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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib notebook\n",
"import logging\n",
"logging.basicConfig(level=logging.INFO)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from sentinelloader import Sentinel2Loader"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from time import time\n",
"logger = logging.getLogger('sentinelloader')\n",
"\n",
"class Timer:\n",
" def __init__(self, name, debug=True):\n",
" self._name = name\n",
" self._debug = debug\n",
" self.start()\n",
" self._lastElapsed = None\n",
" \n",
" def start(self):\n",
" self._start = time()\n",
" if(self._debug):\n",
" logger.info('> [started] ' + self._name + '...')\n",
"\n",
" def stop(self):\n",
" self._lastElapsed = (time()-self._start)\n",
" if(self._debug):\n",
" logger.info('> [done] {} ({:.3f} ms)'.format(self._name, self._lastElapsed*1000))\n",
" \n",
" def elapsed(self):\n",
" if(self._lastElapsed != None):\n",
" return (self._lastElapsed)\n",
" else:\n",
" return (time()-self._start)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.patches as mpatches\n",
"from osgeo import gdal, osr\n",
"import cartopy.crs as ccrs\n",
"import numpy as np\n",
"\n",
"def showGeoTiffs(geoTiffFiles, geometries=None, labels=None, cols=4, size=6, cmap=None, cmap_min=None, cmap_max=None, interpolation=None, legendHandles=None, group_by_label=False, name='image', output_dir=None, transformData=None):\n",
" \"\"\"geometries - list of geoseries from GeoPandas\n",
" labels - list os strings\n",
" getTiffFiles - list of tiff file paths\n",
" \"\"\"\n",
" logger.info('showing ' + str(len(geoTiffFiles)) + ' images')\n",
" fig = plt.figure()\n",
" rows = int(len(geoTiffFiles)/cols)+1\n",
" t = Timer('generating image patches. rows=' + str(rows) + '; cols=' + str(cols))\n",
" fig.set_size_inches(cols*size, rows*size)\n",
"\n",
" image_indexes = range(len(geoTiffFiles))\n",
"\n",
" #order indexes by label\n",
" if(group_by_label==True and image_labels!=None):\n",
" index_label_map = []\n",
" for i,label in enumerate(image_labels):\n",
" index_label_map.append((i,label))\n",
" label_image_map = np.array(index_label_map, dtype=[('index',int),('label',int)])\n",
" label_image_map = np.sort(label_image_map, order='label')\n",
" image_indexes = []\n",
" for a in label_image_map:\n",
" image_indexes.append(a[0])\n",
"\n",
" c = 0\n",
" for i in image_indexes:\n",
" ds = gdal.Open(geoTiffFiles[i])\n",
" data = ds.ReadAsArray()\n",
" gt = ds.GetGeoTransform()\n",
" proj = ds.GetProjection()\n",
"\n",
" inproj = osr.SpatialReference()\n",
" inproj.ImportFromWkt(proj)\n",
"\n",
" projcs = inproj.GetAuthorityCode('PROJCS')\n",
" projection = ccrs.epsg(projcs)\n",
"\n",
" ax = fig.add_subplot(rows,cols,c+1, projection=projection)\n",
"\n",
" extent = (gt[0], gt[0] + ds.RasterXSize * gt[1],\n",
" gt[3] + ds.RasterYSize * gt[5], gt[3])\n",
"\n",
" \n",
" #seems like this data has multiple channels and the channels area indexed from the first position. invert it\n",
" if data.shape[0]<5:\n",
" data = data[:3, :, :].transpose((1, 2, 0))\n",
" else:\n",
" if transformData!=None:\n",
" data = transformData(data)\n",
"\n",
" if data.dtype in [np.uint8, np.uint16, np.uint32]:\n",
" if cmap_min==None:\n",
" cmap_min = np.iinfo(data.dtype).min\n",
" if cmap_max==None:\n",
" cmap_max = np.iinfo(data.dtype).max\n",
"# if(cmap==None):\n",
"# data = data.astype('uint8')\n",
" \n",
" ax.imshow(data, alpha=1.0, transform=projection, extent=extent, cmap=cmap, vmin=cmap_min, vmax=cmap_max, interpolation=interpolation, origin='upper')\n",
" if legendHandles!=None:\n",
" ax.legend(handles=legendHandles, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
"\n",
" if(geometries!=None):\n",
" df_epsg = geometries[i].to_crs(epsg=ax.projection.epsg_code)\n",
" df_epsg.plot(ax=ax, facecolor='none', edgecolor='black', linestyle='--', linewidth=2)\n",
" \n",
" if(labels!=None):\n",
" ax.text(0.5, 0, str(labels[i]), horizontalalignment='center', verticalalignment='bottom', transform=ax.transAxes, fontsize=14, style='normal', color='red')\n",
