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PaddleDetection
未验证 提交 2580dc01 编写于 作者: G Guanghua Yu 提交者： GitHub
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fix picodet cpp infer (#5065)

上级 b367361e
隐藏空白更改
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Showing with 863 addition and 977 deletion
deploy/cpp/include/picodet_postprocess.h
浏览文件 @ 2580dc01
......@@ -14,25 +14,24 @@
#pragma once
#include <string>
#include <vector>
#include <memory>
#include <utility>
#include <cmath>
#include <ctime>
#include <memory>
#include <numeric>
#include <string>
#include <utility>
#include <vector>
#include "include/utils.h"
namespace PaddleDetection {
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 = 0.3,
float nms_threshold = 0.5,
int num_class = 80,
int reg_max = 7);
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 = 0.3, float nms_threshold = 0.5,
int num_class = 80, int reg_max = 7);
} // namespace PaddleDetection
\ No newline at end of file
} // namespace PaddleDetection
deploy/cpp/src/picodet_postprocess.cc
浏览文件 @ 2580dc01
......@@ -20,79 +20,76 @@
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;
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};
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] = fast_exp(src[i] - alpha);
denominator += dst[i];
}
for (int i = 0; i < length; ++i) {
dst[i] /= denominator;
}
for (int i = 0; i < length; ++i) {
dst[i] /= denominator;
}
return 0;
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;
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];
}
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]);
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;
PaddleDetection::ObjectResult result_item;
result_item.rect = {xmin, ymin, xmax, ymax};
result_item.class_id = label;
result_item.confidence = score;
return result_item;
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) {
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 = in_h / fpn_stride[i];
int feature_w = in_w / fpn_stride[i];
int feature_h = std::ceil((float)in_h / fpn_stride[i]);
int feature_w = std::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);
......@@ -107,10 +104,11 @@ void PicoDetPostProcess(std::vector<PaddleDetection::ObjectResult>* results,
}
}
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));
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));
}
}
}
......@@ -118,13 +116,13 @@ void PicoDetPostProcess(std::vector<PaddleDetection::ObjectResult>* results,
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);
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
} // namespace PaddleDetection
deploy/lite/src/picodet_postprocess.cc
浏览文件 @ 2580dc01
......@@ -20,79 +20,76 @@
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;
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};
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] = fast_exp(src[i] - alpha);
denominator += dst[i];
}
for (int i = 0; i < length; ++i) {
dst[i] /= denominator;
}
for (int i = 0; i < length; ++i) {
dst[i] /= denominator;
}
return 0;
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;
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];
}
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]);
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;
PaddleDetection::ObjectResult result_item;
result_item.rect = {xmin, ymin, xmax, ymax};
result_item.class_id = label;
result_item.confidence = score;
return result_item;
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) {
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 = in_h / fpn_stride[i];
int feature_w = in_w / fpn_stride[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);
......@@ -107,10 +104,11 @@ void PicoDetPostProcess(std::vector<PaddleDetection::ObjectResult>* results,
}
}
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));
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));
}
}
}
......@@ -118,13 +116,13 @@ void PicoDetPostProcess(std::vector<PaddleDetection::ObjectResult>* results,
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);
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
} // namespace PaddleDetection
deploy/third_engine/demo_mnn/picodet_mnn.cpp
浏览文件 @ 2580dc01
......@@ -17,223 +17,203 @@
using namespace std;
PicoDet::PicoDet(const std::string &mnn_path,
int input_width, int input_length, int num_thread_,
float score_threshold_, float nms_threshold_)
{
num_thread = num_thread_;
in_w = input_width;
in_h = input_length;
score_threshold = score_threshold_;
nms_threshold = nms_threshold_;
PicoDet_interpreter = std::shared_ptr<MNN::Interpreter>(MNN::Interpreter::createFromFile(mnn_path.c_str()));
MNN::ScheduleConfig config;
config.numThread = num_thread;
MNN::BackendConfig backendConfig;
backendConfig.precision = (MNN::BackendConfig::PrecisionMode) 2;
config.backendConfig = &backendConfig;
PicoDet_session = PicoDet_interpreter->createSession(config);
input_tensor = PicoDet_interpreter->getSessionInput(PicoDet_session, nullptr);
PicoDet::PicoDet(const std::string &mnn_path, int input_width, int input_length,
int num_thread_, float score_threshold_,
float nms_threshold_) {
num_thread = num_thread_;
in_w = input_width;
in_h = input_length;
score_threshold = score_threshold_;
nms_threshold = nms_threshold_;
PicoDet_interpreter = std::shared_ptr<MNN::Interpreter>(
MNN::Interpreter::createFromFile(mnn_path.c_str()));
MNN::ScheduleConfig config;
config.numThread = num_thread;
MNN::BackendConfig backendConfig;
backendConfig.precision = (MNN::BackendConfig::PrecisionMode)2;
config.backendConfig = &backendConfig;
PicoDet_session = PicoDet_interpreter->createSession(config);
input_tensor = PicoDet_interpreter->getSessionInput(PicoDet_session, nullptr);
}
PicoDet::~PicoDet()
{
PicoDet_interpreter->releaseModel();
PicoDet_interpreter->releaseSession(PicoDet_session);
PicoDet::~PicoDet() {
PicoDet_interpreter->releaseModel();
PicoDet_interpreter->releaseSession(PicoDet_session);
}
int PicoDet::detect(cv::Mat &raw_image, std::vector<BoxInfo> &result_list)
{
if (raw_image.empty()) {
std::cout << "image is empty ,please check!" << std::endl;
return -1;
