added copyright notice and license

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 Felix Kleinsteuber and Computer Vision Group, Friedrich Schiller University Jena
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

analyze_dataset.ipynb

@@ -635,11 +635,11 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "Copyright © 2023 Felix Kleinsteuber and Computer Vision Group, Friedrich Schiller University Jena"
+   ]
   }
  ],
  "metadata": {

analyze_labels.ipynb

@@ -43,11 +43,11 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "Copyright © 2023 Felix Kleinsteuber and Computer Vision Group, Friedrich Schiller University Jena"
+   ]
   }
  ],
  "metadata": {

approach1a_basic_frame_differencing.ipynb

@@ -684,11 +684,11 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "Copyright © 2023 Felix Kleinsteuber and Computer Vision Group, Friedrich Schiller University Jena"
+   ]
   }
  ],
  "metadata": {

approach1b_histograms.ipynb

@@ -444,11 +444,11 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "Copyright © 2023 Felix Kleinsteuber and Computer Vision Group, Friedrich Schiller University Jena"
+   ]
   }
  ],
  "metadata": {

approach2_background_estimation.ipynb

@@ -713,11 +713,11 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "Copyright © 2023 Felix Kleinsteuber and Computer Vision Group, Friedrich Schiller University Jena"
+   ]
   }
  ],
  "metadata": {

approach3_boxplot.ipynb

@@ -414,11 +414,11 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "Copyright © 2023 Felix Kleinsteuber and Computer Vision Group, Friedrich Schiller University Jena"
+   ]
   }
  ],
  "metadata": {

approach3_local_features.ipynb

@@ -584,11 +584,11 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "Copyright © 2023 Felix Kleinsteuber and Computer Vision Group, Friedrich Schiller University Jena"
+   ]
   }
  ],
  "metadata": {

approach4_autoencoder.ipynb

@@ -636,11 +636,11 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "Copyright © 2023 Felix Kleinsteuber and Computer Vision Group, Friedrich Schiller University Jena"
+   ]
   }
  ],
  "metadata": {

approach4_boxplot.ipynb

@@ -946,11 +946,11 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "Copyright © 2023 Felix Kleinsteuber and Computer Vision Group, Friedrich Schiller University Jena"
+   ]
   }
  ],
  "metadata": {

check_csv.ipynb

@@ -161,11 +161,11 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "Copyright © 2023 Felix Kleinsteuber and Computer Vision Group, Friedrich Schiller University Jena"
+   ]
   }
  ],
  "metadata": {

