Using Raphael 2.0, I am trying to apply a transform to a set of objects in a way that is relative to all of the objects in the set. However, the effect I am getting is that the transform is applied to each item individually, irrespective of the other objects in the set.
For example: http://jsfiddle.net/tim_iles/VCca9/8/ - if you now uncomment the last line and run the code, each circle is scaled to 0.5x. The actual effect I am trying to achieve would be to scale the whole set of circles, so their relative distances are also scaled, which should put all four of them inside the bounding box of the white square.
Is there a way to achieve this using Raphael's built in tools?
When you scale, the first parameter is the X-scale. If you provide no other parameters, it will use that as the Y-scale, and scale around the center of the object.
When you scaled the rectangle, it scaled around the center of the rectangle. If you want the circles to scale around that point as well, rather than their centers, you should provide that point.
So the last line could be set.transform("s0.5,0.5,100,100"); (100,100 being the center of the rectangle you scaled)
At least, I think this is what you're asking for.
Related
There a is an ellipse on the picture,just as following.
I have got the points of the contour by using opencv. But you can see the pictrue,because the resolution is low, there is a straight line on the contour.How can i fit it into curve like the blue line?
One Of the method to solve your problem is to vectorize your shape (moving from simple intensity space to vectors space).
I am not aware of the state-of-art in this field. However, from school information, I can suggest this solution.
Bezier curves, you can try to model your shape using simple bezier curve.This is not a hard operation you can google for dozen of them. Then, you can resizing it as much as you want after that you may render it to simple image.
Be aware that you may also Splines instead of Bezier.
Another method would be more simple but less efficient. Since you mentioned OpenCV, you can apply the cv::fitEllipse on the points. Be aware that this will return a RotatedRect which contains the ellipse. You can infer your ellipse simply like this:
Center = Center of RotatedRect.
Longest Radius = The Line which pass from the center and intersect with the two small sides of the RotatedRect.
Smallest Radius = The Line which pass from the center and intersect with the two long sides of the RotatedRect.
After you got your Ellipse Parameters, You can resize it as you want then just repaint it in the size you want using cv::ellipse.
I know that this is a pseudo answer. However, I think every thing is easy to apply. If you faced any problem implementing it, just give me a comment.
I am trying to detect a ball in an filtered image.
In this image I've already removed the stuff that can't be part of the object.
Of course I tried the HoughCircle function, but I did not get the expected output.
Either it didn't find the ball or there were too many circles detected.
The problem is that the ball isn't completly round.
Screenshots:
I had the idea that it could work, if I identify single objects, calculate their center and check whether the radius is about the same in different directions.
But it would be nice if it detect the ball also if he isn't completely visible.
And with that method I can't detect semi-circles or something like that.
EDIT: These images are from a video stream (real time).
What other method could I try?
Looks like you've used difference imaging or something similar to obtain the images you have..? Instead of looking for circles, look for a more generic loop. Suggestions:
Separate all connected components.
For every connected component -
Walk around the contour and collect all contour pixels in a list
Suggestion 1: Use least squares to fit an ellipse to the contour points
Suggestion 2: Study the curvature of every contour pixel and check if it fits a circle or ellipse. This check may be done by computing a histogram of edge orientations for the contour pixels, or by checking the gradients of orienations from contour pixel to contour pixel. In the second case, for a circle or ellipse, the gradients should be almost uniform (ask me if this isn't very clear).
Apply constraints on perimeter, area, lengths of major and minor axes, etc. of the ellipse or loop. Collect these properties as features.
You can either use hard-coded heuristics/thresholds to classify a set of features as ball/non-ball, or use a machine learning algorithm. I would first keep it simple and simply use thresholds obtained after studying some images.
Hope this helps.
I am trying to develop box sorting application in qt and using opencv. I want to measure width and length of box.
As shown in image above i want to detect only outermost lines (ie. box edges), which will give me width and length of box, regardless of whatever printed inside the box.
What i tried:
First i tried using Findcontours() and selected contour with max area, but the contour of outer edge is not enclosed(broken somewhere in canny output) many times and hence not get detected as a contour.
Hough line transform gives me too many lines, i dont know how to get only four lines am interested in out of that.
I tried my algorithm as,
Convert image to gray scale.
Take one column of image, compare every pixel with next successive pixel of that column, if difference in there value is greater than some threshold(say 100) that pixel belongs to edge, so store it in array. Do this for all columns and it will give upper line of box parallel to x axis.
Follow the same procedure, but from last column and last row (ie. from bottom to top), it will give lower line parallel to x axis.
Likewise find lines parallel to y axis as well. Now i have four arrays of points, one for each side.
Now this gives me good results if box is placed in such a way that its sides are exactly parallel to X and Y axis. If box is placed even slightly oriented in some direction, it gives me diagonal lines which is obvious as shown in below image.
As shown in image below i removed first 10 and last 10 points from all four arrays of points (which are responsible for drawing diagonal lines) and drew the lines, which is not going to work when box is tilted more and also measurements will go wrong.
Now my question is,
Is there any simpler way in opencv to get only outer edges(rectangle) of box and get there dimensions, ignoring anything printed on the box and oriented in whatever direction?
I am not necessarily asking to correct/improve my algorithm, but any suggestions on that also welcome. Sorry for such a big post.
I would suggest the following steps:
1: Make a mask image by using cv::inRange() (documentation) to select the background color. Then use cv::not() to invert this mask. This will give you only the box.
