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@ -14,20 +14,20 @@ author: |
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\newcommand{\now}[1]{\textcolor{blue}{#1}}
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\newcommand{\todo}[0]{\textcolor{red}{\textbf{TODO}}}
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[ 1 2 3 4 6 7 8 9 10 ]
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[ 1 2 3 6 8 9 ]
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## Reflection and Refraction
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1. \c{Consider a sphere $S$ made of solid glass ($\eta$ = 1.5) that has radius $r =
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3$ and is centered at the location $s = (2, 2, 10)$ in a vaccum ($\eta =
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1. \c{Consider a sphere $S$ made of solid glass ($\eta$ = 1.5) that has radius
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$r = 3$ and is centered at the location $s = (2, 2, 10)$ in a vaccum ($\eta =
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1.0$). If a ray emanating from the point $e = (0, 0, 0)$ intersects $S$ at a
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point $p = (1, 4, 8)$:}
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a. \c{(2 points) What is the angle of incidence $\theta_i$?}
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The incoming ray is in the direction $I = p - e = (1, 4, 8)$, and the normal at
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that point is $N = p - s = (1, 4, 8) - (2, 2, 10) = (1, -2, 2)$. The angle can
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be found by taking the opposite of the incoming ray $-I$ and using the
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The incoming ray is in the direction $I = p - e = (1, 4, 8)$, and the normal
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at that point is $N = p - s = (1, 4, 8) - (2, 2, 10) = (1, -2, 2)$. The angle
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can be found by taking the opposite of the incoming ray $-I$ and using the
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formula $\cos \theta_i = \frac{-I \cdot N}{|I| |N|} = \frac{(-1, -4, -8)
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\cdot (1, -2, 2)}{9 \cdot 3} = \frac{-1 + 8 - 16}{27} = -\frac{1}{3}$. So the
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angle $\boxed{\theta_i = \cos^{-1}(-\frac{1}{3})}$.
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@ -198,21 +198,45 @@ author: |
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pointing in the direction $(1, -1, -1)$, and the camera's 'up' direction is
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$(0, 1, 0)$, what are the entries in $V$?}
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First we can calculate $n$ and $u$:
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- Viewing direction is $(1, -1, -1)$.
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- Normalized $n = (\frac{1}{\sqrt{3}}, -\frac{1}{\sqrt{3}}, -\frac{1}{\sqrt{3}})$.
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- $u = up \times n = (-\frac{1}{\sqrt{2}}, 0, -\frac{1}{\sqrt{2}})$.
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- $v = n \times u = (\frac{\sqrt{6}}{6}, \frac{\sqrt{6}}{3}, -\frac{\sqrt{6}}{6})$
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- $u = up \times n = (\frac{1}{\sqrt{2}}, 0, \frac{1}{\sqrt{2}})$.
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- $v = n \times u = (\frac{\sqrt{6}}{6}, \frac{2}{\sqrt{6}}, -\frac{\sqrt{6}}{6})$
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- $d_x = - (eye \cdot u) = - (2 \times \frac{1}{\sqrt{2}} + 5 \times \frac{1}{\sqrt{2}}) = -\frac{7}{\sqrt{2}}$
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- $d_y = - (eye \cdot v) = - (2 \times \frac{1}{\sqrt{6}} + 3 \times
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\frac{2}{\sqrt{6}} - 5 \times \frac{1}{\sqrt{6}}) = -\frac{3}{\sqrt{6}}$
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- $d_z = - (eye \cdot n) = - (2 \times \frac{1}{\sqrt{3}} - 3 \times
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\frac{1}{\sqrt{3}} - 5 \times \frac{1}{\sqrt{3}}) = -\frac{6}{\sqrt{3}}$
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$$
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\begin{bmatrix}
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-\frac{1}{\sqrt{2}} & 0 & -\frac{1}{\sqrt{2}} & d_x \\
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1 & -1 & -1 & d_y \\
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\frac{1}{\sqrt{2}} & 0 & \frac{1}{\sqrt{2}} & -\frac{7}{\sqrt{2}} \\
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\frac{1}{\sqrt{6}} & \frac{2}{\sqrt{6}} & -\frac{1}{\sqrt{6}} & -\frac{3}{\sqrt{6}} \\
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-\frac{1}{\sqrt{3}} & \frac{1}{\sqrt{3}} & \frac{1}{\sqrt{3}} & -\frac{6}{\sqrt{3}} \\
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0 & 0 & 0 & 1 \\
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\end{bmatrix}
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$$
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\todo
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Also solved using a Python script:
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```py
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def view_matrix(camera_pos, view_dir, up_dir):
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n = unit(-view_dir)
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u = unit(np.cross(up_dir, n))
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v = np.cross(n, u)
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return np.array([
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[u[0], u[1], u[2], -np.dot(camera_pos, u)],
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[v[0], v[1], v[2], -np.dot(camera_pos, v)],
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[n[0], n[1], n[2], -np.dot(camera_pos, n)],
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[0, 0, 0, 1],
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])
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camera_pos = np.array([2, 3, 5])
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view_dir = np.array([1, -1, -1])
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up_dir = np.array([0, 1, 0])
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V = view_matrix(camera_pos, view_dir, up_dir)