deprecated/results.ipynb

@@ -1,139 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Results"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Beaver_01\n",
-    "1734 Lapse images, 695 Motion images.\n",
-    "\n",
-    "| Approach | Configuration | Best AUC | TNR @TPR $\\geq$ 0.9 | TNR @TPR $\\geq$ 0.95 | TNR @TPR $\\geq$ 0.99 | approx train time | approx eval time |\n",
-    "| --- | --- | ---: | ---: | ---: | ---: | ---: | ---: |\n",
-    "| 1a - Basic Frame Differencing | abs var | 0.7414 | 0.4865 | 0.4189 | 0.2432 | 0 | 1:00 min |\n",
-    "| | $\\sigma=2$, sq var | 0.8986 | 0.7162 | 0.6081 | 0.5270 | 0 | 1:30 min |\n",
-    "| | $\\sigma=4$, sq var | 0.9156 | 0.7973 | 0.6486 | 0.5676 | 0 | 1:30 min |\n",
-    "| 1b - Histogram Comparison | p-mean | 0.6707 | | | | | |\n",
-    "| 2 - Background Estimation | sq var | 0.7897 | 0.6622 | 0.5946 | 0.2703 | 0 | < 1 min |\n",
-    "| | $\\sigma=2$, sq var | 0.8735 | 0.7973 | 0.7162 | 0.4865 | 0 | 1:00 min |\n",
-    "| | $\\sigma=4$, sq var | 0.8776 | 0.7838 | 0.7027 | 0.4459 | 0 | 1:00 min |\n",
-    "| 3 - BOW | $k=1024, kp=30$ | 0.7698 | 0.3929 | 0.3800 | 0.0757 | | |\n",
-    "| | $k=2048, kp=30$ | 0.7741 | 0.4976 | 0.3382 | 0.0564 | | |\n",
-    "| | $k=4096, kp=30$ | 0.7837 | 0.5797 | 0.2866 | 0.0451 | 4:00 h | 2:10 min |\n",
-    "| | $k=2048, kp=40$ | 0.7611 | 0.3317 | 0.1610 | 0.1320 | 1:10 h | 1:30 min |\n",
-    "| 3 - BOW +motion | $k=1024, kp=30$, +motion | 0.7056 | 0.2432 | 0.2222 | 0.0821 | | |\n",
-    "| | $k=2048, kp=30$, +motion | 0.7390 | 0.3172 | 0.3092 | 0.0612 | | |\n",
-    "| | $k=4096, kp=30$, +motion | 0.7542 | 0.3768 | 0.2963 | 0.0515 | 5:30 h | 2:10 min |\n",
-    "| | $k=2048, kp=40$, +motion | 0.7388 | 0.1852 | 0.1820 | 0.0467 | 2:40 h | 2:20 min |\n",
-    "| 3 - BOW random | $k=2048, kp=30$, random | 0.8002 | 0.6296 | 0.5588 | 0.0258 | 8 min | 1:30 min |\n",
-    "| | $k=4096, kp=30$, random | 0.8022 | 0.6602 | 0.2738 | 0.1353 | 8 min | 2:00 min |\n",
-    "| | $k=8192, kp=30$, random | 0.7973 | 0.6151 | 0.3913 | 0.2061 | 8 min | 3:30 min |\n",
-    "| | $k=2048, kp=20$, random | 0.7943 | 0.5862 | 0.5539 | 0.2399 | 15 min | 3:00 min |\n",
-    "| | $k=4096, kp=20$, random | 0.8088 | 0.6329 | 0.5459 | 0.2432 | 15 min | 4:00 min |\n",
-    "| 4 - Autoencoder | Deep2 | 0.8678 | 0.5946 | 0.5000 | 0.0405 | 7:00 min | < 0:30 min |\n",
-    "| | Deep2 +Noise(.015) | 0.8930 | 0.7432 | 0.4459 | 0.0000 | 7:00 min | < 0:30 min |\n",
-    "| | Deep2 +Noise(.015) +Sparse(1e-4) | 0.8445 | 0.2703 | 0.1081 | 0.0541 | 7:00 min |  1:00 min |\n",
-    "| | Deep3 | 0.8663 | 0.7703 | 0.5946 | 0.1081 | 7:00 min | < 0:30 min |\n",
-    "| | Deep3 +Noise(.015) | 0.8542 | 0.8486 | 0.4324 | 0.0946 | 7:00 min | < 0:30 min |\n",
-    "| | Deep3 +Noise(.015) +Sparse(1e-4) | 0.7608 | 0.0811 | 0.0676 | 0.0405 | 7:00 min |  1:00 min |\n",
-    "| | Deep +Noise +Sparse Loss (lr=1e-4, 200 epochs, reg=0.1) | 0.7479 | 0.2086 | 0.1138 | 0.0008 | 8:30 min | 1:30 min |\n",
-    "| | Deep +Noise +Sparse KDE | 0.9209 | 0.8514 | 0.6892 | 0.1216 | 6 min | < 0:30 min |"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "## Marten_01\n",
-    "2462 Lapse images (with many doubles), 3105 Motion images.\n",
-    "\n",
-    "| Approach | Configuration | Best AUC | TNR @TPR $\\geq$ 0.9 | TNR @TPR $\\geq$ 0.95 | TNR @TPR $\\geq$ 0.99 | approx train time | approx eval time |\n",
-    "| --- | --- | ---: | ---: | ---: | ---: | ---: | ---: |\n",
-    "| 1a - Basic Frame Differencing | sq var | 0.6363 | 0.0244 | 0.0215 | 0.0160 | 0 | 5 min |\n",
-    "| | $\\sigma=2$, sq var | 0.8004 | 0.3236 | 0.1606 | 0.0434 | 0 | 6 min |\n",
