Develop Reference

Using Java & BoofCV to detect shapes in an image file

I've not found anything here or on google. I'm looking for a way to identify shapes (circle, square, triangle and various other shapes) from a image file. Some examples:
You get the general idea. Not sure if BoofCV is the best choice here but it looks like it should be straightforward enough to use, but again I know nothing about it. I've looked at some of the examples and I though before I get in over my head (which is not hard to do some days), I thought I would ask if there is any info out there.
I'm taking a class on Knowledge Based AI solving Ravens Progressive Matrix problems and the final assignment will use strictly visual based images instead of the text files with attributes. We are not being graded on the visual since we only have a few weeks to work on this section of the project and we are encouraged to share this information. SOF has always been my go to source for information and I'm hoping someone out there might have some ideas on where to start with this...
Essentially what I want to do is detect the shapes (?? convert them into 2D geometry) and then make some assumptions about attributes such as size, fill, placement etc, create a text file with these attributes and then using that, send it through my existing code based that I wrote for my other projects to solve the problems.
Any suggestions????

There are a lot of ways you can do it. One way is to find the contour of the shape then fit a polygon to it or a oval. If you git a polygon to it and there are 4 sides with almost equal length then its a square. The contour can be found with binary blobs (my recommendation for the above images) or canny edge.
http://boofcv.org/index.php?title=Example_Fit_Polygon
http://boofcv.org/index.php?title=Example_Fit_Ellipse

How to Increase accuracy of Eigenface Algorithm?

I have asked this question in this too. But since the topic was a different one, maybe it was not noticed. I got the eigenface algorithm for face recognition working using opencv in java. I wanted to increase the accuracy of the code as its a well known fact that eigenface relies greatly on the light intensity.
What I have Right Now
I get perfect results if I give a check for a image clicked at the same place where the pictures in my database have been clicked, but the results get weird as I give in images clicked in different places.
I figured out that the reason was that my images differ in the light intensity.
Hence , my question is
Is there any way to set a standard to the images saved in the database or the ones that are coming fresh into the system for a recognition check so that I can improve on the accuracy of the face-recognition system that I have currently?
Any kind of positive solution to the problem would be really helpful.

Identifying the lighting intensity and pose is the important factor of face recognition. Try to do histogram comparison with training and testing image (http://docs.opencv.org/doc/tutorials/imgproc/histograms/histogram_comparison/histogram_comparison.html). This parameter helps to avoid the worst lighting situation. And pre processing is one of the successful key factor of Face recognition. Gamma Correction and DOG filtering may reduce the lighting problems.

You can also elliptical filter out only the face,removing the noise created by hair,neck etc.
The OpenCV cookbook provides an excellent and simple tutorial on this.

Below are the following options which may help you boost your accuracy
1] Image Normalization:
Make your image pixel values from 0 to 1 so that to reduce the effect of lighting conditions
2] Image Alignment (This is a very important step to achieve good performance):
Align all the train images and test images so that eyes, nose, mouth of all the faces in all the images have almost the same co-ordinates
Check this post on face alignment (Highly recommended) : https://www.pyimagesearch.com/2017/05/22/face-alignment-with-opencv-and-python/
3] Data augmentation trick:
You can add filters to you faces that will have an effect of the same face in different lighting conditions
So from one face you can make several images in different lighting conditions
4] Removing Noise:
Before performing step 3 apply Gaussian blur to all the images

Scalable clipping mask

I need to to clip variablesized images into puzzle shaped pices like this(not squares): http://www.fernando.com.ar/jquery-puzzle/
I have considered the posibility of doing this with a php library like Cairo or GD, but have little to no experience with these librays, and see no immidiate soulution for creating a clipping mask dynamicaly scalable for different sized images.
I'm looking for guidance/tips on which serverside programing language to use to accomplish this task, and preferably an approach to this problem.

You can create an image using GD with the size of the puzzle piece. and then copy the full image on that image with the right cropping to get the right part of the image.
Then you can just dynamically color in every part of the piece you want to remove with a distinct color (eg #0f0) and then use imagecolorallocatealpha to make that color transparent. Do it for each piece and you have your server side image pieces.
However, if I where you I would create the clipping mask of each puzzle peace in advance in the distinct color. That would make two images per connection (one with the "circle" connecter sticking out and one where this circle connector fits into). That way you can just copy these masks onto the image to create nice edges quickly.

