I would like to compare two video files for the file which is having a better quality (Non blurry) by a c programm.A friend told me to learn about DFT (Discrete Fourier Transform) for image analysis and to use a FFT or DFT tool to learn the difference between blurred vs detailed (non-blurry) copies of same image.So can anyone help me with this ??
You could also do this with SSIM or PSNR. Both can can be done in C and there are C++/C#/Cuda versions of these algorithms on the net.
Doug
Related
Note: This question is originally asked at OpenCV forum a couple of days ago.
I'm building an image processing program which extensively uses 2-dimensional dft, discrete Fourier transform. I'm trying to speed up so as to run in real-time.
In that application I only use a part of dft output specified by a rectangular ROI. My current implementation follows the steps below:
Compute dft of the input image f (typically size of 512x512) and get the entire dft result F
Crop F into a pre-specified region of interest (ROI, typically size of 32x32, located arbitrary), R
This process basically works well, but involves useless calculation since I only need partial information of F. I'm looking for a way to accelerate this calculation only by computing necessary part of dft.
I found OpenCV with Intel IPP computes dft with Intel IPP functions, which results in an order of magnitude faster than naive OpenCV implementation. I'm wondering if I can even accelerate this computation by only computing pre-specified frequency domain of dft.
Since I'm new to OpenCV, I've lost my way here, so I hope you could provide a way to do this.
Kindly note that I don't mean to do a dft to an ROI of an image, i.e. dft(ROI(f)), but I want to compute ROI(dft(f)).
Thanks in advance.
Just a partial idea.
The DFT is separable. It is always computed by first applying the FFT algorithm to rows of the image, then to the columns of the result (or the other way around, the order doesn't matter).
If you want only an ROI of the output, in the second step you only need to process the columns that fall within the ROI.
I don't think you'll find a way to compute only a subset of frequencies along each 1D row/column. That would likely entail hacking your own FFT, which will likely be more computationally expensive than using the one in IPP or FFTW.
Is there way that I can implement Face Recognition using OpenCV? I tried to use LBPH, and train with one image. It gives a confidence score, but I am not sure how accurate this is to use for verification.
My question is how can I create a face recognition system that tells me how similar the two faces are/if they are the same person or not using OpenCV. It doesn't seem like the confidence score is an accurate measure, if I'm doing this correctly.
Also, is a higher confidence score better?
Thanks
OpenCV 3 currently support following algorithms for face recognition:
- Eigenfaces (see createEigenFaceRecognizer())
- Fisherfaces (see createFisherFaceRecognizer())
- Local Binary Patterns Histograms (see createLBPHFaceRecognizer())
Confidence score by these algorithms is the similarity measure between faces, but these methods are really old and perform poorly. I'd suggest you try this article : http://www.robots.ox.ac.uk/~vgg/publications/2015/Parkhi15/parkhi15.pdf
Basically you need to download trained caffe model from here: http://www.robots.ox.ac.uk/~vgg/software/vgg_face/src/vgg_face_caffe.tar.gz
Use opencv to run this classifier like shown is this example:
http://docs.opencv.org/trunk/d5/de7/tutorial_dnn_googlenet.html#gsc.tab=0
Then collect fc8 feature layer of size 4096 floats from caffe network. And calculate your similarity as L2 norm between two fc8 layers calculated for your faces.
I have 5000 images and each image can generate a vector with about 1000 dimensions(hog feature), but some of the images are very similar so I want to remove the similar ones. Is there a way to achieve this?
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As #thedarkside ofthemoon suggested, let me explain a little bit more about what I am trying to do. I am using SVM + HOG features to do image classification. I have prepared some training data but some of the training images are very similar so that I want to remove the similar ones to reduce computation cost. I don't know if the removal of similar images has a side effect on the final classification rate so a good criteria of 'similarity' must be found. That's what i am trying to do.
In another way(not using hog features) you can compute color histogram for each image and compare against others.
Like,
Get the first image and compute the histogram,
Now for each other images calculate histogram and compare with the first one.
If you find close match on the histogram you can discard it. And by using CV_COMP_CORREL you will get match in the range of 0-1.
Well it depends what you mean by similar, currently my favorite image similarity descriptor is the gist descriptor.
http://people.csail.mit.edu/torralba/code/spatialenvelope/
but it is not in opencv. however it is coded in C here, so can be added to a c++ project (extern "C"), if your using the c++ opencv, not sure about python sorry.
http://people.rennes.inria.fr/Herve.Jegou/software.html
I have found this to be pretty good, and quite efficient.
