I am using the Google Custom Search API to find images.
In the API call I can specify image sizes like Small, Medium, Large, X Large, XX Large, Huge.
But I want images that are X Large and upwards. For example, if I select the X large option I don't want to exclude images that are classified as XX Large and Huge.
I can't figure out how to ask for multiple image sizes selections in the API call, and I can't find an option to define "images larger than" which would also solve my problem.
Any ideas?
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.
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
I am currently trying to recognise DVD covers in generic photos. My initial test involved using 100 DVD covers and 10 test cases of photos that contained them, and with some tweaking of the find_obj.cpp example in OpenCV I was able to get recognition working.
However now I need to do this on a much larger database, and I am aware that the FLANN method will not scale up well to meet this requirement. How do people here recommend I scale up my SURF recognition in an SQL database?
If you really want to scale your system to several orders of magnitude, nearest neighbors search (FLANN) will not be sufficient.
In such a case what you need is to build a visual vocabulary (a.k.a bag of words) by quantizing your descriptors, and create an inverted index.
I recommend you to refer to the Scalable Recognition with a Vocabulary Tree paper that is the reference publication for such a topic.
have can i speed up the loading of images - specialy when i open the website for the first time it takes some time for images to load...
Is there anything i can do to improve this (html, css)?
link
Thank to all for your answers.
Crop the size of http://www.ursic-ei.si/datoteke/d4.jpg! It's 900 pixels wide, and most of that (half?) is empty and white. Make the image smaller and then use background-position and backgroud-color to compensate for anything you trimmed off the edges.
You have a lot of extra newlines in your HTML source. Not hugely significant, but theoretically - since in HTML there's no practical difference between one new line and two - you might want to remove some.
For images, you should consider a content delivery network (CDN), which will cache your images and other files and server them faster than you web server.
This is a must for any high-traffic website.
On the client, you can multipart download; e.g. in Firefox there's a bunch of settings under network.http.pipelining that help speed downloads.
On the server, there's little you can do (though you can gzip text-based files). The client must just know how to cache.
Since in your question you only ask about the images, I guess you already know that the cost of php processing and/or javascript is minor. If you want to speed up the images you can reduce their size, increase the compression rate... also try different formats. JPG is not always the best one.
Try GIF and/or PNG, also with these you can reduce the number of colors. Usually this formats are way better than JPG when you have simple pictures with few colors.
Also consider if some of your images are a simple patter that can be reproduced/repeated several times. For example, if you have a background image with a side banner, you just need one line and repeat it several times.
The question
Is there a known benchmark or theoretical substantiation on the optimal (rendering speed wise) image size?
A little background
The problem is as follows: I have a collection of very large images, thousands of pixels wide in each dimension. These should be presented to the user and manipulated somehow. In order to improve performance of my web app, I need to slice them. And here is where my question arises: what should be the dimensions of these slices?
You can only find out by testing, every browser will have different performance parameters and your user base may have anything from a mobile phone to a 16-core Xeon desktop. The larger determining factor may actually be the network performance in loading new tiles which is completely dependent upon how you are hosting and who your users are.
As the others already said, you can save a lot of research by duplicating the sizes already used by similar projects: Google Maps, Bing Maps, any other mapping system, not forgetting some of the gigapixel projects like gigapan.
It's hard to give a definitive dimension, but I successfully used 256x256 tiles.
This is also the size used by Microsoft Deep Zoom technology.
In absence of any other suggestions, I'd just use whatever Google Maps is using. I'd imagine they would have done such tests.