+  
+  std::vector<int> img_size{heatmap_width, heatmap_height};
+  transform_preds(coords, center, scale, img_size, dim, preds);
+}

paddle_detection/deploy/lite/src/main.cc

@@ -0,0 +1,388 @@
+//   Copyright (c) 2021 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 <math.h>
+#include <stdarg.h>
+#include <sys/stat.h>
+#include <sys/types.h>
+#include <algorithm>
+#include <iostream>
+#include <numeric>
+#include <string>
+#include <vector>
+
+#include "include/config_parser.h"
+#include "include/keypoint_detector.h"
+#include "include/object_detector.h"
+#include "include/preprocess_op.h"
+#include "json/json.h"
+
+Json::Value RT_Config;
+
+void PrintBenchmarkLog(std::vector<double> det_time, int img_num) {
+  std::cout << "----------------------- Config info -----------------------"
+            << std::endl;
+  std::cout << "num_threads: " << RT_Config["cpu_threads"].as<int>()
+            << std::endl;
+  std::cout << "----------------------- Data info -----------------------"
+            << std::endl;
+  std::cout << "batch_size_det: " << RT_Config["batch_size_det"].as<int>()
+            << std::endl;
+  std::cout << "----------------------- Model info -----------------------"
+            << std::endl;
+  RT_Config["model_dir_det"].as<std::string>().erase(
+      RT_Config["model_dir_det"].as<std::string>().find_last_not_of("/") + 1);
+  std::cout << "detection model_name: "
+            << RT_Config["model_dir_det"].as<std::string>() << std::endl;
+  std::cout << "----------------------- Perf info ------------------------"
+            << std::endl;
+  std::cout << "Total number of predicted data: " << img_num
+            << " and total time spent(ms): "
+            << std::accumulate(det_time.begin(), det_time.end(), 0.)
+            << std::endl;
+  img_num = std::max(1, img_num);
+  std::cout << "preproce_time(ms): " << det_time[0] / img_num
+            << ", inference_time(ms): " << det_time[1] / img_num
+            << ", postprocess_time(ms): " << det_time[2] / img_num << std::endl;
+}
+
+void PrintKptsBenchmarkLog(std::vector<double> det_time, int img_num) {
+  std::cout << "----------------------- Data info -----------------------"
+            << std::endl;
+  std::cout << "batch_size_keypoint: "
+            << RT_Config["batch_size_keypoint"].as<int>() << std::endl;
+  std::cout << "----------------------- Model info -----------------------"
+            << std::endl;
+  RT_Config["model_dir_keypoint"].as<std::string>().erase(
+      RT_Config["model_dir_keypoint"].as<std::string>().find_last_not_of("/") +
+      1);
+  std::cout << "keypoint model_name: "
+            << RT_Config["model_dir_keypoint"].as<std::string>() << std::endl;
+  std::cout << "----------------------- Perf info ------------------------"
+            << std::endl;
+  std::cout << "Total number of predicted data: " << img_num
+            << " and total time spent(ms): "
+            << std::accumulate(det_time.begin(), det_time.end(), 0.)
+            << std::endl;
+  img_num = std::max(1, img_num);
+  std::cout << "Average time cost per person:" << std::endl
+            << "preproce_time(ms): " << det_time[0] / img_num
+            << ", inference_time(ms): " << det_time[1] / img_num
+            << ", postprocess_time(ms): " << det_time[2] / img_num << std::endl;
+}
+
+void PrintTotalIimeLog(double det_time,
+                       double keypoint_time,
+                       double crop_time) {
+  std::cout << "----------------------- Time info ------------------------"
+            << std::endl;
+  std::cout << "Total Pipeline time(ms) per image: "
+            << det_time + keypoint_time + crop_time << std::endl;
+  std::cout << "Average det time(ms) per image: " << det_time
+            << ", average keypoint time(ms) per image: " << keypoint_time
+            << ", average crop time(ms) per image: " << crop_time << std::endl;
+}
+
+static std::string DirName(const std::string& filepath) {
+  auto pos = filepath.rfind(OS_PATH_SEP);
+  if (pos == std::string::npos) {
+    return "";
+  }
+  return filepath.substr(0, pos);
+}
+
+static bool PathExists(const std::string& path) {
+  struct stat buffer;
+  return (stat(path.c_str(), &buffer) == 0);
+}
+
+static void MkDir(const std::string& path) {
+  if (PathExists(path)) return;
+  int ret = 0;
+  ret = mkdir(path.c_str(), 0755);
+  if (ret != 0) {
+    std::string path_error(path);
+    path_error += " mkdir failed!";
+    throw std::runtime_error(path_error);
+  }
+}
+
+static void MkDirs(const std::string& path) {
+  if (path.empty()) return;
+  if (PathExists(path)) return;
+
+  MkDirs(DirName(path));
+  MkDir(path);
+}
+
+void PredictImage(const std::vector<std::string> all_img_paths,
+                  const int batch_size_det,
+                  const double threshold_det,