"\n",
" c = c + 1\n",
" \n",
" if(output_dir!=None):\n",
" f = output_dir + name + '.jpg'\n",
" plt.savefig(f)\n",
" plt.close(fig)\n",
" else:\n",
" plt.show()\n",
" \n",
" t.stop()\n",
"\n",
"def sclColors():\n",
" colors = ['black','red','dimgray','brown','green','yellow','blue','dimgray','darkgray','lightgray','skyblue','magenta']\n",
" return colors\n",
"\n",
"def sclLegendPatches():\n",
" colors = sclColors()\n",
" labels = ['no data','defective','dark area','cloud shadows','vegetation','not vegetated','water','unclassified','cloud medium prob','cloud high prob','thin cirrus','snow']\n",
" patches = [ mpatches.Patch(color=colors[i], label=labels[i]) for i in range(11) ]\n",
" return patches\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:sentinelloader:Getting region history for band TCI from 2019-04-01 to 2019-04-10 at 60m\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"POLYGON Z ((-55.7005254871978 -13.3255356572483 0, -55.6991419460571 -13.3476412354778 0, -55.6994626679625 -13.3482968267724 0, -55.6993622255233 -13.3497238029627 0, -55.6990879718946 -13.3512050194787 0, -55.6987785623234 -13.3516544880401 0, -55.7009726154231 -13.3521465384388 0, -55.7031391574231 -13.3478350497349 0, -55.70365985481779 -13.3477166261783 0, -55.7042048225458 -13.3481784524516 0, -55.7053983895748 -13.3485070297582 0, -55.7071743431305 -13.350239177507 0, -55.7073679164227 -13.3508664639014 0, -55.707297910641 -13.3536846078021 0, -55.70845582938671 -13.3547391417037 0, -55.7083650623632 -13.3558936045135 0, -55.70920687339019 -13.3573796093408 0, -55.7107993017724 -13.357686953459 0, -55.711524110584 -13.3580807761113 0, -55.71730886861859 -13.3591710955438 0, -55.7198276533255 -13.327129129925 0, -55.7005254871978 -13.3255356572483 0))\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:sentinelloader:Downloading tile uuid='73e01466-6765-4d65-9483-c297f189acf2', resolution='60m', band='TCI', date='2019-03-28'\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[==================================================]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:sentinelloader:Downloading tile uuid='45664263-7a34-4ed0-8a15-82818d2c495a', resolution='60m', band='TCI', date='2019-04-02'\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[==================================================]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:sentinelloader:showing 2 images\n",
"INFO:sentinelloader:> [started] generating image patches. rows=1; cols=10...\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 2880x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:sentinelloader:> [done] generating image patches. rows=1; cols=10 (2423.071 ms)\n",
"INFO:sentinelloader:Getting region history for band SCL from 2019-04-01 to 2019-04-10 at 60m\n",
"INFO:sentinelloader:Downloading tile uuid='73e01466-6765-4d65-9483-c297f189acf2', resolution='60m', band='SCL', date='2019-03-28'\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[==================================================]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:sentinelloader:Downloading tile uuid='45664263-7a34-4ed0-8a15-82818d2c495a', resolution='60m', band='SCL', date='2019-04-02'\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[==================================================]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:sentinelloader:showing 2 images\n",
"INFO:sentinelloader:> [started] generating image patches. rows=1; cols=10...\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 2880x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:sentinelloader:> [done] generating image patches. rows=1; cols=10 (748.521 ms)\n",
"INFO:sentinelloader:Getting region history for band NDVI from 2019-04-01 to 2019-04-10 at 60m\n",
