Basically what I'm trying to do is overlay two images using predefined points on each image.
The images will be of two different sizes probably or scaled differently, don't know this for sure yet. But the images are of the same thing. So what I want to do is say this spot on image one is the same as this spot on image 2. And do this for multiple spots and then have matlab resize or transform to get all those points lined up so that the two images can be overlayed. The thing thats confusing me is having matlab automatically adjust the images so that they can "fit" together.
I have no idea where to start on this, and was just hoping to get a general idea of what i may be able to do.
Just incase someone else knows how to do this I'll throw in what else I need to do. After the two images are on top of each other, one images will be a region map the other a real image. What I need matlab to do is count the amount of dots from the real image in each region of the map.
Thanks for any help.
What you are trying to do is called image registration which is a very common image processing task. You wont need to write much code because matlab has built in functions for this. You use the cp2tform to create a transform from the first to second image and can then apply the transform to the first image using imtransform function. The code will look something like this assuming x,y coordinates of the control points are in an m by 2 matrix called points1 for image1 and points2 for image2.
tform= cp2tform(points1, points2 , 'similarity');
imtransform(image1, tform);
Related
So, I have this dataset of 3k images in grayscale and want to create a colorized respective target to feed them to a model. I decided to do that by changing the RGB color curves. Of course, it is impossible to do this manually for so many images. I couldn't understand how to do this with the python-fu or script-fu. Any ideas?
I know that one way would be to apply the transformation for each channel using OpenCV, the problem with that is that I don't know the equation to the curves that I drew.
I am trying Tensorflow faster_rcnn_resnet101 model to detect multiple objects in a 642*481 image. I tried two ways but none got satisfactory results.
1) In this way, I cropped the objects (50*160 rectangle) from training images (training images might be at a different dim size than 642*481 test image). I use those cropped images to train the faster_rcnn_resnet101. Looks like it has good result if the test set is also a cropped image on the same size. But for the 642*481 test image, it could not detect multiple objects there with good results.
Then I think about maybe the model rescaled the test image to match the 50*160 so the details got lost. In this thought, I tried another way
2) I copied each cropped images into a 642*481 white background respectively (padding basically). So each training image has the same dim size as the test image. The location of the cropped images copied on the background is purposely set as random. However, it still does not have good detection on the test image.
To try its performance, I use GIMP to make windows that contain objects and replace other parts outside the windows with white pixels. The result is much better.
If we only keep one object window in the image and make other parts as white, the result is super.
So my question is what is happening behind those? How could I make it work to detect multiple objects in the test images successfully? Thanks.
The idea is that you should use training images containing multiple instances of objects during training. In other words, you don't have to crop ! Especially that your images are not very big.
However, you DO have to identify (label) the objects. This means to know the bounding box of each object in an image. Furthermore, this data should be assembled together with the original image in a tfrecord file. There is a good guide here with the whole process. In your case, you would label all objects in the source image (642x481), instead of just the "raccoon". If your objects have multiple classes, make sure you label them as so !
If you train with these images, which contain the objects in their contexts, then the network will learn to recognize similar images, which have objects in context.
I have roughly 160 images for an experiment. Some of the images, however, have clearly different levels of brightness and contrast compared to others. For instance, I have something like the two pictures below:
I would like to equalize the two pictures in terms of brightness and contrast (probably find some level in the middle and not equate one image to another - though this could be okay if that makes things easier). Would anyone have any suggestions as to how to go about this? I'm not really familiar with image analysis in Matlab so please bear with my follow-up questions should they arise. There is a question for Equalizing luminance, brightness and contrast for a set of images already on here but the code doesn't make much sense to me (due to my lack of experience working with images in Matlab).
Currently, I use Gimp to manipulate images but it's time consuming with 160 images and also just going with subjective eye judgment isn't very reliable. Thank you!
You can use histeq to perform histogram specification where the algorithm will try its best to make the target image match the distribution of intensities / histogram of a source image. This is also called histogram matching and you can read up about it on my previous answer.
In effect, the distribution of intensities between the two images should hopefully be the same. If you want to take advantage of this using histeq, you can specify an additional parameter that specifies the target histogram. Therefore, the input image would try and match itself to the target histogram. Something like this would work assuming you have the images stored in im1 and im2:
out = histeq(im1, imhist(im2));
However, imhistmatch is the more better version to use. It's almost the same way you'd call histeq except you don't have to manually compute the histogram. You just specify the actual image to match itself:
out = imhistmatch(im1, im2);
Here's a running example using your two images. Note that I'll opt to use imhistmatch instead. I read in the two images directly from StackOverflow, I perform a histogram matching so that the first image matches in intensity distribution with the second image and we show this result all in one window.
im1 = imread('http://i.stack.imgur.com/oaopV.png');
im2 = imread('http://i.stack.imgur.com/4fQPq.png');
out = imhistmatch(im1, im2);
figure;
subplot(1,3,1);
imshow(im1);
subplot(1,3,2);
imshow(im2);
subplot(1,3,3);
imshow(out);
This is what I get:
Note that the first image now more or less matches in distribution with the second image.
We can also flip it around and make the first image the source and we can try and match the second image to the first image. Just flip the two parameters with imhistmatch:
out = imhistmatch(im2, im1);
Repeating the above code to display the figure, I get this:
That looks a little more interesting. We can definitely see the shape of the second image's eyes, and some of the facial features are more pronounced.
As such, what you can finally do in the end is choose a good representative image that has the best brightness and contrast, then loop over each of the other images and call imhistmatch each time using this source image as the reference so that the other images will try and match their distribution of intensities to this source image. I can't really write code for this because I don't know how you are storing these images in MATLAB. If you share some of that code, I'd love to write more.
I am new to Matlab, I am working on a project which will take input an image like this
as we can see it has a plain background (blue), and system will generate it's passport size image with given ratios, first I am working to separate background and person, the approach I searched is like if there is a blue in combinations of rgb matrices of image, then it is background, and rest is a person, but I am little bit confused that if this approach is correct or not, if it is correct then how can I find that current pixel is blue or not, how can I do it with matlab function find. Any help would be appreciated.
If you want to crop your image based on person's face, then there is no need in separating the background from the foreground. Nowadays you will easily find ready implementations of face detection, so, unless you want to implement your own method because the ready one fails, this should be a non-issue. See:
Show[img,
Graphics[{EdgeForm[{Yellow, Thick}], Opacity[0],
Rectangle ###
FindFaces[img = Import["http://i.stack.imgur.com/cSwzj.jpg"]]}]]
Supposing the face is detected correctly, you can expand/retract its bounding box to match the size you are after.
I have two matrices where one is RxCx3 (RGB image) and the other is RxC (labelings). Most of the labels are zero and I would like to paint the non-zero labels on the RGB image. More specifically, I would like to superimpose the figure:
imagesc(labels)
onto the figure:
imshow(rgb)
except the zero values in labels. What is the quickest way to achieve it?
Well here's my interpretation of your problem: You want to superimpose non-zero elements of a matrix onto an image (an example of this might be a heat map).
This page here will give you everything you need regarding the non-zero aspect, but generally you will do something like
find(Labels)
inside a processing section that will then only process those elements of "Labels" > 0. (You can make the expression inside "find" way more complex if your needs change)
Find - Matlab
To actually superimpose the image though (you require labels), you need to make a decision. Physically alter the image to be displayed to show the labels or overlay the labels transparently. The following page has great information on achieving this result:
Overlaying Image in MATLAB
I suspect for your requirements, you'd want to take the transparency route (I would recommend it as well)