forked from kilianv/CoastSat_WRL
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96 lines
2.7 KiB
Python
96 lines
2.7 KiB
Python
7 years ago
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# -*- coding: utf-8 -*-
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"""
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Created on Fri Mar 23 12:46:04 2018
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@author: z5030440
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"""
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# Preamble
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import ee
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import matplotlib.pyplot as plt
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import matplotlib.cm as cm
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import numpy as np
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import pandas as pd
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from datetime import datetime
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import pickle
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import pdb
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import pytz
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from pylab import ginput
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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.morphology as morphology
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import skimage.measure as measure
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# my functions
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import functions.utils as utils
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import functions.sds as sds
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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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#%% Select images
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# parameters
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plot_bool = False # if you want the plots
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prob_high = 99.9 # upper probability to clip and rescale pixel intensity
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min_contour_points = 100 # minimum number of points contained in each water line
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output_epsg = 28356 # GDA94 / MGA Zone 56
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cloud_threshold = 0.8
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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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rect_narra = [[[151.3473129272461,-33.69035274454718],
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[151.2820816040039,-33.68206818063878],
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[151.27281188964844,-33.74775138989556],
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[151.3425064086914,-33.75231878701767],
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[151.3473129272461,-33.69035274454718]]];
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#rect_narra = [[[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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# Dates
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start_date = '2016-01-01'
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end_date = '2016-12-31'
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# filter by location
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flt_col = input_col.filterBounds(ee.Geometry.Polygon(rect_narra)).filterDate(start_date, end_date)
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n_img = flt_col.size().getInfo()
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print('Number of images covering Narrabeen:', n_img)
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im_all = flt_col.getInfo().get('features')
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# find each image in ee database
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im = ee.Image(im_all[0].get('id'))
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# load image as np.array
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im_pan, im_ms, im_cloud, crs, meta = sds.read_eeimage(im, rect_narra, plot_bool)
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# rescale intensities
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im_ms = sds.rescale_image_intensity(im_ms, im_cloud, prob_high, plot_bool)
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im_pan = sds.rescale_image_intensity(im_pan, im_cloud, prob_high, plot_bool)
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# pansharpen rgb image
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im_ms_ps = sds.pansharpen(im_ms[:,:,[0,1,2]], im_pan, im_cloud, plot_bool)
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plt.figure()
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plt.imshow(im_ms_ps[:,:,[2,1,0]])
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plt.show()
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pts = ginput(15)
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points = np.array(pts)
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plt.plot(points[:,0], points[:,1], 'ko')
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plt.show()
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pts_coords = sds.convert_pix2world(points[:,[1,0]], crs['crs_15m'])
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pts = sds.convert_epsg(pts_coords, crs['epsg_code'], output_epsg)
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with open('data/narra_beach.pkl', 'wb') as f:
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pickle.dump(pts, f)
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