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126 lines
4.3 KiB
Python
126 lines
4.3 KiB
Python
# -*- coding: utf-8 -*-
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#==========================================================#
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# Download L8 images of a given area between given dates
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#==========================================================#
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# Initial settings
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import os
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import numpy as np
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import matplotlib.pyplot as plt
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import pdb
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import ee
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# other modules
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from osgeo import gdal, ogr, osr
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from urllib.request import urlretrieve
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import zipfile
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from datetime import datetime
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import pytz
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import pickle
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# image processing modules
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import skimage.filters as filters
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import skimage.exposure as exposure
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import skimage.transform as transform
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import sklearn.decomposition as decomposition
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import skimage.measure as measure
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# import own modules
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import functions.utils as utils
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np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
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ee.Initialize()
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def download_tif(image, polygon, bandsId, filepath):
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"""downloads tif image (region and bands) from the ee server and stores it in a temp file"""
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url = ee.data.makeDownloadUrl(ee.data.getDownloadId({
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'image': image.serialize(),
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'region': polygon,
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'bands': bandsId,
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'filePerBand': 'false',
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'name': 'data',
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}))
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local_zip, headers = urlretrieve(url)
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with zipfile.ZipFile(local_zip) as local_zipfile:
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return local_zipfile.extract('data.tif', filepath)
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# select collection
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input_col = ee.ImageCollection('LANDSAT/LC08/C01/T1_RT_TOA')
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# location (Narrabeen-Collaroy beach)
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#polygon = [[[151.301454, -33.700754],
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# [151.311453, -33.702075],
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# [151.307237, -33.739761],
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# [151.294220, -33.736329],
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# [151.301454, -33.700754]]];
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# location (Oldbar beach)
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polygon = [[[152.664508, -31.896163],
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[152.665827, -31.897112],
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[152.631516, -31.924846],
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[152.629285, -31.923362],
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[152.664508, -31.896163]]]
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# location (Oldbar inlet)
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#polygon = [[[152.676283, -31.866784],
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# [152.709174, -31.869993],
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# [152.678229, -31.892082],
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# [152.670366, -31.886360],
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# [152.676283, -31.866784]]];
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# dates
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start_date = '2013-01-01'
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end_date = '2018-12-31'
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# filter by location
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flt_col = input_col.filterBounds(ee.Geometry.Polygon(polygon))#.filterDate(start_date, end_date)
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n_img = flt_col.size().getInfo()
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print('Number of images covering the area:', n_img)
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im_all = flt_col.getInfo().get('features')
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satname = 'L8'
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#sitename = 'NARRA'
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sitename = 'OLDBAR'
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suffix = '.tif'
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filepath = os.path.join(os.getcwd(), 'data', satname, sitename)
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filepath_pan = os.path.join(filepath, 'pan')
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filepath_ms = os.path.join(filepath, 'ms')
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all_names_pan = []
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all_names_ms = []
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timestamps = []
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# loop through all images
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for i in range(n_img):
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# find each image in ee database
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im = ee.Image(im_all[i].get('id'))
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im_dic = im.getInfo()
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im_bands = im_dic.get('bands')
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im_date = im_dic['properties']['DATE_ACQUIRED']
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t = im_dic['properties']['system:time_start']
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im_timestamp = datetime.fromtimestamp(t/1000, tz=pytz.utc)
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timestamps.append(im_timestamp)
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im_epsg = int(im_dic['bands'][0]['crs'][5:])
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# delete dimensions key from dictionnary, otherwise the entire image is extracted
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for j in range(len(im_bands)): del im_bands[j]['dimensions']
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pan_band = [im_bands[7]]
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ms_bands = [im_bands[1], im_bands[2], im_bands[3], im_bands[4], im_bands[5], im_bands[11]]
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filename_pan = satname + '_' + sitename + '_' + im_date + '_pan' + suffix
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filename_ms = satname + '_' + sitename + '_' + im_date + '_ms' + suffix
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print(i)
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if any(filename_pan in _ for _ in all_names_pan):
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filename_pan = satname + '_' + sitename + '_' + im_date + '_pan' + '_r' + suffix
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filename_ms = satname + '_' + sitename + '_' + im_date + '_ms' + '_r' + suffix
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all_names_pan.append(filename_pan)
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local_data_pan = download_tif(im, polygon, pan_band, filepath_pan)
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os.rename(local_data_pan, os.path.join(filepath_pan, filename_pan))
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local_data_ms = download_tif(im, polygon, ms_bands, filepath_ms)
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os.rename(local_data_ms, os.path.join(filepath_ms, filename_ms))
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with open(os.path.join(filepath, sitename + '_timestamps' + '.pkl'), 'wb') as f:
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pickle.dump(timestamps, f)
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with open(os.path.join(filepath, sitename + '_epsgcode' + '.pkl'), 'wb') as f:
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pickle.dump(im_epsg, f)
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