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C++ 关于cv:：imwrite的速度，是否有.bmp的替代方案

c++ opencv

C++ 关于cv:：imwrite的速度，是否有.bmp的替代方案,c++,opencv,kinect,ros,libfreenect2,C++,Opencv,Kinect,Ros,Libfreenect2,我正在尝试订阅来自kinect2摄像头的主题并保存这些图像。我正在将iai_kinect2软件包与libfreenect2一起使用。我的问题是除了.bmp格式之外，所有其他格式都会在时间戳之间跳转。我不想使用.bmp格式，因为它显然不能提供正确的深度图像。当我尝试在matlab中打开它时，我只有0和255个深度图像。以下是.png的时间戳示例： 1533061840.176977104 1533061840.210227483 1533061840.243339200 1533061840.37

我正在尝试订阅来自kinect2摄像头的主题并保存这些图像。我正在将iai_kinect2软件包与libfreenect2一起使用。我的问题是除了.bmp格式之外，所有其他格式都会在时间戳之间跳转。我不想使用.bmp格式，因为它显然不能提供正确的深度图像。当我尝试在matlab中打开它时，我只有0和255个深度图像。以下是.png的时间戳示例：

1533061840.176977104
1533061840.210227483
1533061840.243339200
1533061840.376478700 //see the jump
1533061840.476526118
1533061840.510053089
1533061840.576983109

      Before depth: 1533067649.437008647 After depth: 1533067649.449930403saved_depth_index: 1
 After color: 1533067649.479346684 Before depth: 1533067649.479786642 After depth: 1533067649.490577579saved_depth_index: 2
 After color: 1533067649.520330356 Before depth: 1533067649.520770286 After depth: 1533067649.531645103saved_depth_index: 3
 After color: 1533067649.561450753 Before depth: 1533067649.570491592 After depth: 1533067649.583154752saved_depth_index: 4
 After color: 1533067649.612989915 Before depth: 1533067649.614441724 After depth: 1533067649.625398696saved_depth_index: 5
 After color: 1533067649.654874365 Before depth: 1533067649.655352960 After depth: 1533067649.666162400saved_depth_index: 6
 After color: 1533067649.695561608 Before depth: 1533067649.696115115 After depth: 1533067649.708304851saved_depth_index: 7
 After color: 1533067649.736989649 Before depth: 1533067649.738013842 After depth: 1533067649.751335296saved_depth_index: 8
 After color: 1533067649.780984546 Before depth: 1533067649.781488371 After depth: 1533067649.792345625saved_depth_index: 9
 After color: 1533067649.822243383 Before depth: 1533067649.822725751 After depth: 1533067649.833530992saved_depth_index: 10
 After color: 1533067649.862818010 Before depth: 1533067649.863264236 After depth: 1533067649.875531476saved_depth_index: 11
 After color: 1533067649.904058584 Before depth: 1533067649.904558682 After depth: 1533067649.917082444saved_depth_index: 12
 After color: 1533067649.946167277 Before depth: 1533067649.946650618 After depth: 1533067649.957500888saved_depth_index: 13
 After color: 1533067649.987059465 Before depth: 1533067649.989056085 After depth: 1533067649.999987811saved_depth_index: 14
 After color: 1533067650.029481919 Before depth: 1533067650.070665790 After depth: 1533067650.083509494saved_depth_index: 15
 After color: 1533067650.113966288 Before depth: 1533067650.115590415 After depth: 1533067650.126528055saved_depth_index: 16
 After color: 1533067650.156366989 Before depth: 1533067650.237325222 After depth: 1533067650.250013465saved_depth_index: 17
 After color: 1533067650.280332343 Before depth: 1533067650.404256322 After depth: 1533067650.416994490saved_depth_index: 18

    1533067649.405156137
    1533067649.438584768
    1533067649.471184334
    1533067649.538291826
    1533067649.571496466
    1533067649.604620511
    1533067649.637874297
    1533067649.671323063
    1533067649.704726902
    1533067649.738140404
    1533067649.771370780
    1533067649.804495997
    1533067649.837931434
    1533067649.938107491
    1533067650.037869503
    1533067650.071517274
    1533067650.204548131
除.bmp外，所有文件扩展名都具有相同的跳转类型。我无法使用.bmp，因为它仅为深度图像提供0或255。以下是一个示例： 另一方面，例如.png提供了如下正确的深度图像：

