I am dealing with an issue while using my model to predict masks on MRI images. The thing is, I have two images which have different dimensions. My goal is to find out how different the mask is. However because my model only takes (256,256) images, I have to resize them. During the resizing process the organ gets very dissimilar in both images because the original dimensions were different. Is there any image processing technique there using which I can resize both my input images in a way their content remains as before.
You could also CenterCrop (https://pytorch.org/vision/stable/generated/torchvision.transforms.CenterCrop.html) your images. Especially if their dimensions are already close to your desired dimension, you won't lose a lot of information, and mostly you are interested in information of the center of your image anyway.
import torchvision.transforms.functional as F
img = F.center_crop(img, crop_size)
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I have been working on Images for few months for my internship, and recently I have been wondering that is there a mathematical way of resizing the images.
This becomes a fairly difficult task to resize the images because many a times freshers like me have little experience about the pre-processing in Images.
Given that my problem statement was Gender classification using the human eye. However I found it difficult because
The images were 3 channel
The images were in rectangular shape (17:11)
I did try to resize the images by following few blogs which said to start small and then go up, while it could have worked I still did not understand how small. I resized them to 800,800 randomly and go Resource Exhaustive error(I was using GPU).
So I ask the community if there is any such mathematical formula or a generalized way of doing the resizing task.
Thank you in advance.
This partially answers your question. But, normally many people use transfer learning and a pre-designed architecture for computer vision tasks. Since almost all architecture is designed for square input shape, you can get a better results by making the shape of your input image squared. Another solution would be only padding your 17X11 to make it square by 0 values. (you need to test to see which one works best in your case, but the common practice is re-shaping to square.)
It is fine to have 3 channel images, almost all images are designed for 3 channel input ( even for BW images it is suggested to repeat the channel to have 3 channel input for the model)
About resizing
About resizing the image, in theory, you need to resize the image to the model you are going to use. For example, LeNet-5 accepts images of Mnist with size 28x28. In theory, larger images result in better model performance, but in your case, the images are super low resolution you can start with 28x28 or 224x224 architectures and later use bigger ones and see if it helps in your case.
About the error it's pretty normal your model size was going to be bigger than your GPU memory so, you see Out of memory error. you can use a smaller model ( and smaller input image size) with your device, or you need to use a device with bigger GPU memory.
Finally, you should consider the size of architecture you are going to reuse to determine the correct resize of the dataset you need. If you are designing your model then best starting point can be something around 28x28 ( basically using Lenet) and later developing based on needs/performance.
the resizing can be as easy as calling a Transform with Pytorch transforms like ( i mean you don't need to manually recreate a copy of the dataset just for resizing)
T.Compose([
T.RandomResize(224)
])
i'm work on graduation project for image forgery detection using CNN , Most of the paper i read before feed the data set to the network they Down scale the image size, i want to know how Does this process effect image information ?
Images are resized/rescaled to a specific size for a few reasons:
(1) It allows the user to set the input size to their network. When designing a CNN you need to know the shape (dimensions) of your data at each step; so, having a static input size is an easy way to make sure your network gets data of the shape it was designed to take.
(2) Using a full resolution image as the input to the network is very inefficient (super slow to compute).
(3) For most cases the features desired to be extracted/learned from an image are also present when downsampling the image. So in a way resizing an image to a smaller size will denoise the image, filtering out much of the unimportant features within the image for you.
Well you change the images size. Of course it changes it's information.
You cannot reduce image size without omitting information. Simple case: Throw away every second pixel to scale image to 50%.
Scaling up adds new pixels. In its simplest form you duplicate pixels, creating redundant information.
More complex solutions create new pixels (less or more) by averaging neighbouring pixels or interpolating between them.
Scaling up is reversible. It doesn't create nor destroy information.
Scaling down divides the amount of information by the square of the downscaling factor*. Upscaling after downscaling results in a blurred image.
(*This is true in a first approximation. If the image doesn't have high frequencies, they are not lost, hence no loss of information.)
I need a dataset(image). So I downloaded images, for training purpose I resized images twice. From random sizes to (200,300), using that resized images I resized them again to (64,64). Is there any possibility that I can face problems while training. Does a picture loss it's data when resized again and again.
can u please explain me in detail. Thanks in advance
Images fundamentally lose their data when down sampling. If a pixel is the fundamental piece of data in an image and you remove pixels, then you have removed data. Different down sample methods lose different amounts of data. For instance a bilinear or bicubic down sample method will use multiple pixels in the larger image to generate a single pixel in the smaller image, whereas nearest neighbor downsampling uses a single pixel in the larger image to generate a single pixel in the smaller image, thereby losing more data.
Whether the down sampling will affect your training depends on more information than you have provided.
I am working on a project, which will intake multiple images (lets say 2 for the moment) and combine them to generate a better image. The resultant image will be a combination of those input images. As a requirement I want to achieve this by using OpenCV. I read about Image Stitching and saw some example images in the process and now I am confused whether image overlapping is equal to image stitching, or can the Stitcher class in OpenCV do Image overlapping? A little clarity as to how can I achieve the above project problem thru OpenCV.
"Image overlapping" is not really a term used in the CV literature. The general concept of matching images via transformations is most often called image registration. Image registration is taking many images and inserting them all into one shared coordinate system. Image stitching relies on that same function, but additionally concerns itself with how to blend multiple images. Furthermore, image stitching tries to take into account multiple images at once and makes small adjustments to the paired image registrations.
But it seems you're interested in producing higher quality images from multiple images of the same space (or from video feed of the space for example). The term for that is not image overlapping but super-resolution; specifically, super-resolution from multiple images. You'll want to look into specialized filters (after warping to the same coordinates) to combine those multiple views into a high resolution image. There are many papers on this topic (e.g.). Even mean or median filters (that is, taking the mean or median at every pixel location across the images) can work well, assuming your transformations are very good.
I have a dataset of about 2000 images. This database contains some blurred images.
How can I automatically remove the blurred images from this database?
I read about fourier transformation to remove the blurred images. First I need to transform my images into fourier domain and then by applying some threshold I will be able to identify the blurred images. Could anybody give me some sample code in matlab for this? I don't know how to determine the threshold. Are there any way to determining this threshold?
This task is really not so simple, if you remove all the images that doesn't contain high frequencies you will end up removing many images that contain smooth scenes even though they are not blurred.
There is no 100% in computer vision, the best thing for you (in my opinion) is to make a human aided software, your software should suggest on the images that it thinks should be removed, but the final call must be made by a human being.