VHR Factory
This commit is contained in:
parent
16ca49cdb5
commit
6b713f6323
@ -233,6 +233,15 @@
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"## Interpolate NoData"
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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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"griddata"
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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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@ -378,31 +387,10 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"def rasterize(spatial, values, resolution=1., method='nearest'):\n",
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" '''\n",
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" Rasterize spatialised data.\n",
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"\n",
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" :param spatial: Coordinates of the points (x, y, z)\n",
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" :type spatial: ndarray\n",
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" :param values: Values of the points\n",
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" :type values: ndarray\n",
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" :param resolution: Resolution of the raster in meters\n",
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" :type resolution: float\n",
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" :param method: Method of interpolation for NoData values {'linear', 'nearest', 'cubic'}\n",
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" :type method: string\n",
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" :return: 2D raster of the values along z axis\n",
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" :rtype: ndarray\n",
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"\n",
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" .. warning:: WIP\n",
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" '''\n",
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" \n",
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" resolution = 0.5\n",
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"\n",
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"spatial_data = np.array((C1['x'], C1['y'], C1['z'])).T\n",
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"\n",
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"bins = np.rint((spatial_data.max(axis=0) - spatial_data.min(axis=0)) / resolution).astype(int)\n",
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"display(bins)\n",
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" "
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"import importlib\n",
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"sys.path.append(\"..\")\n",
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"import rasterizer\n",
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"importlib.reload(rasterizer)"
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]
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},
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{
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@ -411,7 +399,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"rasterize griddata"
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"myraster = rasterizer.rasterize(spatial_data, C1['intensity'], 1.)"
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]
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},
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{
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@ -420,7 +408,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"infile = laspy.file.File('../Data/lidar/C1/272056_3289689.las')"
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"myraster_r = rasterizer.rasterize(spatial_data, C1['intensity'], 1., reverse_alt=True)"
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]
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},
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{
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@ -429,7 +417,34 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"infile.reader.get_dimension('X')"
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"plt.imsave('../Res/myrstr.png', myraster, origin='upper')"
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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.imsave('../Res/myrstr_r.png', myraster_r, origin='upper')"
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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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"mid_layer = myraster - myraster_r"
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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.imsave('../Res/myrstr_layer_1.png', mid_layer, origin='upper')"
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]
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},
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{
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@ -459,6 +474,52 @@
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"plt.show()\n",
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"plt.imsave('../Res/dfc_c1.png', dfc_c1)"
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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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"def rasterize_cache(spatial, values, resolution=1., method='linear', reverse_alt=False, cache_dir='/tmp'):\n",
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" \"\"\"Cache layer for rasterize\"\"\"\n",
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" \n",
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" cache_dir = Path(cache_dir)\n",
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" name = '{}_{}_{}_{}{}'.format(data['name'], field, str(resolution).replace('.', '_'), method,\n",
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" '_reversed' if reverse_alt else '')\n",
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" png_file = cache_dir.joinpath(Path(name + '.png'))\n",
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" tif_file = cache_dir.joinpath(Path(name + '.tif'))\n",
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" \n",
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" if tif_file.exists() :\n",
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" print ('WARNING: Loading cached result {}'.format(tif_file))\n",
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" raster = triskele.read(tif_file)\n",
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" else: \n",
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" raster = rasterizer.rasterize(spatial, values, resolution, method, reverse_alt)\n",
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" triskele.write(tif_file, raster)\n",
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" plt.imsave(png_file, raster)\n",
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" \n",
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" return raster\n",
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"\n",
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"rasterize_cache(spatial_data, C1['z'], 10, cache_dir='../Res/TMP')"
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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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"td = Path(Path('/tmp'))\n",
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"tf = Path('test.png')\n",
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"td.joinpath(tf).j"
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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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271
Notebooks/Raster Factory.ipynb
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271
Notebooks/Raster Factory.ipynb
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@ -0,0 +1,271 @@
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{
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"cells": [
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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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"# Custom Raster Gen for LD2DAPs"
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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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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"sys.path.append(\"..\")\n",
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"import rasterizer\n",
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"import raster_assistant as ra\n",
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"\n",
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"sys.path.append('../triskele/python/')\n",
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"import triskele\n",
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"\n",
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"figsize = np.array((16, 3)) * 1.5"
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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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"## Load LAS data"
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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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"C1 = ra.bulk_load('../Data/lidar/C1', 'C1', filter_treshold=.5)\n",
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"C2 = ra.bulk_load('../Data/lidar/C2', 'C1', filter_treshold=.5)\n",
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"C3 = ra.bulk_load('../Data/lidar/C3', 'C1', filter_treshold=.5)\n",
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"C123 = ra.bulk_load('../Data/lidar', 'C123', filter_treshold=.5)"
