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BaseDetector.h
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184 lines (161 loc) · 4.69 KB
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#pragma once
#include <memory>
#include "defines.h"
///
/// \brief The BaseDetector class
///
class BaseDetector
{
public:
///
/// \brief BaseDetector
/// \param frame
///
BaseDetector(const cv::UMat& frame)
{
m_minObjectSize.width = std::max(5, frame.cols / 100);
m_minObjectSize.height = m_minObjectSize.width;
}
///
/// \brief ~BaseDetector
///
virtual ~BaseDetector(void)
{
}
///
/// \brief Init
/// \param config
///
virtual bool Init(const config_t& config) = 0;
///
/// \brief Detect
/// \param frame
///
virtual void Detect(const cv::UMat& frame) = 0;
///
/// \brief ResetModel
/// \param img
/// \param roiRect
///
virtual void ResetModel(const cv::UMat& /*img*/, const cv::Rect& /*roiRect*/)
{
}
///
/// \brief CanGrayProcessing
///
virtual bool CanGrayProcessing() const = 0;
///
/// \brief SetMinObjectSize
/// \param minObjectSize
///
void SetMinObjectSize(cv::Size minObjectSize)
{
m_minObjectSize = minObjectSize;
}
///
/// \brief GetDetects
/// \return
///
const regions_t& GetDetects() const
{
return m_regions;
}
///
/// \brief CalcMotionMap
/// \param frame
///
virtual void CalcMotionMap(cv::Mat& frame)
{
if (m_motionMap.size() != frame.size())
m_motionMap = cv::Mat(frame.size(), CV_32FC1, cv::Scalar(0, 0, 0));
cv::Mat foreground(m_motionMap.size(), CV_8UC1, cv::Scalar(0, 0, 0));
for (const auto& region : m_regions)
{
#if (CV_VERSION_MAJOR < 4)
cv::ellipse(foreground, region.m_rrect, cv::Scalar(255, 255, 255), CV_FILLED);
#else
cv::ellipse(foreground, region.m_rrect, cv::Scalar(255, 255, 255), cv::FILLED);
#endif
}
cv::normalize(foreground, m_normFor, 255, 0, cv::NORM_MINMAX, m_motionMap.type());
double alpha = 0.95;
cv::addWeighted(m_motionMap, alpha, m_normFor, 1 - alpha, 0, m_motionMap);
const int chans = frame.channels();
const int height = frame.rows;
#pragma omp parallel for
for (int y = 0; y < height; ++y)
{
uchar* imgPtr = frame.ptr(y);
const float* moPtr = reinterpret_cast<float*>(m_motionMap.ptr(y));
for (int x = 0; x < frame.cols; ++x)
{
for (int ci = chans - 1; ci < chans; ++ci)
{
imgPtr[ci] = cv::saturate_cast<uchar>(imgPtr[ci] + moPtr[0]);
}
imgPtr += chans;
++moPtr;
}
}
}
protected:
regions_t m_regions;
cv::Size m_minObjectSize;
// Motion map for visualization current detections
cv::Mat m_motionMap;
cv::Mat m_normFor;
///
std::vector<cv::Rect> GetCrops(float maxCropRatio, cv::Size netSize, cv::Size imgSize) const
{
std::vector<cv::Rect> crops;
const float whRatio = static_cast<float>(netSize.width) / static_cast<float>(netSize.height);
int cropHeight = cvRound(maxCropRatio * netSize.height);
int cropWidth = cvRound(maxCropRatio * netSize.width);
if (imgSize.width / (float)imgSize.height > whRatio)
{
if (cropHeight >= imgSize.height)
cropHeight = imgSize.height;
cropWidth = cvRound(cropHeight * whRatio);
}
else
{
if (cropWidth >= imgSize.width)
cropWidth = imgSize.width;
cropHeight = cvRound(cropWidth / whRatio);
}
//std::cout << "Frame size " << imgSize << ", crop size = " << cv::Size(cropWidth, cropHeight) << ", ratio = " << maxCropRatio << std::endl;
const int stepX = 3 * cropWidth / 4;
const int stepY = 3 * cropHeight / 4;
for (int y = 0; y < imgSize.height; y += stepY)
{
bool needBreakY = false;
if (y + cropHeight >= imgSize.height)
{
y = imgSize.height - cropHeight;
needBreakY = true;
}
for (int x = 0; x < imgSize.width; x += stepX)
{
bool needBreakX = false;
if (x + cropWidth >= imgSize.width)
{
x = imgSize.width - cropWidth;
needBreakX = true;
}
crops.emplace_back(x, y, cropWidth, cropHeight);
if (needBreakX)
break;
}
if (needBreakY)
break;
}
return crops;
};
};
///
/// \brief CreateDetector
/// \param detectorType
/// \param gray
/// \return
///
BaseDetector* CreateDetector(tracking::Detectors detectorType, const config_t& config, cv::UMat& gray);