I have a lot of many mobile screen standard.Example :
240x320
320x480
480x800
........
But in log i received many screen size (This screen size was detected by javascript then send to the webserver, when device access my website) :example 241x322, 239x320, 481x799 ... This size was wrong any pixel at height , weight or both of height and weight
What is the best of algorithm, or library to normalize screen size (know size 241x322 is 240x320).
You could run a simple algorithm that searches for the closest match, and change to that.
Run a loop looking for the distance (absolute value) from all heights / weights, and get the min one.
If you do them in ascending/descending order, you can break once you see the changes grow to improve efficiency.
You could also make sure the aspect radio is right as a measure of extra safety.
Related
Just a straight forward question. I´m trying to make the best possible choice here and there is too much information for a "semi-beginner" like me.
Well, at this point, I´m trying with screen size values for my layout (activity_main.xml (normal, large, small)) and with different densities (xhdpi, xxhdpi, mhdpi) and, if a can say so myself, it is a mess. Do I have to create every possible option to support all screen sizes and densities? Or am I doing something really wrong here? what is the best approach for this?
My layouts are now activity_main(normal_land_xxhdpi) and I have serious doubts about it.
I´m using last version of android studio of course. My app is a single activity with buttons, textview and others. Does not have any fragments or intents whatsoever, and for that reason I think this has to be an easy task, but not for me.
Hope you guys can help. I don't think i need to put any code here, but if needed, i can add it.
If you want to make a responsive UI for every device you need to learn about some things first:
-Difference between PX, DP:
https://developer.android.com/training/multiscreen/screendensities
Here you can understand that dp is a standard measure which android uses to calculate how much pixels, lets say a line, should have to keep the proportions between different screensizes with different densities.
-Resolution, Density and Ratio:
The resolution is how much pixels a screen has over height and width. This pixels can be smaller or bigger, so for instance, if you have a screen A with 10x10 px whose pixels are two times smaller than other screen B with 10 x 10 pixels too, A is two times smaller than B even when both have 10 x 10 px.
For that reason exists the meaning Density, which is how much pixels your screen has for every inch, so you can measure the quality of a screen where most pixels per inch (ppi) is better.
Ratio tells you how much pixels are for height as well as width, for example the ratio of a screen of 1000 x 2000 px is 1:2, a full hd screen of 1920 x 1080 is 16:9 (16 pixels height for every 9 pixels width). A 1:1 ratio is a square screen.
-Standard device's resolutions
You can find the most common measurements on...
https://material.io/resources/devices/
When making a UI, you use the DP measurements. You will realize that even when resolution between screens are different, the DP is the same cause they have different densities.
Now, the right way is going with constraint layout using dp measures to put your views on screen, with correct constraints the content will adapt to other screen sizes
Anyway, you will need to make additional XML for some cases:
-Different orientation
-Different ratio
-Different DP resolution (not px)
For every activity, you need to provide a portrait and landscape design. If other device has different ratio, maybe you will need to adjust the height or width due to the proportions of the screens aren't the same. Finally, even if the ratio is the same, the DP resolution could be different, maybe you designed an activity for a 640x360dp and other device has 853x480dp, which means you will have more vertical space.
You can read more here:
https://developer.android.com/training/multiscreen/screensizes
And learn how to use constraintLayout correctly:
https://developer.android.com/training/constraint-layout?hl=es-419
Note:
Maybe it seems to be so much work for every activity, but you make the first design and then you just need to copy the design to other xml with some qualifiers and change the dp values to adjust the views as you wants (without making from scratch) which is really faster.
I'm building a multi user conference room and I want all of my users coming from both mobile and desktop, so both portrait and landscape orientation, to have a roughly equal amount of space dedicated to their feed, without cutting off too much or adding too many bars. The screen they need to be packed in also can be portrait and landscape.
Since we're limiting the amount of users to 5 I can actually just hard code the ideal layout and be done with it under an hour, allowing for some inefficiency but it would work well enough in all circumstances, but I am curious how to do it with an algorithm.
What I've found so far are algorithms that pack fixed size rectangles and leave some space where rectangles wouldn't fit. What I'm looking for is an algorithm that does the following:
Rectangles should be as close to their original ratio as possible
Rectangles original size is irrelevant, just their ratio
The resulting sizes should be as similar as possible
No space should be left unused
Programming language is irrelevant, but I will implement it in Swift to begin with
Currently I have a program that allows the user to paint on it by capturing the mouse position every 0.05 seconds and drawing a line between a point and the next. With that setup I am looking for a way to identify shapes like a circle, a rectangle or the letter 'P'.
My current algorithm divides the screen on sections, then marks the sections with points recorded by the player and makes a matrix with the marked sections, then compares that matrix with every shape matrix.
This lacks any kind of support for rotations, sizes or positions. Also the control of the threshold is tricky returning in most cases fake results.
I need an algorithm that allows to identify for example a ' P ' as a ' P '.
Note: My current application is running on a c++ framework so any libraries or tools are welcome but I am interested on the algorithm behind.
Edit: After thinking around the problem I have changed the current grid on the screen, instead of that I capture the points and shift them to resize
the shape so it fits on a grid and over that grid compare with the known shapes.
Picture of the process
This solves the position and size problems while being fast enough, also rotating the input and then resizing in a loop may solve the rotation problem (seems though it would have an high cost and won't be very reliable)
I would gladly welcome alternative methods of handling shape comparison or the rotation.
After thinking around the problem I have changed the current grid on the screen, instead of that I capture the points and shift them to resize
the shape so it fits on a grid and over that grid compare with the known shapes.
