I am trying to see if I can cover a pointcloud with disks using an algorithm and I have generated millions of "disks" that have a center, radius, and normal. I am currently doing this:
center, rad, normal = getcirc()
disk = o3d.geometry.TriangleMesh.create_cylinder(radius = rad, height = 1, resolution = 100).translate(center)
I am not sure how to draw to this so that it has the normal I want. The only methods open3d has are rotate (which takes in a 3 by 3 matrix) and transform (which takes in a 4 by 4 matrix) and I am not sure how to incorporate either of those to do this as I have never really studied linear algebra before.
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I currently have 16 tiles, with individual images that make up 1 big map. I pan by transforming right at the beginning before any actual drawing with this:
GL.Translate(G_.Pan(0), G_.Pan(1), 0)
Then I zoom by doing this:
GL.Ortho(-G_.Size * 1.5 ^ G_.ZoomFactor, G_.Size * 1.5 ^ G_.ZoomFactor, G_.Size * 1.5 ^ G_.ZoomFactor, -G_.Size * 1.5 ^ G_.ZoomFactor, -1, 1)
G_.Size is a constant that only varies on startup depending on parameters, zoom factor ranges from -1 to -13
What I want to be able to do is check if 1 of the 16 tiles is within the visible area, so then I stop them drawing when they are not on screen. I had found some quite complex methods for doing it, but it was 3D and seemed like a lot of work for something that should be simple. I would of thought it would of been something like just checking if a point is within the bounds of visible area, but I have no idea on how to get the visible area.
Andon M Coleman already suggested you to implement projection volume culling (a generalized form of frustum culling). This is however outside the scope of OpenGL. You must understand that OpenGL is not a "magical" scene graph that does scene management and the likes. It's mere drawing API; what it does is putting shaded, textured points, lines or triangles on the screen and that's it. The rest is up to you, or the libraries you choose to implement it.
In the case of projection volume culling you're testing if a given piece of geometry intersects with the volume defined by the planes that form the borders of the volume. Your projection matrix defines such planes, specifically it transform the view space vertex position volume into the range [-1;1]×[-1;1]×[0;1] of perspective divided clip space. So by inverting the projection matrix and unprojection the corners of the [-1;1]×[-1;1]×[0;1] cube through that you determine the limiting planes of the projection volume in view space.
You then use that information to intersect your quads with the volume to see if they cross it, i.e. are in any way visible.
I already asked a question about texture mapping and these two are related (this question).
I'm working with Quartz Composer which appears to be kind specific with textures...
I have a complex polygon that I triangulate in a specific coordinate system (-1 -> 1 on x | -0.75 -> 0.75 on y). I obtain an array of triangles vertices in this coordinate system (triangles 1 to 6 on the left pic).
Then I render each polygon separately (it's necessary for my program), by applying a scale function on its vertices from this coordinate system to OpenGL one (0. -> 1.). Here, even if for 0->1 range it's kind of stupid :
return (((1. - 0.) * (**myVertexXorY** - minTriangleBound)) / (maxTriangleBound - minTriangleBound)) + 0.;
But I want one image to be textured on these triangles (like on the picture above). So I begin by getting the whole polygon bounds (1 on the right pic), then the triangle bounds (2 on the right pic). I scale 1 to the picture coordinates (3 on the right pic) in pixels, then I get the triangle bounds (2) in pixels.
It gives me the bounds to lock my texture in OpenGL with Quartz :
NSRect myBounds = NSMakeRect(originXinPixels, originYinPixels, widthForTheTriangle, heightForTheTriangle);
And I lock my texture
[myImage lockTextureRepresentationWithColorSpace:space forBounds:myBounds];
Then, with OpenGL :
for (int32 i = 0; i < vertexCount; ++i)
{
verts[i] = myTriangle.vertices[i];
texcoord[0] = [self myScaleFunctionFor:XinQuartzCoordinateSystem From:0 To:1]
texcoord[1] = [self myScaleFunctionFor:YinQuartzCoordinateSystem From:0 To:1]
glTexCoord2fv(texcoord);
}
And I obtain what you can see : sometimes parts of the image are fitting, sometimes no (well, in fact with this particular polygon, it doesn't fit at all...).
