documentation and ordering

analyze_dataset.ipynb

@@ -581,6 +581,15 @@
     "    print(\"There were files missing!\")\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Check sessions for duplicates\n",
+    "\n",
+    "Using the check_lapse_duplicates() function, the Lapse folder is checked for duplicate dates and duplicate files. We call a duplicate inconsistent or deviant, if the two images show different scenes but have the same associated date."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 7,
@@ -635,7 +644,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.10.4 ('pytorch-gpu')",
+   "display_name": "Python 3.6.9 64-bit",
    "language": "python",
    "name": "python3"
   },
@@ -649,12 +658,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.6.9"
   },
   "orig_nbformat": 4,
   "vscode": {
    "interpreter": {
-    "hash": "17cd5c528a3345b75540c61f907eece919c031d57a2ca1e5653325af249173c9"
+    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
    }
   }
  },

analyze_labels.ipynb

@@ -1,5 +1,13 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Label statistics\n",
+    "This very simple notebook iterates through labels from Labels.py and counts anomalous, normal and not annotated images."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,

autoencoder_experiments.ipynb

@@ -1,218 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/kleinsteuber/anaconda3/envs/pytorch-gpu/lib/python3.10/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
-     ]
-    }
-   ],
-   "source": [
-    "from torch import nn\n",
-    "from torchinfo import summary"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class Autoencoder(nn.Module):\n",
-    "    def __init__(self, dropout=0.1, latent_features=512):\n",
-    "        super(Autoencoder, self).__init__()\n",
-    "        self.encoder = nn.Sequential(\n",
-    "            nn.Dropout(dropout),\n",
-    "            nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3),\n",
-    "            nn.ReLU(True),\n",
-    "\n",
-    "            nn.Dropout(dropout),\n",
-    "            nn.Conv2d(64, 64, kernel_size=5, stride=2, padding=2),\n",
-    "            nn.ReLU(True),\n",
-    "\n",
-    "            nn.Dropout(dropout),\n",
-    "            nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1),\n",
-    "            nn.ReLU(True),\n",
-    "\n",
-    "            nn.Dropout(dropout),\n",
-    "            nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1),\n",
-    "            nn.ReLU(True),\n",
-    "\n",
-    "            nn.Dropout(dropout),\n",
-    "            nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1),\n",
-    "            nn.ReLU(True),\n",
-    "\n",
-    "            nn.Dropout(dropout),\n",
-    "            nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1),\n",
-    "            nn.ReLU(True),\n",
-    "\n",
-    "            nn.Dropout(dropout),\n",
-    "            nn.Flatten(),\n",
-    "            nn.Linear(1024, latent_features),\n",
-    "            nn.ReLU(True),\n",
-    "        )\n",
-    "        self.decoder = nn.Sequential(\n",
-    "            nn.Linear(512, 1024),\n",
-    "            nn.ReLU(True),\n",
-    "            nn.Unflatten(1, (64, 4, 4)),\n",
-    "\n",
-    "            nn.Dropout(dropout),\n",
-    "            nn.ConvTranspose2d(64, 64, kernel_size=4, stride=2, padding=1),\n",
-    "            nn.ReLU(True),\n",
-    "\n",
-    "            nn.Dropout(dropout),\n",
-    "            nn.ConvTranspose2d(64, 64, kernel_size=4, stride=2, padding=1),\n",
-    "            nn.ReLU(True),\n",
-    "\n",
-    "            nn.Dropout(dropout),\n",
-    "            nn.ConvTranspose2d(64, 64, kernel_size=4, stride=2, padding=1),\n",
-    "            nn.ReLU(True),\n",
-    "\n",
-    "            nn.Dropout(dropout),\n",
-    "            nn.ConvTranspose2d(64, 64, kernel_size=4, stride=2, padding=1),\n",
-    "            nn.ReLU(True),\n",
-    "\n",
-    "            nn.Dropout(dropout),\n",
-    "            nn.ConvTranspose2d(64, 64, kernel_size=6, stride=2, padding=2),\n",
-    "            nn.ReLU(True),\n",
-    "\n",
-    "            nn.Dropout(dropout),\n",
-    "            nn.ConvTranspose2d(64, 64, kernel_size=8, stride=2, padding=3),\n",
-    "            nn.ReLU(True),\n",
-    "\n",
-    "            nn.Dropout(dropout),\n",
-    "            nn.Conv2d(64, 3, kernel_size=3, stride=1, padding=\"same\"),\n",
-    "            nn.Tanh(),\n",
-    "        )\n",
-    "    \n",
-    "    def forward(self, x):\n",
-    "        x = self.encoder(x)\n",
-    "        x = self.decoder(x)\n",
-    "        return x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "==========================================================================================\n",
-       "Layer (type:depth-idx)                   Output Shape              Param #\n",
-       "==========================================================================================\n",
-       "Autoencoder                              [32, 3, 256, 256]         --\n",
