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I have this larger image: http://imgur.com/henxc that I need to create a smaller tile of to repeat as a background. However I'm having difficulty knowing what part I need to crop. Can someone please explain how to make this into a small piece that will tile? Is there a technique in Photoshop to make this easier maybe?
You can test if an image will "tile" by cutting it in half (copy half to another layer) and moving the left half to the right side and vice versa. Then you will see how the edges of the tile line up and can make adjustments. You can move one half over the other until they match up, mark them, and use that mark as the edge of the tile. Then repeat the process vertically.
This tut explains it better than I can (and with pictures) starting at step 5.
You should use the offset filter. You need to find the value which is exactly half of your image and enter for both of the numebrs. To get to it go to Filter > Other > Offset.
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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 need a Rubiks cube generator for my project. I searched a lot but I only found websites that provide me a scramble but do not provided a method/function to do so. Is there a way for me to make my own or is an algorithm already available. Thanks
Find yourself a way how to represent the cube (each side and each cell of a side)
Determine, how your representation changes when rotating a horizontal section once clockwise and how your representation changes when rotating a vertical section once clockwise.
n times, randomly select a horizontal or vertical section and rotate it once clockwise.
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I'm working on a project which involves detecting the red blood cells in the blood. RBCs in the blood are never perfectly circular (usually almost eliptical) and they often overlap.
I've searched and found a number of algorithms, but most work for circles only. However, in my case it needs to work for blood from patients with sickle cell disease, where the RBCs are elongated or sickle-shaped. For reference here is an example source image.
Can you suggest an algorithm or approach to solve this problem?
Any help would be greatly appreciated.
As mentioned in the comments, this question is really too broad to answer completely. However, I can give you some pointers in how to address this.
For starters, get yourself the MATLAB Image Processing toolbox.
"Identify red blood cells" is a deceptively simple-sounding task. The first step with any project like this is to figure out what exactly you want to achieve, then start breaking it down into steps of how you will achieve that. Finally, there is the experimental-developmental stage where you try and implement your plan (realise what is wrong with it, then try again).
Cell counting normally uses circularity to identify cells, but that's not possible here because you state you want to identify sickle cells. The other main characteristics distinguishing RBCs from other cells is the colour and size. The colour is more absolute, so start with that. Then think about size. This is a good tutorial on the process of identifying cells although it is in Python the principle is the same.
So we have:
Apply a filter to your image, either isolating the red channel (RGB) or something more complex. Make it monochrome (we don't need colour data).
Smooth the image (e.g. gaussian filter) to reduce the noise and artefacts
Find regional maxima which are (hopefully!) in the center of cells
Label the regional maxima (this should give you the number of cells)
Watershed to find the whole cells an measure size
Hopefully that is enough to get you started!
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I have questions about both sift and phash
First of all, I'm using SIFT to identify similar image in real-time service.
Like pictures by phone-camera, small amount of rotation and blurred effect could be.
And I found Phash. So, I test phash on its demo page. But result made me to sigh.
This is result of above test:
In this test, two images are fixed on x-axis. So they don'
t have rotation. But right images' logo were removed and person was moved to left side. In my eye, This is 'Very Similar'. In addition, SIFT catch this completely.
Now, This is question.
pHash is faster than SIFT?
Is pHash's accuracy reliable?
SIFT's output was too big to use in real-time service. So I must use hash to make output smaller size like LSH(Locality-sensitive hashing). Any other way to I try?
Ok, I got it.
pHash can't recognize rotation and critical movement as same thing.
In case of data space, pHash was dramatically good for using. It is very small size: one image to one hash. SIFT, however, need 128 bytes to get feature point. And there are many feature points in one image.
Eventually, SIFT can identify similar image well than pHash. But more and more size was needed.
In speed bench, I can't test yet. But I think, pHash was faster than SIFT because SIFT have to operate for many features on one images.
If you have another answers for above question, tell me please.
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Is there any way to figure out the pattern from an image?
I need it so that I could create a smaller image from it to repeat so that I could have a background pattern.
p.s: Don't mind the 2 different colors, I just need the almost transparent lines pattern figured out.
Here's a white and black repeating version of the pattern.
What did I do?
Copied some of the top portion of your image
Pasted it into a mask on a solid white layer
Added a photo under the layer to simulate the finished result
Inverted the mask
Adjusted the levels on the mask until I got an effect that was similar to your example
Cropped the image down to one pattern so it would be repeatable
Hit CMD CNTRL SHIFT S to save for web and saved it as a 24-bit PNG
Then inverted the white layer to black and saved for web again.
Does that seem to be what you were looking for?
I would use levels to get it to almost black & white.
Use sliders to get the lightest grey as light as possible
use the highlight/Shadow eyedropers within the levels palette to get it even closer
Use threshold to get it the rest of the way.
Take a look at the image below to see if what I described is what you are wanting.