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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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"## Raster Validation\n",
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"\n",
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"### Rasterize some data"
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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 = rasterizer.rasterize(C1.spatial, C1.intensity, 0.5)"
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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.figure(figsize=figsize)\n",
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"plt.imshow(raster, origin='upper')\n",
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"plt.show()\n",
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"plt.imsave('../Res/raster_validation.png', 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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"plt.hist(raster.flatten(), bins=1000)\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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"### Write TIFF file"
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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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"triskele.write('../Res/validation.tiff', raster)"
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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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"### Compare with DFC dataset"
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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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"dfc = triskele.read('../Data/phase1_rasters/Intensity_C1/UH17_GI1F051_TR.tif')\n",
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"our = triskele.read('../Res/validation.tiff')"
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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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"#### Filter DFC with same parameters"
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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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"ra.auto_filter(dfc, treshold=0.5)"
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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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"#### Display Stats"
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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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"dfc.shape, our.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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"dfc.dtype, our.dtype"
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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.hist(dfc.flatten(), bins=1000, label='DFC')\n",
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"plt.hist(our.flatten(), bins=1000, label='Our', alpha=.8)\n",
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"plt.legend()\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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"#### Display Rasters"
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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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"f, axs = plt.subplots(2, figsize=figsize * 2)\n",
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"\n",
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"axs[0].imshow(dfc)\n",
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"axs[0].set_title('DFC')\n",
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"axs[1].imshow(our)\n",
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"axs[1].set_title('Our')\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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"## Raster Pack"
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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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"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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"from multiprocessing import Pool, Process, Queue\n",
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"import multiprocessing as mp\n",
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"#mp.set_start_method('spawn')\n",
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"\n",
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"def rasterize_cache_mp(data_var, field, res, method, reverse, cache):\n",
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" if data_var == 'C1':\n",
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" ra.rasterize_cache(C1, field, res, method, cache)\n",
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" if data_var == 'C2':\n",
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" ra.rasterize_cache(C2, field, res, method, cache)\n",
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" if data_var == 'C3':\n",
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" ra.rasterize_cache(C3, field, res, method, cache)\n",
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" if data_var == 'C123':\n",
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" ra.rasterize_cache(C123, field, res, method, cache)\n",
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" \n",
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"pool = Pool(processes=20)\n",
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"\n",
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"job_args = list()\n",
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"for field in ('z', 'intensity', 'num_returns', 'gps_time'):\n",
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" for data in ('C1', 'C2', 'C3', 'C123'):\n",
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" job_args.append([data, field, .5, 'linear', False, '../Res/HVR/'])\n",
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" \n",
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"for i in pool.starmap(rasterize_cache_mp, job_args):\n",
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" pass"
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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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60
Notebooks/Test Raster Assistant.ipynb
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60
Notebooks/Test Raster Assistant.ipynb
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{
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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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"sys.path.append(\"..\")\n",
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"import raster_assistant as ra"
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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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"C1 = ra.bulk_load('../Data/lidar/C1/272056_3289689.las', 'C1mini')"
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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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"ra.rasterize_cache(C1, 'z', 10, cache_dir='../Res/TMP')"
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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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96
raster_assistant.py
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96
raster_assistant.py
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#!/usr/bin/python
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# -*- coding: utf-8 -*-
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# \file raster_assistant.py
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# \brief TODO
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# \author Florent Guiotte <florent.guiotte@gmail.com>
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# \version 0.1
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# \date 03 juil. 2018
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#
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# TODO details
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import laspy
|
||||
sys.path.append('../triskele/python/')
|
||||
import triskele
|
||||
import rasterizer
|
||||
|
||||
def rasterize_cache(data, field, resolution=1., method='linear', reverse_alt=False, cache_dir='/tmp'):
|
||||
"""Cache layer for rasterize"""
|
||||
|
||||
cache_dir = Path(cache_dir)
|
||||
name = '{}_{}_{}_{}{}'.format(data.name, field, str(resolution).replace('.', '_'), method,
|
||||
'_reversed' if reverse_alt else '')
|
||||
png_file = cache_dir.joinpath(Path(name + '.png'))
|
||||
tif_file = cache_dir.joinpath(Path(name + '.tif'))
|
||||
|
||||
if tif_file.exists() :
|
||||
print ('WARNING: Loading cached result {}'.format(tif_file))
|
||||
raster = triskele.read(tif_file)
|
||||
else:
|
||||
raster = rasterizer.rasterize(data.spatial, getattr(data, field), resolution, method, reverse_alt)
|
||||
triskele.write(tif_file, raster)
|
||||
plt.imsave(png_file, raster)
|
||||
|
||||
return raster
|
||||
|
||||
def find_las(path):
|
||||
path = Path(path)
|
||||
las = list()
|
||||
|
||||
if path.is_dir():
|
||||
for child in path.iterdir():
|
||||
las.extend(find_las(child))
|
||||
|
||||
if path.is_file() and path.suffix == '.las':
|
||||
las.append(path)
|
||||
|
||||
return las
|
||||
|
||||
def auto_filter(data, treshold=.5):
|
||||
tresholds = np.percentile(data, [treshold, 100 - treshold])
|
||||
|
||||
data[data < tresholds[0]] = tresholds[0]
|
||||
data[data > tresholds[1]] = tresholds[1]
|
||||
|
||||
def bulk_load(path, name=None, filter_treshold=.1):
|
||||
data = {'file': path, 'name': name}
|
||||
|
||||
attributes = ['x', 'y', 'z', 'intensity', 'num_returns']#, 'scan_angle_rank', 'pt_src_id', 'gps_time']
|
||||
for a in attributes:
|
||||
data[a] = list()
|
||||
|
||||
print('Load data...')
