I am not sure if this falls under the criteria of a proper question, but still, I would like to give it a shot.
I am looking for a library or function that takes two SIFT descriptors in a form of a file (or a matrix) of [number_of_keypoints][feature_0...feature_127] - meaning 128 features per file and allows comparison of images (I am using harris-affine alg. to extract them: http://www.robots.ox.ac.uk/~vgg/research/affine/det_eval_files/extract_features2.tar.gz ).
I am interested in a method that would allow me to find mutual nearest neighbours, that would accept number of keypoints in the neighbourhood and success ratio.
E.g.
Lets say I have two files with keypoints (described by SIFT descriptor) (image_1.sift, image_2.sift). I would like the method to accept: number of keypoints in the neighbourhood, match ratio, where match ratio means in pseudo code:
For each keypoint in image_1
Pick 50 nearest neighbours from image_1 -> List<KeyPoints> neighbours_1
For each keypoint in image_2
Pick 50 nearest neighbours from image_2 -> List<KeyPoints> neighbours_2
int numberOfMatches = 0;
foreach(neighbour in neighbours_1)
{
if(neighbour == neighbours_2.Find(neighbour))
numberOfMatches++;
}
The ratio is number of matches to number keypoints taken into consideration.
For example FindMutualKeypoints(image_1, image_2, 50, 0.7)
It can be c#, java, python or matlab implementation. I don't have much to do with image analysis on regular basis and before I start to write my own implementation, I assumed there probably is one out there already. I am having problem finding the correct terms in English from translation from my mother tongue (looks like the terms are quite different), which is probably the reason, why I could not find it yet.
I think openCV is the way to go.
Here is an example for it: link
It uses SURF descriptors, but you can also use SIFT.
You then call the FLANN matcher which also give you information about the quality of the matches.
I need to improve image quality, from low quality to high hd quality. I am using OpenCV libraries. I experimented a lot with GaussianBlur(), Laplacian(), transformation functions, filter functions etc, but all I could succeed is to convert image to hd resolution and keep the same quality. Is it possible to do this? Do I need to implement my own algorithm or is there a way how it's done? I will really appreciate any kind of help. Thanks in advance.
I used this link for my reference. It has other interesting filters that you can play with.
If you are using C++:
detailEnhance(Mat src, Mat dst, float sigma_s=10, float sigma_r=0.15f)
If you are using python:
dst = cv2.detailEnhance(src, sigma_s=10, sigma_r=0.15)
The variable 'sigma_s' determines how big the neighbourhood of pixels must be to perform filtering.
The variable 'sigma_r' determines how the different colours within the neighbourhood of pixels will be averaged with each other. Its range is from: 0 - 1. A smaller value means similar colors will be averaged out while different colors remain as they are.
Since you are looking for sharpness in the image, I would suggest you keep the kernel as minimum as possible.
Here is the result I obtained for a sample image:
1. Original image:
2. Sharpened image for lower sigma_r value:
3. Sharpened image for higher sigma_r value:
Check the above mentioned link for more information.
How about applying Super Resolution in OpenCV? A reference article with more details can be found here: https://learnopencv.com/super-resolution-in-opencv/
So basically you will need to have the Python dependency opencv-contrib-python installed, together with a working version of opencv-python.
There are different techniques for the Super Resolution in OpenCV you can choose from, including EDSR, ESPCN, FSRCNN, and LapSRN. Code examples in both Python and C++ have been included in the tutorial article as well for easy reference.
A correction is needed
dst = cv2.detailEnhance(src, sigma_s=10, sigma_r=0.15)
using kernel will give error.
