forked from kilianv/CoastSat_WRL
shoreline comparison
kvos
7 years ago
parent
67de70cbad
commit
ec290ab323
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# -*- coding: utf-8 -*-
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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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import scipy.io as sio
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import scipy.interpolate
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import os
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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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au_tz = pytz.timezone('Australia/Sydney')
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#%%
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# load SDS shorelines
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with open('data\data_gt_l8.pkl', 'rb') as f:
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data = pickle.load(f)
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# load quadbike dates and convert from datenum to datetime
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suffix = '.mat'
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dir_name = os.getcwd()
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file_name = 'data\quadbike_dates'
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file_path = os.path.join(dir_name, file_name + suffix)
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quad_dates = sio.loadmat(file_path)['dates']
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dt_quad = []
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for i in range(quad_dates.shape[0]):
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dt_quad.append(datetime(quad_dates[i,0], quad_dates[i,1], quad_dates[i,2], tzinfo=au_tz))
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# remove overlapping images, keep the one with lowest cloud_cover
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n = len(data['cloud_cover'])
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idx_worst = []
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for i in range(n):
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date_im = data['date_acquired'][i]
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idx_double = np.isin(data['date_acquired'], date_im)
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if sum(idx_double.astype(int)) > 1:
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idx_worst.append(np.where(idx_double)[0][np.argmax(np.array(data['cloud_cover'])[idx_double])])
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dt_sat = []
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new_meta = {'contours':[],
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'cloud_cover':[],
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'geom_rmse_model':[],
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'gcp_model':[],
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'quality':[],
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'sun_azimuth':[],
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'sun_elevation':[]}
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for i in range(n):
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if not np.isin(i,idx_worst):
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dt_sat.append(data['dt'][i].astimezone(au_tz))
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new_meta['contours'].append(data['contours'][i])
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new_meta['cloud_cover'].append(data['cloud_cover'][i])
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new_meta['geom_rmse_model'].append(data['geom_rmse_model'][i])
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new_meta['gcp_model'].append(data['gcp_model'][i])
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new_meta['quality'].append(data['quality'][i])
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new_meta['sun_azimuth'].append(data['sun_azimuth'][i])
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new_meta['sun_elevation'].append(data['sun_elevation'][i])
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# calculate difference between days
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diff_days = [ [(x - _).days for _ in dt_quad] for x in dt_sat]
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day_thresh = 15
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idx_close = [utils.find_indices(_, lambda e: abs(e) < day_thresh) for _ in diff_days]
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# put everything in a dictionnary and save it
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wl_comp = []
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for i in range(len(dt_sat)):
