I have thousands of images of sheet music, in 4:3 aspect ratio, that I want to crop to something closer to 16:9. The images have a lot of whitespace, so at least I want to cut that away.
All the images are different, even the margins aren't always the same.
What kind of algorithm should I use in C# to detect the edges of the staves, so I can determine where to crop the image?
(The green box is where I'd want to crop this image)
I've tried looping through all the pixels, that seems to work, but is extremely inefficient.
Is there a better way?
Related
I am writing some software that periodically checks a camera image to identify whether an object has been introduced to the viewed scene. I am using ImageMagick in my WinForms software to compare two images to create a third. In the third image, a pixel is white if the first two images had a similar-coloured pixel, and black if they were different. So if the user sees a group of black pixels, they will know something has been placed in the scene that wasn't there previously, as seen here:
The user will not be seeing this image, so I would like my software to identify this for me. I don't need anything too complex from this analysis - just a Boolean for whether something was changed in the scene.
In my mind, there are two approaches to analysing this; counting the number of black pixels in the image, or I could write some algorithm to identify patches of black. My question is about the second approach, since it feels like the more correct approach. The image is a bit too noisy (you can see false positives on straight edges) for me to feel entirely comfortable with counting.
To identify a group, I have considered using some for loops to look at the colours of pixels surrounding every pixel but this seems like it would take forever. My processing time can't take more than a few seconds so I need to be wary of this. Are there cleaner or more-efficient ways to identify groups of similarly-coloured pixels? Or will I need to run loops and be as efficient as possible?
Threshold the image such that black pixels will have value 1 non black will have zero.
Use connected component labeling to find all the groups of connected black pixels. http://www.imagemagick.org/script/connected-components.php
Filter out the components that are too small or doesn't have the correct shape(for example you can have a long line on the sides so they have a lot of black pixels but you are not expecting to see a long line as a valid group of black pixels)
At this point I assume you have a sense of the scale of the objects that you are interested in capturing. For example, if you did have an entirely non-black screen (let's ignore noise for the purpose of this discussion), except you have a black pixel object that is only roughly 10 pixels in diameter. This seems to be frightfully small, and not enough information to be useful.
Once you have determined what is the minimum size of black mass you are willing to accept, I would then go about querying a staggered matrix.
i.e., a pattern like:
Use a little math to determine what acceptable noise is.
Once you have positive results, (pixels = black), investigate in those sectors.
So I have a program that scans cameras from multiple sources and takes a thumbnail of their view at a certain time and saves them as jpg's.
I would like to now scan these through my C# program and check if any of the created jpg files are completely black (either completely obstructed, or no signal in this case).
I am wondering what would be the best way of solving this problem. Not a color depth issue.
Thanks!
Use the GetPixel(x,y) function to check color at x,y location. You can iterate through the whole image and if they're all black then it's black. You can also check if majority of pixels are gray / black - if so then it's probably a very dim image.
Load picture.
Go through all pixels and check their RGB value.
If you find all below a certain threshhold - assume picture is black.
Beware: you should likely ignore single pixels not being black. Sensors are not perfect. Stuck pixels are a known phenomenon.
Currently I'm seeking for a rather fast and reasonably accurate algorithm in C#/.NET to do these steps in code:
Load an image into memory.
Starting from the color at position (0,0), find the unoccupied space.
Crop away this unnecessary space.
I've illustrated what I want to achieve:
What I can imagine is to get the color of the pixel at (0,0) and then do some unsafe line-by-line/column-by-column walking through all pixels until I meet a pixel with another color, then cut away the border.
I just fear that this is really really slow.
So my question is:
Are you aware of any quick algorithmns (ideally without any 3rd party libraries) to cut away "empty" borders from an in-memory image/bitmap?
Side-note: The algorithm should be "reasonable accurate", not 100% accurate. Some tolerance like one line too much or too few cropped would be way OK.
Addition 1:
I've just finished implementing my brute force algorithm in the simplest possible manner. See the code over at Pastebin.com.
If you know your image is centered, you might try walking diagonally ( ie (0,0), (1,1), ...(n,n) ) until you have a hit, then backtrack one line at a time checking until you find an "empty" line (in each dimension). For the image you posted, it would cut a lot of comparisons.
