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Stereo master soundtrack
17 octobre 2011, par
Mis à jour : Octobre 2011
Langue : English
Type : Audio
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RTMP server with OpenCV (python)
12 février, par OvernoutI'm trying to process an RTMP stream in Python, using OpenCV2 but I'm not able to get OpenCV to capture it (i.e. act as RTMP server).


I can run FFmpeg/FFplay from the command line and receive the stream successfully.
What could cause OpenCV to fail opening the stream in listening mode ?


Here is my code :


import cv2

cap = cv2.VideoCapture("rtmp://0.0.0.0:8000/live", cv2.CAP_FFMPEG)

if not cap.isOpened():
 print("Cannot open video source")
 exit()


And the output :


[tcp @ 00000192c490d640] Connection to tcp://0.0.0.0:8000 failed: Error number -138 occurred
[rtmp @ 00000192c490d580] Cannot open connection tcp://0.0.0.0:8000 
Cannot open video source


edit2 : Output with debug logging turned on :


output of the python script with debug logging on:
[DEBUG:0@0.017] global videoio_registry.cpp:218 cv::`anonymous-namespace'::VideoBackendRegistry::VideoBackendRegistry VIDEOIO: Builtin backends(9): FFMPEG(1000); GSTREAMER(990); INTEL_MFX(980); MSMF(970); DSHOW(960); CV_IMAGES(950); CV_MJPEG(940); UEYE(930); OBSENSOR(920)
[DEBUG:0@0.026] global videoio_registry.cpp:242 cv::`anonymous-namespace'::VideoBackendRegistry::VideoBackendRegistry VIDEOIO: Available backends(9): FFMPEG(1000); GSTREAMER(990); INTEL_MFX(980); MSMF(970); DSHOW(960); CV_IMAGES(950); CV_MJPEG(940); UEYE(930); OBSENSOR(920)
[ INFO:0@0.031] global videoio_registry.cpp:244 cv::`anonymous-namespace'::VideoBackendRegistry::VideoBackendRegistry VIDEOIO: Enabled backends(9, sorted by priority): FFMPEG(1000); GSTREAMER(990); INTEL_MFX(980); MSMF(970); DSHOW(960); CV_IMAGES(950); CV_MJPEG(940); UEYE(930); OBSENSOR(920)
[ WARN:0@0.037] global cap.cpp:132 cv::VideoCapture::open VIDEOIO(FFMPEG): trying capture filename='rtmp://192.168.254.101:8000/live' ...
[ INFO:0@0.040] global backend_plugin.cpp:383 cv::impl::getPluginCandidates Found 2 plugin(s) for FFMPEG
[ INFO:0@0.043] global plugin_loader.impl.hpp:67 cv::plugin::impl::DynamicLib::libraryLoad load C:\Users\me\src\opencv\.venv\Lib\site-packages\cv2\opencv_videoio_ffmpeg490_64.dll => OK
[ INFO:0@0.047] global backend_plugin.cpp:50 cv::impl::PluginBackend::initCaptureAPI Found entry: 'opencv_videoio_capture_plugin_init_v1'
[ INFO:0@0.049] global backend_plugin.cpp:169 cv::impl::PluginBackend::checkCompatibility Video I/O: initialized 'FFmpeg OpenCV Video I/O Capture plugin': built with OpenCV 4.9 (ABI/API = 1/1), current OpenCV version is '4.9.0' (ABI/API = 1/1)
[ INFO:0@0.055] global backend_plugin.cpp:69 cv::impl::PluginBackend::initCaptureAPI Video I/O: plugin is ready to use 'FFmpeg OpenCV Video I/O Capture plugin'
[ INFO:0@0.058] global backend_plugin.cpp:84 cv::impl::PluginBackend::initWriterAPI Found entry: 'opencv_videoio_writer_plugin_init_v1'
[ INFO:0@0.061] global backend_plugin.cpp:169 cv::impl::PluginBackend::checkCompatibility Video I/O: initialized 'FFmpeg OpenCV Video I/O Writer plugin': built with OpenCV 4.9 (ABI/API = 1/1), current OpenCV version is '4.9.0' (ABI/API = 1/1)
[ INFO:0@0.065] global backend_plugin.cpp:103 cv::impl::PluginBackend::initWriterAPI Video I/O: plugin is ready to use 'FFmpeg OpenCV Video I/O Writer plugin'
[tcp @ 00000266b2f0d0c0] Connection to tcp://192.168.254.101:8000 failed: Error number -138 occurred
[rtmp @ 00000266b2f0cfc0] Cannot open connection tcp://192.168.254.101:8000
[ WARN:0@5.630] global cap.cpp:155 cv::VideoCapture::open VIDEOIO(FFMPEG): can't create capture
[DEBUG:0@5.632] global cap.cpp:225 cv::VideoCapture::open VIDEOIO: choosen backend does not work or wrong. Please make sure that your computer support chosen backend and OpenCV built with right flags.
Cannot open video source
[ INFO:1@5.661] global plugin_loader.impl.hpp:74 cv::plugin::impl::DynamicLib::libraryRelease unload C:\Users\me\src\opencv\.venv\Lib\site-packages\cv2\opencv_videoio_ffmpeg490_64.dll