"INFO:sentinelloader:Downloading tile uuid='73e01466-6765-4d65-9483-c297f189acf2', resolution='60m', band='B04', date='2019-03-28'\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[==================================================]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:sentinelloader:Downloading tile uuid='73e01466-6765-4d65-9483-c297f189acf2', resolution='10m', band='B08', date='2019-03-28'\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[==================================================]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:sentinelloader:Downloading tile uuid='45664263-7a34-4ed0-8a15-82818d2c495a', resolution='60m', band='B04', date='2019-04-02'\n",
"INFO:sentinelloader:Couldn't get data for 2019-04-06 using the specified filter. err=HTTPSConnectionPool(host='scihub.copernicus.eu', port=443): Max retries exceeded with url: /dhus/odata/v1/Products('45664263-7a34-4ed0-8a15-82818d2c495a')/Nodes('S2B_MSIL2A_20190402T140059_N0211_R067_T21LXF_20190402T175903.SAFE')/Nodes('GRANULE')/Nodes('L2A_T21LXF_A010818_20190402T140055')/Nodes('IMG_DATA')/Nodes('R60m')/Nodes('T21LXF_20190402T140059_B04_60m.jp2')/$value (Caused by SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol (_ssl.c:645)'),))\n",
"INFO:sentinelloader:showing 1 images\n",
"INFO:sentinelloader:> [started] generating image patches. rows=1; cols=10...\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 2880x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:sentinelloader:> [done] generating image patches. rows=1; cols=10 (351.665 ms)\n"
]
}
],
"source": [
"%matplotlib inline\n",
"import logging\n",
"import os\n",
"from osgeo import gdal\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib import rcParams\n",
"import math\n",
"import geopandas as gpd\n",
"from cartopy.io.shapereader import Reader\n",
"from cartopy.feature import ShapelyFeature\n",
"from cartopy.io.shapereader import Reader\n",
"from descartes import PolygonPatch\n",
"import fiona\n",
"from pprint import pprint\n",
"import re\n",
"\n",
"logging.basicConfig(level=logging.INFO)\n",
"\n",
"sl = Sentinel2Loader('/notebooks/data/output/sentinelcache', \n",
" os.environ['COPERNICUS_USER'], os.environ['COPERNICUS_PASSWORD'],\n",
" apiUrl='https://scihub.copernicus.eu/apihub/', showProgressbars=True, cloudCoverage=(0,100))\n",
"\n",
"\n",
"\n",
"df = gpd.read_file(\"/notebooks/test/sample2.shp\")\n",
"df.crs = {'init' :'epsg:4326'}\n",
"\n",
"coords = df['geometry'][0]\n",
"\n",
"\n",
"\n",
"d1 = '2019-04-01'\n",
"d2 = '2019-04-10'\n",
"\n",
"\n",
"\n",
"print(coords)\n",
"geoTiffs = sl.getRegionHistory(coords, 'TCI', '60m', d1, d2, daysStep=5)\n",
"\n",
"geometries = []\n",
"labels = []\n",
"for geoTiff in geoTiffs:\n",
" geometries.append(df)\n",
" d = re.search('[0-9]{4}-[0-9]{2}-[0-9]{2}', geoTiff)\n",
" labels.append(d.group(0))\n",
"\n",
"showGeoTiffs(geoTiffs, geometries=geometries, labels=labels, cols=10, size=4,)\n",
"\n",
"for geoTiff in geoTiffs:\n",
" os.remove(geoTiff)\n",
"\n",
" \n",
"\n",
"\n",
"\n",
"\n",
"geoTiffs = sl.getRegionHistory(coords, 'SCL', '60m', d1, d2, daysStep=5)\n",
"\n",
"geometries = []\n",
"labels = []\n",
"for geoTiff in geoTiffs:\n",
" geometries.append(df)\n",
" d = re.search('[0-9]{4}-[0-9]{2}-[0-9]{2}', geoTiff)\n",
" labels.append(d.group(0))\n",
"\n",
"showGeoTiffs(geoTiffs, geometries=geometries, labels=labels, cols=10, size=4, cmap=mpl.colors.ListedColormap(sclColors()), cmap_min=0, cmap_max=11, legendHandles=sclLegendPatches())\n",
"\n",
"for geoTiff in geoTiffs:\n",
" os.remove(geoTiff)\n",
"\n",
"\n",
" \n",
" \n",
" \n",
" \n",
" \n",
"geoTiffs = sl.getRegionHistory(coords, 'NDVI', '60m', d1, d2, daysStep=5)\n",
"\n",
"geometries = []\n",
"labels = []\n",
"for geoTiff in geoTiffs:\n",
" geometries.append(df)\n",
" d = re.search('[0-9]{4}-[0-9]{2}-[0-9]{2}', geoTiff)\n",
" labels.append(d.group(0))\n",
"\n",
"def td(data):\n",
" return np.negative(data)\n",
" \n",
"showGeoTiffs(geoTiffs, geometries=geometries, labels=labels, cols=10, size=4, transformData=td, cmap='viridis', interpolation='none')\n",
"\n",
"for geoTiff in geoTiffs:\n",
" os.remove(geoTiff)\n",
" \n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}