I have a collection of face images, with 1 or sometimes 2 faces in each image. What I wanna do, is find the face in each image and then crop It.
I've tested a couple of methods, which are implemented in python using openCV, but the results weren't that good. These methods are:
1- Implementation 1
2- Implementation 2
There's one more model that I've tested, but I'm not allowed to post more than two links.
The problem is that these Haar-Feature based algorithms, are not robust to face size, and when I tried them on images which were taken close to the face, they couldn't find any faces.
Someone mentioned to try deep learning based algorithms, but I couldn't find one corresponding to what I want to do. Basically, I guess I need a pre-trained model, which can give me the coordinates of the face bounding box in the image, or better, a pre-trained model which gives out the cropped face image as output.
You don't need machine learning algorithms, Graph-Algorithms is enough. For example Snapchats face recognition algorithm works as follows:
Create a Graph with Nodes and Edges from a most common Face ("Standard Face").
Deform that Graph / Recoordinate the Nodes to the fitted pixels in the Input Image
voila you got the face recognized in the Input Image.
Easy said, but harder to code. We implemented in our university the Dijkstra Algorithm for example and I can hand you my "Graph" Class if you need it. But I wrote it in C++.
With these graph-algorithm you can crop out the faces more efficient.
Related
I am working on a hand detection project. There are many good project on web to do this, but what I need is a specific hand pose detection. It needs a totally open palm and the whole palm face to outwards, like the image below:
The first hand faces to inwards, so it will not be detected, and the right one faces to outwards, it will be detected. Now I can detect hand with OpenCV. but how to tell the hand orientation?
Problem of matching with the forehand belongs to the texture classification, it's a classic pattern recognition problem. I suggest you to try one of the following methods:
Gabor filters: it is good to detect the orientation and pixel intensities (as forehand has different features), opencv has getGaborKernel function, the very important params of this function is theta (orientation) and lambd: (frequencies). To make it simple you can apply this process on a cropped zone of palm (as you have already detected it, it would be easy to crop for example the thumb, or a rectangular zone around the gravity center..etc). Then you can convolute it with a small database of images of the same zone to get the a rate of matching, or you can use the SVM classifier, where you have to train your SVM on a set of images by constructing the training matrix needed for SVM (check this question), this paper
Local Binary Patterns (LBP): it's an important feature descriptor used for texture matching, you can apply it on whole palm image or on a cropped zone or finger of image, it's easy to use in opencv, a lot of tutorials with codes are available for this method. I recommend you to read this paper talking about Invariant Texture Classification
with Local Binary Patterns. here is a good tutorial
Haralick Texture: I've read that it works perfectly when a set of features quantifies the entire image (Global Feature Descriptors). it's not implemented in opencv but easy to be implemented, check this useful tutorial
Training Models: I've already suggested a SVM classifier, to be coupled with some descriptor, that can works perfectly.
Opencv has an interesting FaceRecognizer class for face recognition, it could be an interesting idea to use it replacing the face images by the palm ones, (do resizing and rotation to get an unique pose of palm), this class has three methods can be used, one of them is Local Binary Patterns Histograms, which is recommended for texture recognition. and why not to try the other models (Eigenfaces and Fisherfaces ) , check this tutorial
well if you go for a MacGyver way you can notice that the left hand has bones sticking out in a certain direction, while the right hand has all finger lines and a few lines in the hand palms.
These lines are always sort of the same, so you could try to detect them with opencv edge detection or hough lines. Due to the dark color of the lines, you might even be able to threshold them out of it. Then gather the information from those lines, like angles, regressions, see which features you can collect and train a simple decision tree.
That was assuming you do not have enough data, if you have then you go into deeplearning, just take a basic inceptionV3 model and retrain the last dense layer to classify between two classes with a softmax, or to predict the probablity if the hand being up/down with sigmoid. Check this link, Tensorflow got your back on the training of this one, pure already ready code to execute.
Questions? Ask away
Take a look at what leap frog has done with the oculus rift. I'm not sure what they're using internally to segment hand poses, but there is another paper that produces hand poses effectively. If you have a stereo camera setup, you can use this paper's methods: https://arxiv.org/pdf/1610.07214.pdf.
The only promising solutions I've seen for mono camera train on large datasets.
use Haar-Cascade classifier,
you can get the classifier model file then use it here.
