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wgottwaldwgottwald
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feature: Added a second image_segmenter class and interactive functions to display segmenter data
Added a image_segmenter_overlayed class which reads a secondary image thats overlayed over the primary. This is useful for medical data for example PET where one has an anatomical reference image and then a heatmap overlayed and wants to segment based on both. Also added some functions to go along with it as well as a example notebook. Also had some issues with commiting, these are describes in an issue here: mpl-extensions#265. This changed the pre-commit-config.
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‎.gitignore

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# mpl testing
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result_images/
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# Pycharm paths
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.idea/

‎.pre-commit-config.yaml

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autoupdate_schedule: "quarterly"
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repos:
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- repo: https://github.com/pre-commit/pre-commit-hooks
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rev: v4.3.0
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rev: v4.4.0
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hooks:
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- id: check-docstring-first
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- id: end-of-file-fixer
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- id: trailing-whitespace
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- repo: https://github.com/psf/black
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rev: 22.8.0
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rev: 23.1.0
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hooks:
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- id: black
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- repo: https://github.com/pycqa/isort
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rev: 5.10.1
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rev: 5.12.0
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hooks:
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- id: isort
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- repo: https://github.com/nbQA-dev/nbQA
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rev: 1.5.2
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rev: 1.6.3
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hooks:
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- id: nbqa-black
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- id: nbqa-isort
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- id: nbstripout
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- repo: https://github.com/pre-commit/mirrors-prettier
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rev: v3.0.0-alpha.0
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rev: v3.0.0-alpha.6
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hooks:
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- id: prettier
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- repo: https://github.com/PyCQA/flake8
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rev: 5.0.4
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rev: 6.0.0
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hooks:
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- id: flake8
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additional_dependencies: [flake8-typing-imports==1.7.0]
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additional_dependencies: [flake8-typing-imports==1.12.0]
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- repo: https://github.com/PyCQA/autoflake
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rev: v1.6.1
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rev: v2.0.1
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hooks:
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- id: autoflake
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args:

‎docs/examples/custom-callbacks.ipynb

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"\n",
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"# attach a custom callback\n",
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"\n",
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"\n",
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"# if running from a script you can just delete the widgets.Output and associated code\n",
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"def my_callback(tau, beta):\n",
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" if tau < 7.5:\n",

‎docs/examples/devlop/devlop-controller.ipynb

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" )\n",
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"\n",
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" def update(params, indices):\n",
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"\n",
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" # update plot\n",
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" for i, f in enumerate(funcs):\n",
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" if x is not None and not indexed_x:\n",
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"\n",
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" lines = []\n",
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" for i, f in enumerate(funcs):\n",
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"\n",
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" if x is not None and not indexed_x:\n",
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" lines.append(ax.plot(x, f(x, **params), **plot_kwargs[i])[0])\n",
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" elif indexed_x:\n",

‎docs/examples/image-segmentation-multiple-images.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Image Segmentation of overlayed images and multi-dimensional images"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"%matplotlib widget\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"\n",
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"%load_ext autoreload\n",
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"%autoreload 2\n",
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"import mpl_interactions as mpl"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# load a primary sample image\n",
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"import urllib\n",
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"\n",
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"import PIL\n",
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"\n",
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"url = \"https://github.com/matplotlib/matplotlib/raw/v3.3.0/lib/matplotlib/mpl-data/sample_data/ada.png\"\n",
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"\n",
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"\n",
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"image = np.sum(np.array(PIL.Image.open(urllib.request.urlopen(url))), axis=2) / 255\n",
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"# simulating multiple slices along the 2 dimension\n",
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"image_stack = np.array([image] * 3).transpose((1, 2, 0))\n",
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"\n",
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"# secondary image\n",
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"url_2 = \"https://github.com/matplotlib/matplotlib/raw/v3.3.0/lib/matplotlib/mpl-data/sample_data/Minduka_Present_Blue_Pack.png\"\n",
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"secondary_image = np.sum(np.array(PIL.Image.open(urllib.request.urlopen(url_2))), axis=2) / 255\n",
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"# padding secondard to be same size as primary for illustration\n",
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"secondary_image_padded = np.pad(\n",
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" secondary_image,\n",
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" [\n",
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" (170, 505),\n",
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" (150, 234),\n",
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" ],\n",
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" mode=\"constant\",\n",
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" constant_values=(0, 0),\n",
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")\n",
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"# simulating multiple slices along the 2 dimension\n",
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"secondary_image_stack = np.array([secondary_image_padded] * 3).transpose(1, 2, 0)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"fig, ax = plt.subplots(1, 3, tight_layout=True)\n",
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"ax[0].imshow(image, cmap=\"gray\")\n",
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"ax[1].imshow(secondary_image, cmap=\"magma\")\n",
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"ax[2].imshow(image, cmap=\"gray\")\n",
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"ax[2].imshow(secondary_image_padded, cmap=\"magma\", alpha=0.6)\n",
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"ax[0].set_title(\"Primary image\")\n",
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"ax[1].set_title(\"Secondary image\")\n",
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"ax[2].set_title(\"Overlayed\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Now segment the secondary image over the primary\n",
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"1. First create a stack of image_segmenter_overlayed objects"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"segmenter_stack = mpl.get_segmenter_list(image_stack, secondary_image_stack, n_classes=2)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"2. Call draw_masks function to open an interactive window where one can draw the masks on the seperate slices"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"mpl.draw_masks(segmenter_list=segmenter_stack)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"3. Retrieve mask values from segmenter_list"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"masks_dict = mpl.get_masks(segmenter_stack, plot_res=True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"4. Retrieve contours of the masks"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"contours = mpl.get_mask_contours(segmenter_stack)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"5. Plot contours on segmented images"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

‎docs/examples/usage.ipynb

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"iplt.title(\"the value of tau is: {tau:.2f}\", controls=controls[\"tau\"])\n",
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"\n",
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"\n",
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"# you can still use plt commands if this is the active figure\n",
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"def ylabel(tau):\n",
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" return f\"tau/2 is {np.round(tau/2,3)}\"\n",

‎docs/index.md

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examples/hist.ipynb
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examples/scatter-selector.ipynb
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examples/image-segmentation.ipynb
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examples/image-segmentation-multiple-image.ipynb
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examples/zoom-factory.ipynb
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examples/heatmap-slicer.ipynb
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```

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