+1 to kris stern answer,
If you are looking for practical implementation of super resolution using pretrained model in OpenCV, have a look at below notebook also video describing details.
https://github.com/pankajr141/experiments/blob/master/Reasoning/ComputerVision/super_resolution_enhancing_image_quality_using_pretrained_models.ipynb
https://www.youtube.com/watch?v=JrWIYWO4bac&list=UUplf_LWNn0a9ubnKCZ-95YQ&index=4
Below is a sample code using opencv
model_pretrained = cv2.dnn_superres.DnnSuperResImpl_create()
# setting up the model initialization
model_pretrained.readModel(filemodel_filepath)
model_pretrained.setModel(modelname, scale)
# prediction or upscaling
img_upscaled = model_pretrained.upsample(img_small)
I am trying to run a SVD job in mahout. I have a matrix (say A) created (Document x term) of size 372053 x 21338 (21338 no of unique words say N, 372053 documents say M). So my matrix A is of size (M*N). I ran the svd using mahout and i got the cleaned eigen vectors (i gave the expected rank as 200 say R). Now i have a eigen vectors matrix created of size R*N.
Stating the SVD equation
A = U * S * V' (V' being transpose of V)
I need to convert the matrix A to the new space, to get the compressed vectors of the documents (I am trying to implement LSI)
What is the output i get from mahout SVD? (I would like to know in terms of the equation above) I read mailing list that we can get the eigen values from the NamedVectors in the generated eigen vectors matrix.
Please guide me on how to proceed from here to generate the document-term matrix A in the new space (of size M*R).
Any help is highly appreciated :)
A good starting point for LSI with Stochastic SVD on Mahout can be found here.
The good part is that the paper describes also the folding in process and is explicit on the output format in terms of the svd equation.
The work is integrated in the latest version 0.8 and can be used with SSVDCli job or through mahout CLI with mahout ssvd <options>
I was wondering if anybody knows anything deeper than what I do about the Affinity Propagation clustering algorithm in the python scikit-learn package?
All I know right now is that I input an "affinity matrix" (affmat), which is calculated by applying a heat kernel transformation to the "distance matrix" (distmat). The distmat is the output of a previous analysis step I do. After I obtain the affmat, I pass that into the algorithm by doing something analogous to:
af = AffinityPropagation(affinity = 'precomputed').fit(affmat)
On one hand, I appreciate the simplicity of the inputs. On the other hand, I'm still not sure if I've got the input done correctly?
I have tried evaluating the results by looking at the Silhouette scores, and frequently (but not a majority of the time) I am getting scores close to zero or in the negative ranges, which indicate to me that I'm not getting clusters that are 'naturally grouped'. This, to me, is a problem.
Additionally, when I observe some of the plots, I'm getting weird patterns of clustering, such as the image below, where I clearly see 3 clusters of points, but AP is giving me 10:
I also see other graphs like this, where the clusters overlap a lot.:
How are the cluster centers' euclidean coordinates identified? I notice when I put in percent values (99.2, 99.5 etc.), rather than fraction values (0.992, 0.995 etc.), the x- and y-axes change in scale from [0, 1] to [0, 100] (but of course the axes are scaled appropriately such that I'm getting, on occasion, [88, 100] or [92, 100] and so on).
Sorry for the rambling here, but I'm having quite a hard time understanding the underpinnings of the python implementation of the algorithm. I've gone back to the original paper and read it, but I don't know how the manipulate it in the scikit-learn package to get better clustering.
I need a simple and fast way to compare two images for similarity. I.e. I want to get a high value if they contain exactly the same thing but may have some slightly different background and may be moved / resized by a few pixel.
(More concrete, if that matters: The one picture is an icon and the other picture is a subarea of a screenshot and I want to know if that subarea is exactly the icon or not.)
I have OpenCV at hand but I am still not that used to it.
One possibility I thought about so far: Divide both pictures into 10x10 cells and for each of those 100 cells, compare the color histogram. Then I can set some made up threshold value and if the value I get is above that threshold, I assume that they are similar.
I haven't tried it yet how well that works but I guess it would be good enough. The images are already pretty much similar (in my use case), so I can use a pretty high threshold value.
I guess there are dozens of other possible solutions for this which would work more or less (as the task itself is quite simple as I only want to detect similarity if they are really very similar). What would you suggest?