I'm not really sure if I did understand your question, but:
What hinders you from directly supplying texture coordinates that do match the topology of your source picture? This was far easier than trying to find some per triangle linear mapping that moves the picture in the right way.
How can I do a basic face alignment on a 2-dimensional image with the assumption that I have the position/coordinates of the mouth and eyes.
Is there any algorithm that I could implement to correct the face alignment on images?
Face (or image) alignment refers to aligning one image (or face in your case) with respect to another (or a reference image/face). It is also referred to as image registration. You can do that using either appearance (intensity-based registration) or key-point locations (feature-based registration). The second category stems from image motion models where one image is considered a displaced version of the other.
In your case the landmark locations (3 points for eyes and nose?) provide a good reference set for straightforward feature-based registration. Assuming you have the location of a set of points in both of the 2D images, x_1 and x_2 you can estimate a similarity transform (rotation, translation, scaling), i.e. a planar 2D transform S that maps x_1 to x_2. You can additionally add reflection to that, though for faces this will most-likely be unnecessary.
Estimation can be done by forming the normal equations and solving a linear least-squares (LS) problem for the x_1 = Sx_2 system using linear regression. For the 5 unknown parameters (2 rotation, 2 translation, 1 scaling) you will need 3 points (2.5 to be precise) for solving 5 equations. Solution to the above LS can be obtained through Direct Linear Transform (e.g. by applying SVD or a matrix pseudo-inverse). For cases of a sufficiently large number of reference points (i.e. automatically detected) a RANSAC-type method for point filtering and uncertainty removal (though this is not your case here).
After estimating S, apply image warping on the second image to get the transformed grid (pixel) coordinates of the entire image 2. The transform will change pixel locations but not their appearance. Unavoidably some of the transformed regions of image 2 will lie outside the grid of image 1, and you can decide on the values for those null locations (e.g. 0, NaN etc.).
For more details: R. Szeliski, "Image Alignment and Stitching: A Tutorial" (Section 4.3 "Geometric Registration")
In OpenCV see: Geometric Image Transformations, e.g. cv::getRotationMatrix2D cv::getAffineTransform and cv::warpAffine. Note though that you should estimate and apply a similarity transform (special case of an affine) in order to preserve angles and shapes.
For the face there is lot of variability in feature points. So it won't be possible to do a perfect fit of all feature points by just affine transforms. The only way to align all the points perfectly is to warp the image given the points. Basically you can do a triangulation of image given the points and do a affine warp of each triangle to get the warped image where all the points are aligned.
Face detection could be handled based on the just eye positions.
Herein, OpenCV, Dlib and MTCNN offers to detect faces and eyes. Besides, it is a python based framework but deepface wraps those methods and offers an out-of-the box detection and alignment function.
detectFace function applies detection and alignment in the background respectively.
#!pip install deepface
from deepface import DeepFace
backends = ['opencv', 'ssd', 'dlib', 'mtcnn']
DeepFace.detectFace("img.jpg", detector_backend = backends[0])
Besides, you can apply detection and alignment manually.
from deepface.commons import functions
img = functions.load_image("img.jpg")
backends = ['opencv', 'ssd', 'dlib', 'mtcnn']
detected_face = functions.detect_face(img = img, detector_backend = backends[3])
plt.imshow(detected_face)
aligned_face = functions.align_face(img = img, detector_backend = backends[3])
plt.imshow(aligned_face)
processed_img = functions.detect_face(img = aligned_face, detector_backend = backends[3])
plt.imshow(processed_img)
There's a section Aligning Face Images in OpenCV's Face Recognition guide:
http://docs.opencv.org/trunk/modules/contrib/doc/facerec/facerec_tutorial.html#aligning-face-images
The script aligns given images at the eyes. It's written in Python, but should be easy to translate to other languages. I know of a C# implementation by Sorin Miron:
http://code.google.com/p/stereo-face-recognition/
I am new to GLUT and opengl. I need to draw a scatterplot matrix for n dimensional array.