}
image_h = raw_image.rows;
image_w = raw_image.cols;
cv::Mat image;
cv::resize(raw_image, image, cv::Size(in_w, in_h));
PicoDet_interpreter->resizeTensor(input_tensor, {1, 3, in_h, in_w});
PicoDet_interpreter->resizeSession(PicoDet_session);
std::shared_ptr<MNN::CV::ImageProcess> pretreat(
MNN::CV::ImageProcess::create(MNN::CV::BGR, MNN::CV::BGR, mean_vals, 3,
norm_vals, 3));
pretreat->convert(image.data, in_w, in_h, image.step[0], input_tensor);
auto start = chrono::steady_clock::now();
// run network
PicoDet_interpreter->runSession(PicoDet_session);
// get output data
std::vector<std::vector<BoxInfo>> results;
results.resize(num_class);
for (const auto &head_info : heads_info)
{
MNN::Tensor *tensor_scores = PicoDet_interpreter->getSessionOutput(PicoDet_session, head_info.cls_layer.c_str());
MNN::Tensor *tensor_boxes = PicoDet_interpreter->getSessionOutput(PicoDet_session, head_info.dis_layer.c_str());
MNN::Tensor tensor_scores_host(tensor_scores, tensor_scores->getDimensionType());
tensor_scores->copyToHostTensor(&tensor_scores_host);
MNN::Tensor tensor_boxes_host(tensor_boxes, tensor_boxes->getDimensionType());
tensor_boxes->copyToHostTensor(&tensor_boxes_host);
decode_infer(&tensor_scores_host, &tensor_boxes_host, head_info.stride, score_threshold, results);
}
auto end = chrono::steady_clock::now();
chrono::duration<double> elapsed = end - start;
cout << "inference time:" << elapsed.count() << " s, ";
for (int i = 0; i < (int)results.size(); i++)
{
nms(results[i], nms_threshold);
for (auto box : results[i])
{
box.x1 = box.x1 / in_w * image_w;
box.x2 = box.x2 / in_w * image_w;
box.y1 = box.y1 / in_h * image_h;
box.y2 = box.y2 / in_h * image_h;
result_list.push_back(box);
}
int PicoDet::detect(cv::Mat &raw_image, std::vector<BoxInfo> &result_list) {
if (raw_image.empty()) {
std::cout << "image is empty ,please check!" << std::endl;
return -1;
}
image_h = raw_image.rows;
image_w = raw_image.cols;
cv::Mat image;
cv::resize(raw_image, image, cv::Size(in_w, in_h));
PicoDet_interpreter->resizeTensor(input_tensor, {1, 3, in_h, in_w});
PicoDet_interpreter->resizeSession(PicoDet_session);
std::shared_ptr<MNN::CV::ImageProcess> pretreat(MNN::CV::ImageProcess::create(
MNN::CV::BGR, MNN::CV::BGR, mean_vals, 3, norm_vals, 3));
pretreat->convert(image.data, in_w, in_h, image.step[0], input_tensor);
auto start = chrono::steady_clock::now();
// run network
PicoDet_interpreter->runSession(PicoDet_session);
// get output data
std::vector<std::vector<BoxInfo>> results;
results.resize(num_class);
for (const auto &head_info : heads_info) {
MNN::Tensor *tensor_scores = PicoDet_interpreter->getSessionOutput(
PicoDet_session, head_info.cls_layer.c_str());
MNN::Tensor *tensor_boxes = PicoDet_interpreter->getSessionOutput(
PicoDet_session, head_info.dis_layer.c_str());
MNN::Tensor tensor_scores_host(tensor_scores,
tensor_scores->getDimensionType());
tensor_scores->copyToHostTensor(&tensor_scores_host);
MNN::Tensor tensor_boxes_host(tensor_boxes,
tensor_boxes->getDimensionType());
tensor_boxes->copyToHostTensor(&tensor_boxes_host);
decode_infer(&tensor_scores_host, &tensor_boxes_host, head_info.stride,
score_threshold, results);
}
auto end = chrono::steady_clock::now();
chrono::duration<double> elapsed = end - start;
cout << "inference time:" << elapsed.count() << " s, ";
for (int i = 0; i < (int)results.size(); i++) {
nms(results[i], nms_threshold);
for (auto box : results[i]) {
box.x1 = box.x1 / in_w * image_w;
box.x2 = box.x2 / in_w * image_w;
box.y1 = box.y1 / in_h * image_h;
box.y2 = box.y2 / in_h * image_h;
result_list.push_back(box);
}
cout << "detect " << result_list.size() << " objects" << endl;
}
cout << "detect " << result_list.size() << " objects" << endl;
return 0;
return 0;
}
void PicoDet::decode_infer(MNN::Tensor *cls_pred, MNN::Tensor *dis_pred, int stride, float threshold, std::vector<std::vector<BoxInfo>> &results)
{
int feature_h = in_h / stride;
int feature_w = in_w / stride;
for (int idx = 0; idx < feature_h * feature_w; idx++)
{
const float *scores = cls_pred->host<float>() + (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 > threshold)
{
const float *bbox_pred = dis_pred->host<float>() + (idx * 4 * (reg_max + 1));
results[cur_label].push_back(disPred2Bbox(bbox_pred, cur_label, score, col, row, stride));
}
void PicoDet::decode_infer(MNN::Tensor *cls_pred, MNN::Tensor *dis_pred,
int stride, float threshold,
std::vector<std::vector<BoxInfo>> &results) {
int feature_h = ceil((float)in_h / stride);
int feature_w = ceil((float)in_w / stride);
for (int idx = 0; idx < feature_h * feature_w; idx++) {
const float *scores = cls_pred->host<float>() + (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 > threshold) {
const float *bbox_pred =
dis_pred->host<float>() + (idx * 4 * (reg_max + 1));
results[cur_label].push_back(
disPred2Bbox(bbox_pred, cur_label, score, col, row, stride));
}
}
}
BoxInfo PicoDet::disPred2Bbox(const float *&dfl_det, int label, float score, int x, int y, int stride)
{
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;
BoxInfo PicoDet::disPred2Bbox(const float *&dfl_det, int label, float score,
int x, int y, int stride) {
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];
}
float xmin = (std::max)(ct_x - dis_pred[0], .0f);
float ymin = (std::max)(ct_y - dis_pred[1], .0f);
float xmax = (std::min)(ct_x + dis_pred[2], (float)in_w);
float ymax = (std::min)(ct_y + dis_pred[3], (float)in_h);
return BoxInfo{xmin, ymin, xmax, ymax, score, label};
dis *= stride;
dis_pred[i] = dis;
delete[] dis_after_sm;
}
float xmin = (std::max)(ct_x - dis_pred[0], .0f);
float ymin = (std::max)(ct_y - dis_pred[1], .0f);
float xmax = (std::min)(ct_x + dis_pred[2], (float)in_w);
float ymax = (std::min)(ct_y + dis_pred[3], (float)in_h);
return BoxInfo{xmin, ymin, xmax, ymax, score, label};
}
void PicoDet::nms(std::vector<BoxInfo> &input_boxes, float NMS_THRESH)
{
std::sort(input_boxes.begin(), input_boxes.end(), [](BoxInfo a, BoxInfo b) { return a.score > b.score; });
std::vector<float> vArea(input_boxes.size());
for (int i = 0; i < int(input_boxes.size()); ++i)
{
