diff --git a/module2/exo4/.ipynb b/module2/exo4/.ipynb deleted file mode 100644 index c09cd57ba5258f3e62b6bcff220867edc94954fa..0000000000000000000000000000000000000000 --- a/module2/exo4/.ipynb +++ /dev/null @@ -1,38 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "***Objectif:\n", - "\n", - "- Référencer quelques sites internets fournissant des jeux de données \"datasets\"\n", - "- Extraire un résumé, les formats de fichiers fournis ?\n", - "- Extaire des mots clés, des étiquettes décrivant ces datasets ?\n", - "- Créer quelques statistiques de base de ces datasets ?\n", - "- Créer une représentation graphique de ces datasets ?\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "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": "3.6.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/module2/exo4/datasets.csv b/module2/exo4/datasets.csv new file mode 100644 index 0000000000000000000000000000000000000000..105c01791e946f538dfc5bc5ca3166434edffd1d --- /dev/null +++ b/module2/exo4/datasets.csv @@ -0,0 +1,7 @@ +name;description;website;tags +KITTI Vision Benchmark Suite;We take advantage of our autonomous driving platform Annieway to develop novel challenging real-world computer vision benchmarks. Our tasks of interest are: stereo, optical flow, visual odometry, 3D object detection and 3D tracking;http://www.cvlibs.net/datasets/kitti/;stereo,flow,odometry,tracking,detection,road,maps,city +Audi Autonomous Driving Dataset;We have published the Audi Autonomous Driving Dataset (A2D2) to support startups and academic researchers working on autonomous driving. Equipping a vehicle with a multimodal sensor suite, recording a large dataset, and labelling it, is time and labour intensive.;https://www.a2d2.audi/a2d2/en.html;semantic,cloud,segmentation,detection,road,maps,city +ApolloScape Dataset;Trajectory dataset, 3D Perception Lidar Object Detection and Tracking dataset including about 100K image frames, 80k lidar point cloud and 1000km trajectories for urban traffic. The dataset consisting of varying conditions and traffic densities which includes many challenging scenarios where vehicles, bicycles, and pedestrians move among one another.;http://apolloscape.auto/;stereo,flow,semantic,cloud,segmentation,detection,road,maps,city +Velodyne SLAM;Here, you can find two challenging datasets recorded with the Velodyne HDL64E-S2 scanner in the city of Karlsruhe, Germany.;http://www.mrt.kit.edu/z/publ/download/velodyneslam/dataset.html; +Daimler Urban Segmentation Dataset;The Daimler Urban Segmentation Dataset consists of video sequences recorded in urban traffic. The dataset consists of 5000 rectified stereo image pairs with a resolution of 1024x440. 500 frames come with pixel-level semantic class annotations into 5 classes: ground, building, vehicle, pedestrian, sky. Dense disparity maps are provided as a reference, however these are not manually annotated but computed using semi-global matching.;http://www.6d-vision.com/scene-labeling +nuScenes dataset;The nuScenes dataset is a public large-scale dataset for autonomous driving developed by Aptiv Autonomous Mobility. By releasing a subset of our data to the public, Aptiv aims to support public research into computer vision and autonomous driving.;https://www.nuscenes.org/; diff --git a/module2/exo4/exercice.ipynb b/module2/exo4/exercice.ipynb index 7463e5508092cbd21421f39ca76e990a90bd8c5c..04cada5fc8b9bceb130c34b776cf153f5cb8aba7 100644 --- a/module2/exo4/exercice.ipynb +++ b/module2/exo4/exercice.ipynb @@ -4,24 +4,152 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "***Objectif:\n", + "### Objectif : référencer quelques sites internets fournissant des jeux de données \"datasets\" sur les véhicules autonomes\n", "\n", - "Référencer quelques sites internets fournissant des jeux de données \"datasets\"\n", - "-Extraire un résumé, les formats de fichiers fournis vers un fichier CSV\n", - "-Extaire des mots clés, des étiquettes décrivant ces datasets ?\n", - "-Créer quelques statistiques de base de ces datasets ?