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@cordmaur
Created November 25, 2020 16:27
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import waterdetect as wd\n",
"import rasterio"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For this test we will use maxndwi as detection method and Nir-NDWI as combination. Like so, we need just 2 bands to be passed to the algorithm (B3 and Nir).<br>\n",
"Let's check if they are configured correctly on the WaterDetect.ini"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading configuration file WaterDetect.ini\n",
"File WaterDetect.ini verified.\n"
]
},
{
"data": {
"text/plain": [
"([['ndwi', 'Nir']], 'maxndwi')"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"config = wd.DWConfig(config_file='WaterDetect.ini')\n",
"config.clustering_bands, config.detect_water_cluster"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, let's open the two bands manually using rasterio and check if their shapes matches"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((1, 10980, 10980), (1, 10980, 10980))"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b3 = rasterio.open('D:\\Images\\Input\\Input_Tiete\\SENTINEL2A_20190326-132829-354_L2A_T22KGA_C_V2-0\\SENTINEL2A_20190326-132829-354_L2A_T22KGA_C_V2-0_FRE_B3.tif').read()\n",
"nir = rasterio.open('D:\\Images\\Input\\Input_Tiete\\SENTINEL2A_20190326-132829-354_L2A_T22KGA_C_V2-0\\SENTINEL2A_20190326-132829-354_L2A_T22KGA_C_V2-0_FRE_B8.tif').read()\n",
"b3.shape, nir.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will now prepare the dictionary to be passed to DWImageClustering. We will squeeze the rasters, as we want 2D (N x M) rasters and rasterio creates a new dimension to indicate the layer."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Selection of best number of clusters using Calinski-Harabasz Index:\n",
"k=2 :Calinski_harabaz index=236.895631586871\n",
"k=3 :Calinski_harabaz index=5092.139664424691\n",
"k=4 :Calinski_harabaz index=3422.004257352581\n",
"k=5 :Calinski_harabaz index=2679.80767812912\n",
"k=6 :Calinski_harabaz index=2148.1828279030738\n",
"k=7 :Calinski_harabaz index=1796.8882850591701\n",
"k=8 :Calinski_harabaz index=1633.2718640056298\n",
"k=9 :Calinski_harabaz index=3967.8377514397052\n",
"k=10 :Calinski_harabaz index=3529.059626732085\n",
"Applying clusters based naive bayes classifier\n",
"Assgnin 1 to cluster_id 2\n",
"Skipping cluster_id 2\n"
]
}
],
"source": [
"bands = {'Green': b3.squeeze()/10000, 'Nir': nir.squeeze()/10000}\n",
"wmask = wd.DWImageClustering(bands=bands, bands_keys=['Nir', 'ndwi'], invalid_mask=None, config=config)\n",
"mask = wmask.run_detect_water()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x2a4f9abebc8>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"plt.imshow(wmask.water_mask==1)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x2a4f286f7c8>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(wmask.cluster_matrix)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"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",
"version": "3.7.7"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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