You should be able to do that from 2 opposing corners concurrently to get some multi-core action.
Of course, hopefully you dont it the pathelogical case of 1 pixel wide line in the center of the image :) Or the doubly pathological case of disconnected objects in your image such that the whole image is centered, but nothing crosses the diagonal.
One improvement you could make is to give your "hit color" some tolerance (adjustable maybe?)
The algorithm you are suggesting is a brute force algorithm and will work all the time for all type of images.
but for special cases like, subject image is centered and is a continuous blob of colors (as you have displayed in your example), binary sort kind of algorithm can be applied.
start from center line (0,length/2) and start in one direction at a time, examine the lines as we do in binary search.
do it for all the sides.
this will reduce complexity to log n to the base 2
For starters, your current algorithm is basically the best possible.
If you want it to run faster, you could code it in c++. This tends to be more efficient than managed unsafe code.
If you stay in c#, you can parallel extensions to run it on multiple cores. That wont reduce the load on the machine but it will reduce the latency, if any.
If you happen to have a precomputed thumbnail for the image, you can apply your algo on the thumbnail first to get a rough idea.
First, you can convert your bitmap to a byte[] using LockBits(), this will be much faster than GetPixel() and won't require you to go unsafe.
As long as you don't naively search the whole image and instead search one side at a time, you nailed the algorithm 95%. Just make you are not searching already cropped pixels, as this might actually make the algorithm worse than the naive one if you have two adjacent edges that crop a lot.
A binary search can improve a tiny bit, but it's not that significant as it will maybe save you a line of search for each direction in the best case scenario.
Although i prefer the answer of Tarang, i'd like to give some hints on how to 'isolate' objects in an image by refering to a given foregroundcolor and backgroundcolor, which is called 'segmentation' and used when working in the field of 'optical inspection', where an image is not just cropped to some detected object but objects are counted and also measured, things you can measure on an object is area, contour, diameter etc..
First of all, usually you'll start really to walk through your image beginning at x/y coordinates 0,0 and walk from left to right and top to bottom until you'll find a pixel that has another value as the background. The sensitivity of the segmentation is given by defining the grayscale value of the background as well as the grayscale value of the foreground. You possibly will walk through the image as said, by coordinates, but from the programs view you'll just walk through an array of pixels. That means you'll have to deal with the formula that calculates the x/y coordinate to the pixel's index in the pixel array. This formula sure needs width and height of the image.
For your concern of cropping, i think when you've found the so called 'pivot point' of your foreground object, you'll usually walk along the found object by using a formula that detects neighbor pixels of the same foregeground value. If there is only one object to detect as in your case, it's easy to store those pixels coordinates that are north-most, east-most, south-most and west-most. These 4 coordinates mark the rectangle your object fits in. With this information you can calculate the new images (cropped image) width and height.
With a mobile device I take a picture of a flat light object on a dark surface. (for instance a coupon clipped out of a newspaper).
The image is then run through a brightness/contrast filter. If it is too dark, vital components are left out. If it is too bright, the writing on the coupon is lost.
This image is then converted into a bitonal image. Any pixel that is 50% or more dark is converted to black, everything else is white. (done)
I am left with a skewed bitonal image (think of a white trapezoid inside a larger rectangle with a black background).
I need to figure out how to crop the image - which when it's on a black background is easier than when it's on a white background. Then, I have to de-skew the image so it is rectangular instead of trapezoidal, while attempting to preserve aspect.
The end result should be a nicely cropped, bitonal, readable image of the coupon.
To crop your image, you can use the LockBits method and scan through all your pixels to find the first pixel with content from the top, left, right and bottom, respectively. How to use LockBits is described nicely here: https://web.archive.org/web/20141229164101/http://bobpowell.net/lockingbits.aspx
Assuming your image is not rotated, and that the skewing comes from the camera held at an angle against the table where the coupon is being photographed, you should now have a skewed image of the coupon, fitting perfectly within the bounds of the cropped bitmap. You should also know the four corners of the trapezoid.
"Undistorting" an image is not as easy as you might think though. However, good people have solved this problem and you can probably port their code to your own use. Here is a link I used to explore this problem in a similar case some time ago:
http://ryoushin.com/cmerighi/en-US/2007-10-29_61/Image_Distortion_Enhancements
I also have some code stored somewhere if you can't make any sense of what you find.