vArea[i] = (input_boxes.at(i).x2 - input_boxes.at(i).x1 + 1) * (input_boxes.at(i).y2 - input_boxes.at(i).y1 + 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].x1, input_boxes[j].x1);
float yy1 = (std::max)(input_boxes[i].y1, input_boxes[j].y1);
float xx2 = (std::min)(input_boxes[i].x2, input_boxes[j].x2);
float yy2 = (std::min)(input_boxes[i].y2, input_boxes[j].y2);
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_THRESH)
{
input_boxes.erase(input_boxes.begin() + j);
vArea.erase(vArea.begin() + j);
}
else
{
j++;
}
}
void PicoDet::nms(std::vector<BoxInfo> &input_boxes, float NMS_THRESH) {
std::sort(input_boxes.begin(), input_boxes.end(),
[](BoxInfo a, BoxInfo b) { return a.score > b.score; });
std::vector<float> vArea(input_boxes.size());
for (int i = 0; i < int(input_boxes.size()); ++i) {
vArea[i] = (input_boxes.at(i).x2 - input_boxes.at(i).x1 + 1) *
(input_boxes.at(i).y2 - input_boxes.at(i).y1 + 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].x1, input_boxes[j].x1);
float yy1 = (std::max)(input_boxes[i].y1, input_boxes[j].y1);
float xx2 = (std::min)(input_boxes[i].x2, input_boxes[j].x2);
float yy2 = (std::min)(input_boxes[i].y2, input_boxes[j].y2);
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_THRESH) {
input_boxes.erase(input_boxes.begin() + j);
vArea.erase(vArea.begin() + j);
} else {
j++;
}
}
}
}
string PicoDet::get_label_str(int label)
{
return labels[label];
}
string PicoDet::get_label_str(int label) { return labels[label]; }
inline float fast_exp(float x)
{
union
{
uint32_t i;
float f;
} v{};
v.i = (1 << 23) * (1.4426950409 * x + 126.93490512f);
return v.f;
inline float fast_exp(float x) {
union {
uint32_t i;
float f;
} v{};
v.i = (1 << 23) * (1.4426950409 * x + 126.93490512f);
return v.f;
}
inline float sigmoid(float x)
{
return 1.0f / (1.0f + fast_exp(-x));
}
inline float sigmoid(float x) { return 1.0f / (1.0f + fast_exp(-x)); }
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];
}
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] /= denominator;
}
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;
return 0;
}
deploy/third_engine/demo_ncnn/picodet.cpp
浏览文件 @ 2580dc01
......@@ -17,186 +17,169 @@
#include <benchmark.h>
#include <iostream>
inline float fast_exp(float x)
{
union {
uint32_t i;
float f;
} v{};
v.i = (1 << 23) * (1.4426950409 * x + 126.93490512f);
return v.f;
inline float fast_exp(float x) {
union {
uint32_t i;
float f;
} v{};
v.i = (1 << 23) * (1.4426950409 * x + 126.93490512f);
return v.f;
}
inline float sigmoid(float x)
{
return 1.0f / (1.0f + fast_exp(-x));
}
inline float sigmoid(float x) { return 1.0f / (1.0f + fast_exp(-x)); }
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 };
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] = fast_exp(src[i] - alpha);
denominator += dst[i];
}
for (int i = 0; i < length; ++i) {
dst[i] /= denominator;
}
for (int i = 0; i < length; ++i) {
dst[i] /= denominator;
}
return 0;
return 0;
}
bool PicoDet::hasGPU = false;
PicoDet* PicoDet::detector = nullptr;
PicoDet *PicoDet::detector = nullptr;
PicoDet::PicoDet(const char* param, const char* bin, bool useGPU)
{
this->Net = new ncnn::Net();
PicoDet::PicoDet(const char *param, const char *bin, bool useGPU) {
this->Net = new ncnn::Net();
#if NCNN_VULKAN
this->hasGPU = ncnn::get_gpu_count() > 0;
this->hasGPU = ncnn::get_gpu_count() > 0;
#endif
this->Net->opt.use_vulkan_compute = this->hasGPU && useGPU;
this->Net->opt.use_fp16_arithmetic = true;
this->Net->load_param(param);
this->Net->load_model(bin);
this->Net->opt.use_vulkan_compute = this->hasGPU && useGPU;
this->Net->opt.use_fp16_arithmetic = true;
this->Net->load_param(param);
this->Net->load_model(bin);
}
PicoDet::~PicoDet()
{
delete this->Net;
}
PicoDet::~PicoDet() { delete this->Net; }
void PicoDet::preprocess(cv::Mat& image, ncnn::Mat& in)
{
int img_w = image.cols;
int img_h = image.rows;
in = ncnn::Mat::from_pixels(image.data, ncnn::Mat::PIXEL_BGR, img_w, img_h);
const float mean_vals[3] = { 103.53f, 116.28f, 123.675f };
const float norm_vals[3] = { 0.017429f, 0.017507f, 0.017125f };
in.substract_mean_normalize(mean_vals, norm_vals);
void PicoDet::preprocess(cv::Mat &image, ncnn::Mat &in) {
int img_w = image.cols;
int img_h = image.rows;
in = ncnn::Mat::from_pixels(image.data, ncnn::Mat::PIXEL_BGR, img_w, img_h);
const float mean_vals[3] = {103.53f, 116.28f, 123.675f};
const float norm_vals[3] = {0.017429f, 0.017507f, 0.017125f};
in.substract_mean_normalize(mean_vals, norm_vals);
}
std::vector<BoxInfo> PicoDet::detect(cv::Mat image, float score_threshold, float nms_threshold)
{
ncnn::Mat input;
preprocess(image, input);
auto ex = this->Net->create_extractor();
ex.set_light_mode(false);
ex.set_num_threads(4);
std::vector<BoxInfo> PicoDet::detect(cv::Mat image, float score_threshold,
float nms_threshold) {
ncnn::Mat input;
preprocess(image, input);
auto ex = this->Net->create_extractor();
ex.set_light_mode(false);
ex.set_num_threads(4);
#if NCNN_VULKAN
ex.set_vulkan_compute(this->hasGPU);
ex.set_vulkan_compute(this->hasGPU);
#endif
ex.input("image", input); //picodet
std::vector<std::vector<BoxInfo>> results;
results.resize(this->num_class);
for (const auto& head_info : this->heads_info)
{
ncnn::Mat dis_pred;
ncnn::Mat cls_pred;
ex.extract(head_info.dis_layer.c_str(), dis_pred);
ex.extract(head_info.cls_layer.c_str(), cls_pred);
this->decode_infer(cls_pred, dis_pred, head_info.stride, score_threshold, results);
}
std::vector<BoxInfo> dets;
for (int i = 0; i < (int)results.size(); i++)
{
this->nms(results[i], nms_threshold);
for (auto box : results[i])
{
dets.push_back(box);
}
ex.input("image", input); // picodet
std::vector<std::vector<BoxInfo>> results;
results.resize(this->num_class);
for (const auto &head_info : this->heads_info) {
ncnn::Mat dis_pred;
ncnn::Mat cls_pred;
ex.extract(head_info.dis_layer.c_str(), dis_pred);
ex.extract(head_info.cls_layer.c_str(), cls_pred);
this->decode_infer(cls_pred, dis_pred, head_info.stride, score_threshold,
results);
}
std::vector<BoxInfo> dets;
for (int i = 0; i < (int)results.size(); i++) {
this->nms(results[i], nms_threshold);
for (auto box : results[i]) {