Picture of the process
This solves the position and size problems while being fast enough, also rotating the input and then resizing in a loop may solve the rotation problem (seems though it would have an high cost and won't be very reliable)
I'm building a photographic film scanner. The electronic hardware is done now I have to finish the mechanical advance mechanism then I'm almost done.
I'm using a line scan sensor so it's one pixel width by 2000 height. The data stream I will be sending to the PC over USB with a FTDI FIFO bridge will be just 1 byte values of the pixels. The scanner will pull through an entire strip of 36 frames so I will end up scanning the entire strip. For the beginning I'm willing to manually split them up in Photoshop but I would like to implement something in my program to do this for me. I'm using C++ in VS. So, basically I need to find a way for the PC to detect the near black strips in between the images on the film, isolate the images and save them as individual files.
Could someone give me some advice for this?
That sounds pretty simple compared to the things you've already implemented; you could
calculate an average pixel value per row, and call the resulting signal s(n) (n being the row number).
set a threshold for s(n), setting everything below that threshold to 0 and everything above to 1
Assuming you don't know the exact pixel height of the black bars and the negatives, search for periodicities in s(n). What I describe in the following is total overkill, but that's how I roll:
use FFTw to calculate a discrete fourier transform of s(n), call it S(f) (f being the frequency, i.e. 1/period).
find argmax(abs(S(f))); that f represents the distance between two black bars: number of rows / f is the bar distance.
S(f) is complex, and thus has an argument; arctan(imag(S(f_max))/real(S(f_max)))*number of rows will give you the position of the bars.
To calculate the width of the bars, you could do the same with the second highest peak of abs(S(f)), but it'll probably be easier to just count the average length of 0 around the calculated center positions of the black bars.
To get the exact width of the image strip, only take the pixels in which the image border may lie: r_left(x) would be the signal representing the few pixels in which the actual image might border to the filmstrip material, x being the coordinate along that row). Now, use a simplistic high pass filter (e.g. f(x):= r_left(x)-r_left(x-1)) to find the sharpest edge in that region (argmax(abs(f(x)))). Use the average of these edges as the border location.
By the way, if you want to write a source block that takes your scanned image as input and outputs a stream of pixel row vectors, using GNU Radio would offer you a nice method of having a flow graph of connected signal processing blocks that does exactly what you want, without you having to care about getting data from A to B.
I forgot to add: Use the resulting coordinates with something like openCV, or any other library capable of reading images and specifying sub-images by coordinates as well as saving to new images.
I am currently working on OCR software and my idea is to use templates to try to recognize data inside invoices.
However scanned invoices can have several 'flaws' with them:
Not all invoices, based on a single template, are correctly aligned under the scanner.
People can write on invoices
etc.
Example of invoice: (Have to google it, sadly cannot add a more concrete version as client data is confidential obviously)
I find my data in the invoices based on the x-values of the text.
However I need to know the scale of the invoice and the offset from left/right, before I can do any real calculations with all data that I have retrieved.
What have I tried so far?
1) Making the image monochrome and use the left and right bounds of the first appearance of a black pixel. This fails due to the fact that people can write on invoices.
2) Divide the invoice up in vertical sections, use the sections that have the highest amount of black pixels. Fails due to the fact that the distribution is not always uniform amongst similar templates.
I could really use your help on (1) how to identify important points in invoices and (2) on what I should focus as the important points.
I hope the question is clear enough as it is quite hard to explain.
Detecting rotation
I would suggest you start by detecting straight lines.
Look (perhaps randomly) for small areas with high contrast, i.e. mostly white but a fair amount of very black pixels as well. Then try to fit a line to these black pixels, e.g. using least squares method. Drop the outliers, and fit another line to the remaining points. Iterate this as required. Evaluate how good that fit is, i.e. how many of the pixels in the observed area are really close to the line, and how far that line extends beyond the observed area. Do this process for a number of regions, and you should get a weighted list of lines.
For each line, you can compute the direction of the line itself and the direction orthogonal to that. One of these numbers can be chosen from an interval [0°, 90°), the other will be 90° plus that value, so storing one is enough. Take all these directions, and find one angle which best matches all of them. You can do that using a sliding window of e.g. 5°: slide accross that (cyclic) region and find a value where the maximal number of lines are within the window, then compute the average or median of the angles within that window. All of this computation can be done taking the weights of the lines into account.
Once you have found the direction of lines, you can rotate your image so that the lines are perfectly aligned to the coordinate axes.
Detecting translation
Assuming the image wasn't scaled at any point, you can then try to use a FFT-based correlation of the image to match it to the template. Convert both images to gray, pad them with zeros till the originals take up at most 1/2 the edge length of the padded image, which preferrably should be a power of two. FFT both images in both directions, multiply them element-wise and iFFT back. The resulting image will encode how much the two images would agree for a given shift relative to one another. Simply find the maximum, and you know how to make them match.
Added text will cause no problems at all. This method will work best for large areas, like the company logo and gray background boxes. Thin lines will provide a poorer match, so in those cases you might have to blur the picture before doing the correlation, to broaden the features. You don't have to use the blurred image for further processing; once you know the offset you can return to the rotated but unblurred version.
Now you know both rotation and translation, and assumed no scaling or shearing, so you know exactly which portion of the template corresponds to which portion of the scan. Proceed.
If rotation is solved already, I'd just sum up all pixel color values horizontally and vertically to a single horizontal / vertical "line". This should provide clear spikes where you have horizontal and vertical lines in the form.
p.s. Generated a corresponding horizontal image with Gimp's scaling capabilities, attached below (it's a bit hard to see because it's only one pixel high and may get scaled down because it's > 700 px wide; the url is http://i.stack.imgur.com/Zy8zO.png ).