Thin out the presentation and notes

It was a little too close to the time limit

src/index.html

@@ -31,7 +31,7 @@
           </section>
           <section data-background-iframe="https://www.youtube-nocookie.com/embed/xiDS58Htuh4?autoplay=1&start=24">
             <aside class="notes" data-markdown>
-              - Student Robotics
+              - I volunteer for Student Robotics
               - Charity to help students get into STEM
               - Autonomous robotics competition
               - 16 - 19 year olds
@@ -40,105 +40,24 @@
           </section>
         </section>
         <section>
-          <section data-background-image="https://live.staticflickr.com/8718/17123916289_1cbc4c5210_k.jpg">
+          <section data-background-image="https://live.staticflickr.com/2827/32969459924_164c509e20_k.jpg">
-            <h1 class="r-fit-text text-shadow">Environment</h1>
             <aside class="notes" data-markdown>
-              - Robots need to sense their environments
+              - Lots of these _things_ dotted around the arena
-              - As humans, we rely quite a lot on sight
+              - What are they?
-              - Competitors, as humans, also do the same
             </aside>
           </section>
-          <section data-background-image="https://live.staticflickr.com/2837/33771948196_3cf1b5e3e5_k.jpg">
-            <aside class="notes" data-markdown>
-              - Sight is a powerful sense
-              - Ultrasound sensors can't distinguish between objects
-              - Switches are dull
-              - Eyes can detect objects, get distances, colour etc
-              - Only if they know what they're doing
-            </aside>
-          </section>
-        </section>
-        <section data-background-image="https://images.unsplash.com/photo-1504639725590-34d0984388bd?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1000&q=80">
-          <h2 class="text-shadow">Computer Vision</h2>
-          <aside class="notes" data-markdown>
-            - Requires the world of computer Vision
-            - Been around since late 1960s
-            - Universities pioneering early AI
-              - Not machine learning, the old kind
-              - Lots of `if` statements
-            - Some techniques unchanged to this day
-          </aside>
-        </section>
-        <section>
-          <section data-background-image="./img/object-recognition.png">
-            <h2 class="text-shadow">Object Recognition</h2>
-            <h4 class="text-shadow">The <span class="has-text-success">hot new thing</span></h4>
-            <aside class="notes" data-markdown>
-              - More modern machine learning
-              - 2 stages
-                - First you train a model
-                - Use that model to detect things
-              - Not quite ideal for our use case...
-            </aside>
-          </section>
-          <section data-background-image="./img/not-hotdog.png">
-            <h2 class="text-shadow">Prone to errors</h2>
-            <aside class="notes" data-markdown>
-              - Prone to errors
-              - Different lighting / shadows can affect detection
-              - Black box
-            </aside>
-          </section>
-          <section data-background-image="https://images.unsplash.com/photo-1558494949-ef010cbdcc31?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=1000&q=80">
-            <h2 class="text-shadow">Computationally intensive</h2>
-            <aside class="notes" data-markdown>
-              - Tonnes of computation
-              - However is done once upfront
-              - Fairly fast to detect
-              - Our robos are just Raspberry Pis
-            </aside>
-          </section>
-          <section>
-            <h2 class="r-fit-text">What else?</h2>
-            <aside class="notes" data-markdown>
-              - What other options do we have?
-            </aside>
-          </section>
-        </section>
-        <section>
           <section data-background-image="https://april.eecs.umich.edu/media/apriltag/apriltagrobots_overlay.jpg">
             <h2 class="text-shadow">Fiducial Markers</h2>
             <aside class="notes" data-markdown>
-              - Enter fiducial markers!
+              - Fiducial markers!
               - Look sorta like QR codes
                 - Just a single number
               - Simpler, so they're easier to detect
             </aside>
           </section>
-          <section data-background-image="https://docs.opencv.org/4.x/singlemarkersaxes.jpg">
-            <h2 class="text-shadow">Location</h2>
-            <aside class="notes" data-markdown>
-              - Accurately detect edges, see where in our FoV it is
-              - If we know how big it's meant to be, derive distance
-              - We know it's a square, derive angles
-            </aside>