(Sorry this is not a direct opencv solution, but i feel it is a reasonable answer as gist C code can be added to c++ project, and works nicely.)
EDIT:
if you just want to remove ones with similar hog descriptor you can use the:
http://docs.opencv.org/modules/ml/doc/k_nearest_neighbors.html
or
http://docs.opencv.org/trunk/modules/flann/doc/flann_fast_approximate_nearest_neighbor_search.html
I would like to know, if there is any code or any good documentation available for implementing HOG features? I tried to read the documentation here but it's quite difficult to understand and it needs SVM..
What I need is just to implement a HOG detector for objects.... Like what it does SIFT or SURF
Btw, I'm not interesting in this work.
Thank you..
you can take a look at
http://szproxy.blogspot.com/2010/12/testtest.html
he also published "tutorial" for HOG on source forge here:
http://sourceforge.net/projects/hogtrainingtuto/?_test=beta
I know this since I'm having the same problem as you. The tutorial though isn't what i would call a tutorial, its a bunch of source codes, no documentation, but I assume that it works and can at least get you somewhere.
At the end and simplifying a bit, all that you need to detect specific objects in image is:
Localize "points of interest" to extract the patches:
In order to get points of interest, you can use some algorithms like Harris corner detector, randomly or something simply like sliding windows.
From these points get patches:
You will have to take the decission of the patch size.
From these patches compute the feature descriptor. (like HOG).
Instead of HOG you can use another feature descriptor like SIFT, SURF...
HOG's implementation is not too hard. You have to calculate the gradients of the extracted patch doing applying Sobel X and Y kernels, after that you have to divide the patch in NxM cells, 8x8 for instance, and compute an histogram of gradients, angle and magnitude. In the following link you can see it more detailed explanation:
HOG Person Detector Tutorial
Check your feature vector in the previously trained classifier
Once you got this vector, check if it is the desired object or not with a previously trained classifier like SMV. Instead SVM you could use NeuralNetworks for instance.
SVM implementation is more dificult, but there are some libraries like opencv that you can use.
There is a function extractHOGFeatures in the Computer Vision System Toolbox for MATLAB.
I have a basic understanding in image processing and now studying in-depth the "Digital Image Processing" book by Gonzales.
When image given and object of interest approximated form is known (e.g. circle, triangle),
what is the best algorithm / method to find this object on image?
The object can be slightly deformed, so brute force approach will not help.
You may try using Histograms of Oriented Gradients (also called Edge Orientation Histograms). We have used them for detecting road signs. http://en.wikipedia.org/wiki/Histogram_of_oriented_gradients and the papers by Bill Triggs should get you started.
I recommend you use the Hough transform, which allows you to find any given pattern described by a equation. What's more the Hough transform works also great for deformed objects.
The algorithm and implementation itself is quite simple.
More details can be found here: http://en.wikipedia.org/wiki/Hough_transform , even a source code for this algorithm is included on a referenced page (http://www.rob.cs.tu-bs.de/content/04-teaching/06-interactive/HNF.html).
I hope that helps you.
I would look at your problem in two steps:
first finding your object's outer boundary:
I'm supposing you have contrasted enough image, that you can easily threshold to get a binary image of your object. You need to extract the object boundary chain-code.
then analyzing the boundary's shape to deduce the form (circle, polygon,...):
You can calculate the curvature in each point of the boundary chain and thus determine how many sharp angles (i.e. high curvature value) there are in your shape. Several sharp angles means you have a polygon, none means you have a circle (constant curvature).
You can find a description on how to get your object's boundary from the binary image and ways of analysing it in Gonzalez's Digital Image Processing, chapter 11.
I also found this insightful presentation on binary image analyis (PPT) and a matlab script that implements some of the techniques that Gonzalez talks about in DIP.
I strongly recommend you to use OpenCV, it's a great computer vision library that greatly help with anything related to computer vision. Their website isn't really attractive, nor helpful, but the API is really powerful.
A book that helped me a lot since there isn't a load of documentation on the web is Learning OpenCV. The documentation that comes with the API is good, but not great for learning how to use it.
Related to your problem, you could use a Canny Edge detector to find the border of your item and then analyse it, or you could proceed with and Hough transform to search for lines and or circles.
you can specially try 'face recognition'. Because, you know that is a specific topic. On the other hand 'face detection' etc. EmguCV can be useful for you.. It is .Net wrapper to the Intel OpenCV image processing library.
It looks like professor Jean Rouat from the University of Sherbooke, has found a way to find objects in images by processing neutral spiking neural network. His technology name RN-SPIKES, seems to be available for licencing.