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wl_comp.append({'sat dt': dt_sat[i],
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'quad dt': [dt_quad[_] for _ in idx_close[i]],
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'days diff': [diff_days[i][_] for _ in idx_close[i]],
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'contours': new_meta['contours'][i],
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'cloud_cover': new_meta['cloud_cover'][i],
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'geom_rmse_model': new_meta['geom_rmse_model'][i],
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'gcp_model': new_meta['gcp_model'][i],
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'quality': new_meta['quality'][i],
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'sun_azimuth': new_meta['sun_azimuth'][i],
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'sun_elevation': new_meta['sun_elevation'][i]})
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with open('wl_l8_comparison.pkl', 'wb') as f:
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pickle.dump(wl_comp, f)
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#%%
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with open('data\wl_l8_comparison.pkl', 'rb') as f:
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wl = pickle.load(f)
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# load quadbike dates and convert from datenum to datetime
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suffix = '.mat'
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dir_name = os.getcwd()
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subfolder_name = 'data\quadbike_surveys'
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file_path = os.path.join(dir_name, subfolder_name)
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file_names = os.listdir(file_path)
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for i in range(len(file_names)):
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fn_mat = os.path.join(file_path, file_names[i])
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years = int(file_names[i][6:10])
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months = int(file_names[i][11:13])
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days = int(file_names[i][14:16])
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for j in range(len(wl)):
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if wl[j]['quad dt'][0] == datetime(years, months, days, tzinfo=au_tz):
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quad_mat = sio.loadmat(fn_mat)
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wl[j].update({'quad_data':{'x':quad_mat['x'],
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'y':quad_mat['y'],
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'z':quad_mat['z'],
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'dt': datetime(years, months, days, tzinfo=au_tz)}})
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with open('data\wl_final.pkl', 'wb') as f:
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pickle.dump(wl, f)
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#%%
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with open('data\wl_final.pkl', 'rb') as f:
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wl = pickle.load(f)
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i = 0
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x = wl[i]['quad_data']['x']
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y = wl[i]['quad_data']['y']
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z = wl[i]['quad_data']['z']
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x = x.reshape(x.shape[0] * x.shape[1])
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y = y.reshape(y.shape[0] * y.shape[1])
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z = z.reshape(z.shape[0] * z.shape[1])
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idx_nan = np.isnan(z)
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x_nan = x[idx_nan]
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y_nan = y[idx_nan]
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z_nan = z[idx_nan]
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x_nonan = x[~idx_nan]
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y_nonan = y[~idx_nan]
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z_nonan = z[~idx_nan]
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xs = x_nonan[::10]
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ys = y_nonan[::10]
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zs = z_nonan[::10]
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xq = wl[i]['contours'][:,0]
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yq = wl[i]['contours'][:,1]