2: If you're not concerned about shadow, depth effects making your measurment inaccurate you can proceed right away with trying to use cv::findContours() again. You select the biggest contour and store it's cv::rotatedRect.
3: This cv::rotatedRect will give you a rotatedRect.size that defines the width en the height of your box in pixels
Since the box is placed in a contrasting background, you should be able to use Otsu thresholding.
threshold the image (use Otsu method)
filter out any stray pixels that are outside the box region (let's hope you don't get many such pixels and can easily remove them with a median or a morphological filter)
find contours
combine all contour points and get their convex hull (idea here is to find the convex region that bounds all these contours in the box region regardless of their connectivity)
apply a polygon approximation (approxPolyDP) to this convex hull and check if you get a quadrangle
if there are no perspective distortions, you should get a rectangle, otherwise you will have to correct it
if you get a rectangle, you have its dimensions. You can also find the minimum area rectangle (minAreaRect) of the convexhull, which should directly give you a RotatedRect
Finding Circle Edges :
Here are the two sample images that i have posted.
Need to find the edges of the circle:
Does it possible to develop one generic circle algorithm,that could find all possible circles in all scenarios ?? Like below
1. Circle may in different color ( White , Black , Gray , Red)
2. Background color may be different
3. Different in its size
http://postimage.org/image/tddhvs8c5/
http://postimage.org/image/8kdxqiiyb/
Please suggest some idea to write a algorithm that should work out on above circle
Sounds like a job for the Hough circle transform:
I have not used it myself so far, but it is included in OpenCV. Among other parameters, you can give it a minimum and maximum radius.
Here are links to documentation and a tutorial.
I'd imagine your second example picture will be very hard to detect though
You could apply an edge detection transformation to both images.
Here is what I did in Paint.NET using the outline effect:
You could test edge detect too but that requires more contrast in the images.
Another thing to take into consideration is what it exactly is that you want to detect; in the first image, do you want to detect the white ring or the disc inside. In the second image; do you want to detect the all the circles (there are many tiny ones) or just the big one(s). These requirement will influence what transformation to use and how to initialize these.
After transforming the images into versions that 'highlight' the circles you'll need an algorithm to find them.
Again, there are more options than just one. Here is a paper describing an algoritm
Searching the web for image processing circle recognition gives lots of results.
I think you will have to use a couple of different feature calculations that can be used for segmentation. I the first picture the circle is recognizeable by intensity alone so that one is easy. In the second picture it is mostly the texture that differentiates the circle edge, in that case a feature image based based on some kind of texture filter will be needed, calculating the local variance for instance will result in a scalar image that can segment out the circle. If there are other features that defines the circle in other scenarios (different colors for background foreground etc) you might need other explicit filters that give a scalar difference for those cases.
When you have scalar images where the circles stand out you can use the circular Hough transform to find the circle. Either run it for different circle sizes or modify it to detect a range of sizes.
If you know that there will be only one circle and you know the kind of noise that will be present (vertical/horizontal lines etc) an alternative approach is to design a more specific algorithm e.g. filter out the noise and find center of gravity etc.
Answer to comment:
The idea is to separate the algorithm into independent stages. I do not know how the specific algorithm you have works but presumably it could take a binary or grayscale image where high values means pixel part of circle and low values pixel not part of circle, the present algorithm also needs to give some kind of confidence value on the circle it finds. This present algorithm would then represent some stage(s) at the end of the complete algorithm. You will then have to add the first stage which is to generate feature images for all kind of input you want to handle. For the two examples it should suffice with one intensity image (simply grayscale) and one image where each pixel represents the local variance. In the color case do a color transform an use the hue value perhaps? For every input feed all feature images to the later stage, use the confidence value to select the most likely candidate. If you have other unknowns that your algorithm need as input parameters (circle size etc) just iterate over the possible values and make sure your later stages returns confidence values.
I'm creating a simulator coded in python and based on ODE (Open Dynamics Engine). For visualization I chose VTK.
For every object in the simulation, I create a corresponding source (e.g. vtkCubeSource), mapper and actor. I am able to show objects correctly and update them as the simulation runs.
I want to add axes to have a point of reference and to show the direction of each axis. Doing that I realized that, by default, X and Z are in the plane of the screen and Y points outwards. In my program I have a different convention.
I've been able to display axes in 2 ways:
1) Image
axes = vtk.vtkAxes()
axesMapper = vtk.vtkPolyDataMapper()
axesMapper.SetInputConnection(axes.GetOutputPort())
axesActor = vtk.vtkActor()
axesActor.SetMapper(axesMapper)
axesActor.GetProperty().SetLineWidth(4)
2) Image (colors do not match with the first case)
axesActor = vtk.vtkAxesActor()
axesActor.AxisLabelsOn()
axesActor.SetShaftTypeToCylinder()
axesActor.SetCylinderRadius(0.05)
In the second one, the user is allowed to set many parameters related to how the axis are displayed. In the first one, I only managed to set the line width but nothing else.
So, my questions are:
Which is the correct way to define and display axes in a 3D scene? I just want them in a fixed position and orientation.
How can I set a different convention for the axes orientation, both for their display and the general visualization?
Well, if you do not mess with objects' transformation matrix for display
purposes, it could probably be sufficient to just put your camera into a
different position while using axes approach 2. The easy methods to adjust
your camera position are: Pitch(), Azimuth() and Roll().
If you mess with object transforms, then apply the same transform to the
axes.
Dženan Zukić kindly answered this question in vtkusers#vtk.org mail list.
http://www.vtk.org/pipermail/vtkusers/2011-November/119990.html