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.
Related
The goal is replace all low resolution images by referring to a repository of high resolution images.
I was able to replace the images, but I noticed that the images were rotated and I also need to reflect changed in the images that I am adding. Also, there is no pattern for changing the rotation of the images. The rotation of the image was correct manually and no records were made for almost 50% of the Images.
I was unable to find a way to calculate the rotation since the images were of different quality (same WIDTHxHEIGHT, but different file size)
The following is one of the cases that need to be resolved:
Original Low Quality Image
Added High Quality Image
Like phoenixstudio said, first downsample the images to the same size. With OpenCV, you can do this with the resize function.
Then compare rotations of the images. Beware that even if the images came from the same high resolution source, it is unlikely that they will be bit-identical for the correct rotation. Different downsampling method or distortion from lossy compression could create a minor difference in pixel values. So compare with a tolerance like mean((A - B)^2) < tol.
Another thought: If these are JPEG images, there might be a rotation field in the EXIF metadata, which might help: see https://jdhao.github.io/2019/07/31/image_rotation_exif_info/
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.)
Keras has this function called flow_from_directory and one of the parameters is called target_size. Here is the explanation for it:
target_size: Tuple of integers (height, width), default: (256, 256).
The dimensions to which all images found will be resized.
The thing that is unclear to me is whether it is just cropping the original image into 256x256 matrix (in this case we do not take the entire image) or it is just reducing the resolution of the image (while still showing us the entire image)?
If it is -let's say - just reducing the resolution:
Assume that I have some xray images with the size 1024x1024 each (for breast cancer detection). And if I want to apply transfer learning to a pretrained Convolutional Neural Network which only takes 224x224 input images, will I not be loosing important data/information when I reduce the size of the image (and resolution) from 1024x1024 down to 224x224? Isn't there any such risk?
Thank you in advance!
Reducing the resolution (risizing)
Yes, you are loosing data
The best way for you is to rebuild your CNN to work with your original image size, i.e. 1024*1024
It is reducing the resolution of the image (while still showing us the entire image)
That is true that you are losing data, but you can work with an image size a bit larger than 224224 like 512 * 512 512 as it will keep most of the information and will train in comparatively less time and resources than the original image(10241024).
I have an image and a version that is scaled down to exactly half the width and height. The Lanczos filter (with a = 3) has been used to scale the image. Color spaces can be ignored, all colors are in a linear space.
Since the small image contains one pixel for each 2x2 pixel block of the original I'm thinking it should be possible to restore the original image from the small one with just 3 additional color values per 2x2 pixel block. However, I do not know how to calculate those 3 color values.
The original image has four times as much information as the scaled version. Using the original image I want to calculate the 3/4 of information that is missing in the scaled version such that I can use the scaled version and the calculated missing information to reconstruct the original image.
Consider the following use-case: Over a network you send the scaled image to a user as a thumbnail. Now the user wants to see the image at full size. How can we avoid repeating information that is already in the thumbnail? As far as I can tell progressive image compression algorithms do not manage to do this with more complex filtering.
For the box filter the problem is trivial. But since the kernels of the Lanczos filter overlap each other I do not know how to solve it. Given that this is just a linear system of equations I believe it is solvable. Additionally I would rather avoid deconvolution in frequency space.
How can I calculate the information that is missing in the down-scaled version and use it to restore the original image?
What is the efficient way to compare two images in visual c..?
Also in which format images has to be stored.(bmp, gif , jpeg.....)?
Please provide some suggestions
If the images you are trying to compare have distinctive characteristics that you are trying to differentiate then PCA is an excellent way to go. The question of what format of the file you need is irrelevant really; you need to load it into the program as an array of numbers and do analysis.
Your question opens a can of worms in terms of complexity.
If you want to compare two images to check if they are the same, then you need to perform an md5 on the file (removing possible metainfos which could distort your result).
If you want to compare if they look the same, then it's a completely different story altogether. "Look the same" is intended in a very loose meaning (e.g. they are exactly the same image but stored with two different file formats). For this, you need advanced algorithms, which will give you a probability for two images to be the same. Not being an expert in the field, I would perform the following "invented out of my head" algorithm:
take an arbitrary set of pixel points from the image.
for each pixel "grow" a polygon out of the surrounding pixels which are near in color (according to HSV colorspace)
do the same for the other image
for each polygon of one image, check the geometrical similitude with all the other polygons in the other image, and pick the highest value. Divide this value by the area of the polygon (to normalize).
create a vector out of the highest values obtained
the higher is the norm of this vector, the higher is the chance that the two images are the same.
This algorithm should be insensitive to color drift and image rotation. Maybe also scaling (you normalize against the area). But I restate: not an expert, there's probably much better, and it could make kittens cry.
I did something similar to detect movement from a MJPEG stream and record images only when movement occurs.
For each decoded image, I compared to the previous using the following method.
Resize the image to effectively thumbnail size (I resized fairly hi-res images down by a factor of ten
Compare the brightness of each pixel to the previous image and flag if it is much lighter or darker (threshold value 1)
Once you've done that for each pixel, you can use the count of different pixels to determine whether the image is the same or different (threshold value 2)
Then it was just a matter of tuning the two threshold values.
I did the comparisons using System.Drawing.Bitmap, but as my source images were jpg, there were some artifacting.
It's a nice simple way to compare images for differences if you're going to roll it yourself.
If you want to determine if 2 images are the same perceptually, I believe the best way to do it is using an Image Hashing algorithm. You'd compute the hash of both images and you'd be able to use the hashes to get a confidence rating of how much they match.
One that I've had some success with is pHash, though I don't know how easy it would be to use with Visual C. Searching for "Geometric Hashing" or "Image Hashing" might be helpful.
Testing for strict identity is simple: Just compare every pixel in source image A to the corresponding pixel value in image B. If all pixels are identical, the images are identical.
But I guess don't want this kind of strict identity. You probably want images to be "identical" even if certain transformations have been applied to image B. Examples for these transformations might be:
changing image brightness globally (for every pixel)
changing image brightness locally (for every pixel in a certain area)
changing image saturation golbally or locally
gamma correction
applying some kind of filter to the image (e.g. blurring, sharpening)
changing the size of the image
rotation
e.g. printing an image and scanning it again would probably include all of the above.
In a nutshell, you have to decide which transformations you want to treat as "identical" and then find image measures that are invariant to those transformations. (Alternatively, you could try to revert the translations, but that's not possible if the transformation removes information from the image, like e.g. blurring or clipping the image)