I'm trying to detect an object composed of other objects. Actualy, there are three circles in my binary image which shape up a triangle as shown here:
These circles are correctly detected, but only as single objects as shown here:
What I need to have is an aggregation or composition of these objects, so they get detected as one big object as shown here:
The bigger goal is to get the image moments to get the rotation and scale of the shape. Please share your ideas or code if you have any, it would be well appreciated.
I would suggest using the bounding box functions of opencv
Here is a link to an example of bounding box in C++ OpenCV, however if you are using something like Python, it might be worth your while looking at this link, which is a full set of tutorials for working with binary images and contours (including bounding box/elipse)
Again if you are using the Python port, look at this set of tutorials, they really are great and have a massive supply of information on most functions of OpenCV.
Hope this helps.
Good luck.
Your question is very similar to this question, which has answers with code examples. Alternatively check the documentation of OpenCV. If you are interested in the convex hull of your points, see cv::convexHull().
Related
I need to implement contours detection function in my iOS game, which I'm writing using cocos2d 2.1
For example user will provide me an image(PNG transparent):
So, I need detect shape polygon points and create box2d body from them, and I will able to put this image to my box2d scene.
I expect to have on output NSMutableArray with arrays of points of each polygon detected on the image.
Same do PhysicsEditor, here is result of it:
Here is also result using VertexHelper(shows wrong way of detection, as one polygon... ):
Also SpriteHelper but without detection of other parts of image
My question is: how can I do this? What way is better and faster?
I was looking for a solution in google, however I can't find any that will fit my needs...
I guess you are looking for a Sobel edge detection filter. Check out the GPUImage framework created by Brad Larson. It has an implementation of Sobel edge detection filter using objective-C which might be useful for you.
Finally done this by using Chipmunk Autogeometry feature. Work like a charm.
Just using https://github.com/slembcke/ConcaveSprite/blob/master/ConcaveSprite/ConcaveSprite.m I've saved my time...
I have to implement a contour detection of full human body (from feet to head, in several poses such as raising hands etc.) using opencv. I managed to compile and run code I found here https://gist.github.com/yoggy/1470956, but it only draws a rectangle around the body, and not the exact contour. Can one help me with identifying and displaying the contour itself?
Thanks!!
I'm afraid the answer to this question is:
There's no algorithm that can do this perfectly.
Computer vision has not developed to that extent yet. Take a look at recent papers in CVPR, PAMI, and you will find that most algorithms are "rectangle", or more specifically, bounding-box based, in terms of human labeling and algorithmic detecting.
It is true that you can find the contours within the bounding-box. However the computer just doesn't know which contour belongs to the specified object.
I suggest you search for "human pose estimation" for further information.
One approach that might work is background subtraction:
http://docs.opencv.org/3.1.0/db/d5c/tutorial_py_bg_subtraction.html
This would work for video but perhaps also for single images in a scenario where you were in a controlled (fixed camera) environment where you had an image of the pose and also and image of the background, with no one present.
You can use the function findCountors within the returned bounding box:
http://docs.opencv.org/doc/tutorials/imgproc/shapedescriptors/find_contours/find_contours.html
did somebody tried to find a pizzamarker like this one with "only" OpenCV so far?
I was trying to detect this one but couldn't get good results so far. I do not know where this marker is in picture (no ROI is possible), the marker will be somewhere in the room (different ligthning effects) and not faceing orthoonal towards us. What I want - the corners and later the orientation of this marker extracted with the corners but first of all only the 5Corners. (up, down, left, right, center)
I was trying so far: threshold, noiseclearing, find contours but nothing realy helped for a good result. Chessboards or square markers are normaly found because of their (parallel) lines- i guess this can't help me here...
What is an easy way to find those markers?
How would you start?
Use other colorformat like HSV?
A step-by-step idea or tutorial would be realy helpfull. Cause i couldn't find tuts at the net. Maybe this marker isn't called pizzamarker -> does somebody knows the real name?
thx for help
First - thank you for all of your help.
It seems that several methods are usefull. Some more or less time expansive.
For me it was the easiest with a template matching but not with the same marker.
