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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
//
// Copyright (C) 2020 Intel Corporation
#include "gnnparsers.hpp"
namespace cv
{
namespace gapi
{
namespace nn
{
class YoloParser
{
public:
YoloParser(const float* out, const int side, const int lcoords, const int lclasses)
: m_out(out), m_side(side), m_lcoords(lcoords), m_lclasses(lclasses)
{}
float scale(const int i, const int b)
{
int obj_index = index(i, b, m_lcoords);
return m_out[obj_index];
}
double x(const int i, const int b)
{
int box_index = index(i, b, 0);
int col = i % m_side;
return (col + m_out[box_index]) / m_side;
}
double y(const int i, const int b)
{
int box_index = index(i, b, 0);
int row = i / m_side;
return (row + m_out[box_index + m_side * m_side]) / m_side;
}
double width(const int i, const int b, const float anchor)
{
int box_index = index(i, b, 0);
return std::exp(m_out[box_index + 2 * m_side * m_side]) * anchor / m_side;
}
double height(const int i, const int b, const float anchor)
{
int box_index = index(i, b, 0);
return std::exp(m_out[box_index + 3 * m_side * m_side]) * anchor / m_side;
}
float classConf(const int i, const int b, const int label)
{
int class_index = index(i, b, m_lcoords + 1 + label);
return m_out[class_index];
}
cv::Rect toBox(const double x, const double y, const double h, const double w, const cv::Size& in_sz)
{
auto h_scale = in_sz.height;
auto w_scale = in_sz.width;
cv::Rect r;
r.x = static_cast<int>((x - w / 2) * w_scale);
r.y = static_cast<int>((y - h / 2) * h_scale);
r.width = static_cast<int>(w * w_scale);
r.height = static_cast<int>(h * h_scale);
return r;
}
private:
const float* m_out = nullptr;
int m_side = 0, m_lcoords = 0, m_lclasses = 0;
int index(const int i, const int b, const int entry)
{
return b * m_side * m_side * (m_lcoords + m_lclasses + 1) + entry * m_side * m_side + i;
}
};
struct YoloParams
{
int num = 5;
int coords = 4;
};
struct Detection
{
Detection(const cv::Rect& in_rect, const float in_conf, const int in_label)
: rect(in_rect), conf(in_conf), label(in_label)
{}
cv::Rect rect;
float conf = 0.0f;
int label = 0;
};
class SSDParser
{
public:
SSDParser(const cv::MatSize& in_ssd_dims, const cv::Size& in_size, const float* data)
: m_dims(in_ssd_dims), m_maxProp(in_ssd_dims[2]), m_objSize(in_ssd_dims[3]),
m_data(data), m_surface(cv::Rect({0,0}, in_size)), m_size(in_size)
{
GAPI_Assert(in_ssd_dims.dims() == 4u); // Fixed output layout
GAPI_Assert(m_objSize == 7); // Fixed SSD object size
}
void adjustBoundingBox(cv::Rect& boundingBox)
{
auto w = boundingBox.width;
auto h = boundingBox.height;
boundingBox.x -= static_cast<int>(0.067 * w);
boundingBox.y -= static_cast<int>(0.028 * h);
boundingBox.width += static_cast<int>(0.15 * w);
boundingBox.height += static_cast<int>(0.13 * h);
if (boundingBox.width < boundingBox.height)
{
auto dx = (boundingBox.height - boundingBox.width);
boundingBox.x -= dx / 2;
boundingBox.width += dx;
}
else
{
auto dy = (boundingBox.width - boundingBox.height);
boundingBox.y -= dy / 2;
boundingBox.height += dy;
}
}
std::tuple<cv::Rect, float, float, int> extract(const size_t step)
{
const float* it = m_data + step * m_objSize;
float image_id = it[0];
int label = static_cast<int>(it[1]);
float confidence = it[2];
float rc_left = it[3];
float rc_top = it[4];
float rc_right = it[5];
float rc_bottom = it[6];
cv::Rect rc; // Map relative coordinates to the original image scale
rc.x = static_cast<int>(rc_left * m_size.width);
rc.y = static_cast<int>(rc_top * m_size.height);
rc.width = static_cast<int>(rc_right * m_size.width) - rc.x;
rc.height = static_cast<int>(rc_bottom * m_size.height) - rc.y;
return std::make_tuple(rc, image_id, confidence, label);
}
int getMaxProposals()
{
return m_maxProp;
}
cv::Rect getSurface()
{
return m_surface;
}
private:
const cv::MatSize m_dims;
int m_maxProp = 0, m_objSize = 0;
const float* m_data = nullptr;
