165 lines
3.5 KiB
Plaintext
165 lines
3.5 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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"import matplotlib.pyplot as plt\n",
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"import collections\n",
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"\n",
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"ld2dap_path = Path('../')\n",
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"sys.path.append(str(ld2dap_path.resolve()))\n",
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"import ld2dap\n",
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"from ld2dap.core import Filter"
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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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"layers_files = [\n",
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" '../Data/phase1_rasters/DEM+B_C123/UH17_GEM051_TR.tif',\n",
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" '../Data/phase1_rasters/DEM_C123_3msr/UH17_GEG051_TR.tif',\n",
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" '../Data/phase1_rasters/DEM_C123_TLI/UH17_GEG05_TR.tif',\n",
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" '../Data/phase1_rasters/DSM_C12/UH17c_GEF051_TR.tif',\n",
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" '../Data/phase1_rasters/Intensity_C1/UH17_GI1F051_TR.tif',\n",
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" '../Data/phase1_rasters/Intensity_C2/UH17_GI2F051_TR.tif',\n",
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" '../Data/phase1_rasters/Intensity_C3/UH17_GI3F051_TR.tif'\n",
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"]"
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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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"# Node Streaming Local FeaturesAttribute Profiles\n",
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"\n",
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"We made some custom LFAPs in Python, but now Triskele comes with its owns in C++! Let's try it out then create a streaming node.\n",
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"\n",
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"## Triskele LFAPs"
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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 sys\n",
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"sys.path.append('../triskele/python/')\n",
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"import triskele"
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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(layers_files[0])\n",
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"raster.shape"
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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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"lfaps = triskele.Triskele(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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"results = lfaps.filter(area=[100, 1e4], feature='SD')\n",
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"results.shape, lfaps.process"
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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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"## Streamin Node"
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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 logging\n",
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"logger = logging.getLogger()\n",
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"logger.setLevel(logging.DEBUG)"
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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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"l = ld2dap.LoadTIFF(layers_files)\n",
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"t = ld2dap.Treshold(1e4)\n",
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"a = ld2dap.LocalFeatures([np.var, np.std, ])#, sd=[.4,.6,.8], moi=[.5,.9])\n",
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"d = ld2dap.ShowFig(stack_id='all', symb=False)\n",
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"o = ld2dap.RawOutput()\n",
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"\n",
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"o.input = a\n",
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"d.input = a\n",
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"a.input = t\n",
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"t.input = l\n",
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"\n",
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"d.run()"
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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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"print(o.metadata[0])"
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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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}
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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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