i read the heatmap might crash on many bins and cannot find the feature to put out bitmaps/images of frequently changing data. It suffices if i could shift the image to the left and append another column of pixels on the right for the t+1 timestamp.
Sure most data reasoned about in aggregated fashion, but i try to get as close to the original 'picture' as possible. Does grafana have a pixel panel to quickly draw/update image data? example:
What other tools would you suggest? to visualize serverside generated data at the client in high resolution.
Related
I am currently implementing a few image style transfer algorithms for Tensorflow, but I would like to do it in tiles, so I don't have to run the entire image through the network. Everything works fine, however each image is normalized differently, according to its own statistics, which results in tiles with slightly different characteristics.
I am certain that the only issue is instance normalization, since if I feed the true values (obtained from the entire image) to each tile calculation the result is perfect, however I still have to run the entire image through the network to calculate these values. I also tried calculating these values using a downsampled version of the image, but resolution suffers a lot.
So my question is: is it possible to estimate mean and variance values for instance normalization without feeding the entire image through the network?
You can take a random sample of the pixels of the image, and use the sample mean and sample variance to normalize the whole image. It will not be perfect, but the larger the sample, the better. A few hundred pixels will probably suffice, maybe even less, but you need to experiment.
Use tf.random_uniform() to get random X and Y coordinates, and then use tf.gather_nd() to get the pixel values at the given coordinates.
Suppose we have two similar plots.
Plot1 (already published in a paper)
Plot2 (calculated by using any software)
My question is: How can I compare my calculated plot (pdf, png, jpeg, etc) with the plot in the paper.
Thank You
To the best of my knowledge, there is currently no software that would enable you to re-convert images into their nominal data.
However, it's not that hard to write a piece of code that does it.
Here are the steps (at a high level):
extract the images from the pdf document (use iText)
separate out those images that look like a plot. You can train a neural network to do this, or you can simply look for images that contain a lot of white (assuming that's the background) and have some straight lines in black (assuming that's the foreground). Or do this step manually.
Once you have the image(s), extract axis information. I'll assume a simple lineplot. Extract the minimal x and y value, and the maximum x and y value.
separate out the colors of the lines in your lineplot, and get their exact pixel coordinates. Then, using the axis information, scale them back to their original datapoint.
apply some kind of smoothing algorithm. E.g. Savitzky-Golay
If you ever use this data in another paper, please mention that you gathered this data by approximation of their graph. Make it clear you did not use the original source data.
Reading material:
https://developers.itextpdf.com/examples
https://en.wikipedia.org/wiki/Savitzky%E2%80%93Golay_filter
https://docs.oracle.com/javase/tutorial/2d/images/index.html
My goal is to achieve something that was previously asked in this site (outside from SO). In this external site the questions is unanswered, and in order to give more visibility and to try to get an answer I translate it to here:
The issue is:
I have a small simulation of particles flowing through a wire mesh structure, and I'm interested in calculating the mass flow rate and volume fraction of particles at certain cross sections. I think I understand how to calculate mass flow rate by setting up small regions and dumping particle count and velocity from that region. I assume that volume fraction works in a similar fashion, except I only need to know the size of my particles and my dump region.
What I'm wondering is this - is it possible to do these things in Paraview? I can set up planes and slices and such, but I can't seem to extract much useful information out of them.
Further on down the road, what I would like to do would be to plot contours of volume fraction at certain planes, and plot the volume fraction along the vertical axis so I can see how high the particles are piling up on top of the screen, based on particle size, wire size, etc. Can Paraview do any of this?
This is a visualization issue. I don't know how make it with Paraview. The idea is count how much particles cross the slice.
My first approach was piped: DataReader | Spherical Glyph | Slice with normal fixed handly along z axis but nothing results. Also I tried to adding the filter Surface Flow and nothing too. Probably I am piping the data in a bad way.
To see the pipelining process I add an image (focus in PlotOverLine1 and its above pipes):
We're building a GIS interface to display GPS track data, e.g. imagine the raw data set from a guy wandering around a neighborhood on a bike for an hour. A set of data like this with perhaps a new point recorded every 5 seconds, will be large and displaying it in a browser or a handheld device will be challenging. Also, displaying every single point is usually not necessary since a user can't visually resolve that much data anyway.
So for performance reasons we are looking for algorithms that are good at 'reducing' data like this so that the number of points being displayed is reduced significantly but in such a way that it doesn't risk data mis-interpretation. For example, if our fictional bike rider stops for a drink, we certainly don't want to draw 100 lat/lon points in a cluster around the 7-Eleven.
We are aware of clustering, which is good for when looking at a bunch of disconnected points, however what we need is something that applies to tracks as described above. Thanks.
A more scientific and perhaps more math heavy solution is to use the Ramer-Douglas-Peucker algorithm to generalize your path. I used it when I studied for my Master of Surveying so it's a proven thing. :-)
Giving your path and the minimum angle you can tolerate in your path, it simplifies the path by reducing the number of points.
Typically the best way of doing that is:
Determine the minimum number of screen pixels you want between GPS points displayed.
Determine the distance represented by each pixel in the current zoom level.
Multiply answer 1 by answer 2 to get the minimum distance between coordinates you want to display.
starting from the first coordinate in the journey path, read each next coordinate until you've reached the required minimum distance from the current point. Repeat.
I am currently using OpenCV's built-in patch-based histogram back projection (cv::calcBackProjectPatch()) to identify regions of a target material in an image. With an image resolution of 640 x 480 and a window size of 10 x 10, processing a single image requires ~1200 ms. While the results are great, this far too slow for a real-time application (which should have a processing time of no more than ~100 ms).
I have already tried reducing the window size and switching from CV_COMP_CORREL to CV_COMP_INTERSECT to speed up the processing, but have not seen any appreciable speed up. This may be explained by the OpenCV documentation (emphasis mine):
Each new image is measured and then
converted into an image image array
over a chosen ROI. Histograms are
taken from this image image in an area
covered by a “patch” with an anchor at
center as shown in the picture below.
The histogram is normalized using the
parameter norm_factor so that it may
be compared with hist. The calculated
histogram is compared to the model
histogram; hist uses The function
cvCompareHist() with the comparison
method=method). The resulting
output is placed at the location
corresponding to the patch anchor in
the probability image dst. This
process is repeated as the patch is
slid over the ROI. Iterative histogram
update by subtracting trailing pixels
covered by the patch and adding newly
covered pixels to the histogram can
save a lot of operations, though it is
not implemented yet.
This leaves me with a few questions:
Is there another library that supports iterative histogram updates?
How significant of a speed-up should I expect from using an iterative update?
Are there any other techniques for speeding up this type of operation?
As mentioned in OpenCV Integral Histograms will definitely improve speed.
Please take a look at a sample implementation in the following link
http://smsoftdev-solutions.blogspot.com/2009/08/integral-histogram-for-fast-calculation.html