dets.push_back(box);
}
return dets;
}
return dets;
}
void PicoDet::decode_infer(ncnn::Mat& cls_pred, ncnn::Mat& dis_pred, int stride, float threshold, std::vector<std::vector<BoxInfo>>& results)
{
int feature_h = this->input_size[1] / stride;
int feature_w = this->input_size[0] / stride;
for (int idx = 0; idx < feature_h * feature_w; idx++)
{
const float* scores = cls_pred.row(idx);
int row = idx / feature_w;
int col = idx % feature_w;
float score = 0;
int cur_label = 0;
for (int label = 0; label < this->num_class; label++)
{
if (scores[label] > score)
{
score = scores[label];
cur_label = label;
}
}
if (score > threshold)
{
const float* bbox_pred = dis_pred.row(idx);
results[cur_label].push_back(this->disPred2Bbox(bbox_pred, cur_label, score, col, row, stride));
}
void PicoDet::decode_infer(ncnn::Mat &cls_pred, ncnn::Mat &dis_pred, int stride,
float threshold,
std::vector<std::vector<BoxInfo>> &results) {
int feature_h = ceil((float)this->input_size[1] / stride);
int feature_w = ceil((float)this->input_size[0] / stride);
for (int idx = 0; idx < feature_h * feature_w; idx++) {
const float *scores = cls_pred.row(idx);
int row = idx / feature_w;
int col = idx % feature_w;
float score = 0;
int cur_label = 0;
for (int label = 0; label < this->num_class; label++) {
if (scores[label] > score) {
score = scores[label];
cur_label = label;
}
}
if (score > threshold) {
const float *bbox_pred = dis_pred.row(idx);
results[cur_label].push_back(
this->disPred2Bbox(bbox_pred, cur_label, score, col, row, stride));
}
}
}
BoxInfo PicoDet::disPred2Bbox(const float*& dfl_det, int label, float score, int x, int y, int stride)
{
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[this->reg_max + 1];
activation_function_softmax(dfl_det + i * (this->reg_max + 1), dis_after_sm, this->reg_max + 1);
for (int j = 0; j < this->reg_max + 1; j++)
{
dis += j * dis_after_sm[j];
}
dis *= stride;
dis_pred[i] = dis;
delete[] dis_after_sm;
BoxInfo PicoDet::disPred2Bbox(const float *&dfl_det, int label, float score,
int x, int y, int stride) {
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[this->reg_max + 1];
activation_function_softmax(dfl_det + i * (this->reg_max + 1), dis_after_sm,
this->reg_max + 1);
for (int j = 0; j < this->reg_max + 1; j++) {
dis += j * dis_after_sm[j];
}
float xmin = (std::max)(ct_x - dis_pred[0], .0f);
float ymin = (std::max)(ct_y - dis_pred[1], .0f);
float xmax = (std::min)(ct_x + dis_pred[2], (float)this->input_size[0]);
float ymax = (std::min)(ct_y + dis_pred[3], (float)this->input_size[1]);
return BoxInfo { xmin, ymin, xmax, ymax, score, label };
dis *= stride;
dis_pred[i] = dis;
delete[] dis_after_sm;
}
float xmin = (std::max)(ct_x - dis_pred[0], .0f);
float ymin = (std::max)(ct_y - dis_pred[1], .0f);
float xmax = (std::min)(ct_x + dis_pred[2], (float)this->input_size[0]);
float ymax = (std::min)(ct_y + dis_pred[3], (float)this->input_size[1]);
return BoxInfo{xmin, ymin, xmax, ymax, score, label};
}
void PicoDet::nms(std::vector<BoxInfo>& input_boxes, float NMS_THRESH)
{
std::sort(input_boxes.begin(), input_boxes.end(), [](BoxInfo a, BoxInfo b) { return a.score > b.score; });
std::vector<float> vArea(input_boxes.size());
for (int i = 0; i < int(input_boxes.size()); ++i) {
vArea[i] = (input_boxes.at(i).x2 - input_boxes.at(i).x1 + 1)
* (input_boxes.at(i).y2 - input_boxes.at(i).y1 + 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].x1, input_boxes[j].x1);
float yy1 = (std::max)(input_boxes[i].y1, input_boxes[j].y1);
float xx2 = (std::min)(input_boxes[i].x2, input_boxes[j].x2);
float yy2 = (std::min)(input_boxes[i].y2, input_boxes[j].y2);
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_THRESH) {
input_boxes.erase(input_boxes.begin() + j);
vArea.erase(vArea.begin() + j);
}
else {
j++;
}
}
void PicoDet::nms(std::vector<BoxInfo> &input_boxes, float NMS_THRESH) {
std::sort(input_boxes.begin(), input_boxes.end(),
[](BoxInfo a, BoxInfo b) { return a.score > b.score; });
std::vector<float> vArea(input_boxes.size());
for (int i = 0; i < int(input_boxes.size()); ++i) {
vArea[i] = (input_boxes.at(i).x2 - input_boxes.at(i).x1 + 1) *
(input_boxes.at(i).y2 - input_boxes.at(i).y1 + 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].x1, input_boxes[j].x1);
float yy1 = (std::max)(input_boxes[i].y1, input_boxes[j].y1);
float xx2 = (std::min)(input_boxes[i].x2, input_boxes[j].x2);
float yy2 = (std::min)(input_boxes[i].y2, input_boxes[j].y2);
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_THRESH) {
input_boxes.erase(input_boxes.begin() + j);
vArea.erase(vArea.begin() + j);
} else {
j++;
}
}
}
}
deploy/third_engine/demo_openvino/main.cpp
浏览文件 @ 2580dc01
......@@ -14,338 +14,289 @@
// reference from https://github.com/RangiLyu/nanodet
#include "picodet_openvino.h"
#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <iostream>
#define image_size 416
struct object_rect {
int x;
int y;
int width;
int height;
int x;
int y;
int width;
int height;
};
int resize_uniform(cv::Mat& src, cv::Mat& dst, cv::Size dst_size, object_rect& effect_area)
{
int w = src.cols;
int h = src.rows;
int dst_w = dst_size.width;
int dst_h = dst_size.height;
dst = cv::Mat(cv::Size(dst_w, dst_h), CV_8UC3, cv::Scalar(0));
int resize_uniform(cv::Mat &src, cv::Mat &dst, cv::Size dst_size,
object_rect &effect_area) {
int w = src.cols;
int h = src.rows;
int dst_w = dst_size.width;
int dst_h = dst_size.height;
dst = cv::Mat(cv::Size(dst_w, dst_h), CV_8UC3, cv::Scalar(0));
float ratio_src = w * 1.0 / h;
float ratio_dst = dst_w * 1.0 / dst_h;
float ratio_src = w * 1.0 / h;
float ratio_dst = dst_w * 1.0 / dst_h;
int tmp_w = 0;
int tmp_h = 0;
if (ratio_src > ratio_dst) {
tmp_w = dst_w;
tmp_h = floor((dst_w * 1.0 / w) * h);
}
else if (ratio_src < ratio_dst) {
tmp_h = dst_h;
tmp_w = floor((dst_h * 1.0 / h) * w);
}
else {
cv::resize(src, dst, dst_size);
effect_area.x = 0;
effect_area.y = 0;
effect_area.width = dst_w;
effect_area.height = dst_h;
return 0;
}
cv::Mat tmp;
cv::resize(src, tmp, cv::Size(tmp_w, tmp_h));
int tmp_w = 0;
int tmp_h = 0;
if (ratio_src > ratio_dst) {
tmp_w = dst_w;
tmp_h = floor((dst_w * 1.0 / w) * h);
} else if (ratio_src < ratio_dst) {
