Created
January 12, 2023 12:18
-
-
Save leoimpett/df3695225fb4b70310b103267fda6276 to your computer and use it in GitHub Desktop.
Autogenerated from www.imagegraph.cc, My sketch title
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
{"caption":"My sketch title","offset":{"x":0,"y":0},"nodes":{"49697015-d6a0-4165-bc1a-2e783334599e":{"id":"49697015-d6a0-4165-bc1a-2e783334599e","position":{"x":305,"y":186},"type":"loadIIIFManifest","ports":{"port1":{"id":"port1","type":"bottom","properties":{"type":"imL"},"position":{"x":24,"y":107}}},"properties":{"text":"Loads a IIIF manifest. Paste in any IIIF manifest URL, which you can get from most libraries. ","form":"string","innerDefault":"https://iiif.bodleian.ox.ac.uk/iiif/manifest/e32a277e-91e2-4a6d-8ba6-cc4bad230410.json","viewer":"https://universalviewer.io/uv.html?manifest=","innerValue":"https://iiif.bodleian.ox.ac.uk/iiif/manifest/e32a277e-91e2-4a6d-8ba6-cc4bad230410.json"}},"4363d967-fa60-4a34-b2ab-0ba732d20a44":{"id":"4363d967-fa60-4a34-b2ab-0ba732d20a44","position":{"x":404,"y":316},"type":"getNNEmbedding","ports":{"port1":{"id":"port1","type":"top","properties":{"type":"imL"},"position":{"x":24,"y":-12}},"port2":{"id":"port2","type":"bottom","properties":{"type":"vL"},"position":{"x":24,"y":60}}},"properties":{"text":"Extracts the second-to-last-layer neural network embedding for each image fed in; outputs them as a list of vectors","form":"none","colabform":"True","preform":"myModel = \"vgg16\" #@param [\"vgg16\", \"inceptionv3\", \"efficientnet\"]","argform":"modelType = myModel"}},"ccb411b9-75cd-4ac2-a67f-0ad036168ed2":{"id":"ccb411b9-75cd-4ac2-a67f-0ad036168ed2","position":{"x":456,"y":435},"type":"reduceDims","ports":{"port1":{"id":"port1","type":"top","properties":{"type":"vL"},"position":{"x":24,"y":-12}},"port2":{"id":"port2","type":"bottom","properties":{"type":"fL"},"position":{"x":24,"y":60}},"port3":{"id":"port3","type":"bottom","properties":{"type":"fL"},"position":{"x":156,"y":60}}},"properties":{"text":"Reduce dimensions to 2 using TSNE, UMAP, or PCA. ","form":"none","colabform":"True","preform":"myMethod = \"TSNE\" #@param [\"TSNE\", \"UMAP\", \"PCA\"]","argform":"method = myMethod"}},"a722a7bc-6e35-4173-9c30-152ad4807138":{"id":"a722a7bc-6e35-4173-9c30-152ad4807138","position":{"x":401,"y":573},"type":"scatterImages","ports":{"port1":{"id":"port1","type":"top","properties":{"type":"imL"},"position":{"x":24,"y":-12}},"port2":{"id":"port2","type":"top","properties":{"type":"fL"},"position":{"x":90,"y":-12}},"port3":{"id":"port3","type":"top","properties":{"type":"fL"},"position":{"x":156,"y":-12}}},"properties":{"text":"A *direct visualization* of an image list on an x-y grid. ","form":"none"}}},"selected":{"type":"node","id":"4363d967-fa60-4a34-b2ab-0ba732d20a44"},"links":{"43e4a429-8af1-44ac-a195-f0d7ca483ee9":{"id":"43e4a429-8af1-44ac-a195-f0d7ca483ee9","from":{"nodeId":"49697015-d6a0-4165-bc1a-2e783334599e","portId":"port1"},"to":{"nodeId":"4363d967-fa60-4a34-b2ab-0ba732d20a44","portId":"port1"}},"28598d0d-78f1-4682-9cbc-bec095933ee2":{"id":"28598d0d-78f1-4682-9cbc-bec095933ee2","from":{"nodeId":"4363d967-fa60-4a34-b2ab-0ba732d20a44","portId":"port2"},"to":{"nodeId":"ccb411b9-75cd-4ac2-a67f-0ad036168ed2","portId":"port1"}},"12e5812f-da34-4c02-9fc1-bc961b0a1970":{"id":"12e5812f-da34-4c02-9fc1-bc961b0a1970","from":{"nodeId":"ccb411b9-75cd-4ac2-a67f-0ad036168ed2","portId":"port2"},"to":{"nodeId":"a722a7bc-6e35-4173-9c30-152ad4807138","portId":"port2"}},"8c91381c-1441-4127-b00c-7d675c74ac41":{"id":"8c91381c-1441-4127-b00c-7d675c74ac41","from":{"nodeId":"ccb411b9-75cd-4ac2-a67f-0ad036168ed2","portId":"port3"},"to":{"nodeId":"a722a7bc-6e35-4173-9c30-152ad4807138","portId":"port3"}},"5a46e11a-ede0-4a9e-863c-45aa3e2a9fc2":{"id":"5a46e11a-ede0-4a9e-863c-45aa3e2a9fc2","from":{"nodeId":"49697015-d6a0-4165-bc1a-2e783334599e","portId":"port1"},"to":{"nodeId":"a722a7bc-6e35-4173-9c30-152ad4807138","portId":"port1"}}},"hovered":{}} |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "ImageGraph.ipynb", "version": "0.3.2", "provenance": [], "collapsed_sections": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" } }, "cells": [ { "cell_type": "code", "metadata": { "id": "gsUbIGfNqSjV", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "outputId": "a306f9c8-c721-4280-fba4-f175cffa7090" }, "source": [ " !pip install -q -U git+https:\/\/github.com\/leoimpett\/pyimagegraph\nimport imagegraph as ig\nimL00 = ig.loadIIIFManifest(\"https:\/\/iiif.bodleian.ox.ac.uk\/iiif\/manifest\/e32a277e-91e2-4a6d-8ba6-cc4bad230410.json\")\nmyModel = \"vgg16\" #@param [\"vgg16\", \"inceptionv3\", \"efficientnet\"]\nvL01 = ig.getNNEmbedding(imL00,modelType = myModel)\nmyMethod = \"TSNE\" #@param [\"TSNE\", \"UMAP\", \"PCA\"]\nfL02,fL03 = ig.reduceDims(vL01,method = myMethod)\n_ = ig.scatterImages(imL00,fL02,fL03) " ], "execution_count": 0, "outputs": [ { "output_type": "stream", "text": [ " " ], "name": "stdout" } ] } ]} |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment