Finfa811's profile - activity

Hi all,

I have a 4-channel image (.png, .tif) like this one:

And I would like to add padding of type BORDER_REFLECT around the flower. copyMakeBorder is not useful, since it adds padding to the edges of the image.

I can add certain padding if I split the image in bgr + alpha and apply dilate with BORDER_REFLECT option on the bgr image, but that solution spoils all the pixels of the flower.

Is there any way to perform a selective BORDER_REFLECT padding addition on a ROI defined by a binary mask?

So the steps are first normalizing to unit vectors, then converting to polar coordinates and finally converting back to

So the steps are first normalizing to unit vectors, then converting to polar coordinates and finally converting back to

I have a set of coordinates of a 6-image Cubemap (Front, Back, Left, Right, Top, Bottom) as follows:

[ [160, 314], Front; [253, 231], Front; [345, 273], Left; [347, 92], Bottom; ... ]

Each image is 500x500p, being [0, 0] the top-left corner. I want to convert these coordinates to their equivalents in equirectangular, for a 2500x1250p image.

I don't need to convert the whole image, just the set of coordinates. Is there any straight-forward conversion for a specific pixel?

EDIT:

First of all, I want to insist on the fact that I don't have any image available, neither the 6 input images from the cubemap, nor the output equirectangular pano. What I have available is a set of pixel coordinates from 6 images that shape a skybox cubemap. I add a graphic example using 250x250p images:

Front image:

Right image:

And so on for the other 4 images (Back, Left, Top, Bottom).

I have set in red some points, these points will be my input. Those points would have their equivalents in an equirectangular panorama:

I have used a 1000x500p pano in this case.

So the input is the pixel coordinates [x,y] of the red points in the cubemap, together with the image they belong to. The [0,0] pixel is the top-left corner of each image:

{ Lamp1: [194,175], front; Chair: [151,234], front; TV: [31,81], right; Door: [107,152], back; Lamp2: [125,190], left }

And the output I want to obtain is the pixel coordinates of the red points in the equirectangular panorama:

{ Lamp1: [831,304]; Chair: [784,362]; TV: [898,206]; Door: [228,283]; Lamp2: [500,326]  }

I would like to know how to map from one set of coordinates to the other:

CUBEMAP [194,175], front -> ? -> [831,304] EQUIRECTANGULAR

Convert Cubemap pixel coordinates to equivalents in Equirectangular I have a set of coordinates of a 6-image Cubemap (Fr

Convert Cubemap coordinates to equivalents in Equirectangular I have a set of coordinates of a 6-image Cubemap (Front, B

Convert Cubemap coordinates to equivalents in Equirectangular I have a set of coordinates of a 6-image Cubemap (Front, B

Convert Cubemap coordinates to equivalents in Equirectangular I have a set of coordinates of a 6-image Cubemap (Front, B

Convert Cubemap coordinates to equivalents in Equirectangular I have a set of coordinates of a 6-image Cubemap (Front, B

@berak I've just edited the question. Hope it is clear now for you.

Convert Cubemap coordinates to equivalents in Equirectangular I have a set of coordinates of a 6-image Cubemap (Front, B

@berak I don't get any coordinates, I obtain them from the output of another algorithm. Ok, I thought I was being pretty

@berak I don't get any coordinates, I'm being provided by them. Ok, I thought I was being pretty clear, but I'll edit th

@berak That's a script I am getting as input. It does make sense, e.g.: "[160, 314], Front" means the pixel x=160, y=314

Convert Cubemap coordinates to equivalents in Equirectangular I have a set of coordinates of a 6-image Cubemap (Front, B

@berak That's the point, I don't have the images. I explained it up above, I have a set of [x,y] pixel coordinates, comi

That's the point, I don't have the images. I explained it up above, I have a set of [x,y] pixel coordinates, coming from

Convert Cubemap coordinates to equivalents in Equirectangular I have a set of coordinates of a 6-image Cubemap (Front, B

Thanks, I've fixed it tuning a bit the solution of that post.

Hi,

I have an array of pixels (100x1 Mat) and I would like to rotate it to generate a circle of radius = lenght of image, extending the values into any other direction (0-360º).

i.e. I want to go from this: to this: .

Any idea about how to address this issue?

Sure, the macro should be added to more nonfree algorithms. I'll try to contribute when I have a free spot. Thanks for your answer, now the setup is clear for me.

Hi,

I would like to know why keypoint detectors/feature descriptors are organized in this way. My first thought is that features2d.hpp are basic classes, xfeatures2d.hpp are extra classes and nonfree.hpp are classes that are not free for commercial use (SIFT,SURF).

But if this is the case, shouldn't DAISY (e.g.) be in nonfree.hpp since it's patented too? Is any special license issue the reason to move some classes to opencv_contrib (xfeatures2D), or just a matter of having them as an extra module? If so, why concretely those features and why xfeatures is including nonfree algorithms?

Hi @Eduardo, I commented the commit that should be reverted few months ago, but since I got no reply I've just opened an issue following your tip.

Hi there,

I am using OpenCV to blend a set of pre-warped images. As input I have some 4-channel images (*.png or *.tif) from where I can extract a bgr image and an alpha mask with the region related to the image (white) and the background (black). Both image and mask are the inputs of the Blender module cv::detail::Blender::blend.

When I use feather (alpha) blending the result is ok, however, I would like to avoid ghosting effects. When I use multi-band, some artifacts are appearing on the edges of the images:

The problem is similar to the one raised here, and solved here. The thing is, if the solution is creating a binary mask (that I already extract from the alpha channel), it does not work for me. If I add padding to the ovelap between both images, it takes pixels from the background and messes up the result even more.

I guess that probably it has to do with the functions pyrUp and pyrDown, because maybe the blurring to create the Gaussian and Laplacian pyramids is applied to the whole image, and not only to the positive alpha region. In any case, I don't know how to fix the problem using these functions, and I cannot find another efficient solution.

When I use another implementation of multiresolution blending, it works perfectly, however, I am very interested in integrating the multi-band implementation of OpenCV. Any idea of how to fix this issue?

Hi there,

I've used ImageMagick to calibrate my camera and obtain the a,b,c lens distortion parameters. How do I convert these a,b,c parameters to OpenCV style (k1,k2,p1,p2...)?

Yeah the main problem is that sets of extracted keypoints are way different between images due to radial distortion. Different keypoints may have similar local descriptors, and that's why the number of bad matches is high in my case. I've tried several keypoint detector/feature descriptor combinations with different parameter setups and got no success. I'll have a look to that paper, though. Thanks.