Merge branch 'master' into python-add-lineplot-plugin

_includes/lut/lut_act1_skimage_napari.py

@@ -1,65 +1,58 @@
-# %% [markdown]
-# # Explore LUTs
+# %%
+# Using different Lookup Tables (LUTs) in napari
 
 # %%
 # Instantiate the napari viewer
 import napari
 viewer = napari.Viewer()
 
 # %%
-# Read the image
+# Read an image and its metadata
 from OpenIJTIFF import open_ij_tiff
-image, axes, scales, units = open_ij_tiff('https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__nuclei_high_dynamic_range.tif')
-
-# %% [markdown]
-# ### Napari GUI alternative to load data
-# Drag and drop and rename the layer (alternative for loading data)\
-# Change name of layer `viewer.layers[0].name = 'image_grayscale'` \
-# Get the data as numpy array `image = viewer.layers['image_grayscale'].data` 
-
-# %%
-# Check image type and values
-import numpy as np
-print(image.dtype, np.min(image), np.max(image))
+image, axes, scales, units = open_ij_tiff("https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__nuclei_high_dynamic_range.tif")
 
 # %%
 # View the intensity image as grayscale
 viewer.add_image(image, name='image_grayscale', colormap='gray')
 
 # %%
-# Change brightness and contrast
+# Change contrast programatically
+# Jupyter: Use TAB to to practice auto-completion when entering below command
 viewer.layers['image_grayscale'].contrast_limits=(100, 175)
 
-# %% [markdown]
-# **Napari GUI** explore different contrast limits
-
 # %%
-# View the intensity image as grayscale
-viewer.add_image(image, name='image_grayscale2', colormap='gray')
-
-# %% [markdown]
-# **Napari GUI** visualize images side by side\
-# **Napari GUI** change brightness and contrast to visualize dim nuclei
+# Napari: Change the contrast in the GUI
+# Napari: Right click on "contrast limits" to see the value range 
 
 # %%
-# Check available colormap
-print(list(napari.utils.colormaps.AVAILABLE_COLORMAPS))
+# Add the image a second time
+viewer.add_image(image, name='image_grayscale2', colormap='gray')
 
 # %%
-# Change colormap
-viewer.add_image(image, name='image_turbo', colormap='turbo')
-
-# %% [markdown]
-# **Napari GUI** explore different LUTs
+# Napari: Visualize images side by side by toggling on the "Grid mode"
+# Napari: Change contrast in one image to see the dim nuclei
 
 # %%
 # Extract image data from the layers
 image_grayscale = viewer.layers['image_grayscale'].data
 image_grayscale2 = viewer.layers['image_grayscale2'].data
 
 # %%
-# Compare raw data
+# Check that changing the LUT did not change the data
 print(image_grayscale[0:5,0:5])
 print(image_grayscale2[0:5,0:5])
+
+# %%
+# Check whether all pixels of the two images are identical
 print((image_grayscale == image_grayscale2).all())
 
+# %%
+# Check available LUTs (colormaps)
+print(list(napari.utils.colormaps.AVAILABLE_COLORMAPS))
+
+# %%
+# Add the image again with a multi-color LUT
+viewer.add_image(image, name='image_turbo', colormap='turbo')
+
+# %% 
+# Napari: Explore different LUTs using the GUI

_includes/lut/lut_act2_skimage_napari.py

@@ -1,27 +1,32 @@
-# %% [markdown]
-# # Configure LUTs
+# %% 
+# Show two images with the same LUT settings
 
 # %%
 # Instantiate the napari viewer
 import napari
 viewer = napari.Viewer()
 
 # %%
-# Read the image
+# Read the images
 from OpenIJTIFF import open_ij_tiff
-image_control, axes_control, scales_control, units_control = open_ij_tiff('https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_calibrated_16bit__nuclear_protein_control.tif')
-image_treated, axes_treated, scales_treated, units_treated = open_ij_tiff('https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_calibrated_16bit__nuclear_protein_treated.tif')
+image_control, *_ = open_ij_tiff('https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_calibrated_16bit__nuclear_protein_control.tif')
+image_treated, *_ = open_ij_tiff('https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_calibrated_16bit__nuclear_protein_treated.tif')
 
 # %% 
-# View the intensity image as grayscale
+# View the image as grayscale
 viewer.add_image(image_control, name='control', colormap='gray')
 viewer.add_image(image_treated, name='treated', colormap='gray')
 
-# %% [markdown]
-# **Napari GUI** Show images side by side \
-# **Napari GUI** Inspect possible consistent limits for both images
+# %%
+# Napari: Toggle grid mode on to the see images side by side 
+# Napari: Check the contrast limits for the two images
+
+# %%
+# Check the contrast limits programatically
+print(viewer.layers['control'].contrast_limits)
+print(viewer.layers['treated'].contrast_limits)
 
