Pan*_* Li 3 c++ video buffer opencv avi
我正在使用多光谱相机来收集数据.一个是近红外线,另一个是彩色的.不是两个摄像头,而是一个摄像头可以同时获得两种不同的图像.我可以使用一些API函数,如J_Image_OpenStream.核心代码的两部分如下所示.一个用于打开两个流(实际上它们在一个样本中,我必须使用它们,但我的意思并不太清楚)并设置两个avi文件的保存路径并开始采集.
// Open stream
retval0 = J_Image_OpenStream(m_hCam[0], 0, reinterpret_cast<J_IMG_CALLBACK_OBJECT>(this), reinterpret_cast<J_IMG_CALLBACK_FUNCTION>(&COpenCVSample1Dlg::StreamCBFunc0), &m_hThread[0], (ViewSize0.cx*ViewSize0.cy*bpp0)/8);
if (retval0 != J_ST_SUCCESS) {
AfxMessageBox(CString("Could not open stream0!"), MB_OK | MB_ICONEXCLAMATION);
return;
}
TRACE("Opening stream0 succeeded\n");
retval1 = J_Image_OpenStream(m_hCam[1], 0, reinterpret_cast<J_IMG_CALLBACK_OBJECT>(this), reinterpret_cast<J_IMG_CALLBACK_FUNCTION>(&COpenCVSample1Dlg::StreamCBFunc1), &m_hThread[1], (ViewSize1.cx*ViewSize1.cy*bpp1)/8);
if (retval1 != J_ST_SUCCESS) {
AfxMessageBox(CString("Could not open stream1!"), MB_OK | MB_ICONEXCLAMATION);
return;
}
TRACE("Opening stream1 succeeded\n");
const char *filename0 = "C:\\Users\\shenyang\\Desktop\\test0.avi";
const char *filename1 = "C:\\Users\\shenyang\\Desktop\\test1.avi";
int fps = 10; //frame per second
int codec = -1;//choose the compression method
writer0 = cvCreateVideoWriter(filename0, codec, fps, CvSize(1296,966), 1);
writer1 = cvCreateVideoWriter(filename1, codec, fps, CvSize(1296,964), 1);
// Start Acquision
retval0 = J_Camera_ExecuteCommand(m_hCam[0], NODE_NAME_ACQSTART);
retval1 = J_Camera_ExecuteCommand(m_hCam[1], NODE_NAME_ACQSTART);
// Create two OpenCV named Windows used for displaying "BGR" and "INFRARED" images
cvNamedWindow("BGR");
cvNamedWindow("INFRARED");
另一个是两个流函数,它们看起来非常相似.
void COpenCVSample1Dlg::StreamCBFunc0(J_tIMAGE_INFO * pAqImageInfo)
{
if (m_pImg0 == NULL)
{
// Create the Image:
// We assume this is a 8-bit monochrome image in this sample
m_pImg0 = cvCreateImage(cvSize(pAqImageInfo->iSizeX, pAqImageInfo->iSizeY), IPL_DEPTH_8U, 1);
}
// Copy the data from the Acquisition engine image buffer into the OpenCV Image obejct
memcpy(m_pImg0->imageData, pAqImageInfo->pImageBuffer, m_pImg0->imageSize);
// Display in the "BGR" window
cvShowImage("INFRARED", m_pImg0);
frame0 = m_pImg0;
cvWriteFrame(writer0, frame0);
}
void COpenCVSample1Dlg::StreamCBFunc1(J_tIMAGE_INFO * pAqImageInfo)
{
if (m_pImg1 == NULL)
{
// Create the Image:
// We assume this is a 8-bit monochrome image in this sample
m_pImg1 = cvCreateImage(cvSize(pAqImageInfo->iSizeX, pAqImageInfo->iSizeY), IPL_DEPTH_8U, 1);
}
// Copy the data from the Acquisition engine image buffer into the OpenCV Image obejct
memcpy(m_pImg1->imageData, pAqImageInfo->pImageBuffer, m_pImg1->imageSize);
// Display in the "BGR" window
cvShowImage("BGR", m_pImg1);
frame1 = m_pImg1;
cvWriteFrame(writer1, frame1);
}
问题是如果我不保存avi文件,如
/*writer0 = cvCreateVideoWriter(filename0, codec, fps, CvSize(1296,966), 1);
writer1 = cvCreateVideoWriter(filename1, codec, fps, CvSize(1296,964), 1);*/
//cvWriteFrame(writer0, frame0);
//cvWriteFrame(writer0, frame0);
在两个显示窗口中,类似地捕获的图片意味着它们是同步的.但是如果我必须将数据写入avi文件,由于两种图片的大小不同以及它们的大尺寸,事实证明这会影响两个相机的获取速度并且捕获的图片是非同步的.但我无法创建如此庞大的缓冲区来将整个数据存储在内存中,而I/O设备则相当慢.我该怎么办？非常非常感谢你.
