I have a data set consisting of XYZ data. The dimensions are 5587 rows by 3 columns.
I try to use rasterFromXYZ from the raster package but I get the following error:
Error in rasterFromXYZ(DATA) : x cell sizes are not regular
Any help would be appreciated.
You are not providing example data making it hard to help you out. What the message means is that your data does not appear to be regularly spaced.
Instead of rasterFromXYZ you can use rasterize in which case you specify the required geometry and then transfer the values to it.
Depending on your goals, you may also use interpolate
Related
So this question has been asked a few times, but I think my C++ skills are too deficient to really appreciate the answers. What I need is a way to start with an HEVC encoded video and end with CSV that has all the motion vectors. So far, I've compiled and run the reference decoder, everything seems to be working fine. I'm not sure if this matters, but I'm interested in the motion vectors as a convenient way to analyze motion in a video. My plan at first is to average the MVs in each frame to just get a value expressing something about the average amount of movement in that frame.
The discussion here tells me about the TComDataCU class methods I need to interact with to get the MVs and talks about how to iterate over CTUs. But I still don't really understand the following:
1) what information is returned by these MV methods and in what format? With my limited knowledge, I assume that there are going to be something like 7 values associated with the MV: the frame number, an index identifying a macroblock in that frame, the size of the macroblock, the x coordinate of the macroblock (probably the top left corner?), the y coordinate of the macroblock, the x coordinate of the vector, and the y coordinate of the vector.
2) where in the code do I need to put new statements that save the data? I thought there must be some spot in TComDataCU.cpp where I can put lines in that print the data I want to a file, but I'm confused when the values are actually determined and what they are. The variable declarations look like this:
// create motion vector fields
m_pCtuAboveLeft = NULL;
m_pCtuAboveRight = NULL;
m_pCtuAbove = NULL;
m_pCtuLeft = NULL;
But I can't make much sense of those names. AboveLeft, AboveRight, Above, and Left seem like an asymmetric mix of directions?
Any help would be great! I think I would most benefit from seeing some example code. An explanation of the variables I need to pay attention to would also be very helpful.
At TEncSlice.cpp, you can access every CTU in loop
for( UInt ctuTsAddr = startCtuTsAddr; ctuTsAddr < boundingCtuTsAddr; ++ctuTsAddr )
then you can choose exact CTU by using address of CTU.
pCtu(TComDataCU class)->getCtuRsAddr().
After that,
pCtu->getCUMvField()
will return CTU's motion vector field. You can extract MV of CTU in that object.
For example,
TComMvField->getMv(g_auiRasterToZscan[y * 16 + x])->getHor()
returns specific 4x4 block MV's Horizontal element.
You can save these data after m_pcCuEncoder->compressCtu( pCtu ) because compressCtu determines all data of CTU such as CU partition and motion estimation, etc.
I hope this information helps you and other people!
If I have a 4-D blob, say of size (40,1024,300,1) and I want to average pool across the second channel and generate an output of size (40,1,300,1), how would I do it? I think the reduction layer collapses the whole blob and generates a blob of size (40) by summing elements in all other axises (after 1) also. Is there any work around for this without re-implementing a new layer?
The only easy workaround I found is as follows. Permute your blob to a shape (40,300,1,1024). Use reduction layer to compute the mean with axis = -1 and operation = MEAN. I think the blob will be of shape (40,300,1). You may need to use reshape to append an extra dimension at the end (check if this is needed) and then permute back to shape (40,1,300,1).
You can find an implementation of a Permute layer here or here. I hope this helps.
I have a dataset where the images have VARYING number of labels. The number of labels is between 1 and 5. There are 100 classes.
After googling, it seems like HDF5 db with slice layer can deal with multiple labels, as in the following URL.
The only problem is that it supposes a fixed number of labels. Following this, I would have to create a 1x100 matrix, where entry value is 1 for the labeled classes, and 0 for non-label classes, as in the following definition:
layers {
name: "slice0"
type: SLICE
bottom: "label"
top: "label_matrix"
slice_param {
slice_dim: 1
slice_point: 100
}
}
where each image contains a a label looking like (1,0,0,...1,...0,....,0,1) where the vector size is 100 dimension.
