Add acitivity to open CZI image, #720

_includes/image_file_formats/open_czi.md

@@ -0,0 +1,9 @@
+<h4 id="open_czi"><a href="#open_czi">Open CZI image data</a></h4>
+
+- Open a CZI (Carl Zeiss Image) file and inspect its pixel and metatdata content
+- Observe the data is presented as one file on disk
+- Observe that this one file contains multiple image datasets
+
+##### Data
+
+- [CZI image file](https://github.com/NEUBIAS/training-resources/raw/master/image_data/xyz__multiple_images.czi)

_includes/image_file_formats/open_czi_bioio.py

@@ -0,0 +1,183 @@
+# %% 
+# Open a CZI image file
+# minimal conda env for this module
+# conda create -n ImageFileFormats python=3.10
+# activate ImageFileFormat
+# pip install bioio bioio-tifffile bioio-lif bioio-czi bioio-ome-tiff bioio-ome-zarr notebook
+
+# TODO
+# - Change the below code to only open the CZI image
+# - Implement that it opens both images that are contained in the file (see ImageJ GUI activity)
+
+# %%
+# Load .tif file with minimal metadata
+# - Observe that BioImage chooses the correct reader plugin
+# - Observe that the return object is not the image matrix
+from bioio import BioImage
+image_url = 'https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__nuclei_PLK1_control.tif'
+bioimage = BioImage(image_url)
+print(bioimage)
+print(type(bioimage))
+
+# %%
+# Print some onject attributes
+# - Observe that the object is 5 dimensional with most dimensions being empty
+# - Observe that the dimension order is always time, channel, z, y, x, (TCZYX)
+print(bioimage.dims)
+print(bioimage.shape)
+print(f'Dimension order is: {bioimage.dims.order}')
+print(type(bioimage.dims.order))
+print(f'Size of X dimension is: {bioimage.dims.X}')
+
+# %%
+# Extract image data
+# - Observe that the returned numpy.array is still 5 dimensional
+image_data = bioimage.data
+print(type(image_data))
+print(image_data)
+print(image_data.shape)
+
+# %%
+# Extract specific part of image data
+# - Observe that numpy.array is reduced to populated dimensions only
+yx_image_data = bioimage.get_image_data('YX')
+print(type(yx_image_data))
+print(yx_image_data)
+print(yx_image_data.shape)
+
+# %%
+# Access pixel size
+import numpy as np
+print(bioimage.physical_pixel_sizes)
+print(f'An pixel has a length of {np.round(bioimage.physical_pixel_sizes.X,2)} microns in X dimension.')
+
+# %%
+# Access general metadata
+print(type(bioimage.metadata))
+print(bioimage.metadata)
+
+# %%
+# Load .tif file with extensive metadata
+image_url = "https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_16bit__collagen.md.tif"
+bioimage = BioImage(image_url)
+print(bioimage)
+print(type(bioimage))
+
+# - Observe that the image is larger than the previous
+print(bioimage.dims)
+
+# %%
+# Access image and reduce to only populated dimensions
+yx_image_data = bioimage.data.squeeze()
+print(type(yx_image_data))
+print(yx_image_data)
+print(yx_image_data.shape)
+
+# %%
+# Access pixel size
+print(bioimage.physical_pixel_sizes)
+print(f'An pixel has a length of {np.round(bioimage.physical_pixel_sizes.Y,2)} microns in Y dimension.')
+
+# Access general metadata
+# - Observe that metadata are more extensive than in the previous image
+print(type(bioimage.metadata))
+print(bioimage.metadata)
+
+# %%
+# Load .lif file
+# - Observe that BioImage chooses the correct reader plugin
+# - Observe that the return object has 4 different channels
+# - Observe that the general metadata are an abstract element
+image_url = "https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_xyc__two_images.lif"
+bioimage = BioImage(image_url)
+print(bioimage)
+print(type(bioimage))
+print(bioimage.dims)
+print(bioimage.metadata)
+print(type(bioimage.metadata))
+
+# %%
+# Access channel information
+print(bioimage.channel_names)
+
+# %%
+# Access image data for all channels
+img_4channel = bioimage.data.squeeze()
+
+# Alternative
+img_4channel = bioimage.get_image_data('CYX')
+
+# - Observe that numpy.array shape is 3 dimensional representing channel,y,x
+print(img_4channel.shape)
+
+# Access only one channel
+img_1channel = bioimage.get_image_data('YX',C=0)
+
+# Alternative
+img_1channel = img_4channel[0]
+
+# - Observe that numpy.array shape is 2 dimensional representing y,x
+print(img_1channel.shape)
+
+# %%
+# Access different images in one image file (scenes)
+# - Observe that one image file can contain several scenes
+# - Observe that they can be different in various aspects
+print(bioimage.scenes)
+print(f'Current scene: {bioimage.current_scene}')
+
+# - Observe that the image in the current scene as 4 channel and Y/X dimensions have the size of 1024
+print(bioimage.dims)
+print(bioimage.physical_pixel_sizes)
+
+# Switch to second scene
+# - Observe that the image in the other scene as only one channel and Y/X dimensions are half as large as the first scene
+# - Observe that the pixel sizes are doubled
+bioimage.set_scene(1)
+print(bioimage.dims)
+print(bioimage.physical_pixel_sizes)
+
+# %%
+# Load .czi file
+# file needs first to be downloaded from https://github.com/NEUBIAS/training-resources/raw/master/image_data/xyz__multiple_images.czi
+# save file in the same directory as this notebook
+# - Observe that BioImage chooses the correct reader plugin
+# - Observe that the return object has a z dimension
+bioimage = BioImage('/Users/fschneider/skimage-napari-tutorial/ExampleImages/xyz__multiple_images.czi')
+print(bioimage)
+print(type(bioimage))
+
+# %%
+# little excersise in between
+# Access image dimensions
+print(bioimage.dims)
+
+# Access general metadata
+# - Observe that metadata are abstract
+print(bioimage.metadata)
+print(type(bioimage.metadata))
+
+# Access pixel size
+print(bioimage.physical_pixel_sizes)
+
+# Access image data for all channels
+img_3d = bioimage.data.squeeze()
+
+# Alternative
+img_3d = bioimage.get_image_data('ZYX')
+
+# - Observe that numpy.array shape is 3 dimensional representing z,y,x
+print(img_3d.shape)
+
+# Access only one channel
+img_2d = bioimage.get_image_data('YX',Z=0)
+
+# Alternative
+img_2d = img_3d[0]
+
+# - Observe that numpy.array shape is 2 dimensional representing y,x
+print(img_2d.shape)
+
+# %%
+# little excercise:
+# paticipants should try to open one of their files with python