Here is the output of cv2.getBuildInformation()


General configuration for OpenCV 4.9.0 =====================================
 Version control: 4.9.0

 Platform:
 Timestamp: 2023-12-31T11:21:12Z
 Host: Windows 10.0.17763 AMD64
 CMake: 3.24.2
 CMake generator: Visual Studio 14 2015
 CMake build tool: MSBuild.exe
 MSVC: 1900
 Configuration: Debug Release

 CPU/HW features:
 Baseline: SSE SSE2 SSE3
 requested: SSE3
 Dispatched code generation: SSE4_1 SSE4_2 FP16 AVX AVX2
 requested: SSE4_1 SSE4_2 AVX FP16 AVX2 AVX512_SKX
 SSE4_1 (16 files): + SSSE3 SSE4_1
 SSE4_2 (1 files): + SSSE3 SSE4_1 POPCNT SSE4_2
 FP16 (0 files): + SSSE3 SSE4_1 POPCNT SSE4_2 FP16 AVX
 AVX (8 files): + SSSE3 SSE4_1 POPCNT SSE4_2 AVX
 AVX2 (36 files): + SSSE3 SSE4_1 POPCNT SSE4_2 FP16 FMA3 AVX AVX2

 C/C++:
 Built as dynamic libs?: NO
 C++ standard: 11
 C++ Compiler: C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/x86_amd64/cl.exe (ver 19.0.24247.2)
 C++ flags (Release): /DWIN32 /D_WINDOWS /W4 /GR /D _CRT_SECURE_NO_DEPRECATE /D _CRT_NONSTDC_NO_DEPRECATE /D _SCL_SECURE_NO_WARNINGS /Gy /bigobj /Oi /fp:precise /EHa /wd4127 /wd4251 /wd4324 /wd4275 /wd4512 /wd4589 /wd4819 /MP /O2 /Ob2 /DNDEBUG 
 C++ flags (Debug): /DWIN32 /D_WINDOWS /W4 /GR /D _CRT_SECURE_NO_DEPRECATE /D _CRT_NONSTDC_NO_DEPRECATE /D _SCL_SECURE_NO_WARNINGS /Gy /bigobj /Oi /fp:precise /EHa /wd4127 /wd4251 /wd4324 /wd4275 /wd4512 /wd4589 /wd4819 /MP /Zi /Ob0 /Od /RTC1 
 C Compiler: C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/x86_amd64/cl.exe
 C flags (Release): /DWIN32 /D_WINDOWS /W3 /D _CRT_SECURE_NO_DEPRECATE /D _CRT_NONSTDC_NO_DEPRECATE /D _SCL_SECURE_NO_WARNINGS /Gy /bigobj /Oi /fp:precise /MP /O2 /Ob2 /DNDEBUG 
 C flags (Debug): /DWIN32 /D_WINDOWS /W3 /D _CRT_SECURE_NO_DEPRECATE /D _CRT_NONSTDC_NO_DEPRECATE /D _SCL_SECURE_NO_WARNINGS /Gy /bigobj /Oi /fp:precise /MP /Zi /Ob0 /Od /RTC1 
 Linker flags (Release): /machine:x64 /NODEFAULTLIB:atlthunk.lib /INCREMENTAL:NO /NODEFAULTLIB:libcmtd.lib /NODEFAULTLIB:libcpmtd.lib /NODEFAULTLIB:msvcrtd.lib
 Linker flags (Debug): /machine:x64 /NODEFAULTLIB:atlthunk.lib /debug /INCREMENTAL /NODEFAULTLIB:libcmt.lib /NODEFAULTLIB:libcpmt.lib /NODEFAULTLIB:msvcrt.lib
 ccache: NO
 Precompiled headers: YES
 Extra dependencies: wsock32 comctl32 gdi32 ole32 setupapi ws2_32
 3rdparty dependencies: libprotobuf ade ittnotify libjpeg-turbo libwebp libpng libtiff libopenjp2 IlmImf zlib ippiw ippicv

