{ "cells": [ { "cell_type": "markdown", "id": "96637152-70c6-4f09-99de-a7557de22834", "metadata": {}, "source": [ "# Installing Dependencies" ] }, { "cell_type": "code", "execution_count": 2, "id": "37bb512e-9bb9-4ac6-9334-ff11dbdb03aa", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: ipywidgets in /home/aaichert/.virtualenvs/jupyter/lib/python3.12/site-packages (8.1.8)\n", "Requirement already satisfied: comm>=0.1.3 in /home/aaichert/.virtualenvs/jupyter/lib/python3.12/site-packages (from ipywidgets) (0.2.3)\n", "Requirement already satisfied: ipython>=6.1.0 in /home/aaichert/.virtualenvs/jupyter/lib/python3.12/site-packages (from ipywidgets) (9.13.0)\n", "Requirement already satisfied: traitlets>=4.3.1 in /home/aaichert/.virtualenvs/jupyter/lib/python3.12/site-packages (from ipywidgets) (5.15.0)\n", "Requirement already satisfied: widgetsnbextension~=4.0.14 in 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(from scikit-image) (0.5)\n", "Requirement already satisfied: rich in /home/aaichert/.virtualenvs/jupyter/lib/python3.12/site-packages (15.0.0)\n", "Requirement already satisfied: markdown-it-py>=2.2.0 in /home/aaichert/.virtualenvs/jupyter/lib/python3.12/site-packages (from rich) (4.2.0)\n", "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /home/aaichert/.virtualenvs/jupyter/lib/python3.12/site-packages (from rich) (2.20.0)\n", "Requirement already satisfied: mdurl~=0.1 in /home/aaichert/.virtualenvs/jupyter/lib/python3.12/site-packages (from markdown-it-py>=2.2.0->rich) (0.1.2)\n", "Requirement already satisfied: ProjectiveGeometry23 in /home/aaichert/.virtualenvs/jupyter/lib/python3.12/site-packages (0.0.0)\n", "Requirement already satisfied: numpy in /home/aaichert/.virtualenvs/jupyter/lib/python3.12/site-packages (from ProjectiveGeometry23) (2.4.6)\n", "Requirement already satisfied: scipy in /home/aaichert/.virtualenvs/jupyter/lib/python3.12/site-packages (from ProjectiveGeometry23) (1.17.1)\n", "Requirement already satisfied: svg_snip in /home/aaichert/.virtualenvs/jupyter/lib/python3.12/site-packages (1.1.9)\n", "Requirement already satisfied: numpy>=1.21 in /home/aaichert/.virtualenvs/jupyter/lib/python3.12/site-packages (from svg_snip) (2.4.6)\n" ] } ], "source": [ "!pip install ipywidgets\n", "!pip install ipycanvas\n", "!pip install pynrrd\n", "!pip install pillow\n", "!pip install scikit-image\n", "!pip install rich\n", "\n", "# Libraries by Andre Aichert:\n", "!pip install ProjectiveGeometry23\n", "!pip install svg_snip\n", "\n", "# !python -m pip install -e /home/aaichert/nc_local/work/ProjectiveGeometry23/\n", "# !python -m pip install -e /home/aaichert/nc_local/work/svg_snip/\n", "\n", "!mkdir -p output" ] }, { "cell_type": "markdown", "id": "ee503047-f43f-4dab-b563-3f1a8512c87d", "metadata": {}, "source": [ "# Load Example Data" ] }, { "cell_type": "code", "execution_count": 3, "id": "2230d54e-987e-4a70-b7de-b3526264c6e7", "metadata": {}, "outputs": [], "source": [ "import nrrd\n", "\n", "I0, meta0 = nrrd.read(\"input/proj000.nrrd\")\n", "I1, meta1 = nrrd.read(\"input/proj040.nrrd\")\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "37c40121-157f-4a1d-a753-9b0d00bf0fd5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " ...,\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.]], shape=(1024, 760), dtype=float32)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "I0" ] }, { "cell_type": "code", "execution_count": 5, "id": "a4cbb983-86cd-4925-8f6a-902c08d983ef", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "OrderedDict([('dimension', 2),\n", " ('encoding', 'raw'),\n", " ('endian', 'little'),\n", " ('sizes', array([1024, 760])),\n", " ('spacings', array([0.308, 0.308])),\n", " ('type', 'float'),\n", " ('Projection Matrix',\n", " '[-506.148 -0 -3532.97 376726; -384 -3532.97 -0 285811; -1 -0 -0 744.3]')])" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "meta0" ] }, { "cell_type": "markdown", "id": "6dd3d564-0c5c-4156-b997-1efe28700f1c", "metadata": {}, "source": [ "# Show Projection Images" ] }, { "cell_type": "code", "execution_count": 6, "id": "a08ed734-ff3d-4fad-aa45-755e586702ee", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from PIL import Image\n", "\n", "def to_gray_image(img, scale=1.0, fast_axis_is_x=True):\n", " img = np.asarray(img, dtype=np.float32)\n", " if fast_axis_is_x:\n", " img = img.T # convert (x, y) → (y, x)\n", " img = (img - img.min()) / (img.max() - img.min())\n", " img = (img * 255).astype(np.uint8)\n", " im = Image.fromarray(img, mode=\"L\")\n", " if scale != 1.0:\n", " new_size = (int(im.width * scale), int(im.height * scale))\n", " im = im.resize(new_size, Image.BILINEAR)\n", " return