-       "├─Sequential: 1-1                        [32, 512]                 --\n",
-       "│    └─Dropout: 2-1                      [32, 3, 256, 256]         --\n",
-       "│    └─Conv2d: 2-2                       [32, 32, 128, 128]        4,736\n",
-       "│    └─ReLU: 2-3                         [32, 32, 128, 128]        --\n",
-       "│    └─Dropout: 2-4                      [32, 32, 128, 128]        --\n",
-       "│    └─Conv2d: 2-5                       [32, 64, 64, 64]          51,264\n",
-       "│    └─ReLU: 2-6                         [32, 64, 64, 64]          --\n",
-       "│    └─Dropout: 2-7                      [32, 64, 64, 64]          --\n",
-       "│    └─Conv2d: 2-8                       [32, 64, 32, 32]          36,928\n",
-       "│    └─ReLU: 2-9                         [32, 64, 32, 32]          --\n",
-       "│    └─Dropout: 2-10                     [32, 64, 32, 32]          --\n",
-       "│    └─Conv2d: 2-11                      [32, 64, 16, 16]          36,928\n",
-       "│    └─ReLU: 2-12                        [32, 64, 16, 16]          --\n",
-       "│    └─Dropout: 2-13                     [32, 64, 16, 16]          --\n",
-       "│    └─Conv2d: 2-14                      [32, 128, 8, 8]           73,856\n",
-       "│    └─ReLU: 2-15                        [32, 128, 8, 8]           --\n",
-       "│    └─Dropout: 2-16                     [32, 128, 8, 8]           --\n",
-       "│    └─Conv2d: 2-17                      [32, 64, 4, 4]            73,792\n",
-       "│    └─ReLU: 2-18                        [32, 64, 4, 4]            --\n",
-       "│    └─Dropout: 2-19                     [32, 64, 4, 4]            --\n",
-       "│    └─Flatten: 2-20                     [32, 1024]                --\n",
-       "│    └─Linear: 2-21                      [32, 512]                 524,800\n",
-       "│    └─ReLU: 2-22                        [32, 512]                 --\n",
-       "├─Sequential: 1-2                        [32, 3, 256, 256]         --\n",
-       "│    └─Linear: 2-23                      [32, 1024]                525,312\n",
-       "│    └─ReLU: 2-24                        [32, 1024]                --\n",
-       "│    └─Unflatten: 2-25                   [32, 64, 4, 4]            --\n",
-       "│    └─Dropout: 2-26                     [32, 64, 4, 4]            --\n",
-       "│    └─ConvTranspose2d: 2-27             [32, 128, 8, 8]           131,200\n",
-       "│    └─ReLU: 2-28                        [32, 128, 8, 8]           --\n",
-       "│    └─Dropout: 2-29                     [32, 128, 8, 8]           --\n",
-       "│    └─ConvTranspose2d: 2-30             [32, 64, 16, 16]          131,136\n",
-       "│    └─ReLU: 2-31                        [32, 64, 16, 16]          --\n",
-       "│    └─Dropout: 2-32                     [32, 64, 16, 16]          --\n",
-       "│    └─ConvTranspose2d: 2-33             [32, 64, 32, 32]          65,600\n",
-       "│    └─ReLU: 2-34                        [32, 64, 32, 32]          --\n",
-       "│    └─Dropout: 2-35                     [32, 64, 32, 32]          --\n",
-       "│    └─ConvTranspose2d: 2-36             [32, 64, 64, 64]          65,600\n",
-       "│    └─ReLU: 2-37                        [32, 64, 64, 64]          --\n",
-       "│    └─Dropout: 2-38                     [32, 64, 64, 64]          --\n",
-       "│    └─ConvTranspose2d: 2-39             [32, 32, 128, 128]        73,760\n",
-       "│    └─ReLU: 2-40                        [32, 32, 128, 128]        --\n",
-       "│    └─Dropout: 2-41                     [32, 32, 128, 128]        --\n",
-       "│    └─ConvTranspose2d: 2-42             [32, 16, 256, 256]        32,784\n",
-       "│    └─ReLU: 2-43                        [32, 16, 256, 256]        --\n",
-       "│    └─Dropout: 2-44                     [32, 16, 256, 256]        --\n",
-       "│    └─Conv2d: 2-45                      [32, 3, 256, 256]         435\n",
-       "│    └─Tanh: 2-46                        [32, 3, 256, 256]         --\n",
-       "==========================================================================================\n",
-       "Total params: 1,828,131\n",
-       "Trainable params: 1,828,131\n",
-       "Non-trainable params: 0\n",
-       "Total mult-adds (G): 131.37\n",
-       "==========================================================================================\n",
-       "Input size (MB): 25.17\n",
-       "Forward/backward pass size (MB): 768.21\n",
-       "Params size (MB): 7.31\n",
-       "Estimated Total Size (MB): 800.69\n",
-       "=========================================================================================="
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "summary(Autoencoder(), (32, 3, 256, 256))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.10.4 ('pytorch-gpu')",
-   "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": "3.10.4"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "17cd5c528a3345b75540c61f907eece919c031d57a2ca1e5653325af249173c9"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