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print(V)
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```
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b. \c{(2 points) How will this matrix change if the eye moves forward in the
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direction of view? [which elements in V will stay the same? which elements
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@ -346,39 +370,253 @@ author: |
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6. \c{Consider a cube of width $2\sqrt{3}$ centered at the point $(0, 0,
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-3\sqrt{3})$, whose faces are colored light grey on the top and bottom $(y =
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\pm\sqrt{3})$, dark grey on the front and back $(z = -2\sqrt{3} \textrm{and}
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z = -4\sqrt{3})$, red on the right $(x = \sqrt{3})$, and green on the left $(x
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= -\sqrt{3})$.}
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\pm\sqrt{3})$, dark grey on the front and back ($z = -2\sqrt{3}$ and $z =
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-4\sqrt{3}$), red on the right $(x = \sqrt{3})$, and green on the left $(x =
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-\sqrt{3})$.}
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a. \c{Show how you could project the vertices of this cube to the plane $z =
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0$ using an orthographic parallel projection:}
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i) \c{(2 points) Where will the six vertex locations be after such a
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projection, omitting the normalization step?}
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i) \c{(2 points) Where will the six vertex locations be after such a
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projection, omitting the normalization step?}
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ii) \c{(1 points) Sketch the result, being as accurate as possible and
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labeling the colors of each of the visible faces.}
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- $[\begin{matrix}-1.732 & -1.732 & -6.928\end{matrix}]$ $\rightarrow$ $[\begin{matrix}-1.0 & -1.0 & 7.0\end{matrix}]$
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- $[\begin{matrix}-1.732 & -1.732 & -3.464\end{matrix}]$ $\rightarrow$ $[\begin{matrix}-1.0 & -1.0 & 5.0\end{matrix}]$
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- $[\begin{matrix}-1.732 & 1.732 & -6.928\end{matrix}]$ $\rightarrow$ $[\begin{matrix}-1.0 & 1.0 & 7.0\end{matrix}]$
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- $[\begin{matrix}-1.732 & 1.732 & -3.464\end{matrix}]$ $\rightarrow$ $[\begin{matrix}-1.0 & 1.0 & 5.0\end{matrix}]$
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- $[\begin{matrix}1.732 & -1.732 & -6.928\end{matrix}]$ $\rightarrow$ $[\begin{matrix}1.0 & -1.0 & 7.0\end{matrix}]$
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- $[\begin{matrix}1.732 & -1.732 & -3.464\end{matrix}]$ $\rightarrow$ $[\begin{matrix}1.0 & -1.0 & 5.0\end{matrix}]$
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- $[\begin{matrix}1.732 & 1.732 & -6.928\end{matrix}]$ $\rightarrow$ $[\begin{matrix}1.0 & 1.0 & 7.0\end{matrix}]$
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- $[\begin{matrix}1.732 & 1.732 & -3.464\end{matrix}]$ $\rightarrow$ $[\begin{matrix}1.0 & 1.0 & 5.0\end{matrix}]$
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iii) \c{(2 points) Show how you could achieve this transformation using one or
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more matrix multiplication operations. Specify the matrix entries you would
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use, and, if using multiple matrices, the order in which they would be
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multiplied.}
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ii) \c{(1 points) Sketch the result, being as accurate as possible and
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labeling the colors of each of the visible faces.}
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b. Show how you could project the vertices of this cube to the plane $z = 0$
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using an oblique parallel projection in the direction $d = (1, 0, \sqrt{3})$:
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This is just a square with the dark gray side facing the camera. The other
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sides are not visible because the cube is parallel to the axis, and when you
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do an orthographic projection, those faces are lost.
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i) (3 points) Where will the six vertex locations be after such a projection,
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omitting the normalization step?
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iii) \c{(2 points) Show how you could achieve this transformation using one or
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more matrix multiplication operations. Specify the matrix entries you would
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use, and, if using multiple matrices, the order in which they would be
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multiplied.}
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ii) (2 points) Sketch the result, being as accurate as possible and labeling
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the colors of each of the visible faces.
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Actually, I got the numbers above by using the three transformation matrices
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in this Python script:
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iii) (4 points) Show how you could achieve this transformation using one or
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more matrix multiplication operations. Specify the matrix entries you would
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use, and, if using multiple matrices, the order in which they would be
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multiplied.