我的问题是如何用.png或任何其他文件扩展名修复这个时间戳问题？ 或者如何使用.bmp图像生成正确的深度图像？当我把这个.bmp图像添加到matlab中时，它说它有图像和颜色映射

谢谢你的帮助

你可以在下面找到我的源代码

#include "kinect2_recorder/kinect2_recorder.h"

namespace kinect2_recorder {
// Constructor definition
RecordData::RecordData(const ros::NodeHandle & nh1, const std::string &save_dir_, const std::string &object_name_) : global_image_counter_(1), kinect_node_(nh1), it_(kinect_node_), sub_color_(it_, "color", 5), sub_depth_(it_,"depth", 5) , sync( MySyncPolicy_( 10 ), sub_depth_, sub_color_ ) , local_image_index_(1), padding_digit_number_(6)  
{
    /* Some code*/
    // Exact time policy
  sync.registerCallback(boost::bind(&RecordData::masterkinectCallback, this, _1, _2 ) );              

void RecordData::masterkinectCallback(const sensor_msgs::ImageConstPtr& depth_msg, const sensor_msgs::ImageConstPtr& color_msg) {
    ros::Time last_time1 = ros::Time::now();  
    cv_bridge::CvImageConstPtr cv_depthptr; 
    try{
    cv_depthptr = cv_bridge::toCvShare(depth_msg, sensor_msgs::image_encodings::TYPE_16UC1);
        //cv_depthptr->image.convertTo(cv_depthptr->image, CV_16U);
        //cv::normalize(cv_depthptr->image, cv_depthptr->image, 1, 0, cv::NORM_MINMAX);
    }
    catch (cv_bridge::Exception& e2){
        ROS_ERROR("Could not convert from '%s' to '16UC1'.", e2.what());  
    return;         
    cv_bridge::CvImageConstPtr cv_colorptr;     
    try{
        cv_colorptr = cv_bridge::toCvShare(color_msg, sensor_msgs::image_encodings::BGR8);
    }
    catch (cv_bridge::Exception& e1){
        ROS_ERROR("Could not convert from '%s' to 'BGR8'.", e1.what());
    return;
    } 
    ros::Time last_time2 = ros::Time::now();
    // Save images with increasing index names
    std::ostringstream str_localindex;
    // Ignore First 10 Frames
    if (global_image_counter_ <= 10) {} //do nothing
    else {
    // Save depth images
    std::cout << " Before depth: " << ros::Time::now();
    str_localindex << std::setw(padding_digit_number_) << std::setfill('0') << local_image_index_; 
    cv::imwrite(depth_dir_ + str_localindex.str() + ".bmp",  cv_depthptr->image);
    std::cout << " After depth: " << ros::Time::now();
    std::cout << "saved_depth_index: " << local_image_index_ << std::endl;
    // Save color images    
    cv::imwrite(color_dir_ + str_localindex.str() + ".bmp",  cv_colorptr->image); 
    std::cout << " After color: " << ros::Time::now();
    // Save timestamps
    textfile_ << cv_depthptr->header.stamp << std::endl;
    local_image_index_++;
    }
    global_image_counter_++;

ros::Time last_time3 = ros::Time::now();
    std::cout << "First time difference: " << last_time2-last_time1<< std::endl;
    std::cout << "Second time difference: " << last_time3-last_time2<< std::endl;
    std::cout << "Third time difference: " << last_time3-last_time1<< std::endl;
}    
} //end of namespace
以下是.png的打印定时输出：

1533061840.176977104
1533061840.210227483
1533061840.243339200
1533061840.376478700 //see the jump
1533061840.476526118
1533061840.510053089
1533061840.576983109

