I'm writing an application that displays different color swatches to help people with color coordination. How can I find the RGB values of real world objects?
For example, one of the colors is Red Apple but obviously a red apple isn't just red. It has hints of other colors in it.
Well, it's not an easy task to be honest, but a good place to start would be with a digital camera and/or a flatbed scanner.
Once you have an image in the computer then the task is somewhat easier beacuse all you need is to use a picture / photo editing package such as photoshop or the gimp to sample a selection of colours before using them in your application.
once you have a few different samples, then you need to average them, and that's quite easy to do. Lets say you took 5 samples of RGB values:
255,50,10
250,40,11
253,51,15
248,60,13
254,45,20
You simply need to add up each component and divide by how many samples you took so:
Red = (255 + 250 + 253 + 248 + 254) / 5
Green = (50 + 40 + 51 + 60 + 45) / 5
Blue = (10 + 11 + 15 + 13 + 20) / 5
Now, if what your asking is how do I do this automatically in program code, that's a whole different kettle of fish, first you'll need something like a web cam, then you'll need to write code to capture images from the web-cam, then once you have your image you'll need not just the ability to pick colour, but to actually figure out where in the image the object you want to pick the colour from actually is.
For now, I'd look at using the first method, it's a bit manual I agree, but far easier and will get you started.
The image processing required to do the second maths has given software engineers & comp scientists headaches for years and is still not a perfect science... and that's before we even start thinking about the maths.
For each object, I would do it this way:
Use goolge images to search pictures of the object you want.
Select the one that have the most accurate color, say, to your idea of a "red apple" for example.
--you can skip 1 and 2 if you have a digital picture of the object.
Open that image in Paint; you can do it stroking the "Impr Pant" key on your keyboard, opening Paint, and then "ctrl+v" will paste the screenshoot in paint.
Select the pick color tool on Paint (the one like a dropper) and click on the image, just in the place with the color you want.
Select from the menu, "Colors -> Edit colors" and then in the Colors palette that opens, clic on "Define Custom Colors".
You got it, there RGB values are at your right.
There must be an easier way, but this will work.
If your looking for a programmatic solution then you would look into bitwise operations. The general idea here is you would read the image in it's binary roots and then you could logically convert the bits into RGB values. There are several methods for doing this depending on programming language. Here is a method for Actionscript3.
http://www.flashandmath.com/intermediate/rgbs/explanations.html
also if your looking for the average color look here, (for AS3)
http://blog.soulwire.co.uk/code/actionscript-3/extract-average-colours-from-bitmapdata
a related method and explanation for Java
Bitwise version of finding RGB in java
Related
I am a newbie in CNN and I want to ask what does the channels do in SSD for example? For what reason they exist? For example 18X18X1024 (third number)?
Thanks for any answer.
The dimensions of an image can be represented using 3 numbers. For example, a color image in CIFAR-10 dataset has a height of 32 pixels, width of 32 pixels and is represented as 32 x 32 x 3. Here 3 represents the number of channels in your image. Color images have a channel size of 3 (usually RGB), while a grayscale image will have a channel size of 1.
A CNN will learn features of the images that you feed it, with increasing levels of complexity. These features are represented by the channels. The deeper you go into the network, the more channels you will have that represents these complex features. These features are then used by the network to perform object detection.
In your example, 18X18X1024 means your input image is now represented with 1024 channels, where each channel represents some complex feature/information about the image.
Since you are a beginner, I suggest you look into how CNNs work in general, before diving into object detection. A good start would be image classification using CNNs. I hope this answers your question. Happy learning!! :)
Depending on which skin I use in the simulator, the result from the following method differs :
Display.getInstance().getDeviceDensity();
The results have nothing to do with the real device density since for a Xoom skin it outputs 30 (149 ppi in reality), for a an Iphone 6 it outputs 50 (329 in reality).
I noticed that because I need to translate char height measured in Gimp (72 dpi) into the device world so that it looks alike on an image.
Any help on that topic would be appreciated!
Cheers
The JavaDocs for getDeviceDensity state:
Returns one of the density variables appropriate for this device,
notice that density doesn't always correspond to resolution and an
implementation might decide to change the density based on DPI
constraints.
Returns:
one of the DENSITY constants of Display
The DENSITY constants refers to one of these.
Notice you can also use convertToPixels which is probably a far better API to use. The density API is mostly used to pick the right multi image and should rarely be used in user code.
Given an image (i.e. newspaper, scanned newspaper, magazine etc), how do I detect the region containing text? I only need to know the region and remove it, don't need to do text recognition.
The purpose is I want to remove these text areas so that it will speed up my feature extraction procedure as these text areas are meaningless for my application. Anyone know how to do this?
BTW, it will be good if this can be done in Matlab!
Best!
You can use Stroke Width Transform (SWT) to highlight text regions.