const cv::Rect m_surface;
const cv::Size m_size;
};
} // namespace nn
} // namespace gapi
void ParseSSD(const cv::Mat& in_ssd_result,
const cv::Size& in_size,
const float confidence_threshold,
const int filter_label,
const bool alignment_to_square,
const bool filter_out_of_bounds,
std::vector<cv::Rect>& out_boxes,
std::vector<int>& out_labels)
{
cv::gapi::nn::SSDParser parser(in_ssd_result.size, in_size, in_ssd_result.ptr<float>());
out_boxes.clear();
out_labels.clear();
cv::Rect rc;
float image_id, confidence;
int label;
const size_t range = parser.getMaxProposals();
for (size_t i = 0; i < range; ++i)
{
std::tie(rc, image_id, confidence, label) = parser.extract(i);
if (image_id < 0.f)
{
break; // marks end-of-detections
}
if (confidence < confidence_threshold)
{
continue; // skip objects with low confidence
}
if((filter_label != -1) && (label != filter_label))
{
continue; // filter out object classes if filter is specified
}
if (alignment_to_square)
{
parser.adjustBoundingBox(rc);
}
const auto clipped_rc = rc & parser.getSurface();
if (filter_out_of_bounds)
{
if (clipped_rc.area() != rc.area())
{
continue;
}
}
out_boxes.emplace_back(clipped_rc);
out_labels.emplace_back(label);
}
}
static void checkYoloDims(const MatSize& dims) {
const auto d = dims.dims();
// Accept 1x13x13xN and 13x13xN
GAPI_Assert(d >= 2);
if (d >= 3) {
if (dims[d-2] == 13) {
GAPI_Assert(dims[d-1]%5 == 0);
GAPI_Assert(dims[d-2] == 13);
GAPI_Assert(dims[d-3] == 13);
for (int i = 0; i < d-3; i++) {
GAPI_Assert(dims[i] == 1);
}
return;
}
}
// Accept 1x1x1xN, 1x1xN, 1xN
GAPI_Assert(dims[d-1]%(5*13*13) == 0);
for (int i = 0; i < d-1; i++) {
GAPI_Assert(dims[i] == 1);
}
}
void parseYolo(const cv::Mat& in_yolo_result,
const cv::Size& in_size,
const float confidence_threshold,
const float nms_threshold,
const std::vector<float>& anchors,
std::vector<cv::Rect>& out_boxes,
std::vector<int>& out_labels)
{
const auto& dims = in_yolo_result.size;
checkYoloDims(dims);
int acc = 1;
for (int i = 0; i < dims.dims(); i++) {
acc *= dims[i];
}
const auto num_classes = acc/(5*13*13)-5;
GAPI_Assert(num_classes > 0);
GAPI_Assert(0 < nms_threshold && nms_threshold <= 1);
out_boxes.clear();
out_labels.clear();
gapi::nn::YoloParams params;
constexpr auto side = 13;
constexpr auto side_square = side * side;
const auto output = in_yolo_result.ptr<float>();
gapi::nn::YoloParser parser(output, side, params.coords, num_classes);
std::vector<gapi::nn::Detection> detections;
for (int i = 0; i < side_square; ++i)
{
for (int b = 0; b < params.num; ++b)
{
float scale = parser.scale(i, b);
if (scale < confidence_threshold)
{
continue;
}
double x = parser.x(i, b);
double y = parser.y(i, b);
double height = parser.height(i, b, anchors[2 * b + 1]);
double width = parser.width(i, b, anchors[2 * b]);
for (int label = 0; label < num_classes; ++label)
{
float prob = scale * parser.classConf(i,b,label);
if (prob < confidence_threshold)
{
continue;
}
auto box = parser.toBox(x, y, height, width, in_size);
detections.emplace_back(gapi::nn::Detection(box, prob, label));
}
}
}
std::stable_sort(std::begin(detections), std::end(detections),
[](const gapi::nn::Detection& a, const gapi::nn::Detection& b)
{
return a.conf > b.conf;
});
if (nms_threshold < 1.0f)
{
for (const auto& d : detections)
{
// Reject boxes which overlap with previously pushed ones
// (They are sorted by confidence, so rejected box
// always has a smaller confidence
if (std::end(out_boxes) ==
std::find_if(std::begin(out_boxes), std::end(out_boxes),
[&d, nms_threshold](const cv::Rect& r)
{
float rectOverlap = 1.f - static_cast<float>(jaccardDistance(r, d.rect));
return rectOverlap > nms_threshold;
}))
{
out_boxes. emplace_back(d.rect);
out_labels.emplace_back(d.label);
}
}
}
else
{
for (const auto& d: detections)
{
out_boxes. emplace_back(d.rect);
out_labels.emplace_back(d.label);
}
}
}
} // namespace cv
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