Example #1
0
    void save(Archive & ar, const ::cv::Mat& m, const unsigned int version)
    {
      size_t elem_size = m.elemSize();
      size_t elem_type = m.type();

      ar & m.cols;
      ar & m.rows;
      ar & elem_size;
      ar & elem_type;

      const size_t data_size = m.cols * m.rows * elem_size;
      ar & boost::serialization::make_array(m.ptr(), data_size);
    }
void glTexToCVmat(cv::Mat& image, int width, int height) {
  // http://stackoverflow.com/questions/9097756/converting-data-from-glreadpixels-to-opencvmat/
  image.create(height, width, CV_8UC3);

  // Use fast 4-byte alignment (default anyway) if possible
  glPixelStorei(GL_PACK_ALIGNMENT, (image.step & 3) ? 1 : 4);

  // Set length of one complete row in destination data (doesn't need to equal image.cols)
  glPixelStorei(GL_PACK_ROW_LENGTH, image.step/image.elemSize());

  // Read pixels into Mat. OpenCV stores colors as BGR rather than RGB
  glReadPixels(0, 0, image.cols, image.rows, GL_BGR, GL_UNSIGNED_BYTE, image.data);
}
void cv::ocl::oclMat::download(cv::Mat &m) const
{
    CV_DbgAssert(!this->empty());
    //   int t = type();
    //   if(download_channels == 3)
    //{
    //	t = CV_MAKETYPE(depth(), 3);
    //}
    m.create(wholerows, wholecols, type());

    if(m.channels() == 3)
    {
        int pitch = wholecols * 3 * m.elemSize1();
        int tail_padding = m.elemSize1() * 3072;
        int err;
        cl_mem temp = clCreateBuffer((cl_context)clCxt->oclContext(), CL_MEM_READ_WRITE,
                                     (pitch * wholerows + tail_padding - 1) / tail_padding * tail_padding, 0, &err);
        openCLVerifyCall(err);

        convert_C4C3(*this, temp);
        openCLMemcpy2D(clCxt, m.data, m.step, temp, pitch, wholecols * m.elemSize(), wholerows, clMemcpyDeviceToHost, 3);
        //int* cputemp=new int[wholecols*wholerows * 3];
        //int* cpudata=new int[this->step*this->wholerows/sizeof(int)];
        //openCLSafeCall(clEnqueueReadBuffer(clCxt->impl->clCmdQueue, temp, CL_TRUE,
        //						0, wholecols*wholerows * 3* sizeof(int), cputemp, 0, NULL, NULL));
        //openCLSafeCall(clEnqueueReadBuffer(clCxt->impl->clCmdQueue, (cl_mem)data, CL_TRUE,
        //						0, this->step*this->wholerows, cpudata, 0, NULL, NULL));
        //for(int i=0;i<wholerows;i++)
        //{
        //	int *a = cputemp+i*wholecols * 3,*b = cpudata + i*this->step/sizeof(int);
        //	for(int j=0;j<wholecols;j++)
        //	{
        //		if((a[3*j] != b[4*j])||(a[3*j+1] != b[4*j+1])||(a[3*j+2] != b[4*j+2]))
        //			printf("rows=%d,cols=%d,cputtemp=%d,%d,%d;cpudata=%d,%d,%d\n",
        //			i,j,a[3*j],a[3*j+1],a[3*j+2],b[4*j],b[4*j+1],b[4*j+2]);
        //	}
        //}
        //delete []cputemp;
        //delete []cpudata;
        openCLSafeCall(clReleaseMemObject(temp));
    }
    else
    {
        openCLMemcpy2D(clCxt, m.data, m.step, data, step, wholecols * elemSize(), wholerows, clMemcpyDeviceToHost);
    }
    Size wholesize;
    Point ofs;
    locateROI(wholesize, ofs);
    m.adjustROI(-ofs.y, ofs.y + rows - wholerows, -ofs.x, ofs.x + cols - wholecols);
}
	void ReadMatBin(std::ifstream& stream, cv::Mat &output_mat)
	{
		// Read in the number of rows, columns and the data type
		int row, col, type;

		stream.read((char*)&row, 4);
		stream.read((char*)&col, 4);
		stream.read((char*)&type, 4);

		output_mat = cv::Mat(row, col, type);
		int size = output_mat.rows * output_mat.cols * output_mat.elemSize();
		stream.read((char *)output_mat.data, size);

	}
Example #5
0
bool detectandshow( Alpr* alpr, cv::Mat frame, std::string region, bool writeJson)
{

  timespec startTime;
  getTimeMonotonic(&startTime);

  std::vector<AlprRegionOfInterest> regionsOfInterest;
  regionsOfInterest.push_back(AlprRegionOfInterest(0,0, frame.cols, frame.rows));
  
