onthegrid007's profile - activity

So the idea was essentially to save data in terms of processing and memory. Alternatively, I can scale down the original

There's no simple way to explain his so I will get to the point.

For a neural network, would it be better for me to flatten down a color image to between 0 and 1 like this: 0.RRRGGGBBBAAA whereas the pixel, say 0, would be .255000000255 meaning there is a red pixel at position 0, or just do HSV, thresh it, then greyscale to a grey color between 0 and 1?

Some background. I build what are called PxlDbl's or Pixel Double spelled out, and its exactly how it sounds... Its a pixels' color in a single double between 0 and well .255255255255 would be the max. Why a double? A float didn't have the precision I needed to achieve this. I currently have a custom neural network with many activation functions to be used in each topological layer. The one I am using for images is the well-known sigmoid which takes a number between 0 and 1. My end goal and the question are to place a flattened image of some kind into this Network. My question really relates to the best way to flatten it. Should I take my image size and multiply it by 4 to then have a neuron for each color channel for each pixel? I could take the value between 0 and 255 and map it between 0 and 1 which would give me effectively what I am looking for but say a 600x400 image x4 channels results in something like 960,000 neurons in the first layer which even in my case alone is truly unattainable to get to function no matter how well I programmed the Network... With a theoretical PxlDbl it shrinks the neuron could down to just the image size of 600x400 being only 240,000 neurons which is much more manageable in terms of weights and such. My only concern is with the inputs being so precise my outputs since their classifications might be much more difficult to train, ie after like .255^128076255 it might just drop off the 128076255 in the training because it just can't do that precise of numbers.

Help on this would be greatly appreciated...

OpenCV Clasification Neural Network + Image Flattening Question There's no simple way to explain his so I will get to th

Refactoring Old Code from CV3 and having trouble finding new symbols I have a project from CV3.0 and I'm working in MSVS

@saeid_masumi mingw's ld doesn't have a -z flag therefor you just need to not include the creation or linking of opencv

@saeid_masumi mingw's ld doesn't have a -z flag therefor you just need to not include the creation or linking of opencv

OpenCv 4.0.1, Cuda-6.5, G++, CMake , and an error, or 14... Absolutely NO idea what is wrong here, or how to fix it. Any

@saeid_masumi mingw's ld doesn't have a -z flag therefor you just need to not include the creation or linking of opencv

Error: '_ReturnAddress' was not declared in this scope Hi, I am having an issue and any help is greatly appreciated. I a

I completely just made a new folder and ran through cmake-gui again making sure NOT to select build opencv_world and min

I have compiled OpenCV 3.2.0 only once back in 2016 and it compiled using a 32bit flavor of MinGW to produce a folder co

ld.exe -z unknown flag generated by CMake that wont let me continue compiling OpenCV 3.4.3 using MinGW-64 8.1.0 I have a

Qt and wxWidgets are two great cross platform compatible GUI librarys. Hope these can be of some help. Happy Coding! -P

CameraObject = cv.VideoCapture.release() should do the trick... I am not familiar with there ever being a release(OBJECT

I looked a little closer into the the linking file CMake created and found these flags being tagged on the g++ execution

OpenCV 3.4.3 compiling with MinGW-64 8.1.0 using CMake...New Issue Another issue if anyone knows how to remove the -z fl

OpenCV 3.4.3 compiling with MinGW-64 8.1.0 and CMake for Eclipse So, new error while doing this, with the latest version

Attempting to compiling OpenCV Libs using MinGW and CMake for Eclipse Having an error saying SIZE_LIMIT is not defined i