 OpenCV modules:
 To be built: calib3d core dnn features2d flann gapi highgui imgcodecs imgproc ml objdetect photo python3 stitching video videoio
 Disabled: java world
 Disabled by dependency: -
 Unavailable: python2 ts
 Applications: -
 Documentation: NO
 Non-free algorithms: NO

 Windows RT support: NO

 GUI: WIN32UI
 Win32 UI: YES
 VTK support: NO

 Media I/O: 
 ZLib: build (ver 1.3)
 JPEG: build-libjpeg-turbo (ver 2.1.3-62)
 SIMD Support Request: YES
 SIMD Support: NO
 WEBP: build (ver encoder: 0x020f)
 PNG: build (ver 1.6.37)
 TIFF: build (ver 42 - 4.2.0)
 JPEG 2000: build (ver 2.5.0)
 OpenEXR: build (ver 2.3.0)
 HDR: YES
 SUNRASTER: YES
 PXM: YES
 PFM: YES

 Video I/O:
 DC1394: NO
 FFMPEG: YES (prebuilt binaries)
 avcodec: YES (58.134.100)
 avformat: YES (58.76.100)
 avutil: YES (56.70.100)
 swscale: YES (5.9.100)
 avresample: YES (4.0.0)
 GStreamer: NO
 DirectShow: YES
 Media Foundation: YES
 DXVA: YES

 Parallel framework: Concurrency

 Trace: YES (with Intel ITT)

 Other third-party libraries:
 Intel IPP: 2021.11.0 [2021.11.0]
 at: D:/a/opencv-python/opencv-python/_skbuild/win-amd64-3.7/cmake-build/3rdparty/ippicv/ippicv_win/icv
 Intel IPP IW: sources (2021.11.0)
 at: D:/a/opencv-python/opencv-python/_skbuild/win-amd64-3.7/cmake-build/3rdparty/ippicv/ippicv_win/iw
 Lapack: NO
 Eigen: NO
 Custom HAL: NO
 Protobuf: build (3.19.1)
 Flatbuffers: builtin/3rdparty (23.5.9)

 OpenCL: YES (NVD3D11)
 Include path: D:/a/opencv-python/opencv-python/opencv/3rdparty/include/opencl/1.2
 Link libraries: Dynamic load

 Python 3:
 Interpreter: C:/hostedtoolcache/windows/Python/3.7.9/x64/python.exe (ver 3.7.9)
 Libraries: C:/hostedtoolcache/windows/Python/3.7.9/x64/libs/python37.lib (ver 3.7.9)
 numpy: C:/hostedtoolcache/windows/Python/3.7.9/x64/lib/site-packages/numpy/core/include (ver 1.17.0)
 install path: python/cv2/python-3

 Python (for build): C:\hostedtoolcache\windows\Python\3.7.9\x64\python.exe

 Java: 
 ant: NO
 Java: YES (ver 1.8.0.392)
 JNI: C:/hostedtoolcache/windows/Java_Temurin-Hotspot_jdk/8.0.392-8/x64/include C:/hostedtoolcache/windows/Java_Temurin-Hotspot_jdk/8.0.392-8/x64/include/win32 C:/hostedtoolcache/windows/Java_Temurin-Hotspot_jdk/8.0.392-8/x64/include
 Java wrappers: NO
 Java tests: NO

 Install to: D:/a/opencv-python/opencv-python/_skbuild/win-amd64-3.7/cmake-install
-----------------------------------------------------------------


edit : Receiving the stream with ffplay from command line :


