Please provide complete information as applicable to your setup.
• Hardware Platform (Jetson / GPU) • DeepStream Version • JetPack Version (valid for Jetson only) • TensorRT Version • NVIDIA GPU Driver Version (valid for GPU only) • Issue Type( questions, new requirements, bugs) • How to reproduce the issue ? (This is for bugs. Including which sample app is using, the configuration files content, the command line used and other details for reproducing) • Requirement details( This is for new requirement. Including the module name-for which plugin or for which sample application, the function description)
about the pic,I have 2 questions: 1)for the deepstream pipline like streammux!nvinfer(seg model)!nvtracker!nvosd!,if I add a pad in the osd src,the channel params in the pic should set to 3? or 1? 2)when i use the function nvds_mask_utils_resize_to_binary_argb32, the params argb_32,interpolation,stream should set to what?any suggestions?
thank you so much,the answer is important for me !looking forward!
so glad to receive your reply,thanks! What do you mean add a pad in the osd src ?
sorry,it’s add a probe in osd src pad. Please refer to our source code sources\includes\nvds_mask_utils.h .
In fact, I have carefully read these parameter definitions, but I am not very familiar with them, especially the stream parameter. I really don’t know how to get it. I tried to set it to NULL, but there was an segmentation error when the program runing.
We recommend that you learn some cuda and NvbufSurface basics before customizing your own code, otherwise there will be some exceptions in your own code.
hi,listen to youradvice,I learn some easy function about cuda,now i have two questions:
Please check if this process is correct,
Memory request
cudaMalloc(&src_data, mask_width * mask_height * sizeof(float));
cudaMalloc(&dst_data, stream_width * stream_height * sizeof(uint32_t));
Perform memory requests for both the original and target mask data separately;
Two questions:
What are the loopholes in the above process?
2. Is the size of the memory request for src mask data calculated using the size in mask_params or through mask_weight * mask_height * sizeof (float)?
about the introduction of the nvds_mask_utils_resize_to_binary_argb32 function said “ARGB32 For resized pixels > threshold, transparency is set to 50% and other pixels are set to 100% transparent; RGB = 0 for all pixels” two questions:
Why is the transparency of the data I receive equal to others (not 50% and 100%)? What does this mean?
I set the nvds_mask_utils_resize_to_binary_argb32 param argb32_px equal to 0xFFFFFFFF,why the r,g,b isn’t equal to 255?
How can I use the parsed data to convert them into binary masks?
by the way the threshold that i set is equal to 0.5.
cudaMalloc(&dst_data, stream_width * stream_height * sizeof(uint32_t));its the sizes stream_width * stream_height * sizeof(uint32_t) is right? Is it means a pic have 3 channels and the pixel of each channel can get in uint32_t?
everything work well but only one question: the dst uint32_t data is not equal to my setting value, 1. I set the nvds_mask_utils_resize_to_binary_argb32 param argb32_px equal to 0xFFFFFFFF,why the r,g,b isn’t equal to 255?
Let’s not debug this non-open source code for a while.
Could you describe in more detail the problems you encountered in our demo and the final results you want?
Our latest nvsegvisual version supports this scenario. You can set the alpha and original-background parameter to meet your needs. Please refer to our demo tao_segmentation.
I tried your method to add the nvsegvisual element , but it not work , my model is yolo-seg, it don’t have the NvDsInferSegmentationMeta data,it’s seg results contained in the obj_meta->mask_params,any method can i decrease the running time of the nvds_mask_utils_resize_to_binary_argb32 ?
OK, now I understand. The yolo-seg is an instance segmetation model, so we cannot use nvsegvisual plugin to draw the mask. Can you attach a picture to illustrate the result you want? We can support the instance segvisual model too. You can refer to our demo mask2former.