  AlprResults results = alpr->recognize(frame.data, frame.elemSize(), frame.cols, frame.rows, regionsOfInterest );

  timespec endTime;
  getTimeMonotonic(&endTime);
  double totalProcessingTime = diffclock(startTime, endTime);
  if (measureProcessingTime)
    std::cout << "Total Time to process image: " << totalProcessingTime << "ms." << std::endl;
  
  
  if (writeJson)
  {
    std::cout << alpr->toJson( results ) << std::endl;
  }
  else
  {
    for (int i = 0; i < results.plates.size(); i++)
    {
      std::cout << "plate" << i << ": " << results.plates[i].topNPlates.size() << " results";
      if (measureProcessingTime)
        std::cout << " -- Processing Time = " << results.plates[i].processing_time_ms << "ms.";
      std::cout << std::endl;

      if (results.plates[i].regionConfidence > 0)
        std::cout << "State ID: " << results.plates[i].region << " (" << results.plates[i].regionConfidence << "% confidence)" << std::endl;
      
      for (int k = 0; k < results.plates[i].topNPlates.size(); k++)
      {
        std::cout << "    - " << results.plates[i].topNPlates[k].characters << "\t confidence: " << results.plates[i].topNPlates[k].overall_confidence;
        if (templatePattern.size() > 0)
          std::cout << "\t pattern_match: " << results.plates[i].topNPlates[k].matches_template;
        
        std::cout << std::endl;
      }
    }
  }



  return results.plates.size() > 0;
}
//! Read cv::Mat from binary
void readMatBinary(std::ifstream& ifs, cv::Mat& in_mat)
{
	if(!ifs.is_open()){
		throw new orArgException("Not opened");
	}
	
	int rows, cols, type;
	ifs.read((char*)(&rows), sizeof(int));
	ifs.read((char*)(&cols), sizeof(int));
	ifs.read((char*)(&type), sizeof(int));

	in_mat.release();
	in_mat.create(rows, cols, type);
	ifs.read((char*)(in_mat.data), in_mat.elemSize() * in_mat.total());
}
    // Function turn a cv::Mat into a texture
    void matToTexture(GLuint textureID, cv::Mat& mat)
    {
      // Bind to our texture handle
      glBindTexture(GL_TEXTURE_2D, textureID);
      // Set texture interpolation methods for minification and magnification
      glTexParameterf(GL_TEXTURE_2D,GL_TEXTURE_MIN_FILTER, GL_NEAREST); 
      glTexParameterf(GL_TEXTURE_2D,GL_TEXTURE_MAG_FILTER, GL_NEAREST); 
      glTexParameterf( GL_TEXTURE_2D, GL_TEXTURE_WRAP_S , GL_REPEAT );
      glTexParameterf( GL_TEXTURE_2D, GL_TEXTURE_WRAP_T, GL_REPEAT );

      //use fast 4-byte alignment (default anyway) if possible
      glPixelStorei(GL_PACK_ALIGNMENT, (mat.step & 3) ? 1 : 4);
      //set length of one complete row in destination data (doesn't need to equal img.cols)
      glPixelStorei(GL_PACK_ROW_LENGTH, mat.step/mat.elemSize());
 
      // Set incoming texture format
      GLenum inputColourFormat = GL_BGR;
      GLenum inputType = GL_UNSIGNED_BYTE;
      GLenum internalFormat = GL_RGBA;
      if (mat.channels() == 1)
      {
        if (mat.depth() == CV_8U)
        {
          inputColourFormat = GL_RED_INTEGER;
          inputType = GL_UNSIGNED_BYTE;
          internalFormat = GL_RGBA8UI;
        }
        else
        {
          inputColourFormat = GL_RED_INTEGER;
          inputType = GL_UNSIGNED_SHORT;
          internalFormat = GL_RGBA16UI;
        }
      }

      // Create the texture
      glTexImage2D(GL_TEXTURE_2D,     // Type of texture
                   0,                 // Pyramid level (for mip-mapping) - 0 is the top level
                   internalFormat,    // Internal colour format to convert to
                   mat.cols,          // Image width  i.e. 640 for Kinect in standard mode
                   mat.rows,          // Image height i.e. 480 for Kinect in standard mode
                   0,                 // Border width in pixels (can either be 1 or 0)
                   inputColourFormat, // Input image format (i.e. GL_RGB, GL_RGBA, GL_BGR etc.)
                   inputType,         // Image data type
                   mat.data);         // The actual image data itself

      glBindTexture(GL_TEXTURE_2D, 0);
    }
void hulo::readMatBin(std::string filename, cv::Mat& mat) {
	std::ifstream ifs(filename, std::ios::binary);
	CV_Assert(ifs.is_open());

	int rows, cols, type;
	ifs.read((char*) (&rows), sizeof(int));
	if (rows == 0) {
		return;
	}
	ifs.read((char*) (&cols), sizeof(int));
	ifs.read((char*) (&type), sizeof(int));