一些类变量是:
public:
FACTORY_HANDLE m_hFactory; // Factory Handle
CAM_HANDLE m_hCam[MAX_CAMERAS]; // Camera Handles
THRD_HANDLE m_hThread[MAX_CAMERAS]; // Stream handles
char m_sCameraId[MAX_CAMERAS][J_CAMERA_ID_SIZE]; // Camera IDs
IplImage *m_pImg0 = NULL; // OpenCV Images
IplImage *m_pImg1 = NULL; // OpenCV Images
CvVideoWriter* writer0;
IplImage *frame0;
CvVideoWriter* writer1;
IplImage *frame1;
BOOL OpenFactoryAndCamera();
void CloseFactoryAndCamera();
void StreamCBFunc0(J_tIMAGE_INFO * pAqImageInfo);
void StreamCBFunc1(J_tIMAGE_INFO * pAqImageInfo);
void InitializeControls();
void EnableControls(BOOL bIsCameraReady, BOOL bIsImageAcquiring);
在没有帧丢失的情况下记录视频的正确方法是隔离两个任务(帧获取和帧序列化),使它们不会相互影响(特别是这样,序列化的波动不会消耗时间来捕获帧,必须没有延迟发生,以防止帧丢失).
这可以通过委派序列化(将帧编码并将它们写入视频文件)来分离线程,并使用某种同步队列将数据提供给工作线程来实现.
以下是一个简单的示例,说明如何完成此操作.由于我只有一台摄像机,而不是你所拥有的那种,我只需使用网络摄像头并复制框架,但一般原则也适用于你的场景.
一开始我们有一些包括:
#include <opencv2/opencv.hpp>
#include <chrono>
#include <condition_variable>
#include <iostream>
#include <mutex>
#include <queue>
#include <thread>
// ============================================================================
using std::chrono::high_resolution_clock;
using std::chrono::duration_cast;
using std::chrono::microseconds;
// ============================================================================
第一步是定义我们的同步队列,我们将使用它来与编写视频的工作线程进行通信.
我们需要的主要功能是能够:
我们std::queue用来保存cv::Mat实例,并std::mutex提供同步.A std::condition_variable用于在图像插入队列(或取消标志集)时通知消费者,并使用简单的布尔标志来通知取消.
最后,我们使用empty struct cancelled作为抛出的异常pop(),因此我们可以通过取消队列来干净地终止worker.
// ============================================================================
class frame_queue
{
public:
struct cancelled {};
public:
frame_queue();
void push(cv::Mat const& image);
cv::Mat pop();
void cancel();
private:
std::queue<cv::Mat> queue_;
std::mutex mutex_;
std::condition_variable cond_;
bool cancelled_;
};
// ----------------------------------------------------------------------------
frame_queue::frame_queue()
: cancelled_(false)
{
}
// ----------------------------------------------------------------------------
void frame_queue::cancel()
{
std::unique_lock<std::mutex> mlock(mutex_);
cancelled_ = true;
cond_.notify_all();
}
// ----------------------------------------------------------------------------
void frame_queue::push(cv::Mat const& image)
{
std::unique_lock<std::mutex> mlock(mutex_);
queue_.push(image);
cond_.notify_one();
}
// ----------------------------------------------------------------------------
cv::Mat frame_queue::pop()
{
std::unique_lock<std::mutex> mlock(mutex_);
while (queue_.empty()) {
if (cancelled_) {
throw cancelled();
}
cond_.wait(mlock);
if (cancelled_) {
throw cancelled();
}
}
cv::Mat image(queue_.front());
queue_.pop();
return image;
}
// ============================================================================
下一步是定义一个简单的storage_worker,它将负责从同步队列中获取帧,并将它们编码为视频文件,直到队列被取消.
我添加了简单的时序,因此我们对编码帧花费了多少时间以及简单的登录控制台有了一些了解,因此我们对程序中发生的事情有了一些了解.