I found an article on image processing from here: http://www.switchonthecode.com/tutorials/csharp-tutorial-image-editing-saving-cropping-and-resizing Everything works fine.
I want to keep the high quality when resizing the image. I think if I can increase the DPI value I can achieve this. Does anyone know if this is possible? And if so, how can I implement it in C#?
For starters, it's worth pointing out that there are two general categories of images; vector [e.g. SVG, WMF, Adobe Illustrator and Corel Draw Graphics] and bitmap (also called raster) images [e.g. Bitmap, JPEG and PNG Images].
Vector images are formed from a series of mathematical equations and/or calculations. Bitmap images, on the other hand, are made up of individual dots (pixels) each corresponding to a particular feature on the object the image is taken of.
If it should happen that you want to resize an image, the first thing to consider is if it is a bitmap or vector image. By virtue of the fact that vector images are obtained from calculations, they can be perfectly resized without losing any detail. The case is different for bitmap images. Since each pixel is independent of the other, when you desire to resize it, you are simply increasing or decreasing the source to target pixel ratio.
So in order to double the size of a vector image, simply multiply the target dimensions by two and everything comes out all right. If you should apply the same effect on a bitmap, you are actually increasing each source pixel to cover four pixels (two rows of two horizontal pixels).
Of course, by applying interpolation and filtering, the computer can "smooth" out the edges of the target pixels so they seem to blend into each other and give the appearance of a reasonably resized image but this output is never the same as resizing a vector image; vector images resize perfectly.
You also mentioned DPI in your question. DPI is essentially the number of pixels that correspond to an inch when the image is printed not when it is viewed on a screen. Therefore by increasing the DPI of the image, you do not increase the size of the image on the screen. You only increase the quality of print [which needless to say depends on the maximum resolution of the printer].
If you really desire to resize the image and the image is a bitmap, as a rule of thumb, do not increase the size beyond 200% of the original image's size else you'll lose the quality.
You can see this answer for code to resize bitmap images.
To see a sample vector image, go to this link.
Note Try zooming in and out of the image to see how well it resizes.
A typical bitmap are the StackOverflow sprites. They do not keep their quality resized.
Further Reading
Vector Graphics: http://en.wikipedia.org/wiki/Vector_image
Bitmap Graphics: http://en.wikipedia.org/wiki/Bitmap_image
Simply If the original image is smaller then the re-sized image then there is hardly anything you can do. Rest is a research problem.
This would only be possible if it's a vector graphic. Look into SVG. Otherwise, I think you might need Silverlight or Flex for that part.
What you're asking isn't really possible. You can't enlarge an image while maintaining the same quality.
If you think about an image as a mapped array of pixels (literally, a "bit-map"), this makes sense. The image is saved with a fixed amount of data, and that's all you have to work with when you resize it. Any examples to the contrary (like TV shows, as suggested by one of the comments) are purely fictional.
The best that you can do is set the InterpolationMode property of the Graphics object you're using to do the resizing to "HighQualityBicubic", which is the highest quality smoothing algorithm supported by GDI+ and in fact by every major graphics package on the market. It's the best that even Adobe Photoshop has to offer. Essentially, interpolation means that the computer is calculating the approximate value of the new pixels you're adding to make the image larger from the relative values of neighboring pixels. It's a "best guess" method, but it's the best compromise we've come up with yet.
At the very least, the resulting images won't have "jaggies" or rough, pixelated lines.
Sample code:
Graphics g;
g.InterpolationMode = Drawing.Drawing2D.InterpolationMode.HighQualityBicubic;
// ... insert the rest of your code here
Beyond that, it's worth noting that GDI+ (which the .NET Framework uses internally for graphics routines) works best with image sizes that are multiples of 16. So if it all possible, you should try and make the width and height of your resized images a multiple of 16. This will preserve as much of the original image quality as possible.
The ideal solution is to switch to vector graphics that can be resized at will. Instead of pixel information, they contain mathematical information used to draw or "render" the image. Read more on Wikipedia.
let's try metadata in GDI+, may be it can suit your request