-    "| | $\\sigma=4$, sq var | 0.8030 | 0.3536 | 0.2031 | 0.0801 | 0 | 6 min |\n",
-    "| 2 - Background Estimation | sqmean | 0.5056 | 0.0295 | 0.0219 | 0.0169 | 0 | 2:30 min |\n",
-    "| | $\\sigma=4$, sqvar | 0.7403 | 0.2090 | 0.1150 | 0.0253 | 0 | 4:00 min |\n",
-    "| 3 - BOW | $k = 4096, kp = 30$, random | 0.6619 | 0.4973 | 0.2186 | 0.1298 | 12 min | 13 min |\n",
-    "| 4 - Autoencoder | Deep3 | 0.6912 | 0.1471 | 0.0721 | 0.0013 | 10:00 min |  1:00 min |\n",
-    "| | Deep3 +Noise(.015) | 0.7582 | 0.3767 | 0.0206 | 0.0206 | 10:00 min |  1:00 min |\n",
-    "| | Deep3 +Noise(.015) +Sparse(1e-4) | 0.6120 | 0.1753 | 0.1037 | 0.0013 | 10:00 min |  1:00 min |\n",
-    "| | Deep2 | 0.7207 | 0.1745 | 0.0464 | 0.0198 | 20:00 min | < 0:30 min |\n",
-    "| | Deep2 +Noise(.015) | 0.7200 | 0.1884 | 0.0881 | 0.0088 | 20:00 min | < 0:30 min |\n",
-    "| | Deep2 +Noise(.015) +Sparse(1e-4) | 0.6553 | 0.0194 | 0.0114 | 0.0038 | 20:00 min | < 0:30 min |\n",
-    "| | Deep +Noise +Sparse Loss (lr=1e-4, 200 epochs, reg=0.1) | 0.7479 | 0.2086 | 0.1138 | 0.0008 | 8:30 min | 1:30 min |"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## GFox_03\n",
-    "**Generated set:** The Lapse set was randomly selected from the labeled Motion set. This was necessary due to a lack of Lapse images (only one per day instead of per hour).\n",
-    "\n",
-    "**Lapse generation procedure:** Take a random set of consecutively taken Motion images. If all images are annotated as images, add the whole set to Lapse and remove it from Motion. Repeat until at least 1200 Lapse images were selected.\n",
-    "\n",
-    "| Approach | Configuration | Best AUC | TNR @TPR $\\geq$ 0.9 | TNR @TPR $\\geq$ 0.95 | TNR @TPR $\\geq$ 0.99 | approx train time | approx eval time |\n",
-    "| --- | --- | ---: | ---: | ---: | ---: | ---: | ---: |\n",
-    "| 1a - Basic Frame Differencing | n.a. | | | | | | |\n",
-    "| 2 - Background Estimation | sqmean | 0.4745 | 0.0903 | 0.0493 | 0.0189 | 0 | 4:00 min |\n",
-    "| | $\\sigma=4$, sqmean | 0.4793 | 0.0650 | 0.0434 | 0.0096 | 0 | 4:30 min |\n",
-    "| 3 - BOW | $k = 4096, kp = 30$, random | 0.9743 | 0.9715 | 0.8333 | 0.4837 | 3:00 min | 8:00 min |\n",
-    "| 4 - Autoencoder | Deep2 | 0.9713 | 0.9510 | 0.9049 | 0.6701 | 6:00 min | < 0:30 min |\n",
-    "| | Deep2 (Loss) | 0.9861 | 0.9892 | 0.9470 | 0.6481 | 6:00 min | < 0:30 min |\n",
-    "| | Deep2 +Noise(.015) | 0.9684 | 0.9274 | 0.8347 | 0.6838 | 6:00 min | < 0:30 min |\n",
-    "| | Deep2 +Noise(.015) (Loss) | 0.9858 | 0.9852 | 0.9551 | 0.6344 | 6:00 min | < 0:30 min |\n",
-    "| | Deep2 +Noise(.015) +Sparse(1e-4) | 0.9749 | 0.9631 | 0.8668 | 0.6320 | 6:00 min |  1:00 min |\n",
-    "| | Deep2 +Noise(.015) +Sparse(1e-4) (Loss) | 0.9847 | 0.9916 | 0.9474 | 0.6140 | 6:00 min |  1:00 min |\n",
-    "| | Deep3 | 0.9012 | 0.8455 | 0.7472 | 0.6746 | 6:00 min | < 0:30 min |\n",
-    "| | Deep3 (Loss) | 0.9887 | 0.9932 | 0.9835 | 0.7913 | 6:00 min | < 0:30 min |\n",
-    "| | Deep3 +Noise(.015) | 0.9042 | 0.8387 | 0.7476 | 0.6465 | 6:00 min | < 0:30 min |\n",
-    "| | Deep3 +Noise(.015) (Loss) | 0.9889 | 0.9920 | 0.9715 | 0.8363 | 6:00 min | < 0:30 min |\n",
-    "| | Deep3 +Noise(.015) +Sparse(1e-4) | 0.9356 | 0.8732 | 0.8351 | 0.1597 | 6:00 min |  1:00 min |\n",
-    "| | Deep3 +Noise(.015) +Sparse(1e-4) (Loss) | 0.9829 | 0.9872 | 0.9117 | 0.6726 | 6:00 min |  1:00 min |\n",
-    "| | Deep +Noise +Sparse KDE (lr=1e-4) | 0.9684 | 0.9579 | 0.9041 | 0.4964 | 5:00 min | < 0:30 min |"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.6.9 64-bit",
-   "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": "3.6.9"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

