WORK IN PROGRESS: CVG
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@ -10,7 +10,7 @@
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import numpy as np
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class CVG:
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class CVG_legacy:
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def __init__(self, attributes, ground_truth, n_test=2, order_dim=0):
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self._order = order_dim
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self._ntests = n_test
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@ -48,3 +48,11 @@ class CVG:
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self._actual_ntest += 1
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return (Xtrain, Xtest, Ytrain, Ytest, train_filter)
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class APsCVG:
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"""Cross Validation Generator for Attribute Profiles Descriptors"""
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def __init__(self, ground_truth, attributes, cv_count=5, label_ignore=None):
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d
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return xtrain, xtest, ytrain, ytest, test_index
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@ -1,5 +1,25 @@
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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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"# 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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@ -11,29 +31,34 @@
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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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"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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"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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"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 raster files"
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"## List of raster files"
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]
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},
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{
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@ -50,7 +75,10 @@
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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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" #'../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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@ -58,111 +86,59 @@
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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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"**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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"## Load rasters data"
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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": "code",
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"execution_count": null,
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"cell_type": "markdown",
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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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"Basic APs classification flow:\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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"- 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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"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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"dfc_filter.input = loader\n",
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"rasters_disp.input = dfc_filter\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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"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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"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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"### 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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@ -171,18 +147,17 @@
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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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"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": "code",
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"execution_count": null,
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"cell_type": "markdown",
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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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"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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@ -191,7 +166,15 @@
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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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"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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@ -200,14 +183,30 @@
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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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"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 vectors"
