{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# ImageNode\n", "The ImageNode class in Geomapi represents the data and metadata of image data. The data itself and methods build upon Open3D and OpenCV concepts while the metadata builds upon the RDFlib framework:\n", "\n", "[https://docs.opencv.org/4.x/d3/df2/tutorial_py_basic_ops.html](https://docs.opencv.org/4.x/d3/df2/tutorial_py_basic_ops.html)\n", "\n", "[https://rdflib.readthedocs.io/](https://docs.opencv.org/4.x/d3/df2/tutorial_py_basic_ops.html)\n", "\n", "The code below shows how to create a ImageNode from various inputs. " ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "#IMPORT PACKAGES\n", "from rdflib import Graph, URIRef, Literal, RDF\n", "import open3d as o3d\n", "import os\n", "from pathlib import Path\n", "import cv2\n", "from PIL import Image\n", "import matplotlib.pyplot as plt\n", "\n", "\n", "#IMPORT MODULES\n", "from context import geomapi \n", "from geomapi.nodes import *\n", "import geomapi.utils as ut\n", "from geomapi.utils import geometryutils as gmu\n", "import geomapi.tools as tl" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The autoreload extension is already loaded. To reload it, use:\n", " %reload_ext autoreload\n" ] } ], "source": [ "%load_ext autoreload" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [], "source": [ "%autoreload 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ImageNode from properties" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A placeholder ImageNode can be initialised without any data or metadata." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'_xmlPath': None,\n", " '_xmpPath': None,\n", " 'imageWidth': None,\n", " 'imageHeight': None,\n", " 'focalLength35mm': None,\n", " '_subject': rdflib.term.URIRef('file:///myNode'),\n", " '_graph': None,\n", " '_graphPath': None,\n", " '_path': None,\n", " '_name': 'myName',\n", " '_timestamp': None,\n", " '_resource': None,\n", " '_cartesianTransform': None}" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "node=ImageNode(subject='myNode',\n", " name='myName')\n", "{key:value for key, value in node.__dict__.items() if not key.startswith('__') and not callable(key)} " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ImageNode from Path" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Instead, it is much more likely to initialise a ImageNode from a path containing an image file. This sets the:
\n", "1. subject\n", "2. name\n", "3. timestamp\n", "4. path\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'_xmlPath': None,\n", " '_xmpPath': None,\n", " 'imageWidth': 5472,\n", " 'imageHeight': 3078,\n", " 'focalLength35mm': None,\n", " '_subject': rdflib.term.URIRef('file:///DJI_0067'),\n", " '_graph': None,\n", " '_graphPath': None,\n", " '_path': 'd:\\\\Scan-to-BIM repository\\\\geomapi\\\\test\\\\testfiles\\\\IMG\\\\DJI_0067.JPG',\n", " '_name': 'DJI_0067',\n", " '_timestamp': '2018-06-06T15:21:17',\n", " '_resource': None,\n", " '_cartesianTransform': None,\n", " 'resolutionUnit': 2,\n", " 'geospatialTransform': [51.05982777777778, 3.7198242777777777, 1.814],\n", " 'coordinateSystem': 'geospatial-wgs84'}" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "filePath=os.path.join(Path(os.getcwd()).parents[2],'test','testfiles','IMG','DJI_0067.JPG')\n", "node=ImageNode(path=filePath)\n", "{key:value for key, value in node.__dict__.items() if not key.startswith('__') and not callable(key)} " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Additionally, any relevant properties from the EXIF data will be stored such as the geospatial transform. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ImageNode from Structure-from-Motion pipelines\n", "\n", "As GEOMAPI is a geomatics API, the cartesianTransform of nodes is crucial to many of its functionality. For images, this is generally derived from Structure-from-Motion (SfM) pipelines. These pipelines allign the images in a common coordinate system of which the geospatial components are stored in software specific formats.\n", "\n", "
