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‎.gitignore

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.DS_STORE
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backup

‎courses.html

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<div class="contentItem">
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<table class="course">
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<tr>
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<!-- course 1 -->
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<td>
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<div class="cropTeaser" style="background-image: url('courses/16385.png');">
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</div>
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</td>
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<td class="courseDescription">
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<h2 class="courseTitle"> 16-385 / Computer Vision </h2>
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This course provides a comprehensive introduction to computer vision. Major topics
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include image processing, detection
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and recognition, geometry-based and physics-based vision and video analysis.
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Students will learn basic concepts of
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computer vision as well as hands on experience to solve real-life vision problems.
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<p class="offeringList"><a href="./index.html">Course Website</a></p>
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</td>
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<!-- autogen courses -->
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<!-- course 2 -->
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<td>
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<div class="cropTeaser" style="background-image: url('courses/l43d.png');">
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</div>
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</td>
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<!--------------------------------------------------------------------------->
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<table class="course" id=16889>
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<tr>
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<td>
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<div class="cropTeaser" style="background-image: url('courses/l43d.png');">
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</div>
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</td>
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<td class="courseDescription">
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<h2 class="courseTitle"> 16-889 : Learning for 3D Vision </h2>
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Any autonomous agent we develop must perceive and act in a 3D world. The ability to infer, model, and utilize 3D representations is therefore of central importance in AI, with applications ranging from robotic manipulation and self-driving to virtual reality and image manipulation. While 3D understanding has been a longstanding goal in computer vision, it has witnessed several impressive advances due to the rapid recent progress in (deep) learning techniques. The goal of this course is to explore this confluence of 3D Vision and Learning-based methods.
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<p class="offeringList"><a href="https://learning3d.github.io/">Course Website</a></p>
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</td>
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</tr>
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</table>
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<!--------------------------------------------------------------------------->
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<td class="courseDescription">
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<h2 class="courseTitle"> 16-889 / Learning for 3D Vision </h2>
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While 3D understanding has
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been a longstanding goal in computer
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vision, it has witnessed several impressive advances due to the rapid recent
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progress in (deep) learning techniques. The
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goal of this course is to explore this confluence of 3D Vision and Learning-based
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methods.
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<!--------------------------------------------------------------------------->
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<table class="course" id=16385>
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<tr>
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<td>
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<div class="cropTeaser" style="background-image: url('courses/16385.png');">
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</div>
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</td>
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<td class="courseDescription">
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<h2 class="courseTitle"> 16-385 : Computer Vision </h2>
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This course provides a comprehensive introduction to computer vision. Major topics include image processing, detection and recognition, geometry-based and physics-based vision and video analysis. Students will learn basic concepts of computer vision as well as hands on experience to solve real-life vision problems.
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<p class="offeringList"><a href="http://16385.courses.cs.cmu.edu/spring2022/">Course Website</a></p>
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</td>
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</tr>
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</table>
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<!--------------------------------------------------------------------------->
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<!--------------------------------------------------------------------------->
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<table class="course" id=16824>
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<tr>
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<td>
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<div class="cropTeaser" style="background-image: url('https://visual-learning.cs.cmu.edu/images/teaser.jpg');">
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</div>
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</td>
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<td class="courseDescription">
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<h2 class="courseTitle"> 16-824 : Visual Learning and Recognition </h2>
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This graduate-level computer vision course focuses on representation and reasoning for large amounts of data (images, videos, and associated tags, text, GPS locations, etc.) toward the ultimate goal of understanding the visual world surrounding us. We will be reading an eclectic mix of classic and recent papers on topics including Theories of Perception, Mid-level Vision (Grouping, Segmentation, Poses), Object and Scene Recognition, 3D Scene Understanding, Action Recognition, Contextual Reasoning, Joint Language and Vision Models, Deep Generative Models, etc. We will be covering a wide range of supervised, semi-supervised and unsupervised approaches for each of the topics above.
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<p class="offeringList"><a href="https://visual-learning.cs.cmu.edu/">Course Website</a></p>
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</td>
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</tr>
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</table>
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<!--------------------------------------------------------------------------->
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<p class="offeringList"><a href="https://learning3d.github.io/">Course Website</a></p>
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</td>
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</tr>
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<tr>
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<!-- course 1 -->
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<td> <div class="cropTeaser" style="background-image: url('courses/16385.png');"> </div> </td>
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<td class="courseDescription">
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<h2 class="courseTitle"> 16-385 / Computer Vision </h2>
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This course provides a comprehensive introduction to computer vision. Major topics
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include image processing, detection
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and recognition, geometry-based and physics-based vision and video analysis.
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Students will learn basic concepts of
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computer vision as well as hands on experience to solve real-life vision problems.
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<p class="offeringList"><a href="./index.html">Course website</a></p>
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</td>
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</tr>
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</table>
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</div> <!-- content -->
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</div>

‎courses/.DS_Store

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‎courses/16385.txt

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number:: 16-385
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title:: Computer Vision
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url:: http://16385.courses.cs.cmu.edu/spring2022/
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img:: courses/16385.png
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description:: This course provides a comprehensive introduction to computer vision. Major topics include image processing, detection and recognition, geometry-based and physics-based vision and video analysis. Students will learn basic concepts of computer vision as well as hands on experience to solve real-life vision problems.

‎courses/16824.txt

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number:: 16-824
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title:: Visual Learning and Recognition
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url:: https://visual-learning.cs.cmu.edu/
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img:: https://visual-learning.cs.cmu.edu/images/teaser.jpg
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description:: This graduate-level computer vision course focuses on representation and reasoning for large amounts of data (images, videos, and associated tags, text, GPS locations, etc.) toward the ultimate goal of understanding the visual world surrounding us. We will be reading an eclectic mix of classic and recent papers on topics including Theories of Perception, Mid-level Vision (Grouping, Segmentation, Poses), Object and Scene Recognition, 3D Scene Understanding, Action Recognition, Contextual Reasoning, Joint Language and Vision Models, Deep Generative Models, etc. We will be covering a wide range of supervised, semi-supervised and unsupervised approaches for each of the topics above.