\n", - "-Créer une représentation graphique de ces datasets ?" + "- [x] Extraire un résumé, des informations vers un fichier CSV (datasets.csv)\n", + "- [x] Lire et afficher les données du fichier CSV pour vérification\n", + "- [ ] Extraire des mots-clés, des étiquettes décrivant ces datasets (colonne tags)\n", + "- [ ] Créer quelques statistiques de base de ces datasets\n", + "- [ ] Créer une représentation graphique de ces datasets\n" ] }, { - "cell_type": "raw", + "cell_type": "markdown", "metadata": {}, "source": [ - "name;description;website;tags\n", - "KITTI Vision Benchmark Suite;We take advantage of our autonomous driving platform Annieway to develop novel challenging real-world computer vision benchmarks. Our tasks of interest are: stereo, optical flow, visual odometry, 3D object detection and 3D tracking;http://www.cvlibs.net/datasets/kitti/;stereo,flow,odometry,tracking,detection,road,maps,city;\n", - "Audi Autonomous Driving Dataset (A2D2);We have published the Audi Autonomous Driving Dataset (A2D2) to support startups and academic researchers working on autonomous driving. Equipping a vehicle with a multimodal sensor suite, recording a large dataset, and labelling it, is time and labour intensive.;https://www.a2d2.audi/a2d2/en.html;semantic,cloud,segmentation,detection,road,maps,city;\n", - "ApolloScape Dataset;Trajectory dataset, 3D Perception Lidar Object Detection and Tracking dataset including about 100K image frames, 80k lidar point cloud and 1000km trajectories for urban traffic. The dataset consisting of varying conditions and traffic densities which includes many challenging scenarios where vehicles, bicycles, and pedestrians move among one another.;http://apolloscape.auto/;stereo,flow,semantic,cloud,segmentation,detection,road,maps,city;" + "#### Lire et afficher les données du fichier CSV" ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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namedescriptionwebsite
0KITTI Vision Benchmark SuiteWe take advantage of our autonomous driving pl...http://www.cvlibs.net/datasets/kitti/
1Audi Autonomous Driving DatasetWe have published the Audi Autonomous Driving ...https://www.a2d2.audi/a2d2/en.html
2ApolloScape DatasetTrajectory dataset, 3D Perception Lidar Object...http://apolloscape.auto/
3Velodyne SLAMHere, you can find two challenging datasets re...http://www.mrt.kit.edu/z/publ/download/velodyn...
4Daimler Urban Segmentation DatasetThe Daimler Urban Segmentation Dataset consist...http://www.6d-vision.com/scene-labeling
5nuScenes datasetThe nuScenes dataset is a public large-scale d...https://www.nuscenes.org/
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" + ], + "text/plain": [ + " name \\\n", + "0 KITTI Vision Benchmark Suite \n", + "1 Audi Autonomous Driving Dataset \n", + "2 ApolloScape Dataset \n", + "3 Velodyne SLAM \n", + "4 Daimler Urban Segmentation Dataset \n", + "5 nuScenes dataset \n", + "\n", + " description \\\n", + "0 We take advantage of our autonomous driving pl... \n", + "1 We have published the Audi Autonomous Driving ... \n", + "2 Trajectory dataset, 3D Perception Lidar Object... \n", + "3 Here, you can find two challenging datasets re... \n", + "4 The Daimler Urban Segmentation Dataset consist... \n", + "5 The nuScenes dataset is a public large-scale d... \n", + "\n", + " website \n", + "0 http://www.cvlibs.net/datasets/kitti/ \n", + "1 https://www.a2d2.audi/a2d2/en.html \n", + "2 http://apolloscape.auto/ \n", + "3 http://www.mrt.kit.edu/z/publ/download/velodyn... \n", + "4 http://www.6d-vision.com/scene-labeling \n", + "5 https://www.nuscenes.org/ " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Le fichier datasets.csv est dans le dossier module2/exo4/datasets.csv\n", + "# Structure de l'entête/données : name;description;website;tags (avec des points virgules pour les champs)\n", + "\n", + "# https://pandas.pydata.org\n", + "# Version 0.22.0 sur ce Jupyter\n", + "import pandas as pd\n", + "\n", + "datasets = pd.read_csv('datasets.csv', delimiter = ';', usecols=[0,1,2])\n", + "datasets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Extraire des mots-clés, des étiquettes décrivant ces datasets" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": {