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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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@ -50,7 +50,7 @@
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"outputs": [],
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"source": [
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"plt.figure(figsize=fs)\n",
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"plt.imshow(gt)\n",
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"plt.imshow(gt, cmap=plt.get_cmap('GnBu'))\n",
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"plt.colorbar()\n",
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"plt.show()"
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]
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@ -118,30 +118,49 @@
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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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"## First Semantic Cross Validation Generator Prototype\n",
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"\n",
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"**WORK IN PROGRESS** you 'll have fun"
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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 matplotlib.pyplot as plt\n",
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"import matplotlib.animation\n",
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"import numpy as np\n",
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"nb_split = 10\n",
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"step = 0\n",
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"\n",
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"t = np.linspace(0,2*np.pi)\n",
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"x = np.sin(t)\n",
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"test_part = 1 / nb_split\n",
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"\n",
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"fig, ax = plt.subplots()\n",
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"ax.axis([0,2*np.pi,-1,1])\n",
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"l, = ax.plot([],[])\n",
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"split = np.zeros_like(gt)\n",
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"\n",
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"def animate(i):\n",
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" l.set_data(t[:i], x[:i])\n",
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"\n",
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"ani = matplotlib.animation.FuncAnimation(fig, animate, frames=len(t))\n",
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"\n",
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"from IPython.display import HTML\n",
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"HTML(ani.to_jshtml())"
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"for lbli, lblc in zip(count[0][1:], count[1][1:]):\n",
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" treshold = int(lblc * test_part)\n",
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" #print('lbli:{}, count:{}, train:{}'.format(lbli, lblc, treshold))\n",
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" f = np.nonzero(gt == lbli)\n",
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" train, test\n",
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" split[f[0][treshold * step:treshold * (step + 1)], f[1][:treshold]] = 2\n",
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" split[f[0][treshold:], f[1][treshold:]] = 1\n",
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" \n",
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"plt.figure(figsize=fs)\n",
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"plt.imshow(split)\n",
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"#plt.imshow(split * (gt==1))\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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"gt == lbli"
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]
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},
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{
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"# Metaclasses "
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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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"gt"
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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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@ -200,15 +210,6 @@
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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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"lbl"
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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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@ -244,7 +245,81 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"all_meta_view[all_meta_view['metaclass_index'] == 0]['index']"
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"np.array(all_meta_view[all_meta_view['metaclass_label'] == 'Roads']['index'])"
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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=fs)\n",
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"plt.imshow(gt * np.isin(gt, np.array(all_meta_view[all_meta_view['metaclass_label'] == 'Roads']['index'])))\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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"count = np.unique(gt, return_counts=True)\n",
