I am writing a script to extract text from the images of questions like the one given below using tesseract. But I am talking about 1000-10000 images per day so manualy applying the methods won't be feasible. Is it possible that I can apply some general things to all the images?
This image is here to show the quality of images not that it is handwritten.
So this image is having some noise, blur and all. I have used otsu thresholding and closing already. What other methods should be there for pre-processing functions that I can use on each image safely so that it does not hurt good quality images but improves bad quality.
In other terms, can I use a sharpening kernel to every image? If yes, What should be average (minimal safe )filter size/value so that it does not hurt the good quality images
sharpen_kernel_choice1 = np.array([[-1,-1,-1], [-1,9,-1], [-1,-1,-1]])
sharpen_kernel_choise2 = np.array[[0,-1,0], [-1,9,-1], [0,-1,0]]
I am also thinking about using Wiener Filter so that it can de-blur the image for example,
psf = np.ones((5, 5)) / 25 # what should be a safe kernel size
img = convolve2d(img, psf, 'same')
img += 0.1 * img.std() * np.random.standard_normal(img.shape) # what should be this value?
deconvolved_img = restoration.unsupervised_wiener(img, psf)
You want to use edge preserving filters like anisotropic diffusion. The only implementation in python I know of can be found in medpy.
medpy.filter.smoothing.anisotropic_diffusion(gray_image, option = 3)
I tried your image and found unsharp masking works even better due to the edge augmenting. And for exposure modification you should defenitely use a local filter, CLAHE as mentioned by Ziri is the best and easiest to use that I can think of.
from skimage.exposure import equalize_adapthist
from skimage.filters import threshold_otsu, unsharp_mask
img = unsharp_mask(image)
equalize_adapthist(img) > threshold_otsu(equalize_adapthist(img))
The Tesseract project also provides a page dedicated to Image Preprocessing.
How do i do a gaussi smoothing in the 3th dimension?
I have this detection pyramid, votes accumulated at four scales. Objects are found at each peak.
I already smoothed each of them in 2d, and reading in my papers that i need to filter the third dimension with a \sigma = 1, which i havent tried before, i am not even sure what it means.
I Figured out how to do it in Matlab, and need something simular in opencv/c++.
Matlab Raw Values:
Matlab Smoothen with M0 = smooth3(M0,'gaussian'); :
Gaussian filters are separable. You apply 1D filter at each dimension as follows:
for (dim = 0; dim < D; dim++)
tensor = gaussian_filter(tensor, dim);
I would recommend OpenCV for an implementation of a gaussian filter (and image processing in general) in C++.
Note that this assumes that your pyramid levels are all of the same size.
You can have your own functions that sample your scale-space pyramid on the fly while convolving the third dimension, but if you have enough memory I believe that it would be faster to scale up your coarser level to have the same size of the finest level.
Long ago (in 2008-2009) I have developed a small C++ template lib to apply some simple transformations and convolution filters. The library's source can be found in the Linderdaum Engine - it has nothing to do with the rest of the engine and does not use any of the engine's features. The license is MIT, so do whatever you want with it.
Take a look into the Linderdaum's source code (http://www.linderdaum.com) at Src/Linderdaum/Images/VolumeLib.*
The function to prepare the kernel is PrepareGaussianFilter() and MakeScalarVolumeConvolution() applies the filter. It is easy to adapt the library for the different data sources because the I/O is implemented using callback functions.
Hi (sorry for my english) .. i'm working in a project for University in this project i need to use the MBA (Multilevel B-Spline Approximation) algorithm to get some points (control points) of a image to use in other operations.
I'm reading a lot of papers about this algorithm, and i think i understand, but i can't writing.
The idea is: Read a image, process a image (OpenCV), then get control points of the image, use this points.
So the problem here is:
The algorithm use a set of points {(x,y,z)} , this set of points are approximated with a surface generated with the control points obtained from MBA. the set of points {(x,y,z)} represents de data we need to approximate (the image)..
So, the image is in a cv::Mat format , how can transform this format to an ordinary array to simply access to the data an manipulate...
Here are one paper with an explanation of the method:
(Paper) REGULARIZED MULTILEVEL B-SPLINE REGISTRATION
(Paper)Scattered Data Interpolation with Multilevel B-splines
(Matlab)MBA
If someone can help, maybe a guideline, idea or anything will be appreciate ..
Thanks in advance.
EDIT: Finally i wrote the algorithm in C++ using armadillo and OpenCV ...
Well i'm using armadillo a C++ linear algebra library to works with matrix for the algorithm
May anyone give me a quick guide on how to use Cimg to compute SVD for a 3-dimension array?
I just want to get the decomposition of the array in order to compress it small for speeding up further process.
What value should I input at where, and how to get the output?
I've been searched around and still can't understand how it works. and not really fully understand how SVD works as well..only know that it can used to decompress matrix.
At the same time I found that OpenCV and Eigen library also can done the job, do let me know their steps if is much more easier..
(Alternative for me instead of SVD is PCA, which I found its source/library but also don't know how to use..)
Thanks!
See http://cimg.sourceforge.net/reference/structcimg__library_1_1CImg.html#a9a79f3a0849388b3ec13bd140b67a12e
CImg<float> A(3,3); // A = U'*S*V
A.rand(0,1);
CImgList<float> USV = A.get_SVD(); //USV[0] = U and so forth
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.