Compute gradient magnitude and orientation at each image location.
If input image has k>1 channels and channel=0, keeps gradient with
maximum magnitude (over all channels) at each location. Otherwise if
channel is between 1 and k computes gradient for the given channel.
If full==1 orientation is computed in [0,2*pi) else it is in [0,pi).
If normRad>0, normalization is performed by first computing S, a smoothed
version of the gradient magnitude, then setting: M = M./(S + normConst).
S is computed by S = convTri( M, normRad ).
This code requires SSE2 to compile and run (most modern Intel and AMD
processors support SSE2). Please see: http://en.wikipedia.org/wiki/SSE2.
USAGE
[M,O] = gradientMag( I, [channel], [normRad], [normConst], [full] )
INPUTS
I - [hxwxk] input k channel single image
channel - [0] if>0 color channel to use for gradient computation
normRad - [0] normalization radius (no normalization if 0)
normConst - [.005] normalization constant
full - [0] if true compute angles in [0,2*pi) else in [0,pi)
OUTPUTS
M - [hxw] gradient magnitude at each location
O - [hxw] approximate gradient orientation modulo PI
EXAMPLE
I=rgbConvert(imread('peppers.png'),'gray');
[Gx,Gy]=gradient2(I); M=sqrt(Gx.^2+Gy.^2); O=atan2(Gy,Gx);
full=0; [M1,O1]=gradientMag(I,0,0,0,full);
D=abs(M-M1); mean2(D), if(full), o=pi*2; else o=pi; end
D=abs(O-O1); D(~M)=0; D(D>o*.99)=o-D(D>o*.99); mean2(abs(D))
See also gradient, gradient2, gradientHist, convTri
Piotr's Computer Vision Matlab Toolbox Version 3.23
Copyright 2014 Piotr Dollar & Ron Appel. [pdollar-at-gmail.com]
Licensed under the Simplified BSD License [see external/bsd.txt]