Added a parameterized example notebook to simplify the usage

notebooks/run_cellpose_script.ipynb

@@ -0,0 +1,185 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Script\n",
+    "\n",
+    "Compatible with [`papermill`](https://github.com/nteract/papermill).\n",
+    "Based on [`run_cellpose.ipynb`](./run_cellpose.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#!pip install matplotlib\n",
+    "import numpy as np\n",
+    "from cellpose import models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "parameters"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "## parameters\n",
+    "input_path=None\n",
+    "output_path=None\n",
+    "\n",
+    "gpu=False\n",
+    "model_type='cyto' # model_type='cyto' or model_type='nuclei'\n",
+    "kws_eval={}\n",
+    "plot=True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Input"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "if not input_path.endswith('.npy'):\n",
+    "    img = io.imread(input_path)\n",
+    "else:\n",
+    "    img = np.load(input_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prediction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# DEFINE CELLPOSE MODEL\n",
+    "model = models.Cellpose(gpu=gpu, model_type=model_type)\n",
+    "masks, flows, styles, diams = model.eval(img, diameter=None, **kws_eval)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if output_path is None:\n",
+    "    from cellpose import io\n",
+    "    # save results so you can load in gui\n",
+    "    io.masks_flows_to_seg(img, masks, flows, diams, input_path)\n",
+    "    # save results as png\n",
+    "    io.save_to_png(img, masks, flows, input_path)\n",
+    "elif output_path.endswith('.npy'):\n",
+    "    np.save(\n",
+    "        output_path,\n",
+    "        masks,\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "if plot:\n",
+    "    import matplotlib.pyplot as plt\n",
+    "    from cellpose import plot    \n",
+    "    fig = plt.figure(figsize=(12,5))\n",
+    "    plot.show_segmentation(fig, img, masks, flows[0])\n",
+    "    plt.tight_layout()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "cellpose",
+   "language": "python",
+   "name": "cellpose"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.18"
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}