      Before depth: 1533067649.437008647 After depth: 1533067649.449930403saved_depth_index: 1
 After color: 1533067649.479346684 Before depth: 1533067649.479786642 After depth: 1533067649.490577579saved_depth_index: 2
 After color: 1533067649.520330356 Before depth: 1533067649.520770286 After depth: 1533067649.531645103saved_depth_index: 3
 After color: 1533067649.561450753 Before depth: 1533067649.570491592 After depth: 1533067649.583154752saved_depth_index: 4
 After color: 1533067649.612989915 Before depth: 1533067649.614441724 After depth: 1533067649.625398696saved_depth_index: 5
 After color: 1533067649.654874365 Before depth: 1533067649.655352960 After depth: 1533067649.666162400saved_depth_index: 6
 After color: 1533067649.695561608 Before depth: 1533067649.696115115 After depth: 1533067649.708304851saved_depth_index: 7
 After color: 1533067649.736989649 Before depth: 1533067649.738013842 After depth: 1533067649.751335296saved_depth_index: 8
 After color: 1533067649.780984546 Before depth: 1533067649.781488371 After depth: 1533067649.792345625saved_depth_index: 9
 After color: 1533067649.822243383 Before depth: 1533067649.822725751 After depth: 1533067649.833530992saved_depth_index: 10
 After color: 1533067649.862818010 Before depth: 1533067649.863264236 After depth: 1533067649.875531476saved_depth_index: 11
 After color: 1533067649.904058584 Before depth: 1533067649.904558682 After depth: 1533067649.917082444saved_depth_index: 12
 After color: 1533067649.946167277 Before depth: 1533067649.946650618 After depth: 1533067649.957500888saved_depth_index: 13
 After color: 1533067649.987059465 Before depth: 1533067649.989056085 After depth: 1533067649.999987811saved_depth_index: 14
 After color: 1533067650.029481919 Before depth: 1533067650.070665790 After depth: 1533067650.083509494saved_depth_index: 15
 After color: 1533067650.113966288 Before depth: 1533067650.115590415 After depth: 1533067650.126528055saved_depth_index: 16
 After color: 1533067650.156366989 Before depth: 1533067650.237325222 After depth: 1533067650.250013465saved_depth_index: 17
 After color: 1533067650.280332343 Before depth: 1533067650.404256322 After depth: 1533067650.416994490saved_depth_index: 18

    1533067649.405156137
    1533067649.438584768
    1533067649.471184334
    1533067649.538291826
    1533067649.571496466
    1533067649.604620511
    1533067649.637874297
    1533067649.671323063
    1533067649.704726902
    1533067649.738140404
    1533067649.771370780
    1533067649.804495997
    1533067649.837931434
    1533067649.938107491
    1533067650.037869503
    1533067650.071517274
    1533067650.204548131
以下是.png对应的timestamp.txt行：

1533061840.176977104
1533061840.210227483
1533061840.243339200
1533061840.376478700 //see the jump
1533061840.476526118
1533061840.510053089
1533061840.576983109

      Before depth: 1533067649.437008647 After depth: 1533067649.449930403saved_depth_index: 1
 After color: 1533067649.479346684 Before depth: 1533067649.479786642 After depth: 1533067649.490577579saved_depth_index: 2
 After color: 1533067649.520330356 Before depth: 1533067649.520770286 After depth: 1533067649.531645103saved_depth_index: 3
 After color: 1533067649.561450753 Before depth: 1533067649.570491592 After depth: 1533067649.583154752saved_depth_index: 4
 After color: 1533067649.612989915 Before depth: 1533067649.614441724 After depth: 1533067649.625398696saved_depth_index: 5
 After color: 1533067649.654874365 Before depth: 1533067649.655352960 After depth: 1533067649.666162400saved_depth_index: 6
 After color: 1533067649.695561608 Before depth: 1533067649.696115115 After depth: 1533067649.708304851saved_depth_index: 7
 After color: 1533067649.736989649 Before depth: 1533067649.738013842 After depth: 1533067649.751335296saved_depth_index: 8
 After color: 1533067649.780984546 Before depth: 1533067649.781488371 After depth: 1533067649.792345625saved_depth_index: 9
 After color: 1533067649.822243383 Before depth: 1533067649.822725751 After depth: 1533067649.833530992saved_depth_index: 10
 After color: 1533067649.862818010 Before depth: 1533067649.863264236 After depth: 1533067649.875531476saved_depth_index: 11
 After color: 1533067649.904058584 Before depth: 1533067649.904558682 After depth: 1533067649.917082444saved_depth_index: 12
 After color: 1533067649.946167277 Before depth: 1533067649.946650618 After depth: 1533067649.957500888saved_depth_index: 13
 After color: 1533067649.987059465 Before depth: 1533067649.989056085 After depth: 1533067649.999987811saved_depth_index: 14
 After color: 1533067650.029481919 Before depth: 1533067650.070665790 After depth: 1533067650.083509494saved_depth_index: 15
 After color: 1533067650.113966288 Before depth: 1533067650.115590415 After depth: 1533067650.126528055saved_depth_index: 16
 After color: 1533067650.156366989 Before depth: 1533067650.237325222 After depth: 1533067650.250013465saved_depth_index: 17
 After color: 1533067650.280332343 Before depth: 1533067650.404256322 After depth: 1533067650.416994490saved_depth_index: 18