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```py
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def ortho(points):
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left = min(map(lambda p: p[0], points))
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right = max(map(lambda p: p[0], points))
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bottom = min(map(lambda p: p[1], points))
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top = max(map(lambda p: p[1], points))
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near = min(map(lambda p: p[2], points))
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far = max(map(lambda p: p[2], points))
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7.
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step_1 = np.array([
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[1, 0, 0, 0],
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[0, 1, 0, 0],
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[0, 0, -1, 0],
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[0, 0, 0, 1],
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])
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step_2 = np.array([
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[1, 0, 0, -(left + right) / 2.0],
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[0, 1, 0, -(bottom + top) / 2.0],
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[0, 0, 1, -(near + far) / 2.0],
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[0, 0, 0, 1],
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])
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step_3 = np.array([
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[2.0 / (right - left), 0, 0, 0],
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[0, 2.0 / (top - bottom), 0, 0],
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[0, 0, 2.0 / (far - near), 0],
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[0, 0, 0, 1],
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])
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M_ortho = step_3 @ step_2 @ step_1
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```
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b. \c{Show how you could project the vertices of this cube to the plane $z =
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0$ using an oblique parallel projection in the direction $d = (1, 0,
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\sqrt{3})$:}
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i) \c{(3 points) Where will the six vertex locations be after such a
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projection, omitting the normalization step?}
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ii) \c{(2 points) Sketch the result, being as accurate as possible and
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labeling the colors of each of the visible faces.}
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iii) \c{(4 points) Show how you could achieve this transformation using one
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or more matrix multiplication operations. Specify the matrix entries you
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would use, and, if using multiple matrices, the order in which they would be
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multiplied.}
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7. \c{Consider the simple scene shown in the image below, where two cubes, one
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of height 1 and one of height 2, are both resting on a horizontal groundplane
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($y = -\frac{1}{2}$), with the smaller cube’s front face aligned with $z =
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-4$ and the larger cube’s front face aligned with $z = -7$.}
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a. \c{(5 points) Let the camera location be (0, 0, 0), looking down the $-z$
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axis, with the field of view set at $90^\circ$. Determine the points, in the
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image plane, to which each of the cube vertices will be projected and sketch
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the result to scale. Please clearly label the coordinates to avoid
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ambiguity.}
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For this part, I reimplemented the perspective rendering algorithm using
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Python.
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```py
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def perspective_matrix(left, right, bottom, top, near, far):
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return np.array([
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[2.0 * near / (right - left), 0, (right + left) / (right - left), 0],
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[0, 2.0 * near / (top - bottom), (top + bottom) / (top - bottom), 0],
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[0, 0, -(far + near) / (far - near), -(2.0 * far * near) / (far - near)],
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[0, 0, -1, 0],
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])
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def view_matrix(camera_pos, view_dir, up_dir):
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n = unit(-view_dir)
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u = unit(np.cross(up_dir, n))
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v = np.cross(n, u)
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return np.array([
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[u[0], u[1], u[2], -np.dot(camera_pos, u)],
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[v[0], v[1], v[2], -np.dot(camera_pos, v)],
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[n[0], n[1], n[2], -np.dot(camera_pos, n)],
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[0, 0, 0, 1],
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])
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```
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The perspective and view matrices are:
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$$
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PV =
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\begin{bmatrix}
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1.0 & 0.0 & 0.0 & 0.0 \\
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0.0 & 1.0 & 0.0 & 0.0 \\
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0.0 & 0.0 & -1.2222 & -2.2222 \\
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0.0 & 0.0 & -1.0 & 0.0 \\
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\end{bmatrix}
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\begin{bmatrix}
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1.0 & 0.0 & 0.0 & -0.0 \\
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0.0 & 1.0 & 0.0 & -0.0 \\
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0.0 & 0.0 & 1.0 & -0.0 \\
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0.0 & 0.0 & 0.0 & 1.0 \\
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\end{bmatrix}
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$$
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Then I ran the transformation using the data given in this particular scene:
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```py
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def compute_view(near, vfov, hfov):
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left = -math.tan(hfov / 2.0) * near
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right = math.tan(hfov / 2.0) * near
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bottom = -math.tan(vfov / 2.0) * near
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top = math.tan(vfov / 2.0) * near