// ============================================================================
class storage_worker
{
public:
storage_worker(frame_queue& queue
, int32_t id
, std::string const& file_name
, int32_t fourcc
, double fps
, cv::Size frame_size
, bool is_color = true);
void run();
double total_time_ms() const { return total_time_ / 1000.0; }
private:
frame_queue& queue_;
int32_t id_;
std::string file_name_;
int32_t fourcc_;
double fps_;
cv::Size frame_size_;
bool is_color_;
double total_time_;
};
// ----------------------------------------------------------------------------
storage_worker::storage_worker(frame_queue& queue
, int32_t id
, std::string const& file_name
, int32_t fourcc
, double fps
, cv::Size frame_size
, bool is_color)
: queue_(queue)
, id_(id)
, file_name_(file_name)
, fourcc_(fourcc)
, fps_(fps)
, frame_size_(frame_size)
, is_color_(is_color)
, total_time_(0.0)
{
}
// ----------------------------------------------------------------------------
void storage_worker::run()
{
cv::VideoWriter writer(file_name_, fourcc_, fps_, frame_size_, is_color_);
try {
int32_t frame_count(0);
for (;;) {
cv::Mat image(queue_.pop());
if (!image.empty()) {
high_resolution_clock::time_point t1(high_resolution_clock::now());
++frame_count;
writer.write(image);
high_resolution_clock::time_point t2(high_resolution_clock::now());
double dt_us(static_cast<double>(duration_cast<microseconds>(t2 - t1).count()));
total_time_ += dt_us;
std::cout << "Worker " << id_ << " stored image #" << frame_count
<< " in " << (dt_us / 1000.0) << " ms" << std::endl;
}
}
} catch (frame_queue::cancelled& /*e*/) {
// Nothing more to process, we're done
std::cout << "Queue " << id_ << " cancelled, worker finished." << std::endl;
}
}
// ============================================================================
最后,我们可以把这些放在一起.
我们首先初始化和配置我们的视频源.然后我们创建两个frame_queue实例,每个实例用于一个图像流.我们通过创建两个实例来实现storage_worker这一点,每个实例对应一个队列.为了让事情变得有趣,我为每个设置了不同的编解码器.
下一步是创建和启动工作线程,它将执行run()每个线程的方法storage_worker.让我们的消费者做好准备,我们可以开始从相机捕获帧,并将它们提供给frame_queue实例.如上所述,我只有一个源,所以我将同一帧的副本插入两个队列.
注意:我需要使用clone()方法cv::Mat来进行深度复制,否则我会插入对OpenCV VideoCapture使用的单个缓冲区的引用,这是出于性能原因.这意味着工作线程将获得对此单个映像的引用,并且对于访问此共享映像缓冲区将没有同步.您需要确保在您的方案中也不会发生这种情况.
一旦我们读取了适当数量的帧(您可以实现任何其他类型的停止条件),我们取消工作队列,并等待工作线程完成.
最后,我们写了一些关于不同任务所需时间的统计数据.
// ============================================================================
int main()
{
// The video source -- for me this is a webcam, you use your specific camera API instead
// I only have one camera, so I will just duplicate the frames to simulate your scenario
cv::VideoCapture capture(0);
// Let's make it decent sized, since my camera defaults to 640x480
capture.set(CV_CAP_PROP_FRAME_WIDTH, 1920);
capture.set(CV_CAP_PROP_FRAME_HEIGHT, 1080);
capture.set(CV_CAP_PROP_FPS, 20.0);
// And fetch the actual values, so we can create our video correctly
int32_t frame_width(static_cast<int32_t>(capture.get(CV_CAP_PROP_FRAME_WIDTH)));
int32_t frame_height(static_cast<int32_t>(capture.get(CV_CAP_PROP_FRAME_HEIGHT)));
double video_fps(std::max(10.0, capture.get(CV_CAP_PROP_FPS))); // Some default in case it's 0
std::cout << "Capturing images (" << frame_width << "x" << frame_height
<< ") at " << video_fps << " FPS." << std::endl;
// The synchronized queues, one per video source/storage worker pair
std::vector<frame_queue> queue(2);
// Let's create our storage workers -- let's have two, to simulate your scenario
// and to keep it interesting, have each one write a different format
std::vector <storage_worker> storage;
storage.emplace_back(std::ref(queue[0]), 0