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"df_count = pd.DataFrame(np.array(count).T, columns=['index', 'count'])\n",
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"df_count.head()"
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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 seaborn as sns\n",
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"\n",
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"df_tmp = meta_idx.merge(df_count).merge(meta_lbl).merge(dfc_lbl).drop(index=0)\n",
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"display(df_tmp.head())\n",
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"\n",
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"g = sns.barplot(data=df_tmp, x='label', y='count')\n",
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"plt.xticks(rotation=80)\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": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"meta_count_view = pd.merge(meta_idx, df_count)\n",
|
||||
"display(meta_count_view.head())\n",
|
||||
"meta_count_view.drop('index', 1, inplace=True)\n",
|
||||
"meta_count_view = meta_count_view.groupby('metaclass_index', as_index=False).sum()\n",
|
||||
"display(meta_count_view.head())\n",
|
||||
"\n",
|
||||
"pd.merge(meta_lbl, meta_count_view)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"meta_index = np.array(meta_idx['metaclass_index'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"plt.figure(figsize=fs)\n",
|
||||
"plt.imshow(meta_index[gt])\n",
|
||||
"plt.colorbar()\n",
|
||||
"plt.show()"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
||||
412
Notebooks/Legacy Attribute Profiles Classifier.ipynb
Normal file
412
Notebooks/Legacy Attribute Profiles Classifier.ipynb
Normal file
@ -0,0 +1,412 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import sys\n",
|
||||
"from pathlib import Path\n",
|
||||
"import numpy as np\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"\n",
|
||||
"triskele_path = Path('../triskele/python/')\n",
|
||||
"sys.path.append(str(triskele_path.resolve()))\n",
|
||||
"import triskele"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def show(im):\n",
|
||||
" plt.figure(figsize=(16*2,3*2))\n",
|
||||
" plt.imshow(im)\n",
|
||||
" plt.colorbar()\n",
|
||||
" plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## List raster files"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"layers_files = [\n",
|
||||
" '../Data/phase1_rasters/DEM+B_C123/UH17_GEM051_TR.tif',\n",
|
||||
" '../Data/phase1_rasters/DEM_C123_3msr/UH17_GEG051_TR.tif',\n",
|
||||
" '../Data/phase1_rasters/DEM_C123_TLI/UH17_GEG05_TR.tif',\n",
|
||||
" '../Data/phase1_rasters/DSM_C12/UH17c_GEF051_TR.tif',\n",
|
||||
" '../Data/phase1_rasters/Intensity_C1/UH17_GI1F051_TR.tif',\n",
|
||||
" '../Data/phase1_rasters/Intensity_C2/UH17_GI2F051_TR.tif',\n",
|
||||
" '../Data/phase1_rasters/Intensity_C3/UH17_GI3F051_TR.tif',\n",
|
||||
" #'../Data/ground_truth/2018_IEEE_GRSS_DFC_GT_TR.tif'\n",
|
||||
"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Define dataset dependent raster filtering"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def DFC_filter(raster):\n",
|
||||
" ## Remove extrem values\n",
|
||||
" #raster[raster == raster.max()] = raster[raster != raster.max()].max()\n",
|
||||
" raster[raster > 1e4] = raster[raster < 1e4].max()\n",
|
||||
" #raster[raster == np.finfo(raster.dtype).max] = raster[raster != raster.max()].max()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Load rasters data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"layers = list()\n",
|
||||
"\n",
|
||||
"for file in layers_files:\n",
|
||||
" print('Loading {}'.format(file))\n",
|
||||
" layer = triskele.read(file)\n",
|
||||
" DFC_filter(layer)\n",
|
||||
" layers.append(layer)\n",
|
||||
"\n",
|
||||
"layers_stack = np.stack(layers, axis=2)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Display rasters"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"for i in range(layers_stack.shape[2]):\n",
|
||||
" plt.figure(figsize=(16*2,3*2))\n",
|
||||
" plt.imshow(layers_stack[:,:,i])\n",
|
||||
" plt.colorbar()\n",
|
||||
" plt.title(layers_files[i])\n",
|
||||
" plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Attributes filter with TRISKELE !"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"area = np.array([10, 100, 1e3, 1e4, 1e5])\n",
|
||||
"sd = np.array([0.5,0.9,0.99,0.999,0.9999])#,1e4,1e5,5e5])\n",
|
||||
"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",
|
||||
"\n",
|
||||
"t = triskele.Triskele(layers_stack[:,:,:], verbose=False)\n",
|
||||
"attributes = t.filter(tree='tos-tree',\n",
|
||||
" area=area,\n",
|
||||
" #standard_deviation=sd,\n",
|
||||
" #moment_of_inertia=moi\n",
|
||||
" )\n",
|
||||
"attributes.shape"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"for i in range(attributes.shape[2]-1):\n",
|
||||
" plt.figure(figsize=(16*2,3*2))\n",
|
||||
" plt.imshow(attributes[:,:,i])\n",
|
||||
" plt.colorbar()\n",
|
||||
" plt.show()\n",
|
||||
" plt.figure(figsize=(16*2,3*2))\n",
|
||||
" plt.imshow(attributes[:,:,i+1].astype(np.float) - attributes[:,:,i])\n",
|
||||
" plt.colorbar()\n",
|
||||
" #plt.title(layers_files[i])\n",
|
||||
"plt.show()\n",
|
||||
"plt.figure(figsize=(16*2,3*2))\n",
|
||||
"plt.imshow(attributes[:,:,-1])\n",
|
||||
"plt.colorbar()\n",
|
||||