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return left, right, bottom, top
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def solve(camera_pos, angle):
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angle_radians = math.radians(angle)
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near = 1
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far = 10
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view_dir = np.array([0, 0, -1])
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up_dir = np.array([0, 1, 0])
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left, right, bottom, top = compute_view(near, angle_radians, angle_radians)
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P = perspective_matrix(left, right, bottom, top, near, far)
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V = view_matrix(camera_pos, view_dir, up_dir)
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return P @ V
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camera_pos = np.array([0, 0, 0])
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angle = 90
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m = np.around(solve(camera_pos, angle), 4)
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```
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This performed the transformation on the front face of the small cube:
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- $[\begin{matrix}0.5 & 0.5 & -4.0\end{matrix}]$ $\rightarrow$ $[\begin{matrix}0.5 & 0.5 & 2.6666\end{matrix}]$
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- $[\begin{matrix}0.5 & -0.5 & -4.0\end{matrix}]$ $\rightarrow$ $[\begin{matrix}0.5 & -0.5 & 2.6666\end{matrix}]$
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- $[\begin{matrix}-0.5 & -0.5 & -4.0\end{matrix}]$ $\rightarrow$ $[\begin{matrix}-0.5 & -0.5 & 2.6666\end{matrix}]$
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- $[\begin{matrix}-0.5 & 0.5 & -4.0\end{matrix}]$ $\rightarrow$ $[\begin{matrix}-0.5 & 0.5 & 2.6666\end{matrix}]$
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and this transformation on the front face of the large cube:
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- $[\begin{matrix}1.0 & 1.5 & -7.0\end{matrix}]$ $\rightarrow$ $[\begin{matrix}1.0 & 1.5 & 6.3332\end{matrix}]$
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- $[\begin{matrix}1.0 & -0.5 & -7.0\end{matrix}]$ $\rightarrow$ $[\begin{matrix}1.0 & -0.5 & 6.3332\end{matrix}]$
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- $[\begin{matrix}-1.0 & -0.5 & -7.0\end{matrix}]$ $\rightarrow$ $[\begin{matrix}-1.0 & -0.5 & 6.3332\end{matrix}]$
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- $[\begin{matrix}-1.0 & 1.5 & -7.0\end{matrix}]$ $\rightarrow$ $[\begin{matrix}-1.0 & 1.5 & 6.3332\end{matrix}]$
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Here's a render using Blender:
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![](7a.jpg){width=40%}
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b. \c{(4 points) How would the image change if the camera were moved forward by
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2 units, leaving all of the other parameter settings the same? Determine the
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points, in the image plane, to which each of the cube vertices would be
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projected in this case and sketch the result to scale. Please clearly label
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the coordinates to avoid ambiguity.}
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Here is the updated Blender render:
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![](7b.jpg){width=40%}
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As you can see, the cubes now take up more of the frame, and in particular
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the red cube has been warped to take up more camera width than the blue.
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c. \c{(4 points) How would the image change if, instead of moving the camera,
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the field of view were reduced by half, to $45^\circ$, leaving all of the
|
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other parameter settings the same? Determine the points, in the image plane,
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to which each of the cube vertices would be projected and sketch the result
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to scale. Please clearly label the coordinates to avoid ambiguity.}
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Here is the updated Blender render:
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![](7c.jpg){width=40%}
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Because of the reduced FOV, there is less of the scene shown so the cubes
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take up more of the view. However, there is less of the perspective
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foreshortening effect, so the front cube doesn't get warped into being wider
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or bigger than the back cube.
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d. (2 points)
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- \c{Briefly describe what you notice.}
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The cubes aren't warped except when they change distance from the eye.
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- \c{When looking at two cube faces that are equal sizes in reality (e.g. front
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and back) does one appear smaller than the other when one is more distant
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from the camera than the other?}
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Yes.
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- \c{When looking at two objects that are resting on a common horizontal
|
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groundplane, does the groundplane appear to be tiled in the image, so that
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the objects that are farther away appear to be resting on a base that is
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higher as their distance from the camera increases?}
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Yes.
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- \c{What changes do you observe in the relative heights, in the image, of the
|
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smaller and larger cubes as the camera position changes?}
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When the camera position is closer to the cubes, the front cube takes up
|
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more space overall and so it takes up more height as well. But once the
|
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camera is far, the big cube has a bigger relative height since their
|
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heights aren't really warped from each other anymore.
|
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|
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- \c{Is there a point at which the camera could be so close to the smaller cube
|
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(but not touching it) that the larger cube would be completely obscured in
|
||||
the camera’s image?}
|
||||
|
||||
Yes. You can imagine that if the camera was a microscopically small
|
||||
distance from the front cube (and the $near$ value was also small enough to
|
||||
accommodate!), then the front cube would take up the entire image.
|
||||
|
||||
- \c{Based on these insights, what can you say about the idea to create an
|
||||
illusion of "getting closer" to an object in a photographed scene by
|
||||
zooming in on the image and cropping it so that the object looks bigger?}
|
||||
|
||||
It's not entirely accurate, because of perspective warp.
