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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Convert `.mat`\n",
"Converts Camera Signal and Radiant Temperature of `.mat` files to `.p` filetype."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import imageio\n",
"import numpy as np\n",
"import pickle\n",
"import scipy"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"folder = \"powder_plate_7_bare_pad_195_w_800_mm_s\""
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(501, 127, 360)\n",
"16367\n"
]
}
],
"source": [
"filename = \"camera_signal\"\n",
"\n",
"mat = scipy.io.loadmat(f\"data/{folder}/{filename}.mat\")\n",
"video = mat[\"CameraSignal\"]\n",
"\n",
"# Reshapes from (y, x, f) to (f, x, y)\n",
"video_reshaped = np.transpose(video, (2, 0, 1))\n",
"print(video_reshaped.shape)\n",
"print(np.max(video_reshaped))\n",
"\n",
"with open(f\"data/{folder}/{filename}.pkl\", \"wb\") as file:\n",
" pickle.dump(video_reshaped, file)\n",
"\n",
"# Normalizes the video for visual output\n",
"video_normalized = np.interp(\n",
" video_reshaped,\n",
" (video_reshaped.min(), video_reshaped.max()),\n",
" (0, 255)\n",
")\n",
"\n",
"frames = []\n",
"for frame in video_normalized:\n",
" frames.append(frame)\n",
"\n",
"imageio.mimsave(f\"data/{folder}/{filename}.gif\", frames)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(501, 127, 360)\n",
"1073.1382220207333\n"
]
}
],
"source": [
"filename = \"radiant_temperature\"\n",
"\n",
"mat = scipy.io.loadmat(f\"data/{folder}/{filename}.mat\")\n",
"video = mat[\"RadiantTemperature\"]\n",
"\n",
"# Reshapes from (x, y, f) to (f, x, y)\n",
"video_reshaped = np.transpose(video, (2, 0, 1))\n",
"print(video_reshaped.shape)\n",
"print(np.max(video_reshaped))\n",
"\n",
"with open(f\"data/{folder}/{filename}.pkl\", \"wb\") as file:\n",
" pickle.dump(video_reshaped, file)\n",
"\n",
"# Normalizes the video for visual output\n",
"video_normalized = np.interp(\n",
" video_reshaped,\n",
" (video_reshaped.min(), video_reshaped.max()),\n",
" (0, 255)\n",
")\n",
"\n",
"frames = []\n",
"for frame in video_normalized:\n",
" frames.append(frame)\n",
"\n",
"imageio.mimsave(f\"data/{folder}/{filename}.gif\", frames)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "venv",
"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": "3.12.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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