Skip to content

M
mooc-rr
Commit 6c0f247e authored by c6b76b23b68ff2880e300802cab9a6e3's avatar c6b76b23b68ff2880e300802cab9a6e3
Browse files
Options

MAJ exercice 4 (module 2)

parent 48defb2f
Hide whitespace changes
Inline Side-by-side
Showing with 204 additions and 7 deletions
module2/exo4/datasets.csv
View file @ 6c0f247e
...@@ -2,6 +2,6 @@ name;description;website;tags ...@@ -2,6 +2,6 @@ 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 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 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 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; 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;detection,images,city
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 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;stereo,labelling,detection,road,maps,city
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/; 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/;labelling,detection,road,maps,city
module2/exo4/exercice.ipynb
View file @ 6c0f247e
...@@ -8,7 +8,7 @@ ...@@ -8,7 +8,7 @@
"\n", "\n",
"- [x] Extraire un résumé, des informations vers un fichier CSV (datasets.csv)\n", "- [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", "- [x] Lire et afficher les données du fichier CSV pour vérification\n",
"- [ ] Extraire des mots-clés, des étiquettes décrivant ces datasets (colonne tags)\n", "- [x] Extraire des mots-clés, des étiquettes décrivant ces datasets (colonne tags)\n",
"- [ ] Créer quelques statistiques de base de ces datasets\n", "- [ ] Créer quelques statistiques de base de ces datasets\n",
"- [ ] Créer une représentation graphique de ces datasets\n" "- [ ] Créer une représentation graphique de ces datasets\n"
] ]
...@@ -22,7 +22,7 @@ ...@@ -22,7 +22,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 1,
"metadata": { "metadata": {
"scrolled": false "scrolled": false
}, },
...@@ -120,7 +120,7 @@ ...@@ -120,7 +120,7 @@
"5 https://www.nuscenes.org/ " "5 https://www.nuscenes.org/ "
] ]
}, },
"execution_count": 4, "execution_count": 1,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -130,9 +130,13 @@ ...@@ -130,9 +130,13 @@
"# Structure de l'entête/données : name;description;website;tags (avec des points virgules pour les champs)\n", "# Structure de l'entête/données : name;description;website;tags (avec des points virgules pour les champs)\n",
"\n", "\n",
"# https://pandas.pydata.org\n", "# https://pandas.pydata.org\n",
"# Version 0.22.0 sur ce Jupyter\n", "# Version 0.22.0 (December 29, 2017) sur ce Jupyter !\n",
"import pandas as pd\n", "import pandas as pd\n",
"\n", "\n",
"# print(pd.__version__) \n",
"# pd.show_versions() # Toutes les extensions installées\n",
"\n",
"# Afficher les colonnes principales\n",
"datasets = pd.read_csv('datasets.csv', delimiter = ';', usecols=[0,1,2])\n", "datasets = pd.read_csv('datasets.csv', delimiter = ';', usecols=[0,1,2])\n",
"datasets" "datasets"
] ]
...@@ -144,6 +148,199 @@ ...@@ -144,6 +148,199 @@
"#### Extraire des mots-clés, des étiquettes décrivant ces datasets" "#### Extraire des mots-clés, des étiquettes décrivant ces datasets"
] ]
}, },
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>tags</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>stereo,flow,odometry,tracking,detection,road,m...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>semantic,cloud,segmentation,detection,road,map...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>stereo,flow,semantic,cloud,segmentation,detect...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>detection,images,city</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>stereo,labelling,detection,road,maps,city</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>labelling,detection,road,maps,city</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" tags\n",
"0 stereo,flow,odometry,tracking,detection,road,m...\n",
"1 semantic,cloud,segmentation,detection,road,map...\n",
"2 stereo,flow,semantic,cloud,segmentation,detect...\n",
"3 detection,images,city\n",
"4 stereo,labelling,detection,road,maps,city\n",
"5 labelling,detection,road,maps,city"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"# Vérifier les mots-clés\n",
"tags = pd.read_csv('datasets.csv', delimiter = ';', usecols=[3])\n",
"tags"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Créer quelques statistiques de base de ces datasets"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>tags</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>unique</th>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>top</th>\n",
" <td>stereo,labelling,detection,road,maps,city</td>\n",
" </tr>\n",
" <tr>\n",
" <th>freq</th>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" tags\n",
"count 6\n",
"unique 6\n",
"top stereo,labelling,detection,road,maps,city\n",
"freq 1"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"# Vérifier les mots-clés\n",
"tags = pd.read_csv('datasets.csv', delimiter = ';', usecols=[3])\n",
"tags.describe()"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['stereo,flow,odometry,tracking,detection,road,maps,city']\n",
"['semantic,cloud,segmentation,detection,road,maps,city']\n",
"['stereo,flow,semantic,cloud,segmentation,detection,road,maps,city']\n",
"['detection,images,city']\n",
"['stereo,labelling,detection,road,maps,city']\n",
"['labelling,detection,road,maps,city']\n"
]
}
],
"source": [
"import pandas as pd\n",
"# import re\n",
"\n",
"# Extraire les mots-clés\n",
"tags = pd.read_csv('datasets.csv', delimiter = ';', usecols=[3])\n",
"\n",
"for t in tags.values:\n",
" print(t)\n",
" \n",
" \n",
"# array = re.split('\\,',t)\n",
"# str = np.array_split(t,1)\n",
"# print(str)\n"
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
......
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment