Notebook Explo API Python Myoquant

Author: Corentin

notebooks/example_myoquant_api.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import urllib.request\n",
+    "from os import path\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import numpy as np\n",
+    "from PIL import Image\n",
+    "\n",
+    "from myoquant.common_func import (\n",
+    "    load_cellpose,\n",
+    "    load_sdh_model,\n",
+    "    load_stardist,\n",
+    "    run_cellpose,\n",
+    "    run_stardist,\n",
+    ")\n",
+    "from myoquant.HE_analysis import run_he_analysis\n",
+    "from myoquant.SDH_analysis import run_sdh_analysis\n",
+    "\n",
+    "try:\n",
+    "    from imageio.v2 import imread\n",
+    "except:\n",
+    "    from imageio import imread"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ANALYSE SDH\n",
+    "\n",
+    "# Importer l'image et dossier de sauvegarde\n",
+    "image_path = \"LE CHEMIN VERS L'IMAGE SDH A ANALYSER\"\n",
+    "output_path = image_path.parents[0]\n",
+    "Path(output_path).mkdir(parents=True, exist_ok=True)\n",
+    "\n",
+    "# Télécharger le modèle SDH\n",
+    "model_path_abs = Path(os.path.abspath(__file__)).parents[0] / \"model.h5\"\n",
+    "if not path.exists(model_path_abs):\n",
+    "    urllib.request.urlretrieve(\n",
+    "        \"https://lbgi.fr/~meyer/SDH_models/model.h5\",\n",
+    "        model_path_abs,\n",
+    "    )\n",
+    "model_path = model_path_abs\n",
+    "\n",
+    "# Charger Cellpose\n",
+    "model_cellpose = load_cellpose()\n",
+    "# Charger l'image\n",
+    "image_ndarray_sdh = imread(image_path)\n",
+    "\n",
+    "# Faire tourner CellPose sur l'image & sauvegarder\n",
+    "mask_cellpose = run_cellpose(image_ndarray_sdh, model_cellpose)\n",
+    "mask_cellpose = mask_cellpose.astype(np.uint16)\n",
+    "cellpose_mask_filename = image_path.stem + \"_cellpose_mask.tiff\"\n",
+    "Image.fromarray(mask_cellpose).save(output_path / cellpose_mask_filename)\n",
+    "\n",
+    "# Faire tourner le modèle SDH sur l'image et récupérer le tableau de sommaire (result_df)\n",
+    "model_SDH = load_sdh_model(model_path)\n",
+    "result_df, full_label_map = run_sdh_analysis(\n",
+    "    image_ndarray_sdh, model_SDH, mask_cellpose\n",
+    ")\n",
+    "csv_name = image_path.stem + \"_results.csv\"\n",
+    "result_df.to_csv(\n",
+    "    output_path / csv_name,\n",
+    "    index=False,\n",
+    ")\n",
+    "label_map_name = image_path.stem + \"_label_map.tiff\"\n",
+    "Image.fromarray(full_label_map).save(output_path / label_map_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ANALYSE HE\n",
+    "\n",
+    "# Importer l'image et dossier de sauvegarde\n",
+    "image_path = \"LE CHEMIN VERS L'IMAGE HE A ANALYSER\"\n",
+    "output_path = image_path.parents[0]\n",
+    "Path(output_path).mkdir(parents=True, exist_ok=True)\n",
+    "\n",
+    "# Charger les modèles CellPose et Stardist\n",
+    "model_cellpose = load_cellpose()\n",
+    "model_stardist = load_stardist()\n",
+    "# Charger l'image\n",
+    "image_ndarray = imread(image_path)\n",
+    "# Faire tourner Cellpose (fibres) puis sauvegarder l'image\n",
+    "mask_cellpose = run_cellpose(image_ndarray, model_cellpose)\n",
+    "mask_cellpose = mask_cellpose.astype(np.uint16)\n",
+    "cellpose_mask_filename = image_path.stem + \"_cellpose_mask.tiff\"\n",
+    "Image.fromarray(mask_cellpose).save(output_path / cellpose_mask_filename)\n",
+    "# Faire tourner Stardist (noyaux) puis sauvegarder l'image\n",
+    "mask_stardist = run_stardist(image_ndarray, model_stardist)\n",
+    "mask_stardist = mask_stardist.astype(np.uint16)\n",
+    "stardist_mask_filename = image_path.stem + \"_stardist_mask.tiff\"\n",
+    "Image.fromarray(mask_stardist).save(output_path / stardist_mask_filename)\n",
+    "# Analyser la position des noyaux et fibres et sauvegarder le tableau de sommaire (result_df)\n",
+    "result_df, full_label_map = run_he_analysis(image_ndarray, mask_cellpose, mask_stardist)\n",
+    "csv_name = image_path.stem + \"_results.csv\"\n",
+    "result_df.to_csv(\n",
+    "    output_path / csv_name,\n",
+    "    index=False,\n",
+    ")\n",
+    "label_map_name = image_path.stem + \"_label_map.tiff\"\n",
+    "Image.fromarray(full_label_map).save(output_path / label_map_name)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.12 ('myoquant-WpXbYFOG-py3.9')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.9.12"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "d376be9f186256918b4977b6bd310794ac5a9b1babe0c9318e787478ad5da552"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}