This is one of the 100 recipes of the IPython Cookbook, the definitive guide to high-performance scientific computing and data science in Python.

5.5. Ray tracing: naive Cython

In this example, we will render a sphere with a diffuse and specular material. The principle is to model a scene with a light source and a camera, and use the physical properties of light propagation to calculate the light intensity and color of every pixel of the screen.

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import numpy as np
import matplotlib.pyplot as plt
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%matplotlib inline
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#%load_ext cythonmagic
%load_ext Cython
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%%cython
import numpy as np
cimport numpy as np

w, h = 200, 200  # Size of the screen in pixels.

def normalize(x):
    # This function normalizes a vector.
    x /= np.linalg.norm(x)
    return x

def intersect_sphere(O, D, S, R):
    # Return the distance from O to the intersection 
    # of the ray (O, D) with the sphere (S, R), or 
    # +inf if there is no intersection.
    # O and S are 3D points, D (direction) is a 
    # normalized vector, R is a scalar.
    a = np.dot(D, D)
    OS = O - S
    b = 2 * np.dot(D, OS)
    c = np.dot(OS, OS) - R*R
    disc = b*b - 4*a*c
    if disc > 0:
        distSqrt = np.sqrt(disc)
        q = (-b - distSqrt) / 2.0 if b < 0 \
            else (-b + distSqrt) / 2.0
        t0 = q / a
        t1 = c / q
        t0, t1 = min(t0, t1), max(t0, t1)
        if t1 >= 0:
            return t1 if t0 < 0 else t0
    return np.inf

def trace_ray(O, D):
    # Find first point of intersection with the scene.
    t = intersect_sphere(O, D, position, radius)
    # No intersection?
    if t == np.inf:
        return
    # Find the point of intersection on the object.
    M = O + D * t
    N = normalize(M - position)
    toL = normalize(L - M)
    toO = normalize(O - M)
    # Ambient light.
    col = ambient
    # Lambert shading (diffuse).
    col += diffuse * max(np.dot(N, toL), 0) * color
    # Blinn-Phong shading (specular).
    col += specular_c * color_light * \
        max(np.dot(N, normalize(toL + toO)), 0) \
           ** specular_k
    return col

def run():
    img = np.zeros((h, w, 3))
    # Loop through all pixels.
    for i, x in enumerate(np.linspace(-1., 1., w)):
        for j, y in enumerate(np.linspace(-1., 1., h)):
            # Position of the pixel.
            Q[0], Q[1] = x, y
            # Direction of the ray going through the optical center.
            D = normalize(Q - O)
            depth = 0
            # Launch the ray and get the color of the pixel.
            col = trace_ray(O, D)
            if col is None:
                continue
            img[h - j - 1, i, :] = np.clip(col, 0, 1)
    return img

# Sphere properties.
position = np.array([0., 0., 1.])
radius = 1.
color = np.array([0., 0., 1.])
diffuse = 1.
specular_c = 1.
specular_k = 50

# Light position and color.
L = np.array([5., 5., -10.])
color_light = np.ones(3)
ambient = .05

# Camera.
O = np.array([0., 0., -1.])  # Position.
Q = np.array([0., 0., 0.])  # Pointing to.
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img = run()
plt.imshow(img);
plt.xticks([]); plt.yticks([]);
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%timeit -n1 -r1 run()

You'll find all the explanations, figures, references, and much more in the book (to be released later this summer).

IPython Cookbook, by Cyrille Rossant, Packt Publishing, 2014 (500 pages).