• Rasterizer: Zbuffer, BlinnPhong, Texture (Python)


    import numpy as np
    from matplotlib import pyplot as plt, image as mpimg
    import random
    import math
    import copy
    
    from numpy.linalg.linalg import norm
    
    
    def checksgn(x):
        return x >= 0
    
    
    def check(p, i, j):
        if i < min(x[0] for x in p):
            return 0
        if i > max(x[0] for x in p):
            return 0
        if j < min(x[1] for x in p):
            return 0
        if j > max(x[1] for x in p):
            return 0
        p = np.array([[x[0], x[1], 0] for x in p])
        return checksgn(np.cross(p[1]-p[0], p[0]-[i, j, 0])[2]) == checksgn(np.cross(p[2]-p[1], p[1]-[i, j, 0])[2]) == checksgn(np.cross(p[0]-p[2], p[2]-[i, j, 0])[2])
    
    
    def barycentric(p, i, j):
        # p must be 3d and in screen space (i.e. z=0)
        q = np.array([i, j, 0])
        total = norm(np.cross(p[1]-p[0], p[2]-p[0]), ord=2)
        w0 = norm(np.cross(p[1]-q, p[2]-q), ord=2)
        w1 = norm(np.cross(p[2]-q, p[0]-q), ord=2)
        w2 = norm(np.cross(p[0]-q, p[1]-q), ord=2)
        return [w0/total, w1/total, w2/total]
    
    
    def getTexVal(tex, x, y):
        tex_w = len(tex[0])
        tex_h = len(tex)
        if x < 0 or y < 0 or x >= tex_w or y >= tex_h:
            return np.array([0, 0, 0])
        return tex[y][x] / 255
    
    
    def getTexValI(tex, x, y):
        tex_w = len(tex[0])
        tex_h = len(tex)
        x *= tex_w
        y *= tex_h
        x0 = math.floor(x)
        y0 = math.floor(y)
        x1 = x0 + 1
        y1 = y0 + 1
        dx = x - x0
        dy = y - y0
        c00 = getTexVal(tex, x0, y0)
        c01 = getTexVal(tex, x0, y1)
        c10 = getTexVal(tex, x1, y0)
        c11 = getTexVal(tex, x1, y1)
        w00 = (1 - dx) * (1-dy)
        w01 = (1 - dx) * dy
        w10 = dx * (1-dy)
        w11 = dx * dy
        ans = np.array(w00 * c00 + w01 * c01 + w10 * c10 + w11 * c11)
        return ans
    
    
    img_w = 512
    img_h = 512
    clip_n, clip_f, clip_l, clip_r, clip_t, clip_b = -3, -10, -1, 1, 1, -1
    
    img = [[np.array([0., 0, 0]) for j in range(img_w)] for i in range(img_h)]
    zbuf = [[abs(clip_f)+1 for j in range(img_w)] for i in range(img_h)]
    
    camera_pos_vs = np.array([0., 1, 2])
    camera_gaze = np.array([0., -0.5, -2])
    camera_gaze /= np.linalg.norm(camera_gaze, ord=2)
    camera_up = [
        np.cross(np.cross(camera_gaze, np.array([0., 1, 0])), camera_gaze)]
    camera_up /= np.linalg.norm(camera_up, ord=2)
    camera_handle = np.cross(camera_gaze, camera_up)
    camera_handle /= np.linalg.norm(camera_handle, ord=2)
    
    camera_pos_vs = np.append(camera_pos_vs, 1)
    camera_gaze = np.append(camera_gaze, 0)
    camera_up = np.append(camera_up, 0)
    camera_handle = np.append(camera_handle, 0)
    
    transform_view_translate = np.array(
        [[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [-camera_pos_vs[0], -camera_pos_vs[1], -camera_pos_vs[2], 1]]).T
    transform_view_rotate = np.concatenate(
        ([camera_handle], [camera_up], [-camera_gaze], [[0, 0, 0, 1]]))
    transform_view = transform_view_rotate @ transform_view_translate
    
    transform_proj_persp = np.array([[clip_n, 0, 0, 0], [0, clip_n, 0, 0], [
                                    0, 0, clip_n+clip_f, -clip_n*clip_f], [0, 0, 1, 0]])
    transform_proj_ortho = np.array([[2/(clip_r-clip_l), 0, 0, 0], [0, 2/(clip_t-clip_b), 0, 0], [0, 0, 2/(clip_n-clip_f), 0],
                                    [(clip_l+clip_r)/(clip_r-clip_l), (clip_t+clip_b)/(clip_t-clip_b), (clip_n+clip_f)/(clip_n-clip_f), 1]]).T
    transform_proj = transform_proj_ortho @ transform_proj_persp
    
    transform_viewport = np.array(
        [[img_w / 2, 0, 0, img_w/2], [0, -img_h/2, 0, img_h/2], [0, 0, 0, 0], [0, 0, 0, 1]])
    transform = transform_viewport @ transform_proj @ transform_view
    