+                  const bool run_benchmark,
+                  PaddleDetection::ObjectDetector* det,
+                  PaddleDetection::KeyPointDetector* keypoint,
+                  const std::string& output_dir = "output") {
+  std::vector<double> det_t = {0, 0, 0};
+  int steps = ceil(static_cast<float>(all_img_paths.size()) / batch_size_det);
+  int kpts_imgs = 0;
+  std::vector<double> keypoint_t = {0, 0, 0};
+  double midtimecost = 0;
+  for (int idx = 0; idx < steps; idx++) {
+    std::vector<cv::Mat> batch_imgs;
+    int left_image_cnt = all_img_paths.size() - idx * batch_size_det;
+    if (left_image_cnt > batch_size_det) {
+      left_image_cnt = batch_size_det;
+    }
+    for (int bs = 0; bs < left_image_cnt; bs++) {
+      std::string image_file_path = all_img_paths.at(idx * batch_size_det + bs);
+      cv::Mat im = cv::imread(image_file_path, 1);
+      batch_imgs.insert(batch_imgs.end(), im);
+    }
+    // Store all detected result
+    std::vector<PaddleDetection::ObjectResult> result;
+    std::vector<int> bbox_num;
+    std::vector<double> det_times;
+
+    // Store keypoint results
+    std::vector<PaddleDetection::KeyPointResult> result_kpts;
+    std::vector<cv::Mat> imgs_kpts;
+    std::vector<std::vector<float>> center_bs;
+    std::vector<std::vector<float>> scale_bs;
+    std::vector<int> colormap_kpts = PaddleDetection::GenerateColorMap(20);
+    bool is_rbox = false;
+    if (run_benchmark) {
+      det->Predict(
+          batch_imgs, threshold_det, 50, 50, &result, &bbox_num, &det_times);
+    } else {
+      det->Predict(
+          batch_imgs, threshold_det, 0, 1, &result, &bbox_num, &det_times);
+    }
+
+    // get labels and colormap
+    auto labels = det->GetLabelList();
+    auto colormap = PaddleDetection::GenerateColorMap(labels.size());
+    int item_start_idx = 0;
+    for (int i = 0; i < left_image_cnt; i++) {
+      cv::Mat im = batch_imgs[i];
+      std::vector<PaddleDetection::ObjectResult> im_result;
+      int detect_num = 0;
+      for (int j = 0; j < bbox_num[i]; j++) {
+        PaddleDetection::ObjectResult item = result[item_start_idx + j];
+        if (item.confidence < threshold_det || item.class_id == -1) {
+          continue;
+        }
+        detect_num += 1;
+        im_result.push_back(item);
+        if (item.rect.size() > 6) {
+          is_rbox = true;
+          printf("class=%d confidence=%.4f rect=[%d %d %d %d %d %d %d %d]\n",
+                 item.class_id,
+                 item.confidence,
+                 item.rect[0],
+                 item.rect[1],
+                 item.rect[2],
+                 item.rect[3],
+                 item.rect[4],
+                 item.rect[5],
+                 item.rect[6],
+                 item.rect[7]);
+        } else {
+          printf("class=%d confidence=%.4f rect=[%d %d %d %d]\n",
+                 item.class_id,
+                 item.confidence,
+                 item.rect[0],
+                 item.rect[1],
+                 item.rect[2],
+                 item.rect[3]);
+        }
+      }
+      std::cout << all_img_paths.at(idx * batch_size_det + i)
+                << " The number of detected box: " << detect_num << std::endl;
+      item_start_idx = item_start_idx + bbox_num[i];
+
+      std::vector<int> compression_params;
+      compression_params.push_back(cv::IMWRITE_JPEG_QUALITY);
+      compression_params.push_back(95);
+      std::string output_path(output_dir);
+      if (output_dir.rfind(OS_PATH_SEP) != output_dir.size() - 1) {
+        output_path += OS_PATH_SEP;
+      }
+      std::string image_file_path = all_img_paths.at(idx * batch_size_det + i);
+      if (keypoint) {
+        int imsize = im_result.size();
+        for (int i = 0; i < imsize; i++) {
+          auto keypoint_start_time = std::chrono::steady_clock::now();
+          auto item = im_result[i];
+          cv::Mat crop_img;
+          std::vector<double> keypoint_times;
+          std::vector<int> rect = {
+              item.rect[0], item.rect[1], item.rect[2], item.rect[3]};
+          std::vector<float> center;
+          std::vector<float> scale;
+          if (item.class_id == 0) {
+            PaddleDetection::CropImg(im, crop_img, rect, center, scale);
+            center_bs.emplace_back(center);
+            scale_bs.emplace_back(scale);
+            imgs_kpts.emplace_back(crop_img);
+            kpts_imgs += 1;
+          }
+          auto keypoint_crop_time = std::chrono::steady_clock::now();
+
+          std::chrono::duration<float> midtimediff =
+              keypoint_crop_time - keypoint_start_time;
+          midtimecost += static_cast<double>(midtimediff.count() * 1000);
+
+          if (imgs_kpts.size() == RT_Config["batch_size_keypoint"].as<int>() ||
+              ((i == imsize - 1) && !imgs_kpts.empty())) {
+            if (run_benchmark) {
