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pipnet98.cpp
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pipnet98.cpp
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//
// Created by DefTruth on 2022/3/20.
//
#include "pipnet98.h"
#include "lite/ort/core/ort_utils.h"
using ortcv::PIPNet98;
Ort::Value PIPNet98::transform(const cv::Mat &mat)
{
cv::Mat canvas;
cv::resize(mat, canvas, cv::Size(input_node_dims.at(3),
input_node_dims.at(2)));
cv::cvtColor(canvas, canvas, cv::COLOR_BGR2RGB);
canvas.convertTo(canvas, CV_32FC3, 1.f / 255.f, 0.f);
ortcv::utils::transform::normalize_inplace(canvas, mean_vals, scale_vals); // float32
// (1,3,256,256)
return ortcv::utils::transform::create_tensor(
canvas, input_node_dims, memory_info_handler,
input_values_handler, ortcv::utils::transform::CHW); // deepcopy inside
}
void PIPNet98::detect(const cv::Mat &mat, types::Landmarks &landmarks)
{
if (mat.empty()) return;
float img_height = static_cast<float>(mat.rows);
float img_width = static_cast<float>(mat.cols);
// 1. make input tensor
Ort::Value input_tensor = this->transform(mat);
// 2. inference
auto output_tensors = ort_session->Run(
Ort::RunOptions{nullptr}, input_node_names.data(),
&input_tensor, 1, output_node_names.data(), num_outputs
);
// 3. generate landmarks
this->generate_landmarks(landmarks, output_tensors, img_height, img_width);
}
void PIPNet98::generate_landmarks(types::Landmarks &landmarks,
std::vector<Ort::Value> &output_tensors,
float img_height, float img_width)
{
Ort::Value &outputs_cls = output_tensors.at(0); // (1,98,8,8)
Ort::Value &outputs_x = output_tensors.at(1); // (1,98,8,8)
Ort::Value &outputs_y = output_tensors.at(2); // (1,98,8,8)
Ort::Value &outputs_nb_x = output_tensors.at(3); // (1,98*10,8,8)
Ort::Value &outputs_nb_y = output_tensors.at(4); // (1,98*10,8,8)
const unsigned int grid_h = output_node_dims.at(0).at(2); // 8
const unsigned int grid_w = output_node_dims.at(0).at(3); // 8
const unsigned int grid_length = grid_h * grid_w; // 8 * 8 = 64
const unsigned int input_h = input_node_dims.at(2);
const unsigned int input_w = input_node_dims.at(3);
// fetch data from pointers
const float *outputs_cls_ptr = outputs_cls.GetTensorMutableData<float>();
const float *outputs_x_ptr = outputs_x.GetTensorMutableData<float>();
const float *outputs_y_ptr = outputs_y.GetTensorMutableData<float>();
const float *outputs_nb_x_ptr = outputs_nb_x.GetTensorMutableData<float>();
const float *outputs_nb_y_ptr = outputs_nb_y.GetTensorMutableData<float>();
// find max_ids
std::vector<unsigned int> max_ids(num_lms);
for (unsigned int i = 0; i < num_lms; ++i)
{
const float *score_ptr = outputs_cls_ptr + i * grid_length;
unsigned int max_id = 0;
float max_score = score_ptr[0];
for (unsigned int j = 0; j < grid_length; ++j)
{
if (score_ptr[j] > max_score)
{
max_score = score_ptr[j];
max_id = j;
}
}
max_ids[i] = max_id; // range 0~64
}
// find x & y offsets
std::vector<float> output_x_select(num_lms);
std::vector<float> output_y_select(num_lms);
for (unsigned int i = 0; i < num_lms; ++i)
{
const float *offset_x_ptr = outputs_x_ptr + i * grid_length;
const float *offset_y_ptr = outputs_y_ptr + i * grid_length;
