Remove some demos and stray slides

examples/adaptive-threshold.py

@@ -1,18 +0,0 @@
-import cv2
-import numpy
-
-cap = cv2.VideoCapture(2)
-
-cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
-cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
-
-while True:
-    _, frame = cap.read()
-    grey = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-    t1 = cv2.adaptiveThreshold(grey, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 3, 7)
-    t2 = cv2.adaptiveThreshold(grey, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 13, 7)
-    t3 = cv2.adaptiveThreshold(grey, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 23, 7)
-
-    threshed = numpy.concatenate((t1, t2, t3), axis=1)
-    cv2.imshow('threshed', threshed)
-    cv2.waitKey(1)

examples/threshold.py

@@ -1,14 +0,0 @@
-import cv2
-
-cap = cv2.VideoCapture(2)
-
-cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
-cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
-
-while True:
-    _, frame = cap.read()
-    grey = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-    _, thresh = cv2.threshold(grey, 127, 255, cv2.THRESH_BINARY)
-    cv2.imshow('original', frame)
-    cv2.imshow('thresh', thresh)
-    cv2.waitKey(1)

src/index.html

@@ -72,13 +72,12 @@
         </section>
         <section data-background-image="https://docs.opencv.org/4.x/singlemarkersthresh.png">
           <h2 class="r-fit-text text-shadow">2. Thresholding</h2>
-          <p class="fragment text-shadow">Demo 🤞</p>
           <aside class="notes" data-markdown>
             - Images aren't black and white
               - This slide is
             - Markers _are_
             - Black and white!
-              - Not greyscale
+              - Not even greyscale
               - Much less data to be working with
             - Thresholding achieves this
               - Naive thresholding based on the entire image
@@ -86,7 +85,6 @@
                 - Useful in future stages
           </aside>
         </section>
-        <section>
         <section>
           <h2 class="r-fit-text">3. Edge Detection</h2>
           <aside class="notes" data-markdown>
@@ -99,19 +97,6 @@
             - Find collections with 4 sides
           </aside>
         </section>
-          <section>
-            <h2 class="r-fit-text">3a. Contour Refinement</h2>
-            <aside class="notes" data-markdown>
-              - Only care about the square (ish) ones
-                - Get rid of anything else
-                - Ignore rectangles, too skewed etc
-                - Remove contours within contours
-              - Refinement is fast and simple
-                - Latter stages are more complex
-                - Exclude now whilst it's easy and cheap
-            </aside>
-          </section>
-        </section>
         <section data-background-image="https://docs.opencv.org/4.x/singlemarkersoriginal.jpg">
           <h2 class="r-fit-text text-shadow">4. <em>Distortion</em></h2>
           <aside class="notes" data-markdown>
@@ -149,16 +134,6 @@
               - Search "Hamming code" for more
             </aside>
         </section>
-        <section data-background-image="https://docs.opencv.org/4.x/singlemarkersdetection.jpg">
-            <h2 class="r-fit-text text-shadow">And done!</h2>
-            <p class="fragment text-shadow">...Almost</p>
-            <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>
@@ -178,7 +153,7 @@
             </aside>
         </section>
         <section>
-          <h2 class="r-fit-text">Another demo 🙏</h2>
+          <h2 class="r-fit-text">Demo 🙏</h2>
         </section>
         <section>
           <section>