I'm developing an iPhone app where the user chooses an image and then is allowed to draw on it (dots) that maybe stored on different zoomscales (he's allowed to zoom in and out).
I store the location of every point drawn in an array but when I calculate the distance I come to realize the result isn't correct if the points were stored on different zoomscales. Would someone kindly help me with this?
Probably you should store points in normalized unit.
Assuming that you are using UIScrollView for zooming, divide both x and y by current scrollView.zoomScale before storing. When calculating the distance, multiply the distance back by scrollView.zoomScale.
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Leaflet consists of multiple self worlds. General limit of latitude n longitude is -90 to +90 and -180 to +180 respectively. So for a different number of world in map area i receive {lat: 76.12621315046384, lng: 370.70826412409673}, which i normalize to the limit format and send as a param to server in order to receive points based on an algorithm. However the points that i receive are in normalized format already which will plot on the initial first map only, however i would like to plot them on that number of world on map area from which i retrieved longitude as 370.70826412409673.
I tried getting getting pane, scaleZoom, zoomScale, scale, zoom but nothing seem to work in order to get me the world number or anything that helps me de-normalize the geopoints.
You can use the function getBounds() that gives you the coordinates of the two corners of the current view and/or getCenter(). You can then change your longitude to fit this view.
An other solution is to keep the offset between the normalized and "true" coordinates, and add it back to the answer of your algorithm.
I am writing a program that captures real time images from a scene by two calibrated cameras (so the internal parameters of the cameras are known to us). Using two view geometry, I can find the essential matrix and use OpenCV or MATLAB to find the relative position and orientation of one camera with respect to another. Having the essential matrix, it is shown in Hartley and Zisserman's Multiple View Geometry that one can reconstruct the scene using triangulation up to scale. Now I want to use a reference length to determine the scale of reconstruction and resolve ambiguity.
I know the height of the front wall and I want to use it for determining the scale of reconstruction to measure other objects and their dimensions or their distance from the center of my first camera. How can it be done in practice?
Thanks in advance.
Edit: To add more information, I have already done linear trianglation (minimizing the algebraic error) but I am not sure if it is any useful because there is still a scale ambiguity that I don't know how to get rid of it. My ultimate goal is to recognize an object (like a Pepsi can) and separate it in a rectangular area (which is going to be written as a separate module by someone else) and then find the distance of each pixel in this rectangular area, i.e. the region of interest, to the camera. Then the distance from the camera to the object will be the minimum of the distances from the camera to the 3D coordinates of the pixels in the region of interest.
Might be a bit late, but at least for someone struggling with the same staff.
As far as I remember it is actually linear problem. You got essential matrix, which gives you rotation matrix and normalized translation vector specifying relative position of cameras. If you followed Hartley and Zissermanm you probably chose one of the cameras as origin of world coordinate system. Meaning all your triangulated points are in normalized distance from this origin. What is important is, that the direction of every triangulated point is correct.
If you have some reference in the scene (lets say height of the wall), then you just have to find this reference (2 points are enough - so opposite ends of the wall) and calculate "normalization coefficient" (sorry for terminology) as
coeff = realWorldDistanceOf2Points / distanceOfTriangulatedPoints
Once you have this coeff, just mulptiply all your triangulated points with it and you got real world points.
Example:
you know that opposite corners of the wall are 5m from each other. you find these corners in both images, triangulate them (lets call triangulated points c1 and c2), calculate their distance in the "normalized" world as ||c1 - c2|| and get the
coeff = 5 / ||c1 - c2||
and you get real 3d world points as triangulatedPoint*coeff.
Maybe easier option is to have both cameras in fixed relative position and calibrate them together by stereoCalibrate openCV/Matlab function (there is actually pretty nice GUI in Matlab for that) - it returns not just intrinsic params, but also extrinsic. But I don't know if this is your case.
How do you determine that the intrinsic and extrinsic parameters you have calculated for a camera at time X are still valid at time Y?
My idea would be
to use a known calibration object (a chessboard) and place it in the camera's field of view at time Y.
Calculate the chessboard corner points in the camera's image (at time Y).
Define one of the chessboard corner points as world origin and calculate the world coordinates of all remaining chessboard corners based on that origin.
Relate the coordinates of 3. with the camera coordinate system.
Use the parameters calculated at time X to calculate the image points of the points from 4.
Calculate distances between points from 2. with points from 5.
Is that a clever way to go about it? I'd eventually like to implement it in MATLAB and later possibly openCV. I think I'd know how to do steps 1)-2) and step 6). Maybe someone can give a rough implementation for steps 2)-5). Especially I'd be unsure how to relate the "chessboard-world-coordinate-system" with the "camera-world-coordinate-system", which I believe I would have to do.