There are a few very related / similar questions about obtaining a signature/fingerprint/hash from an image:
OpenCV / SURF How to generate a image hash / fingerprint / signature out of the descriptors?
Image fingerprint to compare similarity of many images
Near-Duplicate Image Detection
OpenCV: Fingerprint Image and Compare Against Database.
more, more, more, more, more, more, more
Also, I stumbled upon these implementations which have such functions to obtain a fingerprint:
pHash
imgSeek (GitHub repo) (GPL) based on the paper Fast Multiresolution Image Querying
image-match. Very similar to what I was searching for. Similar to pHash, based on An image signature for any kind of image, Goldberg et al. Uses Python and Elasticsearch.
iqdb
ImageHash. supports pHash.
Image Deduplicator (imagededup). Supports CNN, PHash, DHash, WHash, AHash.
Some discussions about perceptual image hashes: here
A bit offtopic: There exists many methods to create audio fingerprints. MusicBrainz, a web-service which provides fingerprint-based lookup for songs, has a good overview in their wiki. They are using AcoustID now. This is for finding exact (or mostly exact) matches. For finding similar matches (or if you only have some snippets or high noise), take a look at Echoprint. A related SO question is here. So it seems like this is solved for audio. All these solutions work quite good.
A somewhat more generic question about fuzzy search in general is here. E.g. there is locality-sensitive hashing and nearest neighbor search.
Can the screenshot or icon be transformed (scaled, rotated, skewed ...)? There are quite a few methods on top of my head that could possibly help you:
Simple euclidean distance as mentioned by #carlosdc (doesn't work with transformed images and you need a threshold).
(Normalized) Cross Correlation - a simple metrics which you can use for comparison of image areas. It's more robust than the simple euclidean distance but doesn't work on transformed images and you will again need a threshold.
Histogram comparison - if you use normalized histograms, this method works well and is not affected by affine transforms. The problem is determining the correct threshold. It is also very sensitive to color changes (brightness, contrast etc.). You can combine it with the previous two.
Detectors of salient points/areas - such as MSER (Maximally Stable Extremal Regions), SURF or SIFT. These are very robust algorithms and they might be too complicated for your simple task. Good thing is that you do not have to have an exact area with only one icon, these detectors are powerful enough to find the right match. A nice evaluation of these methods is in this paper: Local invariant feature detectors: a survey.
Most of these are already implemented in OpenCV - see for example the cvMatchTemplate method (uses histogram matching): http://dasl.mem.drexel.edu/~noahKuntz/openCVTut6.html. The salient point/area detectors are also available - see OpenCV Feature Detection.
I face the same issues recently, to solve this problem(simple and fast algorithm to compare two images) once and for all, I contribute an img_hash module to opencv_contrib, you can find the details from this link.
img_hash module provide six image hash algorithms, quite easy to use.
Codes example
origin lena
blur lena
resize lena
shift lena
#include <opencv2/core.hpp>
#include <opencv2/core/ocl.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/img_hash.hpp>
#include <opencv2/imgproc.hpp>
#include <iostream>
void compute(cv::Ptr<cv::img_hash::ImgHashBase> algo)
{
auto input = cv::imread("lena.png");
cv::Mat similar_img;
//detect similiar image after blur attack
cv::GaussianBlur(input, similar_img, {7,7}, 2, 2);
cv::imwrite("lena_blur.png", similar_img);
cv::Mat hash_input, hash_similar;
algo->compute(input, hash_input);
algo->compute(similar_img, hash_similar);
std::cout<<"gaussian blur attack : "<<
algo->compare(hash_input, hash_similar)<<std::endl;
//detect similar image after shift attack
similar_img.setTo(0);
input(cv::Rect(0,10, input.cols,input.rows-10)).
