How to draw orthographic projection from equirectangular projection

Question

I have this image :

I don’t know exactly what kind on projection it is, I guess equirectangular or mercator by the shape. It's the texture for an attitude indicator, b.

I want to draw a orthographic projection, b or maybe a General Perspective projection (which one looks better) of it according to a direction vector defined by two angles (heading and pitch). This direction define a point on the sphere, this point should be the center of the projection.

I want it to look from the pilot point of view, so only half of the sphere should be drawn.

I use python, and I have not yet chosen a graphic library, I will probably be using pygame though.

I’ve found something related : http://www.pygame.org/project-Off-Center+Map+Projections-2881-.html but it uses OpenGL and I have no experience with it, but I can try if needed.

How should I do that ? I probably can draw it manually by calculating every pixel from the calculation formulas but I think there are some kind of library tools to do that efficiently (hardware accelerated probably ?).

Answer 1

For an all-Python solution (using numpy/scipy array ops, which will be faster than any explicit per-pixel looping), this:

#!/usr/bin/env python

import math
import numpy as np
import scipy
import scipy.misc
import scipy.ndimage.interpolation
import subprocess

src=scipy.misc.imread("ji80w.png")

size=256
frames=50

for frame in xrange(0,frames):

    # Image pixel co-ordinates
    px=np.arange(-1.0,1.0,2.0/size)+1.0/size
    py=np.arange(-1.0,1.0,2.0/size)+1.0/size
    hx,hy=scipy.meshgrid(px,py)

    # Compute z of sphere hit position, if pixel's ray hits
    r2=hx*hx+hy*hy
    hit=(r2<=1.0)
    hz=np.where(
        hit,
        -np.sqrt(1.0-np.where(hit,r2,0.0)),
        np.NaN
        )

    # Some spin and tilt to make things interesting
    spin=2.0*np.pi*(frame+0.5)/frames
    cs=math.cos(spin)
    ss=math.sin(spin)
    ms=np.array([[cs,0.0,ss],[0.0,1.0,0.0],[-ss,0.0,cs]])

    tilt=0.125*np.pi*math.sin(2.0*spin)
    ct=math.cos(tilt)
    st=math.sin(tilt)
    mt=np.array([[1.0,0.0,0.0],[0.0,ct,st],[0.0,-st,ct]])

    # Rotate the hit points
    xyz=np.dstack([hx,hy,hz])
    xyz=np.tensordot(xyz,mt,axes=([2],[1]))
    xyz=np.tensordot(xyz,ms,axes=([2],[1]))
    x=xyz[:,:,0]
    y=xyz[:,:,1]
    z=xyz[:,:,2]

    # Compute map position of hit
    latitude =np.where(hit,(0.5+np.arcsin(y)/np.pi)*src.shape[0],0.0)
    longitude=np.where(hit,(1.0+np.arctan2(z,x)/np.pi)*0.5*src.shape[1],0.0)
    latlong=np.array([latitude,longitude])

    # Resample, and zap non-hit pixels
    dst=np.zeros((size,size,3))
    for channel in [0,1,2]:
        dst[:,:,channel]=np.where(
            hit,
            scipy.ndimage.interpolation.map_coordinates(
                src[:,:,channel],
                latlong,
                order=1
                ),
            0.0
            )

    # Save to f0000.png, f0001.png, ... 
    scipy.misc.imsave('f{:04}.png'.format(frame),dst)

# Use imagemagick to make an animated gif
subprocess.call('convert -delay 10 f????.png anim.gif',shell=True)

will get you

.

OpenGL is really the place to be doing this sort of pixel wrangling though, especially if it's for anything interactive.