im" ] }, { "cell_type": "code", "execution_count": 7, "id": "a94e2b2e-02d1-45bd-b74f-2424962687fb", "metadata": {}, "outputs": [ { "data": { "image/jpeg": 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", "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "to_gray_image(I0, .25)" ] }, { "cell_type": "code", "execution_count": 8, "id": "488eaae5-11f8-4bf0-8729-07390283da11", "metadata": {}, "outputs": [ { "data": { "image/jpeg": 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", "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "to_gray_image(I1, .25)" ] }, { "cell_type": "markdown", "id": "9b959a25-8bc8-4d62-9503-53c3acc2c645", "metadata": {}, "source": [ "# Projection Matrix" ] }, { "cell_type": "code", "execution_count": 9, "id": "210fac76-ea5d-482a-90b2-664e8412c225", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([[-5.06148e+02, -0.00000e+00, -3.53297e+03, 3.76726e+05],\n", " [-3.84000e+02, -3.53297e+03, -0.00000e+00, 2.85811e+05],\n", " [-1.00000e+00, -0.00000e+00, -0.00000e+00, 7.44300e+02]]),\n", " array([[-1.97544e+03, -0.00000e+00, 2.97249e+03, 3.76726e+05],\n", " [ 2.86441e+02, -3.53297e+03, 2.55749e+02, 2.85811e+05],\n", " [ 7.45941e-01, -0.00000e+00, 6.66012e-01, 7.44300e+02]]),\n", " array([0.308, 0.308]))" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "import numpy as np\n", "\n", "def parse_matrix(s):\n", " s = s.strip().replace('[', '').replace(']', '')\n", " rows = s.split(';')\n", " return np.array([[float(x) for x in row.split()] for row in rows])\n", "\n", "\n", "P0_np = parse_matrix(meta0['Projection Matrix'])\n", "P1_np = parse_matrix(meta1['Projection Matrix'])\n", "\n", "spacing = meta0['spacings']\n", "\n", "P0_np, P1_np, spacing" ] }, { "cell_type": "code", "execution_count": 10, "id": "09c2d25d-407c-4776-b57c-c7e45354e5c2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(ProjectionMatrix:\n", " [[-5.06148e+02 -0.00000e+00 -3.53297e+03 3.76726e+05]\n", " [-3.84000e+02 -3.53297e+03 -0.00000e+00 2.85811e+05]\n", " [-1.00000e+00 -0.00000e+00 -0.00000e+00 7.44300e+02]]\n", " Image Size: [1024 760]\n", " Pixel Spacing: 0.308,\n", " ProjectionMatrix:\n", " [[-1.97544e+03 -0.00000e+00 2.97249e+03 3.76726e+05]\n", " [ 2.86441e+02 -3.53297e+03 2.55749e+02 2.85811e+05]\n", " [ 7.45941e-01 -0.00000e+00 6.66012e-01 7.44300e+02]]\n", " Image Size: [1024 760]\n", " Pixel Spacing: 0.308)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from ProjectiveGeometry23.central_projection import ProjectionMatrix\n", "\n", "P0 = ProjectionMatrix(P0_np, I0.shape, pixel_spacing=spacing[0])\n", "P1 = ProjectionMatrix(P1_np, I1.shape, pixel_spacing=spacing[0])\n", "\n", "P0, P1" ] }, { "cell_type": "code", "execution_count": 11, "id": "6562f096-ce0d-4e69-b990-364850933444", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[31mInit signature:\u001b[39m ProjectionMatrix(P, image_size=(\u001b[32m400\u001b[39m, \u001b[32m300\u001b[39m), pixel_spacing=\u001b[32m1.0\u001b[39m)\n", "\u001b[31mDocstring:\u001b[39m \n", "\u001b[31mInit docstring:\u001b[39m\n", "Pinhole projection model as 3x4 projection matrix with optional\n", "information of detector size and pixel spacing [mm per px].\n", "\u001b[31mFile:\u001b[39m ~/.virtualenvs/jupyter/lib/python3.12/site-packages/ProjectiveGeometry23/central_projection.py\n", "\u001b[31mType:\u001b[39m type\n", "\u001b[31mSubclasses:\u001b[39m " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ProjectionMatrix?" ] }, { "cell_type": "code", "execution_count": 12, "id": "ff2cda03-e073-451e-b766-1a3308d7910e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "backproject\n", "computeFundamentalMatrix\n", "decomposition\n", "from_dict\n", "from_json\n", "getCenterOfProjection\n", "getDetectorAxisDirections\n", "getDetectorAxisDirectionsPx\n", "getDetectorPlane\n", "getFocalLengthPx\n", "getPrincipalPoint\n", "getPrincipalRay\n", "normalize\n", "perspective_look_at\n", "principal_ray\n", "pseudoinverse\n", "to_dict\n", "to_json\n", "to_ompl\n" ] } ], "source": [ "for f in dir(ProjectionMatrix):\n", " if not f.startswith('__'):\n", " print(f)" ] }, { "cell_type": "code", "execution_count": 13, "id": "0dfc630b-4e26-4a78-af3e-c7b9e25c4e15", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[-1.60102035e-10, 1.08105982e-03, -4.15126676e-01],\n", " [ 1.08106000e-03, -4.30956671e-18, -2.00411569e+00],\n", " [-4.15126960e-01, 9.09762940e-01, 4.20231307e+02]])" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "P0.computeFundamentalMatrix(P1)" ] }, { "cell_type": "code", "execution_count": null, "id": "b314c1b7-4e1a-4b81-8eed-82cccadde8d6", "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": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 5 }