	mat.release();
	mat.create(rows, cols, type);
	ifs.read((char*) (mat.data), mat.elemSize() * mat.total());
}
void hulo::saveMatBin(std::string filename, cv::Mat& mat) {
	std::ofstream ofs(filename, std::ios::binary);
	CV_Assert(ofs.is_open());

	if (mat.empty()) {
		int s = 0;
		ofs.write((const char*) (&s), sizeof(int));
		return;
	}

	int type = mat.type();
	ofs.write((const char*) (&mat.rows), sizeof(int));
	ofs.write((const char*) (&mat.cols), sizeof(int));
	ofs.write((const char*) (&type), sizeof(int));
	ofs.write((const char*) (mat.data), mat.elemSize() * mat.total());
}
Example #10
0
void DiffImage::applyIntensityCorrection(cv::Mat &image)
{
    if (image.total() != intensityCorrectionImage_.total()) return;

    #pragma omp parallel for private(x, y, c)
    for (int x = 0; x < image.cols; ++x) {
        for (int y = 0; y < image.rows; ++y) {
            for (int c = 0; c < image.channels(); ++c) {
                const int index = y * image.step + x * image.elemSize() + c;
                auto val = image.data[index] * (1.0 * intensityCorrectionImage_.data[index] / 100);
                if (val > 255) val = 255;
                image.data[index] = static_cast<int>(pow(val / 255.0, gamma_) * 255);
            }
        }
    }
}
Example #11
0
void SensorData::setUserDataRaw(const cv::Mat & userDataRaw)
{
	if(!userDataRaw.empty() && (!_userDataCompressed.empty() || !_userDataRaw.empty()))
	{
		UWARN("Cannot write new user data (%d bytes) over existing user "
			  "data (%d bytes, %d compressed). Set user data of %d to null "
			  "before setting a new one.",
			  int(userDataRaw.total()*userDataRaw.elemSize()),
			  int(_userDataRaw.total()*_userDataRaw.elemSize()),
			  _userDataCompressed.cols,
			  this->id());
		return;
	}
	_userDataRaw = userDataRaw;
	_userDataCompressed = cv::Mat();
}
    ptrobot2D_test_world_impl(const std::string& aFileName, double aRobotRadius = 1.0) {
#ifdef REAK_HAS_OPENCV
      world_map_image = cv::imread(aFileName);
      if(world_map_image.empty())
        throw std::ios_base::failure("Could not open the world map image '" + aFileName + "'! File is missing, empty or invalid!");

      world_map_output = world_map_image.clone();
      grid_width = world_map_image.size().width;
      grid_height = world_map_image.size().height;

      bpp = world_map_image.elemSize();

      for(int y = 0; y < grid_height; ++y) {
        uchar* color_bits = world_map_image.ptr(y);

        for(int x = 0; x < grid_width; ++x) {
          if( (color_bits[2] == 0) &&
              (color_bits[0] == 255) &&
              (color_bits[1] == 0) ) {
            //this is the start position.
            start_pos[0] = x;
            start_pos[1] = y;
          } else if( (color_bits[2] == 0) &&
                     (color_bits[0] == 0) &&
                     (color_bits[1] == 255) ) {
            //this is the goal position.
            goal_pos[0] = x;
            goal_pos[1] = y;
          };
          color_bits += bpp;
        };
      };

//       int iRobotRadius = int(std::fabs(aRobotRadius));
//       if(iRobotRadius > 0) {
//         cv::GaussianBlur(world_map_output,world_map_image,
//                          cv::Size(iRobotRadius * 2 + 1, iRobotRadius * 2 + 1),
//                          aRobotRadius
//                         );
//       };
#else
      grid_width = 500;
      grid_height = 500;
      start_pos = ptrobot2D_test_world::point_type(1.0,1.0);
      goal_pos  = ptrobot2D_test_world::point_type(498.0,498.0);
#endif
    };
Example #13
0
void CFast::convertKeypoints(const cv::Mat& cvmKeyPoints_, std::vector<cv::KeyPoint>* pvKeyPoints_)
{
	if (cvmKeyPoints_.empty())	return;

	CV_Assert(cvmKeyPoints_.rows == ROWS_COUNT && cvmKeyPoints_.elemSize() == 4);

	int nPoints = cvmKeyPoints_.cols;
	pvKeyPoints_->resize(nPoints);

	const short2* ps2LocationRow = cvmKeyPoints_.ptr<short2>(LOCATION_ROW);
	const float* pfResponseRow = cvmKeyPoints_.ptr<float>(RESPONSE_ROW);