    1533067649.405156137
    1533067649.438584768
    1533067649.471184334
    1533067649.538291826
    1533067649.571496466
    1533067649.604620511
    1533067649.637874297
    1533067649.671323063
    1533067649.704726902
    1533067649.738140404
    1533067649.771370780
    1533067649.804495997
    1533067649.837931434
    1533067649.938107491
    1533067650.037869503
    1533067650.071517274
    1533067650.204548131
以下是.bmp的输出：

Before depth: 1533068550.007569541 After depth: 1533068550.039293417saved_depth_index: 1
After color: 1533068550.040323334 Before depth: 1533068550.041525631 After depth: 1533068550.041995796saved_depth_index: 2
 After color: 1533068550.043473161 Before depth: 1533068550.074098761 After depth: 1533068550.074672501saved_depth_index: 3
 After color: 1533068550.075766884 Before depth: 1533068550.106968347 After depth: 1533068550.107476638saved_depth_index: 4
 After color: 1533068550.108505096 Before depth: 1533068550.141064749 After depth: 1533068550.141878365saved_depth_index: 5
 After color: 1533068550.144248827 Before depth: 1533068550.174576245 After depth: 1533068550.175276900saved_depth_index: 6
 After color: 1533068550.176800271 Before depth: 1533068550.207376338 After depth: 1533068550.207835761saved_depth_index: 7
 After color: 1533068550.209161683 Before depth: 1533068550.242071242 After depth: 1533068550.242618620saved_depth_index: 8
 After color: 1533068550.243642589 Before depth: 1533068550.275326665 After depth: 1533068550.275932215saved_depth_index: 9
 After color: 1533068550.278269832 Before depth: 1533068550.307122129 After depth: 1533068550.307663711saved_depth_index: 10
 After color: 1533068550.309189049 Before depth: 1533068550.341019501 After depth: 1533068550.341644754saved_depth_index: 11
 After color: 1533068550.343060063 Before depth: 1533068550.374125217 After depth: 1533068550.374740065saved_depth_index: 12
 After color: 1533068550.376692289 Before depth: 1533068550.407086067 After depth: 1533068550.407683886saved_depth_index: 13
 After color: 1533068550.409053003 Before depth: 1533068550.441498083 After depth: 1533068550.442042331saved_depth_index: 14
 After color: 1533068550.444182590 Before depth: 1533068550.507368335 After depth: 1533068550.507842169saved_depth_index: 15
 After color: 1533068550.509398491 Before depth: 1533068550.541008375 After depth: 1533068550.541669296saved_depth_index: 16
 After color: 1533068550.543557788 Before depth: 1533068550.575678313 After depth: 1533068550.576168080saved_depth_index: 17
 After color: 1533068550.577603736 Before depth: 1533068550.608481247 After depth: 1533068550.608980560saved_depth_index: 18
 After color: 1533068550.610480415 Before depth: 1533068550.674148550 After depth: 1533068550.674846522saved_depth_index: 19
 After color: 1533068550.676181557 Before depth: 1533068550.707998985 After depth: 1533068550.708643165saved_depth_index: 20
如何生成时间戳？（您提供的列表）我订阅的rostopics已经有时间戳了。我使用ros精确时间策略来同步彩色图像和深度图像，这样我只在彩色图像和深度图像的时间戳相同的情况下保存图像？对吗？如果增加被忽略的帧的数量会发生什么？如果要确保不发生帧丢失，可以使用另一个线程并使用要保存的图像队列。当心你可能会很快地填满你的记忆。有关示例，请参见。