Merge pull request #20 from petebankhead/videos

Add video tutorials, standardize headings

docs/tutorials/cell_classification.rst

@@ -24,6 +24,22 @@ They can be applied for the *classification* of all *detections* within QuPath,
     It is a good idea to read through the :doc:`cell_detection` section before this one.
 
 
+==============
+Video tutorial
+==============
+
+.. raw:: html
+
+    <div style="text-align: center; margin-bottom: 2em;">
+    <iframe width="100%" height="350" src="https://www.youtube-nocookie.com/embed/-4JZ73ZEicY?rel=0" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
+    </div>
+
+
+============
+Step-by-step
+============
+
+
 Annotate the main region of interest
 ====================================
 

docs/tutorials/cell_detection.rst

@@ -27,12 +27,29 @@ This is sometimes referred to as the 'Ki67 labelling index'.
 In this case, tumor cells are identified by manually drawing around them to create annotations, after which QuPath is able to very quickly detect the cells and calculate the positive percentage inside each annotation.
 In :doc:`cell_classification` we will look at improving on this by training QuPath to identify tumor cells itself.
 
-.. figure:: images/ki67_detecting_origin.jpg
-  :class: shadow-image
-  :width: 75%
-  :align: center
 
-  Ki67 image
+==============
+Video tutorial
+==============
+
+.. raw:: html
+
+    <div style="text-align: center; margin-bottom: 2em;">
+    <iframe width="100%" height="350" src="https://www.youtube-nocookie.com/embed/14ZQCSmICCA?rel=0" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
+    </div>
+
+
+.. .. figure:: images/ki67_detecting_origin.jpg
+..   :class: shadow-image
+..   :width: 75%
+..   :align: center
+.. 
+..   Ki67 image
+
+
+============
+Step-by-step
+============
 
 
 Annotate a region of interest

docs/tutorials/density_maps.rst

@@ -16,6 +16,20 @@ In some cases, density maps can even be a replacement for :doc:`pixel_classifica
     Like most commands in QuPath, Density Maps are currently calculated only in 2D.
     And, like many other commands, you should create Density Maps within a :ref:`project <Projects>` if you want to reuse them later.
 
+==============
+Video tutorial
+==============
+
+.. raw:: html
+
+    <div style="text-align: center; margin-bottom: 2em;">
+    <iframe width="100%" height="350" src="https://www.youtube-nocookie.com/embed/lGtYOlv8XFU?rel=0" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
+    </div>
+
+
+============
+Step-by-step
+============
 
 
 Getting started with density maps

docs/tutorials/exporting_measurements.rst

@@ -19,7 +19,10 @@ Which one to use is up to you and depends on what you want to do.
   * **a single image**: use the measurement table
   * **multiple images**: use the *Measurement Exporter* or a script
 
-=========================
+============
+Step-by-step
+============
+
 Via the measurement table
 =========================
 
@@ -45,7 +48,6 @@ You can then save your measurement by pressing **Save** and choosing an appropri
 
   If your analysis involves combining measurements from different images, it is recommended to use the Measurement Exporter, detailed in the next subsection.
 
-============================
 Via the Measurement Exporter
 ============================
 
@@ -83,7 +85,6 @@ Below the image selection, a small number of parameters will allow you to shape
   A small red-colored warning will appear to remind you.
 
 
-=============
 Via scripting
 =============
 

docs/tutorials/measuring_areas.rst

@@ -6,14 +6,22 @@ Perhaps one of the earliest and most familiar applications of image analysis in
 
 We can apply this to :doc:`OS-3.ndpi <../intro/acknowledgements>` to answer the question: what is the area of the brown region, and what proportion of the tissue does it occupy?
 
-.. figure:: images/areas_image.jpg
-  :class: shadow-image
-  :align: center
-  :width: 90%
-
-  OS-3.ndpi
+==============
+Video tutorial
+==============
 
+.. raw:: html
 
+    <div style="text-align: center; margin-bottom: 2em;">
+    <iframe width="100%" height="350" src="https://www.youtube-nocookie.com/embed/KGE7v4VIQ_0?rel=0" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
+    </div>
+
+
+============
+Step-by-step
+============
+
+
 Define the region of interest
 =============================
 

docs/tutorials/multiplex_analysis.rst

@@ -25,12 +25,15 @@ There are three main steps involved:
 
 But first we have a few routine things we need to take care of so that things can run smoothly.
 
-==================
+============
+Step-by-step
+============
+
 Before we begin...
 ==================
 
 Create a project containing your images
-=======================================
+---------------------------------------
 
 Many things in QuPath work best if you create a :doc:`Project <projects>`.
 Here, it is really necessary so that classifiers generated along the way are saved in the right place to become available later.
@@ -42,7 +45,7 @@ Here, it is really necessary so that classifiers generated along the way are sav
 
 
 Set the image type
-==================
+------------------
 
 As usual when working with an image in QuPath, it is important to ensure the :doc:`Image type <../starting/first_steps>` is appropriate.
 In this case, the best choice is *Fluorescence*.
@@ -64,7 +67,7 @@ In this case, the best choice is *Fluorescence*.
 
 
 Set up the channel names
-========================
+------------------------
 
 The *channel names* are particularly important for multiplexed analysis, since these typically correspond to the markers of interest.
 They will also be reused within the names for the cell classifications.
@@ -113,7 +116,7 @@ Here, I would remove any '(Opal)' parts.
 