copyTo(similar_img(cv::Rect(0,0,input.cols,input.rows-10)));
cv::imwrite("lena_shift.png", similar_img);
algo->compute(similar_img, hash_similar);
std::cout<<"shift attack : "<<
algo->compare(hash_input, hash_similar)<<std::endl;
//detect similar image after resize
cv::resize(input, similar_img, {120, 40});
cv::imwrite("lena_resize.png", similar_img);
algo->compute(similar_img, hash_similar);
std::cout<<"resize attack : "<<
algo->compare(hash_input, hash_similar)<<std::endl;
}
int main()
{
using namespace cv::img_hash;
//disable opencl acceleration may(or may not) boost up speed of img_hash
cv::ocl::setUseOpenCL(false);
//if the value after compare <= 8, that means the images
//very similar to each other
compute(ColorMomentHash::create());
//there are other algorithms you can try out
//every algorithms have their pros and cons
compute(AverageHash::create());
compute(PHash::create());
compute(MarrHildrethHash::create());
compute(RadialVarianceHash::create());
//BlockMeanHash support mode 0 and mode 1, they associate to
//mode 1 and mode 2 of PHash library
compute(BlockMeanHash::create(0));
compute(BlockMeanHash::create(1));
}
In this case, ColorMomentHash give us best result
gaussian blur attack : 0.567521
shift attack : 0.229728
resize attack : 0.229358
Pros and cons of each algorithm
The performance of img_hash is good too
Speed comparison with PHash library(100 images from ukbench)
If you want to know the recommend thresholds for these algorithms, please check this post(http://qtandopencv.blogspot.my/2016/06/introduction-to-image-hash-module-of.html).
If you are interesting about how do I measure the performance of img_hash modules(include speed and different attacks), please check this link(http://qtandopencv.blogspot.my/2016/06/speed-up-image-hashing-of-opencvimghash.html).
Does the screenshot contain only the icon? If so, the L2 distance of the two images might suffice. If the L2 distance doesn't work, the next step is to try something simple and well established, like: Lucas-Kanade. Which I'm sure is available in OpenCV.
If you want to get an index about the similarity of the two pictures, I suggest you from the metrics the SSIM index. It is more consistent with the human eye. Here is an article about it: Structural Similarity Index
It is implemented in OpenCV too, and it can be accelerated with GPU: OpenCV SSIM with GPU
If you can be sure to have precise alignment of your template (the icon) to the testing region, then any old sum of pixel differences will work.
If the alignment is only going to be a tiny bit off, then you can low-pass both images with cv::GaussianBlur before finding the sum of pixel differences.
If the quality of the alignment is potentially poor then I would recommend either a Histogram of Oriented Gradients or one of OpenCV's convenient keypoint detection/descriptor algorithms (such as SIFT or SURF).
If for matching identical images - code for L2 distance
// Compare two images by getting the L2 error (square-root of sum of squared error).
double getSimilarity( const Mat A, const Mat B ) {
if ( A.rows > 0 && A.rows == B.rows && A.cols > 0 && A.cols == B.cols ) {
// Calculate the L2 relative error between images.
double errorL2 = norm( A, B, CV_L2 );
// Convert to a reasonable scale, since L2 error is summed across all pixels of the image.
double similarity = errorL2 / (double)( A.rows * A.cols );
return similarity;
}
else {
//Images have a different size
return 100000000.0; // Return a bad value
}
Fast. But not robust to changes in lighting/viewpoint etc.
Source
If you want to compare image for similarity,I suggest you to used OpenCV. In OpenCV, there are few feature matching and template matching. For feature matching, there are SURF, SIFT, FAST and so on detector. You can use this to detect, describe and then match the image. After that, you can use the specific index to find number of match between the two images.
Hu invariant moments is very powerful tool to compare two images
Hash functions are used in the undouble library to detect (near-)identical images (disclaimer: I am also the author). This is a simple and fast way to compare two or more images for similarity. It works using a multi-step process of pre-processing the images (grayscaling, normalizing, and scaling), computing the image hash, and the grouping of images based on a threshold value.