{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Video Analysis"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import math\n",
"import matplotlib.pyplot as plt\n",
"from scipy.interpolate import interp1d\n",
"import numpy as np\n",
"from glob import glob\n",
"\n",
"# set decimal precision\n",
"pd.set_option('precision', 3)\n",
"\n",
"# set plot parameters\n",
"%matplotlib inline\n",
"%config InlineBackend.figure_formats = ['svg']\n",
"plt.rcParams.update(\n",
" {\n",
" 'axes.grid': True,\n",
" 'axes.spines.bottom': False,\n",
" 'axes.spines.left': False,\n",
" 'axes.spines.right': False,\n",
" 'axes.spines.top': False,\n",
" 'font.family': 'Iosevka sbruder'\n",
" }\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"project = 'chaospendel/coole_bewegung_0'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"tracking_data = pd.read_csv(f'data/{project}.csv')\n",
"# remove all unneeded columns and rename them for easier handling\n",
"tracking_data = tracking_data[['time (s)', 'x (m)', 'y (m)', 'rotation']]\n",
"tracking_data = tracking_data.rename(columns={'time (s)': 't', 'x (m)': 'x', 'y (m)': 'y'})"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# add new columns for distance, velocity and acceleration\n",
"tracking_data['s'] = 0.0 * len(tracking_data)\n",
"tracking_data['v'] = 0.0 * len(tracking_data)\n",
"tracking_data['a'] = 0.0 * len(tracking_data)\n",
"\n",
"# calculate velocities and insert them\n",
"for idx in tracking_data.index[1:]:\n",
" xdelta = tracking_data.loc[idx]['x'] - tracking_data.loc[idx - 1]['x']\n",
" ydelta = tracking_data.loc[idx]['y'] - tracking_data.loc[idx - 1]['y']\n",
" tdelta = tracking_data.loc[idx]['t'] - tracking_data.loc[idx - 1]['t']\n",
" distance = math.sqrt(xdelta**2 + ydelta**2)\n",
" velocity = distance / tdelta\n",
" tracking_data.at[idx, 's'] = distance + tracking_data.loc[idx - 1]['s']\n",
" tracking_data.at[idx, 'v'] = velocity\n",
" \n",
" vdelta = tracking_data.loc[idx]['v'] - tracking_data.loc[idx - 1]['v']\n",
" acceleration = vdelta / tdelta\n",
" tracking_data.at[idx, 'a'] = acceleration\n",
" \n",
" tracking_data.at[idx, 'rotation'] = tracking_data.loc[idx]['rotation'] - 180"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# remove first two rows (too high acceleration)\n",
"tracking_data = tracking_data.drop([0, 1])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
"\n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(nrows=3, ncols=2, figsize=(12, 12))\n",
"tracking_data.plot(x='t', y=['v', 'a', 's', 'rotation'], subplots=True, ax=[*ax[0], *ax[1]])\n",
"tracking_data.plot(x='t', y=['x', 'y'], ax=ax[2][0])\n",
"tracking_data.plot(x='x', y='y', ax=ax[2][1])\n",
"plt.savefig(f'data/{project}.svg', bbox_inches='tight')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"project_ = '_'.join(project.split('_')[:-1])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"tracking_data = sorted(glob(f'data/{project_}*.csv'))\n",
"tracking_data = [pd.read_csv(file) for file in tracking_data]\n",
"tracking_data = [df[['x (m)', 'y (m)']] for df in tracking_data]\n",
"tracking_data = [df.rename(columns={'x (m)': f'x ({idx})', 'y (m)': f'y ({idx})'}) for idx, df in enumerate(tracking_data)]\n",
"tracking_data = pd.concat(tracking_data, axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
"\n",
"\n",
"\n",
"\n"
],
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"xs = list(zip(*tracking_data.T[[col.startswith('x') for col in tracking_data.columns]].T.values))\n",
"ys = list(zip(*tracking_data.T[[col.startswith('y') for col in tracking_data.columns]].T.values))\n",
"plt.figure(figsize=(6, 6))\n",
"plt.gca().set_aspect('equal', adjustable='box')\n",
"plt.gca().invert_yaxis()\n",
"for idx in range(len(xs)):\n",
" plt.plot(xs[idx], ys[idx])\n",
"plt.savefig(f'data/{project_}_movement.svg', bbox_inches='tight')"
]
}
],
"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": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}