Minor maintenance fixes, with thanks to @Koushouu

Author: WhoIsJack

README.md

@@ -8,10 +8,11 @@ Python BioImage Analysis Tutorial
 
 ----
 
-⚠️ Note that some of the materials in this tuorial are slightly out of date by now (2022), but recent feedback has shown that the tutorial is still very useful and approachable for many learners. I would like to create an updated and slightly extended version by the end of the year, but academic pressures limit my ability to work on this, so we'll have to see how it goes. 🤞 (Jonas, May 22)
+⚠️ Note that some of the materials in this tuorial are slightly out of date by now (2023), but recent feedback has shown that the tutorial is still very useful and approachable for many learners. I would like to create an updated and slightly extended version at some point, but academic pressures limit my ability to work on this, so we'll have to see how it goes... 🤞
 
------
+(The latest maintenance update was done on 17.02.2023 for python 3.9.16 (basic Anaconad distro), with thanks to @Koushouu!)
 
+-----
 
 ## Aims and Overview
 
@@ -23,39 +24,36 @@ Everything you need to know to get started can be found in the jupyter notebook
 
 Note that this tutorial was part of a course aimed at people with basic knowledge of python. The course included introductory sessions/lectures on scientific python (in particular `numpy` and `matplotlib`) as well as on image analysis (see the slides in this repository). For those tackling this tutorial on their own, it is therefore recommended to first acquire basic scientific python knowledge elsewhere (e.g. at [python-course.eu](https://www.python-course.eu)).
 
-
 ## Content Overview
 
 - Lecture
-    - Working with digital images
-        - Images as arrays of numbers
-        - Look-up tables (LUTs)
-        - Dimensions
-        - Bit-depth
-    - Image analysis pipelines
-        - Preprocessing: filters, kernels, convolution, background subtraction
-        - Foreground detection: thresholding, morphological operations
-        - Segmentation: labels, seeds, watershed
-        - Postprocessing: object filtering
-        - Making measurements
-
+  - Working with digital images
+    - Images as arrays of numbers
+    - Look-up tables (LUTs)
+    - Dimensions
+    - Bit-depth
+  - Image analysis pipelines
+    - Preprocessing: filters, kernels, convolution, background subtraction
+    - Foreground detection: thresholding, morphological operations
+    - Segmentation: labels, seeds, watershed
+    - Postprocessing: object filtering
+    - Making measurements
 
 - Tutorial
-    - Importing Modules & Packages
-    - Loading & Handling Image Data
-    - Preprocessing
-    - Manual Thresholding & Threshold Detection
-    - Adaptive Thresholding
-    - Improving Masks with Binary Morphology
-    - Connected Components Labeling
-    - Cell Segmentation by Seeding & Expansion
-    - Postprocessing: Removing Cells at the Image Border
-    - Identifying Cell Edges
-    - Extracting Quantitative Measurements
-    - Simple Analysis & Visualization
-    - Writing Output to Files
-    - Batch Processing
-
+  - Importing Modules & Packages
+  - Loading & Handling Image Data
+  - Preprocessing
+  - Manual Thresholding & Threshold Detection
+  - Adaptive Thresholding
+  - Improving Masks with Binary Morphology
+  - Connected Components Labeling
+  - Cell Segmentation by Seeding & Expansion
+  - Postprocessing: Removing Cells at the Image Border
+  - Identifying Cell Edges
+  - Extracting Quantitative Measurements
+  - Simple Analysis & Visualization
+  - Writing Output to Files
+  - Batch Processing
 
 ## Old Versions and Other Sources
 
@@ -65,7 +63,6 @@ If you are looking for the python 2 version from 2017, see the `2017_legacy_pyth
 
 The original 2016 materials can be found in Karin Sasaki's corresponding Github [repo](https://github.com/karinsasaki/python-workshop-image-processing).
 
-
 ## Acknowledgements
 
 The first version of this tutorial was created for the `EMBL Python Workshop - Image Processing` course organized by Karin Sasaki and Jonas Hartmann in 2016. Additional lecturers and TAs contributing to this course were Kota Miura, Volker Hilsenstein, Aliaksandr Halavatyi, Imre Gaspar, and Toby Hodges.
@@ -76,7 +73,6 @@ The third version of this tutorial was part of the `EMBL Bio-IT/ALMF Image Analy
 
 Many thanks to all the helpful collaborators and the interested students who were instrumental in making these courses a success.
 