, std::string("foo_0.avi")
, CV_FOURCC('I', 'Y', 'U', 'V')
, video_fps
, cv::Size(frame_width, frame_height)
, true);
storage.emplace_back(std::ref(queue[1]), 1
, std::string("foo_1.avi")
, CV_FOURCC('D', 'I', 'V', 'X')
, video_fps
, cv::Size(frame_width, frame_height)
, true);
// And start the worker threads for each storage worker
std::vector<std::thread> storage_thread;
for (auto& s : storage) {
storage_thread.emplace_back(&storage_worker::run, &s);
}
// Now the main capture loop
int32_t const MAX_FRAME_COUNT(10);
double total_read_time(0.0);
int32_t frame_count(0);
for (; frame_count < MAX_FRAME_COUNT; ++frame_count) {
high_resolution_clock::time_point t1(high_resolution_clock::now());
// Try to read a frame
cv::Mat image;
if (!capture.read(image)) {
std::cerr << "Failed to capture image.\n";
break;
}
// Insert a copy into all queues
for (auto& q : queue) {
q.push(image.clone());
}
high_resolution_clock::time_point t2(high_resolution_clock::now());
double dt_us(static_cast<double>(duration_cast<microseconds>(t2 - t1).count()));
total_read_time += dt_us;
std::cout << "Captured image #" << frame_count << " in "
<< (dt_us / 1000.0) << " ms" << std::endl;
}
// We're done reading, cancel all the queues
for (auto& q : queue) {
q.cancel();
}
// And join all the worker threads, waiting for them to finish
for (auto& st : storage_thread) {
st.join();
}
if (frame_count == 0) {
std::cerr << "No frames captured.\n";
return -1;
}
// Report the timings
total_read_time /= 1000.0;
double total_write_time_a(storage[0].total_time_ms());
double total_write_time_b(storage[1].total_time_ms());
std::cout << "Completed processing " << frame_count << " images:\n"
<< " average capture time = " << (total_read_time / frame_count) << " ms\n"
<< " average write time A = " << (total_write_time_a / frame_count) << " ms\n"
<< " average write time B = " << (total_write_time_b / frame_count) << " ms\n";
return 0;
}
// ============================================================================
运行这个小样本,我们在控制台中获得以下日志输出,以及磁盘上的两个视频文件.
注意:由于这实际上比捕获编码速度快得多,我在storage_worker中添加了一些等待以更好地显示分隔.
Capturing images (1920x1080) at 20 FPS.
Captured image #0 in 111.009 ms
Captured image #1 in 67.066 ms
Worker 0 stored image #1 in 94.087 ms
Captured image #2 in 62.059 ms
Worker 1 stored image #1 in 193.186 ms
Captured image #3 in 60.059 ms
Worker 0 stored image #2 in 100.097 ms
Captured image #4 in 78.075 ms
Worker 0 stored image #3 in 87.085 ms
Captured image #5 in 62.061 ms
Worker 0 stored image #4 in 95.092 ms
Worker 1 stored image #2 in 193.187 ms
Captured image #6 in 75.074 ms
Worker 0 stored image #5 in 95.093 ms
Captured image #7 in 63.061 ms
Captured image #8 in 64.061 ms
Worker 0 stored image #6 in 102.098 ms
Worker 1 stored image #3 in 201.195 ms
Captured image #9 in 76.074 ms
Worker 0 stored image #7 in 90.089 ms
Worker 0 stored image #8 in 91.087 ms
Worker 1 stored image #4 in 185.18 ms
Worker 0 stored image #9 in 82.08 ms
Worker 0 stored image #10 in 94.092 ms
Queue 0 cancelled, worker finished.
Worker 1 stored image #5 in 179.174 ms
Worker 1 stored image #6 in 106.102 ms
Worker 1 stored image #7 in 105.104 ms
Worker 1 stored image #8 in 103.101 ms
Worker 1 stored image #9 in 104.102 ms
Worker 1 stored image #10 in 104.1 ms
Queue 1 cancelled, worker finished.
Completed processing 10 images:
average capture time = 71.8599 ms
average write time A = 93.09 ms
average write time B = 147.443 ms
average write time B = 176.673 ms
目前,当序列化无法跟上相机生成新图像的速率时,无法防止队列过满.设置队列大小的一些上限,并在推送帧之前检查生成器.您需要确定您希望如何处理这种情况.
| 归档时间： |
|
| 查看次数： |
735 次 |
| 最近记录： |