+              keypoint->Predict(imgs_kpts,
+                                center_bs,
+                                scale_bs,
+                                10,
+                                10,
+                                &result_kpts,
+                                &keypoint_times);
+            } else {
+              keypoint->Predict(imgs_kpts,
+                                center_bs,
+                                scale_bs,
+                                0,
+                                1,
+                                &result_kpts,
+                                &keypoint_times);
+            }
+            imgs_kpts.clear();
+            center_bs.clear();
+            scale_bs.clear();
+            keypoint_t[0] += keypoint_times[0];
+            keypoint_t[1] += keypoint_times[1];
+            keypoint_t[2] += keypoint_times[2];
+          }
+        }
+        std::string kpts_savepath =
+            output_path + "keypoint_" +
+            image_file_path.substr(image_file_path.find_last_of('/') + 1);
+        cv::Mat kpts_vis_img = VisualizeKptsResult(
+            im, result_kpts, colormap_kpts, keypoint->get_threshold());
+        cv::imwrite(kpts_savepath, kpts_vis_img, compression_params);
+        printf("Visualized output saved as %s\n", kpts_savepath.c_str());
+      } else {
+        // Visualization result
+        cv::Mat vis_img = PaddleDetection::VisualizeResult(
+            im, im_result, labels, colormap, is_rbox);
+        std::string det_savepath =
+            output_path + "result_" +
+            image_file_path.substr(image_file_path.find_last_of('/') + 1);
+        cv::imwrite(det_savepath, vis_img, compression_params);
+        printf("Visualized output saved as %s\n", det_savepath.c_str());
+      }
+    }
+
+    det_t[0] += det_times[0];
+    det_t[1] += det_times[1];
+    det_t[2] += det_times[2];
+  }
+  PrintBenchmarkLog(det_t, all_img_paths.size());
+  if (keypoint) {
+    PrintKptsBenchmarkLog(keypoint_t, kpts_imgs);
+    PrintTotalIimeLog(
+        (det_t[0] + det_t[1] + det_t[2]) / all_img_paths.size(),
+        (keypoint_t[0] + keypoint_t[1] + keypoint_t[2]) / all_img_paths.size(),
+        midtimecost / all_img_paths.size());
+  }
+}
+
+int main(int argc, char** argv) {
+  std::cout << "Usage: " << argv[0] << " [config_path] [image_dir](option)\n";
+  if (argc < 2) {
+    std::cout << "Usage: ./main det_runtime_config.json" << std::endl;
+    return -1;
+  }
+  std::string config_path = argv[1];
+  std::string img_path = "";
+
+  if (argc >= 3) {
+    img_path = argv[2];
+  }
+  // Parsing command-line
+  PaddleDetection::load_jsonf(config_path, RT_Config);
+  if (RT_Config["model_dir_det"].as<std::string>().empty()) {
+    std::cout << "Please set [model_det_dir] in " << config_path << std::endl;
+    return -1;
+  }
+  if (RT_Config["image_file"].as<std::string>().empty() &&
+      RT_Config["image_dir"].as<std::string>().empty() && img_path.empty()) {
+    std::cout << "Please set [image_file] or [image_dir] in " << config_path
+              << " Or use command: <" << argv[0] << " [image_dir]>"
+              << std::endl;
+    return -1;
+  }
+  if (!img_path.empty()) {
+    std::cout << "Use image_dir in command line overide the path in config file"
+              << std::endl;
+    RT_Config["image_dir"] = img_path;
+    RT_Config["image_file"] = "";
+  }
+  // Load model and create a object detector
+  PaddleDetection::ObjectDetector det(
+      RT_Config["model_dir_det"].as<std::string>(),
+      RT_Config["cpu_threads"].as<int>(),
+      RT_Config["batch_size_det"].as<int>());
+
+  PaddleDetection::KeyPointDetector* keypoint = nullptr;
+  if (!RT_Config["model_dir_keypoint"].as<std::string>().empty()) {
+    keypoint = new PaddleDetection::KeyPointDetector(
+        RT_Config["model_dir_keypoint"].as<std::string>(),
+        RT_Config["cpu_threads"].as<int>(),
+        RT_Config["batch_size_keypoint"].as<int>(),
+        RT_Config["use_dark_decode"].as<bool>());
+    RT_Config["batch_size_det"] = 1;
+    printf(
+        "batchsize of detection forced to be 1 while keypoint model is not "
+        "empty()");
+  }
+  // Do inference on input image
+
+  if (!RT_Config["image_file"].as<std::string>().empty() ||
+      !RT_Config["image_dir"].as<std::string>().empty()) {
+    if (!PathExists(RT_Config["output_dir"].as<std::string>())) {
+      MkDirs(RT_Config["output_dir"].as<std::string>());
+    }
+    std::vector<std::string> all_img_paths;
+    std::vector<cv::String> cv_all_img_paths;
+    if (!RT_Config["image_file"].as<std::string>().empty()) {
+      all_img_paths.push_back(RT_Config["image_file"].as<std::string>());
+      if (RT_Config["batch_size_det"].as<int>() > 1) {
+        std::cout << "batch_size_det should be 1, when set `image_file`."
