I have lots of image (about 40 GB).
My images are small but they don't have same size.
My images aren't from natural things because I made them from a signal so all pixels are important and I can't crop or delete any pixel.
Is it possible to use deep learning for this kind of images with different shapes?
All pixels are important, please take this into consideration.
I want a model which does not depend on a fixed size input image. Is it possible?
Without knowing what you're trying to learn from the data, it's tough to give a definitive answer:
You could pad all the data at the beginning (or end) of the signal so
they're all the same size. This allows you to keep all the important
pixels, but adds irrelevant information to the image that the network
will most likely ignore.
I've also had good luck with activations where you take a pretrained
network and pull features from the image at a certain part of the
network regardless of size (as long as it's larger than the network
input size). Then run through a classifier.
https://www.mathworks.com/help/deeplearning/ref/activations.html#d117e95083
Or you could window your data, and only process smaller chunks at one
time.
https://www.mathworks.com/help/audio/examples/cocktail-party-source-separation-using-deep-learning-networks.html
Related
I have a bunch of poor quality photos that I extracted from a pdf. Somebody I know has the good quality photo's somewhere on her computer(Mac), but it's my understanding that it will be difficult to find them.
I would like to
loop through each poor quality photo
perform a reverse image search using each poor quality photo as the query image and using this persons computer as the database to search for the higher quality images
and create a copy of each high quality image in one destination folder.
Example pseudocode
for each image in poorQualityImages:
search ./macComputer for a higherQualityImage of image
copy higherQualityImage to ./higherQualityImages
I need to perform this action once.
I am looking for a tool, github repo or library which can perform this functionality more so than a deep understanding of content based image retrieval.
There's a post on reddit where someone was trying to do something similar
imgdupes is a program which seems like it almost achieves this, but I do not want to delete the duplicates, I want to copy the highest quality duplicate to a destination folder
Update
Emailed my previous image processing prof and he sent me this
Off the top of my head, nothing out of the box.
No guaranteed solution here, but you can narrow the search space.
You’d need a little program that outputs the MSE or SSIM similarity
index between two images, and then write another program or shell
script that scans the hard drive and computes the MSE between each
image on the hard drive and each query image, then check the images
with the top X percent similarity score.
Something like that. Still not maybe guaranteed to find everything
you want. And if the low quality images are of different pixel
dimensions than the high quality images, you’d have to do some image
scaling to get the similarity index. If the poor quality images have
different aspect ratios, that’s even worse.
So I think it’s not hard but not trivial either. The degree of
difficulty is partly dependent on the nature of the corruption in the
low quality images.
UPDATE
Github project I wrote which achieves what I want
What you are looking for is called image hashing
. In this answer you will find a basic explanation of the concept, as well as a go-to github repo for plug-and-play application.
Basic concept of Hashing
From the repo page: "We have developed a new image hash based on the Marr wavelet that computes a perceptual hash based on edge information with particular emphasis on corners. It has been shown that the human visual system makes special use of certain retinal cells to distinguish corner-like stimuli. It is the belief that this corner information can be used to distinguish digital images that motivates this approach. Basically, the edge information attained from the wavelet is compressed into a fixed length hash of 72 bytes. Binary quantization allows for relatively fast hamming distance computation between hashes. The following scatter plot shows the results on our standard corpus of images. The first plot shows the distances between each image and its attacked counterpart (e.g. the intra distances). The second plot shows the inter distances between altogether different images. While the hash is not designed to handle rotated images, notice how slight rotations still generally fall within a threshold range and thus can usually be matched as identical. However, the real advantage of this hash is for use with our mvp tree indexing structure. Since it is more descriptive than the dct hash (being 72 bytes in length vs. 8 bytes for the dct hash), there are much fewer false matches retrieved for image queries.
"
Another blogpost for an in-depth read, with an application example.
Available Code and Usage
A github repo can be found here. There are obviously more to be found.
After importing the package you can use it to generate and compare hashes:
>>> from PIL import Image
>>> import imagehash
>>> hash = imagehash.average_hash(Image.open('test.png'))
>>> print(hash)
d879f8f89b1bbf
>>> otherhash = imagehash.average_hash(Image.open('other.bmp'))
>>> print(otherhash)
ffff3720200ffff
>>> print(hash == otherhash)
False
>>> print(hash - otherhash)
36
The demo script find_similar_images also on the mentioned github, illustrates how to find similar images in a directory.
Premise
I'll focus my answer on the image processing part, as I believe implementation details e.g. traversing a file system is not the core of your problem. Also, all that follows is just my humble opinion, I am sure that there are better ways to retrieve your image of which I am not aware. Anyway, I agree with what your prof said and I'll follow the same line of thought, so I'll share some ideas on possible similarity indexes you might use.
