Hi, I’m trying to make use of the videoSource component available at the dev branch of jetson-utils, but I can’t find a way to convert its result (cudaImage) to a numpy array … I’m able to inspect this cudaImage properties (width, height, channels, ptr, format, mapped) and it appears to be a valid frame and everything, but apparently I can’t feed it to cudaToNumpy() because its not mapped (it says mapped=false) … while other examples I’ve checked are able to convert cudaImages to numpy successfully. The only difference that I found is that the cudaImages that were converted succesfully have the mapped flag in true (as they were created for example via cudaAllocMapped.
Is there a way to turn an unmapped cudaImage to a mapped one so that I can then convert it to numpy? Would that be the way to go?
Here’s an example:
>>> cudaImage_mapped = jetson.utils.cudaAllocMapped(width=1280, height=960, format='rgb32f')
>>> str(cudaImage_mapped)
'<cudaImage object>\n -- ptr: 0x101370000\n -- size: 14745600\n -- width: 1280\n -- height: 960\n -- channels: 3\n -- format: rgb32f\n -- mapped: true\n -- freeOnDelete: true\n'
>>> numpy_mapped = jetson.utils.cudaToNumpy(cudaImage_mapped)
>>> cudaImage_not_mapped = vs.Capture() # vs is a jetson.utils.videoSource instance
>>> str(cudaImage_not_mapped)
'<cudaImage object>\n -- ptr: 0xf00db0000\n -- size: 3686400\n -- width: 1280\n -- height: 960\n -- channels: 3\n -- format: rgb8\n -- mapped: false\n -- freeOnDelete: false\n'
>>> numpy_not_mapped = jetson.utils.cudaToNumpy(cudaImage_not_mapped)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
Exception: jetson.utils -- cudaToNumpy() needs to use CUDA mapped memory as input (allocate with mapped=1)
Thank you.
Hi,
* @returns `true` if a frame was captured, `false` if there was an error or a timeout occurred.
*/
template<typename T> bool Capture( T** image, uint64_t timeout=UINT64_MAX ) { return Capture((void**)image, imageFormatFromType<T>(), timeout); }
/**
* Capture the next image from the video stream.
*
* The image formats supported by Capture() are `IMAGE_RGB8` (uchar3), `IMAGE_RGBA8` (uchar4),
* `IMAGE_RGB32F` (float3), and `IMAGE_RGBA32F` (float4). @see imageFormat for more info.
*
* @param[out] image output pointer that will be set to the memory containing the image.
* If this interface has it's videoOptions::zeroCopy flag set to true,
* the memory was allocated in mapped CPU/GPU memory and is be accessible
* from both CPU and CUDA. Otherwise, it's accessible only from CUDA.
*
* @param[in] timeout timeout in milliseconds to wait to capture the image before returning.
* A timeout value of `UINT64_MAX` (the default) will wait forever, and
* a timeout of 0 will return instantly if a frame wasn't immediately ready.
*
* @returns `true` if a frame was captured, `false` if there was an error or a timeout occurred.
*/
Suppose you can get a mapped memory if the configure is well set.
Would you mind to check it further.
Thanks.
Hmm, what URI are you creating your videoSource with? I think by default I create them all to use mapped memory…so curious why it is not already mapped.
Hi, it’s an rtsp source. The videoSource was created like this:
>>> vs = jetson.utils.videoSource('rtsp://user:pass@192.168.0.20:554', sys.argv)
The whole snippet would be:
>>> cudaImage_mapped = jetson.utils.cudaAllocMapped(width=1280, height=960, format='rgb32f')
>>> str(cudaImage_mapped)
'<cudaImage object>\n -- ptr: 0x101370000\n -- size: 14745600\n -- width: 1280\n -- height: 960\n -- channels: 3\n -- format: rgb32f\n -- mapped: true\n -- freeOnDelete: true\n'
>>> numpy_mapped = jetson.utils.cudaToNumpy(cudaImage_mapped)
>>> vs = jetson.utils.videoSource('rtsp://user:pass@192.168.0.20:554', sys.argv)
>>> vs.Open()
>>> cudaImage_not_mapped = vs.Capture() # vs is a jetson.utils.videoSource instance
>>> str(cudaImage_not_mapped)
'<cudaImage object>\n -- ptr: 0xf00db0000\n -- size: 3686400\n -- width: 1280\n -- height: 960\n -- channels: 3\n -- format: rgb8\n -- mapped: false\n -- freeOnDelete: false\n'
>>> numpy_not_mapped = jetson.utils.cudaToNumpy(cudaImage_not_mapped)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
Exception: jetson.utils -- cudaToNumpy() needs to use CUDA mapped memory as input (allocate with mapped=1)
OK gotcha, yep it was a bug - thanks for letting me know. I just fixed it in the dev branch - if you run this and pull the latest, it should work now:
$ cd jetson-inference
$ git pull origin dev
$ cd build
$ cmake ../
$ make
$ sudo make install
Great news! it works now, thank you!
dusty_nv:
make
hi, this failed for me (jatson nano)
[ 34%] Building NVCC (Device) object CMakeFiles/jetson-inference.dir/c/jetson-inference_generated_tensorConvert.cu.o
In file included from /home/jetbot/workspace/jetson-inference/c/tensorConvert.cu:23:0:
/home/jetbot/workspace/jetson-inference/c/tensorConvert.h:28:10: fatal error: imageFormat.h: No such file or directory
#include "imageFormat.h"
^~~~~~~~~~~~~~~
compilation terminated.
CMake Error at jetson-inference_generated_tensorConvert.cu.o.cmake:219 (message):
Error generating
/home/jetbot/workspace/jetson-inference/build/CMakeFiles/jetson-inference.dir/c/./jetson-inference_generated_tensorConvert.cu.o
CMakeFiles/jetson-inference.dir/build.make:501: recipe for target 'CMakeFiles/jetson-inference.dir/c/jetson-inference_generated_tensorConvert.cu.o' failed
make[2]: *** [CMakeFiles/jetson-inference.dir/c/jetson-inference_generated_tensorConvert.cu.o] Error 1
CMakeFiles/Makefile2:67: recipe for target 'CMakeFiles/jetson-inference.dir/all' failed
make[1]: *** [CMakeFiles/jetson-inference.dir/all] Error 2
Makefile:129: recipe for target 'all' failed
make: *** [all] Error 2
Hi @avidan2012 , I think that your jetson-inference/utils submodule hadn’t been updated. Can you try updating your repo?
$ cd jetson-inference/
$ git submodule foreach git pull origin master
$ cd build/
$ cmake ../
$ make
$ sudo make install
If that still doesn’t work, then you may want to totally re-clone a fresh copy of the repo from master.
1 Like
thanks dusty for the super quick response :)