Affine Transform Refactoring

.github/workflows/notebook_tests.yml

@@ -9,7 +9,8 @@ jobs:
       max-parallel: 4
       matrix:
         python-version: [3.7]
-
+    env:
+      TEST_ENV: TRUE
     steps:
     - uses: actions/checkout@v1
     - name: Set up Python ${{ matrix.python-version }}

notebooks/transformations.ipynb

@@ -0,0 +1,279 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-02-15T09:57:50.791332Z",
+     "start_time": "2020-02-15T09:57:46.068701Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "!pip install napari\n",
+    "!pip install SimpleITK"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-02-16T16:54:54.920459Z",
+     "start_time": "2020-02-16T16:54:54.669509Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%reload_ext autoreload\n",
+    "%autoreload 2\n",
+    "%matplotlib inline\n",
+    "%gui qt\n",
+    "import os\n",
+    "if 'TEST_ENV' in os.environ:\n",
+    "    TEST_ENV = os.environ['TEST_ENV'].lower() == \"true\"\n",
+    "else:\n",
+    "    TEST_ENV = 0\n",
+    "print(f\"Running test environment: {bool(TEST_ENV)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-02-15T11:55:06.130717Z",
+     "start_time": "2020-02-15T11:54:22.119553Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from io import BytesIO\n",
+    "from zipfile import ZipFile\n",
+    "from urllib.request import urlopen\n",
+    "\n",
+    "resp = urlopen(\"http://www.fmrib.ox.ac.uk/primers/intro_primer/ExBox3/ExBox3.zip\")\n",
+    "zipfile = ZipFile(BytesIO(resp.read()))\n",
+    "\n",
+    "img_file = zipfile.extract(\"ExBox3/T1_brain.nii.gz\")\n",
+    "mask_file = zipfile.extract(\"ExBox3/T1_brain_seg.nii.gz\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-02-16T16:55:01.394975Z",
+     "start_time": "2020-02-16T16:55:00.893340Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import SimpleITK as sitk\n",
+    "import numpy as np\n",
+    "\n",
+    "# load image and mask\n",
+    "img_file = \"./ExBox3/T1_brain.nii.gz\"\n",
+    "mask_file = \"./ExBox3/T1_brain_seg.nii.gz\"\n",
+    "img = sitk.GetArrayFromImage(sitk.ReadImage(img_file))\n",
+    "img = img.astype(np.float32)\n",
+    "mask = mask = sitk.GetArrayFromImage(sitk.ReadImage(mask_file))\n",
+    "mask = mask.astype(np.float32)\n",
+    "\n",
+    "assert mask.shape == img.shape\n",
+    "print(f\"Image shape {img.shape}\")\n",
+    "print(f\"Image shape {mask.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-02-16T16:55:04.255613Z",
+     "start_time": "2020-02-16T16:55:03.213336Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "if TEST_ENV:\n",
+    "    def view_batch(batch):\n",
+    "        pass\n",
+    "else:\n",
+    "    %gui qt\n",
+    "    import napari\n",
+    "    def view_batch(batch):\n",
+    "            viewer = napari.view_image(batch[\"data\"].cpu().numpy(), name=\"data\")\n",
+    "            viewer.add_image(batch[\"mask\"].cpu().numpy(), name=\"mask\", opacity=0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-02-16T16:55:54.082493Z",
+     "start_time": "2020-02-16T16:55:54.019599Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from rising.transforms import *\n",
+    "\n",
+    "batch = {\n",
+    "    \"data\": torch.from_numpy(img).float()[None, None],\n",
+    "    \"mask\": torch.from_numpy(mask).long()[None, None],\n",
+    "}\n",
+    "\n",
+    "def apply_transform(trafo, batch):\n",
+    "    transformed = trafo(**batch)\n",
+    "    print(f\"Transformed data shape: {transformed['data'].shape}\")\n",
+    "    print(f\"Transformed mask shape: {transformed['mask'].shape}\")\n",
+    "    print(f\"Transformed data min: {transformed['data'].min()}\")\n",
+    "    print(f\"Transformed data max: {transformed['data'].max()}\")\n",
+    "    print(f\"Transformed data mean: {transformed['data'].mean()}\")\n",
+    "    return transformed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-02-16T16:55:06.109008Z",
+     "start_time": "2020-02-16T16:55:06.069336Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "print(f\"Transformed data shape: {batch['data'].shape}\")\n",
+    "print(f\"Transformed mask shape: {batch['mask'].shape}\")\n",
+    "print(f\"Transformed data min: {batch['data'].min()}\")\n",
+    "print(f\"Transformed data max: {batch['data'].max()}\")\n",
+    "print(f\"Transformed data mean: {batch['data'].mean()}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-02-16T16:55:57.391117Z",
+     "start_time": "2020-02-16T16:55:55.675294Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "trafo = Scale(1.5, adjust_size=False)\n",
+    "transformed = apply_transform(trafo, batch)\n",
+    "view_batch(transformed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-02-16T17:03:58.535489Z",
+     "start_time": "2020-02-16T17:03:57.964843Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "trafo = Rotate([0, 0, 45], degree=True, adjust_size=False)\n",
+    "transformed = apply_transform(trafo, batch)\n",
+    "view_batch(transformed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2020-02-16T16:00:26.032367Z",
+     "start_time": "2020-02-16T16:00:25.466391Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "trafo = Translate([0.1, 0, 0], adjust_size=False)\n",
+    "transformed = apply_transform(trafo, batch)\n",
+    "view_batch(transformed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.7.5"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  },
+  "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
+}

rising/transforms/__init__.py

@@ -8,3 +8,4 @@
 from rising.transforms.spatial import *
 from rising.transforms.utility import *
 from rising.transforms.tensor import *
+from rising.transforms.affine import *