_includes/image_file_formats/open_czi_imagejgui.md

@@ -0,0 +1,8 @@
+- Open the file mentioned in the activity using:
+  - [Plugins > Bio-Formats > Bio-Format Importer]
+    - [X] Display metadata
+    - [X] Display OME-XML Metadata
+  - Press [OK]
+  - Select both "Series"
+  - Look at the images
+  - Inspect the metadata

_layouts/module.html

@@ -188,22 +188,26 @@
             document.getElementById("{{ platform[0] | slugify }}-activity-div").style.display = 'none';{% endfor %}{% unless site.default-platform == "NONE" %}document.getElementById("{{ site.default-platform | slugify }}-activity-div").style.display = 'block';{% endunless %}
         }
 
-        function change_activity_content_by_platform(form_control) {
+        function change_activity_content_by_platform(form_control) 
+        {
           console.log(`change_activity_content_by_platform called with form_control: ${form_control}`);
           updateUrlWithSelection(form_control);
           {% for platform in page.activities %}
             document.getElementById("{{ platform[0] | slugify }}-activity-div").style.display = 'none';
           {% endfor %}
-          if (! form_control || document.getElementById(form_control).value == "null-activity") {
+          if (! form_control || document.getElementById(form_control).value == "null-activity") 
+          {
             set_activity_view_defaults();
             {% for platform in page.activities %}
             } else if (document.getElementById(form_control).value == "{{ platform[0] | slugify }}-activity") {
               document.getElementById("{{ platform[0] | slugify }}-activity-div").style.display = 'block';
             {% endfor %}
-        } else {
+          } 
+          else 
+          {
           alert("Error: Missing platform value for 'change_activity_content_by_platform()' script!");
+          }
         }
-      }
 
       function set_exercises_view_defaults () {
         {% for platform in page.exercises %}

_modules/commenting.md

@@ -1,7 +1,7 @@
 ---
 title: Commenting
 layout: module
-tags: ["scripting","draft"]
+tags: ["scripting", "draft"]
 prerequisites:
   - "[Binarization](../binarization)"
   - "[Connected component labeling](../connected_components)"

_modules/image_file_formats.md

@@ -29,6 +29,7 @@ figure: /figures/image_file_formats.png
 figure_legend: Image pixel data and metadata
 
 multiactivities:
+  - ["image_file_formats/open_czi.md", [["ImageJ GUI", "image_file_formats/open_czi_imagejgui.md"],["python BioIO", "image_file_formats/open_czi_bioio.py"]]]
   - ["image_file_formats/open_diverse_file_formats.md", [["ImageJ GUI", "image_file_formats/open_diverse_file_formats_imagejgui.md"],["python BioIO", "image_file_formats/open_diverse_file_formats_bioio.py"]]]
   - ["image_file_formats/resaving_images.md", [["ImageJ GUI", "image_file_formats/resaving_images_imagejgui.md", "markdown"]]]
 

_modules/script_for_loop.md

@@ -1,7 +1,7 @@
 ---
 title:     Loops
 layout:    module
-tags: ["scripting", "draft"]
+tags: ["scripting"]
 objectives:
   - "Use for loops to repeat operations multiple times"
   - "Running a script for multiple files"

_modules/script_functions.md

@@ -1,7 +1,7 @@
 ---
 title: Functions
 layout: module
-tags: ["draft","scripting"]
+tags: ["scripting"]
 prerequisites:
   - "[Variables](../script_variables)"
   - "[Loops](../script_for_loops)"

_modules/tiff.md

@@ -41,9 +41,8 @@ assessment: >
     {: .solution}
 
 learn_next:
-  - "[Automatic threshold for binarization](../auto_threshold)"
+  - "[OME-Zarr](../ome-zarr)"
 
 external_links:
-  - "[Wikipedia: Binary image](https://en.wikipedia.org/wiki/Binary_image)"
+  - "[Wikipedia: TIFF](https://en.wikipedia.org/wiki/TIFF)"
 ---
-