eval_autoencoder.py

@@ -1,3 +1,5 @@
+# Copyright (c) 2023 Felix Kleinsteuber and Computer Vision Group, Friedrich Schiller University Jena
+
 # Approach 4: Autoencoder
 # This script is used for evaluating an autoencoder on Motion and Lapse images.
 # See train_autoencoder.py for training.

eval_bow.py

@@ -1,3 +1,5 @@
+# Copyright (c) 2023 Felix Kleinsteuber and Computer Vision Group, Friedrich Schiller University Jena
+
 # Approach 3: Local features
 # This script is used for calculating BOW features of Motion images
 # using a BOW vocabulary.

generate_lapseless_session.ipynb

@@ -189,6 +189,13 @@
     "full_folder = os.path.join(TARGET_DIR, os.path.basename(target_session.folder), \"Full\")\n",
     "shutil.copytree(target_session.get_full_folder(), full_folder)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright © 2023 Felix Kleinsteuber and Computer Vision Group, Friedrich Schiller University Jena"
+   ]
   }
  ],
  "metadata": {

get_image_exif.ipynb

@@ -123,11 +123,11 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "Copyright © 2023 Felix Kleinsteuber and Computer Vision Group, Friedrich Schiller University Jena"
+   ]
   }
  ],
  "metadata": {

index.ipynb

@@ -52,6 +52,13 @@
     "## Early experiments\n",
     " - *[deprecated/experiments.ipynb](deprecated/experiments.ipynb)*: Early experiments with lapse images and frame differencing"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright © 2023 Felix Kleinsteuber and Computer Vision Group, Friedrich Schiller University Jena"
+   ]
   }
  ],
  "metadata": {

py/Autoencoder.py

@@ -1,3 +1,5 @@
+# Copyright (c) 2023 Felix Kleinsteuber and Computer Vision Group, Friedrich Schiller University Jena
+
 # This is the initial autoencoder architecture.
 # Convolutional with 5 conv layers + 1 dense layer per encoder and decoder.
 # relu on hidden layers, tanh on output layer

py/Autoencoder2.py

@@ -1,3 +1,5 @@
+# Copyright (c) 2023 Felix Kleinsteuber and Computer Vision Group, Friedrich Schiller University Jena
+
 # This is the preferred autoencoder architecture.
 # Fully convolutional with 7 layer encoder and decoder.
 # Dropout, relu on hidden layers, tanh on output layer

py/Autoencoder3.py

@@ -1,3 +1,5 @@
+# Copyright (c) 2023 Felix Kleinsteuber and Computer Vision Group, Friedrich Schiller University Jena
+
 # Experimental architecture; not used for paper results.
 # Convolutional with 6 conv layers + 1 dense layer per encoder and decoder.
 # Dropout, relu on hidden layers, tanh on output layer

py/Dataset.py

@@ -1,3 +1,5 @@
+# Copyright (c) 2023 Felix Kleinsteuber and Computer Vision Group, Friedrich Schiller University Jena
+
 import os
 from tqdm import tqdm
 from py.DatasetStatistics import DatasetStatistics

py/DatasetStatistics.py

@@ -1,3 +1,5 @@
+# Copyright (c) 2023 Felix Kleinsteuber and Computer Vision Group, Friedrich Schiller University Jena
+
 from turtle import pd
 from warnings import warn
 

py/FileUtils.py

@@ -1,3 +1,5 @@
+# Copyright (c) 2023 Felix Kleinsteuber and Computer Vision Group, Friedrich Schiller University Jena
+
 # This file defines helper functions for processing files.
 from glob import glob
 import os

py/ImageAnnotator.py

@@ -1,3 +1,5 @@
+# Copyright (c) 2023 Felix Kleinsteuber and Computer Vision Group, Friedrich Schiller University Jena
+
 import ipywidgets as widgets
 from IPython.display import display
 from py.Session import Session

py/ImageClassifier.py

@@ -1,3 +1,5 @@
+# Copyright (c) 2023 Felix Kleinsteuber and Computer Vision Group, Friedrich Schiller University Jena
+
 from py.Session import MotionImage
 
