{ "cells": [ { "cell_type": "markdown", "id": "7ed71dc7-d4b0-45fb-82fe-33824e4ceea7", "metadata": {}, "source": [ "# Load Example Data (see 01_getting_started.ipnb)" ] }, { "cell_type": "code", "execution_count": 1, "id": "2230d54e-987e-4a70-b7de-b3526264c6e7", "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": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import nrrd\n", "import numpy as np\n", "from PIL import Image\n", "from ProjectiveGeometry23.central_projection import ProjectionMatrix\n", "\n", "\n", "def to_gray_image(img, scale=1.0, fast_axis_is_x=True):\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\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", "I0, meta0 = nrrd.read(\"input/proj000.nrrd\")\n", "I1, meta1 = nrrd.read(\"input/proj040.nrrd\")\n", "\n", "P0_np = parse_matrix(meta0['Projection Matrix'])\n", "P1_np = parse_matrix(meta1['Projection Matrix'])\n", "\n", "spacing = meta0['spacings']\n", "\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": "markdown", "id": "d4daeaa2-3ce6-41bb-a09f-6f593e80b663", "metadata": {}, "source": [ "# Visualizing Projection Geometry" ] }, { "cell_type": "code", "execution_count": 2, "id": "11a0730e-e320-479c-bbd7-583664a79ede", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[31mSignature:\u001b[39m svg_world_geometry(P, composer=\u001b[38;5;28;01mNone\u001b[39;00m, **kwargs)\n", "\u001b[31mDocstring:\u001b[39m\n", "Draw a coordinate system of size 100 and a wire cube\n", "of the same size centered in the origin.\n", "\u001b[31mFile:\u001b[39m ~/.virtualenvs/jupyter/lib/python3.12/site-packages/ProjectiveGeometry23/svg_utils.py\n", "\u001b[31mType:\u001b[39m function" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from ProjectiveGeometry23.svg_utils import svg_world_geometry, svg_coordinate_frame\n", "svg_world_geometry?" ] }, { "cell_type": "code", "execution_count": 3, "id": "25657733-ed9b-4f2c-b905-eccf47ee0ba2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[31mInit signature:\u001b[39m\n", "SourceDetectorGeometry(\n", " projection: ProjectiveGeometry23.central_projection.ProjectionMatrix,\n", ")\n", "\u001b[31mDocstring:\u001b[39m \n", "\u001b[31mInit docstring:\u001b[39m\n", "From a 3x4 projection matrix and pixel spacing, compute physical location of detector.\n", "This allows for a full visualization of a source-detector geometry as a pyramid or frustum.\n", "In case you have a left-handed coordinate frame, use negative pixel spacing to swap the\n", "viewing direction of the system (i.e. negate pixel spacing if projection looks backwards.\n", "\u001b[31mFile:\u001b[39m ~/.virtualenvs/jupyter/lib/python3.12/site-packages/ProjectiveGeometry23/source_detector_geometry.py\n", "\u001b[31mType:\u001b[39m type\n", "\u001b[31mSubclasses:\u001b[39m " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from ProjectiveGeometry23.source_detector_geometry import SourceDetectorGeometry\n", "SourceDetectorGeometry?" ] }, { "cell_type": "code", "execution_count": 4, "id": "1fce66c4-d64e-423d-b0bc-e0ac9653c5ce", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\u001b[31mSignature:\u001b[39m\n", "svg_source_detector(\n", " P,\n", " projection: ProjectiveGeometry23.central_projection.ProjectionMatrix,\n", " draw_on_detector=\u001b[38;5;28;01mNone\u001b[39;00m,\n", " composer=\u001b[38;5;28;01mNone\u001b[39;00m,\n", " **kwargs,\n", ")\n", "\u001b[31mDocstring:\u001b[39m\n", "Draw X-ray source-detector geometry.\n", "Define draw_on_detector as any SVG drawing function to project\n", "additional 3D geometry to the detector plane.\n", "\u001b[31mFile:\u001b[39m ~/.virtualenvs/jupyter/lib/python3.12/site-packages/ProjectiveGeometry23/svg_utils.py\n", "\u001b[31mType:\u001b[39m function" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from ProjectiveGeometry23.svg_utils import svg_source_detector\n", "svg_source_detector?" ] }, { "cell_type": "markdown", "id": "e3a7d20b-6933-459e-ac61-a2d1976e70fe", "metadata": {}, "source": [ "# Visualizing a projectoin image with overlay" ] }, { "cell_type": "code", "execution_count": 5, "id": "d2d0c71d-c6c3-48ad-996a-58dc7692e76a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from svg_snip.Composer import Composer\n", "import svg_snip.Elements as e2d\n", "import svg_snip.Elements3D as e3d\n", "\n", "svg = Composer(to_gray_image(I1))\n", "svg.add(e3d.cube, min=[-50,-50,-50], max=[50,50,50])\n", "svg.add(svg_world_geometry)\n", "svg.scale=0.5\n", "svg.display(P=P1.P, stroke_width=2, head='arrow-large')" ] }, { "cell_type": "code", "execution_count": 6, "id": "ff4adf52-c848-4839-9881-2751b98c4ef0", "metadata": {}, "outputs": [], "source": [ "with open('output/example_overlay.svg', 'w') as file:\n", " file.write(svg.render(P=P1.P, stroke_width=2, head='arrow-large'))\n" ] }, { "cell_type": "markdown", "id": "7b899055-ea9c-4960-97fc-df8fd299da61", "metadata": {}, "source": [ "# Visualize Geometry of Both Projections" ] }, { "cell_type": "code", "execution_count": 7, "id": "63907b66-b471-4481-a80d-7992929bf064", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "fc14a9967e1740e39272c80244b14ec6", "version_major": 2, "version_minor": 0 }, "text/plain": [ "FloatSlider(value=0.0, description='angle', max=3.14, min=-3.14, step=0.06343434343434344)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5156efe40c694dd8ac0f1dbcff6ec81a", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value='\\n