I am working on raspberry pi in image processing project using opencv
But i found that it was inefficient with raspberry pi as it works on CPU and don't use powerful GPU and i must switch to openCL
Are there any method to convert from openCV to openCL
Or how can i able GPU for opencv ?
Or how i can i use opencv with acceptable fast response processing on raspberry pi ?
OpenCL modules are included in OpenCV. http://docs.opencv.org/modules/ocl/doc/introduction.html
use it.
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I am trying to estimate pose of a Aruco Marker using opencv library.
But the performance of the above Library is a bit slow since it runs on CPU.Is there a way so that I can run the above Library on GPU?
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I tried researching in OpenCV's documentation, it has some cuda enabled functions but for aruco functions it is not available
I need to build standalone module which records video from rasberry pi camera to sd card (through external module) when the motion on video is detected.
So, I need to run OpenCV that I will use for motion detection. Is it possible to run it on Raspberry Pi Pico on board? How much FPS will it have in case for i.e. background subtraction?
The RPi Pico uses an RP2040.
RP2040 is a dual-core ARM Cortex-M0+. It comes with "264kB on-chip SRAM". You shouldn't expect this to have any power that's useful for image processing. It doesn't even run Linux. Were those 264 kB fully available to you, you could fit a single grayscale image of size 593x445 in there.
OpenCV can target ARM but not such tiny microcontrollers.
Here's some evaluations by OpenCV itself: https://opencv.org/arm/
You should investigate "OpenVX".
I'm trying to figure out if http://www.solid-run.com/products/hummingboard/ will be able to run OpenCV with OpenCL accelerations. Attempting to do this on the Raspberry Pi was fruitless.
The Ocl library for OpenCl acceleration for OpenCV requires OpenCL with FULL PROFILE. Vivate GPU in Freescale iMX6q only supports Embedded Profile(EP).
You cannot use OpenCL acceleration in OpenCV in this case.
I would like to know if it is possible to run the OpenCV HOG Detector using a Raspberry Pi in real time using the Raspberry Pi camera.
Unfortunately not, even overclocked to 1000MHz and with 64MB for video it's not enough.
On my old mac with a 2.1 GHz Dual Core Intel CPU and 2GB of ram I could barely get between 8-12 FPS for a 640x480 stream.
I haven't tried OpenCV 3.0 (just 2.4.8) on Raspberry PI so don't have any softcascades test results to share, but it sounds promising.
Another idea I can think of is using LBP cascades. You could start with a HAAR since there's one already for detecting bodies so it would be easy to test, but LBP should be a bit faster. Perhaps you could train a cascade that works really well for a set environment.
Also, if it helps, you can use my little OpenCV wrapper for the PiCamera for tests. It basically returns frames from the Pi Camera module as cv::Mat.
I've had openCV running on a PI, using a USB video grabber, as I am using a CCTV camera. I use Python.
It runs fine (for what I want to do), but you need to limit the resolution.
It's slower than a PC (2ghz dual core) but still works.
I need to develop an image processing program for my project in which I have to count the number of cars on the road. I am using GPU programming. Should I go for OpenCV program with GPU processing feature or should I develop my entire program on CUDA without any OpenCV library?
The algorithms which I am using for counting the number of cars is background subtraction, segmentation and edge detection.
You can use GPU functions in OpenCV.
First visit the introduction about this : http://docs.opencv.org/modules/gpu/doc/introduction.html
Secondly, I think above mentioned processes are already implemented in OpenCV optimized for GPU. So It will be much easier to develop with OpenCV.
Canny Edge Detection : http://docs.opencv.org/modules/gpu/doc/image_processing.html#gpu-canny
PerElement Operations (including subtraction): http://docs.opencv.org/modules/gpu/doc/per_element_operations.html#per-element-operations
For other functions, visit OpenCV docs.
OpenCV, no doubt, has the biggest collection of Image processing functionality and recently they've started porting functions to CUDA as well. There's a new GPU module in latest OpenCV with few functions ported to CUDA.
Being said that, OpenCV is not the best option to build a CUDA based application as there are many dedicated CUDA libraries like CUVI that beat OpenCV in Performance. If you're looking for an optimized solution, you should also give them a try.