+                  << std::endl;
+        return -1;
+      }
+    } else {
+      cv::glob(RT_Config["image_dir"].as<std::string>(), cv_all_img_paths);
+      for (const auto& img_path : cv_all_img_paths) {
+        all_img_paths.push_back(img_path);
+      }
+    }
+    PredictImage(all_img_paths,
+                 RT_Config["batch_size_det"].as<int>(),
+                 RT_Config["threshold_det"].as<float>(),
+                 RT_Config["run_benchmark"].as<bool>(),
+                 &det,
+                 keypoint,
+                 RT_Config["output_dir"].as<std::string>());
+  }
+  delete keypoint;
+  keypoint = nullptr;
+  return 0;
+}

paddle_detection/deploy/lite/src/object_detector.cc

@@ -0,0 +1,329 @@
+//   Copyright (c) 2021 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 <sstream>
+// for setprecision
+#include <chrono>
+#include <iomanip>
+#include "include/object_detector.h"
+
+namespace PaddleDetection {
+
+// Load Model and create model predictor
+void ObjectDetector::LoadModel(std::string model_file, int num_theads) {
+  MobileConfig config;
+  config.set_threads(num_theads);
+  config.set_model_from_file(model_file + "/model.nb");
+  config.set_power_mode(LITE_POWER_HIGH);
+
+  predictor_ = CreatePaddlePredictor<MobileConfig>(config);
+}
+
+// Visualiztion MaskDetector results
+cv::Mat VisualizeResult(const cv::Mat& img,
+                        const std::vector<PaddleDetection::ObjectResult>& results,
+                        const std::vector<std::string>& lables,
+                        const std::vector<int>& colormap,
+                        const bool is_rbox = false) {
+  cv::Mat vis_img = img.clone();
+  for (int i = 0; i < results.size(); ++i) {
+    // Configure color and text size
+    std::ostringstream oss;
+    oss << std::setiosflags(std::ios::fixed) << std::setprecision(4);
+    oss << lables[results[i].class_id] << " ";
+    oss << results[i].confidence;
+    std::string text = oss.str();
+    int c1 = colormap[3 * results[i].class_id + 0];
+    int c2 = colormap[3 * results[i].class_id + 1];
+    int c3 = colormap[3 * results[i].class_id + 2];
+    cv::Scalar roi_color = cv::Scalar(c1, c2, c3);
+    int font_face = cv::FONT_HERSHEY_COMPLEX_SMALL;
+    double font_scale = 0.5f;
+    float thickness = 0.5;
+    cv::Size text_size =
+        cv::getTextSize(text, font_face, font_scale, thickness, nullptr);
+    cv::Point origin;
+
+    if (is_rbox) {
+      // Draw object, text, and background
+      for (int k = 0; k < 4; k++) {
+        cv::Point pt1 = cv::Point(results[i].rect[(k * 2) % 8],
+                                  results[i].rect[(k * 2 + 1) % 8]);
+        cv::Point pt2 = cv::Point(results[i].rect[(k * 2 + 2) % 8],
+                                  results[i].rect[(k * 2 + 3) % 8]);
+        cv::line(vis_img, pt1, pt2, roi_color, 2);
+      }
+    } else {
+      int w = results[i].rect[2] - results[i].rect[0];
+      int h = results[i].rect[3] - results[i].rect[1];
+      cv::Rect roi = cv::Rect(results[i].rect[0], results[i].rect[1], w, h);
+      // Draw roi object, text, and background
+      cv::rectangle(vis_img, roi, roi_color, 2);
+    }
+
+    origin.x = results[i].rect[0];
+    origin.y = results[i].rect[1];
+
+    // Configure text background
+    cv::Rect text_back = cv::Rect(results[i].rect[0],
+                                  results[i].rect[1] - text_size.height,
+                                  text_size.width,
+                                  text_size.height);
+    // Draw text, and background
+    cv::rectangle(vis_img, text_back, roi_color, -1);
+    cv::putText(vis_img,
+                text,
+                origin,
+                font_face,
+                font_scale,
+                cv::Scalar(255, 255, 255),
+                thickness);
+  }
+  return vis_img;
+}
+
+void ObjectDetector::Preprocess(const cv::Mat& ori_im) {
+  // Clone the image : keep the original mat for postprocess
+  cv::Mat im = ori_im.clone();
+  cv::cvtColor(im, im, cv::COLOR_BGR2RGB);
+  preprocessor_.Run(&im, &inputs_);
+}
+
+void ObjectDetector::Postprocess(const std::vector<cv::Mat> mats,
+                                 std::vector<PaddleDetection::ObjectResult>* result,
+                                 std::vector<int> bbox_num,
+                                 bool is_rbox = false) {
+  result->clear();
+  int start_idx = 0;
+  for (int im_id = 0; im_id < mats.size(); im_id++) {
+    cv::Mat raw_mat = mats[im_id];
+    int rh = 1;
+    int rw = 1;
+    if (config_.arch_ == "Face") {
+      rh = raw_mat.rows;
+      rw = raw_mat.cols;
+    }
+    for (int j = start_idx; j < start_idx + bbox_num[im_id]; j++) {
+      if (is_rbox) {
+        // Class id
+        int class_id = static_cast<int>(round(output_data_[0 + j * 10]));
+        // Confidence score