tmp_h = dst_h;
tmp_w = floor((dst_h * 1.0 / h) * w);
} else {
cv::resize(src, dst, dst_size);
effect_area.x = 0;
effect_area.y = 0;
effect_area.width = dst_w;
effect_area.height = dst_h;
return 0;
}
cv::Mat tmp;
cv::resize(src, tmp, cv::Size(tmp_w, tmp_h));
if (tmp_w != dst_w) {
int index_w = floor((dst_w - tmp_w) / 2.0);
for (int i = 0; i < dst_h; i++) {
memcpy(dst.data + i * dst_w * 3 + index_w * 3, tmp.data + i * tmp_w * 3, tmp_w * 3);
}
effect_area.x = index_w;
effect_area.y = 0;
effect_area.width = tmp_w;
effect_area.height = tmp_h;
if (tmp_w != dst_w) {
int index_w = floor((dst_w - tmp_w) / 2.0);
for (int i = 0; i < dst_h; i++) {
memcpy(dst.data + i * dst_w * 3 + index_w * 3, tmp.data + i * tmp_w * 3,
tmp_w * 3);
}
else if (tmp_h != dst_h) {
int index_h = floor((dst_h - tmp_h) / 2.0);
memcpy(dst.data + index_h * dst_w * 3, tmp.data, tmp_w * tmp_h * 3);
effect_area.x = 0;
effect_area.y = index_h;
effect_area.width = tmp_w;
effect_area.height = tmp_h;
}
else {
printf("error\n");
}
return 0;
effect_area.x = index_w;
effect_area.y = 0;
effect_area.width = tmp_w;
effect_area.height = tmp_h;
} else if (tmp_h != dst_h) {
int index_h = floor((dst_h - tmp_h) / 2.0);
memcpy(dst.data + index_h * dst_w * 3, tmp.data, tmp_w * tmp_h * 3);
effect_area.x = 0;
effect_area.y = index_h;
effect_area.width = tmp_w;
effect_area.height = tmp_h;
} else {
printf("error\n");
}
return 0;
}
const int color_list[80][3] =
{
{216 , 82 , 24},
{236 ,176 , 31},
{125 , 46 ,141},
{118 ,171 , 47},
{ 76 ,189 ,237},
{238 , 19 , 46},
{ 76 , 76 , 76},
{153 ,153 ,153},
{255 , 0 , 0},
{255 ,127 , 0},
{190 ,190 , 0},
{ 0 ,255 , 0},
{ 0 , 0 ,255},
{170 , 0 ,255},
{ 84 , 84 , 0},
{ 84 ,170 , 0},
{ 84 ,255 , 0},
{170 , 84 , 0},
{170 ,170 , 0},
{170 ,255 , 0},
{255 , 84 , 0},
{255 ,170 , 0},
{255 ,255 , 0},
{ 0 , 84 ,127},
{ 0 ,170 ,127},
{ 0 ,255 ,127},
{ 84 , 0 ,127},
{ 84 , 84 ,127},
{ 84 ,170 ,127},
{ 84 ,255 ,127},
{170 , 0 ,127},
{170 , 84 ,127},
{170 ,170 ,127},
{170 ,255 ,127},
{255 , 0 ,127},
{255 , 84 ,127},
{255 ,170 ,127},
{255 ,255 ,127},
{ 0 , 84 ,255},
{ 0 ,170 ,255},
{ 0 ,255 ,255},
{ 84 , 0 ,255},
{ 84 , 84 ,255},
{ 84 ,170 ,255},
{ 84 ,255 ,255},
{170 , 0 ,255},
{170 , 84 ,255},
{170 ,170 ,255},
{170 ,255 ,255},
{255 , 0 ,255},
{255 , 84 ,255},
{255 ,170 ,255},
{ 42 , 0 , 0},
{ 84 , 0 , 0},
{127 , 0 , 0},
{170 , 0 , 0},
{212 , 0 , 0},
{255 , 0 , 0},
{ 0 , 42 , 0},
{ 0 , 84 , 0},
{ 0 ,127 , 0},
{ 0 ,170 , 0},
{ 0 ,212 , 0},
{ 0 ,255 , 0},
{ 0 , 0 , 42},
{ 0 , 0 , 84},
{ 0 , 0 ,127},
{ 0 , 0 ,170},
{ 0 , 0 ,212},
{ 0 , 0 ,255},
{ 0 , 0 , 0},
{ 36 , 36 , 36},
{ 72 , 72 , 72},
{109 ,109 ,109},
{145 ,145 ,145},
{182 ,182 ,182},
{218 ,218 ,218},
{ 0 ,113 ,188},
{ 80 ,182 ,188},
{127 ,127 , 0},
const int color_list[80][3] = {
{216, 82, 24}, {236, 176, 31}, {125, 46, 141}, {118, 171, 47},
{76, 189, 237}, {238, 19, 46}, {76, 76, 76}, {153, 153, 153},
{255, 0, 0}, {255, 127, 0}, {190, 190, 0}, {0, 255, 0},
{0, 0, 255}, {170, 0, 255}, {84, 84, 0}, {84, 170, 0},
{84, 255, 0}, {170, 84, 0}, {170, 170, 0}, {170, 255, 0},
{255, 84, 0}, {255, 170, 0}, {255, 255, 0}, {0, 84, 127},
{0, 170, 127}, {0, 255, 127}, {84, 0, 127}, {84, 84, 127},
{84, 170, 127}, {84, 255, 127}, {170, 0, 127}, {170, 84, 127},
{170, 170, 127}, {170, 255, 127}, {255, 0, 127}, {255, 84, 127},
{255, 170, 127}, {255, 255, 127}, {0, 84, 255}, {0, 170, 255},
{0, 255, 255}, {84, 0, 255}, {84, 84, 255}, {84, 170, 255},
{84, 255, 255}, {170, 0, 255}, {170, 84, 255}, {170, 170, 255},
{170, 255, 255}, {255, 0, 255}, {255, 84, 255}, {255, 170, 255},
{42, 0, 0}, {84, 0, 0}, {127, 0, 0}, {170, 0, 0},
{212, 0, 0}, {255, 0, 0}, {0, 42, 0}, {0, 84, 0},
{0, 127, 0}, {0, 170, 0}, {0, 212, 0}, {0, 255, 0},
{0, 0, 42}, {0, 0, 84}, {0, 0, 127}, {0, 0, 170},
{0, 0, 212}, {0, 0, 255}, {0, 0, 0}, {36, 36, 36},
{72, 72, 72}, {109, 109, 109}, {145, 145, 145}, {182, 182, 182},
{218, 218, 218}, {0, 113, 188}, {80, 182, 188}, {127, 127, 0},
};
void draw_bboxes(const cv::Mat& bgr, const std::vector<BoxInfo>& bboxes, object_rect effect_roi)
{
static const char* class_names[] = { "person", "bicycle", "car", "motorcycle", "airplane", "bus",
"train", "truck", "boat", "traffic light", "fire hydrant",
"stop sign", "parking meter", "bench", "bird", "cat", "dog",
"horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe",
"backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee",
"skis", "snowboard", "sports ball", "kite", "baseball bat",
"baseball glove", "skateboard", "surfboard", "tennis racket",
"bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl",
"banana", "apple", "sandwich", "orange", "broccoli", "carrot",
"hot dog", "pizza", "donut", "cake", "chair", "couch",
"potted plant", "bed", "dining table", "toilet", "tv", "laptop",
"mouse", "remote", "keyboard", "cell phone", "microwave", "oven",
"toaster", "sink", "refrigerator", "book", "clock", "vase",
"scissors", "teddy bear", "hair drier", "toothbrush"
};
void draw_bboxes(const cv::Mat &bgr, const std::vector<BoxInfo> &bboxes,
object_rect effect_roi) {
static const char *class_names[] = {
"person", "bicycle", "car",
"motorcycle", "airplane", "bus",
"train", "truck", "boat",
"traffic light", "fire hydrant", "stop sign",
"parking meter", "bench", "bird",
"cat", "dog", "horse",
"sheep", "cow", "elephant",
"bear", "zebra", "giraffe",
"backpack", "umbrella", "handbag",
"tie", "suitcase", "frisbee",
"skis", "snowboard", "sports ball",
"kite", "baseball bat", "baseball glove",
"skateboard", "surfboard", "tennis racket",
"bottle", "wine glass", "cup",
"fork", "knife", "spoon",
"bowl", "banana", "apple",
"sandwich", "orange", "broccoli",
"carrot", "hot dog", "pizza",
"donut", "cake", "chair",
"couch", "potted plant", "bed",
"dining table", "toilet", "tv",
"laptop", "mouse", "remote",
"keyboard", "cell phone", "microwave",
"oven", "toaster", "sink",
"refrigerator", "book", "clock",
"vase", "scissors", "teddy bear",
"hair drier", "toothbrush"};
cv::Mat image = bgr.clone();
int src_w = image.cols;
int src_h = image.rows;
int dst_w = effect_roi.width;
int dst_h = effect_roi.height;
float width_ratio = (float)src_w / (float)dst_w;
