Mjpeg Video Sample
Magnhild Mongolo
Below you will find a selection of sample .mjpeg video files for you to download. On the right there are some details about the file such as its size so you can best decide which one will fit your needs.
Edit: I found the cause. The stream always begins with something which is not a JPEG. Only after it there is a normal MJPEG stream. Interestingly, not all of the small examples of using V4L2/MJPEG decoders can divide what the camera produces properly into frames. Something called capturev4l2.c is a rare example of doing it properly. Possibly there is some detail, which decides if the camera's bugginess is worked around or not.
I have a noname almost-UVC-compliant camera (it fails several compatibility tests). This is a relatively cheap global shutter camera, and thus I would like to use it instead of something properly documented. It outputs what is reported (and properly played) by mplayer as
I thus tried 0_hello_world.c from libavcodec/libavformat, but its stops at avformat_open_input() with an error Invalid data found when processing input. A 100-frame sample file is sitting here a.raw. Do you have any idea how to determine a method of decoding it in C into anything plain bitmap?
I chagned as per your first point but the result is same very low fps 1-2.
Yes i am using /opt/nvidia/deepstream/deepstream-6.0/samples/streams/sample_720p.mjpeg this video the standard video given by deepstream.
Also want to know the benchmark performance of jetson nano 4gb developer kit of this python deepstream samples.
I tested it and it behaves the same way as the motion mjpeg stream, i.e. video codec crashes when hardware accelerated requiring a reboot to have hardware acceleration for video again. Restarting Kodi plays media with software decoding only.
Thanks
The FPS number shown on the console when deepstream-app runs is an average of the most recent five seconds. The number in brackets is average FPS over the entire run. The numbers are displayed per stream. The performance measurement interval is set by the perf-measurement-interval-sec setting in the configuration file.
You can enable remote display by adding an RTSP sink in the application configuration file. The sample configuration file source30_1080p_dec_infer-resnet_tiled_display_int8.txt has an example of this in the [sink2] section. You must set the enable flag to 1.Once you enable remote display, the application prints the RTSP URL, which you can open in any media player like VLC.
nvdrmvideosink plugin is not supported in docker. It is an unsupported use case, nvdrmvideosink only work well when there is no compositorrunning in background, and that is not possible inside a docker.
If the host machine is running X, starting VNC is trivial. Otherwise you must start X, then start VNC.To determine whether X is running, check the DISPLAY environment variable.If X is not running you must start it first, then run DeepStream with GUI, or set type to 1 or 3 under sink groups to select fakesink or save to a file. If you are using an NVIDIA Tesla GPU Accelerators (compute-only cards without a display), you can set type to 4 for DeepStream output RTSP streaming. See the NVIDIA DeepStream SDK Development Guide for sink settings or create a virtual display to visualize the output using VNC. Refer to How to visualize the output if the display is not attached to the system in Quickstart Guide for more details
Multiple source components like decoder, camera, etc. are connected to the Gst-nvstreammux plugin to form a batch.This plugin is responsible for creating batch metadata, which is stored in the structure NvDsBatchMeta. This is the primary form of metadata in DeepStream 4.0.1.All plugins downstream from Gst-nvstreammux work on NvDsBatchMeta to access metadata and fill in the metadata they generate.
If input stream resolution and Gst-nvstreammux resolution (set in the configuration file) are the same, no additional GPU memory is allocated in Gst-nvstreammux.If input stream resolution is not same as Gst-nvstreammux resolution, Gst-nvstreammux allocates memory of size:
This may happen when you are running thirty 1080p streams at 30 frames/second. The issue is caused by initial load. I/O operations bog down the CPU, and with qos=1 as a default property of the [sink0] group, decodebin starts dropping frames. To avoid this, set qos=0 in the [sink0] group in the configuration file.
This Gst pipeline must be run from the dewarper test application directory, sources/apps/sample_apps/deepstream-dewarper-test.This pipeline runs only for four surfaces. To run for one, two, or three surfaces, use the dewarper test application.
Up to 5 (k0 to k4) distortion coefficients can be configured in Dewarper plugin using the property distortion.The plugin supports two types of distortion coefficients based on the type of input/source camera:
Set rtsp-reconnect-interval-sec=30 or 60, so that it can wait for sufficient time required for camera to reboot and start before. DS application attempts camera reconnection after waiting for this duration.