+        float score = output_data_[1 + j * 10];
+        int x1 = (output_data_[2 + j * 10] * rw);
+        int y1 = (output_data_[3 + j * 10] * rh);
+        int x2 = (output_data_[4 + j * 10] * rw);
+        int y2 = (output_data_[5 + j * 10] * rh);
+        int x3 = (output_data_[6 + j * 10] * rw);
+        int y3 = (output_data_[7 + j * 10] * rh);
+        int x4 = (output_data_[8 + j * 10] * rw);
+        int y4 = (output_data_[9 + j * 10] * rh);
+
+        PaddleDetection::ObjectResult result_item;
+        result_item.rect = {x1, y1, x2, y2, x3, y3, x4, y4};
+        result_item.class_id = class_id;
+        result_item.confidence = score;
+        result->push_back(result_item);
+      } else {
+        // Class id
+        int class_id = static_cast<int>(round(output_data_[0 + j * 6]));
+        // Confidence score
+        float score = output_data_[1 + j * 6];
+        int xmin = (output_data_[2 + j * 6] * rw);
+        int ymin = (output_data_[3 + j * 6] * rh);
+        int xmax = (output_data_[4 + j * 6] * rw);
+        int ymax = (output_data_[5 + j * 6] * rh);
+        int wd = xmax - xmin;
+        int hd = ymax - ymin;
+
+        PaddleDetection::ObjectResult result_item;
+        result_item.rect = {xmin, ymin, xmax, ymax};
+        result_item.class_id = class_id;
+        result_item.confidence = score;
+        result->push_back(result_item);
+      }
+    }
+    start_idx += bbox_num[im_id];
+  }
+}
+
+void ObjectDetector::Predict(const std::vector<cv::Mat>& imgs,
+                             const double threshold,
+                             const int warmup,
+                             const int repeats,
+                             std::vector<PaddleDetection::ObjectResult>* result,
+                             std::vector<int>* bbox_num,
+                             std::vector<double>* times) {
+  auto preprocess_start = std::chrono::steady_clock::now();
+  int batch_size = imgs.size();
+
+  // in_data_batch
+  std::vector<float> in_data_all;
+  std::vector<float> im_shape_all(batch_size * 2);
+  std::vector<float> scale_factor_all(batch_size * 2);
+  // Preprocess image
+  for (int bs_idx = 0; bs_idx < batch_size; bs_idx++) {
+    cv::Mat im = imgs.at(bs_idx);
+    Preprocess(im);
+    im_shape_all[bs_idx * 2] = inputs_.im_shape_[0];
+    im_shape_all[bs_idx * 2 + 1] = inputs_.im_shape_[1];
+
+    scale_factor_all[bs_idx * 2] = inputs_.scale_factor_[0];
+    scale_factor_all[bs_idx * 2 + 1] = inputs_.scale_factor_[1];
+
+    // TODO: reduce cost time
+    in_data_all.insert(
+        in_data_all.end(), inputs_.im_data_.begin(), inputs_.im_data_.end());
+  }
+  auto preprocess_end = std::chrono::steady_clock::now();
+  std::vector<const float *> output_data_list_;
+  // Prepare input tensor
+
+  auto input_names = predictor_->GetInputNames();
+  for (const auto& tensor_name : input_names) {
+    auto in_tensor = predictor_->GetInputByName(tensor_name);
+    if (tensor_name == "image") {
+      int rh = inputs_.in_net_shape_[0];
+      int rw = inputs_.in_net_shape_[1];
+      in_tensor->Resize({batch_size, 3, rh, rw});
+      auto* inptr = in_tensor->mutable_data<float>();
+      std::copy_n(in_data_all.data(), in_data_all.size(), inptr);
+    } else if (tensor_name == "im_shape") {
+      in_tensor->Resize({batch_size, 2});
+      auto* inptr = in_tensor->mutable_data<float>();
+      std::copy_n(im_shape_all.data(), im_shape_all.size(), inptr);
+    } else if (tensor_name == "scale_factor") {
+      in_tensor->Resize({batch_size, 2});
+      auto* inptr = in_tensor->mutable_data<float>();
+      std::copy_n(scale_factor_all.data(), scale_factor_all.size(), inptr);
+    }
+  }
+
+  // Run predictor
+  // warmup
+  for (int i = 0; i < warmup; i++) {
+    predictor_->Run();
+    // Get output tensor
+    auto output_names = predictor_->GetOutputNames();
+    if (config_.arch_ == "PicoDet") {
+      for (int j = 0; j < output_names.size(); j++) {
+        auto output_tensor = predictor_->GetTensor(output_names[j]);
+        const float* outptr = output_tensor->data<float>();
+        std::vector<int64_t> output_shape = output_tensor->shape();
+        output_data_list_.push_back(outptr);
+      }
+    } else {
+      auto out_tensor = predictor_->GetTensor(output_names[0]);
+      auto out_bbox_num = predictor_->GetTensor(output_names[1]);
+    }
+  }
+
+  bool is_rbox = false;
+  auto inference_start = std::chrono::steady_clock::now();
+  for (int i = 0; i < repeats; i++) {
+    predictor_->Run();
+  }
+  auto inference_end = std::chrono::steady_clock::now();
+  auto postprocess_start = std::chrono::steady_clock::now();
+  // Get output tensor
+  output_data_list_.clear();
+  int num_class = 80;
+  int reg_max = 7;
+  auto output_names = predictor_->GetOutputNames();
+  // TODO: Unified model output.