 # %% 
-# Apply limits to both images
-viewer.layers['control'].contrast_limits=(0, 1024)
-viewer.layers['treated'].contrast_limits=(0, 1024)
+# Apply the same contrast to both images
+viewer.layers['control'].contrast_limits=(0, 2500)
+viewer.layers['treated'].contrast_limits=(0, 2500)

_includes/pixels/pixels_3d_image_inspection.md

@@ -0,0 +1,8 @@
+<h4 id="3d"><a href="#2d">Inspect a 3D image</a></h4>
+
+Explore the content of a 3D image:
+- Essentially follow the instructions for inspecting a 2D image (see above actitvity)
+- Depending on the software that you are using it may be more or less convenient to deal with the 3rd dimension 
+
+Example data:
+- [xyz_8bit__mri_head.tif](https://github.com/NEUBIAS/training-resources/raw/master/image_data/xyz_8bit__mri_head.tif)

_includes/pixels/pixels_3d_image_inspection_skimage_napari.py

@@ -0,0 +1,45 @@
+# %%
+# 3D image inspection using skimage and napari
+
+# %%
+# Load an image
+from OpenIJTIFF import open_ij_tiff
+image_url = "https://github.com/NEUBIAS/training-resources/raw/master/image_data/xyz_8bit__mri_head.tif"
+image, axes, *_ = open_ij_tiff(image_url)
+
+# %%
+# Inspect the image shape
+print(image.shape)
+
+# %%
+# Inspect the image axes
+print(axes)
+
+# %%
+# Inspect all image pixel values, and appreciate that this is not useful for larger 3D data
+print(image)
+
+# %%
+# Create a napari viewer and add the image
+from napari.viewer import Viewer
+napari_viewer = Viewer()
+napari_viewer.add_image(image)
+
+# %%
+# Napari: 
+# - However with the mouse over the image and observe the pixel indices and values
+# - Use the slider to change the position of the 3rd dimension
+
+# %%
+# Extract the pixels that belong to the tip of the nose
+print(image[1, 9:19, 89:102])
+
+# %%
+# Compute the image min and max
+print(image.min(), image.max())
+
+# %%
+# Compute the image histogram
+import matplotlib.pyplot as plt
+import numpy as np
+plt.hist(image.flatten(), bins=np.arange(image.min(), image.max() + 1));

_includes/pixels/pixels_act1.md

@@ -1,7 +1,13 @@
 <h4 id="2d"><a href="#2d">Inspect a 2D image</a></h4>
- - Open image: [xy_8bit__nuclei_noisy_different_intensity.tif](https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__nuclei_noisy_different_intensity.tif)
- - Explore different ways to inspect pixel values and indices, e.g.,
- - Examine image dimensions
- - Examine individual (or a range of) pixel values
- - Plot line profiles
- - Compute and plot pixel value histograms
+
+Explore the content of a 2D image:
+- Examine image dimensions
+- Examine individual (or a range of) pixel values
+- Plot line profiles
+- Compute and plot pixel value histograms
+ - This is, e.g., important for [image thresholding](https://neubias.github.io/training-resources/binarization/index.html)
+- Compute pixel value statistics, such as min, max
+ - This is, e.g., important for checking for [intensity clipping](https://neubias.github.io/training-resources/datatypes/index.html)
+
+Example data:
+- [xy_8bit__nuclei_noisy_different_intensity.tif](https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__nuclei_noisy_different_intensity.tif)

_includes/pixels/pixels_act1_skimage_napari.py

@@ -1,72 +1,93 @@
-# %% [markdown]
-# ## Inspect a 2D image
+# %%
+# 2D image inspection using skimage and napari
+
 
 # %%
-# Load the image
-# You can also load a local image by providing the path to the file
+# Load an image
 from OpenIJTIFF import open_ij_tiff
 image_url = "https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__nuclei_noisy_different_intensity.tif"
-image, axes, scales, units  = open_ij_tiff(image_url)
+image, *_ = open_ij_tiff(image_url)
 
 # %%
-# Create a new napari viewer.
-from napari.viewer import Viewer
-napari_viewer = Viewer()
+# Inspect all image pixel values
+print(image)
 
-# %% [markdown]
-# ### Code completion and help in Jupyter notebook 
-# * **Code completion** type `napari_viewer.` and press `TAB`
-# * **Help** type `napari_viewer.add_image` and press `SHIFT-TAB` this will open a help associated to the add_image method/command
+# %%
+# Inspect the image shape
+print(image.shape)
 
 # %%
+# Create a napari viewer
+from napari.viewer import Viewer
+napari_viewer = Viewer()
 
+# %%
+# Jupyter notebook exercise:
+# Code completion: Type `napari_viewer.` and press `TAB`
+# Get help: Type `napari_viewer.add_image` and press `SHIFT-TAB` 
 