Now, I apologize that my question becomes somehow vague, but is this a feasible idea? I.e., is there a better approach to this problem?
I get that you have 5 types of labels that are not always present for each data point. 1 of the 5 labels is for 100-way classification. Correct so far?
I would suggest always writing all 5 labels into your HDF5 and use a special value for when the label is missing. You can then use the missing_value option to skip computing the loss for that layer for that iteration. Using it requires add loss_param{ ignore_label = Y } to the loss layer in your network prototxt definition where Y is a scalar.
The backpropagated error will only be a function of labels that are present. If input X does not have a valid value for a label, the network will still produce an estimate for that label. But it will not be penalized for it. The output is produced without any effect on how the weights are updated in that iteration. Only outputs for non-missing labels contribute to the error signal and the weight gradients.
It seems that only the Accuracy and SoftmaxWithLossLayer layers support missing_values.
Each label is a 1x5 matrix. The first entry can be for the 100-way classification (e.g. [0-99]) and entries 2:5 have scalars that reflect the values that the other labels can take. The order of the columns is the same for all entries in your dataset. A missing label is marked by a special value of your choosing. This special value has to lie outside the set of valid label values. This will depend on what those labels represent. If a label value of -1 never occurs you can use this to flag a missing label.
I'm using scanner (not IRM) images to load 2D volumes, the contrast seems different more saturated comparing to ITK-Snap. Here is the screenshot of what I get :
http://i.stack.imgur.com/IHX5M.jpg
And with IT -snap :
http://i.stack.imgur.com/8tIv7.jpg
Any idea why this differences ?
Thank you
Right now, XTK doesn't read this dicom tag and crops all the negative values.
https://github.com/xtk/X/blob/master/io/parserDCM.js
Do you have one image you can share for testing?
I would add this tag to the parserDCM.js, and if this flag is ON, increment all the pixels values to make sure they are all positive.
Thanks
I've got the BumbleBee 2 stereo camera and two mentioned SDKs.
I've managed to capture a video from it in my program, rectify stereo images and get a disparity map. Next thing I'd like to have is a depth map similar to one, the Kinect gives.
The Triclops' documentation is rather short, it only references functions, without typical workflow description. The workflow is described in examples.
Up to now I've found 2 relevant functions: family of triclopsRCDxxToXYZ() functions and triclopsExtractImage3d() function.
Functions from the first family calculate x, y and z coordinate for a single pixel. Z coordinate perfectly corresponds to the depth in meters. However, to use this function I should create two nested loops, as shown in the stereo3dpoints example. That gives too much overhead, because each call returns two more coordinates.
The second function, triclopsExtractImage3d(), always returns error TriclopsErrorInvalidParameter. The documentation says only that "there is a geometry mismatch between the context and the TriclopsImage3d", which is not clear for me.
Examples of Triclops 3.3.1 SDK do not show how to use it. Google brings example from Triclops SDK 3.2, which is absent in 3.3.1.
I've tried adding lines 253-273 from the link above to current stereo3dpoints - got that error.
Does anyone have an experience with it?
Is it valid to use triclopsExtractImage3d() or is it obsolete?
I also tried plotting values of disparity vs. z, obtained from triclopsRCDxxToXYZ().
The plot shows almost exact inverse proportionality: .
That is z = k / disparity. But k is not constant across the image, it varies from approximately 2.5e-5 to 1.4e-3, that is two orders of magnitude. Therefore, it is incorrect to calculate this value once and use forever.
Maybe it is a bit to late and you figured it out by yourself but:
To use triclopsExtractImage3d you have to create a 3dImage first.
TriclopsImage3d *depthImage;
triclopsCreateImage3d(triclopsContext, &depthImage);
triclopsExtractImage3d(triclopsContext, depthImage);
triclopsDestroyImage3d(&depthImage);