+  if (config_.arch_ == "PicoDet") {
+    for (int i = 0; i < output_names.size(); i++) {
+      auto output_tensor = predictor_->GetTensor(output_names[i]);
+      const float* outptr = output_tensor->data<float>();
+      std::vector<int64_t> output_shape = output_tensor->shape();
+      if (i == 0) {
+        num_class = output_shape[2];
+      }
+      if (i == config_.fpn_stride_.size()) {
+        reg_max = output_shape[2] / 4 - 1;
+      }
+      output_data_list_.push_back(outptr);
+    }
+  } else {
+    auto output_tensor = predictor_->GetTensor(output_names[0]);
+    auto output_shape = output_tensor->shape();
+    auto out_bbox_num = predictor_->GetTensor(output_names[1]);
+    auto out_bbox_num_shape = out_bbox_num->shape();
+    // Calculate output length
+    int output_size = 1;
+    for (int j = 0; j < output_shape.size(); ++j) {
+      output_size *= output_shape[j];
+    }
+    is_rbox = output_shape[output_shape.size() - 1] % 10 == 0;
+
+    if (output_size < 6) {
+      std::cerr << "[WARNING] No object detected." << std::endl;
+    }
+    output_data_.resize(output_size);
+    std::copy_n(
+        output_tensor->mutable_data<float>(), output_size, output_data_.data());
+
+    int out_bbox_num_size = 1;
+    for (int j = 0; j < out_bbox_num_shape.size(); ++j) {
+      out_bbox_num_size *= out_bbox_num_shape[j];
+    }
+    out_bbox_num_data_.resize(out_bbox_num_size);
+    std::copy_n(out_bbox_num->mutable_data<int>(),
+                out_bbox_num_size,
+                out_bbox_num_data_.data());
+  }
+  // Postprocessing result
+  result->clear();
+  if (config_.arch_ == "PicoDet") {
+    PaddleDetection::PicoDetPostProcess(
+        result, output_data_list_, config_.fpn_stride_, 
+        inputs_.im_shape_, inputs_.scale_factor_,
+        config_.nms_info_["score_threshold"].as<float>(), 
+        config_.nms_info_["nms_threshold"].as<float>(), num_class, reg_max);
+    bbox_num->push_back(result->size());
+  } else {
+    Postprocess(imgs, result, out_bbox_num_data_, is_rbox);
+    bbox_num->clear();
+    for (int k = 0; k < out_bbox_num_data_.size(); k++) {
+      int tmp = out_bbox_num_data_[k];
+      bbox_num->push_back(tmp);
+    }
+  }
+  auto postprocess_end = std::chrono::steady_clock::now();
+
+  std::chrono::duration<float> preprocess_diff =
+      preprocess_end - preprocess_start;
+  times->push_back(double(preprocess_diff.count() * 1000));
+  std::chrono::duration<float> inference_diff = inference_end - inference_start;
+  times->push_back(double(inference_diff.count() / repeats * 1000));
+  std::chrono::duration<float> postprocess_diff =
+      postprocess_end - postprocess_start;
+  times->push_back(double(postprocess_diff.count() * 1000));
+}
+
+std::vector<int> GenerateColorMap(int num_class) {
+  auto colormap = std::vector<int>(3 * num_class, 0);
+  for (int i = 0; i < num_class; ++i) {
+    int j = 0;
+    int lab = i;
+    while (lab) {
+      colormap[i * 3] |= (((lab >> 0) & 1) << (7 - j));
+      colormap[i * 3 + 1] |= (((lab >> 1) & 1) << (7 - j));
+      colormap[i * 3 + 2] |= (((lab >> 2) & 1) << (7 - j));
+      ++j;
+      lab >>= 3;
+    }
+  }
+  return colormap;
+}
+
+}  // namespace PaddleDetection

paddle_detection/deploy/lite/src/picodet_postprocess.cc

@@ -0,0 +1,128 @@
+//   Copyright (c) 2021 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.
+//
+// The code is based on:
+// https://github.com/RangiLyu/nanodet/blob/main/demo_mnn/nanodet_mnn.cpp
+
+#include "include/picodet_postprocess.h"
+
+namespace PaddleDetection {
+
+float fast_exp(float x) {
+  union {
+    uint32_t i;
+    float f;
+  } v{};
+  v.i = (1 << 23) * (1.4426950409 * x + 126.93490512f);
+  return v.f;
+}
+
+template <typename _Tp>
+int activation_function_softmax(const _Tp *src, _Tp *dst, int length) {
+  const _Tp alpha = *std::max_element(src, src + length);
+  _Tp denominator{0};
+
+  for (int i = 0; i < length; ++i) {
+    dst[i] = fast_exp(src[i] - alpha);
+    denominator += dst[i];
+  }
+
+  for (int i = 0; i < length; ++i) {
+    dst[i] /= denominator;
+  }
+
+  return 0;
+}
+
+// PicoDet decode
+PaddleDetection::ObjectResult
+disPred2Bbox(const float *&dfl_det, int label, float score, int x, int y,
+             int stride, std::vector<float> im_shape, int reg_max) {
+  float ct_x = (x + 0.5) * stride;
+  float ct_y = (y + 0.5) * stride;
+  std::vector<float> dis_pred;
+  dis_pred.resize(4);
+  for (int i = 0; i < 4; i++) {
+    float dis = 0;
+    float *dis_after_sm = new float[reg_max + 1];
+    activation_function_softmax(dfl_det + i * (reg_max + 1), dis_after_sm,
+                                reg_max + 1);
+    for (int j = 0; j < reg_max + 1; j++) {
+      dis += j * dis_after_sm[j];
+    }
+    dis *= stride;
+    dis_pred[i] = dis;
+    delete[] dis_after_sm;
+  }
+  int xmin = (int)(std::max)(ct_x - dis_pred[0], .0f);
+  int ymin = (int)(std::max)(ct_y - dis_pred[1], .0f);