-
 ## Feedback
 
 Feedback on this tutorial is always welcome! Please open an issue on GitHub or write to *jonas.hartmann(AT)ucl.ac.uk*.

batch_processing_solution.py

@@ -53,7 +53,7 @@ def run_pipeline(dirpath, filename):
     ## Preprocessing
 
     sigma = 3
-    img_smooth = ndi.filters.gaussian_filter(img, sigma)
+    img_smooth = ndi.gaussian_filter(img, sigma)
 
 
     ## Adaptive Thresholding
@@ -85,7 +85,7 @@ def run_pipeline(dirpath, filename):
     ### Seeding by Distance Transform
 
     dist_trans = ndi.distance_transform_edt(~mem_final)
-    dist_trans_smooth = ndi.filters.gaussian_filter(dist_trans, sigma=5)
+    dist_trans_smooth = ndi.gaussian_filter(dist_trans, sigma=5)
 
     from skimage.feature import peak_local_max
     seed_coords = peak_local_max(dist_trans_smooth, min_distance=10)

image_analysis_tutorial.ipynb

@@ -94,8 +94,7 @@
     "\n",
     "#### Python version\n",
     "\n",
-    "- The solutions are provided in **python 3.6** (but any version of python 3 should work without changes)\n",
-    "- Everything shown in the solutions also works in python 2.7 with only very minor changes.\n",
+    "- The solutions were last tested with **python 3.9**, but any version of python 3 should work without changes\n",
     "- To install python, jupyter notebook and the required modules, we recommend the **Anaconda Distribution**. The installer can be downloaded [here](https://www.anaconda.com/download/).\n",
     "\n",
     "\n",
@@ -106,6 +105,7 @@
     "    - scipy\n",
     "    - matplotlib\n",
     "    - scikit-image\n",
+    "    - ipywidgets\n",
     "- All required modules come pre-installed if you are using the **[Anaconda distribution](https://www.anaconda.com/download/)** of python."
    ]
   },
@@ -481,7 +481,7 @@
    "source": [
     "# (ii) Perform the smoothing on the image\n",
     "\n",
-    "# To do so, use the Gaussian filter function 'ndi.filters.gaussian_filter' from the \n",
+    "# To do so, use the Gaussian filter function 'ndi.gaussian_filter' from the \n",
     "# image processing module 'scipy.ndimage', which was imported at the start of the tutorial. \n",
     "# Check out the documentation of scipy to see how to use this function. \n",
     "# Allocate the output to a new variable.\n",
@@ -639,9 +639,9 @@
     "\n",
     "*Important note for those who get a static image (no slider) or a text warning:*\n",
     "\n",
-    "For some users, it is necessary to specifically activate the widgets plugin for Jupyter notebook. To do so, save and exit Jupyter notebook, then go to a terminal and write `jupyter nbextension enable --py --sys-prefix widgetsnbextension`. After this, you should be able to restart Jupyter notebook and the widget should display correctly. \n",
+    "For some users, it is necessary to specifically activate the widgets plugin for Jupyter notebook. To do so, save and exit Jupyter notebook, then go to a terminal (or Anaconda prompt) and write `jupyter nbextension enable --py --sys-prefix widgetsnbextension`. After this, you should be able to restart Jupyter notebook and the widget should display correctly. \n",
     "\n",
-    "If it still doesn't work, you may instead have to type `jupyter nbextension enable --py widgetsnbextension` in the terminal. However, note that this implies that your installation of Conda/Jupyter is not optimally configured (see [this GitHub issue](https://github.com/jupyter-widgets/ipywidgets/issues/541) for more information, although this is not something you necessarily need to worry about in the context of this course).\n",
+    "If it still doesn't work, you may instead have to type `jupyter nbextension enable --py widgetsnbextension` in the terminal (or Anaconda prompt). However, note that this implies that your installation of Conda/Jupyter is not optimally configured (see [this GitHub issue](https://github.com/jupyter-widgets/ipywidgets/issues/541) for more information, although this is not something you necessarily need to worry about in the context of this course).\n",
     "\n",
     "----"
    ]
@@ -1077,7 +1077,7 @@
    "source": [
     "# (iii) Smoothen the distance transform\n",
     "\n",
-    "# Use 'ndi.filters.gaussian_filter' to do so.\n",
+    "# Use 'ndi.gaussian_filter' to do so.\n",
     "# You will have to optimize your choice of 'sigma' based on the outcome below.\n",
     "### YOUR CODE HERE!"
    ]
@@ -1126,7 +1126,7 @@
     "# using the function 'np.ma.array' with the keyword argument 'mask'. \n",
     "# Check the docs or Stack Overflow to figure out how to do this.\n",
     "\n",
-    "# BONUS: As an additional improvement for the visualization, use 'ndi.filters.maximum_filter' to dilate the \n",
+    "# BONUS: As an additional improvement for the visualization, use 'ndi.maximum_filter' to dilate the \n",
     "# seeds a little bit, making them bigger and thus better visible.\n",
     "\n",
     "### YOUR CODE HERE!"

