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Florent Guiotte 2018-04-05 13:57:46 +02:00
parent c900add842
commit 648991bf51
2 changed files with 269 additions and 94 deletions
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230
Notebooks/APs.ipynb Normal file
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@ -0,0 +1,230 @@
{
"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": [
"# Specific Utils\n",
"\n",
"def DFC_filter(raster):\n",
" raster[raster > 1e4] = raster[raster < 1e4].max()\n",
"\n",
"def show(im, im_size=1, save=None):\n",
" plt.figure(figsize=(16*im_size,3*im_size))\n",
" plt.imshow(im)\n",
" plt.colorbar()\n",
" \n",
" if save is not None:\n",
" plt.savefig(save, bbox_inches='tight', pad_inches=1)\n",
" \n",
" plt.show()\n",
"\n",
"def mshow(Xs, titles=None, im_size=1, save=None):\n",
" s = len(Xs)\n",
"\n",
" plt.figure(figsize=(16*im_size,3*im_size*s))\n",
"\n",
" for i in range(s):\n",
" plt.subplot(s,1,i+1)\n",
" plt.imshow(Xs[i])\n",
" \n",
" if titles is not None:\n",
" plt.title(titles[i])\n",
" \n",
" plt.colorbar()\n",
" \n",
" if save is not None:\n",
" plt.savefig(save, bbox_inches='tight', pad_inches=1)\n",
" \n",
" plt.show()"
]
},
{
"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[:,:,0], verbose=False)\n",
"attributes_min = t.filter(tree='min-tree',\n",
" area=area,\n",
" #standard_deviation=sd,\n",
" #moment_of_inertia=moi\n",
" )\n",
"attributes_max = t.filter(tree='max-tree',\n",
" area=area,\n",
" #standard_deviation=sd,\n",
" #moment_of_inertia=moi\n",
" )\n",
"\n",
"attributes_min_lbl = ['origin']\n",
"attributes_min_lbl.extend(['Thickening area {}'.format(x) for x in area])\n",
"attributes_max_lbl = ['origin']\n",
"attributes_max_lbl.extend(['Thinning area {}'.format(x) for x in area])\n",
"\n",
"attributes_min.shape, attributes_max.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"attributes = np.dstack((attributes_min[:,:,:0:-1], attributes_max))\n",
"attributes_lbl = attributes_min_lbl[:0:-1]\n",
"attributes_lbl.extend(attributes_max_lbl)\n",
"\n",
"attributes.shape, attributes_lbl"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"figs = list()\n",
"\n",
"attributes[0,0,:] = 255 # J'ai honte...\n",
"\n",
"for i in range(attributes.shape[-1]):\n",
" figs.append(attributes[:,:,i])\n",
"\n",
"mshow(figs, attributes_lbl, 2)"
]
}
],
"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
}

133
Notebooks/Attribute Profiles Classifier.ipynb
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@ -16,6 +16,19 @@
"import triskele" "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", "cell_type": "markdown",
"metadata": {}, "metadata": {},
@ -271,19 +284,6 @@
" " " "
] ]
}, },
{
"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": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
@ -295,12 +295,10 @@
] ]
}, },
{ {
"cell_type": "code", "cell_type": "markdown",
"execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [],
"source": [ "source": [
"labels.shape" "## Scores"
] ]
}, },
{ {
@ -309,90 +307,37 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"np.arange(238400).reshape(-1, 4768)" "scores(actual=labels.reshape(-1), prediction=cv_labels.reshape(-1))"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "markdown",
"execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [],
"source": [ "source": [
"with open('../Res/classifier_0.pkl', 'wb') as f:\n", "#### Labels\n",
" pickle.dump(rfc, f)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Yp = Y.copy()\n",
"\n", "\n",
"Yp[training == False] = rfc.predict(X[training == False])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.figure(figsize=(16*2,3*2))\n",
"plt.imshow(Y.reshape(labels.shape))\n",
"plt.colorbar()\n",
"plt.show()\n",
"\n", "\n",
"plt.figure(figsize=(16*2,3*2))\n", " 0 – Unclassified\n",
"plt.imshow(Yp.reshape(labels.shape))\n", " 1 – Healthy grass\n",
"plt.colorbar()\n", " 2 – Stressed grass\n",
"plt.show()\n", " 3 – Artificial turf\n",
"\n", " 4 – Evergreen trees\n",
"plt.figure(figsize=(16*2,3*2))\n", " 5 – Deciduous trees\n",
"plt.imshow(Yp.reshape(labels.shape).astype(np.float) - labels)\n", " 6 – Bare earth\n",
"plt.colorbar()\n", " 7 – Water\n",
"plt.show()" " 8 – Residential buildings\n",
] " 9 – Non-residential buildings\n",
}, " 10 – Roads\n",
{ " 11 – Sidewalks\n",
"cell_type": "code", " 12 – Crosswalks\n",
"execution_count": null, " 13 – Major thoroughfares\n",
"metadata": {}, " 14 – Highways\n",
"outputs": [], " 15 – Railways\n",
"source": [ " 16 – Paved parking lots\n",
"class cvg:\n", " 17 – Unpaved parking lots\n",
" def __init__(self, attributes, ground_truth, order_dim=0, n_test=2): \n", " 18 – Cars\n",
" self._tests_left = n_test\n", " 19 – Trains\n",
" \n", " 20 – Stadium seats\n"
" if attributes.shape[0] != ground_truth.shape[0] or \\\n",
" attributes.shape[1] != ground_truth.shape[1] :\n",
" raise ValueError('attributes and ground_truth must have the same 2D shape')\n",
" \n",
" def __iter__(self):\n",
" return self\n",
" \n",
" def __next__(self):\n",
" if self._tests_left == 0:\n",
" raise StopIteration\n",
" \n",
" train_filter = np.arange(attributes.shape) < (Y.size * .50)\n",
"\n",
" Xtrain = 42\n",
" Xtest = 432\n",
" Ytrain = 12\n",
" Ytest = 123\n",
" \n",
" return (Xtrain, Xtest, Ytrain, Ytest)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cvg(attributes, labels[:,:-1])"
] ]
} }
], ],