Using my mex implementation posted here, you can
img = imread('http://i.stack.imgur.com/Eyepc.jpg');
[swt swtcc] = SWT( img, 0, 10 );
Playing with internal parameters of the edge-map extraction and image filtering in SWT.m can help you tweak the resulting mask to your needs.
To get this result:
I used these parameters for the edge map computation in SWT.m:
edgeMap = single( edge( img, 'canny', [0.05 0.25] ) );
Text detection in natural images is an active area of research in computer vision community. U can refer to ICDAR papers. But in your case I think it should be simple enough. As you have text from newspaper or magazines, it should be of fixed size and horizontally oriented.
So, you can apply scanning window of a fixed size, say 32x32. Train it on ICDAR 2003 training dataset for positive windows having text in it. U can use a small feature set of color and gradients and train an SVM which would give a positive or negative result for a window having text or not.
For reference go to http://crypto.stanford.edu/~dwu4/ICDAR2011.pdf . For code, you can try their homepages
This example in the Computer Vision System Toolbox in Matlab shows how to detect text using MSER regions.
If your image is well binarized and you know the usual size of the text you could use the HorizontalRunLengthSmoothing and VerticalRunLengthSmoothing algorithms. They are implemented in the open source library Aforge.Net but it should be easy to reimplement them in Matlab.
The intersection of the result image from these algorithm will give you a good indication that the region contains text, it is not perfect but it is fast.
The question isn't exactly concerned with touch develop rather just basic programming "structure" or syntax.
what I am trying to do is create a simple compass working on the phones heading capability. The heading capability just spits out degree readings to several (like 12) decimal places.
Anyway, even just letting the phone spit out the heading, eventually the phone will crash, why is that? Running out of memory?
The reason I came here is because of this:
I want to update the page with a photo of an associated rotation based on degree readout. I can't figure out how to do something like if 0 < x < 1 post this picture. Since the heading readout varies like 321.18364947363 and 321.10243635471
So currently I am testing this: several if / if else statements saying if heading output is 1 post picture with 1 degree rotation, 2 post picture with 2 degree rotation. This definitely and guaranteed crashes the phone. Why? Memory?
If you are a touch developer, would it be easier and more sane to simply take a round object, center it in relation to a square image and use it as a sprite or object which then you can dictate what angular velocity and position the object has without doing / using 360 individual images.
GAH! Damn character limits / thread format
this is what follows what I last wrote below for anyone that cares :
The concept seems simple enough but I am basically a programming noob, I was all over the place trying to learn Python, Java and C/C#/C++. ( I wrote this on my Windows Phone 8 but I was unable to copy the text ( GAY ) ) I am happy to have come across Touch Develop because it is better for me as a visual learner. (Thanks for the life story )right ? haha
The idea would have been to use this dumb pink against black giant compass with three headings / points of interests namely A fixed relative north, the heading and a position given by the person to be found's lat and long coordinates relative to the finder's phone's current location (lat and long ). This app in my mind would be used for party scenarios. I would have benefited from this app had the circumstances been right, I was lost at a party and I had to take a cab home for $110.00 because I didn't drive to that party.
I am now working on an eye tracking project. In this project I am tracking eyes in a webcam video (resolution if 640X480).
I can locate and track the eye in every frame, but I need to locate the pupil. I read a lot of papers and most of them refer to Alan Yuille's deformable template method to extract and track the eye features. Can anyone help me with the code of this method in any languages (matlab/OpenCV)?
I have tried with different thresholds, but due to the low resolution in the eye regions, it does not work very well. I will really appreciate any kind of help regarding finding pupil or even iris in the video.
What you need to do is to convert your webcam to a Near-Infrared Cam. There are plenty of tutorials online for that. Try this.
A Image taken from an NIR cam will look something like this -
You can use OpenCV then to threshold.
Then use the Erode function.
After this fill the image with some color takeing a corner as the seed point.
Eliminate the holes and invert the image.
Use the distance transform to the nearest non-zero value.
Find the max-value's coordinate and draw a circle.
If you're still working on this, check out my OptimEyes project: https://github.com/LukeAllen/optimeyes
It uses Python with OpenCV, and works fairly well with images from a 640x480 webcam. You can check out the "Theory Paper" and demo video on that page also. (It was a class project at Stanford earlier this year; it's not very polished but we made some attempts to comment the code.)
Depending on the application for tracking the pupil I would find a bounding box for the eyes and then find the darkest pixel within that box.
Some psuedocode:
box left_location = findlefteye()
box right_location = findrighteye()
image_matrix left = image[left_location]
image_matrix right = image[right_location]
image_matrix average = left + right
pixel min = min(average)
pixel left_pupil = left_location.corner + min
pixel right_pupil = right_location.corner + min
In the first answer suggested by Anirudth...
Just apply the HoughCirles function after thresholding function (2nd step).
Then you can directly draw the circles around the pupil and using radius(r) and center of eye(x,y) you can easily find out the Center of Eye..