float height_ratio = (float)src_h / (float)dst_h;
cv::Mat image = bgr.clone();
int src_w = image.cols;
int src_h = image.rows;
int dst_w = effect_roi.width;
int dst_h = effect_roi.height;
float width_ratio = (float)src_w / (float)dst_w;
float height_ratio = (float)src_h / (float)dst_h;
for (size_t i = 0; i < bboxes.size(); i++) {
const BoxInfo &bbox = bboxes[i];
cv::Scalar color =
cv::Scalar(color_list[bbox.label][0], color_list[bbox.label][1],
color_list[bbox.label][2]);
cv::rectangle(image,
cv::Rect(cv::Point((bbox.x1 - effect_roi.x) * width_ratio,
(bbox.y1 - effect_roi.y) * height_ratio),
cv::Point((bbox.x2 - effect_roi.x) * width_ratio,
(bbox.y2 - effect_roi.y) * height_ratio)),
color);
for (size_t i = 0; i < bboxes.size(); i++)
{
const BoxInfo& bbox = bboxes[i];
cv::Scalar color = cv::Scalar(color_list[bbox.label][0], color_list[bbox.label][1], color_list[bbox.label][2]);
cv::rectangle(image, cv::Rect(cv::Point((bbox.x1 - effect_roi.x) * width_ratio, (bbox.y1 - effect_roi.y) * height_ratio),
cv::Point((bbox.x2 - effect_roi.x) * width_ratio, (bbox.y2 - effect_roi.y) * height_ratio)), color);
char text[256];
sprintf(text, "%s %.1f%%", class_names[bbox.label], bbox.score * 100);
int baseLine = 0;
cv::Size label_size =
cv::getTextSize(text, cv::FONT_HERSHEY_SIMPLEX, 0.4, 1, &baseLine);
int x = (bbox.x1 - effect_roi.x) * width_ratio;
int y =
(bbox.y1 - effect_roi.y) * height_ratio - label_size.height - baseLine;
if (y < 0)
y = 0;
if (x + label_size.width > image.cols)
x = image.cols - label_size.width;
char text[256];
sprintf(text, "%s %.1f%%", class_names[bbox.label], bbox.score * 100);
int baseLine = 0;
cv::Size label_size = cv::getTextSize(text, cv::FONT_HERSHEY_SIMPLEX, 0.4, 1, &baseLine);
int x = (bbox.x1 - effect_roi.x) * width_ratio;
int y = (bbox.y1 - effect_roi.y) * height_ratio - label_size.height - baseLine;
if (y < 0)
y = 0;
if (x + label_size.width > image.cols)
x = image.cols - label_size.width;
cv::rectangle(image, cv::Rect(cv::Point(x, y),
cv::Size(label_size.width,
label_size.height + baseLine)),
color, -1);
cv::putText(image, text, cv::Point(x, y + label_size.height),
cv::FONT_HERSHEY_SIMPLEX, 0.4, cv::Scalar(255, 255, 255));
}
cv::rectangle(image, cv::Rect(cv::Point(x, y), cv::Size(label_size.width, label_size.height + baseLine)),
color, -1);
cv::putText(image, text, cv::Point(x, y + label_size.height),
cv::FONT_HERSHEY_SIMPLEX, 0.4, cv::Scalar(255, 255, 255));
}
cv::imwrite("../predict.jpg",image);
cv::imwrite("../predict.jpg", image);
}
int image_demo(PicoDet &detector, const char *imagepath) {
std::vector<std::string> filenames;
cv::glob(imagepath, filenames, false);
int image_demo(PicoDet& detector, const char* imagepath)
{
std::vector<std::string> filenames;
cv::glob(imagepath, filenames, false);
for (auto img_name : filenames)
{
cv::Mat image = cv::imread(img_name);
if (image.empty())
{
return -1;
}
object_rect effect_roi;
cv::Mat resized_img;
resize_uniform(image, resized_img, cv::Size(image_size, image_size), effect_roi);
auto results = detector.detect(resized_img, 0.4, 0.5);
draw_bboxes(image, results, effect_roi);
for (auto img_name : filenames) {
cv::Mat image = cv::imread(img_name);
if (image.empty()) {
return -1;
}
return 0;
object_rect effect_roi;
cv::Mat resized_img;
resize_uniform(image, resized_img, cv::Size(image_size, image_size),
effect_roi);
auto results = detector.detect(resized_img, 0.4, 0.5);
draw_bboxes(image, results, effect_roi);
}
return 0;
}
int webcam_demo(PicoDet& detector, int cam_id)
{
cv::Mat image;
cv::VideoCapture cap(cam_id);
while (true)
{
cap >> image;
object_rect effect_roi;
cv::Mat resized_img;
resize_uniform(image, resized_img, cv::Size(image_size, image_size), effect_roi);
auto results = detector.detect(resized_img, 0.4, 0.5);
draw_bboxes(image, results, effect_roi);
cv::waitKey(1);
}
return 0;
int webcam_demo(PicoDet &detector, int cam_id) {
cv::Mat image;
cv::VideoCapture cap(cam_id);
while (true) {
cap >> image;
object_rect effect_roi;
cv::Mat resized_img;
resize_uniform(image, resized_img, cv::Size(image_size, image_size),
effect_roi);
auto results = detector.detect(resized_img, 0.4, 0.5);
draw_bboxes(image, results, effect_roi);
cv::waitKey(1);
}
return 0;
}
int video_demo(PicoDet& detector, const char* path)
{
cv::Mat image;
cv::VideoCapture cap(path);
int video_demo(PicoDet &detector, const char *path) {
cv::Mat image;
cv::VideoCapture cap(path);
while (true)
{
cap >> image;
object_rect effect_roi;
cv::Mat resized_img;
resize_uniform(image, resized_img, cv::Size(image_size, image_size), effect_roi);
auto results = detector.detect(resized_img, 0.4, 0.5);
draw_bboxes(image, results, effect_roi);
cv::waitKey(1);
}
return 0;
while (true) {
cap >> image;
object_rect effect_roi;
cv::Mat resized_img;
resize_uniform(image, resized_img, cv::Size(image_size, image_size),
effect_roi);
auto results = detector.detect(resized_img, 0.4, 0.5);
draw_bboxes(image, results, effect_roi);
cv::waitKey(1);
}
return 0;
}
int benchmark(PicoDet& detector)
{
int loop_num = 100;
int warm_up = 8;
int benchmark(PicoDet &detector) {
int loop_num = 100;
int warm_up = 8;
double time_min = DBL_MAX;
double time_max = -DBL_MAX;
double time_avg = 0;
cv::Mat image(image_size, image_size, CV_8UC3, cv::Scalar(1, 1, 1));
double time_min = DBL_MAX;
double time_max = -DBL_MAX;
double time_avg = 0;
cv::Mat image(image_size, image_size, CV_8UC3, cv::Scalar(1, 1, 1));
for (int i = 0; i < warm_up + loop_num; i++)
{
auto start = std::chrono::steady_clock::now();
std::vector<BoxInfo> results;
results = detector.detect(image, 0.4, 0.5);
auto end = std::chrono::steady_clock::now();
double time = std::chrono::duration<double, std::milli>(end - start).count();
if (i >= warm_up)
{
time_min = (std::min)(time_min, time);
time_max = (std::max)(time_max, time);
time_avg += time;
}
for (int i = 0; i < warm_up + loop_num; i++) {
auto start = std::chrono::steady_clock::now();
std::vector<BoxInfo> results;
results = detector.detect(image, 0.4, 0.5);
auto end = std::chrono::steady_clock::now();
double time =
std::chrono::duration<double, std::milli>(end - start).count();