I have saved the data from csv to a vector of vectors and each vector corresponds to a row. I have plotted just one scatterplot. And used GL_LINES to draw the grid. My questions
1. How do I draw points in a particular grid? Using GL_POINTS I can only draw points in the entire window.
Please let me know need any further info to answer this question
Thanks
What you need to do is be able to transform your data's (x,y) coordinates into screen coordinates. The most straightforward way to do it actually does not rely on OpenGL or GLUT. All you have to do is use a little math. Determine the screen (x,y) coordinates of the place where you want a datapoint for (0,0) to be on the screen, and then determine how far apart you want one increment to be on the screen. Simply take your original data points, apply the offset, and then scale them, to get your screen coordinates, which you then pass into glVertex2f() (or whatever function you are using to specify points in your API).
For instance, you might decide you want point (0,0) in your data to be at location (200,0) on your screen, and the distance between 0 and 1 in your data to be 30 pixels on the screen. This operation will look like this:
int x = 0, y = 0; //Original data points
int scaleX = 30, scaleY = 30; //Scaling values for each component
int offsetX = 100, offsetY = 100; //Where you want the origin of your graph to be
// Apply the scaling values and offsets:
int screenX = x * scaleX + offsetX;
int screenY = y * scaleY + offsetY;
// Calls to your drawing functions using screenX and screenY as your coordinates
You will have to determine values that make sense for the scalaing and offsets. You can also have your program use different values for different sets of data, so you can display multiple graphs on the same screen. But this is a simple way to do it.
There are also other ways you can go about this. OpenGL has very powerful coordinate transformation functions and matrix math capabilities. Those may become more useful when you develop increasingly elaborate programs. They're most useful if you're going to be moving things around the screen in real-time, or operating on incredibly large data sets, as they allow you to perform these mathematical calculations very quickly using your graphics hardware (which is able to do them much faster than the CPU). However, the time it takes for the CPU to do simple calculations like those where you only are going to do them once or very infrequently on limited sets of data is not a problem for computers today.
I have a webcam directly over a chicken nest. This camera takes images and uploads them to a folder on a server. I'd like to detect if an egg has been laid from this image.
I'm thinking the best method would be to compare the contrast as the egg will be much more reflective than the straw nest. (The camera has Infrared so the image is partly grey scale)
I'd like to do this in .NET if possible.
Try to resize your image to a smaller size, maybe 10 x 10 pixel. This averages out any small disturbing details.
Const N As Integer = 10
Dim newImage As New Bitmap(N, N)
Dim fromCamera As Image = Nothing ' Get image from camera here
Using gr As Graphics = Graphics.FromImage(newImage)
gr.SmoothingMode = SmoothingMode.HighSpeed
gr.InterpolationMode = InterpolationMode.Bilinear
gr.PixelOffsetMode = PixelOffsetMode.HighSpeed
gr.DrawImage(fromCamera, New Rectangle(0, 0, N, N))
End Using
Note: you do not need a high quality, but you need a good averaging. Maybe you will have to test different quality settings.
Since now, a pixel covers a large area of your original image, a bright pixel is very likely part of an egg. It might also be a good idea to compare the brightness of the brightest pixel to the average image brightness, since that would reduce problems due to global illumination changes.
EDIT (in response to comment):
Your code is well structured and makes sense. Here some thoughts:
Calculate the gray value from the color value with:
Dim grayValue = c.R * 0.3 + c.G * 0.59 + c.B * 0.11
... instead of comparing the three color components separately. The different weights are due to the fact, that we perceive green stronger than red and red stronger than blue. Again, we do not want a beautiful thumbnail we want a good contrast. Therefore, you might want to do some experiments here as well. May be it is sufficient to use only the red component. Dependent on lighting conditions one color component might yield a better contrast than others. I would recommend, to make the gray conversion part of the thumbnail creation and to write the thumbnails to a file or to the screen. This would allow you to play with the different settings (size of the thumbnail, resizing parameters, color to gray conversion, etc.) and to compare the (intermediate) results visually. Creating a bitmap (bmp) with the (end-)result is a very good idea.
The Using statement does the Dispose() for you. It does it even if an exception should occur before End Using (There is a hidden Try Finally involved).