	for (int i = 0; i < nPoints; ++i){
		cv::KeyPoint kp(ps2LocationRow[i].x, ps2LocationRow[i].y, static_cast<float>(FEATURE_SIZE), -1, pfResponseRow[i]);
		(*pvKeyPoints_)[i] = kp;
	}
	return;
}
Example #14
0
/*!
\param[out] ofs output file stream
\param[in] out_mat mat to save
*/
bool writeMatBinary(std::ofstream& ofs, const cv::Mat& out_mat)
{
	if(!ofs.is_open()){
		return false;
	}
	if(out_mat.empty()){
		int s = 0;
		ofs.write((const char*)(&s), sizeof(int));
		return true;
	}
	int type = out_mat.type();
	ofs.write((const char*)(&out_mat.rows), sizeof(int));
	ofs.write((const char*)(&out_mat.cols), sizeof(int));
	ofs.write((const char*)(&type), sizeof(int));
	ofs.write((const char*)(out_mat.data), out_mat.elemSize() * out_mat.total());

	return true;
}
Example #15
0
// image는 흑백 영상이어야 한다.
// 크기는 640/480 이어야 한다.
int cStreamingSender::Send(const cv::Mat &image)
{
	if (READY == m_state)
	{
		// 이미지를 변조한다.
		uchar *data = image.data;
		int buffSize = image.total() * image.elemSize();

		// Gray Scale
		if (m_isConvertGray)
		{
			cvtColor(image, m_gray, CV_RGB2GRAY);
			data = m_gray.data;
			buffSize = m_gray.total() * m_gray.elemSize();
		}

		// 압축
		if (m_isCompressed)
		{
			vector<int> p(3);
			p[0] = CV_IMWRITE_JPEG_QUALITY;
			p[1] = m_jpgCompressQuality;
			p[2] = 0;
			cv::imencode(".jpg", m_isConvertGray ? m_gray : image, m_compBuffer, p);
			data = (uchar*)&m_compBuffer[0];
			buffSize = m_compBuffer.size();
		}

		return SendImage(data, buffSize, m_isConvertGray, m_isCompressed);
	}
	else if (SPLIT == m_state)
	{
		if (SendSplit())
		{
			m_state = READY;
			++m_chunkId;
			if (m_chunkId > 100)
				m_chunkId = 0;
		}
	}

	return 0;
}
Example #16
0
// Save cv::Mat as binary
bool BinaryMat::SaveMatBinary(const std::string& filename, const cv::Mat& out_mat)
{
	std::ofstream ofs(filename, std::ios::binary);
	if (!ofs.is_open()) {
		return false;
	}
	if (out_mat.empty()) {
		int s = 0;
		ofs.write((const char*)(&s), sizeof(int));
		return true;
	}
	int type = out_mat.type();
	ofs.write((const char*)(&out_mat.rows), sizeof(int));
	ofs.write((const char*)(&out_mat.cols), sizeof(int));
	ofs.write((const char*)(&type), sizeof(int));
	ofs.write((const char*)(out_mat.data), out_mat.elemSize() * out_mat.total());

	return true;
}
Example #17
0
    virtual bool write( const char * table, const char * name, const cv::Mat & mat ) 
    { 
        int t = mat.type();
        int w = mat.cols;
        int h = mat.rows;
        string q = format("insert into %s values(?, ?, ?, ?, ?);", table);

        MYSQL_STMT * stmt = mysql_stmt_init(mysql);

        if ( ! stmt )
            return _error("write init statement");
        
        if ( mysql_stmt_prepare(stmt,q.c_str(),q.length()) ) 
            return _error("write prepare statement"); 

        MYSQL_BIND bind[5] = {0};
        bind[0].buffer_type= MYSQL_TYPE_STRING;
        bind[0].buffer= (void*)name;
        bind[0].buffer_length= strlen(name);

        bind[1].buffer_type= MYSQL_TYPE_LONG;
        bind[1].buffer= (char *)(&t);

        bind[2].buffer_type= MYSQL_TYPE_LONG;
        bind[2].buffer= (char *)(&w);

        bind[3].buffer_type= MYSQL_TYPE_LONG;
        bind[3].buffer= (char *)(&h);

        bind[4].buffer_type= MYSQL_TYPE_MEDIUM_BLOB;
        bind[4].buffer= (char *)(mat.data);
        bind[4].buffer_length= mat.total()*mat.elemSize();

        if ( mysql_stmt_bind_param(stmt,bind) ) 
            return _error(stmt,"write bind params"); 
        
        if ( mysql_stmt_execute(stmt) ) 
            return _error(stmt,"write execute"); 

        mysql_stmt_close(stmt);
        return true; 
    }
Example #18
0
double multiplyImage(cv::Mat &image, cv::Mat gain)
{
	cv::Mat stub, b, g, r, stubGain;
	std::vector<cv::Mat> arrayColor;
	arrayColor.push_back(b);
	arrayColor.push_back(g);
	arrayColor.push_back(r);
	cv::split(image, arrayColor);
	int64 begin, end;

