413 lines
9.7 KiB
Plaintext
413 lines
9.7 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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"\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"
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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 show(im):\n",
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" plt.figure(figsize=(16*2,3*2))\n",
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" plt.imshow(im)\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": "markdown",
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"metadata": {},
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"source": [
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"## List 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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"]"
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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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"## Define dataset dependent raster filtering"
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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 DFC_filter(raster):\n",
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" ## Remove extrem values\n",
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" #raster[raster == raster.max()] = raster[raster != raster.max()].max()\n",
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" raster[raster > 1e4] = raster[raster < 1e4].max()\n",
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" #raster[raster == np.finfo(raster.dtype).max] = raster[raster != raster.max()].max()"
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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 rasters 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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"layers = list()\n",
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"\n",
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"for file in layers_files:\n",
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" print('Loading {}'.format(file))\n",
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" layer = triskele.read(file)\n",
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" DFC_filter(layer)\n",
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" layers.append(layer)\n",
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"\n",
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"layers_stack = np.stack(layers, axis=2)"
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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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"for i in range(layers_stack.shape[2]):\n",
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" plt.figure(figsize=(16*2,3*2))\n",
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" plt.imshow(layers_stack[:,:,i])\n",
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" plt.colorbar()\n",
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" plt.title(layers_files[i])\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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"## Attributes filter with 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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"area = np.array([10, 100, 1e3, 1e4, 1e5])\n",
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"sd = np.array([0.5,0.9,0.99,0.999,0.9999])#,1e4,1e5,5e5])\n",
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"moi = np.array([0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0.09,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,0.99])\n",
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"\n",
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"t = triskele.Triskele(layers_stack[:,:,:], verbose=False)\n",
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"attributes = t.filter(tree='tos-tree',\n",
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" area=area,\n",
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" #standard_deviation=sd,\n",
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" #moment_of_inertia=moi\n",
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" )\n",
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"attributes.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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"for i in range(attributes.shape[2]-1):\n",
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" plt.figure(figsize=(16*2,3*2))\n",
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" plt.imshow(attributes[:,:,i])\n",
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" plt.colorbar()\n",
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" plt.show()\n",
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" plt.figure(figsize=(16*2,3*2))\n",
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" plt.imshow(attributes[:,:,i+1].astype(np.float) - attributes[:,:,i])\n",
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" plt.colorbar()\n",
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" #plt.title(layers_files[i])\n",
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"plt.show()\n",
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"plt.figure(figsize=(16*2,3*2))\n",
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"plt.imshow(attributes[:,:,-1])\n",
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"plt.colorbar()\n",
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"plt.show()\n"
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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.imshow((attributes[:,:,4].astype(np.float) - attributes[:,:,3])>0)\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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"plt.imshow((attributes[:,:,4].astype(np.float) - attributes[:,:,3])<0)\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((attributes[:,:,6].astype(np.float) - attributes[:,:,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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"attributes[0,0,:] = 0"
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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 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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