 # %%
-# Add an image to the napari_viewer.
+# Add an image to the napari_viewer
 napari_viewer.add_image(image)
 
-# %% [markdown]
-# ### Alternative loading of data
-# **Napari GUI** drag and drop image from browser\
-# rename the layer for convenience\
-# `napari_viewer.layers[0].name = 'image'` 
-# Get the data as numpy array\
-# `image = napari_viewer.layers['image'].data` 
+# %%
+# Pixel value inspection in napari: 
+# However with the mouse over the image and observe the pixel indices and values
 
 # %%
-# Print image shape
-print(image.shape)
+# Inspect the pixel at the top left corner
+print(image[0, 0])
 
 # %%
-# Print the image pixel values.
-print(image)
+# Inspect the pixel at the bottom right corner
+print(image[49, 58])
+print(image.shape[0], image.shape[1])
+print(image[image.shape[0]-1, image.shape[1]-1))
 
 # %%
-# Top left corner is [y, x] = [r, c] = [0, 0]
-print(image[0, 0])
+# Fetch a pixel value from the background
+# Beware the index order: [y, x] = [r, c]
+print(image[4, 8])
 
 # %%
-# [y, x] = [r, c] = [1, 0]
-print(image[1, 0])
+# Fetch a pixel value from within an object
+print(image[31, 42])
 
 # %%
-# [y, x] = [r, c] = [0, 2]
-print(image[0, 2])
+# Extract a line of pixel values across the objects
+print(image[20,:])
 
-# %% [markdown]
-# **Napari GUI** Explore the napari-plot-profile plugin (optional)
+# %% 
+# Napari:
+# Use the plot profile plugin to inspect a line of pixel values
+
+# %%
+# Extract one object as a square of pixel values
+print(image[7:30,10:26])
 
 # %%
-import numpy as np
-# Compute min and max.
+# Compute the image min and max
 print(image.min(), image.max())
 
 # %%
+# Compute the image histogram
 import matplotlib.pyplot as plt
-# Use matplotlib to quickly plot a histogram.
+import numpy as np
 plt.hist(image.flatten(), bins=np.arange(image.min(), image.max() + 1));
 
 # %%
-# Most frequent pixel value (the mode)
+# Compute the most frequent pixel value (the mode)
 from scipy.stats import mode
 mode(image, axis = None, keepdims = True)
+
+# %%
+# Napari:
+# Alternative loading of data
+# Remove the currently shown image
+# Drag and drop image from browser onto napari
+# Rename the layer to: image
+
+# %%
+# Fetch the image data from napari and check its shape
+image = napari_viewer.layers['image'].data
+print(image.shape)

_modules/pixels.md

@@ -29,41 +29,15 @@ multiactivities:
  - ["pixels/pixels_act1.md", [["ImageJ GUI", "pixels/pixels_act1_imagejgui.md"], ["skimage napari", "pixels/pixels_act1_skimage_napari.py"], ["MATLAB", "pixels/pixels_act1_matlab.m"]]]
  - ["pixels/collagen_inspection.md", [["ImageJ GUI", "pixels/collagen_inspection_imagejgui.md"]]]
 
-assessment: >
-
- ### 2-D image inspection
- Open image
- [xy_8bit__nuclei_noisy_different_intensity.tif](https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__nuclei_noisy_different_intensity.tif).
- Hint: For certain exercises the inspection of the histogram will help
- 1. What is the value of the pixel at the indices (x=20,y=20)?
- 1. What is the highest value in the image?
- 1. What is the most frequently occurring value in the image?
- 1. Where is this pixel with the indices (x=0,y=0)? Why is this potentially confusing?
- 1. How many pixels does this image have in the x direction?
- 1. What is the highest pixel index in the x direction?
- 1. If you were to rotate the image by 90 degrees, would it change the image histogram?
-
- > ## Solution
- > 1. 82
- > 1. 129
- > 1. 55
- > 1. Top left; normally x/y coordinate systems have their origin at the bottom left
- > 1. 59
- > 1. 58
- > 1. No, the gray value histogram is independent of the pixel locations
- {: .solution}
-
- ### 3-D image inspection
- Open image: [xyz_8bit__mri_head.tif](https://github.com/NEUBIAS/training-resources/raw/master/image_data/xyz_8bit__mri_head.tif)
- 1. What is the value of the voxel at the indices (x=93,y=124,z=13)?
- 1. Which is the highest value in the image?
-
- > ## Solution
- > 1. 47
- > 1. 255
- {: .solution}
-
-exercises:
+assessment: |
+
+ ### Answer the question
+
+ 1. If someome gives you a 2D image file and tells you to measure the value of the pixel at the indices `(7,8)` without telling you which programming language to use. Is that a precise enough instruction? If not, how many different pixels could that actually refer to? 
+
+ > ## Solution
+ > 1. Unfortunately this instruction is not precise enough and, in practice it could refer to four different pixels, depending on whether this is meant to be zero or one-based indexing and depending whether this is row or column-major ordering. See [here for more details](https://en.wikipedia.org/wiki/Row-_and_column-major_order).
+ {: .solution} 
 
 learn_next:
  - "[Lookup tables](../lut)"