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I have an image on mat-lab and i would like to analyze the intensity of the light in the image.
I want to do some kind of 3-D plot so i can see it clearly and get from it the average of the intensity and other stuff.
Can someone tell me what command should i use and how? my knowledge on mat-lab is very poor.
I have tried to do a surf plot but it keeps telling me "Warning: CData must be double or single unless it is used only as a texture data "
and i don't know how to change the type or what can i do to overcome this problem.
Convert your image from uint8 to double type. This way you'll be able to do more operations on Matlab in a more flexible way:
>> doubleImg = im2double( uint8Img );
Alternatively (if you do not have image processing toolbox):
>> doubleImg = double( uint8Img ) / 255.0 ;
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How can I detect QR codes when processing the pixels of an image?
The image might be pixelated (low-fidelity), noisy (missing or extra pixels in a line), blurry (antialiasing), or be at an angle (image skew). Simply looping through the pixels seems like it would require some sort of line detection and then you could figure out a good calculate of the number of expected blocks between the three (or four) different corners.
You will need to use an image processing library to detect QRCodes. I’ve used go-zxing in the past which is 100% native go.
You will need to be familiar with the go image package that’s part of the standard library.
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I got this aerial image of a warehouse from Google Map, and I'd like to analyze the characteristics of the building inside the image. How can I find the approximate building footprint?
I learned some functions for matlab image processing. But I'm still a newbie for image processing. I'll be very appreciated if anyone can help me.
Or is it easier to figure out the area using the roadmap image below?
Import the roadmap image in Matlab, convert in in an 8bit greyscale image and use the following binarization.
BW = imbinarize(I,'adaptive','Sensitivity',0.68);
figure, imshow(BW,[0,1])
From Here you can either use regionprops (link) or extract lines using a Hough transform.
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I have a final project in which I have to combine two images from optic disk segmentation and optic cup segmentation on binary images. I can't combine both images.
i wish anyone can help me. thankyou
If the images are binary, you may want to take the logical AND to get the pixels that are white in both of them.
im3=im1 & im2;
Assuming that the two images have the same size and the values are in {0,1}, I think
result = image1 .* image2;
would be the most natural choice, yielding a result like this:
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I am trying to find out a way to "score" images that are likely to be a logo. I have thought on using the concept of contrast ratio. I will be comparing one image that is clearly to be a logo with other images that basically will be just a background color or a background color with a column in another color. For example:
vs
So, how can I measure the contrast ratio using any Ruby library? I am currently using Minimagick but I haven't found a way to get the contrast ratio. There are options to modify it, but not to get the ratio per say.
You can do this in roughly two steps.
First get the histogram of the image using RMagick:
http://www.simplesystems.org/RMagick/doc/image1.html#color_histogram
The next step is applying some formula. The one I provide below is one I just came up with off the top of my head. However, there are a few others:
http://en.wikipedia.org/wiki/Contrast_(vision)#Formula
Next compute the average color by multiplying each pixel by it's weight and then dividing by the total number of pixels. Something like this:
color_histogram.each do |pixel, count|
total_red += pixel.red*count
total_blue += pixel.blue*count
total_green += pixel.green*count
end
average_red = total_red / pixel_count
average_blue = total_blue / pixel_count
average_green = total_green / pixel_count
Once you have your "average color", determine the total distance between each pixel and the average:
color_histogram.each do |pixel, count|
distance_red += Math.abs(average_red - pixel.red)
distance_blue += Math.abs(average_blue - pixel.blue)
distance_green += Math.abs(average_green - pixel.green)
end
The sum distance_red + distance_blue + distance_green should roughly be equivalent to the "contrast" of the image.
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I have created this using the mathematica bezier curves. I just want to know how could I fill these fonts with colour using some mathematica routines.
Thanks
If you look at all the examples hidden behind the drop down links on
http://reference.wolfram.com/mathematica/ref/FilledCurve.html
this shows a number of examples of closed bezier curves filled with colors.