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# cut xq around xs
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np.min(xs)
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np.max(xs)
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np.min(ys)
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np.max(ys)
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idx_x = np.logical_and(xq < np.max(xs), xq > np.min(xs))
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idx_y = np.logical_and(yq < np.max(ys), yq > np.min(ys))
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idx_in = np.logical_and(idx_x, idx_y)
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xq = xq[idx_in]
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yq = yq[idx_in]
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for i in range(len(xq)):
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idx_x = np.logical_and(xs < xq[i] + 10, xs > xq[i] - 10)
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idx_y = np.logical_and(ys < yq[i] + 10, ys > yq[i] - 10)
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xint = xs[idx_x]
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yint = ys[idx_y]
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f = interpolate.interp2d(xs, ys, zs, kind='linear')
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zq = f(xq,yq)
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plt.figure()
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plt.grid()
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plt.scatter(xs, ys, s=10, c=zs, marker='o', cmap=cm.get_cmap('jet'),
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label='quad data')
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plt.plot(xq,yq,'r-o', markersize=5, label='SDS')
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plt.axis('equal')
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plt.legend()
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plt.colorbar(label='mAHD')
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plt.xlabel('Eastings [m]')
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plt.ylabel('Northings [m]')
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plt.show()
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plt.figure()
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plt.plot(zq[:,0])
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plt.show()
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plt.figure()
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plt.grid()
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plt.scatter(x_nonan, y_nonan, s=10, c=z_nonan, marker='o', cmap=cm.get_cmap('jet'),
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label='quad data')
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#plt.plot(x_nan, y_nan, 'k.', label='nans')
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plt.plot(xq,yq,'r-o', markersize=5, label='SDS')
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plt.axis('equal')
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plt.legend()
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plt.colorbar(label='mAHD')
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plt.xlabel('Eastings [m]')
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plt.ylabel('Northings [m]')
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plt.show()
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z2 = scipy.interpolate.griddata([x, y], z, [xq, yq], method='linear')
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f_interp = scipy.interpolate.interp2d(x1,y1,z1, kind='linear')
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sio.savemat('shoreline1.mat', {'x':xq, 'y':yq})
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from scipy import interpolate
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x = np.arange(-5.01, 5.01, 0.01)
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y = np.arange(-5.01, 5.01, 0.01)
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xx, yy = np.meshgrid(x, y)
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z = np.sin(xx**2+yy**2)
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f = interpolate.interp2d(x, y, z, kind='cubic')
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xnew = np.arange(-5.01, 5.01, 1e-2)
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ynew = np.arange(-5.01, 5.01, 1e-2)
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znew = f(xnew, ynew)
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plt.plot(x, z[:, 0], 'ro-', xnew, znew[:, 0], 'b-')
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plt.show()
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# -*- coding: utf-8 -*-