if (i >= warm_up) {
time_min = (std::min)(time_min, time);
time_max = (std::max)(time_max, time);
time_avg += time;
}
time_avg /= loop_num;
fprintf(stderr, "%20s min = %7.2f max = %7.2f avg = %7.2f\n", "picodet", time_min, time_max, time_avg);
return 0;
}
time_avg /= loop_num;
fprintf(stderr, "%20s min = %7.2f max = %7.2f avg = %7.2f\n", "picodet",
time_min, time_max, time_avg);
return 0;
}
int main(int argc, char **argv) {
if (argc != 3) {
fprintf(stderr, "usage: %s [mode] [path]. \n For webcam mode=0, path is "
"cam id; \n For image demo, mode=1, path=xxx/xxx/*.jpg; \n "
"For video, mode=2; \n For benchmark, mode=3 path=0.\n",
argv[0]);
return -1;
}
std::cout << "start init model" << std::endl;
auto detector = PicoDet("../weight/picodet_m_416.xml");
std::cout << "success" << std::endl;
int main(int argc, char** argv)
{
if (argc != 3)
{
fprintf(stderr, "usage: %s [mode] [path]. \n For webcam mode=0, path is cam id; \n For image demo, mode=1, path=xxx/xxx/*.jpg; \n For video, mode=2; \n For benchmark, mode=3 path=0.\n", argv[0]);
return -1;
}
std::cout<<"start init model"<<std::endl;
auto detector = PicoDet("../weight/picodet_m_416.xml");
std::cout<<"success"<<std::endl;
int mode = atoi(argv[1]);
switch (mode)
{
case 0:{
int cam_id = atoi(argv[2]);
webcam_demo(detector, cam_id);
break;
}
case 1:{
const char* images = argv[2];
image_demo(detector, images);
break;
}
case 2:{
const char* path = argv[2];
video_demo(detector, path);
break;
}
case 3:{
benchmark(detector);
break;
}
default:{
fprintf(stderr, "usage: %s [mode] [path]. \n For webcam mode=0, path is cam id; \n For image demo, mode=1, path=xxx/xxx/*.jpg; \n For video, mode=2; \n For benchmark, mode=3 path=0.\n", argv[0]);
break;
}
}
int mode = atoi(argv[1]);
switch (mode) {
case 0: {
int cam_id = atoi(argv[2]);
webcam_demo(detector, cam_id);
break;
}
case 1: {
const char *images = argv[2];
image_demo(detector, images);
break;
}
case 2: {
const char *path = argv[2];
video_demo(detector, path);
break;
}
case 3: {
benchmark(detector);
break;
}
default: {
fprintf(stderr, "usage: %s [mode] [path]. \n For webcam mode=0, path is "
"cam id; \n For image demo, mode=1, path=xxx/xxx/*.jpg; \n "
"For video, mode=2; \n For benchmark, mode=3 path=0.\n",
argv[0]);
break;
}
}
}
deploy/third_engine/demo_openvino/picodet_openvino.cpp
浏览文件 @ 2580dc01
......@@ -15,218 +15,195 @@
#include "picodet_openvino.h"
inline float fast_exp(float x)
{
union {
uint32_t i;
float f;
} v{};
v.i = (1 << 23) * (1.4426950409 * x + 126.93490512f);
return v.f;
inline float fast_exp(float x) {
union {
uint32_t i;
float f;
} v{};
v.i = (1 << 23) * (1.4426950409 * x + 126.93490512f);
return v.f;
}
inline float sigmoid(float x)
{
return 1.0f / (1.0f + fast_exp(-x));
}
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;
}
inline float sigmoid(float x) { return 1.0f / (1.0f + fast_exp(-x)); }
return 0;
}
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};
PicoDet::PicoDet(const char* model_path)
{
InferenceEngine::Core ie;
InferenceEngine::CNNNetwork model = ie.ReadNetwork(model_path);
// prepare input settings
InferenceEngine::InputsDataMap inputs_map(model.getInputsInfo());
input_name_ = inputs_map.begin()->first;
InferenceEngine::InputInfo::Ptr input_info = inputs_map.begin()->second;
//prepare output settings
InferenceEngine::OutputsDataMap outputs_map(model.getOutputsInfo());
for (auto &output_info : outputs_map)
{
output_info.second->setPrecision(InferenceEngine::Precision::FP32);
}
for (int i = 0; i < length; ++i) {
dst[i] = fast_exp(src[i] - alpha);
denominator += dst[i];
}
//get network
network_ = ie.LoadNetwork(model, "CPU");
infer_request_ = network_.CreateInferRequest();
for (int i = 0; i < length; ++i) {
dst[i] /= denominator;
}
return 0;
}
PicoDet::~PicoDet()
{
PicoDet::PicoDet(const char *model_path) {
InferenceEngine::Core ie;
InferenceEngine::CNNNetwork model = ie.ReadNetwork(model_path);
// prepare input settings
InferenceEngine::InputsDataMap inputs_map(model.getInputsInfo());
input_name_ = inputs_map.begin()->first;
InferenceEngine::InputInfo::Ptr input_info = inputs_map.begin()->second;
// prepare output settings
InferenceEngine::OutputsDataMap outputs_map(model.getOutputsInfo());
for (auto &output_info : outputs_map) {
output_info.second->setPrecision(InferenceEngine::Precision::FP32);
}
// get network
network_ = ie.LoadNetwork(model, "CPU");
infer_request_ = network_.CreateInferRequest();
}
void PicoDet::preprocess(cv::Mat& image, InferenceEngine::Blob::Ptr& blob)
{
int img_w = image.cols;
int img_h = image.rows;
int channels = 3;
InferenceEngine::MemoryBlob::Ptr mblob = InferenceEngine::as<InferenceEngine::MemoryBlob>(blob);
if (!mblob)
{
THROW_IE_EXCEPTION << "We expect blob to be inherited from MemoryBlob in matU8ToBlob, "
<< "but by fact we were not able to cast inputBlob to MemoryBlob";
}
auto mblobHolder = mblob->wmap();
float *blob_data = mblobHolder.as<float *>();
for (size_t c = 0; c < channels; c++)
{
for (size_t h = 0; h < img_h; h++)
{
for (size_t w = 0; w < img_w; w++)
{
blob_data[c * img_w * img_h + h * img_w + w] =
(float)image.at<cv::Vec3b>(h, w)[c];
}
}
PicoDet::~PicoDet() {}
void PicoDet::preprocess(cv::Mat &image, InferenceEngine::Blob::Ptr &blob) {
int img_w = image.cols;
int img_h = image.rows;
int channels = 3;
InferenceEngine::MemoryBlob::Ptr mblob =
InferenceEngine::as<InferenceEngine::MemoryBlob>(blob);
if (!mblob) {
THROW_IE_EXCEPTION
<< "We expect blob to be inherited from MemoryBlob in matU8ToBlob, "
<< "but by fact we were not able to cast inputBlob to MemoryBlob";
}
auto mblobHolder = mblob->wmap();
float *blob_data = mblobHolder.as<float *>();
for (size_t c = 0; c < channels; c++) {
for (size_t h = 0; h < img_h; h++) {
for (size_t w = 0; w < img_w; w++) {
blob_data[c * img_w * img_h + h * img_w + w] =
(float)image.at<cv::Vec3b>(h, w)[c];
}
}
}
}
std::vector<BoxInfo> PicoDet::detect(cv::Mat image, float score_threshold, float nms_threshold)
{
InferenceEngine::Blob::Ptr input_blob = infer_request_.GetBlob(input_name_);
preprocess(image, input_blob);
// do inference
infer_request_.Infer();
// get output
std::vector<std::vector<BoxInfo>> results;