+  int xmax = (int)(std::min)(ct_x + dis_pred[2], (float)im_shape[0]);
+  int ymax = (int)(std::min)(ct_y + dis_pred[3], (float)im_shape[1]);
+
+  PaddleDetection::ObjectResult result_item;
+  result_item.rect = {xmin, ymin, xmax, ymax};
+  result_item.class_id = label;
+  result_item.confidence = score;
+
+  return result_item;
+}
+
+void PicoDetPostProcess(std::vector<PaddleDetection::ObjectResult> *results,
+                        std::vector<const float *> outs,
+                        std::vector<int> fpn_stride,
+                        std::vector<float> im_shape,
+                        std::vector<float> scale_factor, float score_threshold,
+                        float nms_threshold, int num_class, int reg_max) {
+  std::vector<std::vector<PaddleDetection::ObjectResult>> bbox_results;
+  bbox_results.resize(num_class);
+  int in_h = im_shape[0], in_w = im_shape[1];
+  for (int i = 0; i < fpn_stride.size(); ++i) {
+    int feature_h = ceil((float)in_h / fpn_stride[i]);
+    int feature_w = ceil((float)in_w / fpn_stride[i]);
+    for (int idx = 0; idx < feature_h * feature_w; idx++) {
+      const float *scores = outs[i] + (idx * num_class);
+
+      int row = idx / feature_w;
+      int col = idx % feature_w;
+      float score = 0;
+      int cur_label = 0;
+      for (int label = 0; label < num_class; label++) {
+        if (scores[label] > score) {
+          score = scores[label];
+          cur_label = label;
+        }
+      }
+      if (score > score_threshold) {
+        const float *bbox_pred =
+            outs[i + fpn_stride.size()] + (idx * 4 * (reg_max + 1));
+        bbox_results[cur_label].push_back(
+            disPred2Bbox(bbox_pred, cur_label, score, col, row, fpn_stride[i],
+                         im_shape, reg_max));
+      }
+    }
+  }
+  for (int i = 0; i < (int)bbox_results.size(); i++) {
+    PaddleDetection::nms(bbox_results[i], nms_threshold);
+
+    for (auto box : bbox_results[i]) {
+      box.rect[0] = box.rect[0] / scale_factor[1];
+      box.rect[2] = box.rect[2] / scale_factor[1];
+      box.rect[1] = box.rect[1] / scale_factor[0];
+      box.rect[3] = box.rect[3] / scale_factor[0];
+      results->push_back(box);
+    }
+  }
+}
+
+} // namespace PaddleDetection

paddle_detection/deploy/lite/src/preprocess_op.cc

@@ -0,0 +1,185 @@
+//   Copyright (c) 2021 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 <string>
+#include <thread>
+#include <vector>
+
+#include "include/preprocess_op.h"
+
+namespace PaddleDetection {
+
+void InitInfo::Run(cv::Mat* im, ImageBlob* data) {
+  data->im_shape_ = {static_cast<float>(im->rows),
+                     static_cast<float>(im->cols)};
+  data->scale_factor_ = {1., 1.};
+  data->in_net_shape_ = {static_cast<float>(im->rows),
+                         static_cast<float>(im->cols)};
+}
+
+void NormalizeImage::Run(cv::Mat* im, ImageBlob* data) {
+  double e = 1.0;
+  if (is_scale_) {
+    e *= 1./255.0;
+  }
+  (*im).convertTo(*im, CV_32FC3, e);
+  for (int h = 0; h < im->rows; h++) {
+    for (int w = 0; w < im->cols; w++) {
+      im->at<cv::Vec3f>(h, w)[0] =
+          (im->at<cv::Vec3f>(h, w)[0] - mean_[0]) / scale_[0];
+      im->at<cv::Vec3f>(h, w)[1] =
+          (im->at<cv::Vec3f>(h, w)[1] - mean_[1]) / scale_[1];
+      im->at<cv::Vec3f>(h, w)[2] =
+          (im->at<cv::Vec3f>(h, w)[2] - mean_[2]) / scale_[2];
+    }
+  }
+}
+
+void Permute::Run(cv::Mat* im, ImageBlob* data) {
+  (*im).convertTo(*im, CV_32FC3);
+  int rh = im->rows;
+  int rw = im->cols;
+  int rc = im->channels();
+  (data->im_data_).resize(rc * rh * rw);
+  float* base = (data->im_data_).data();
+  for (int i = 0; i < rc; ++i) {
+    cv::extractChannel(*im, cv::Mat(rh, rw, CV_32FC1, base + i * rh * rw), i);
+  }
+}
+
+void Resize::Run(cv::Mat* im, ImageBlob* data) {
+  auto resize_scale = GenerateScale(*im);
+  data->im_shape_ = {static_cast<float>(im->cols * resize_scale.first),
+                     static_cast<float>(im->rows * resize_scale.second)};
+  data->in_net_shape_ = {static_cast<float>(im->cols * resize_scale.first),
+                         static_cast<float>(im->rows * resize_scale.second)};
+  cv::resize(
+      *im, *im, cv::Size(), resize_scale.first, resize_scale.second, interp_);
+  data->im_shape_ = {
+      static_cast<float>(im->rows), static_cast<float>(im->cols),
+  };
+  data->scale_factor_ = {
+      resize_scale.second, resize_scale.first,
+  };
+}
+
+std::pair<float, float> Resize::GenerateScale(const cv::Mat& im) {
+  std::pair<float, float> resize_scale;
+  int origin_w = im.cols;
+  int origin_h = im.rows;
+
+  if (keep_ratio_) {
+    int im_size_max = std::max(origin_w, origin_h);
+    int im_size_min = std::min(origin_w, origin_h);
+    int target_size_max =
+        *std::max_element(target_size_.begin(), target_size_.end());
+    int target_size_min =
+        *std::min_element(target_size_.begin(), target_size_.end());
+    float scale_min =
+        static_cast<float>(target_size_min) / static_cast<float>(im_size_min);
