I have to make some calculations concerning the size of a skeleton capture file without having an actual Kinect device...
So I came to the conclusion that for a 15 minutes skeletal tracking, the file size would approximately be 8Mbytes. Is this right ?
Ok, I just answer my own question.
If you only save the skeleton view, you are only saving the joints coordinates and their orientation. It doesnt take much space, and should be about the file size I mentioned in the question.
Related
I started to write a small engine to render a 2d isometric map. A friend of mine made a small basic image of a train station to use example art for my engine. I tried to import the .png into tiled and create a tileset for it, to then use the information for the rendering of that house.
When I import the image, tiled cuts off the edges of the picture (see attachment "tiled .png import to tileset") on the right and bottom side. I looked into the menus and tried to find information about it but I could'nt find any helpful advice why it happens.
Another thing I find curious is the information within the .tsx file:
<?xml version="1.0" encoding="UTF-8"?>
<tileset version="1.2" tiledversion="1.2.1" name="bAHNHOF" tilewidth="30"
tileheight="30" tilecount="195" columns="13">
<image source="bAHNHOF.png" width="401" height="468"/>
</tileset>
Shouldn't columns(13) multiplied by tile width(30) result in width of the imported image (i.e. 401). It only is 390 though, so roughly 11 pixels less then the original width.
I probably made a mistake somewhere or am confusing something. Maybe someone can help me?
Thanks in advance :)
Seem like whatever editor you are using wants "whole tile" sizes. This is not uncommon. Increase the size of your base image so that the X and Y align to tile size boundaries to prevent this. 30 for a tile size is also very unusual. I'd expect a power of 2 like "32" or "16".
In short, your importer is culling tiles that are not full size. I'd expect it to display a warning about image size before it did this, but who knows as you didn't state the programs.
When this goes onto whatever platform you are using, a power of 2 tile size will help as well in terms of efficiency, so consider making that change sooner rather than later as well.
Finally, often tiling is done to save memory. If, when you divide up your image into tiles (tile it), you can create identical tiles, the computer can use that knowledge to lessen the amount of memory is needed.
Lately, I have been interested in Watermark & steganography.
And as a part of research i am working on, i have a mp4 video file that has a hidden/invisible Watermark.
My question is is there an easy/simple way/tool (that you experienced) to detect the hidden/invisible Watermark in videos ?
I would compare each frame to the previous frame and then look for pixels that have changed. Then compare the amount that the pixels changed relative to their neighbours. And keep track of where the pixels consistently change by a slightly different amount to their neighbours. Similar to what I do here to detect rapid eye movements in the dark: https://lsdbase.files.wordpress.com/2016/01/wingless-drone1.gif?w=245
I'm learning about the Viola-James detection framework and I read that it uses a 24x24 base detection window[1][2]. I'm having problems understanding this base detection window.
Let's say I have an image of size 1280x960 pixels and 3 people in it. When I try to perform face detection on this image, will the algorithm:
Shrink the picture to 24x24 pixels,
Tile the picture with 24x24 pixel large sections and then test each section,
Position the 24x24 window in the top left of the image and then move it by 1px over the whole image area?
Any help is appreciated, even a link to another explanation.
Source: https://www.cs.cmu.edu/~efros/courses/LBMV07/Papers/viola-cvpr-01.pdf
[1] - page 2, last paragraph before Integral images
[2] - page 4, Results
Does this video help? It is 40 minutes long.
Adam Harvey Explains Viola-Jones Face Detection
Also called Haar Cascades, the algorithm is very popular for face-detection.
About half way down that page is another video which shows a super slow-mo scan in progress so you can see how the window starts small (although much larger than 24x24 for the purpose of demonstration) and shifts around the image pixel by pixel, then does it again and again on successively larger square portions. At each stage, it's still only looking at those windows as though they were resampled to the 24x24 size.
You can also see how it quickly rejects many of those windows and spends most of its time in areas that seem face-like while it computes more and more complex comparisons that become more stringent. This is where the term "cascade" comes into play.
