Access ROI from nvdspreprocess in python

Accelerated Computing Intelligent Video Analytics DeepStream SDK
1

I am using-
• Hardware Platform Jetson
• DeepStream Version 6.2
• JetPack Version 5.1

I am working on a deepstream inference pipeline in python. I am able to run inference on RGB stream with detection model. My pipeline is: source → nvstreammux → nvdspreprocess → nvinfer → nvdsosd → sink. Nvdspreprocess plugin processes the frame for multiple ROIs in single frame and then nvinfer runs inference on it as batch. This is based on apps available in deepstream_python_apps.

My requirement is to access the frame’s ROIs that are getting sent for inference in the python code, so as to visualize them.

For example, in the pipeline below -

gst-launch-1.0 v4l2src ! “video/x-raw, height=1080, width=1920” ! queue ! nvvidconv ! “video/x-raw(memory:NVMM), format=NV12” ! m.sink_0 nvstreammux name=m batch-size=1 width=1920 height=1080 ! nvvideoconvert ! nvdspreprocess config-file=/opt/nvidia/deepstream/deepstream/sources/gst-plugins/gst-nvdspreprocess/config_preprocess.txt ! nvinfer config-file-path=/opt/nvidia/deepstream/deepstream/samples/configs/deepstream-app/config_infer_primary.txt input-tensor-meta=1 batch-size=2 ! nvvideoconvert ! nvdsosd ! nvvideoconvert ! autovideoconvert ! autovideosink sync=false

we can see that there are two ROIs in the frame and batch size 2 is used in nvinfer. If I convert this pipeline in python, can I access the ROIs? I do not want to visualize the detections or get full frame (like they have done in deepstream-imagedata-multistream), I want to check the images that are being sent to the model. Is there a way to do that?

3

If you want to get the image that are being sent to the model, you need to dump the image from our C/C++ open source code.
You can refer to the queueInputBatch function in the sources\libs\nvdsinfer\nvdsinfer_context_impl.cpp dir.

4

I tried changes in queueBatchOutput refering to the following:

But the images were not getting saved. After some debugging I found that the function queueInputBatch is not called, but queueInputBatchPreprocessed is called instead. So I replicated the same changes from the patch to the later function. I am now getting the following error when I run it:

Caught SIGSEGV
#0 0x0000ffff8acc3098 in __GI___poll
#1 0x0000ffff8adf0b38 in () at /lib/aarch64-linux-gnu/libglib-2.0.so.0
#2 0x0000ffff8adf0ef8 in g_main_loop_run ()
#3 0x0000ffff8af96e48 in gst_bus_poll ()
#4 0x0000aaaab3f84980 in event_loop
#5 0x0000aaaab3f83868 in main (argc=, argv=)

After further debug, I found that the issue is in line “float scale = m_Preprocessor->getScale();” of the patch.
I do not have enough knowledge about nvinfer and c++ to solve this. Please help.

5

Yes. If you use preprocess plugin, you refer to the queueInputBatchPreprocessed function.
About the patch, did you refer to the 2nd part of the Dump the Inference Input?

6

Yes. I have used that patch itself to make the changes before enqueue() function.

7

OK. For DS 7.0, there are no getScale() for the m_Preprocessor class. You may consider using the following parameters m_Scale.

Also since you are using the nvdspreprocess, you can dump that from the nvdspreprocess plugin. You can refer to prepare_tensor in the sources\gst-plugins\gst-nvdspreprocess\nvdspreprocess_lib\nvdspreprocess_impl.cpp. Just open the DEBUG_LIB macro according to your needs.

8

Okay, but I am using:

9

I will check how I can do this from nvdspreprocess. Thank you!

10

I tried saving the .bin files in nvdspreprocess. I defined DEBUG_LIB as 1, no other change was done.
The .bin files that are saved with this are 0 bytes in size.

Is there anything else that needs to be done?

11

Yes. The default buffer should be the GPU buffer. If you want to dump that to a file, you need to change to CPU buffer. You can refer to the code below.

#ifdef DEBUG_LIB
    static int batch_num2 = 0;
    int debug_size = 4*m_NetworkSize.channels*m_NetworkSize.width*m_NetworkSize.height;
    char *debug_buffer = (char *)malloc(debug_size);
    std::ofstream outfile2("impl_out_batch_" + std::to_string(batch_num2) + ".bin");
    cudaMemcpy (debug_buffer, outPtr, debug_size, cudaMemcpyDeviceToHost);
    outfile2.write((char*) debug_buffer, debug_size);
    outfile2.close();
    free(debug_buffer);
    batch_num2 ++;
#endif
12

Thank you, this worked. Now the .bin files are not empty. But when I tried to read them in python using

numpy.fromfile(“path_to_bin_file.bin”)

I am getting an array of shape (353280,); whereas when I printed the debug_size, channels, width and height in nvdspreprocess code, I got

size = 2826240 channels = 3 width = 640 height = 368

Now how do I reshape the numpy array so as to see the actual image?

Edit : 353280 is actually 640 x 368 x 1.5

Edit2 : Also, I have observed that all values in the array are zero

13

It’s just a demo code, You need to confirm the debug_size according to your specific configuration.
Like below
bytesPerElement(tensorParam.params.data_type)*m_NetworkSize.channels*m_NetworkSize.width*m_NetworkSize.height

The data here already separates the channels of R G B. You can use OpenCV to show the 3 channels separately.

14

Okay. I am now able to save the images, but there is a height and width mismatch, and I can see mixture of the images I want.

15

Yes. As I attached before

16

There is no update from you for a period, assuming this is not an issue anymore. Hence we are closing this topic. If need further support, please open a new one. Thanks

17

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