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update notebooks and COLAB (DeepLabCut#2411)
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-59
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‎examples/COLAB/COLAB_3miceDemo.ipynb

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"- assemble animals and tracklets.\n",
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"- create quality check plots and video.\n",
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"\n",
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"### To create a full maDLC pipeline please see our full docs: https://deeplabcut.github.io/DeepLabCut/docs/intro.html \n",
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"### To create a full maDLC pipeline please see our full docs: https://deeplabcut.github.io/DeepLabCut/README.html\n",
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"- Of interest is a full how-to for maDLC: https://deeplabcut.github.io/DeepLabCut/docs/maDLC_UserGuide.html\n",
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"- a quick guide to maDLC: https://deeplabcut.github.io/DeepLabCut/docs/tutorial.html\n",
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"- a demo COLAB for how to use maDLC on your own data: https://github.com/DeepLabCut/DeepLabCut/blob/master/examples/COLAB_maDLC_TrainNetwork_VideoAnalysis.ipynb\n",
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"- a demo COLAB for how to use maDLC on your own data: https://github.com/DeepLabCut/DeepLabCut/blob/main/examples/COLAB/COLAB_maDLC_TrainNetwork_VideoAnalysis.ipynb\n",
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"\n",
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"### To get started, please go to \"Runtime\" ->\"change runtime type\"->select \"Python3\", and then select \"GPU\"\n"
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]

‎examples/COLAB/COLAB_YOURDATA_TrainNetwork_VideoAnalysis.ipynb

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"- create simple quality check plots\n",
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"- analyze novel videos!\n",
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"\n",
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"###This notebook assumes you already have a project folder with labeled data! \n",
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"### This notebook assumes you already have a project folder with labeled data! \n",
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"\n",
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"This notebook demonstrates the necessary steps to use DeepLabCut for your own project.\n",
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"\n",

‎examples/COLAB/COLAB_maDLC_TrainNetwork_VideoAnalysis.ipynb

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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"accelerator": "GPU",
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"colab": {
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"collapsed_sections": [],
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"include_colab_link": true,
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"name": "COLAB_maDLC_TrainNetwork_VideoAnalysis.ipynb",
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3",
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"name": "python3"
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}
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},
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"cells": [
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{
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"cell_type": "markdown",
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"- assemble animals and tracklets\n",
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"- create quality check plots!\n",
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"\n",
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"###This notebook assumes you already have a DLC project folder with labeled data and you uploaded it to your own Google Drive.\n",
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"### This notebook assumes you already have a DLC project folder with labeled data and you uploaded it to your own Google Drive.\n",
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"\n",
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"This notebook demonstrates the necessary steps to use DeepLabCut for your own project.\n",
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"\n",
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "-MVvZ13_FMvP"
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},
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"outputs": [],
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"source": [
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"#a few colab specific things needed:\n",
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"!pip install --upgrade scikit-image\n",
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"!pip3 install pickle5"
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],
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"execution_count": null,
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"outputs": []
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]
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},
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{
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"cell_type": "markdown",
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"id": "oTwAcbq2-FZz",
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "oTwAcbq2-FZz",
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"outputId": "9cfd8dcf-a0a8-4801-ed1d-fbcd5ec056af"
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"DLC loaded in light mode; you cannot use any GUI (labeling, relabeling and standalone GUI)\n"
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]
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" filtered=True)"
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]
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}
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]
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],
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"metadata": {
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"accelerator": "GPU",
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"colab": {
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"collapsed_sections": [],
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"include_colab_link": true,
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"name": "COLAB_maDLC_TrainNetwork_VideoAnalysis.ipynb",
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3",
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"name": "python3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}

‎examples/COLAB/COLAB_transformer_reID.ipynb

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"- use the transformer to do unsupervised ID tracking.\n",
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"- create quality check plots and video.\n",
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"\n",
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"### To create a full maDLC pipeline please see our full docs: https://deeplabcut.github.io/DeepLabCut/docs/intro.html \n",
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"### To create a full maDLC pipeline please see our full docs: https://deeplabcut.github.io/DeepLabCut/README.html\n",
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"- Of interest is a full how-to for maDLC: https://deeplabcut.github.io/DeepLabCut/docs/maDLC_UserGuide.html\n",
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"- a quick guide to maDLC: https://deeplabcut.github.io/DeepLabCut/docs/tutorial.html\n",
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"- a demo COLAB for how to use maDLC on your own data: https://github.com/DeepLabCut/DeepLabCut/blob/master/examples/COLAB_maDLC_TrainNetwork_VideoAnalysis.ipynb\n",
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"- a demo COLAB for how to use maDLC on your own data: https://github.com/DeepLabCut/DeepLabCut/blob/main/examples/COLAB/COLAB_maDLC_TrainNetwork_VideoAnalysis.ipynb\n",
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"\n",
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"### To get started, please go to \"Runtime\" ->\"change runtime type\"->select \"Python3\", and then select \"GPU\"\n"
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]

‎examples/JUPYTER/Demo_labeledexample_Openfield.ipynb

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},
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"outputs": [],
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"source": [
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"%gui wx\n",
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"%gui qt6\n",
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"deeplabcut.refine_labels(path_config_file)"
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]
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},

‎examples/JUPYTER/Demo_napari.ipynb

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"outputs": [],
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"source": [
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"# napari will pop up! Please go to plugin > deeplabcut to start:\n",
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"%gui qt5\n",
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"%gui qt6\n",
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"import napari\n",
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"napari.Viewer()"
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]
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"source": [
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"#now you can edit the \"machine-labeled file\" within napari; \n",
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"#just again drop the file and images into the workspace after you load the plugin\n",
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"%gui qt5\n",
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"%gui qt6\n",
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"import napari\n",
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"napari.Viewer()"
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]

‎examples/JUPYTER/Demo_yourowndata.ipynb

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},
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{
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"cell_type": "code",
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"execution_count": 0,
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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},
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{
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"cell_type": "code",
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"execution_count": 0,
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"deeplabcut.extract_frames(path_config_file) "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#AND/OR:\n",
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"#SELECT RARE EVENTS MANUALLY:\n",
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"%gui wx\n",
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"deeplabcut.extract_frames(path_config_file,'manual')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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},
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{
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"cell_type": "code",
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"execution_count": 0,
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "iyROSOiEoEJI"
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},
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"outputs": [],
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"source": [
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"%gui wx\n",
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"deeplabcut.label_frames(path_config_file)"
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"# napari will pop up!\n",
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"# Please go to plugin > deeplabcut to start\n",
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"# then, drag-and-drop the project configuration file into the viewer (the value of path_config_file)\n",
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"# finally, drop the folder containing the images (in 'labeled-data') in the viewer\n",
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"\n",
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"%gui qt6\n",
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"import napari\n",
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"napari.Viewer()"
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]
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},
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{
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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},
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{
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"cell_type": "code",
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"execution_count": 0,
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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},
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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},
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"colab_type": "code",
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {},
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"colab_type": "code",
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"id": "n_FpEXtyoEJy"
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},
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"outputs": [],
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"source": [
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"%gui wx\n",
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"%gui qt6\n",
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"deeplabcut.refine_labels(path_config_file)"
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]
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},
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},
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},
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"metadata": {
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"colab": {},
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"colab_type": "code",

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