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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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import scipy.io as sio
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import scipy.interpolate
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import os
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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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with open('data\wl_final.pkl', 'rb') as f:
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wl = pickle.load(f)
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i = 0
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x = wl[i]['quad_data']['x']
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y = wl[i]['quad_data']['y']
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z = wl[i]['quad_data']['z']
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x = x.reshape(x.shape[0] * x.shape[1])
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y = y.reshape(y.shape[0] * y.shape[1])
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z = z.reshape(z.shape[0] * z.shape[1])
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idx_nan = np.isnan(z)
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x = x[~idx_nan]
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y = y[~idx_nan]
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z = z[~idx_nan]
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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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@ -0,0 +1,119 @@
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# -*- coding: utf-8 -*-
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"""
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Created on Thu Mar 1 14:32:08 2018
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@author: z5030440
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Main code to extract shorelines from Landsat imagery
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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 numpy as np
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import pandas as pd
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from datetime import datetime
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import pytz
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import pdb
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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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# 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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# select collection
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input_col = ee.ImageCollection('LANDSAT/LC08/C01/T1_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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# 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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props = {'cloud_cover_cropped':[],
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'cloud_cover':[],
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'cloud_cover_land':[],
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'date_acquired':[],
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'geom_rmse_model':[],
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'geom_rmse_verify':[],
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'gcp_model':[],
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'gcp_verify':[],
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'quality':[],
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'sun_azimuth':[],
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'sun_elevation':[]}
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t = []
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# loop through all images
|
||||
for i in range(n_img):
|
||||
|
||||
# find each image in ee database
|
||||
im = ee.Image(im_all[i].get('id'))
|
||||
im_bands = im_all[i].get('bands')
|
||||
im_props = im_all[i]['properties']
|
||||
|
||||
# compute cloud cover on cropped image
|
||||
for j in range(len(im_bands)): del im_bands[j]['dimensions']
|
||||
qa_band = [im_bands[11]]
|
||||
im_qa, crs_qa = sds.load_image(im, rect_narra, qa_band)
|
||||
im_qa = im_qa[:,:,0]
|
||||
im_cloud = sds.create_cloud_mask(im_qa)
|
||||
props['cloud_cover_cropped'].append(100*sum(sum(im_cloud.astype(int)))/(im_cloud.shape[0]*im_cloud.shape[1]))
|
||||
|
||||
# extract image metadata
|
||||
props['cloud_cover'].append(im_props['CLOUD_COVER'])
|
||||
props['cloud_cover_land' ].append(im_props['CLOUD_COVER_LAND'])
|
||||
props['date_acquired'].append(im_props['DATE_ACQUIRED'])
|
||||
props['geom_rmse_model'].append(im_props['GEOMETRIC_RMSE_MODEL'])
|
||||
props['gcp_model'].append(im_props['GROUND_CONTROL_POINTS_MODEL'])
|
||||