|
||||
for f in find_las(path):
|
||||
print('{}: '.format(f), end='')
|
||||
infile = laspy.file.File(f)
|
||||
for i, a in enumerate(attributes):
|
||||
print('\r {}: [{:3d}%]'.format(f, int(i/len(attributes) * 100)), end='')
|
||||
data[a].extend(getattr(infile, a))
|
||||
infile.close()
|
||||
print('\r {}: [Done]'.format(f))
|
||||
|
||||
|
||||
print('Create matrices...', end='')
|
||||
for i, a in enumerate(attributes):
|
||||
print('\rCreate matrices: [{:3d}%]'.format(int(i/len(attributes) * 100)), end='')
|
||||
data[a] = np.array(data[a])
|
||||
print('\rCreate matrices: [Done]')
|
||||
|
||||
print('Filter data...', end='')
|
||||
for i, a in enumerate(attributes):
|
||||
print('\rFilter data: [{:3d}%]'.format(int(i/len(attributes) * 100)), end='')
|
||||
if a == 'x' or a == 'y':
|
||||
continue
|
||||
auto_filter(data[a], filter_treshold)
|
||||
print('\rFilter data: [Done]')
|
||||
|
||||
data['spatial'] = np.array((data['x'], data['y'], data['z'])).T
|
||||
|
||||
class TMPLAS(object):
|
||||
def __init__(self, d):
|
||||
self.__dict__.update(d)
|
||||
|
||||
return TMPLAS(data)
|
||||
71
rasterizer.py
Normal file
71
rasterizer.py
Normal file
@ -0,0 +1,71 @@
|
||||
#!/usr/bin/python
|
||||
# -*- coding: utf-8 -*-
|
||||
# \file rasterizer.py
|
||||
# \brief TODO
|
||||
# \author Florent Guiotte <florent.guiotte@gmail.com>
|
||||
# \version 0.1
|
||||
# \date 03 juil. 2018
|
||||
#
|
||||
# TODO details
|
||||
|
||||
import numpy as np
|
||||
from scipy.interpolate import griddata
|
||||
|
||||
def rasterize(spatial, values, resolution=1., method='linear', reverse_alt=False):
|
||||
'''
|
||||
Rasterize spatialised data.
|
||||
|
||||
:param spatial: Coordinates of the points [x, y, z] shape (n, 3)
|
||||
:type spatial: ndarray
|
||||
:param values: Values of the points
|
||||
:type values: ndarray
|
||||
:param resolution: Resolution of the raster in meters
|
||||
:type resolution: float
|
||||
:param method: Method of interpolation for NoData values {'linear', 'nearest', 'cubic'}
|
||||
:type method: string
|
||||
:return: 2D raster of the values along z axis
|
||||
:rtype: ndarray
|
||||
|
||||
.. warning:: WIP
|
||||
'''
|
||||
|
||||
bins = np.rint((spatial.max(axis=0) - spatial.min(axis=0)) / resolution).astype(int)
|
||||
|
||||
print('Compute histogram...')
|
||||
voxels_histo, edges = np.histogramdd(spatial, bins=bins)
|
||||
|
||||
print('Compute weighted histogram...')
|
||||
voxels_histo_v, edges = np.histogramdd(spatial, bins=bins, weights=values)
|
||||
|
||||
layers = voxels_histo.shape[2]
|
||||
|
||||
raster = np.zeros_like(voxels_histo.T[0])
|
||||
raster[:] = np.nan
|
||||
|
||||
print('Compute voxels value...')
|
||||
voxels = (voxels_histo_v / voxels_histo).T
|
||||
|
||||
print('Stack voxels...')
|
||||
for i in reversed(range(layers)) if not reverse_alt else range(layers):
|
||||
nan_filter = np.isnan(raster)
|
||||
raster[nan_filter] = voxels[i][nan_filter]
|
||||
|
||||
print('Interpolate NoData...')
|
||||
if method != 'nearest':
|
||||
raster = interpolate(raster, method)
|
||||
|
||||
print('Final touches...')
|
||||
raster = interpolate(raster, 'nearest')
|
||||
|
||||
return np.flip(raster, 0)
|
||||
|
||||
def interpolate(raster, method='linear'):
|
||||
nan_filter = np.isnan(raster)
|
||||
val_filter = np.logical_not(nan_filter)
|
||||
coords = np.argwhere(val_filter)
|
||||
values = raster[val_filter]
|
||||
grid = np.argwhere(nan_filter)
|
||||
|
||||
raster[nan_filter] = griddata(coords, values, grid, method=method)
|
||||
return raster
|
||||
|
||||
Loading…
Reference in New Issue
Block a user