image_analysis_tutorial_solutions.ipynb

@@ -284,11 +284,11 @@
    "source": [
     "# (ii) Perform the smoothing on the image\n",
     "\n",
-    "# To do so, use the Gaussian filter function 'ndi.filters.gaussian_filter' from the \n",
+    "# To do so, use the Gaussian filter function 'ndi.gaussian_filter' from the \n",
     "# image processing module 'scipy.ndimage', which was imported at the start of the tutorial. \n",
     "# Check out the documentation of scipy to see how to use this function. \n",
     "# Allocate the output to a new variable.\n",
-    "img_smooth = ndi.filters.gaussian_filter(img, sigma)"
+    "img_smooth = ndi.gaussian_filter(img, sigma)"
    ]
   },
   {
@@ -776,11 +776,11 @@
    "source": [
     "# (iii) Smoothen the distance transform\n",
     "\n",
-    "# Use 'ndi.filters.gaussian_filter' to do so.\n",
+    "# Use 'ndi.gaussian_filter' to do so.\n",
     "# You will have to optimize your choice of 'sigma' based on the outcome below.\n",
     "\n",
     "# Applying the filter\n",
-    "dist_trans_smooth = ndi.filters.gaussian_filter(dist_trans, sigma=5)\n",
+    "dist_trans_smooth = ndi.gaussian_filter(dist_trans, sigma=5)\n",
     "\n",
     "# Visualizing\n",
     "plt.figure(figsize=(7,7))\n",
@@ -834,11 +834,11 @@
     "# using the function 'np.ma.array' with the keyword argument 'mask'. \n",
     "# Check the docs or Stack Overflow to figure out how to do this.\n",
     "\n",
-    "# BONUS: As an additional improvement to the visualization, use 'ndi.filters.maximum_filter' to dilate the \n",
+    "# BONUS: As an additional improvement to the visualization, use 'ndi.maximum_filter' to dilate the \n",
     "# seeds a little bit, making them bigger and thus better visible.\n",
     "\n",
     "# Dilate seeds\n",
-    "seeds_dil = ndi.filters.maximum_filter(seeds, size=10)\n",
+    "seeds_dil = ndi.maximum_filter(seeds, size=10)\n",
     "\n",
     "# Create plot\n",
     "plt.figure(figsize=(7,7))\n",
@@ -859,7 +859,7 @@
     "seeds_labeled = ndi.label(seeds)[0]\n",
     "\n",
     "# Visualize the final result (the labeled seeds) as an overlay on the raw (or smoothed) image\n",
-    "seeds_labeled_dil = ndi.filters.maximum_filter(seeds_labeled, size=10)  # Expand a bit for visualization\n",
+    "seeds_labeled_dil = ndi.maximum_filter(seeds_labeled, size=10)  # Expand a bit for visualization\n",
     "plt.figure(figsize=(10,10))\n",
     "plt.imshow(img_smooth, interpolation='none', cmap='gray')\n",
     "plt.imshow(np.ma.array(seeds_labeled_dil, mask=seeds_labeled_dil==0), interpolation='none', cmap='prism')\n",
@@ -1748,9 +1748,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python [conda env:py39_tutorial_test]",
    "language": "python",
-   "name": "python3"
+   "name": "conda-env-py39_tutorial_test-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1762,7 +1762,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.9.16"
   }
  },
  "nbformat": 4,