Question

This is my first post here, therefore apologize for any blunders.
I’m developing a simple action game with the usage of OpenGL ES 2.0 and Android 2.3. My game framework on which I’m currently working on is based on two dimensional sprites which exists in three dimensional world. Of course my world entities possess information such as position within the imaginary world, rotational value in form of float[] matrix, OpenGL texture handle as well as Android’s Bitmap handle (I’m not sure if the latter is necessary as I’m doing the rasterisation with the usage of OpenGl machine, but for the time being it is just there, for my convenience). This is briefly the background, now to the problematic issue.
Presently I’m stuck with the pixel based collision detection as I’m not sure which object (here OGL texture, or Android Bitmap) I need to sample. I mean, I’ve already tried to sample Android’s Bitmap, but it completely didn’t worked for me – many run-time crashes in relation to reading outside of the bitmap. Of course to be able to read the pixels from the bitmap, I’ve used Bitmap.create method to obtain properly rotated sprite. Here’s the code snippet:

android.graphics.Matrix m = new android.graphics.Matrix();
if(o1.angle != 0.0f) {          
    m.setRotate(o1.angle);
    b1 = Bitmap.createBitmap(b1, 0, 0, b1.getWidth(), b1.getHeight(), m, false);
}

Another issue, which might add to the problem, or even be the main problem, is that my rectangle of intersection (rectangle indicating two dimensional space mutual for both objects) is build up from parts of two bounding boxes which were computed with the usage of OpenGL matrices Matrix.multiplyMV functionality (code below). Could it be, that those two Android and OpenGL matrices computation methods aren’t equal?

Matrix.rotateM(mtxRotate, 0, -angle, 0, 0, 1);

// original bitmap size, equal to sprite size in it's model space,
// as well as in world's space
float[] rect = new float[] {
    origRect.left, origRect.top, 0.0f, 1.0f, 
    origRect.right, origRect.top, 0.0f, 1.0f,
    origRect.left, origRect.bottom, 0.0f, 1.0f,
    origRect.right, origRect.bottom, 0.0f, 1.0f 
};

android.opengl.Matrix.multiplyMV(rect, 0, mtxRotate, 0, rect, 0);
android.opengl.Matrix.multiplyMV(rect, 4, mtxRotate, 0, rect, 4);
android.opengl.Matrix.multiplyMV(rect, 8, mtxRotate, 0, rect, 8);
android.opengl.Matrix.multiplyMV(rect, 12, mtxRotate, 0, rect, 12);

// computation of object's bounding box (it is necessary as object has been
// rotated second ago and now it's bounding rectangle doesn't match it's host
float left = rect[0];
float top = rect[1];
float right = rect[0];
float bottom = rect[1];
for(int i = 4; i < 16; i += 4) {
    left = Math.min(left, rect[i]);
    top = Math.max(top, rect[i+1]);
    right = Math.max(right, rect[i]);
    bottom = Math.min(bottom, rect[i+1]);
};

Answer 1

Cheers,

first note that there is a bug in your code. You can not use Matrix.multiplyMV() with source and destination vector being the same (the function will correctly calculate an x coordinate which it will overwrite in the source vector. However, it needs the original x to calculate the y, z and w coordinates – which are in turn flawed). Also note that it would be easier for you to use bounding spheres for the first detection collision step, as they do not require such a complicated code to perform matrix transformation.

Then, the collision detection. You should not read from bitmaps nor textures. What you should do is to build a silhouette for your object (that is pretty easy, silhouette is just a list of positions). After that you need to build convex objects that fill the (non-convex) silhouette. It can be acheived by eg. ear clipping algorithm. It may not be the fastest, but it is very easy to implement and will be done only one time. Once you have the convex objects, you can transform their coordinates using a matrix and detect collisions with your world (there are many nice articles on ray-triangle intersections you can use), and you get the same precision as if you were to use pixel-based collision detection.

I hope it helps …