For 12bit NV12 format, the output caps shows I420_12LE. But the data is handled as semiplanar 12 bit data. This is a workaround since gstreamer v1.16 does not have the particular caps for 12 bit NV12. Downstream component should process it considering as 12bit NV12 only.
DeepStream 7.0 uses explicit batch dimension for caffemodels. Some caffemodels use TensorRT plugins/layers which have not been updated for explicit batch dimensions. Add force-implicit-batch-dim=1 in the nvinfer config file for such models to build the models using implicit batch dimension networks.
If TensorRT OSS plugins library is not already available, please follow instructions from to build the library. To use the library in DeepStream, export LD_PRELOAD=/path/to/oss/libnvinfer_plugin.so before running any DeepStream app.
At times the requested muxer pad gets deleted before linking happens, as streams might contain both video and audio. If queue element is added between nvstreammux and the uridecodebin then the above pipeline will work. As uridecodebin will link to queue pad and not nvstreammux pad. This problem is not observed programmatically as the linking takes place new pad callback of decoder on video stream.
Now that you have access to NvBufSurface structure, you can access actual frame memory and save it. At the end you need to unmap gst buffer using gst_buffer_unmap (inbuf, &in_map_info)For more details, see gst_dsexample_transform_ip() in gst-dsexample plugin source code.
Memory of type Surface Array which is 2D pitched allocated by default: Used by all hardware accelerator on the platform. Accessible by CPU using NvBufSurfaceMap and NvBufSurfaceSyncForCpu or NvBufSurfaceSyncForDevice based on read write usage. GPU Access using EGLImageCreate and Map APIs
Memory of type Cuda Device is allocated by default, accessible only by GPU. User might need to have custom Cuda kernels to access or modify memory. Or NvBufSurfaceCopy to copy content into CPU accessible memory
Memory of type Surface Array which is 2D pitched allocated by default; used by all hardware accelerator on the platform; accessible by CPU using NvBufSurfaceMap () and NvBufSurfaceSyncForCpu () or NvBufSurfaceSyncForDevice () based on usage read write usage.GPU Access using EGLImageCreate and Map APIs
dGPU: User can use NvBufSurfaceCopy() to copy from one memory type to another. If transformation is required, nvvideoconvert plugin support nvbuf-memory-type property to allow different type of memory. NvBufSurfTransform() can also be used to do the transformation between various CUDA types of memories. CUDA to NVBUF_MEM_SYSTEM transformation is not supported by NvBufSurfTransform directly, user can use NvBufSurfaceCopy() to copy into CUDA memory and perform transformation on that memory.
If you are not using NvBufSurfaceCreate for allocation, ensure the pitch of the allocated memory is multiple of 32. Also ensure that the starting address of each plane of the input is 128-byte aligned.
You can insert a probe on sink pad of the decoder, measure the time at which input buffer arrives. Insert another probe on sink pad of the sink component and measure the time at which output buffer arrives corresponding to the input buffer . Time difference between these two will give you the latency of the buffer.
With DS 6.2+, DeepStream docker containers do not package libraries necessary for certain multimedia operations like audio data parsing, CPU decode, and CPU encode.This change could affect processing certain video streams/files like mp4 that include audio track.
The actual number of objects in all the streams exceeds the memory allocated for tracker re-identification embedding output. When outputting re-identification tensor is enabled, users are recommended to set maxTargetsPerStream to be larger than the total number of objects across all streams in the pipeline.
NVIDIA Tools Extension SDK (NVTX) provides APIs for annotating events, code ranges, and resources in an application. The core GStreamer plugins in DeepStreamSDK have integrated NVTX and the events and ranges can be captured and visualized using NVIDIA Nsight tool, Tegra System Profiler, and Visual Profiler to capture and visualize these events and ranges. For more information on NVTX, visit _tools_extension_library_nvtx.htm. Following are the instructions on how to capture and visualize these events using deepstream-app with NVIDIA Nsight tool:
To have better performance and optimization, smart record avoids transcoding and caches only encoded frames. To that extent recording the video with overlaid bounding boxes is not possible. But you can implement that use case in two ways:
Not exactly. dGPU can support most models such as TensorRT, Tensorflow (and TF-TRT), ONNX(and with TRT optimization), Pytorch.Jetson can support TensorRT, Tensorflow (and TF-TRT). Support for other models are coming in future releases.For more details, see Deepstream Plugin Guide section.
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