props['quality'].append(im_props['IMAGE_QUALITY_OLI'])
|
||||
props['sun_azimuth'].append(im_props['SUN_AZIMUTH'])
|
||||
props['sun_elevation'].append(im_props['SUN_ELEVATION'])
|
||||
|
||||
# try structure as sometimes the geometry cannot be verified
|
||||
try:
|
||||
props['geom_rmse_verify'].append(im_props['GEOMETRIC_RMSE_VERIFY'])
|
||||
props['gcp_verify'].append(im_props['GROUND_CONTROL_POINTS_VERIFY'])
|
||||
except:
|
||||
props['geom_rmse_verify'].append(np.nan)
|
||||
props['gcp_verify'].append(np.nan)
|
||||
|
||||
# record exact time of acquisition
|
||||
t.append(im_props['system:time_start'])
|
||||
|
||||
#%% create pd.DataFrame with datetime index
|
||||
dt = [];
|
||||
fmt = '%Y-%m-%d %H:%M:%S %Z%z'
|
||||
au_tz = pytz.timezone('Australia/Sydney')
|
||||
for k in range(len(t)): dt.append(datetime.fromtimestamp(t[k]/1000, tz=au_tz))
|
||||
|
||||
df = pd.DataFrame(data = props, index=dt , columns=list(props.keys()))
|
||||
|
||||
df.to_pickle('meta_l8.pkl')
|
||||
|
||||
#df['cloud_cover_cropped'].groupby(df.index.month).count().plot.bar()
|
||||
|
||||
#df_monthly = df['cloud_cover_cropped'].groupby(df.index.month)
|
||||
@ -0,0 +1,260 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Thu Mar 1 14:32:08 2018
|
||||
|
||||
@author: z5030440
|
||||
|
||||
Main code to extract shorelines from Landsat imagery
|
||||
"""
|
||||
# Preamble
|
||||
|
||||
import ee
|
||||
import matplotlib.pyplot as plt
|
||||
import matplotlib.cm as cm
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from datetime import datetime
|
||||
import pickle
|
||||
import pdb
|
||||
import pytz
|
||||
from pylab import ginput
|
||||
|
||||
|
||||
# image processing modules
|
||||
import skimage.filters as filters
|
||||
import skimage.exposure as exposure
|
||||
import skimage.transform as transform
|
||||
import sklearn.decomposition as decomposition
|
||||
import skimage.morphology as morphology
|
||||
import skimage.measure as measure
|
||||
|
||||
# my functions
|
||||
import functions.utils as utils
|
||||
import functions.sds as sds
|
||||
|
||||
np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
|
||||
ee.Initialize()
|
||||
|
||||
#%% Select images
|
||||
|
||||
# parameters
|
||||
plot_bool = False # if you want the plots
|
||||
prob_high = 99.9 # upper probability to clip and rescale pixel intensity
|
||||
min_contour_points = 100 # minimum number of points contained in each water line
|
||||
output_epsg = 28356 # GDA94 / MGA Zone 56
|
||||
cloud_threshold = 0.7
|
||||
|
||||
# select collection
|
||||
input_col = ee.ImageCollection('LANDSAT/LC08/C01/T1_RT_TOA')
|
||||
|
||||
# location (Narrabeen-Collaroy beach)
|
||||
rect_narra = [[[151.3473129272461,-33.69035274454718],
|
||||
[151.2820816040039,-33.68206818063878],
|
||||
[151.27281188964844,-33.74775138989556],
|
||||
[151.3425064086914,-33.75231878701767],
|
||||
[151.3473129272461,-33.69035274454718]]];
|
||||
|
||||
with open('data/narra_beach.pkl', 'rb') as f:
|
||||
pts_beach = pickle.load(f)
|
||||
|
||||
with open('data/idx_nogt.pkl', 'rb') as f:
|
||||
idx_nogt = pickle.load(f)
|
||||
idx_nogt = np.array(idx_nogt)
|
||||
|
||||
#rect_narra = [[[151.301454, -33.700754],
|
||||
# [151.311453, -33.702075],
|
||||
# [151.307237, -33.739761],
|
||||
# [151.294220, -33.736329],
|
||||
# [151.301454, -33.700754]]];
|
||||
|
||||
# Dates
|
||||
start_date = '2016-01-01'
|
||||
end_date = '2016-12-31'
|
||||
# filter by location
|
||||
flt_col = input_col.filterBounds(ee.Geometry.Polygon(rect_narra))#.filterDate(start_date, end_date)
|
||||
|
||||
n_img = flt_col.size().getInfo()
|
||||
print('Number of images covering Narrabeen:', n_img)
|
||||
im_all = flt_col.getInfo().get('features')
|
||||
|
||||
#%% Extract shorelines
|
||||
metadata = {'timestamp':[],
|
||||
'date_acquired':[],
|
||||
'cloud_cover':[],
|
||||
'geom_rmse_model':[],
|
||||
'gcp_model':[],
|
||||
'quality':[],
|
||||
'sun_azimuth':[],
|
||||
'sun_elevation':[]}
|
||||
skipped_images = np.zeros((n_img,1)).astype(bool)
|
||||
output_wl = []
|
||||
# loop through all images
|
||||
for i in range(n_img):
|
||||
|
||||
if np.isin(i, idx_nogt):
|
||||
continue
|
||||
|
||||
# find each image in ee database
|
||||
im = ee.Image(im_all[i].get('id'))
|
||||
# load image as np.array
|
||||
im_pan, im_ms, im_cloud, crs, meta = sds.read_eeimage(im, rect_narra, plot_bool)
|
||||
|
||||
# if clouds -> skip the image
|
||||
if sum(sum(im_cloud.astype(int)))/(im_cloud.shape[0]*im_cloud.shape[1]) > cloud_threshold:
|
||||
skipped_images[i] = True
|
||||
continue
|
||||
|
||||
# rescale intensities
|
||||
im_ms = sds.rescale_image_intensity(im_ms, im_cloud, prob_high, plot_bool)
|
||||
im_pan = sds.rescale_image_intensity(im_pan, im_cloud, prob_high, plot_bool)
|
||||
|
||||
# pansharpen rgb image
|
||||
im_ms_ps = sds.pansharpen(im_ms[:,:,[0,1,2]], im_pan, im_cloud, plot_bool)
|
||||
