187 lines
4.0 KiB
Plaintext
187 lines
4.0 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys\n",
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"from pathlib import Path\n",
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"import numpy as np\n",
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"from scipy import stats\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"triskele_path = Path('../triskele/python/')\n",
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"sys.path.append(str(triskele_path.resolve()))\n",
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"import triskele\n",
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"\n",
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"# Specific Utils\n",
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"\n",
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"def DFC_filter(raster):\n",
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" raster[raster > 1e4] = raster[raster < 1e4].max()\n",
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"\n",
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"def show(im, im_size=1, save=None):\n",
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" plt.figure(figsize=(16*im_size,3*im_size))\n",
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" plt.imshow(im)\n",
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" plt.colorbar()\n",
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" \n",
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" if save is not None:\n",
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" plt.savefig(save, bbox_inches='tight', pad_inches=1)\n",
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" \n",
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" plt.show()\n",
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"\n",
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"def mshow(Xs, titles=None, im_size=1, save=None):\n",
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" s = len(Xs)\n",
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"\n",
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" plt.figure(figsize=(16*im_size,3*im_size*s))\n",
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"\n",
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" for i in range(s):\n",
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" plt.subplot(s,1,i+1)\n",
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" plt.imshow(Xs[i])\n",
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" \n",
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" if titles is not None:\n",
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" plt.title(titles[i])\n",
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" \n",
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" plt.colorbar()\n",
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" \n",
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" if save is not None:\n",
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" plt.savefig(save, bbox_inches='tight', pad_inches=1)\n",
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" \n",
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" plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Kernel Density Estimation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"raster = triskele.read('../Data/test.tiff')\n",
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"show(raster)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"kernel = stats.gaussian_kde(raster.reshape(-1))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import cv2\n",
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"\n",
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"test = cv2.imread('/home/florent/Pictures/Jura-Panorama.jpg')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"bins = [x for x in range(100)]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"kernel.pdf(bins)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.plot(bins, kernel.pdf(bins))\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"show(test)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"kB = stats.gaussian_kde(test[:,:,0].reshape(-1))\n",
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"kG = stats.gaussian_kde(test[:,:,1].reshape(-1))\n",
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"kR = stats.gaussian_kde(test[:,:,2].reshape(-1))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"bins = [x for x in range(255)]\n",
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"plt.plot(bins, kB.pdf(bins))\n",
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"plt.plot(bins, kG.pdf(bins))\n",
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"plt.plot(bins, kR.pdf(bins))\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"bins = [x for x in range(10)]\n",
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"plt.plot(bins, kB.pdf(bins))\n",
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"plt.plot(bins, kG.pdf(bins))\n",
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"plt.plot(bins, kR.pdf(bins))\n",
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"plt.show()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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