\n", "\n", "GEOMAPI currently supports two formats. [Agisoft Metashape](https://www.agisoft.com/) and [Capturing Reality](https://www.capturingreality.com/)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Capturing Reality\n", "To import the pose of an image from the Capturing Reality pipeline, one should export the coordinate information of the image to an [XMP](https://support.capturingreality.com/hc/en-us/articles/360012410660-Coordinate-System-Preservation-with-XMPs-Full-body-Scans) file. These files are xml formatted and stored per image." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![rendering](../../pics/capturingreality1.PNG)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'_xmlPath': None,\n", " '_xmpPath': 'd:\\\\Scan-to-BIM repository\\\\geomapi\\\\test\\\\testfiles\\\\IMG\\\\IMG_2173.xmp',\n", " 'imageWidth': 5616,\n", " 'imageHeight': 3744,\n", " 'focalLength35mm': 24.3771542355288,\n", " '_subject': rdflib.term.URIRef('file:///IMG_2173'),\n", " '_graph': None,\n", " '_graphPath': None,\n", " '_path': 'd:\\\\Scan-to-BIM repository\\\\geomapi\\\\test\\\\testfiles\\\\IMG\\\\IMG_2173.JPG',\n", " '_name': 'IMG_2173',\n", " '_timestamp': '2022-03-13T13:55:26',\n", " '_resource': None,\n", " '_cartesianTransform': array([[-0.05442451, 0.08978218, 0.99447329, -8.94782375],\n", " [-0.78368672, -0.62101649, 0.01317728, 11.25314019],\n", " [ 0.6187674 , -0.77863835, 0.10415962, 6.54284524],\n", " [ 0. , 0. , 0. , 1. ]]),\n", " 'coordinateSystem': 'geospatial-wgs84',\n", " 'principalPointU': -0.00291855667677505,\n", " 'principalPointV': -0.00415035446181888,\n", " 'distortionCoeficients': [-0.126115439984335,\n", " 0.0981832072267781,\n", " 0.0312044509604729,\n", " 0.0,\n", " 0.0,\n", " 0.0],\n", " 'resolutionUnit': 2,\n", " 'geospatialTransform': [None, None, None]}" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xmpPath=os.path.join(Path(os.getcwd()).parents[2],'test','testfiles','IMG','IMG_2173.xmp')\n", "node=ImageNode(xmpPath=xmpPath)\n", "{key:value for key, value in node.__dict__.items() if not key.startswith('__') and not callable(key)} " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "which yields cartesianTransform and some internal camera parameters such as the focalLength35mm, which is needed for digital renders. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Agisoft Metashape\n", "Alternatively, Metashape exports its camera poses and properties in a common [XML file ](https://www.agisoft.com/forum/index.php?topic=6211.0). Therefore, one should provide both the subject in the form of the camera label, and the XML file." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'_xmlPath': 'd:\\\\Scan-to-BIM repository\\\\geomapi\\\\test\\\\testfiles\\\\IMG\\\\ReferenceLAMBERT08_TAW.xml',\n", " '_xmpPath': None,\n", " 'imageWidth': 5472,\n", " 'imageHeight': 3648,\n", " 'focalLength35mm': None,\n", " '_subject': rdflib.term.URIRef('file:///101_0366_0037'),\n", " '_graph': None,\n", " '_graphPath': None,\n", " '_path': 'd:\\\\Scan-to-BIM repository\\\\geomapi\\\\test\\\\testfiles\\\\IMG\\\\101_0366_0037.jpg',\n", " '_name': '101_0366_0037',\n", " '_timestamp': '2021-06-07T16:49:33',\n", " '_resource': None,\n", " '_cartesianTransform': array([[-9.02585284e-01, -4.30511097e-01, 0.00000000e+00,\n", " 6.00578072e+05],\n", " [ 4.30511097e-01, -9.02585284e-01, 0.00000000e+00,\n", " 6.96277256e+05],\n", " [ 0.00000000e+00, 0.00000000e+00, 1.00000000e+00,\n", " 3.13904134e+01],\n", " [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", " 1.00000000e+00]]),\n", " 'sxy': 0.02,\n", " 'sz': 0.05,\n", " 'resolutionUnit': 2,\n", " 'geospatialTransform': [51.074569000007855,\n", " 3.6636115834699305,\n", " 74.1250002610359],\n", " 'coordinateSystem': 'geospatial-wgs84'}" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xmlPath=os.path.join(Path(os.getcwd()).parents[2],'test','testfiles','IMG','ReferenceLAMBERT08_TAW.xml')\n", "node=ImageNode(subject='101_0366_0037',xmlPath=xmlPath)\n", "{key:value for key, value in node.