{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "3b987de5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pyclesperanto_prototype as cle\n", "from dask.threaded import get as dask_get\n", "from skimage.io import imread\n", "import warnings\n", "import inspect\n", "from skimage.measure import label\n", "\n", "\n", "cle.get_device()" ] }, { "cell_type": "code", "execution_count": 11, "id": "86b4a6bf", "metadata": {}, "outputs": [], "source": [ "class Workflow():\n", " \n", " def __init__(self):\n", " self.tasks = {}\n", " \n", "\n", " def set(self, name, func_or_data, *args, **kwargs):\n", " if name in self.tasks.keys():\n", " warnings.warn(\"Overwriting {}\".format(name))\n", " if not callable(func_or_data):\n", " self.tasks[name] = func_or_data\n", " return\n", " \n", " sig = inspect.signature(func_or_data)\n", " bound = sig.bind(*args, **kwargs)\n", " bound.apply_defaults()\n", " \n", " self.tasks[name] = tuple([func_or_data] + [value for key, value in bound.arguments.items()]\n", " ) \n", "\n", " def get(self, name):\n", " ## Actually, all this should work with dask. But I don't manage.\n", " return dask_get(self.tasks, name)\n", "\n", "w = Workflow()\n", "# define background subtraction\n", "w.set(\"background_subtracted\", cle.top_hat_box, \"input\", radius_x=10, radius_y=10)\n", "# define segmentation\n", "w.set(\"binarized\", cle.threshold_otsu, \"background_subtracted\")\n", "\n", "# add a function from another library\n", "w.set(\"alias\", label, \"binarized\")\n", "\n", "# link an alias\n", "w.set(\"segmented\", \"alias\")" ] }, { "cell_type": "code", "execution_count": 8, "id": "c26e000d", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "w.set(\"input\", imread(\"C:/structure/data/blobs.tif\"))\n", "result = w.get(\"segmented\")\n", "\n", "cle.imshow(result, labels=True)" ] }, { "cell_type": "code", "execution_count": 9, "id": "231ac156", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\rober\\AppData\\Local\\Temp/ipykernel_33764/1092359316.py:12: UserWarning: Overwriting input\n", " warnings.warn(\"Overwriting {}\".format(name))\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "w.set(\"input\", imread(\"C:/structure/data/blobs_edges.tif\"))\n", "result = w.get(\"segmented\")\n", "\n", "cle.imshow(result, labels=True)" ] }, { "cell_type": "code", "execution_count": null, "id": "11220c1e", "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": "3.8.12" } }, "nbformat": 4, "nbformat_minor": 5 }