Image segmenter of overlayed images and functions for segmenting interactively

.gitignore

@@ -139,3 +139,7 @@ _version.py
 
 # mpl testing
 result_images/
+
+
+# Pycharm paths
+.idea/

.pre-commit-config.yaml

@@ -2,24 +2,24 @@ ci:
   autoupdate_schedule: "quarterly"
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.3.0
+    rev: v4.4.0
     hooks:
       - id: check-docstring-first
       - id: end-of-file-fixer
       - id: trailing-whitespace
 
   - repo: https://github.com/psf/black
-    rev: 22.8.0
+    rev: 23.1.0
     hooks:
       - id: black
 
   - repo: https://github.com/pycqa/isort
-    rev: 5.10.1
+    rev: 5.12.0
     hooks:
       - id: isort
 
   - repo: https://github.com/nbQA-dev/nbQA
-    rev: 1.5.2
+    rev: 1.6.3
     hooks:
       - id: nbqa-black
       - id: nbqa-isort
@@ -30,18 +30,18 @@ repos:
       - id: nbstripout
 
   - repo: https://github.com/pre-commit/mirrors-prettier
-    rev: v3.0.0-alpha.0
+    rev: v3.0.0-alpha.6
     hooks:
       - id: prettier
 
   - repo: https://github.com/PyCQA/flake8
-    rev: 5.0.4
+    rev: 6.0.0
     hooks:
       - id: flake8
-        additional_dependencies: [flake8-typing-imports==1.7.0]
+        additional_dependencies: [flake8-typing-imports==1.12.0]
 
   - repo: https://github.com/PyCQA/autoflake
-    rev: v1.6.1
+    rev: v2.0.1
     hooks:
       - id: autoflake
         args:

docs/contributing.md

@@ -17,7 +17,7 @@ pre-commit install
 The `conda env create` command installs all Python packages that are useful when working on the source code of `mpl_interactions` and its documentation. You can also install these packages separately:
 
 ```bash
-pip install -e .[dev]
+pip install -e ".[dev]"
 ```
 
 The {command}`-e .` flag installs the `mpl_interactions` folder in ["editable" mode](https://pip.pypa.io/en/stable/cli/pip_install/#editable-installs) and {command}`[dev]` installs the [optional dependencies](https://setuptools.readthedocs.io/en/latest/userguide/dependency_management.html#optional-dependencies) you need for developing `mpl_interacions`.

docs/examples/custom-callbacks.ipynb

@@ -157,6 +157,7 @@
     "\n",
     "# attach a custom callback\n",
     "\n",
+    "\n",
     "# if running from a script you can just delete the widgets.Output and associated code\n",
     "def my_callback(tau, beta):\n",
     "    if tau < 7.5:\n",

docs/examples/devlop/devlop-controller.ipynb

@@ -310,7 +310,6 @@
     "    )\n",
     "\n",
     "    def update(params, indices):\n",
-    "\n",
     "        # update plot\n",
     "        for i, f in enumerate(funcs):\n",
     "            if x is not None and not indexed_x:\n",
@@ -379,7 +378,6 @@
     "\n",
     "    lines = []\n",
     "    for i, f in enumerate(funcs):\n",
-    "\n",
     "        if x is not None and not indexed_x:\n",
     "            lines.append(ax.plot(x, f(x, **params), **plot_kwargs[i])[0])\n",
     "        elif indexed_x:\n",

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

@@ -0,0 +1,201 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Image Segmentation of overlayed images and multi-dimensional images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib widget\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "import mpl_interactions as mpl"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# load a primary sample image\n",
+    "import urllib\n",
+    "\n",
+    "import PIL\n",
+    "\n",
+    "url = \"https://github.com/matplotlib/matplotlib/raw/v3.3.0/lib/matplotlib/mpl-data/sample_data/ada.png\"\n",
+    "\n",
+    "\n",
+    "image = np.sum(np.array(PIL.Image.open(urllib.request.urlopen(url))), axis=2) / 255\n",
+    "# simulating multiple slices along the 2 dimension\n",
+    "image_stack = np.array([image] * 3).transpose((1, 2, 0))\n",
+    "\n",
+    "# secondary image\n",
+    "url_2 = \"https://github.com/matplotlib/matplotlib/raw/v3.3.0/lib/matplotlib/mpl-data/sample_data/Minduka_Present_Blue_Pack.png\"\n",
+    "secondary_image = np.sum(np.array(PIL.Image.open(urllib.request.urlopen(url_2))), axis=2) / 255\n",
+    "# padding secondard to be same size as primary for illustration\n",
+    "secondary_image_padded = np.pad(\n",
+    "    secondary_image,\n",
+    "    [\n",
+    "        (170, 505),\n",
+    "        (150, 234),\n",
+    "    ],\n",
+    "    mode=\"constant\",\n",
+    "    constant_values=(0, 0),\n",
+    ")\n",
+    "# simulating multiple slices along the 2 dimension\n",
+    "secondary_image_stack = np.array([secondary_image_padded] * 3).transpose(1, 2, 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(1, 3, tight_layout=True)\n",
+    "ax[0].imshow(image, cmap=\"gray\")\n",
+    "ax[1].imshow(secondary_image, cmap=\"magma\")\n",
+    "ax[2].imshow(image, cmap=\"gray\")\n",
+    "ax[2].imshow(secondary_image_padded, cmap=\"magma\", alpha=0.6)\n",
+    "ax[0].set_title(\"Primary image\")\n",
+    "ax[1].set_title(\"Secondary image\")\n",
+    "ax[2].set_title(\"Overlayed\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Now segment the secondary image over the primary\n",
+    "1. First create a stack of image_segmenter_overlayed objects"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "segmenter_stack = mpl.get_segmenter_list(image_stack, secondary_image_stack, n_classes=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2. Call draw_masks function to open an interactive window where one can draw the masks on the seperate slices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "mpl.draw_masks(segmenter_list=segmenter_stack)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "3. Retrieve mask values from segmenter_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "masks_dict = mpl.get_masks(segmenter_stack, plot_res=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "4. Retrieve contours of the masks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "contours = mpl.get_mask_contours(segmenter_stack)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "5. Plot contours on segmented images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig,ax=plt.subplots(1,image_stack.shape[2])\n",
+    "\n",
+    "for n in range(len(image_stack.shape[2])):\n",
+    "    ax[n].imshow(image_stack[:,:,n]) for n in range(len(image_stack.shape[2])\n",
+    "    if len(contours[slice])>0:\n",
+    "        for roi_num in range(len(contours[n])):\n",
+    "            ax.plot(contours[n][roi_num][:,1], contours[n][roi_num][:,0], linewidth=2, color='C'+str(roi_num),label='ROI Nr.'+str(roi_num))\n",
+    "\n",
+    "ax.legend()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

docs/examples/usage.ipynb

@@ -452,6 +452,8 @@
     ")\n",
     "\n",
     "iplt.title(\"the value of tau is: {tau:.2f}\", controls=controls[\"tau\"])\n",
+    "\n",
+    "\n",
     "# you can still use plt commands if this is the active figure\n",
     "def ylabel(tau):\n",
     "    return f\"tau/2 is {np.round(tau/2,3)}\"\n",

docs/index.md

@@ -116,6 +116,7 @@ examples/imshow.ipynb
 examples/hist.ipynb
 examples/scatter-selector.ipynb
 examples/image-segmentation.ipynb
+examples/image-segmentation-multiple-image.ipynb
 examples/zoom-factory.ipynb
 examples/heatmap-slicer.ipynb
 ```