I found this video that perfectly explains how the detection window moves and scales on a picture. I wanted to draw a flowchart how this looks but I think the video illustrates it better:
https://vimeo.com/12774628
Credits to the original author of the video.
I'm using a 40 x 40 sized image as a search result suggestion image in Windows 8 search. Only advice about the image format I can find is to have correct size for it (http://msdn.microsoft.com/en-us/library/windows/apps/Hh700542.aspx: "Windows will scale or crop smaller or larger images").
However, the correctly sized image blurs annoyingly. The same thing happens whether I use jpg or png. Original image looks fine, but the result suggestion in the search charm is very ugly, being still of same size! Is Windows converting the image somehow, and how could I get the image to stay crisp?
I haven't noticed blurring with photo-like images, but this image contains clear lines and areas which are vulnerable to any scaling etc.
Update Sep 24:
Here is the test image I used when trying to figure out the problem. I also created different scale versions, but in my case the 100% version was used (that's why the "100" marking) - as I supposed because the resulting image really is 40x40. As you can see, the resulting image (right) is of same size as original (left), but blurry.
it does not happen that often but it seems the right solution in this case was simply to wait ;) I haven't done anything new regarding result suggestion images in my solution and today I realized that the images became crisp. Probably fixed by any of the windows updates.
[Took a stab at answering what seems the related question mentioned in the comments, so I'm posting here as well.]
It sounds like this could be related to automatic scaling of the images. Windows will automatically scale up/down based on pixel density, and you can help things scale well by either using vector-based images or, for bitmap images, supplying scale-specific versions.
For example, to scale an image referenced in markup as "AppLogo.jpg", you'd include these images:
AppLogo.scale-100.jpg
AppLogo.scale-140.jpg
AppLogo.scale-180.jpg
You can also use folders, e.g. "\scale-140\AppLogo.jpg".
For search result images, the 100% image is the 40x40 pixel version, 140 is 56x56, and 180 is 72x72. Just reference the image as "AppLogo.jpg" and the appropriate version will be used automatically. (You can also detect scale with DisplayProperties.ResolutionScale and manually choose an image.)
Here's a couple of articles with more examples/details:
"Guidelines for scaling to pixel density"
"Quickstart: Using file or image resources"
There's also some scaling discussion in the forums (general, not specific to search) here and here.
I've had theft problems outside my house so I setup a simple webcam to capture every second with Dorgem (http://dorgem.sf.net).
Dorgem does offer a feature to use motion detection to only capture frames where something is moving on the screen. The problem is that the motion detection algorithm it uses is extremely sensitive. It goes off because of variations in color between successive shots on my cheap webcam, and it also goes off because the trees in front of the house are blowing in the wind. Additionally, the front of my house is a high traffic area so there is also a large number of legitimately captured frames.
I average capturing 2800/3600 frames every second using Dorgem's motion detection. This is too much for me to search through to find out where the interesting activity is.
I wish I could re-position the camera to a more optimal position where it would only capture the areas I'm interested in, so that motion detection would be simpler, however this is not an option for me.
I think that because my camera has a fixed position and each picture frames the same area in front of my house, then I should be able to scan the images and figure out which ones have motion in some interesting region of that image, throwing out all other frames.
For example: if there's a change in pixel 320,240 then someone has stepped in front of my house and I want to see that frame, but if there's a change in pixel 1,1 then its just the trees blowing in the wind and the frame can be discarded.
I've looked at pdiff, a tool for finding diffs in sets of pictures, but it seems to be also focused on diffing the entire picture, rather than a specific region of it:
http://pdiff.sourceforge.net/
I've also looked at phash, a tool for calculating a hash based on human perception of an image, but it seems too complex:
http://www.phash.org/
I suppose I could implement it in a shell script using imagemagick's mogrify -crop to cherry pick the regions of the image I'm interested in, then running pdiff to find the interesting ones, and using that to pick out the interesting frames.
Any thoughts? ideas? existing tools?
cropping and then using pdiff seems like the best choice to me.