day_vs_night.ipynb

@@ -1,77 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from skimage import io\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "img = io.imread(\"sample.jpg\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "99.88014299849979"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.mean([abs(img[:,:,0] - img[:,:,1]), abs(img[:,:,1] - img[:,:,2]), abs(img[:,:,2] - img[:,:,0])])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.10.4 ('pytorch-gpu')",
-   "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": "3.10.4"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "17cd5c528a3345b75540c61f907eece919c031d57a2ca1e5653325af249173c9"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

index.ipynb

@@ -20,12 +20,30 @@
     " - *approach2_background_estimation.ipynb*: Implementation.\n",
     "\n",
     "## Approach 3: Bag of Visual Words\n",
+    "### Notebooks\n",
     " - *approach3_local_features.ipynb*: Visualizations and evaluation of single trainings.\n",
     " - *approach3_boxplot.ipynb*: Boxplot to compare multiple vocabularies generated using random prototypes.\n",
     "\n",
+    "### Scripts\n",
+    " - *train_bow.py*: Training of BOW model\n",
+    " - *eval_bow.py*: Evaluation of BOW model\n",
+    "\n",
     "## Approach 4: Autoencoder\n",
+    "### Notebooks\n",
     " - *approach4_autoencoder.ipynb*: Visualizations and evaluation of single trainings.\n",
-    " - *approach4_boxplot.ipynb*: Boxplot to compare multiple trainings."
+    " - *approach4_boxplot.ipynb*: Boxplot to compare different trainings.\n",
+    "\n",
+    "### Scripts\n",
+    " - *train_autoencoder.py*: Training of autoencoder\n",
+    " - *eval_autoencoder.py*: Evaluation of trained autoencoder\n",
+    "\n",
+    "## Helpers\n",
+    " - *analyze_dataset.ipynb*: Dataset statistics, check for duplicates\n",
+    " - *analyze_labels.ipynb*: Annotation statistics (number of normal/anomalous motion samples)\n",
+    " - *check_csv.ipynb*: Loads annotations from *Kadaverbilder_leer.csv*\n",
+    "\n",
+    "## Early experiments\n",
+    " - *deprecated/experiments.ipynb*: Early experiments with lapse images and frame differencing"
    ]
   },
   {