|
||||
|
||||
8. \c{Consider the perspective projection-normalization matrix $P$ which maps
|
||||
the contents of the viewing frustum into a cube that extends from -1 to 1 in
|
||||
|
@ -477,11 +715,24 @@ author: |
|
|||
- \c{After the six vertices v0 .. v5 are sent to be clipped, what will the
|
||||
vertex list be after clipping process has finished?}
|
||||
|
||||
![](10a.jpg){width=40%}
|
||||
|
||||
- \c{How can this new result be expressed as a triangle strip? (Try to be as
|
||||
efficient as possible)}
|
||||
|
||||
The only way to get this to be represented as a triangle strip is to
|
||||
change around the some of the existing lines. Otherwise, the order of the
|
||||
vertices prevents the exact same configuration from working.
|
||||
|
||||
See below for a working version (only consider the green lines, ignore the
|
||||
red lines):
|
||||
|
||||
![](10b.jpg){width=40%}
|
||||
|
||||
- \c{How many triangles will be encoded in the clipped triangle strip?}
|
||||
|
||||
Based on the image above, 8 triangles will be used.
|
||||
|
||||
## Ray Tracing vs Scan Conversion
|
||||
|
||||
11. \c{(8 points) List the essential steps in the scan-conversion (raster
|
||||
|
|
350
exam-2/exam2.py
350
exam-2/exam2.py
|
@ -1,136 +1,290 @@
|
|||
import itertools
|
||||
import numpy as np
|
||||
from sympy import *
|
||||
import math
|
||||
|
||||
unit = lambda v: v/np.linalg.norm(v)
|
||||
|
||||
def perspective_matrix(vfov, width, height, left, right, bottom, top, near, far):
|
||||
aspect = width / height
|
||||
return np.array([
|
||||
[1.0 / math.tan(vfov / 2.0) / aspect, 0, 0, 0],
|
||||
[0, 1.0 / math.tan(vfov / 2.0), 0, 0],
|
||||
[0, 0, -(far + near) / (far - near), -2.0 * far * near / (far - near)],
|
||||
[0, 0, -1, 0]
|
||||
])
|
||||
# return np.array([
|
||||
# [2.0 * near / (right - left), 0, (right + left) / (right - left), 0],
|
||||
# [0, 2.0 * near / (top - bottom), (top + bottom) / (top - bottom), 0],
|
||||
# [0, 0, -(far + near) / (far - near), -(2.0 * far * near) / (far - near)],
|
||||
# [0, 0, -1, 0],
|
||||
# ])
|
||||
|
||||
def view_matrix(camera_pos, view_dir, up_dir):
|
||||
n = unit(-view_dir)
|
||||
u = unit(np.cross(up_dir, n))
|
||||
v = np.cross(n, u)
|
||||
return np.array([
|
||||
[u[0], u[1], u[2], -np.dot(camera_pos, u)],
|
||||
[v[0], v[1], v[2], -np.dot(camera_pos, v)],
|
||||
[n[0], n[1], n[2], -np.dot(camera_pos, n)],
|
||||
[0, 0, 0, 1],
|
||||
])
|
||||
|
||||
def print_trans(before, after):
|
||||
def style_vec(v):
|
||||
start = "$[\\begin{matrix}"
|
||||
mid = str(v[0]) + " & " + str(v[1]) + " & " + str(v[2])
|
||||
end = "\\end{matrix}]$"
|
||||
return f"{start}{mid}{end}"
|
||||
return style_vec(before) + " $\\rightarrow$ " + style_vec(after)
|
||||
|
||||
def compute_view(near, vfov, hfov):
|
||||
width = 2.0 * near * math.tan(hfov / 2.0)
|
||||
height = 2.0 * near * math.tan(hfov / 2.0)
|
||||
|
||||
left = -width / 2.0
|
||||
right = width / 2.0
|
||||
bottom = -height / 2.0
|
||||
top = height / 2.0
|
||||
return width, height, left, right, bottom, top
|
||||
|
||||
def print_bmatrix(arr):
|
||||
for row in arr:
|
||||
for j, col in enumerate(row):
|
||||
end = " " if j == len(row) - 1 else " & "
|
||||
print(col, end=end)
|
||||
print("\\\\")
|
||||
|
||||
def problem_1():
|
||||
p = np.array([1, 4, 8])
|
||||
e = np.array([0, 0, 0])
|
||||
s = np.array([2, 2, 10])