	begin = cv::getTickCount();
	switch (gain.elemSize())
	{
	case 1:
		gain.convertTo(stubGain, CV_32FC1, 1/255.0f);
		for (unsigned int i = 0; i < arrayColor.size(); i++)
		{
			arrayColor[i].convertTo(stub, CV_32FC1);
			multiply(stub, stubGain, arrayColor[i], 1.0, CV_8UC1);
		}
		break;
	case 2:
		for (unsigned int i = 0; i < arrayColor.size(); i++)
		{
			multiply(arrayColor[i].data, (short*)gain.data, arrayColor[i].data, arrayColor[i].rows*arrayColor[i].cols);
		}
		break;
	case 4:
	default:
		for (unsigned int i = 0; i < arrayColor.size(); i++)
		{
			arrayColor[i].convertTo(stub, CV_32FC1);
			multiply(stub, gain, arrayColor[i], 1.0, CV_8UC1);
		}
		break;
	}
	end = cv::getTickCount();
	cv::merge(arrayColor, image);

	double tickCountElapsed = double(end - begin);
	return tickCountElapsed/(double)cv::getTickFrequency();
}
Example #19
0
/*!
\param[in] ifs input file stream
\param[out] in_mat mat to load
*/
bool readMatBinary(std::ifstream& ifs, cv::Mat& in_mat)
{
	if(!ifs.is_open()){
		return false;
	}
	
	int rows, cols, type;
	ifs.read((char*)(&rows), sizeof(int));
	if(rows==0){
		return true;
	}
	ifs.read((char*)(&cols), sizeof(int));
	ifs.read((char*)(&type), sizeof(int));

	in_mat.release();
	in_mat.create(rows, cols, type);
	ifs.read((char*)(in_mat.data), in_mat.elemSize() * in_mat.total());

	return true;
}
Example #20
0
void copyMatToImageMSg(const cv::Mat& in, mobots_msgs::ImageWithPoseAndID& out2){
  sensor_msgs::Image* out = &out2.image;
  out->height = in.rows;
  out->width = in.cols;
  out->encoding = "bgr8";
  out->is_bigendian = 0;
  out->step = in.cols * in.elemSize();
  out->data.resize(in.rows * out->step);
  if(in.isContinuous()){
    memcpy(&out->data[0], in.data, in.rows * out->step);
  }else{
    // Copy row by row
    uchar* ros_data_ptr = (uchar*)(&out->data[0]);
    uchar* cv_data_ptr = in.data;
    for (int i = 0; i < in.rows; i++){
      memcpy(ros_data_ptr, cv_data_ptr, out->step);
      ros_data_ptr += out->step;
      cv_data_ptr += in.step;
    }
  }
}
Example #21
0
double multiplyImageCuda(cv::Mat &image, cv::Mat gain)
{
	unsigned int image_width  = image.cols;
	unsigned int image_height = image.rows;
	unsigned int imageSizeGray  = image_width * image_height;
	unsigned int imageSizeColor = imageSizeGray * 3;

	cudaMemcpy(imageCuda, image.data, imageSizeColor*sizeof(char), cudaMemcpyHostToDevice);

	// calculate grid size
    dim3 block(16, 16, 1);
    dim3 grid(image_width / block.x, image_height / block.y, 1);
	int64 begin, end;

	switch (gain.elemSize())
	{
	case 1:
		begin = cv::getTickCount();
		cudaMemcpy(gainByteCuda,  gain.data,  imageSizeGray*sizeof(char),  cudaMemcpyHostToDevice);
		end = cv::getTickCount();
		launchCudaProcessByte(grid, block, 0, gainByteCuda, imageCuda, imageResult, image_width);
		break;
	case 2:
		begin = cv::getTickCount();
		cudaMemcpy(gainCuda,  gain.data,  imageSizeGray*sizeof(short),  cudaMemcpyHostToDevice);
		end = cv::getTickCount();
		launchCudaProcessHalf(grid, block, 0, gainCuda, imageCuda, imageResult, image_width);
		break;
	case 4:
		begin = cv::getTickCount();
		cudaMemcpy(gainFloatCuda,  gain.data,  imageSizeGray*sizeof(float),  cudaMemcpyHostToDevice);
		end = cv::getTickCount();
		launchCudaProcessFloat(grid, block, 0, gainFloatCuda, imageCuda, imageResult, image_width);
		break;
	}
	cudaMemcpy(image.data, imageResult, imageSizeColor*sizeof(char), cudaMemcpyDeviceToHost);