    
    
    ####################################
    
    # mat: {ambi: [r, g, b], diff: "tex", spec: [r, g, b], specpow: p}
    # mesh: [[x, y, z, u, v], [x, y, z, u, v], [x, y, z, u, v], [matid]]
    
    mat = [
        {"ambi": np.array([0.1, 0.1, 0.1]), "diff": "assets/wood.jpg",
         "spec": np.array([1, 1, 1]), "specpow": 10}
    ]
    
    mesh = [
        [[0, 0, 0, 0, 0], [0, 1, 0, 1, 0], [1, 0, -1, 0, 1], [0]],
        [[0, 0, 0, 0, 0], [0, 1, 0, 1, 0], [-1, 0, -1, 0, 1], [0]],
        [[0, 0, -2, 0, 0], [0, 1, 0, 1, 0], [-1, 0, -1, 0, 1], [0]],
        [[0, 0, -2, 0, 0], [0, 1, 0, 1, 0], [-1, 0, -1, 0, 1], [0]]
    ]
    
    lights = [
        {"int": np.array([10, 10, 10]), "pos":np.array([1, 1, 2]) },
        {"int": np.array([10, 10, 10]), "pos":np.array([0, 1, 10]) }
    ]
    
    light_vs_cached = [
        {
            "int": i["int"],
            "pos": (transform_view @ np.array(i["pos"].tolist() + [1]))[:3]
        }
        for i in lights
    ]
    
    
    # for each triangle
    for tri in mesh:
        mat_id = tri[3][0]
    
        triangle_vert = [np.array(i[:3] + [1]) for i in tri[:3]]
        triangle_uv = [np.array(i[3:]) for i in tri[:3]]
    
        ma = mat[mat_id]
        triangle_ambi = ma["ambi"]
        triangle_spec = ma["spec"]
        triangle_specpow = ma["specpow"]
        triangle_tex_filename = ma["diff"]
        triangle_tex = mpimg.imread(triangle_tex_filename).copy()
    
        vert_ws = triangle_vert
        vert_vs = [transform_view @ i for i in vert_ws]
        vert_ss = [transform @ i for i in vert_ws]
        vert_ss = [i[:3]/i[3] for i in vert_ss]
    
        for i in range(img_h):
            for j in range(img_w):
                if check(vert_ss, j, i):
                    px = j
                    py = i
                    # Evaluate interpolated z, u, v
                    bc = barycentric(vert_ss, px, py)
                    z = abs(1 / sum(bc[k] / vert_vs[k][2] for k in range(3)))
                    u = -z * sum(triangle_uv[k][0] * bc[k] /
                                vert_vs[k][2] for k in range(3))
                    v = -z * sum(triangle_uv[k][1] * bc[k] /
                                vert_vs[k][2] for k in range(3))
                    x = (px/img_w-0.5)*clip_r*z/abs(clip_n)
                    y = (py/img_h-0.5)*clip_t*z/abs(clip_n)
                    p_vs = np.array([x, y, z])
    
                    if z >= zbuf[i][j]:
                        continue
    
                    zbuf[i][j] = z
    
                    # Fragment Shader
                    coef_ambi = copy.deepcopy(triangle_ambi)
                    coef_spec = copy.deepcopy(triangle_spec)
                    coef_specpow = copy.deepcopy(triangle_specpow)
                    coef_diff = getTexValI(triangle_tex, u, v)
                    camera_pos_vs = np.array([0, 0, 0])
    
                    view_dir_vs = camera_pos_vs - p_vs
                    view_dir_vs /= norm(view_dir_vs, ord=2)
                    normal_dir_vs = np.cross(
                        vert_vs[1][:3]-vert_vs[0][:3], vert_vs[2][:3]-vert_vs[0][:3])
                    normal_dir_vs /= norm(normal_dir_vs, ord=2)
    
                    color = coef_ambi
    
                    for light in light_vs_cached:
                        light_intensity = light["int"]
                        light_pos_vs = light["pos"]   # already in vs!
    
                        light_dist_vec = light_pos_vs - p_vs
                        light_dir_vs = light_pos_vs - p_vs
                        light_dir_vs /= norm(light_dir_vs, ord=2)
                        half_dir_vs = light_dir_vs + view_dir_vs
                        half_dir_vs /= norm(half_dir_vs, ord=2)
    
                        color += coef_diff * light_intensity / \
                            light_dist_vec.dot(light_dist_vec) * \
                            max(0, light_dir_vs.dot(normal_dir_vs))
                        color += coef_spec * light_intensity / \
                            light_dist_vec.dot(
                                light_dist_vec) * (max(0, half_dir_vs.dot(normal_dir_vs)) ** coef_specpow)
    
                    img[i][j] = color
    
    plt.imshow(img)
    plt.show()
    

    image

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  • 原文地址:https://www.cnblogs.com/mollnn/p/15788779.html
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