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Try kernel density estimation

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Florent Guiotte 2018-04-12 10:34:00 +02:00
parent 3274683983
commit d3f9b66b9a
1 changed files with 186 additions and 0 deletions
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Notebooks/Kernel Density Estimation.ipynb Normal file
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"from pathlib import Path\n",
"import numpy as np\n",
"from scipy import stats\n",
"import matplotlib.pyplot as plt\n",
"\n",
"triskele_path = Path('../triskele/python/')\n",
"sys.path.append(str(triskele_path.resolve()))\n",
"import triskele\n",
"\n",
"# Specific Utils\n",
"\n",
"def DFC_filter(raster):\n",
" raster[raster > 1e4] = raster[raster < 1e4].max()\n",
"\n",
"def show(im, im_size=1, save=None):\n",
" plt.figure(figsize=(16*im_size,3*im_size))\n",
" plt.imshow(im)\n",
" plt.colorbar()\n",
" \n",
" if save is not None:\n",
" plt.savefig(save, bbox_inches='tight', pad_inches=1)\n",
" \n",
" plt.show()\n",
"\n",
"def mshow(Xs, titles=None, im_size=1, save=None):\n",
" s = len(Xs)\n",
"\n",
" plt.figure(figsize=(16*im_size,3*im_size*s))\n",
"\n",
" for i in range(s):\n",
" plt.subplot(s,1,i+1)\n",
" plt.imshow(Xs[i])\n",
" \n",
" if titles is not None:\n",
" plt.title(titles[i])\n",
" \n",
" plt.colorbar()\n",
" \n",
" if save is not None:\n",
" plt.savefig(save, bbox_inches='tight', pad_inches=1)\n",
" \n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Kernel Density Estimation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"raster = triskele.read('../Data/test.tiff')\n",
"show(raster)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"kernel = stats.gaussian_kde(raster.reshape(-1))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import cv2\n",
"\n",
"test = cv2.imread('/home/florent/Pictures/Jura-Panorama.jpg')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"bins = [x for x in range(100)]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"kernel.pdf(bins)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.plot(bins, kernel.pdf(bins))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"show(test)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"kB = stats.gaussian_kde(test[:,:,0].reshape(-1))\n",
"kG = stats.gaussian_kde(test[:,:,1].reshape(-1))\n",
"kR = stats.gaussian_kde(test[:,:,2].reshape(-1))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"bins = [x for x in range(255)]\n",
"plt.plot(bins, kB.pdf(bins))\n",
"plt.plot(bins, kG.pdf(bins))\n",
"plt.plot(bins, kR.pdf(bins))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"bins = [x for x in range(10)]\n",
"plt.plot(bins, kB.pdf(bins))\n",
"plt.plot(bins, kG.pdf(bins))\n",
"plt.plot(bins, kR.pdf(bins))\n",
"plt.show()"
]
}
],
"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"
}
},
"nbformat": 4,
"nbformat_minor": 2
}