Just search for 'Haarcascade detection of Palm in Google' or use below code.
import cv2
cam=cv2.VideoCapture(0)
ccfr2=cv2.CascadeClassifier('haar-cascade-files-master/palm.xml')
while True:
retval,image=cam.read()
grey=cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
palm=ccfr2.detectMultiScale(grey,scaleFactor=1.05,minNeighbors=3)
for x,y,w,h in palm:
image=cv2.rectangle(image,(x,y),(x+w,y+h),(256,256,256),2)
cv2.imshow("Window",image)
if cv2.waitKey(1) & 0xFF==ord('q'):
cv2.destroyAllWindows()
break
del(cam)
Best of Luck for your experience using HaarCascade.
I have created and guns and knives image classifier using fastai model resnet34. I am able to predict the class in an image but I want to extend it for real time analysis of a video footage. I want to get bounding boxes around the guns and knives but am not able to understand how I can get this. Can somebody please guide me through.
So I would do it in two steps. First, learn how to get bounding boxes in fastai. Example here.
If you have that you can use opencv to load a video frame by frame and label it. OpenCV example.
When you have both it's quite easy to do it in real time. Just label frames loaded from your webcam. This process is quite expensive so you probably want to lower image resolution before inference. I've done it in tensorflow and it worked quite nicely. This repository was extremely useful
I am working on a project that aims to build a program which automatically gives a relatively accurate detection of pupil region in eye pictures. I am currently using simplecv in Python, given that Python is easier to experiment with. Since I just started, the eye pictures I am working with are fairly standardized. However, the size of iris and pupil as well as the color of iris can vary. And the position of the eye can shift a little among pictures. Here's a picture from wikipedia that is similar to the pictures I am using:
"MyStrangeIris.JPG" by Epicstessie is licensed under CC BY-SA 3.0
I have tried simple thresholding. Since different eyes have different iris colors, a fixed thresholding would not work on all pictures.
In addition, I tried simplecv's build-in sobel and canny edge detection, it's not working especially for eyes with darker iris. I also doubt that sobel or canny alone can solve the problem, given sometimes there are noises on the edge of the pupil (e.g., reflection of eyelash)
I have entry-level knowledge about image processing and machine learning. Right now, I am thinking about three possibilities:
Do a regression on the threshold value base on some variables
Make a specific mask only for edge detection for the pupil
classification on each pixel (this looks like lots of work to build the training set)
Am I on the right track? I would like to reach out to anyone with more experience on this type of problem. Any tips/suggestions are more than welcome. Thanks!
I think that for start you should put aside the machine learning. You have so much more to try in "regular" computer vision.
You need to try and describe a model for your problem. A good way to do this is to sit and think how you as a person detect iris. For example, i can think of:
It is near the center of image.
It is is Brown/green/blue circle, with distinct black center, surrounded by mostly white ellipse.
You have a skin color around the white ellipse.
It can't be too small or too large (depends on your images..)
After you build your model, try to find better ways to find these features. Hard to point on specific stuff, but you can start from: HSV color space, Correlation, Hough transform, Morphological operations..
Only after you feel you have exhausted all conventional tools, start thinking on features extraction and machine learning..
And BTW, because you are not the first person that try to detect iris, you can look at other projects for ideas.
I have written a small matlab code for image (link you have provided), function which i have used is hough transform for circle detection, which has also implemented in opencv, so porting will not create problem, i just want to know that i am on write way or not.
my result and code is as follows:
clc
clear all
close all
im = imresize(imread('irisdet.JPG'),0.5);
gray = rgb2gray(im);
Rmin = 50; Rmax = 100;
[centersDark, radiiDark] = imfindcircles(gray,[Rmin Rmax],'ObjectPolarity','dark');
figure,imshow(im,[])
viscircles(centersDark, radiiDark,'EdgeColor','b');
Input Image:
Result of Algorithm:
Thank You
Not sure about iris classification, but I've done written digit recognition from photos. I would recommend tuning up the contrast and saturation, then use a k-nearest neighbour algorithm to classify your images. Depending on your training set, you can get as high as 90% accuracy.
I think you are on the right track. Do image preprocessing to make classification easier, then train an algorithm of your choice. You would want to treat each image as one input vector though, instead of classifying each pixel!
I think you can try Active Shape Modelling or if you want a really feature rich modelling and do not care about the time it takes execute the algorithm you can try Active appearance modelling. You might want to look into these papers for better understanding:
Active Shape Models: Their Training and Application
Statistical Models of Appearance for Computer Vision - In Depth
I am working on the project and part of it is to recognize objects recorded on camera. So to be more specific:
I am using OpenCV
I have correctly setup camera and am able to retrieve pictures from it
I have compiled and experimented with number of demos from OpenCV
I need a scale- AND rotation- invariant algorithm for detection
Pictures of original objects are ONLY available as edge-images
All feature detection/extraction/matching algorithms I have seen so far are working reasonably well with gray-scale images (like photos), however due to my project specs I need to work with edge images (kinda like output of canny edge detector) which are typically BW and contain only edges found within the image. In this case the performance of algorithms I was trying to use (SURF, SIFT, MSER, etc) decreases dramatically.