>ffplay.exe -i "rtmp://0.0.0.0:8000/live" -listen 1 -f flv
ffplay version 2024-02-04-git-7375a6ca7b-full_build-www.gyan.dev Copyright (c) 2003-2024 the FFmpeg developers
 built with gcc 12.2.0 (Rev10, Built by MSYS2 project)
 configuration: --enable-gpl --enable-version3 --enable-static --pkg-config=pkgconf --disable-w32threads --disable-autodetect --enable-fontconfig --enable-iconv --enable-gnutls --enable-libxml2 --enable-gmp --enable-bzlib --enable-lzma --enable-libsnappy --enable-zlib --enable-librist --enable-libsrt --enable-libssh --enable-libzmq --enable-avisynth --enable-libbluray --enable-libcaca --enable-sdl2 --enable-libaribb24 --enable-libaribcaption --enable-libdav1d --enable-libdavs2 --enable-libuavs3d --enable-libzvbi --enable-librav1e --enable-libsvtav1 --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxavs2 --enable-libxvid --enable-libaom --enable-libjxl --enable-libopenjpeg --enable-libvpx --enable-mediafoundation --enable-libass --enable-frei0r --enable-libfreetype --enable-libfribidi --enable-libharfbuzz --enable-liblensfun --enable-libvidstab --enable-libvmaf --enable-libzimg --enable-amf --enable-cuda-llvm --enable-cuvid --enable-ffnvcodec --enable-nvdec --enable-nvenc --enable-dxva2 --enable-d3d11va --enable-libvpl --enable-libshaderc --enable-vulkan --enable-libplacebo --enable-opencl --enable-libcdio --enable-libgme --enable-libmodplug --enable-libopenmpt --enable-libopencore-amrwb --enable-libmp3lame --enable-libshine --enable-libtheora --enable-libtwolame --enable-libvo-amrwbenc --enable-libcodec2 --enable-libilbc --enable-libgsm --enable-libopencore-amrnb --enable-libopus --enable-libspeex --enable-libvorbis --enable-ladspa --enable-libbs2b --enable-libflite --enable-libmysofa --enable-librubberband --enable-libsoxr --enable-chromaprint
 libavutil 58. 36.101 / 58. 36.101
 libavcodec 60. 38.100 / 60. 38.100
 libavformat 60. 20.100 / 60. 20.100
 libavdevice 60. 4.100 / 60. 4.100
 libavfilter 9. 17.100 / 9. 17.100
 libswscale 7. 6.100 / 7. 6.100
 libswresample 4. 13.100 / 4. 13.100
 libpostproc 57. 4.100 / 57. 4.100
[rtmp @ 0000018a564ed340] Unexpected stream , expecting livef=0/0
 Last message repeated 1 times
Input #0, flv, from 'rtmp://0.0.0.0:8000/live':KB sq= 0B f=0/0
 Metadata:
 fileSize : 0
 audiochannels : 2
 2.1 : false
 3.1 : false
 4.0 : false
 4.1 : false
 5.1 : false
 7.1 : false
 encoder : obs-output module (libobs version 30.0.2)
 Duration: 00:00:00.00, start: 0.000000, bitrate: N/A
 Stream #0:0: Audio: aac (LC), 48000 Hz, stereo, fltp, 163 kb/s
 Stream #0:1: Video: h264 (Constrained Baseline), yuv420p(tv, bt709, progressive), 1280x720 [SAR 1:1 DAR 16:9], 2560 kb/s, 30 fps, 30 tbr, 1k tbn
 7.54 A-V: -0.024 fd= 18 aq= 24KB vq= 498KB sq= 0B f=0/0


