I want to display Radar information on the vessel identified in the Camera image.
At this point, the work is complete.
Camera Image: The ship was identified by object recognition.
Radar information: Identified the latitude, longitude, distance, and azimuth of the vessel (A, B, C).
Three sea-going vessels and a radar plot
The camera and radar are located at the same position and know the latitude, longitude, roll, and pitch values.
How can we match GPS information by converting pixel coordinates?
Same three vessels annotated with distances
You want to project the 3D coordinates to the image plane. A more detailed information about the theory behind can be found in the OpenCV documentation.
This is a brief description of how it could be done:
Compute your vessel's 3D coordinates relative to your camera position.
You need a calibrated camera. You need the interior camera parameters to compute the projection from 3D coordinates to 2D image coordinates.
Use e.g. OpenCV and projectPoints (see doc here) to compute the 2D image coordinates based on the relative 3D coordinates and your camera parameters. Because you use the relative position of the vessel you don't need the exterior orientation of your camera. So translation and rotation become zero.
Related
How can I calculate the distance of an object of known size (e.g. aruco marker of 0.14m printed on paper) from camera. I know the camera matrix (camMatx) and my fx,fy ~= 600px assuming no distortion. From this data I am able to calculate the pose of the aruco marker and have obtained [R|t]. Now the task is to get the distance of the aruco marker from the camera. I also know the height of the camera from ground plane (15m).
How should I go about solving this problem. Any help would be appreciated. Also please note I have also seen approach of similar triangles, but that would work on knowing the distance of the object, which doesnt apply in my case as I have to calculate the distance.
N.B: I dont know the camera sensor height. But I know how high the camera is located above ground.
I know the dimensions of the area in which my object is moving (70m x 45m). In the end I would like to plot the coordinate of the moving object on a 2D map drawn to the scale.
I have 2D image data with respective camera location in latitude and longitude. I want to translate pixel co-ordinates to 3D world co-ordinates. I have access to intrinsic calibration parameters and Yaw, pitch and roll. Using Yaw, pitch and roll I can derive rotation matrix but I am not getting how to calculate translation matrix. As I am working on data set, I don't have access to camera physically. Please help me to derive translation matrix.
Cannot be done at all if you don't have the elevation of the camera with respect to the ground (AGL or ASL) or another way to resolve the scale from the image (e.g. by identifying in the image an object of known size, for example a soccer stadium in an aerial image).
Assuming you can resolve the scale, the next question is how precisely you can (or want to) model the terrain. For a first approximation you can use a standard geodetical ellipsoid (e.g. WGS-84). For higher precision - especially for images shot from lower altitudes - you will need use a DTM and register it to the images. Either way, it is a standard back-projection problem: you compute the ray from the camera centre to the pixel, transform it into world coordinates, then intersect with the ellipsoid or DTM.
There are plenty of open source libraries to help you do that in various languages (e.g GeographicLib)
Edited to add suggestions:
Express your camera location in ECEF.
Transform the ray from the camera in ECEF as well taking into account the camera rotation. You can both transformations using a library, e.g. nVector.
Then proceeed to intersect the ray with the ellipsoid, as explained in this answer.
After to calibrated a camera using Jean- Yves Bouget's Camera Calibration Toolbox and checkerboard-patterns printed on cardboard, I´ve obtained extrinsic and intrinsic parameters, I can use the informations to find camera coordinates:
Pc = R * Pw + T
After that, how to obtain the world coordinates of an image using the Pc and calibration parametesr?
thanks in advance.
EDIT
The goal is to use the calibrated camera parameters to measure planar objects with a calibrated Camera). To perform this task i dont know to use the camera parameters. in other words i have to convert the pixels coordinates of the image to world coordinates using the calibrated parameters. I already have the parameters and the new image. How can i do this convertion?
thanks in advance.
I was thinking about problem, and came to the result:
You can't find the object size. The problem is by a single shot, when you have no idea how far the Object is from your camera you can't say something about the size of the object. The calibration just say how far is the image plane from the camera (focal length) and the open angles of the lense. When the focal length changes the calbriation changes too.
But there are some possibiltys:
How to get the real life size of an object from an image, when not knowing the distance between object and the camera?
So how I understand you can approximate the size of the objects.
Your problem can be solved if (and only if) you can express the plane of your object in calibrated camera coordinates.
The calibration procedure outputs, along with the camera intrinsic parameters K, a coordinate transform matrix for every calibration image Qwc_i = [Rwc_i |Twc_i] matrix, that expresses the location and pose of a particular scene coordinate frame in the camera coordinates at that calibration image. IIRC, in Jean-Yves toolbox this is the frame attached to the top-left corner of the calibration checkerboard.
So, if your planar object is on the same plane as the checkerboard in one of the calibration images, all you have to do in order to find its location in space is intersect the checkerboard plane with camera rays cast from the camera center (0,0,0) to the pixels into which the object is imaged.
If your object is NOT in one of those planes, all you can do is infer the object's own plane from additional information, if available, e.g. from a feature of known size and shape.
We are currently using opencv to track a planar rectangular target. While directly straight(no pitch), this works perfectly using findContours with solvePnp and returns a very accurate location of the target.
The problem is, is that obviously we get the different results once we increase the pitch. We know the pitch of the camera at all time.
How would I "cancel out" the pitch of the camera, and obtain coordinates as if the camera was facing straight ahead?
In the general case you can use an affine transform to map the quadrilateral seen by the camera back to the original rectangle. In your case the quadrilateral seen by the camera may be a good approximation of a parallelogram since only one angle is changing, but in real-world applications you can generally assume that the camera can have non-zero values for each of the three rotations (e.g. in pitch, yaw, and roll).
http://opencv.itseez.com/doc/tutorials/imgproc/imgtrans/warp_affine/warp_affine.html
The transform allows you to calculate the matching coordinates (x,y) within the rectangle's plane given coordinates (x', y') in the image of the rectangle.
I have a problem that involves a UAV flying with a camera mounted below it. Following information is provided:
GPS Location of the UAV in Lat/Long
GPS Height of the UAV in meters
Attitude of the UAV i.e. roll, pitch, and yaw in degrees
Field of View (FOV) of the camera in degrees
Elevation of the camera w.r.t UAV in degrees
Azimuth of camera w.r.t UAV in degrees
I have some some images taken from that camera during a flight and my task is to compute the locations (in Lat/Long) of 4 corners points and the center points of the image so that the image can be placed on the map at proper location.
I found a document while searching the internet that can be downloaded at the following link:
http://www.siaa.asn.au/get/2411853249.pdf
My maths background is very weak so I am not able to translate the document into a working solution.
Can somebody provide me a solution to my problem in the form of a simple algorithm or preferable in the form of code of some programming language?
Thanks.
As I see, it does not related to image-processing, because you need to determine coordinates of center of image (you even do not need FOV). You have to find intersection of camera principal ray and earth surface (if I've understood your task well). This is nothing more then basic matrix math.
See wiki:Transformation.