So the actual question is: Has anyone come across algorithm that would be specific for matching edge images or is there a certain setup that can improve performance of SIFR/SURF/? in order to work well with that kind of input.
I would appretiate any advice or links to any relevant resources
PS: this is my first question of stackoverflow
Edge images have a problem: The information they contain about the objects of interest is very, very scarce.
So, a general algorithm to classify edge images is probably not to be found. However, if your images are simple, clear and specific, you can employ a number of techniques to classify them. Among them: find contours, and select by shape, area, positioning, tracking.
A good list of shape information (from Matlab help site) includes:
'Area'
'EulerNumber'
'Orientation'
'BoundingBox'
'Extent'
'Perimeter'
'Centroid'
'Extrema'
'PixelIdxList'
'ConvexArea'
'FilledArea'
'PixelList'
'ConvexHull'
'FilledImage'
'Solidity'
'ConvexImage'
'Image'
'SubarrayIdx'
'Eccentricity'
'MajorAxisLength'
'EquivDiameter'
'MinorAxisLength'
An important condition to use shapes in your algorithm is to be able to select them individually. Shape analysis is very sensitive to noise, overlap, etc
Update
I found a paper that may be interesting in this context - it is an object classifier that only uses shape information, and it can be applied on Canny images - it sounds like it's your solution
http://www.vision.ee.ethz.ch/publications/papers/articles/eth_biwi_00664.pdf
If I take a picture with a camera, so I know the distance from the camera to the object, such as a scale model of a house, I would like to turn this into a 3D model that I can maneuver around so I can comment on different parts of the house.
If I sit down and think about taking more than one picture, labeling direction, and distance, I should be able to figure out how to do this, but, I thought I would ask if someone has some paper that may help explain more.
What language you explain in doesn't matter, as I am looking for the best approach.
Right now I am considering showing the house, then the user can put in some assistance for height, such as distance from the camera to the top of that part of the model, and given enough of this it would be possible to start calculating heights for the rest, especially if there is a top-down image, then pictures from angles on the four sides, to calculate relative heights.
Then I expect that parts will also need to differ in color to help separate out the various parts of the model.
As mentioned, the problem is very hard and is often also referred to as multi-view object reconstruction. It is usually approached by solving the stereo-view reconstruction problem for each pair of consecutive images.
Performing stereo reconstruction requires that pairs of images are taken that have a good amount of visible overlap of physical points. You need to find corresponding points such that you can then use triangulation to find the 3D co-ordinates of the points.
Epipolar geometry
Stereo reconstruction is usually done by first calibrating your camera setup so you can rectify your images using the theory of epipolar geometry. This simplifies finding corresponding points as well as the final triangulation calculations.
If you have:
the intrinsic camera parameters (requiring camera calibration),
the camera's position and rotation (it's extrinsic parameters), and
8 or more physical points with matching known positions in two photos (when using the eight-point algorithm)
you can calculate the fundamental and essential matrices using only matrix theory and use these to rectify your images. This requires some theory about co-ordinate projections with homogeneous co-ordinates and also knowledge of the pinhole camera model and camera matrix.
If you want a method that doesn't need the camera parameters and works for unknown camera set-ups you should probably look into methods for uncalibrated stereo reconstruction.
Correspondence problem
Finding corresponding points is the tricky part that requires you to look for points of the same brightness or colour, or to use texture patterns or some other features to identify the same points in pairs of images. Techniques for this either work locally by looking for a best match in a small region around each point, or globally by considering the image as a whole.
If you already have the fundamental matrix, it will allow you to rectify the images such that corresponding points in two images will be constrained to a line (in theory). This helps you to use faster local techniques.
There is currently still no ideal technique to solve the correspondence problem, but possible approaches could fall in these categories:
Manual selection: have a person hand-select matching points.
Custom markers: place markers or use specific patterns/colours that you can easily identify.
Sum of squared differences: take a region around a point and find the closest whole matching region in the other image.
Graph cuts: a global optimisation technique based on optimisation using graph theory.
For specific implementations you can use Google Scholar to search through the current literature. Here is one highly cited paper comparing various techniques:
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms.
Multi-view reconstruction
Once you have the corresponding points, you can then use epipolar geometry theory for the triangulation calculations to find the 3D co-ordinates of the points.