Thanks!
If you have a single camera you can easily follow the steps from this article:
Evaluating the Accuracy of Single Camera Calibration
For achieving step 2, you can easily use detectCheckerboardPoints function from MATLAB.
[imagePoints, boardSize, imagesUsed] = detectCheckerboardPoints(imageFileNames);
Assuming that you are talking about stereo-cameras, for stereo pairs, imagePoints(:,:,:,1) are the points from the first set of images, and imagePoints(:,:,:,2) are the points from the second set of images. The output contains M number of [x y] coordinates. Each coordinate represents a point where square corners are detected on the checkerboard. The number of points the function returns depends on the value of boardSize, which indicates the number of squares detected. The function detects the points with sub-pixel accuracy.
As you can see in the following image the points are estimated relative to the first point that covers your third step.
[The image is from this page at MATHWORKS.]
You can consider point 1 as the origin of your coordinate system (0,0). The directions of the axes are shown on the image and you know the distance between each point (in the world coordinate), so it is just the matter of depth estimation.
To find a transformation matrix between the points in the world CS and the points in the camera CS, you should collect a set of points and perform an SVD to estimate the transformation matrix.
But,
I would estimate the parameters of the camera and compare them with the initial parameters at time X. This is easier, if you have saved the images that were used when calibrating the camera at time X. By repeating the calibrating process using those images you should get very similar results, if the camera calibration is still valid.
Edit: Why you need the set of images used in the calibration process at time X?
You have a set of images to do the calibrations for the first time, right? To recalibrate the camera you need to use a new set of images. But for checking the previous calibration, you can use the previous images. If the parameters of the camera are changes, there would be an error between the re-estimation and the first estimation. This can be used for evaluating the validity of the calibration not for recalibrating the camera.
I have data describing a rotated ellipse (the center of the ellipse in latitude longitude coordinates, the lengths of the major and minor axes in kilometers, and the angle that the ellipse is oriented). I do not know the location of the foci, but assume there is a way to figure them out somehow. I would like to determine if a specific latitude longitude point is within this ellipse. I have found a good way to determine if a point is within an ellipse on a Cartesian grid, but don't know how to deal with latitude longitude points.
Any help would be appreciated.
-Cody O.
The standard way of doing this on a Cartesian plane would be with a ray-casting algorithm. Since you're on a sphere, you will need to use great circle distances to accurately represent the ellipse.
EDIT: The standard ray-casting algorithm will work on your ellipse, but its accuracy depends on a) how small your ellipse is, and b) how close to the equator it is. Keep in mind, you'd have to be aware of special cases like the date line, where it goes from 179 -> 180/-180 -> -179.
Since you already have a way to solve the problem on a cartesian grid, I would just convert your points to UTM coordinates. The points and lengths will all be in meters then and the check should be easy. Lots of matlab code is available to do this conversion from LL to UTM. Like this.
You don't mention how long the axes of the ellipse are in the description. If they are very long (say hundreds of km), this approach may not work for you and you will have to resort to thinking about great circles and so on. You will have to make sure to specify the UTM zone to which you are converting. You want all your points to end up in the same UTM zone or you won't be able to relate the points.
After some more research into my problem and posting in another forum I was able to figure out a solution. My ellipse is relatively small so I assumed it was a true (flat) ellipse. I was able to locate the lat lon of the foci of the ellipse then if the sum of the distances from the point of interest to each focus is less than 2a (the major axis radius), then it is within the ellipse. Thanks for the suggestions though.
-Cody
I have done some drawing on one layer now I want to draw same thing on another layer.So I have stored all the points of drawing that user has drawn on first layer.And then using convertPoint: toLayer: method converting stored point into points of another layer. And its working.But creates problem in orientation. If I have done drawing in portrait. Then it will work only for portrait in landscape position will be change. So please suggest any way to get out of this.
Thanks
Normalise your stored points, such that the x, y positions are relative to a surface of size 0..1, 0..1 (divide the x, y by the width and height of the current surface). Then, whenever you want to change the size of the underlying surface, multiply each point by this new surface's width and height. All points will now appear in the same relative positions regardless of the surface dimensions.
Note the above will scale (going from portrait to landscape, the Y will be compressed and the X expanded). If you don't want to do this, you will need to take physical dimensions of the surface on-screen into account too. That is, normalise your points to some physical dimension instead.
Note: I have no idea what system, language, package, library, etc. you are using as you don't state in your question!