Answer
MSE and SSIM - This is a possible solution, as suggested by your prof. As I assume the low quality images also have a different resolution than the good ones, remember to downsample the good ones (and not upsample the bad ones).
Image subtraction (1-norm distance) - Subtract two images -> if they are equal you'll get a black image. If they are slightly different, the non-black pixels (or the sum of the pixel intensity) can be used as a similarity index. This is actually the 1-norm distance.
Histogram distance - You can refer to this paper: https://www.cse.huji.ac.il/~werman/Papers/ECCV2010.pdf. Comparing two images' histograms might be potentially robust for your task. Check out this question too: Comparing two histograms
Embedding learning - As I see you included tensorflow, keras or pytorch as tags, let's consider deep learning. This paper came to my
mind: https://arxiv.org/pdf/1503.03832.pdf The idea is to learn a
mapping from the image space to a Euclidian space - i.e. compute an
embedding of the image. In the embedding hyperspace, images are
points. This paper learns an embedding function by minimizing the
triplet loss. The triplet loss is meant to maximize the distance
between images of different classes and minimize the distance between
images of the same class. You could train the same model on a Dataset
like ImageNet. You could augment the dataset with by lowering the
quality of the images, in order to make the model "invariant" to
difference in image quality (e.g. down-sampling followed by
up-sampling, image compression, adding noise, etc.). Once you can
compute embedding, you could compute the Euclidian distance (as a
substitute of the MSE). This might work better than using MSE/SSIM as a similarity indexes. Repo of FaceNet: https://github.com/timesler/facenet-pytorch. Another general purpose approach (not related to faces) which might help you: https://github.com/zegami/image-similarity-clustering.
Siamese networks for predicting similarity score - I am referring to this paper on face verification: http://bmvc2018.org/contents/papers/0410.pdf. The siamese network takes two images as input and outputs a value in the [0, 1]. We can interpret the output as the probability that the two images belong to the same class. You can train a model of this kind to predict 1 for image pairs of the following kind: (good quality image, artificially degraded image). To degrade the image, again, you can combine e.g. down-sampling followed by
up-sampling, image compression, adding noise, etc. Let the model predict 0 for image pairs of different classes (e.g. different images). The output of the network can e used as a similarity index.
Remark 1
These different approaches can also be combined. They all provide you with similarity indexes, so you can very easily average the outcomes.
Remark 2
If you only need to do it once, the effort you need to put in implementing and training deep models might be not justified. I would not suggest it. Still, you can consider it if you can't find any other solution and that Mac is REALLY FULL of images and a manual search is not possible.
If you look at the documentation of imgdupes you will see there is the following option:
--dry-run
dry run (do not delete any files)
So if you run imgdupes with --dry-run you will get a listing of all the duplicate images but it will not actually delete anything. You should be able to process that output to move the images around as you need.
Try similar image finder I have developed to address this problem.
There is an explanation and the algorithm there, so you can implement your own version if needed.
The SSD paper details its random-crop data augmentation scheme as:
Data augmentation To make the model more robust to various input object sizes and
shapes, each training image is randomly sampled by one of the following options:
– Use the entire original input image.
– Sample a patch so that the minimum jaccard overlap with the objects is 0.1, 0.3,
0.5, 0.7, or 0.9.
– Randomly sample a patch.
The size of each sampled patch is [0.1, 1] of the original image size, and the aspect ratio
is between 1 and 2. We keep the overlapped part of the ground truth box if the center of
it is in the sampled patch. After the aforementioned sampling step, each sampled patch
is resized to fixed size and is horizontally flipped with probability of 0.5, in addition to
applying some photo-metric distortions similar to those described in [14].
https://arxiv.org/pdf/1512.02325.pdf
My question is: what is the reasoning for resizing crops that range in aspect ratios between 0.5 and 2.0?
For instance if your input image is 300x300, reshaping a crop with AR=2.0 back to square resolution will severely stretch objects (square features become rectangular, circles become ellipses, etc.) I understand small distortions may be good to improve generalization, but training the network on objects distorted up to 2x in either dimension seems counter-productive. Am I misunderstanding how random-crop works?
[Edit] I completely understand that augmented images need to be the same size as the original -- I'm more wondering why the authors don't fix the Aspect Ratio to 1.0 to preserve object proportions.
GPU architecture enforces us to use batches to speedup training, and these batches should be of the same size. Using not-so-distorted image crops could make training more efficient, but much slower.
Personally I consider that any transformation makes sense as long as you as a human can still identify the object/subject, and as long as they make sense in the receptive field of the network. Also I guess somehow that the aspect ratio might help to learn some kind of perspective distortion (look at the cow in fig 5, it's kind of "compressed"). Objects like a cup, a tree, a chair, even stretched are still identifiable. Otherwise you could also consider that some point-controlled or skew transforms just don't make sense as well.