GD is quite complicated, I've heard very good things about Image Magick for which there is a PHP version and lots of documentation on php.net. However, not all web servers would have this installed by default.
http://www.php.net/manual/en/book.imagick.php

If you choose to do it using PHP with GD then the code here may help:
http://php.amnuts.com/index.php?do=view&id=15&file=class.imagemask.php
Essentially what you need to do with GD is to start with a mask at a particular size and then use the imagecopyresampled function to copy the mask image resource to a larger or smaller size. To see what I mean, check out the _getMaskImage method class shown at the url above. A working example of the output can be seen at:
http://php.amnuts.com/demos/image-mask/
The problem with doing it via GD, as far as I can tell, is that you need to do it a pixel at a time if you want to achieve varying opacity levels, so processing a large image could take a few seconds. With ImageMagick this may not be the case.

Object detection with a generic webcam

Here’s my task which I want to solve with as little effort as possible (preferrably with QT & C++ or Java): I want to use webcam video input to detect if there’s a (or more) crate(s) in front of the camera lens or not. The scene can change from "clear" to "there is a crate in front of the lens" and back while the cam feeds its video signal to my application. For prototype testing/ learning I have 2-3 images of the “empty” scene, and 2-3 images with one or more crates.
Do you know straightforward idea how to tackle this task? I found OpenCV, but isn't this framework too bulky for this simple task? I'm new to the field of computer vision. Is this generally a hard task or is it simple and robust to detect if there's an obstacle in front of the cam in live feeds? Your expert opinion is deeply appreciated!

Here's an approach I've heard of, which may yield some success:
Perform edge detection on your image to translate it into a black and white image, whereby edges are shown as black pixels.
Now create a histogram to record the frequency of black pixels in each vertical column of pixels in the image. The theory here is that a high frequency value in the histogram in or around one bucket is indicative of a vertical edge, which could be the edge of a crate.
You could also consider a second histogram to measure pixels on each row of the image.
Obviously this is a fairly simple approach and is highly dependent on "simple" input; i.e. plain boxes with "hard" edges against a blank background (preferable a background that contrasts heavily with the box).

You dont need a full-blown computer-vision library to detect if there is a crate or no crate in front of the camera. You can just take a snapshot and make a color-histogram (simple). To capture the snapshot take a look here:
http://msdn.microsoft.com/en-us/library/dd742882%28VS.85%29.aspx

Lots of variables here including any possible changes in ambient lighting and any other activity in the field of view. Look at implementing a Canny edge detector (which OpenCV has and also Intel Performance Primitives have as well) to look for the outline of the shape of interest. If you then kinda know where the box will be, you can perhaps sum pixels in the region of interest. If the box can appear anywhere in the field of view, this is more challenging.

This is not something you should start in Java. When I had this kind of problems I would start with Matlab (OpenCV library) or something similar, see if the solution would work there and then port it to Java.
To answer your question I did something similar by XOR-ing the 'reference' image (no crate in your case) with the current image then either work on the histogram (clustered pixels at right means large difference) or just sum the visible pixels and compare them with a threshold. XOR is not really precise but it is fast.
My point is, it took me 2hrs to install Scilab and the toolkits and write a proof of concept. It would have taken me two days in Java and if the first solution didn't work each additional algorithm (already done in Mat-/Scilab) another few hours. IMHO you are approaching the problem from the wrong angle.
If really Java/C++ are just some simple tools that don't matter then drop them and use Scilab or some other Matlab clone - prototyping and fine tuning would be much faster.

There are 2 parts involved in object detection. One is feature extraction, the other is similarity calculation. Some obvious features of the crate are geometry, edge, texture, etc...
So you can find some algorithms to extract these features from your crate image. Then comparing these features with your training sample images.

Auto scale and rotate images

Given:
two images of the same subject matter;
the images have the same resolution, colour depth, and file format;
the images differ in size and rotation; and
two lists of (x, y) co-ordinates that correlate the images.
I would like to know:
How do you transform the larger image so that it visually aligns to the second image?
(Optional.) What are the minimum number of points needed to get an accurate transformation?
(Optional.) How far apart do the points need to be to get an accurate transformation?
The transformation would need to rotate, scale, and possibly shear the larger image. Essentially, I want to create (or find) a program that does the following:
Input two images (e.g., TIFFs).
Click several anchor points on the small image.
Click the several corresponding anchor points on the large image.
Transform the large image such that it maps to the small image by aligning the anchor points.
This would help align pictures of the same stellar object. (For example, a hand-drawn picture from 1855 mapped to a photograph taken by Hubble in 2000.)
Many thanks in advance for any algorithms (preferably Java or similar pseudo-code), ideas or links to related open-source software packages.

This is called Image Registration.
Mathworks discusses this, Matlab has this ability, and more information is in the Elastix Manual.
Consider:
Open source Matlab equivalents
IRTK
IRAF
Hugin

you can use the javax.imageio or Java Advanced Imaging api's for rotating, shearing and scaling the images once you found out what you want to do with them.

For a C++ implementation (without GUI), try the old KLT (Kanade-Lucas-Tomasi) tracker.
http://www.ces.clemson.edu/~stb/klt/