在不丢帧的情况下录制视频的正确方法是隔离两个任务（帧采集和帧序列化），使它们不会相互影响（特别是序列化的波动不会占用捕获帧的时间） ，这必须立即发生，以防止帧丢失）。
这可以通过将序列化（帧编码并将它们写入视频文件）委托给单独的线程，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，展示了如何做到这一点。由于我只有一台相机，而不是您拥有的那种，因此我将简单地使用网络摄像头并复制帧，但一般原则也适用于您的场景。
示例代码
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;
// ============================================================================
同步队列
第一步是定义我们的同步队列，我们将用它来与写入视频的工作线程进行通信。
我们需要的主要功能是：
- 将新图像推送到队列中
- 从队列中弹出图像，等待队列为空。
- 当我们完成后，能够取消所有待处理的弹出窗口。
We use std::queue http://en.cppreference.com/w/cpp/container/queue持有cv::Mat http://docs.opencv.org/3.1.0/d3/d63/classcv_1_1Mat.html#gsc.tab=0实例，以及std::mutex http://en.cppreference.com/w/cpp/thread/mutex提供同步。 Astd::condition_variable http://en.cppreference.com/w/cpp/thread/condition_variable用于通知消费者何时将图像插入队列（或设置取消标志），并且使用一个简单的布尔标志来通知取消。
最后我们使用空的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工作线程。
// ============================================================================
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;
}
// ============================================================================
存储工人
下一步是定义一个简单的storage_worker，它将负责从同步队列中取出帧，并将它们编码成视频文件，直到队列被取消。
我添加了简单的计时，这样我们就可以了解编码帧所花费的时间，以及简单地记录到控制台，这样我们就可以了解程序中发生的情况。
// ============================================================================
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;
}
}
// ============================================================================
加工
最后，我们可以把这一切放在一起。
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，每个图像流一个。我们通过创建两个实例来遵循这一点storage_worker，每个队列一个。为了让事情变得有趣，我为每个设置了不同的编解码器。
下一步是创建并启动工作线程，它将执行run()各的方法storage_worker。让我们的消费者做好准备后，我们就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，因此我将同一帧的副本插入到两个队列中。
NB:我需要使用clone()的方法cv::Mat进行深度复制，否则我将插入对单个缓冲区 OpenCV 的引用VideoCapture出于性能原因使用。这意味着工作线程将获取对此单个图像的引用，并且不会同步访问此共享图像缓冲区。您需要确保这种情况也不会发生在您的场景中。
一旦我们读取了适当数量的帧（您可以实现您想要的任何其他类型的停止条件），我们就取消工作队列，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;
}
// ============================================================================
控制台输出
运行这个小示例，我们在控制台中得到以下日志输出，以及磁盘上的两个视频文件。
NB:由于这实际上编码比捕获快得多，因此我在 storage_worker 中添加了一些等待以更好地显示分离。
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
可能的改进
目前，当序列化根本无法跟上相机生成新图像的速率时，无法防止队列太满。为队列大小设置一些上限，并在推送帧之前检查生产者。您需要决定如何准确地处理这种情况。