const unsigned int max_id = max_ids.at(i);
output_x_select[i] = offset_x_ptr[max_id];
output_y_select[i] = offset_y_ptr[max_id];
}
// find nb_x & nb_y offsets
std::unordered_map<unsigned int, std::vector<float>> output_nb_x_select;
std::unordered_map<unsigned int, std::vector<float>> output_nb_y_select;
// initialize offsets map
for (unsigned int i = 0; i < num_lms; ++i)
{
std::vector<float> nb_x_offset(num_nb);
std::vector<float> nb_y_offset(num_nb);
output_nb_x_select[i] = nb_x_offset;
output_nb_y_select[i] = nb_y_offset;
}
for (unsigned int i = 0; i < num_lms; ++i)
{
for (unsigned int j = 0; j < num_nb; ++j)
{
const unsigned int max_id = max_ids.at(i);
const float *offset_nb_x_ptr = outputs_nb_x_ptr + (i * num_nb + j) * grid_length;
const float *offset_nb_y_ptr = outputs_nb_y_ptr + (i * num_nb + j) * grid_length;
output_nb_x_select[i][j] = offset_nb_x_ptr[max_id];
output_nb_y_select[i][j] = offset_nb_y_ptr[max_id];
}
}
// calculate coords
std::vector<float> lms_pred_x(num_lms); // 98
std::vector<float> lms_pred_y(num_lms); // 98
std::unordered_map<unsigned int, std::vector<float>> lms_pred_nb_x; // 98,10
std::unordered_map<unsigned int, std::vector<float>> lms_pred_nb_y; // 98,10
// initialize pred maps
for (unsigned int i = 0; i < num_lms; ++i)
{
std::vector<float> nb_x_offset(num_nb);
std::vector<float> nb_y_offset(num_nb);
lms_pred_nb_x[i] = nb_x_offset;
lms_pred_nb_y[i] = nb_y_offset;
}
for (unsigned int i = 0; i < num_lms; ++i)
{
float cx = static_cast<float>(max_ids.at(i) % grid_w);
float cy = static_cast<float>(max_ids.at(i) / grid_w);
// calculate coords & normalize
lms_pred_x[i] = ((cx + output_x_select[i]) * (float) net_stride) / (float) input_w;
lms_pred_y[i] = ((cy + output_y_select[i]) * (float) net_stride) / (float) input_h;
for (unsigned int j = 0; j < num_nb; ++j)
{
lms_pred_nb_x[i][j] = ((cx + output_nb_x_select[i][j]) * (float) net_stride) / (float) input_w;
lms_pred_nb_y[i][j] = ((cy + output_nb_y_select[i][j]) * (float) net_stride) / (float) input_h;
}
}
// reverse indexes
std::unordered_map<unsigned int, std::vector<float>> tmp_nb_x; // 98,max_len
std::unordered_map<unsigned int, std::vector<float>> tmp_nb_y; // 98,max_len
// initialize reverse maps
for (unsigned int i = 0; i < num_lms; ++i)
{
std::vector<float> tmp_x(max_len);
std::vector<float> tmp_y(max_len);
tmp_nb_x[i] = tmp_x;
tmp_nb_y[i] = tmp_y;
}
for (unsigned int i = 0; i < num_lms; ++i)
{
for (unsigned int j = 0; j < max_len; ++j)
{
unsigned int ri = reverse_index1[i * max_len + j];
unsigned int rj = reverse_index2[i * max_len + j];
tmp_nb_x[i][j] = lms_pred_nb_x[ri][rj];
tmp_nb_y[i][j] = lms_pred_nb_y[ri][rj];
}
}
// merge predictions
landmarks.points.clear();
for (unsigned int i = 0; i < num_lms; ++i)
{
float total_x = lms_pred_x[i];
float total_y = lms_pred_y[i];
for (unsigned int j = 0; j < max_len; ++j)
{
total_x += tmp_nb_x[i][j];
total_y += tmp_nb_y[i][j];
}
float x = total_x / ((float) max_len + 1.f);
float y = total_y / ((float) max_len + 1.f);
x = std::min(std::max(0.f, x), 1.0f);
y = std::min(std::max(0.f, y), 1.0f);
landmarks.points.push_back(cv::Point2f(x * img_width, y * img_height));
}
landmarks.flag = true;
}