added lfs files

.gitattributes

@@ -1,2 +1,5 @@
 *.doc filter=lfs diff=lfs merge=lfs -text
 *.bmp filter=lfs diff=lfs merge=lfs -text
+*.odt filter=lfs diff=lfs merge=lfs -text
+presentation/main.pdf filter=lfs diff=lfs merge=lfs -text
+presentation/main.pptx filter=lfs diff=lfs merge=lfs -text

model.dat

@@ -24,4 +24,4 @@
 10  10  4
 10  10  6
 10  10  8
-10  10  10
+10  10  10

presentation/main.typ

@@ -0,0 +1,35 @@
+#import "@preview/xwysyy:0.4.0": *
+
+#show: xwysyy-pre.with(
+  theme: "midnight",
+  config-info(
+    title: [Project 1],
+    subtitle: [Computer Vision],
+    author: "Zander Johnson",
+    date: datetime.today(),
+    institution: "University of Central Arkansas",
+  ),
+)
+
+#title-slide()
+
+#outline-slide()
+
+= Motivation
+
+== One Minute Setup
+
+#textbox(
+  [*Reusable components*
+
+  `textbox`, #red[red highlights], #yellow[yellow highlights], tables, code blocks, and touying animations share one theme.],
+
+  [*Theme control*
+
+  Switch built-in themes with `theme: "sunset"` or pass a custom color dictionary directly.],
+)
+
+#end-slide(
+  title: [Thank You!],
+  body: [Questions?],
+)

project/Cargo.lock

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project/Cargo.toml

@@ -0,0 +1,8 @@
+[package]
+name = "project"
+version = "0.1.0"
+edition = "2024"
+
+[dependencies]
+anyhow = "1.0.104"
+nalgebra = "0.35.0"

project/src/main.rs

@@ -0,0 +1,33 @@
+use anyhow::Result as AnyResult;
+use std::{fs::File, io::Read};
+
+use nalgebra as ng;
+
+fn get_data(file_path: &str) -> AnyResult<Vec<u16>> {
+    let mut file = File::open(file_path)?;
+
+    let mut buf = "".to_string();
+    file.read_to_string(&mut buf)?;
+
+    Ok(buf
+        .split_whitespace()
+        .filter_map(|f| f.parse::<u16>().ok())
+        .collect())
+}
+
+fn main() -> AnyResult<()> {
+    let observe_data = get_data("../observe.dat")?;
+    let observe_mat = ng::MatrixXx2::from_vec_generic(
+        ng::Dyn(observe_data.len() / 2),
+        ng::Const::<2>,
+        observe_data,
+    );
+    println!("Observe: {:?}", observe_mat);
+
+    let model_data = get_data("../model.dat")?;
+    let model_mat =
+        ng::MatrixXx3::from_vec_generic(ng::Dyn(model_data.len() / 3), ng::Const::<3>, model_data);
+    println!("Observe: {:?}", model_mat);
+
+    Ok(())
+}

project1.md

@@ -0,0 +1,100 @@
+**ENGR4350 Computer Vision**
+
+**Project 1. Linear Approach to Camera Calibration**
+
+**Due Data: September 20, 2026**
+
+**Objective:** To use a linear approach to calibrate a camera and to use
+the camera parameters to predict the 2D image-domain location of a set
+of 3D points or a 3D moving object.
+
+Procedure:
+
+1.  Study the lecture note by showing the use of projection matrix that
+    maps objects from 3-D to 2-D, the definition of intrinsic and
+    extrinsic parameters of a camera, and the linear approach to
+    geometric camera calibration.
+2.  Given an image *test_image.bmp* captured by a camera which is posed
+    toward a 3D chess board, we manually selected 27 points whose 2-D
+    image coordinates are saved in the file *observe.dat*, and whose 3-D
+    coordinates in a world coordinate are stored in the file
+    *model.dat*. Please use the linear approach to calibrate the camera
+    system by computing the projection matrix (M) from which you are
+    required to compute the intrinsic and extrinsic parameters,
+    including , u0, v0, α, β, and the rotation matrix (R), and the
+    shift vector (t).
+3.  In order to verify the correctness of the camera calibration, please
+    use the projection matrix to map the three set of 3D points, whose
+    3-D coordinates are {x,y,0\|x,y=0,...,10}, {x,10,z\|x,z=0,...,10},
+    {10,y,z\|y,z=0,...,10}, respectively, into the 2-D image space.
+    Discuss your results.
+4.  Create a video file that shows a 3D object moving in the 3D scene
+    along a specific pre-defined path. For example, a 3D cube (1x1x1)
+    can be displayed by nine lines connecting seven vertexes (see the
+    example below). Each line can be drawn with around 100 samples. (You
+    can see a video example from Slide 13 of the Lecture 8 handout in
+    the slideshow mode.)
+
+**Report Requirements:**
+
+1.  Briefly discuss the basics of geometric camera modeling.
+2.  Briefly discuss the linear approach to camera calibration.
+3.  Show the simulation results by figures (Part 3).
+4.  Include some sample frames of the video (Part 4) in the report.
+5.  The Python source code should be included as the appendix of the
+    report with detailed comments. A separate Python file should also be
+    provided for testing.
+6.  Zip all files (DOC, AVI, M-file) into one package and upload it to
+    Blackboard by the due date.
+
+**Useful Python functions:**
+
+You need to import the required libraries:
+
+> import torch\
+> import numpy as np\
+> import matplotlib.pyplot as plt\
+> from PIL import Image\
+> import time
+
+> import cv2\
+> import os
+
+- Inner product: torch.dot(t1,t2), Cross product : torch.cross(t1,t2).
+
+> v0 = (s \*\* 2) \* torch.dot(a2, a3)\
+> \
+> cross_a1_a3 = torch.cross(a1, a3)
+
+> 
+
+- Singular value decomposition (SVD) :
+
+> \# \-\-- Solve for the projection matrix M \-\--\
+> \# The solution to Qm=0 is the last column of V from the SVD of Q\
+> \# In PyTorch, torch.linalg.svd returns U, S, Vh (V transpose)\
+> U, S, Vh = torch.linalg.svd(Q)\
+> \
+> \# The solution is the last row of Vh, which corresponds to the
+> smallest singular value\
+> m = Vh\[-1, :\]\
+> \
+> \# Reshape the 12x1 vector m into the 3x4 projection matrix M\
+> M = m.reshape(3, 4)
+
+- Norm of a vector: torch.linalg.norm(vector).
+
+> torch.linalg.norm(cross_a1_a3)
+
+> 
+
+- Inverse of a matrix: torch.linalg.inv(M)
+
+> torch.linalg.inv(M)
+
+> 
+
+- Reshape of a vector to a matrix:
+
+> \# Reshape the 12x1 vector m into the 3x4 projection matrix M\
+> M = m.reshape(3, 4)