 # Abstract class which represents an image classifier.

py/ImageUtils.py

@@ -1,3 +1,5 @@
+# Copyright (c) 2023 Felix Kleinsteuber and Computer Vision Group, Friedrich Schiller University Jena
+
 # This file defines helper functions for processing images.
 from datetime import datetime
 from PIL import Image

py/Labels.py

@@ -1,3 +1,5 @@
+# Copyright (c) 2023 Felix Kleinsteuber and Computer Vision Group, Friedrich Schiller University Jena
+
 # Annotations for all sessions that were evaluated in the paper.
 # Annotations generated using quick_label.py can be pasted here.
 # Each session is labeled using the "normal", "anomalous", "not_annotated", and "max" keys.

py/LocalFeatures.py

@@ -1,3 +1,5 @@
+# Copyright (c) 2023 Felix Kleinsteuber and Computer Vision Group, Friedrich Schiller University Jena
+
 # Functions related to approach 3 (local features).
 # For training and evaluation scripts, see ./train_bow.py and ./eval_bow.py.
 

py/PlotUtils.py

@@ -1,3 +1,5 @@
+# Copyright (c) 2023 Felix Kleinsteuber and Computer Vision Group, Friedrich Schiller University Jena
+
 # This file defines helper functions for plotting.
 import matplotlib.pyplot as plt
 from sklearn.metrics import roc_curve, auc

py/PyTorchData.py

@@ -1,3 +1,5 @@
+# Copyright (c) 2023 Felix Kleinsteuber and Computer Vision Group, Friedrich Schiller University Jena
+
 # Functions related to approach 4 (autoencoder).
 # For training and evaluation scripts, see ./train_autoencoder.py and ./eval_autoencoder.py.
 import os

py/Session.py

@@ -1,3 +1,5 @@
+# Copyright (c) 2023 Felix Kleinsteuber and Computer Vision Group, Friedrich Schiller University Jena
+
 from datetime import datetime, timedelta
 import pickle
 import random

quick_label.py

@@ -1,3 +1,5 @@
+# Copyright (c) 2023 Felix Kleinsteuber and Computer Vision Group, Friedrich Schiller University Jena
+
 # Quick labeling script.
 # The user is displayed every image and can then assign an image as "normal" (1-key) or "anomalous" (2-key).
 # The list of all normal and anomalous images will be printed after every image to be copied to Labels.py.

read_csv_annotations.ipynb

@@ -560,11 +560,11 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "Copyright © 2023 Felix Kleinsteuber and Computer Vision Group, Friedrich Schiller University Jena"
+   ]
   }
  ],
  "metadata": {

resize_session.ipynb

@@ -107,6 +107,13 @@
    "source": [
     "copy_session(session, \"ResizedSessions256_NoBackup\", size=(256, 256), truncate_y=(40, 40))"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright © 2023 Felix Kleinsteuber and Computer Vision Group, Friedrich Schiller University Jena"
+   ]
   }
  ],
  "metadata": {

scan_sessions.ipynb

@@ -1791,11 +1791,11 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "Copyright © 2023 Felix Kleinsteuber and Computer Vision Group, Friedrich Schiller University Jena"
+   ]
   }
  ],
  "metadata": {

train_autoencoder.py

@@ -1,3 +1,5 @@
+# Copyright (c) 2023 Felix Kleinsteuber and Computer Vision Group, Friedrich Schiller University Jena
+
 # Approach 4: Autoencoder
 # This script is used for training an autoencoder on Lapse images.
 # See eval_autoencoder.py for evaluation.

train_bow.py

@@ -1,3 +1,5 @@
+# Copyright (c) 2023 Felix Kleinsteuber and Computer Vision Group, Friedrich Schiller University Jena
+
 # Approach 3: Local features
 # This script is used for generating a BOW vocabulary using
 # densely sampeled SIFT features on Lapse images.

visualization/activation_functions.ipynb

@@ -83,11 +83,11 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "Copyright © 2023 Felix Kleinsteuber and Computer Vision Group, Friedrich Schiller University Jena"
+   ]
   }
  ],
  "metadata": {

visualization/kernel_density_estimation.ipynb

@@ -65,11 +65,11 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "Copyright © 2023 Felix Kleinsteuber and Computer Vision Group, Friedrich Schiller University Jena"
+   ]
   }
  ],
  "metadata": {

visualization/roc_curves.ipynb

@@ -155,11 +155,11 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "Copyright © 2023 Felix Kleinsteuber and Computer Vision Group, Friedrich Schiller University Jena"
+   ]
   }
  ],
  "metadata": {