	double tickCountElapsed = double(end - begin);
	return tickCountElapsed/(double)cv::getTickFrequency();
}
Example #22
0
QImage Listener::cvtCvMat2QImage(const cv::Mat & image)
{
	QImage qtemp;
	if(!image.empty() && image.depth() == CV_8U)
	{
		const unsigned char * data = image.data;
		qtemp = QImage(image.cols, image.rows, QImage::Format_RGB32);
		for(int y = 0; y < image.rows; ++y, data += image.cols*image.elemSize())
		{
			for(int x = 0; x < image.cols; ++x)
			{
				QRgb * p = ((QRgb*)qtemp.scanLine (y)) + x;
				*p = qRgb(data[x * image.channels()+2], data[x * image.channels()+1], data[x * image.channels()]);
			}
		}
	}
	else if(!image.empty() && image.depth() != CV_8U)
	{
		printf("Wrong image format, must be 8_bits\n");
	}
	return qtemp;
}
Example #23
0
static void randu(cv::Mat& m)
{
    const int bigValue = 0x00000FFF;
    if (m.depth() < CV_32F)
    {
        int minmax[] = {0, 256};
        cv::Mat mr = cv::Mat(m.rows, (int)(m.cols * m.elemSize()), CV_8U, m.ptr(), m.step[0]);
        cv::randu(mr, cv::Mat(1, 1, CV_32S, minmax), cv::Mat(1, 1, CV_32S, minmax + 1));
    }
    else if (m.depth() == CV_32F)
    {
        //float minmax[] = {-FLT_MAX, FLT_MAX};
        float minmax[] = {-bigValue, bigValue};
        cv::Mat mr = m.reshape(1);
        cv::randu(mr, cv::Mat(1, 1, CV_32F, minmax), cv::Mat(1, 1, CV_32F, minmax + 1));
    }
    else
    {
        //double minmax[] = {-DBL_MAX, DBL_MAX};
        double minmax[] = {-bigValue, bigValue};
        cv::Mat mr = m.reshape(1);
        cv::randu(mr, cv::Mat(1, 1, CV_64F, minmax), cv::Mat(1, 1, CV_64F, minmax + 1));
    }
}
Example #24
0
static inline int ippiSuggestThreadsNum(const cv::Mat &image, double multiplier)
{
    return ippiSuggestThreadsNum(image.cols, image.rows, image.elemSize(), multiplier);
}
Example #25
0
std::string describe(cv::Mat const &mat)
{
	std::ostringstream out;
	out << mat.rows << "x" << mat.cols;
	out << ", " << mat.channels() << " channels";
	out << ", type " << CV_MAT_DEPTH(mat.type()) << ", " << mat.elemSize1() << " byte elemsize1, " << mat.elemSize() << " byte elemsize";
	return out.str();
}
Example #26
0
File: hist.cpp Project: sensq/home
/**
@brief 色相だけのヒストグラムを計算する
@param src ヒストグラムを計算する画像
*/
void HIST::calcHistgramHue(cv::Mat &src)
{
	if(src.data == NULL)
		return;
	
	// グラフのデータ間の距離
	stepH = (double)ui.histgramH->width()/180;

	cv::cvtColor(src, src, cv::COLOR_BGR2HSV);
	int count[180];
	int max = 0;

	// 初期化
	for (int i = 0; i < 180; i++)
		count[i] = 0;

	// 色相の各値の個数を取得
	for (int j = 0; j < src.cols; j++)
		for (int i = 0; i < src.rows; i++)
			// ほぼ白(彩度≒0)は無視
				if(src.data[i * src.step + j * src.elemSize() + 1] > 3)
					count[src.data[i * src.step + j * src.elemSize()]]++;
	cv::cvtColor(src, src, cv::COLOR_HSV2BGR);

	// スケーリング定数(一番多い個数)の取得
	for (int i = 0; i < 180; i++)
		if(max < count[i])
			max = count[i];

	// スケーリング
	double histgram[180];
	for (int i = 0; i < 180; i++)
		histgram[i] = (double)count[i] / max * 200;


	/** 簡易グラフ作成 **/
	int gWidth = 180 * stepH;
	int gHeight = 200;
	// 格子画像
	cv::Mat baseGraph(gHeight, gWidth, CV_8UC3, cv::Scalar(255, 255, 255));
	// 色相のヒストグラム画像
	cv::Mat hueGraph(gHeight, gWidth, CV_8UC3, cv::Scalar(255, 255, 255));
	// 上記2つを乗算ブレンディングした最終的に描画する画像
	cv::Mat graph(gHeight, gWidth, CV_8UC3, cv::Scalar(255, 255, 255));