‎courses/16889.txt

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number:: 16-889
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title:: Learning for 3D Vision
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url:: https://learning3d.github.io/
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img:: courses/l43d.png
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description:: Any autonomous agent we develop must perceive and act in a 3D world. The ability to infer, model, and utilize 3D representations is therefore of central importance in AI, with applications ranging from robotic manipulation and self-driving to virtual reality and image manipulation. While 3D understanding has been a longstanding goal in computer vision, it has witnessed several impressive advances due to the rapid recent progress in (deep) learning techniques. The goal of this course is to explore this confluence of 3D Vision and Learning-based methods.

‎courses_base.html

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<!DOCTYPE html>
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<html>
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<head>
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<meta charset="UTF-8">
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<meta name="description" content="Course offerings in computer vision at Carnegie Mellon.">
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<title>Carnegie Mellon Computer Vision - Courses</title>
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<link href='http://fonts.googleapis.com/css?family=EB+Garamond' rel='stylesheet' type='text/css'>
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<link href='http://fonts.googleapis.com/css?family=Roboto+Condensed:700' rel='stylesheet' type='text/css'>
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<link href='http://fonts.googleapis.com/css?family=Source+Sans+Pro:300,400,300italic,400italic' rel='stylesheet'
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type='text/css'>
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<link rel="icon" type="image/x-icon" href="./assets/ri-favicon.ico">
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<link rel="stylesheet" href="./style.css">
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</head>
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<body class="page page-id-16 page-parent page-child parent-pageid-24 page-template-default">
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<div id="wrapper">
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<div id="header">
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<div id="logo"><a href="./index.html"><img alt="Computer Vision @ Carnegie Mellon" src="./assets/logo.svg"></a>
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</div>
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<div id="navBar">
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<a href="./index.html">People</a>
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<a href="./research.html">Research</a>
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<a href="./courses.html">Courses</a>
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</div>
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</div>
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<div id="main" role="main">
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<div class="contentItem">
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<!-- autogen courses -->
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</div> <!-- content -->
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</div>
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<!--main-->
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<div id="footWrapper">
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<div id="footer">
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<span id="footerLogo"><a href="http://www.cmu.edu/index.shtml"><img src="./assets/cmu.svg"
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alt="Carnegie Mellon University"></a></span>
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<span id="footerAddress"><a
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href="https://www.google.com/maps/place/Carnegie+Mellon+University/@40.442492,-79.942553,17z/data=!3m1!4b1!4m2!3m1!1s0x8834f21f58679a9f:0x88716b461fc4daf4">5000
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Forbes Ave Pittsburgh, PA 15213</a></span>
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</div>
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</div>
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</div>
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</body>
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</html>

‎gen_course_page.py

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import os
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import glob
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import random
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MAX_PROJECTS = 24
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CURRENT_YEAR = 2022
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def read_file(fpath):
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with open(fpath, 'r') as f:
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content = f.read()
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return content
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def get_txtfile_ids(folder):
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txtfiles = glob.glob('{}/*.txt'.format(folder))
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ids = [os.path.basename(txtfile)[:-4] for txtfile in txtfiles]
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return ids
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# <img class="paper static" title="paper" src="src.png" />
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def parse_course_info(course_id, course_folder):
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elems = {}
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keys = []
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fpath = os.path.join(course_folder, course_id + '.txt')
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with open(fpath, 'r') as f:
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for ln in f.readlines():
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ln_items = ln.split('::')
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if len(ln_items) != 2:
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continue
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key, val = ln_items[0], ln_items[1]
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key = key.rstrip().lstrip()
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val = val.rstrip().lstrip()
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elems[key] = val
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keys.append(key)
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if 'img' not in keys:
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elems['img'] = 'courses/16385.jpeg'
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return elems, keys
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def get_course_html_string(id, elems, keys):
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comment_str = '<!--------------------------------------------------------------------------->'
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course_str = ''
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course_str += '\n' + comment_str + '\n'
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course_str += '<table class="course" id={}>\n'.format(id)
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course_str += '<tr>\n<td>\n'
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course_str += '<div class="cropTeaser" style="background-image: url(\'{}\');">\n</div>\n'.format(elems['img'])
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course_str += '</td>\n<td class="courseDescription">\n'
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course_str += '<h2 class="courseTitle"> {} : {} </h2> \n {} \n'.format(elems['number'], elems['title'], elems['description'])
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if 'url' in keys:
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course_str += '<p class="offeringList"><a href="{}">Course Website</a></p>\n'.format(elems['url'])
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course_str += '</td>\n</tr>\n</table>\n'
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## Meta fields
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# add some javascript here if needed using the id for the added element
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course_str += comment_str + '\n'
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return course_str
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if __name__ == '__main__':
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page_string = read_file('./courses_base.html')
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###########################################################################
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###########################################################################
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folder = './courses'
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courses_string = ''
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ids = get_txtfile_ids(folder)
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random.shuffle(ids)
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for id in ids:
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elems, keys = parse_course_info(id, folder)
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courses_string += get_course_html_string(id, elems, keys)
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page_string = page_string.replace('<!-- autogen courses -->', '<!-- autogen courses -->\n' + courses_string)
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###########################################################################
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###########################################################################
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fpath = './courses.html'
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with open(fpath, 'w') as f:
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f.write(page_string)

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