__dict__.items() if not key.startswith('__') and not callable(key)} " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "which also yields the accuracies of the SfM pose estimation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![rendering](../../pics/Georeferencedoutput.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ImageNode with getResource" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Given the above XMP or XML files, all metadata of the node can be set. However, one can call getResource to also load the image." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**NOTE**: GetResource is optional and might slow down any analysis. Only work with data when all metadata options have been exhausted." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "xmpPath=os.path.join(Path(os.getcwd()).parents[2],'test','testfiles','IMG','IMG_7257.xmp')\n", "node=ImageNode(xmpPath=xmpPath, getResource=True)\n", "print(type(node.resource))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![rendering](../../pics/IMG_7257.JPG)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ImageNode from resource" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A similar result is achieved by initialising a ImageNode from a PIL, Open3D or nd.array(OPENCV) instance. In this case, GetResource (bool) means nothing. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "OpenCV images are stored as np.ndarrays " ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "filePath=os.path.join(Path(os.getcwd()).parents[2],'test','testfiles','IMG','IMG_202200706_00___70_.png')\n", "img=cv2.imread(filePath) \n", "node=ImageNode(resource=img)\n", "print(type(node.resource))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "PIL images are a seperate class but are transferred to np.ndarrays" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "filePath=os.path.join(Path(os.getcwd()).parents[2],'test','testfiles','IMG','IMG_202200706_00___70_.png')\n", "img=Image.open( filePath) \n", "node=ImageNode(resource=img)\n", "print(type(node.resource))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, Open3D also has a seperate class but also uses np.ndarrays to buffer the data" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Image of size 5472x3078, with 3 channels.\n", "Use numpy.asarray to access buffer data.\n", "\n" ] } ], "source": [ "filePath=os.path.join(Path(os.getcwd()).parents[2],'test','testfiles','IMG','IMG_202200706_00___70_.png')\n", "img=o3d.io.read_image( filePath) \n", "print(img)\n", "node=ImageNode(resource=img)\n", "print(type(node.resource))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ImageNode from Graph and graphPath" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If a Image was already serialized, a node can be initialised from the graph or graphPath. \n", "\n", "**NOTE**: The graphPath is the more complete option as it is used to absolutize the node's path information. However, it is also the slower option as the entire graph encapsulation the node is parsed multiple times.\n", "\n", "**USE**: linkeddatatools.graph_to_nodes resolves this issue." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "@prefix e57: .\n", "@prefix exif: .\n", "@prefix gom: .\n", "@prefix openlabel: .\n", "@prefix v4d: .\n", "@prefix xsd: .