+    float scale_max =
+        static_cast<float>(target_size_max) / static_cast<float>(im_size_max);
+    float scale_ratio = std::min(scale_min, scale_max);
+    resize_scale = {scale_ratio, scale_ratio};
+  } else {
+    resize_scale.first =
+        static_cast<float>(target_size_[1]) / static_cast<float>(origin_w);
+    resize_scale.second =
+        static_cast<float>(target_size_[0]) / static_cast<float>(origin_h);
+  }
+  return resize_scale;
+}
+
+void PadStride::Run(cv::Mat* im, ImageBlob* data) {
+  if (stride_ <= 0) {
+    return;
+  }
+  int rc = im->channels();
+  int rh = im->rows;
+  int rw = im->cols;
+  int nh = (rh / stride_) * stride_ + (rh % stride_ != 0) * stride_;
+  int nw = (rw / stride_) * stride_ + (rw % stride_ != 0) * stride_;
+  cv::copyMakeBorder(
+      *im, *im, 0, nh - rh, 0, nw - rw, cv::BORDER_CONSTANT, cv::Scalar(0));
+  data->in_net_shape_ = {
+      static_cast<float>(im->rows), static_cast<float>(im->cols),
+  };
+}
+
+void TopDownEvalAffine::Run(cv::Mat* im, ImageBlob* data) {
+  cv::resize(*im, *im, cv::Size(trainsize_[0], trainsize_[1]), 0, 0, interp_);
+  // todo: Simd::ResizeBilinear();
+  data->in_net_shape_ = {
+      static_cast<float>(trainsize_[1]), static_cast<float>(trainsize_[0]),
+  };
+}
+
+// Preprocessor op running order
+const std::vector<std::string> Preprocessor::RUN_ORDER = {"InitInfo",
+                                                          "TopDownEvalAffine",
+                                                          "Resize",
+                                                          "NormalizeImage",
+                                                          "PadStride",
+                                                          "Permute"};
+
+void Preprocessor::Run(cv::Mat* im, ImageBlob* data) {
+  for (const auto& name : RUN_ORDER) {
+    if (ops_.find(name) != ops_.end()) {
+      ops_[name]->Run(im, data);
+    }
+  }
+}
+
+void CropImg(cv::Mat& img,
+             cv::Mat& crop_img,
+             std::vector<int>& area,
+             std::vector<float>& center,
+             std::vector<float>& scale,
+             float expandratio) {
+  int crop_x1 = std::max(0, area[0]);
+  int crop_y1 = std::max(0, area[1]);
+  int crop_x2 = std::min(img.cols - 1, area[2]);
+  int crop_y2 = std::min(img.rows - 1, area[3]);
+  
+  int center_x = (crop_x1 + crop_x2) / 2.;
+  int center_y = (crop_y1 + crop_y2) / 2.;
+  int half_h = (crop_y2 - crop_y1) / 2.;
+  int half_w = (crop_x2 - crop_x1) / 2.;
+
+  if (half_h * 3 > half_w * 4) {
+    half_w = static_cast<int>(half_h * 0.75);
+  } else {
+    half_h = static_cast<int>(half_w * 4 / 3);
+  }
+
+  crop_x1 =
+      std::max(0, center_x - static_cast<int>(half_w * (1 + expandratio)));
+  crop_y1 =
+      std::max(0, center_y - static_cast<int>(half_h * (1 + expandratio)));
+  crop_x2 = std::min(img.cols - 1,
+                     static_cast<int>(center_x + half_w * (1 + expandratio)));
+  crop_y2 = std::min(img.rows - 1,
+                     static_cast<int>(center_y + half_h * (1 + expandratio)));
+  crop_img =
+      img(cv::Range(crop_y1, crop_y2 + 1), cv::Range(crop_x1, crop_x2 + 1));
+
+  center.clear();
+  center.emplace_back((crop_x1 + crop_x2) / 2);
+  center.emplace_back((crop_y1 + crop_y2) / 2);
+  scale.clear();
+  scale.emplace_back((crop_x2 - crop_x1));
+  scale.emplace_back((crop_y2 - crop_y1));
+}
+
+}  // namespace PaddleDetection

paddle_detection/deploy/lite/src/utils.cc

@@ -0,0 +1,49 @@
+//   Copyright (c) 2021 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 "include/utils.h"
+
+namespace PaddleDetection {
+
+void nms(std::vector<ObjectResult> &input_boxes, float nms_threshold) {
+  std::sort(input_boxes.begin(),
+  input_boxes.end(), 
+  [](ObjectResult a, ObjectResult b) { return a.confidence > b.confidence; });
+  std::vector<float> vArea(input_boxes.size());
+  for (int i = 0; i < int(input_boxes.size()); ++i) {
+    vArea[i] = (input_boxes.at(i).rect[2] - input_boxes.at(i).rect[0] + 1) 
+            * (input_boxes.at(i).rect[3] - input_boxes.at(i).rect[1] + 1);
+  }
+  for (int i = 0; i < int(input_boxes.size()); ++i) {
+    for (int j = i + 1; j < int(input_boxes.size());) {
+      float xx1 = (std::max)(input_boxes[i].rect[0], input_boxes[j].rect[0]);
+      float yy1 = (std::max)(input_boxes[i].rect[1], input_boxes[j].rect[1]);
+      float xx2 = (std::min)(input_boxes[i].rect[2], input_boxes[j].rect[2]);
+      float yy2 = (std::min)(input_boxes[i].rect[3], input_boxes[j].rect[3]);
+      float w = (std::max)(float(0), xx2 - xx1 + 1);
+      float h = (std::max)(float(0), yy2 - yy1 + 1);
+      float inter = w * h;
+      float ovr = inter / (vArea[i] + vArea[j] - inter);
+      if (ovr >= nms_threshold) {
+          input_boxes.erase(input_boxes.begin() + j);
+          vArea.erase(vArea.begin() + j);
+      }
+      else {
+          j++;
+      }
+    }
+  }
+}
+
+}  // namespace PaddleDetection