"plt.show()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"plt.imshow((attributes[:,:,4].astype(np.float) - attributes[:,:,3])>0)\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"plt.imshow((attributes[:,:,4].astype(np.float) - attributes[:,:,3])<0)\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"show((attributes[:,:,6].astype(np.float) - attributes[:,:,5]))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"attributes[0,0,:] = 0"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Classification vectors"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"X = attributes.reshape(-1, attributes.shape[2])\n",
|
||||
"\n",
|
||||
"(attributes[0,0] == X[0]).all()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"labels_file = Path('../Data/ground_truth/2018_IEEE_GRSS_DFC_GT_TR.tif')\n",
|
||||
"labels = triskele.read(labels_file)\n",
|
||||
"display(labels.shape)\n",
|
||||
"\n",
|
||||
"plt.figure(figsize=(16*2,3*2))\n",
|
||||
"plt.imshow(labels)\n",
|
||||
"plt.colorbar()\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"Y = labels.reshape(-1)\n",
|
||||
"\n",
|
||||
"X.shape, Y.shape"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Random Forest Classifier"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import importlib\n",
|
||||
"from sklearn import metrics\n",
|
||||
"from sklearn.ensemble import RandomForestClassifier\n",
|
||||
"import pickle\n",
|
||||
"sys.path.insert(0, '..')\n",
|
||||
"import CrossValidationGenerator as cvg"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"importlib.reload(cvg)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn import metrics\n",
|
||||
"import pandas as pd\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def scores(actual, prediction):\n",
|
||||
" ct = pd.crosstab(prediction, actual,\n",
|
||||
" rownames=['Prediction'], colnames=['Reference'],\n",
|
||||
" margins=True, margins_name='Total',\n",
|
||||
" normalize=False # all, index, columns\n",
|
||||
" )\n",
|
||||
" display(ct)\n",
|
||||
" \n",
|
||||
" scores = metrics.precision_recall_fscore_support(actual, prediction)\n",
|
||||
" print(metrics.classification_report(actual, prediction)) "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"cv_labels = np.zeros(labels[:].shape)\n",
|
||||
"\n",
|
||||
"for xtrain, xtest, ytrain, ytest, train_index in cvg.CVG_legacy(attributes[:], labels[:], 10, 1): \n",
|
||||
" rfc = RandomForestClassifier(n_jobs=-1, random_state=0, n_estimators=100, verbose=True)\n",
|
||||
" rfc.fit(xtrain, ytrain)\n",
|
||||
" \n",
|
||||
" ypred = rfc.predict(xtest)\n",
|
||||
" \n",
|
||||
" display(ytest.shape, ypred.shape)\n",
|
||||
" \n",
|
||||
" scores(ytest, ypred)\n",
|
||||
" \n",
|
||||
" cv_labels[:,train_index == False] = ypred.reshape(cv_labels.shape[0], -1)\n",
|
||||
" "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"show(labels)\n",
|
||||
"show(cv_labels)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"plt.imsave('../Res/labels.png', labels)\n",
|
||||
"plt.imsave('../Res/prediction.png', cv_labels)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Scores"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"scores(actual=labels.reshape(-1), prediction=cv_labels.reshape(-1))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Labels\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" 0 – Unclassified\n",
|
||||
" 1 – Healthy grass\n",
|
||||
" 2 – Stressed grass\n",
|
||||
" 3 – Artificial turf\n",
|
||||
" 4 – Evergreen trees\n",
|
||||
" 5 – Deciduous trees\n",
|
||||
" 6 – Bare earth\n",
|
||||
" 7 – Water\n",
|
||||
" 8 – Residential buildings\n",
|
||||
" 9 – Non-residential buildings\n",
|
||||
" 10 – Roads\n",
|
||||
" 11 – Sidewalks\n",
|
||||
" 12 – Crosswalks\n",
|
||||
" 13 – Major thoroughfares\n",
|
||||
" 14 – Highways\n",
|
||||
" 15 – Railways\n",
|
||||
" 16 – Paved parking lots\n",
|
||||
" 17 – Unpaved parking lots\n",
|
||||
" 18 – Cars\n",
|
||||
" 19 – Trains\n",
|
||||
" 20 – Stadium seats\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@ -48,9 +48,9 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"l = ld2dap.LoadTIFF(layers_files[5:7])\n",
|
||||
"l = ld2dap.LoadTIFF(layers_files[0])\n",
|
||||
"t = ld2dap.Treshold(1e4)\n",
|
||||
"a = ld2dap.SelfDualAttributeProfiles(area = [1e3, 1e6], sd=[.4,.6,.8], moi=[.5,.9])\n",
|
||||
"a = ld2dap.SelfDualAttributeProfiles(area = [1e3, 1e6])#, sd=[.4,.6,.8], moi=[.5,.9])\n",
|
||||
"f = ld2dap.Differential()\n",
|
||||
"d = ld2dap.ShowFig(stack_id='all', symb=False)\n",
|
||||
"o = ld2dap.RawOutput()\n",
|
||||
|
||||
0
Notebooks/None0000000.png
Normal file
0
Notebooks/None0000000.png
Normal file
@ -1,6 +0,0 @@
|
||||
{
|
||||
"cells": [],
|
||||
"metadata": {},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@ -18,6 +18,9 @@ class Input(Node):
|
||||
|
||||
def register(self, output):
|
||||
self.outputs.append(output)
|
||||
|
||||
def unregister(self, output):
|
||||
self.outputs.remove(output)
|
||||
|
||||
def process(self, data, metadata=None):
|
||||
"""Override abstract method"""
|
||||
|
||||
@ -24,10 +24,16 @@ class Output(Node):
|
||||
self.__dict__[name] = value
|
||||
|
||||
def _input(self, inode):
|
||||
if not isinstance(inode, (Input)):
|
||||
if not isinstance(inode, (Input)) and inode is not None:
|
||||
raise NotImplementedError('{} is not an Input'.format(inode))
|
||||
|
||||
if self.__dict__['input'] is not None:
|
||||
self.__dict__['input'].unregister(self)
|
||||
|
||||
self.__dict__['input'] = inode
|
||||
inode.register(self)
|
||||
|
||||
if inode is not None:
|
||||
inode.register(self)
|
||||
|
||||
def _run(self):
|
||||
if self.input is None:
|
||||
|
||||
Loading…
Reference in New Issue
Block a user