\n", "\n", " a v4d:ImageNode ;\n", " e57:cartesianTransform \"\"\"[[-9.02585284e-01 -4.30511097e-01 0.00000000e+00 6.00578072e+05]\n", " [ 4.30511097e-01 -9.02585284e-01 0.00000000e+00 6.96277256e+05]\n", " [ 0.00000000e+00 0.00000000e+00 1.00000000e+00 3.13904134e+01]\n", " [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\"\"\" ;\n", " e57:geospatialTransform \"[51.074569000007855, 3.6636115834699305, 74.1250002610359]\" ;\n", " exif:imageHeight 3648 ;\n", " exif:imageWidth 5472 ;\n", " exif:resolutionUnit 2 ;\n", " gom:coordinateSystem \"geospatial-wgs84\" ;\n", " v4d:name \"101_0366_0037\" ;\n", " v4d:path \"IMG\\\\101_0366_0037.jpg\" ;\n", " v4d:sxy \"0.02\"^^xsd:float ;\n", " v4d:sz \"0.05\"^^xsd:float ;\n", " v4d:xmlPath \"IMG\\\\ReferenceLAMBERT08_TAW.xml\" ;\n", " openlabel:timestamp \"2021-06-07T16:49:33\" .\n", "\n", "\n" ] } ], "source": [ "graphPath = os.path.join(Path(os.getcwd()).parents[2],'test','testfiles','imgGraph.ttl')\n", "graph=Graph().parse(graphPath)\n", "\n", "#only print first node\n", "newGraph=Graph()\n", "newGraph=ut.bind_ontologies(newGraph)\n", "newGraph+=graph.triples((URIRef('file:///101_0366_0037'),None,None))\n", "print(newGraph.serialize())" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'_xmlPath': 'd:\\\\Scan-to-BIM repository\\\\geomapi\\\\test\\\\testfiles\\\\IMG\\\\ReferenceLAMBERT08_TAW.xml',\n", " '_xmpPath': None,\n", " 'imageWidth': 5472.0,\n", " 'imageHeight': 3648.0,\n", " 'focalLength35mm': None,\n", " '_subject': rdflib.term.URIRef('file:///101_0366_0036'),\n", " '_graph': )>,\n", " '_graphPath': 'd:\\\\Scan-to-BIM repository\\\\geomapi\\\\test\\\\testfiles\\\\imgGraph.ttl',\n", " '_path': 'd:\\\\Scan-to-BIM repository\\\\geomapi\\\\test\\\\testfiles\\\\IMG\\\\101_0366_0036.jpg',\n", " '_name': '101_0366_0036',\n", " '_timestamp': '2021-06-07T16:49:28',\n", " '_resource': None,\n", " '_cartesianTransform': array([[-9.01077021e-01, -4.33659085e-01, 0.00000000e+00,\n", " 6.00575993e+05],\n", " [ 4.33659085e-01, -9.01077021e-01, 0.00000000e+00,\n", " 6.96281654e+05],\n", " [ 0.00000000e+00, 0.00000000e+00, 1.00000000e+00,\n", " 3.13845765e+01],\n", " [ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n", " 1.00000000e+00]]),\n", " 'type': 'https://w3id.org/v4d/core#ImageNode',\n", " 'geospatialTransform': array([51.07460836, 3.66358133, 74.11900087]),\n", " 'resolutionUnit': 2,\n", " 'coordinateSystem': 'geospatial-wgs84',\n", " 'sxy': 0.02,\n", " 'sz': 0.05}" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "node=ImageNode(graphPath=graphPath)\n", "{key:value for key, value in node.__dict__.items() if not key.startswith('__') and not callable(key)}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ImageNode to Graph" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Graph serialisation is inherited from Node functionality." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "@prefix exif: .\n", "@prefix openlabel: .\n", "@prefix v4d: .\n", "@prefix xsd: .\n", "\n", " a v4d:ImageNode ;\n", " exif:imageHeight 3078 ;\n", " exif:imageWidth 5472 ;\n", " v4d:name \"IMG_202200706_00___70_\" ;\n", " v4d:path \"..\\\\..\\\\..\\\\test\\\\testfiles\\\\IMG\\\\IMG_202200706_00___70_.png\" ;\n", " openlabel:timestamp \"2022-08-08T14:32:56\" .\n", "\n", "\n" ] } ], "source": [ "node=ImageNode(subject='myNode',\n", " path=filePath,\n", " getResource=True)\n", "\n", "newGraphPath = os.path.join(os.getcwd(),'myGraph.ttl')\n", "node.to_graph(newGraphPath)\n", "\n", "newNode=Node(graphPath=newGraphPath)\n", "print(node.graph.serialize())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ImageNode analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "ImageNodes can be attributed with a range of relationships. This is extremely usefull for Graph navigation and linking together different resources. In [Semantic Web Technologies](https://rdflib.readthedocs.io/en/stable/intro_to_creating_rdf.html), relationships are defined by triples that have other subjects as literals. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this first example, we create both virtual images of the BIM and a mesh from the same location as a geolocalised image. " ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TriangleMesh with 330263 points and 485077 triangles.\n" ] } ], "source": [ "meshNode=MeshNode(path=os.path.join(Path(os.getcwd()).parents[2],'test','testfiles','MESH','week22.obj'),getResource=True)\n", "print(meshNode.resource)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[Open3D WARNING] Unable to load file d:\\Scan-to-BIM repository\\geomapi\\test\\testfiles\\282_SC_f2_Round:Ø30:883870.obj with ASSIMP\n", "[Open3D WARNING] Unable to load file d:\\Scan-to-BIM repository\\geomapi\\test\\testfiles\\B1_CL.obj with ASSIMP\n", "[Open3D WARNING] Unable to load file d:\\Scan-to-BIM repository\\geomapi\\test\\testfiles\\Default Grid.obj with ASSIMP\n", "[Open3D WARNING] Unable to load file d:\\Scan-to-BIM repository\\geomapi\\test\\testfiles\\Precast Stair:Stair:1194239.obj with ASSIMP\n", "[Open3D WARNING] Unable to load file d:\\Scan-to-BIM repository\\geomapi\\test\\testfiles\\Precast Stair:Stair:1195754.obj with ASSIMP\n", "100\n" ] } ], "source": [ "bimGraphPath= os.path.join(Path(os.getcwd()).parents[2],'test','testfiles','bimGraph1.ttl')\n", "bimGraph=Graph().parse(bimGraphPath)\n", "bimNodes=[]\n", "for s in bimGraph.subjects(RDF.type):\n", " bimNodes.append(BIMNode(subject=s,graphPath=bimGraphPath,getResource=True))\n", "print(len(bimNodes))\n", "geometry=gmu.join_geometries([n.resource for n in bimNodes])" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "[[-9.73302276e-01 2.28468791e-01 2.20156949e-02 4.27609137e+01]\n", " [ 3.17698956e-02 2.29092449e-01 -9.72886079e-01 9.87548132e+01]\n", " [-2.27317735e-01 -9.46212799e-01 -2.30234632e-01 5.42623981e+00]\n", " [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00]]\n" ] } ], "source": [ "xmpPath=os.path.join(Path(os.getcwd()).parents[2],'test','testfiles','IMG','IMG_7255.xmp')\n", "node=ImageNode(xmpPath=xmpPath,getResource=True)\n", "print(type(node.resource))\n", "print(node.cartesianTransform)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Downsampling is advised as geometry repositories typically are not as dense as actual image footage." ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [], "source": [ "im=node.get_virtual_image(meshNode.resource, downsampling=2)" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.subplot(1, 2, 1)\n", "plt.title('Real image')\n", "plt.imshow(node.resource)\n", "plt.subplot(1, 2, 2)\n", "plt.title('Virtual image')\n", "plt.imshow(im)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**NOTE**: there is a rotational error on the virtual image, perhaps due to a misalignemtn of the mesh or the image within the structure-from-motion pipeline." ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "@prefix omg: .\n", "@prefix v4d: .\n", "\n", " a v4d:ImageNode ;\n", " omg:isDerivedFromGeometry \"file:///week22\" ;\n", " v4d:analysisTimestamp \"2022-08-02T08:25:01\" ;\n", " v4d:isDerivedFromImage \"file:///IMG_7255\" .\n", "\n", "\n" ] } ], "source": [ "virtualNode=ImageNode(subject=node.subject.toPython() + '-virtual',\n", " isDerivedFromGeometry=meshNode.subject,\n", " isDerivedFromImage=node.subject,\n", " analysisTimestamp=meshNode.timestamp)\n", "virtualNode.to_graph()\n", "print(virtualNode.graph.serialize())" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3.8.13 ('conda_environment3')", "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": "3.8.13" }, "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "801b4083378541fd050d6c91abf6ec053c863905e8162e031d57b83e7cdb3051" } } }, "nbformat": 4, "nbformat_minor": 2 }