	cv::cvtColor(hueGraph, hueGraph, cv::COLOR_BGR2HSV);
	for (int j = 0; j < hueGraph.rows; j++){
		for (int i = 0; i < hueGraph.cols; i++){
			hueGraph.data[j * hueGraph.step + i * hueGraph.elemSize() + 0] = (int)((double)i/stepH);
			hueGraph.data[j * hueGraph.step + i * hueGraph.elemSize() + 1] = 220;
			hueGraph.data[j * hueGraph.step + i * hueGraph.elemSize() + 2] = 180;
		}
	}
	cv::cvtColor(hueGraph, hueGraph, cv::COLOR_HSV2BGR);
	// 横のメモリ
	for (int i = 0; i < 20; i++)
		if(!(i%4))
			cv::line(baseGraph, cv::Point(0, i*10), cv::Point(gWidth, i*10), cv::Scalar(180, 180, 180), 2);
		else
			cv::line(baseGraph, cv::Point(0, i*10), cv::Point(gWidth, i*10), cv::Scalar(200, 200, 200), 1);
	// 色相のグラフ
	for (int i = 0; i < 180; i++)
		cv::line(hueGraph, cv::Point((int)(i*stepH), 0), cv::Point((int)(i*stepH), (int)histgram[i]), cv::Scalar(180, 180, 180), 2);
	// 折れ線
	for (int i = 0; i < 180; i++)
		cv::line(hueGraph, cv::Point((int)(i*stepH), (int)histgram[i]), cv::Point((int)((i+1)*stepH), (int)histgram[i+1]), cv::Scalar(90, 90, 90), 2, CV_AA);
	// 合成
	blend(baseGraph, hueGraph, graph, blendType::MULTI);
	// 上下を反転
	cv::flip(graph, graph, 0);

	drawForQtLabel(graph, ui.histgramH, false);
}
Example #27
0
unsigned long ipa_Utils::FilterSpeckles(cv::Mat& img, int maxSpeckleSize, double maxDiff, cv::Mat& _buf )
{
    CV_Assert( img.type() == CV_32FC3 );

 
    float newVal = 0;
    int width = img.cols, height = img.rows, npixels = width*height;
    size_t bufSize = npixels*(int)(sizeof(cv::Point_<short>) + sizeof(int) + sizeof(uchar));
    if( !_buf.isContinuous() || !_buf.data || _buf.cols*_buf.rows*_buf.elemSize() < bufSize )
        _buf.create(1, bufSize, CV_8U);
    
    uchar* buf = _buf.data;
    int i, j, dstep = img.step/sizeof(cv::Vec3f);
    int* labels = (int*)buf;
    buf += npixels*sizeof(labels[0]);
    cv::Point_<short>* wbuf = (cv::Point_<short>*)buf;
    buf += npixels*sizeof(wbuf[0]);
    uchar* rtype = (uchar*)buf;
    int curlabel = 0;
    
    // clear out label assignments
    memset(labels, 0, npixels*sizeof(labels[0]));
    
    for( i = 0; i < height; i++ )
    {
		cv::Vec3f* ds = img.ptr<cv::Vec3f>(i);
        int* ls = labels + width*i;
        
        for( j = 0; j < width; j++ )
        {
            if( ds[j][2] != newVal )	// not a bad disparity
            {
                if( ls[j] )		// has a label, check for bad label
                {  
                    if( rtype[ls[j]] ) // small region, zero out disparity
					{
                        ds[j][0] = (float)newVal;
						ds[j][1] = (float)newVal;
						ds[j][2] = (float)newVal;
					}
                }
                // no label, assign and propagate
                else
                {
                    cv::Point_<short>* ws = wbuf;	// initialize wavefront
                    cv::Point_<short> p((short)j, (short)i);	// current pixel
                    curlabel++;	// next label
                    int count = 0;	// current region size
                    ls[j] = curlabel;
                    
                    // wavefront propagation
                    while( ws >= wbuf ) // wavefront not empty
                    {
                        count++;
                        // put neighbors onto wavefront
						cv::Vec3f* dpp = &img.at<cv::Vec3f>(p.y, p.x);
						cv::Vec3f dp = *dpp;
                        int* lpp = labels + width*p.y + p.x;
                        
                        if( p.x < width-1 && !lpp[+1] && dpp[+1][2] != newVal && std::abs(dp[2] - dpp[+1][2]) <= maxDiff )
                        {
                            lpp[+1] = curlabel;
                            *ws++ = cv::Point_<short>(p.x+1, p.y);
                        }
                        
						if( p.x > 0 && !lpp[-1] && dpp[-1][2] != newVal && std::abs(dp[2] - dpp[-1][2]) <= maxDiff )
                        {
                            lpp[-1] = curlabel;
                            *ws++ = cv::Point_<short>(p.x-1, p.y);
                        }
                        
                        if( p.y < height-1 && !lpp[+width] && dpp[+dstep][2] != newVal && std::abs(dp[2] - dpp[+dstep][2]) <= maxDiff )
                        {
                            lpp[+width] = curlabel;
                            *ws++ = cv::Point_<short>(p.x, p.y+1);
                        }
                        
                        if( p.y > 0 && !lpp[-width] && dpp[-dstep][2] != newVal && std::abs(dp[2] - dpp[-dstep][2]) <= maxDiff )
                        {
                            lpp[-width] = curlabel;
                            *ws++ = cv::Point_<short>(p.x, p.y-1);
                        }
                        
                        // pop most recent and propagate
                        // NB: could try least recent, maybe better convergence
                        p = *--ws;
                    }
                    