|
||||
p = np.array([1, 4, 8])
|
||||
e = np.array([0, 0, 0])
|
||||
s = np.array([2, 2, 10])
|
||||
|
||||
i = p - e
|
||||
print("incoming", i)
|
||||
print("|I| =", np.linalg.norm(i))
|
||||
n = s - p
|
||||
print("normal", n)
|
||||
i = p - e
|
||||
print("incoming", i)
|
||||
print("|I| =", np.linalg.norm(i))
|
||||
n = s - p
|
||||
print("normal", n)
|
||||
|
||||
n_norm = unit(n)
|
||||
print("normal_norm", n_norm)
|
||||
cos_theta_i = np.dot(-i, n) / (np.linalg.norm(i) * np.linalg.norm(n))
|
||||
print("part a = cos^{-1} of ", cos_theta_i)
|
||||
print(np.arccos(cos_theta_i))
|
||||
n_norm = unit(n)
|
||||
print("normal_norm", n_norm)
|
||||
cos_theta_i = np.dot(-i, n) / (np.linalg.norm(i) * np.linalg.norm(n))
|
||||
print("part a = cos^{-1} of ", cos_theta_i)
|
||||
print(np.arccos(cos_theta_i))
|
||||
|
||||
proj = n_norm * np.linalg.norm(i) * cos_theta_i
|
||||
print("proj", proj)
|
||||
proj = n_norm * np.linalg.norm(i) * cos_theta_i
|
||||
print("proj", proj)
|
||||
|
||||
p_ = p + proj
|
||||
print("proj point", p_)
|
||||
p_ = p + proj
|
||||
print("proj point", p_)
|
||||
|
||||
v2 = p_ - e
|
||||
print("v2", v2)
|
||||
v2 = p_ - e
|
||||
print("v2", v2)
|
||||
|
||||
sin_theta_i = np.sin(np.arccos(cos_theta_i))
|
||||
print("sin theta_i =", sin_theta_i)
|
||||
sin_theta_i = np.sin(np.arccos(cos_theta_i))
|
||||
print("sin theta_i =", sin_theta_i)
|
||||
|
||||
theta_t = np.arcsin(1.0 / 1.5 * sin_theta_i)
|
||||
print("approx answer for part d", theta_t)
|
||||
theta_t = np.arcsin(1.0 / 1.5 * sin_theta_i)
|
||||
print("approx answer for part d", theta_t)
|
||||
|
||||
uin = unit(n_norm)
|
||||
print("unit inv normal", uin)
|
||||
uin = unit(n_norm)
|
||||
print("unit inv normal", uin)
|
||||
|
||||
uperp = unit(v2)
|
||||
print("uperp", uperp)
|
||||
uperp = unit(v2)
|
||||
print("uperp", uperp)
|
||||
|
||||
answer_1e = uin + math.tan(theta_t) * uperp - p
|
||||
print("1e answer", unit(answer_1e))
|
||||
answer_1e = uin + math.tan(theta_t) * uperp - p
|
||||
print("1e answer", unit(answer_1e))
|
||||
|
||||
def problem_4():
|
||||
print("part 4a.")
|
||||
up = np.array([0, 1, 0])
|
||||
viewing_dir = np.array([1, -1, -1])
|
||||
n = unit(viewing_dir)
|
||||
print(f"{n = }")
|
||||
camera_pos = np.array([2, 3, 5])
|
||||
view_dir = np.array([1, -1, -1])
|
||||
up_dir = np.array([0, 1, 0])
|
||||
V = view_matrix(camera_pos, view_dir, up_dir)
|
||||
print(V)
|
||||
|
||||
u = unit(np.cross(up, n))
|
||||
print(f"{u = }")
|
||||
f = np.vectorize(lambda c: (c * c))
|
||||
print(f(V))
|
||||
|
||||
v = np.cross(n, u)
|
||||
print(f"{v = }")
|
||||
|
||||
print(math.sqrt(1 / 6.0))
|
||||
print(math.sqrt(2 / 3.0))
|
||||
|
||||
def build_translation_matrix(vec):
|
||||
return np.array([
|
||||
[1, 0, 0, vec[0]],
|
||||
[0, 1, 0, vec[1]],
|
||||
[0, 0, 1, vec[2]],
|
||||
[0, 0, 0, 1],
|
||||
])
|
||||
return np.array([
|
||||
[1, 0, 0, vec[0]],
|
||||
[0, 1, 0, vec[1]],
|
||||
[0, 0, 1, vec[2]],
|
||||
[0, 0, 0, 1],
|
||||
])
|
||||
|
||||
def problem_5():
|
||||
b1 = build_translation_matrix(np.array([0, 0, 5]))
|
||||
theta = math.radians(-90)
|
||||
sin_theta = round( math.sin(theta), 5)
|
||||
cos_theta = round(math.cos(theta), 5)
|
||||
b2 = np.array([
|
||||
[cos_theta, 0, sin_theta, 0],
|
||||
[0, 1, 0, 0],
|
||||
[-sin_theta, 0, cos_theta, 0],
|
||||
[0, 0, 0, 1],
|
||||
])
|
||||