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What is White Label Analytics ? Everything You Need to Know
6 février, par ErinReports are a core part of a marketing agency’s offering. It’s how you build trust with clients by highlighting your efforts and demonstrating your results.
But all too often, those reports deliver a jarring and incohesive experience. The culprit ? The logos, colours and names of third-party brands your agency uses to deliver work and create the reports.
Luckily, there’s a way to make sure your reports elevate your agency’s stature ; not undermine it.
By white labelling your tools, you can deliver a clear and cohesive brand experience — one that strengthens the client relationship rather than diminishing it.
In this article, we explain what white label analytics tools are, why it’s important to white label your analytics solution and how you can do it using Matomo.
What is white label analytics ?
White labelling is the process of redesigning a product or service using your company’s brand. The term comes from the act of putting a white label on a product that covers the original branding and allows the reseller to personalise the product.
White label analytics, then, is a way to customise your analytics software with your agency’s logo and colours. When you white label your analytics, you ensure your reports, dashboards and interface provide a consistent and familiar user experience.
The alternative is to provide your clients with an analytics report containing the logo and branding of your analytics software provider — whether that’s Google Analytics, Matomo, or another tool.
For some clients, it can create a confusing experience that takes attention away from your agency’s results.
Why white label analytics is important
There are plenty of reasons to white label your analytics tool, from improving your client’s experience to generating additional revenue. Here are four of the most important benefits to know :
Improve the client experience
You want your clients to have a seamless user experience with your agency’s brand, whether they visit your website, log into their client portal, or read one of your reports.
By white labelling your analytics platform, you can give your clients a visually appealing experience that stays in line with the rest of your branding and doesn’t leave them confused about who they are interacting with or which company is providing the service they pay for.
This is especially important if your agency uses other third-party tools like a client portal or productivity platform that also allows for custom branding.
Strengthen client relationships
When you use white labelling to remove solution providers’ logos, you ensure your brand gets all of the credit for the hard work you’ve been doing. This can strengthen the agency-client relationship and reaffirm the importance of your agency.
But, white labelling allows you to tell a better story through your reports and increases the perceived value you offer. There are no other brands, logos, or names to confuse the narrative or detract from your key points — or to stop the client from understanding just how much value you provide.
Save time and increase productivity
White labelling your analytics platform can save your team a significant amount of time when creating client reports.
There’s no need to carefully screenshot graphs to add them to your own branded report. You can simply email clients a report using your white labelled analytics platform, assuring them of a seamlessly branded experience.
The upshot is that your team can spend more time on billable work, improving the value they deliver to existing clients or opening up capacity to take on even more work.
Increase monetisation opportunities
Whether you are an agency or consultant, white labelling an analytics solution gives you the opportunity to package and sell analytics as part of your own services. This can open up new revenue streams, help you to diversify your income, and reach a wider audience.
The beauty of a white label offering is that there is no allusion to the company providing the underlying service.
The most important elements of an analytics platform to white label
A white label analytics solution should offer a broad range of customisation options that range from surface-level branding to functional elements like tracking codes.
Below we take a look at the top components you should be able to customise with your chosen platform.
Logo and Favicon
The logo is the first thing clients will see when they open up their analytics platform or look at your reports. It should make your services instantly recognisable, which is why it’s so jarring when clients read a report with another company’s brand slapped on every chart.
This should be the very first thing you change since it will be on almost every page and report your client views. Don’t stop there, however. If you send clients web-based reports, you’ll also want to change the platform’s favicon — the small logo you see next to your website in a browser.
Customising both your logo and favicon is easy with Matomo.
Just head to Administration, then General Settings and click Use a custom Logo under Brand settings.
Upload your brand, click Save, and it will automatically populate your brand in place of the Matomo logo across the platform, just like in the image above.
Brand name
Most analytics platforms will mention their brand names repeatedly across the site, so it’s important to change these, too.
Otherwise, you risk clients reading your analytics reports in detail or playing around with your platform’s settings and getting confused when another seemingly unrelated name keeps popping up.
Again, this is easily done with Matomo’s White Label plugin.
Head to Administration, then General Settings. Scroll to the bottom of the page to find WhiteLabel settings.
Enter your brand or product name in the first box and click Save.
Just like your logo, this will replace every instance of Matomo’s brand name with your own.
Brand colours
Changing your analytics platform’s colours to match your own is almost as important as swapping out the logo.
Failure to do so could mean the charts and graphs you add to your client reports could cause confusion.
You can also use Matomo’s WhiteLabel settings to change the platform’s background and font colours.
Just enter a new header background and font colour using hexadecimal values.
This change will also apply to automated email reports.
Custom tracking
Tracking requests and links are an overlooked element of analytics when it comes to white labelling. Most people wouldn’t think twice about them, but they are an easy way for someone in the know to identify which platform you are using.
With Matomo’s White Label plugin, it’s possible to customise every request Matomo makes to your clients’ websites.
If left unbranded, tracking requests contain the following references : matomo.js and matomo.php.
By clicking the Whitelabel tracking endpoint box on the WhiteLabel settings page, those references will be replaced with js/tracker.js and js/tracker.php
You’ll need to update your tracking code to reflect these changes, otherwise, requests will still contain Matomo branding.
Try Matomo for Free
Get the web insights you need, without compromising data accuracy.
Links
Finally, you’ll want to remove any links to any additional content offered by the analytics company. These are usually included to improve the user experience, but they are best removed if you are letting clients access your platform.
With Matomo, you can remove all links by clicking the relevant box in WhiteLabel settings.
You can also use the Show Marketplace only to Super Users checkbox to limit the visibility of Matomo’s Marketplace to everyone bar Super Users.
Can you white label Google Analytics ?
In a word : no.
Google Analytics might be the most popular analytics platform, but it comes up short if you want to customise its appearance.
This can be a particular problem for agencies that need to stand out from competitors offering the same generic reports. You can add more context, detail and graphs to your analytics reports, of course. But you’ll never be able to create completely custom, brand-cohesive reports using Google Analytics.
3 analytics platforms you can white label
While you can’t white label Google Analytics, there are several web analytics providers that do offer a white labelling service. Here are three of the best :
Matomo
As you’ve already seen, Matomo is the ideal web analytics platform if you want to let your own brand shine through. Matomo lets you personalise the entire dashboard and all of your reports. That includes :
- Adding your brand logo and favicon
- Changing the font and background colours
- Removing third-party links
- Tracking using custom URLs
- Develop your own custom theme
Matomo offers a 21-day free trial (no credit card required). If you want to get remove the Matomo branding, you need the White Label plugin, which starts at just $179 per year after a free trial.
Try Matomo for Free
Get the web insights you need, without compromising data accuracy.
Clicky
Clicky is a simple, privacy-focused web analytics platform with a white label offering. Like Matomo, you can add your logo and change the platform’s colours.
Clicky offers a seven-day free trial and charges a $99 setup fee, with prices starting from $49 and rising to $399.
Plausible
Plausible is another privacy-focused Google Analytics alternative that offers white labelling. The difference here is that it’s pretty complex to set up.
Rather than customising Plausible’s platform, for instance, you need to embed its dashboard into your own user interface. If you want to create your own custom dashboard, you’ll need to use an API.
Plausible offers a 30-day free trial.
Leverage white label analytics today with Matomo
Don’t put up with confusing unbranded clients a moment longer. White label your analytics platform so the next time you sit down to share insights with your clients, they’ll only see one brand : yours.
Matomo makes it quick and easy to customise the look of your analytics platform and all of the reports you generate. If you already use Matomo, try the White Label plugin free for 30 days.
If not, try Matomo with a free 21-day trial. No credit card required.
Try Matomo for Free
21 day free trial. No credit card required.
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Why does every encoded frame's size increase after I had use to set one frame to be key in intel qsv of ffmpeg
22 avril 2021, par TONYI used intel's qsv to encode h264 video in ffmpeg. My av codec context settings is like as below :