results.resize(this->num_class_);
for (const auto& head_info : this->heads_info_)
{
const InferenceEngine::Blob::Ptr dis_pred_blob = infer_request_.GetBlob(head_info.dis_layer);
const InferenceEngine::Blob::Ptr cls_pred_blob = infer_request_.GetBlob(head_info.cls_layer);
auto mdis_pred = InferenceEngine::as<InferenceEngine::MemoryBlob>(dis_pred_blob);
auto mdis_pred_holder = mdis_pred->rmap();
const float *dis_pred = mdis_pred_holder.as<const float *>();
auto mcls_pred = InferenceEngine::as<InferenceEngine::MemoryBlob>(cls_pred_blob);
auto mcls_pred_holder = mcls_pred->rmap();
const float *cls_pred = mcls_pred_holder.as<const float *>();
this->decode_infer(cls_pred, dis_pred, head_info.stride, score_threshold, results);
}
std::vector<BoxInfo> dets;
for (int i = 0; i < (int)results.size(); i++)
{
this->nms(results[i], nms_threshold);
for (auto& box : results[i])
{
dets.push_back(box);
}
std::vector<BoxInfo> PicoDet::detect(cv::Mat image, float score_threshold,
float nms_threshold) {
InferenceEngine::Blob::Ptr input_blob = infer_request_.GetBlob(input_name_);
preprocess(image, input_blob);
// do inference
infer_request_.Infer();
// get output
std::vector<std::vector<BoxInfo>> results;
results.resize(this->num_class_);
for (const auto &head_info : this->heads_info_) {
const InferenceEngine::Blob::Ptr dis_pred_blob =
infer_request_.GetBlob(head_info.dis_layer);
const InferenceEngine::Blob::Ptr cls_pred_blob =
infer_request_.GetBlob(head_info.cls_layer);
auto mdis_pred =
InferenceEngine::as<InferenceEngine::MemoryBlob>(dis_pred_blob);
auto mdis_pred_holder = mdis_pred->rmap();
const float *dis_pred = mdis_pred_holder.as<const float *>();
auto mcls_pred =
InferenceEngine::as<InferenceEngine::MemoryBlob>(cls_pred_blob);
auto mcls_pred_holder = mcls_pred->rmap();
const float *cls_pred = mcls_pred_holder.as<const float *>();
this->decode_infer(cls_pred, dis_pred, head_info.stride, score_threshold,
results);
}
std::vector<BoxInfo> dets;
for (int i = 0; i < (int)results.size(); i++) {
this->nms(results[i], nms_threshold);
for (auto &box : results[i]) {
dets.push_back(box);
}
return dets;
}
return dets;
}
void PicoDet::decode_infer(const float*& cls_pred, const float*& dis_pred, int stride, float threshold, std::vector<std::vector<BoxInfo>>& results)
{
int feature_h = input_size_ / stride;
int feature_w = input_size_ / stride;
for (int idx = 0; idx < feature_h * feature_w; idx++)
{
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 (cls_pred[idx * num_class_ +label] > score)
{
score = cls_pred[idx * num_class_ + label];
cur_label = label;
}
}
if (score > threshold)
{
const float* bbox_pred = dis_pred + idx * (reg_max_ + 1) * 4;
results[cur_label].push_back(this->disPred2Bbox(bbox_pred, cur_label, score, col, row, stride));
}
void PicoDet::decode_infer(const float *&cls_pred, const float *&dis_pred,
int stride, float threshold,
std::vector<std::vector<BoxInfo>> &results) {
int feature_h = ceil((float)input_size_ / stride);
int feature_w = ceil((float)input_size_ / stride);
for (int idx = 0; idx < feature_h * feature_w; idx++) {
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 (cls_pred[idx * num_class_ + label] > score) {
score = cls_pred[idx * num_class_ + label];
cur_label = label;
}
}
if (score > threshold) {
const float *bbox_pred = dis_pred + idx * (reg_max_ + 1) * 4;
results[cur_label].push_back(
this->disPred2Bbox(bbox_pred, cur_label, score, col, row, stride));
}
}
}
BoxInfo PicoDet::disPred2Bbox(const float*& dfl_det, int label, float score, int x, int y, int stride)
{
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;
BoxInfo PicoDet::disPred2Bbox(const float *&dfl_det, int label, float score,
int x, int y, int stride) {
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];
}
float xmin = (std::max)(ct_x - dis_pred[0], .0f);
float ymin = (std::max)(ct_y - dis_pred[1], .0f);
float xmax = (std::min)(ct_x + dis_pred[2], (float)this->input_size_);
float ymax = (std::min)(ct_y + dis_pred[3], (float)this->input_size_);
return BoxInfo { xmin, ymin, xmax, ymax, score, label };
dis *= stride;
dis_pred[i] = dis;
delete[] dis_after_sm;
}
float xmin = (std::max)(ct_x - dis_pred[0], .0f);
float ymin = (std::max)(ct_y - dis_pred[1], .0f);
float xmax = (std::min)(ct_x + dis_pred[2], (float)this->input_size_);
float ymax = (std::min)(ct_y + dis_pred[3], (float)this->input_size_);
return BoxInfo{xmin, ymin, xmax, ymax, score, label};
}
void PicoDet::nms(std::vector<BoxInfo>& input_boxes, float NMS_THRESH)
{
std::sort(input_boxes.begin(), input_boxes.end(), [](BoxInfo a, BoxInfo b) { return a.score > b.score; });
std::vector<float> vArea(input_boxes.size());
for (int i = 0; i < int(input_boxes.size()); ++i)
{
vArea[i] = (input_boxes.at(i).x2 - input_boxes.at(i).x1 + 1)
* (input_boxes.at(i).y2 - input_boxes.at(i).y1 + 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].x1, input_boxes[j].x1);
float yy1 = (std::max)(input_boxes[i].y1, input_boxes[j].y1);
float xx2 = (std::min)(input_boxes[i].x2, input_boxes[j].x2);
float yy2 = (std::min)(input_boxes[i].y2, input_boxes[j].y2);
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_THRESH)
{
input_boxes.erase(input_boxes.begin() + j);
vArea.erase(vArea.begin() + j);
}
else
{
j++;
}
}
void PicoDet::nms(std::vector<BoxInfo> &input_boxes, float NMS_THRESH) {
std::sort(input_boxes.begin(), input_boxes.end(),
[](BoxInfo a, BoxInfo b) { return a.score > b.score; });
std::vector<float> vArea(input_boxes.size());
for (int i = 0; i < int(input_boxes.size()); ++i) {
vArea[i] = (input_boxes.at(i).x2 - input_boxes.at(i).x1 + 1) *
(input_boxes.at(i).y2 - input_boxes.at(i).y1 + 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].x1, input_boxes[j].x1);
float yy1 = (std::max)(input_boxes[i].y1, input_boxes[j].y1);
float xx2 = (std::min)(input_boxes[i].x2, input_boxes[j].x2);
float yy2 = (std::min)(input_boxes[i].y2, input_boxes[j].y2);
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_THRESH) {
input_boxes.erase(input_boxes.begin() + j);
vArea.erase(vArea.begin() + j);
} else {
j++;
}
}
}
}
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