b3 = build_translation_matrix(np.array([0, 0, -5]))
|
||||
b1 = build_translation_matrix(np.array([0, 0, 5]))
|
||||
theta = math.radians(-90)
|
||||
sin_theta = round( math.sin(theta), 5)
|
||||
cos_theta = round(math.cos(theta), 5)
|
||||
b2 = np.array([
|
||||
[cos_theta, 0, sin_theta, 0],
|
||||
[0, 1, 0, 0],
|
||||
[-sin_theta, 0, cos_theta, 0],
|
||||
[0, 0, 0, 1],
|
||||
])
|
||||
b3 = build_translation_matrix(np.array([0, 0, -5]))
|
||||
|
||||
print("b1", b1)
|
||||
print("b2", b2)
|
||||
print("b3", b3)
|
||||
M = b3 @ b2 @ b1
|
||||
print("M", M)
|
||||
print("b1", b1)
|
||||
print("b2", b2)
|
||||
print("b3", b3)
|
||||
M = b3 @ b2 @ b1
|
||||
print("M", M)
|
||||
|
||||
ex1 = np.array([1, 1, -4, 1])
|
||||
print("ex1", ex1, M @ ex1)
|
||||
ex1 = np.array([1, 1, -4, 1])
|
||||
print("ex1", ex1, M @ ex1)
|
||||
|
||||
up = np.array([0, 1, 0])
|
||||
n = np.array([1, 0, 0])
|
||||
u = unit(np.cross(up, n))
|
||||
v = np.cross(n, u)
|
||||
up = np.array([0, 1, 0])
|
||||
n = np.array([1, 0, 0])
|
||||
u = unit(np.cross(up, n))
|
||||
v = np.cross(n, u)
|
||||
|
||||
print(f"{up = }, {n = }, {u = }, {v = }")
|
||||
print(f"{up = }, {n = }, {u = }, {v = }")
|
||||
|
||||
eye = np.array([5, 0, -5])
|
||||
dx = -(np.dot(eye, u))
|
||||
dy = -(np.dot(eye, v))
|
||||
dz = -(np.dot(eye, n))
|
||||
print(f"{dx = }, {dy = }, {dz = }")
|
||||
eye = np.array([5, 0, -5])
|
||||
dx = -(np.dot(eye, u))
|
||||
dy = -(np.dot(eye, v))
|
||||
dz = -(np.dot(eye, n))
|
||||
print(f"{dx = }, {dy = }, {dz = }")
|
||||
|
||||
def problem_8():
|
||||
def P(left, right, bottom, top, near, far):
|
||||
return np.array([
|
||||
[2.0 * near / (right - left), 0, (right + left) / (right - left), 0],
|
||||
[0, 2.0 * near / (top - bottom), (top + bottom) / (top - bottom), 0],
|
||||
[0, 0, -(far + near) / (far - near), -(2.0 * far * near) / (far - near)],
|
||||
[0, 0, -1, 0],
|
||||
])
|
||||
|
||||
near = 0.5
|
||||
far = 20
|
||||
|
||||
def compute_view(vfov, hfov):
|
||||
left = -math.tan(hfov) * near
|
||||
right = math.tan(hfov) * near
|
||||
bottom = -math.tan(vfov) * near
|
||||
top = math.tan(vfov) * near
|
||||
return left, right, bottom, top
|
||||
near = 0.5
|
||||
far = 20
|
||||
|
||||
|
||||
print("part 8a")
|
||||
vfov = hfov = math.radians(60)
|
||||
left, right, bottom, top = compute_view(vfov, hfov)
|
||||
print(P(left, right, bottom, top, near, far))
|
||||
print()
|
||||
|
||||
print("\nPROBLEM 4 -------------------------"); problem_4()
|
||||
print("part 8a")
|
||||
vfov = hfov = math.radians(60)
|
||||
width, height, left, right, bottom, top = compute_view(near, vfov, hfov)
|
||||
print(perspective_matrix(vfov, width, height, left, right, bottom, top, near, far))
|
||||
print()
|
||||
|
||||
def problem_7():
|
||||
|
||||
def solve(camera_pos, angle):
|
||||
angle_radians = math.radians(angle)
|
||||
near = 1
|
||||
far = 10
|
||||
view_dir = np.array([0, 0, -1])
|
||||
up_dir = np.array([0, 1, 0])
|
||||
width, height, left, right, bottom, top = compute_view(near, angle_radians, angle_radians)
|
||||
print("faces of the viewing frustum", left, right, bottom, top)
|
||||
P = perspective_matrix(angle_radians, width, height, left, right, bottom, top, near, far)
|
||||
V = view_matrix(camera_pos, view_dir, up_dir)
|
||||
print("P")
|
||||
print_bmatrix(np.around(P, 4))
|
||||
print("V")
|
||||
print_bmatrix(np.around(V, 4))
|