I used only a small part of it...
this can be found 5 times(4 times negative and one positive) in this new marker:
now I use only the 4 most negatives Points and the most positive and got my 5 points that I finaly wanted. To make this more sure, I check if they are close to each other and will do a cornerSubPix().
If you need something which can operate in real-time I'd go down the edge detection route and look for intersecting lines like these guys did. Seems fast and robust to lighting changes.
Read up on the Hough Line Transform in openCV to get started.
Addendum:
Black to White is the strongest edge you can have. If you create a gradient image and use the strongest edges found in the scene (via histogram or other) you will be able to limit the detection to only the black/white edges. Look for intersections. This should give you a small number of center points to apply Hough ellipse detection (or alternate) to. You could rotate in a template as a further check if you wish.
BTW.. OpenCV has Edge Detection, Hough transform and FitEllipse if you do go down this route.
actually this 'pizza' pattern is one of the building blocks of the haar featured used in the
Viola–Jones object detection framework.
So what I would do is compute the summed area table, or integral image using cv::integral(img) and then run exhaustive search for this pattern, on various scales (size dependant).
In each window you are using only 9 points (top-left, top-center, ..., bottom left).
You can train and use cvHaarDetectObjects to detect the marker using VJ.
Probably not the fastest method but it should work.
You can find more info on object detection methods using OpenCV here: http://opencv.willowgarage.com/documentation/object_detection.html
I am looking for an efficient way to detect the small boxes around the numbers (see images)?
I already tried to use hough transformation with no success. Any ideas? I need some hints! I am using opencv...
For inspiration, you can have a look at the
Matlab video sudoku solver demo and explanation
Sudoku Grab, an Iphone App, whose author explains the computer vision part on his blog
Alternatively, if you are always hunting for the same grid you could deploy something like this:
Make a perfect artificial template of the grid and detect or save all coordinates from all corners.
In the target image, do the same thing, for example with Harris points. Be creative, you might also be able to use the distinct triangles that can be found in your images.
Using the coordinates from the template and the found harris points, determine the affine transformation x = Ax' between the template and the target image. That transformation can then be used to map the template grid onto the target image. At the very least this will give you some prior information to help guide further segmentation.
The gist of the idea and examples of the estimation of affine matrix A can be found on the site of Zissermans book Multiple View Geometry in Computer Vision and Peter Kovesi
I'd start by trying to detect the rectangular boundary of the overall sheet, then applying a perspective transform to make it truly rectangular. Crop that portion of the image out. If possible, then try to make the alternating white and grey sub-rectangles have an equal background brightness - maybe try adaptive histogram equalization.
Then the Hough transform might perform better. Alternatively, you could then take an approach that's broadly similar to this demonstration by Robert Bemis on MATLAB Central (it's analysing a DNA microarray image rather than Lotto cards, but it's essentially finding bounding boxes of items arranged in a grid). At a high level, the approach is to calculate the autocorrelation along columns and rows of pixels to detect the periodicity of the items in the grid, and use that to impose a bounding box on each item.
Sorry the above advice is mostly MATLAB-based; I'm afraid I'm not an opencv user, but hopefully it will give you some ideas at least.
I want to make an apps detect an square/rectangle in my webcam using EMGU CV (an OPENCV wrapper). The square/rectangle will have a solid color.
if it's posible I would like to obtain the width and heigth of the square/rectangle
In this video you can see what I would like to do.
http://www.youtube.com/watch?v=ytvO2dijZ7A&NR=1
I'm working with C#
If you already know the color of the desired object then you can segment the image based on that color. (Which may be why the rectangle disapears when the guy movies the direction to and away from the camera [differences in lighting]. Once you have the object segmented out of the image you can do region calculations on the image. [In matlab think regionprops]
Once you have the blob you can attempt to do model fitting to get a good approximation of the object being represented.
In the video link provided what is probably being done is Surf feature detection. Take a look at the SURFFeture example that ships with EMGU. Rather than drawing lines in this case however the four corner points are detected and a shape drawn on top. Similar examples which will help you are ShapeDetection and TrafficSignRecognition both in the EMGU.CV.Examples folder. ShapeDetection will teach you how to classify the square and the StopSignDetector.cs class will show you another example of how to apply a surf feature detection algorithm.
It will require a little reconfiguration but if you get stuck feel free to ask another question.
Cheers
Chris