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{
"cells": [
{
"cell_type": "markdown",
"source": [
"# Video Face Manipulation Detection Through Ensemble of CNNs\n",
"Image and Sound Processing Lab - Politecnico di Milano\n",
"- Nicolò Bonettini\n",
"- Edoardo Daniele Cannas\n",
"- Sara Mandelli\n",
"- Luca Bondi\n",
"- Paolo Bestagini"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%% md\n"
}
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"pycharm": {
"is_executing": false
}
},
"outputs": [],
"source": [
"from pathlib import Path\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import sklearn.metrics as M\n",
"from scipy.special import expit\n",
"from tqdm.notebook import tqdm"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"pycharm": {
"is_executing": false,
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"results_root = Path('results/')\n",
"results_model_folder = list(results_root.glob('net-*'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"pycharm": {
"is_executing": false,
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"def compute_metrics(df_res:pd.DataFrame,train_tag:str) -> dict:\n",
" numreal = sum(df_res['label']==False)\n",
" numfake = sum(df_res['label']==True\n",
")\n",
" \n",
" netname = train_tag.split('net-')[1].split('_')[0]\n",
" traindb = train_tag.split('traindb-')[1].split('_')[0]\n",
" \n",
" loss = M.log_loss(df_res['label'],expit(df_res['score']))\n",
" acc = M.accuracy_score(df_res['label'],df_res['score']>0)\n",
" accbal = M.balanced_accuracy_score(df_res['label'],df_res['score']>0)\n",
" rocauc = M.roc_auc_score(df_res['label'],df_res['score'])\n",
" \n",
" res_dict = {'traintag':train_tag,\n",
" 'net':netname,\n",
" 'traindb': traindb,\n",
" 'testdb':testdb,'testsplit':testsplit,\n",
" 'numreal':numreal,'numfake':numfake,\n",
" 'loss':loss,\n",
" 'acc':acc,'accbal':accbal,\n",
" 'rocauc':rocauc} \n",
" return res_dict"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"pycharm": {
"is_executing": false,
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"results_frame_list = []\n",
"results_video_list = []\n",
"\n",
"for model_folder in tqdm(results_model_folder):\n",
" train_model_tag = model_folder.name\n",
" model_results = model_folder.glob('*.pkl')\n",
" for model_path in model_results:\n",
" testdb,testsplit = model_path.with_suffix('').name.rsplit('_',1)\n",
" \n",
" df_frames = pd.read_pickle(model_path)\n",
" results_frame_list.append(compute_metrics(df_frames,train_model_tag))\n",
" \n",
" df_videos = df_frames[['video','label','score']].groupby('video').mean()\n",
" df_videos['label'] = df_videos['label'].astype(np.bool)\n",
" results_video_list.append(compute_metrics(df_videos,train_model_tag))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"pycharm": {
"is_executing": false,
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"df_res_frames = pd.DataFrame(results_frame_list)\n",
"df_res_frames"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"pycharm": {
"is_executing": false,
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"df_res_video = pd.DataFrame(results_video_list)\n",
"df_res_video"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"df_res_frames.to_csv(results_root.joinpath('frames.csv'),index=False)\n",
"df_res_video.to_csv(results_root.joinpath('videos.csv'),index=False)\n"
]
}
],
"metadata": {
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"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.9"
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"pycharm": {
"stem_cell": {
"cell_type": "raw",
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