||||
m = P @ V
|
||||
|
||||
points = [
|
||||
np.array([0.5, 0.5, -4]),
|
||||
np.array([0.5, -0.5, -4]),
|
||||
np.array([-0.5, -0.5, -4]),
|
||||
np.array([-0.5, 0.5, -4]),
|
||||
|
||||
np.array([0.5, 0.5, -5]),
|
||||
np.array([0.5, -0.5, -5]),
|
||||
np.array([-0.5, -0.5, -5]),
|
||||
np.array([-0.5, 0.5, -5]),
|
||||
|
||||
np.array([1, 1.5, -7]),
|
||||
np.array([1, -0.5, -7]),
|
||||
np.array([-1, -0.5, -7]),
|
||||
np.array([-1, 1.5, -7]),
|
||||
|
||||
np.array([1, 1.5, -9]),
|
||||
np.array([1, -0.5, -9]),
|
||||
np.array([-1, -0.5, -9]),
|
||||
np.array([-1, 1.5, -9]),
|
||||
]
|
||||
|
||||
for point in points:
|
||||
point_ = np.r_[point, [1]]
|
||||
trans = m @ point_
|
||||
def style_vec(v):
|
||||
start = "$[\\begin{matrix}"
|
||||
mid = str(v[0]) + " & " + str(v[1]) + " & " + str(v[2])
|
||||
end = "\\end{matrix}]$"
|
||||
return f"{start}{mid}{end}"
|
||||
print("-", style_vec(point), "$\\rightarrow$", style_vec(np.around(trans[:3], 4)))
|
||||
|
||||
print("Part A")
|
||||
camera_pos = np.array([0, 0, 0])
|
||||
angle = 90
|
||||
solve(camera_pos, angle)
|
||||
|
||||
print("Part B")
|
||||
camera_pos = np.array([0, 0, -2])
|
||||
angle = 90
|
||||
solve(camera_pos, angle)
|
||||
|
||||
print("Part C")
|
||||
camera_pos = np.array([0, 0, 0])
|
||||
angle = 45
|
||||
solve(camera_pos, angle)
|
||||
|
||||
def problem_6():
|
||||
def calculate(points):
|
||||
left = min(map(lambda p: p[0], points))
|
||||
right = max(map(lambda p: p[0], points))
|
||||
bottom = min(map(lambda p: p[1], points))
|
||||
top = max(map(lambda p: p[1], points))
|
||||
near = min(map(lambda p: p[2], points))
|
||||
far = max(map(lambda p: p[2], points))
|
||||
|
||||
step_1 = np.array([
|
||||
[1, 0, 0, 0],
|
||||
[0, 1, 0, 0],
|
||||
[0, 0, -1, 0],
|
||||
[0, 0, 0, 1],
|
||||
])
|
||||
|
||||
step_2 = np.array([
|
||||
[1, 0, 0, -(left + right) / 2.0],
|
||||
[0, 1, 0, -(bottom + top) / 2.0],
|
||||
[0, 0, 1, -(near + far) / 2.0],
|
||||
[0, 0, 0, 1],
|
||||
])
|
||||
|
||||
step_3 = np.array([
|
||||
[2.0 / (right - left), 0, 0, 0],
|
||||
[0, 2.0 / (top - bottom), 0, 0],
|
||||
[0, 0, 2.0 / (far - near), 0],
|
||||
[0, 0, 0, 1],
|
||||
])
|
||||
|
||||
M_ortho = step_3 @ step_2 @ step_1
|
||||
|
||||
for point in points:
|
||||
point_ = np.r_[point, [1]]
|
||||
trans_ = M_ortho @ point_
|
||||
trans = trans_[:3]
|
||||
print("-", print_trans(np.around(point, 3), np.around(trans, 3)))
|
||||
|
||||
sqrt3 = math.sqrt(3)
|
||||
width = 2.0 * sqrt3
|
||||
cube_center = np.array([0, 0, -3.0 * sqrt3])
|
||||
print("HELLOSU")
|
||||
|
||||
points = []
|
||||
for (dx, dy, dz) in itertools.product([-1, 1], [-1, 1], [-1, 1]):
|
||||
point = cube_center + np.array([dx * width / 2, dy * width / 2, dz * width / 2])
|
||||
points.append(point)
|
||||
|
||||
calculate(points)
|
||||
|
||||
def problem_9():
|
||||
pass
|
||||
|
||||
print("\nPROBLEM 8 -------------------------"); problem_8()
|
||||
print("\nPROBLEM 1 -------------------------"); problem_1()
|
||||
print("\nPROBLEM 5 -------------------------"); problem_5()
|
||||
print("\nPROBLEM 9 -------------------------"); problem_9()
|
||||
print("\nPROBLEM 7 -------------------------"); problem_7()
|
||||
print("\nPROBLEM 6 -------------------------"); problem_6()
|
||||
print("\nPROBLEM 4 -------------------------"); problem_4()
|
||||
|
|
Loading…
Reference in a new issue