This whole stereo reconstruction would then be repeated for each pair of consecutive images (implying that you need an order to the images or at least knowledge of which images have many overlapping points). For each pair you would calculate a different fundamental matrix.
Of course, due to noise or inaccuracies at each of these steps you might want to consider how to solve the problem in a more global manner. For instance, if you have a series of images that are taken around an object and form a loop, this provides extra constraints that can be used to improve the accuracy of earlier steps using something like bundle adjustment.
As you can see, both stereo and multi-view reconstruction are far from solved problems and are still actively researched. The less you want to do in an automated manner the more well-defined the problem becomes, but even in these cases quite a bit of theory is required to get started.
Alternatives
If it's within the constraints of what you want to do, I would recommend considering dedicated hardware sensors (such as the XBox's Kinect) instead of only using normal cameras. These sensors use structured light, time-of-flight or some other range imaging technique to generate a depth image which they can also combine with colour data from their own cameras. They practically solve the single-view reconstruction problem for you and often include libraries and tools for stitching/combining multiple views.
Epipolar geometry references
My knowledge is actually quite thin on most of the theory, so the best I can do is to further provide you with some references that are hopefully useful (in order of relevance):
I found a PDF chapter on Multiple View Geometry that contains most of the critical theory. In fact the textbook Multiple View Geometry in Computer Vision should also be quite useful (sample chapters available here).
Here's a page describing a project on uncalibrated stereo reconstruction that seems to include some source code that could be useful. They find matching points in an automated manner using one of many feature detection techniques. If you want this part of the process to be automated as well, then SIFT feature detection is commonly considered to be an excellent non-real-time technique (since it's quite slow).
A paper about Scene Reconstruction from Multiple Uncalibrated Views.
A slideshow on Methods for 3D Reconstruction from Multiple Images (it has some more references below it's slides towards the end).
A paper comparing different multi-view stereo reconstruction algorithms can be found here. It limits itself to algorithms that "reconstruct dense object models from calibrated views".
Here's a paper that goes into lots of detail for the case that you have stereo cameras that take multiple images: Towards robust metric reconstruction
via a dynamic uncalibrated stereo head. They then find methods to self-calibrate the cameras.
I'm not sure how helpful all of this is, but hopefully it includes enough useful terminology and references to find further resources.
Research has made significant progress and these days it is possible to obtain pretty good-looking 3D shapes from 2D images. For instance, in our recent research work titled "Synthesizing 3D Shapes via Modeling Multi-View Depth Maps and Silhouettes With Deep Generative Networks" took a big step in solving the problem of obtaining 3D shapes from 2D images. In our work, we show that you can not only go from 2D to 3D directly and get a good, approximate 3D reconstruction but you can also learn a distribution of 3D shapes in an efficient manner and generate/synthesize 3D shapes. Below is an image of our work showing that we are able to do 3D reconstruction even from a single silhouette or depth map (on the left). The ground-truth 3D shapes are shown on the right.
The approach we took has some contributions related to cognitive science or the way the brain works: the model we built shares parameters for all shape categories instead of being specific to only one category. Also, it obtains consistent representations and takes the uncertainty of the input view into account when producing a 3D shape as output. Therefore, it is able to naturally give meaningful results even for very ambiguous inputs. If you look at the citation to our paper you can see even more progress just in terms of going from 2D images to 3D shapes.
This problem is known as Photogrammetry.
Google will supply you with endless references, just be aware that if you want to roll your own, it's a very hard problem.
Check out The Deadalus Project, althought that website does not contain a gallery with illustrative information about the solution, it post several papers and info about the working method.
I watched a lecture from one of the main researchers of the project (Roger Hubbold), and the image results are quite amazing! Althought is a complex and long problem. It has a lot of tricky details to take into account to get an approximation of the 3d data, take for example the 3d information from wall surfaces, for which the heuristic to work is as follows: Take a photo with normal illumination of the scene, and then retake the picture in same position with full flash active, then substract both images and divide the result by a pre-taken flash calibration image, apply a box filter to this new result and then post-process to estimate depth values, the whole process is explained in detail in this paper (which is also posted/referenced in the project website)
Google Sketchup (free) has a photo matching tool that allows you to take a photograph and match its perspective for easy modeling.
EDIT: It appears that you're interested in developing your own solution. I thought you were trying to obtain a 3D model of an image in a single instance. If this answer isn't helpful, I apologize.
Hope this helps if you are trying to construct 3d volume from 2d stack of images !! You can use open source tool such as ImageJ Fiji which comes with 3d viewer plugin..
https://quppler.com/creating-a-classifier-using-image-j-fiji-for-3d-volume-data-preparation-from-stack-of-images/