Then, if you are working with different images than natural images, without perspective, it is probably not a good idea to do so. If your image shows objects of a fixed known size like in a microscope or other medical imaging device, and if your object has more or less a fixed size (let's say a cell), then it's probably not a good idea to perform strong distortion on the scale (like a cell twice as large), maybe then a cell twice as an ellipse actually makes more sense.
With this library, you can perform strong augmentations, but not all of them make sense if you look at the image here:
I am currently implementing a few image style transfer algorithms for Tensorflow, but I would like to do it in tiles, so I don't have to run the entire image through the network. Everything works fine, however each image is normalized differently, according to its own statistics, which results in tiles with slightly different characteristics.
I am certain that the only issue is instance normalization, since if I feed the true values (obtained from the entire image) to each tile calculation the result is perfect, however I still have to run the entire image through the network to calculate these values. I also tried calculating these values using a downsampled version of the image, but resolution suffers a lot.
So my question is: is it possible to estimate mean and variance values for instance normalization without feeding the entire image through the network?
You can take a random sample of the pixels of the image, and use the sample mean and sample variance to normalize the whole image. It will not be perfect, but the larger the sample, the better. A few hundred pixels will probably suffice, maybe even less, but you need to experiment.
Use tf.random_uniform() to get random X and Y coordinates, and then use tf.gather_nd() to get the pixel values at the given coordinates.
I've read some of posts here about png/jpg/gif but still I'm quite confused..
I've got a big header on my website :
width:850px height:380px weight:108kb
And it's jpg. A woman + gradient + some layers on top and behing her..
Do you think 108kb it's too much? I was thinking about cut it to png pieces..Would that be a bad idea? What's your suggestions?;) THX for help;]
It depends on the nature of the image, if it's a photograph, JPEG would give the highest quality/compression ratio, if it was pixel stuff like writing or clipart, or have some transparency, then it's GIF/PNG to choose (GIF,PNG8 offers one level transparency, while PNG24 offers a levelled transparency for each pixel).
When I'm confused, I usually save the picture in all three formats and decide what gives the best quality/size deal within the size limits I need, I also try to lower down JPEG quality to the level where the image is still good quality (because that varies from image to another).
Also, if it was a photograph with some writing, split it into a JPEG photograph with a transparent GIF writing overlay (because writing edges look distorted in JPEG).
So, when you are confused, give the three a try and decide, with time you'll gain experience what format suits what content the most.
Actually, 108kb for an image of that size isn't abnormal. It's even more optimal to have it in 1 image: your browser only needs to perform 1 get-request. If you break it up into multiple images, the user needs to wait longer for the site to load.
PNG probably wouldn't help you since JPG is usually much more efficient at handling gradients. PNG would be better if you had big unicolored spaces in your image.
Does anyone know of any code that does streaming Jpeg resizing. What I mean by this is reading a chunk of an image (depending on the original source and destination size this would obviously vary), and resizing it, allowing for lower memory consumption when resizing very large jpegs. Obviously this wouldn't work for progressive jpegs (or at least it would become much more complicated), but it should be possible for standard jpegs.
The design of JPEG data allows simple resizing to 1/2, 1/4 or 1/8 size. Other variations are possible. These same size reductions are easy to do on progressive jpegs as well and the quantity of data to parse in a progressive file will be much less if you want a reduced size image. Beyond that, your question is not specific enough to know what you really want to do.
Another simple trick to reduce the data size by 33% is to render the image into a RGB565 bitmap instead of RGB24 (if you don't need the full color space).
I don't know of a library that can do this off the shelf, but it's certainly possible.
Lets say your JPEG is using 8x8 pixel MCUs (the units in which pixels are grouped). Lets also say you are reducing by a factor to 12 to 1. The first output pixel needs to be the average of the 12x12 block of pixels at the top left of the input image. To get to the input pixels with a y coordinate greater than 8, you need to have decoded the start of the second row of MCUs. You can't really get to decode those pixels before decoding the whole of the first row of MCUs. In practice, that probably means you'll need to store two rows of decoded MCUs. Still, for a 12000x12000 pixel image (roughly 150 mega pixels) you'd reduce the memory requirements by a factor of 12000/16 = 750. That should be enough for a PC. If you're looking at embedded use, you could horizontally resize the rows of MCUs as you read them, reducing the memory requirements by another factor of 12, at the cost of a little more code complexity.
I'd find a simple jpeg decoder library like Tiny Jpeg Decoder and look at the main loop in the jpeg decode function. In the case of Tiny Jpeg Decoder, the main loop calls decode_MCU, Modify from there. :-)
You've got a bunch of fiddly work to do to make the code work for non 8x8 MCUs and a load more if you want to reduce by a none integer factor. Sounds like fun though. Good luck.