                    // assign label type
                    if( count <= maxSpeckleSize )	// speckle region
                    {
                        rtype[ls[j]] = 1;	// small region label
                        ds[j][0] = (float)newVal;
						ds[j][1] = (float)newVal;
						ds[j][2] = (float)newVal;
                    }
                    else
                        rtype[ls[j]] = 0;	// large region label
                }
            }
        }
    }
	return ipa_Utils::RET_OK;
} 
Example #28
0
File: hist.cpp Project: sensq/home
/**
@brief RGBのヒストグラムを計算する
@param src ヒストグラムを計算する画像
*/
void HIST::calcHistgram(cv::Mat &src)
{
	if(src.data == NULL)
		return;
	
	// グラフのデータ間の距離
	step = (double)ui.histgramRGB->width()/256;

	int count[256][3];
	int max = 0;

	// 初期化
	for (int i = 0; i < 256; i++)
		for (int c = 0; c < 3; c++)
			count[i][c] = 0;

	// RGBごとに輝度の個数を取得
	for (int j = 0; j < src.cols; j++)
		for (int i = 0; i < src.rows; i++)
			for (int c = 0; c < 3; c++)
				count[src.data[i * src.step + j * src.elemSize() + c]][c]++;

	// 0と255はだいたい極端に多くなるので無視する
	for (int c = 0; c < 3; c++)
		count[0][c] = count[255][c] = 0;

	// スケーリング定数(一番多い輝度の個数)の取得
	for (int i = 0; i < 256; i++)
		for (int c = 0; c < 3; c++)
			if(max < count[i][c])
				max = count[i][c];

	// スケーリング
	double histgram[256][3];
	for (int i = 0; i < 256; i++)
		for (int c = 0; c < 3; c++)
			histgram[i][c] = (double)count[i][c] / max * 200;


	/** 簡易グラフ作成 **/
	int gWidth = 256 * step;
	int gHeight = 200;
	// 格子画像
	cv::Mat baseGraph(gHeight, gWidth, CV_8UC3, cv::Scalar(255, 255, 255));
	// RGBごとのヒストグラム画像
	cv::Mat rgbGraph[3] = {
		cv::Mat(gHeight, gWidth, CV_8UC3, cv::Scalar(255, 255, 255)),
		cv::Mat(gHeight, gWidth, CV_8UC3, cv::Scalar(255, 255, 255)),
		cv::Mat(gHeight, gWidth, CV_8UC3, cv::Scalar(255, 255, 255))
	};
	// 上記4つを乗算ブレンディングした最終的に描画する画像
	cv::Mat graph(gHeight, gWidth, CV_8UC3, cv::Scalar(255, 255, 255));

	// 横のメモリ
	for (int i = 0; i < 20; i++)
		if(!(i%4))
			cv::line(baseGraph, cv::Point(0, i*10), cv::Point(gWidth, i*10), cv::Scalar(180, 180, 180), 2);
		else
			cv::line(baseGraph, cv::Point(0, i*10), cv::Point(gWidth, i*10), cv::Scalar(200, 200, 200), 1);
	// 縦のメモリ
	for (int i = 0; i < 5; i++)
		cv::line(baseGraph, cv::Point(i*50*step, 0), cv::Point(i*50*step, gHeight), cv::Scalar(180, 180, 180), 2);
	// RGBごとのグラフ
	for (int i = 0; i < 256; i++){
		cv::line(rgbGraph[0], cv::Point(i*step, 0), cv::Point(i*step, (int)histgram[i][0]), cv::Scalar(255, 200, 200), 2);
		cv::line(rgbGraph[1], cv::Point(i*step, 0), cv::Point(i*step, (int)histgram[i][1]), cv::Scalar(200, 255, 200), 2);
		cv::line(rgbGraph[2], cv::Point(i*step, 0), cv::Point(i*step, (int)histgram[i][2]), cv::Scalar(200, 200, 255), 2);
	}
	// 折れ線
	for (int i = 0; i < 255; i++){
		cv::line(rgbGraph[0], cv::Point(i*step, (int)histgram[i][0]), cv::Point((i+1)*step, (int)histgram[i+1][0]), cv::Scalar(255, 30, 30), 2, CV_AA);
		cv::line(rgbGraph[1], cv::Point(i*step, (int)histgram[i][1]), cv::Point((i+1)*step, (int)histgram[i+1][1]), cv::Scalar(30, 255, 30), 2, CV_AA);
		cv::line(rgbGraph[2], cv::Point(i*step, (int)histgram[i][2]), cv::Point((i+1)*step, (int)histgram[i+1][2]), cv::Scalar(30, 30, 255), 2, CV_AA);
	}
	// 合成
	cv::Mat tmp;
	blend(rgbGraph[0], rgbGraph[1], tmp, blendType::MULTI);
	blend(tmp, rgbGraph[2], tmp, blendType::MULTI);
	blend(baseGraph, tmp, graph, blendType::MULTI);
	// 上下を反転
	cv::flip(graph, graph, 0);

	drawForQtLabel(graph, ui.histgramRGB, false);
}