m_ctx->width = m_width;
 m_ctx->height = m_height;
 m_ctx->time_base = { 1, (int)fps };
 m_ctx->qmin = 10;
 m_ctx->qmax = 35;
 m_ctx->gop_size = 3000;
 m_ctx->max_b_frames = 0;
 m_ctx->has_b_frames = false;
 m_ctx->refs = 2;
 m_ctx->slices = 0;
 m_ctx->codec_id = m_encoder->id;
 m_ctx->codec_type = AVMEDIA_TYPE_VIDEO;
 m_ctx->pix_fmt = m_h264InputFormat;
 m_ctx->compression_level = 4;
 m_ctx->flags &= ~AV_CODEC_FLAG_CLOSED_GOP;
 AVDictionary *param = nullptr;
 av_dict_set(&param, "idr_interval", "0", 0);
 av_dict_set(&param, "async_depth", "1", 0);
 av_dict_set(&param, "forced_idr", "1", 0);


and in the encoding, I set the AVFrame to be AV_PICTURE_TYPE_I when key frame is needed :


if(key_frame){
 encodeFrame->pict_type = AV_PICTURE_TYPE_I;
 }else{
 encodeFrame->pict_type = AV_PICTURE_TYPE_NONE;
 }
 avcodec_send_frame(m_ctx, encodeFrame);
 avcodec_receive_packet(m_ctx, m_packet);
 std::cerr<<"packet size is "<size<<",is key frame "<code>

The strange phenomenon is that if I had set one frame to AV_PICTURE_TYPE_I, then every encoded frame's size after the key frame would increase. If I change the h264 encoder to x264, then it's ok.


The packet size is as below before I call "encodeFrame->pict_type = AV_PICTURE_TYPE_I" :


packet size is 26839
packet size is 2766
packet size is 2794
packet size is 2193
packet size is 1820
packet size is 2542
packet size is 2024
packet size is 1692
packet size is 2095
packet size is 2550
packet size is 1685
packet size is 1800
packet size is 2276
packet size is 1813
packet size is 2206
packet size is 2745
packet size is 2334
packet size is 2623
packet size is 2055


If I call "encodeFrame->pict_type = AV_PICTURE_TYPE_I", then the packet size is as below :


packet size is 23720,is key frame 1
packet size is 23771,is key frame 0
packet size is 23738,is key frame 0
packet size is 23752,is key frame 0
packet size is 23771,is key frame 0
packet size is 23763,is key frame 0
packet size is 23715,is key frame 0
packet size is 23686,is key frame 0
packet size is 23829,is key frame 0
packet size is 23774,is key frame 0
packet size is 23850,is key frame 0


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