-          </section>
-          <section data-background-image="https://live.staticflickr.com/2827/32969459924_164c509e20_k.jpg">
-            <h2 class="text-shadow r-fit-text">We put them <strong>everywhere</strong>!</h2>
-            <aside class="notes" data-markdown>
-              - Abundance of sources
-                - Arena walls
-                - Game props (tokens etc)
-              - Location of any of them
-              - If you know where the marker is, you know where you are
-            </aside>
-          </section>
           <section>
             <h2 class="r-fit-text">How do fiducial markers work?</h2>
             <aside class="notes" data-markdown>
-              - All well and good knowing they exist
-              - Tools out there to help make it easier
-              - Not good enough
               - How does it actually _work_?
             </aside>
           </section>
@@ -158,24 +77,25 @@
             - Images aren't black and white
               - This slide is
             - Markers _are_
-            - Discard the colour so the data we're working with is easier
-            - Grey is still more than enough data
             - Black and white!
-            - Thresholding
+              - Not greyscale
-              - Naive thresholding based on a value
+              - Much less data to be working with
+            - Thresholding achieves this
+              - Naive thresholding based on the entire image
               - Adaptive thresholding looks for hotspots and edges
-                - Useful in future
+                - Useful in future stages
           </aside>
         </section>
         <section>
           <section>
             <h2 class="r-fit-text">3. Edge Detection</h2>
             <aside class="notes" data-markdown>
+              - Basically "find the squares"
               - Marker edges are straight lines
-              - Markers are squares
-                - Well, quadralaterals
               - Filter to find hard edges
                 - Are neighbouring pixels sufficiently different from eachother?
+              - Markers are squares
+                - Well, quadralaterals
               - Find collections with 4 sides
             </aside>
           </section>
@@ -235,18 +155,20 @@
             <aside class="notes" data-markdown>
               - And that's it
               - We can now find markers in a given image
+              - ... Almost
               - But what if we wanted to do a little more?
             </aside>
         </section>
         <section data-background-image="https://docs.opencv.org/4.x/singlemarkersaxes.jpg">
             <h2 class="r-fit-text text-shadow">7. Pose Estimation</h2>
             <aside class="notes" data-markdown>
-              - Now know where there's a marker
+              - Now we know where there's a marker
                 - Which way is it facing?
               - We know where the corners are
-              - With some calibration, if it knows what a known-size marker looks like, it can be used to determine how far away a marker is
+                - We can get the angle from that
-              - Camera lenses have some distortion (lenses aren't completely flat), so as the image moves around the camera, it skews.
+                - If we know the size of the marker, we can calculate its distance
-                - If we account for that, we can get accurate angles
+              - Some calibration per camera model is required
+                - Done once upfront
               - Out the end, we get rotation and translations from the camera
                 - _complicated maths_
                 - `solvePnP` in OpenCV (entertaining name)
@@ -296,9 +218,6 @@
                 - Working out what something is
             </aside>
           </section>
-          <section data-background-image="https://github.com/ju1ce/April-Tag-VR-FullBody-Tracker/raw/master/images/demo.gif"></section>
-          <section data-background-iframe="https://www.youtube-nocookie.com/embed/5iV_hB08Uns?autoplay=1"></section>
-          <section data-background-iframe="https://www.youtube-nocookie.com/embed/4sRnVUHdM4A?autoplay=1&start=17"></section>
         </section>
         <section>
           <section>

src/index.js

@@ -9,16 +9,3 @@ let deck = new Reveal({
    hash: true
 })
 deck.initialize();
-
-// HACK: Manually transform parcel's hashed file paths for background images
-const FILE_MAPPING = {
-   "./img/not-hotdog.png": new URL("./img/not-hotdog.png", import.meta.url).toString(),
-   "./img/object-recognition.png": new URL("./img/object-recognition.png", import.meta.url).toString(),
-};
-
-for (const src in FILE_MAPPING) {
-   const dest = FILE_MAPPING[src];
-   document.querySelectorAll(`[data-background-image='${src}']`).forEach(e => {
-      e.dataset.backgroundImage = dest;
-   })
-}