412 lines
9.4 KiB
Plaintext
412 lines
9.4 KiB
Plaintext
{
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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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"# Attribute Profiles Classification Prototype\n",
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"\n",
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"Ground classification (2D) of LiDAR data with Attribute Profiles (APs) on pre-calculated rasters from LiDAR point cloud processing (DEMs, intensity maps...).\n",
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"\n",
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"We will use the LD2DAPs package to compute profiles and try to generalize the process to automatise the classification."
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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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"## Setup\n",
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"\n",
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"### Packages"
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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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"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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"\n",
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"sys.path.append(str(Path('..').resolve()))\n",
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"import ld2dap\n",
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"\n",
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"sys.path.append(str(Path('../triskele/').resolve()))\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": "markdown",
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"metadata": {},
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"source": [
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"### Fuctions and constants"
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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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"figsize=np.array([16,9]) * 1."
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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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"## List of raster files"
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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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" #'../Data/ground_truth/2018_IEEE_GRSS_DFC_GT_TR.tif',\n",
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" #'../Res/HVR/C123_num_returns_0_5_nearest.tif',\n",
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" '../Res/HVR noisy/C123_num_returns_0_5_nearest.tif'\n",
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"\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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"**IDEA:** We could try to combinate rasters into new ones (e.g. $R_{DSM} - R_{DTM}$ to obtain trees and building height map)"
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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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"## Create the Profiles Pattern"
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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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"Basic APs classification flow:\n",
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"\n",
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"- Load rasters\n",
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"- Filter input rasters with a treshold value: for reasons DFC rasters are noisy with very high values\n",
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"- Construct filtered rasters with basic attributes profiles\n",
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" + Area: [10, 100, 1e3, ..., 1e4]\n",
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" + ...\n"
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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 and filter 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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"loader = ld2dap.LoadTIFF(layers_files)\n",
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"dfc_filter = ld2dap.Treshold(1e4)\n",
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"rasters_disp = ld2dap.ShowFig('all')\n",
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"\n",
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"dfc_filter.input = loader\n",
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"rasters_disp.input = dfc_filter\n",
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"\n",
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"loader.run()"
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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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"### Compute APs\n",
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"\n",
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"Choose area filter tresholds."
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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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"areas = [10., 100.]\n",
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"areas.extend([x * 1e3 for x in range(1,100,2)])\n",
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"plt.plot(areas, '.')\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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"Disable previous display then add the APs node and the vectors output to the flow ."
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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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"rasters_disp.input = None\n",
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"\n",
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"aps = ld2dap.AttributeProfiles(area=areas)\n",
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"aps.input = dfc_filter\n",
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"\n",
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"out_vectors = ld2dap.RawOutput()\n",
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"out_vectors.input = aps\n",
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"\n",
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"out_vectors.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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"out_vectors.data.shape"
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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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"## Classification"
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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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"- Concatenate filtered rasters into pixel description vector\n",
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"- Split the vectors in train and test sets for cross validation with a spatial approach: random sampling is not good for spatial descriptors!\n",
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"- Random forests"
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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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"### Vectors"
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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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"X = attributes.reshape(-1, attributes.shape[2])\n",
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"\n",
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"(attributes[0,0] == X[0]).all()"
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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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"labels_file = Path('../Data/ground_truth/2018_IEEE_GRSS_DFC_GT_TR.tif')\n",
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"labels = triskele.read(labels_file)\n",
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"display(labels.shape)\n",
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"\n",
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"plt.figure(figsize=(16*2,3*2))\n",
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"plt.imshow(labels)\n",
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"plt.colorbar()\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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"Y = labels.reshape(-1)\n",
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"\n",
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"X.shape, Y.shape"
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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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"## Random Forest Classifier"
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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 importlib\n",
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"from sklearn import metrics\n",
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"from sklearn.ensemble import RandomForestClassifier\n",
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"import pickle\n",
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"sys.path.insert(0, '..')\n",
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"import CrossValidationGenerator as cvg"
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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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"importlib.reload(cvg)"
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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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"from sklearn import metrics\n",
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"import pandas as pd\n",
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"\n",
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"\n",
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"def scores(actual, prediction):\n",
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" ct = pd.crosstab(prediction, actual,\n",
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" rownames=['Prediction'], colnames=['Reference'],\n",
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" margins=True, margins_name='Total',\n",
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" normalize=False # all, index, columns\n",
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" )\n",
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" display(ct)\n",
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" \n",
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" scores = metrics.precision_recall_fscore_support(actual, prediction)\n",
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" print(metrics.classification_report(actual, prediction)) "
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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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"cv_labels = np.zeros(labels[:].shape)\n",
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"\n",
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"for xtrain, xtest, ytrain, ytest, train_index in cvg.CVG(attributes[:], labels[:], 10, 1): \n",
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" rfc = RandomForestClassifier(n_jobs=-1, random_state=0, n_estimators=100, verbose=True)\n",
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" rfc.fit(xtrain, ytrain)\n",
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" \n",
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" ypred = rfc.predict(xtest)\n",
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" \n",
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" display(ytest.shape, ypred.shape)\n",
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" \n",
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" scores(ytest, ypred)\n",
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" \n",
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" cv_labels[:,train_index == False] = ypred.reshape(cv_labels.shape[0], -1)\n",
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" "
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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(labels)\n",
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"show(cv_labels)"
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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/labels.png', labels)\n",
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"plt.imsave('../Res/prediction.png', cv_labels)"
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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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"## Scores"
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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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"scores(actual=labels.reshape(-1), prediction=cv_labels.reshape(-1))"
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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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"#### Labels\n",
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"\n",
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"\n",
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" 0 – Unclassified\n",
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" 1 – Healthy grass\n",
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" 2 – Stressed grass\n",
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" 3 – Artificial turf\n",
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" 4 – Evergreen trees\n",
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" 5 – Deciduous trees\n",
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" 6 – Bare earth\n",
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" 7 – Water\n",
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" 8 – Residential buildings\n",
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" 9 – Non-residential buildings\n",
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" 10 – Roads\n",
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" 11 – Sidewalks\n",
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" 12 – Crosswalks\n",
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" 13 – Major thoroughfares\n",
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" 14 – Highways\n",
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" 15 – Railways\n",
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" 16 – Paved parking lots\n",
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" 17 – Unpaved parking lots\n",
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" 18 – Cars\n",
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" 19 – Trains\n",
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" 20 – Stadium seats\n"
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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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