 
 Setting up the classifications
-==============================
+------------------------------
 
 We now want to make the channel names available as *classifications*.
 
@@ -125,7 +128,7 @@ You can either right-click this list or select the |vertical_ellipsis| button an
       :align: center
       :class: shadow-image
 
-======================
+
 Detect & measure cells
 ======================
 
@@ -148,7 +151,6 @@ Because these measurements are based on the channel names, it is important to ha
       :class: shadow-image
 
 
-===================================
 Create a classifier for each marker
 ===================================
 
@@ -164,7 +166,7 @@ You do not have to choose the same method for every marker, but can switch betwe
 
 
 Option #1. Simple thresholding
-==============================
+------------------------------
 
 QuPath v0.2.0 introduces a new command, :menuselection:`Classify --> Object classification --> Create single measurement classifier`.
 This gives us a quick way to classify based on the value of one measurement.
@@ -202,14 +204,14 @@ Now you can return to the **Channel filter** and work through the steps for the
 
 
 Option #2. Machine learning
-===========================
+---------------------------
 
 If you are entirely happy with the process above, you can skip this section.
 But sometimes thresholding a single measurement isn't sufficient to generate a usable classification - and taking a machine learning approach can help.
 This process is a bit more involved, but the effort is often worth it.
 
 Create training images
-----------------------
+^^^^^^^^^^^^^^^^^^^^^^
 
 It is very difficult and confusing to try to train multiple classifiers by annotating the same image.
 
@@ -227,7 +229,7 @@ To do this, choose :menuselection:`Classify --> Extras --> Duplicate channel tra
 
 
 Train & save classifiers
-------------------------
+^^^^^^^^^^^^^^^^^^^^^^^^
 
 Now you should have multiple duplicate images in your project, with names derived from the original channel names.
 Because you ran this after cell detection (right?!), these duplicate images will bring across all the original cells.
@@ -296,7 +298,6 @@ Then save the image data and open the image associated with the next marker of i
   You can then proceed to annotate cells, largely free of the distraction of what QuPath had actually previously detected.
 
 
-=======================
 Combine the classifiers
 =======================
 
@@ -325,7 +326,6 @@ Then, choose *any combination* of classifiers and press :guilabel:`Apply classif
 
 
 
-======================
 Making sense of it all
 =======================
 

docs/tutorials/pixel_classification.rst

@@ -15,8 +15,12 @@ But pixel classifiers can also do much more sophisticated things.
   Please read :doc:`thresholding` and :doc:`measuring_areas` first!
 
 
+============
+Step-by-step
+============
+
 Stained areas (again)
-*********************
+=====================
 
 Returning to the example in :doc:`measuring_areas`, we could replace either of the thresholding steps with :menuselection:`Classify --> Pixel classification --> Train pixel classifier`.
 
@@ -38,7 +42,7 @@ When you are done, you can enter the classifier name, save it, and create measur
 
 
 More complex classifications
-****************************
+============================
 
 Training a pixel classifier makes it possible to incorporate a lot more information than is possible with a simple threshold, and to determine the output in a much more sophisticated way.
 

docs/tutorials/projects.rst

@@ -21,13 +21,28 @@ However, if you will be saving and reloading data associated with multiple image
   Some commands and scripts *only* work within projects.
 
 
-================
+==============
+Video tutorial
+==============
+
+.. raw:: html
+
+    <div style="text-align: center; margin-bottom: 2em;">
+    <iframe width="100%" height="350" src="https://www.youtube-nocookie.com/embed/kCEp89ypmAY?rel=0" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
+    </div>
+
+
+============
+Step-by-step
+============
+
+
 Create a project
 ================
 
 
 Choose a folder
-===============
+---------------
 
 The first step of creating a project is to create an empty folder somewhere on your computer.
 You can then set this to be the project directory in one of two ways:
@@ -43,7 +58,7 @@ You can then set this to be the project directory in one of two ways:
 
 
 Add images
-==========
+----------
 
 The easiest way to add images to a project is usually to drag them on top of QuPath.
 
@@ -75,7 +90,7 @@ The options are:
 
 
 Remove images
-=============
+-------------
 
 You can remove images by right-clicking one or more entries under the *Project* tab and choosing :menuselection:`Delete image(s)`.
 
@@ -86,13 +101,11 @@ If you choose not to, these files will linger around - you won't be able to acce
   On platforms that support it, QuPath will try to remove data by sending it to the recycle bin rather than permanently deleting it immediately.
 
 
-
-=====================
 Working with projects
 =====================
 
 Reopen a project
-================
+----------------
 
 There are four ways to reopen an existing project:
 
@@ -103,7 +116,7 @@ There are four ways to reopen an existing project:
 
 
 Fix paths
-=========
+---------
 
 It is important to recognize that your project folder does **not** (usually) contain your images.
 Rather, it contains QuPath's associated data files only - and the paths to where your images are located.
@@ -128,12 +141,3 @@ If you choose :guilabel:`Ignore` then your project will not be overwritten, but
 
 .. tip::
   In the event that your images and project *both* changed location together, QuPath will try to resolve the paths relative to the project and suggest how you should update them automatically.
-
-
-.. Set metadata
-.. ============
-..
-..
-..
-.. .. attention::
-..   For more detail on working with projects