keypoint_detector.cc

//   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