|
||||
# add down-sized bands for NIR and SWIR (since pansharpening is not possible)
|
||||
im_ms_ps = np.append(im_ms_ps, im_ms[:,:,[3,4]], axis=2)
|
||||
|
||||
# calculate NDWI
|
||||
im_ndwi = sds.nd_index(im_ms_ps[:,:,3], im_ms_ps[:,:,1], im_cloud, plot_bool)
|
||||
|
||||
# edge detection
|
||||
wl_pix = sds.find_wl_contours(im_ndwi, im_cloud, min_contour_points, plot_bool)
|
||||
# convert from pixels to world coordinates
|
||||
wl_coords = sds.convert_pix2world(wl_pix, crs['crs_15m'])
|
||||
# convert to output epsg spatial reference
|
||||
wl = sds.convert_epsg(wl_coords, crs['epsg_code'], output_epsg)
|
||||
|
||||
# find contour closest to narrabeen beach
|
||||
sum_dist = np.zeros(len(wl))
|
||||
for k,contour in enumerate(wl):
|
||||
min_dist = np.zeros(len(pts_beach))
|
||||
for j,pt in enumerate(pts_beach):
|
||||
min_dist[j] = np.min(np.linalg.norm(contour - pt, axis=1))
|
||||
sum_dist[k] = np.sum(min_dist)/len(min_dist)
|
||||
try:
|
||||
wl_beach = wl[np.argmin(sum_dist)]
|
||||
# plt.figure()
|
||||
# plt.axis('equal')
|
||||
# plt.plot(pts_beach[:,0], pts_beach[:,1], 'ko')
|
||||
# plt.plot(wl_beach[:,0], wl_beach[:,1], 'r')
|
||||
# plt.show()
|
||||
except:
|
||||
wl_beach = []
|
||||
|
||||
plt.figure()
|
||||
plt.imshow(im_ms_ps[:,:,[2,1,0]])
|
||||
for k,contour in enumerate(wl_pix): plt.plot(contour[:, 1], contour[:, 0], linewidth=2)
|
||||
if len(wl_beach) > 0:
|
||||
plt.plot(wl_pix[np.argmin(sum_dist)][:,1], wl_pix[np.argmin(sum_dist)][:,0], linewidth=3, color='w')
|
||||
plt.axis('image')
|
||||
plt.title('im ' + str(i) + ' : ' + datetime.strftime(datetime
|
||||
.fromtimestamp(meta['timestamp']/1000, tz=pytz.utc)
|
||||
.astimezone(pytz.timezone('Australia/Sydney')), '%Y-%m-%d %H:%M:%S %Z%z'))
|
||||
plt.show()
|
||||
|
||||
# manually validate shoreline detection
|
||||
input_pt = np.array(ginput(1))
|
||||
if input_pt[0,1] > 300:
|
||||
skipped_images[i] = True
|
||||
continue
|
||||
|
||||
# store metadata of each image in dict
|
||||
metadata['timestamp'].append(meta['timestamp'])
|
||||
metadata['date_acquired'].append(meta['date_acquired'])
|
||||
metadata['cloud_cover'].append(sum(sum(im_cloud.astype(int)))/(im_cloud.shape[0]*im_cloud.shape[1]))
|
||||
metadata['geom_rmse_model'].append(meta['geom_rmse_model'])
|
||||
metadata['gcp_model'].append(meta['gcp_model'])
|
||||
metadata['quality'].append(meta['quality'])
|
||||
metadata['sun_azimuth'].append(meta['sun_azimuth'])
|
||||
metadata['sun_elevation'].append(meta['sun_elevation'])
|
||||
# store water lines
|
||||
output_wl.append(wl_beach)
|
||||
|
||||
print(i)
|
||||
|
||||
# generate datetimes
|
||||
#fmt = '%Y-%m-%d %H:%M:%S %Z%z'
|
||||
#au_tz = pytz.timezone('Australia/Sydney')
|
||||
dt = [];
|
||||
t = metadata['timestamp']
|
||||
for k in range(len(t)): dt.append(datetime.fromtimestamp(t[k]/1000, tz=pytz.utc))
|
||||
|
||||
# save outputs
|
||||
data = metadata.copy()
|
||||
data.update({'dt':dt})
|
||||
data.update({'contours':output_wl})
|
||||
|
||||
with open('data_gt15d_32_56.pkl', 'wb') as f:
|
||||
pickle.dump(data, f)
|
||||
#%% Load data
|
||||
|
||||
##with open('data_2016.pkl', 'rb') as f:
|
||||
## data = pickle.load(f)
|
||||
#
|
||||
#
|
||||
## load backgroud image
|
||||
#i = 0
|
||||
#im = ee.Image(im_all[i].get('id'))
|
||||
#im_pan, im_ms, im_cloud, crs, meta = sds.read_eeimage(im, rect_narra, plot_bool)
|
||||
#im_ms = sds.rescale_image_intensity(im_ms, im_cloud, prob_high, plot_bool)
|
||||
#im_pan = sds.rescale_image_intensity(im_pan, im_cloud, prob_high, plot_bool)
|
||||
#im_ms_ps = sds.pansharpen(im_ms[:,:,[0,1,2]], im_pan, im_cloud, plot_bool)
|
||||
#
|
||||
#plt.figure()
|
||||
#plt.imshow(im_ms_ps[:,:,[2,1,0]])
|
||||
#plt.axis('image')
|
||||
#plt.title('2016 shorelines')
|
||||
#
|
||||
#n = len(data['cloud_cover'])
|
||||
#idx_best = []
|
||||
## remove overlapping images, based on cloud cover
|
||||
#for i in range(n):
|
||||
# date_im = data['date_acquired'][i]
|
||||
# idx = np.isin(data['date_acquired'], date_im)
|
||||
# best = np.where(idx)[0][np.argmin(np.array(data['cloud_cover'])[idx])]
|
||||
# if ~np.isin(best, idx_best):
|
||||
# idx_best.append(best)
|
||||
#
|
||||
#point_narra = np.array([342500, 6266990])
|
||||
#plt.figure()
|
||||
#plt.axis('equal')
|
||||
#plt.grid()
|
||||
#cmap = cm.get_cmap('jet')
|
||||
#colours = cmap(np.linspace(0, 1, num=len(idx_best)))
|
||||
#for i, idx in enumerate(idx_best):
|
||||
# for j in range(len(data['contours'][i])):
|
||||
# if np.any(np.linalg.norm(data['contours'][i][j][:,[0,1]] - point_narra, axis=1) < 200):
|
||||
# plt.plot(data['contours'][i][j][:,0], data['contours'][i][j][:,1],
|
||||
# label=str(data['date_acquired'][i]),
|
||||
# linewidth=2, color=colours[i,:])
|
||||
#
|
||||
#plt.legend()
|
||||
#plt.show()
|
||||
#
|
||||
#pts_narra = sds.convert_epsg(pts_narra, output_epsg, 4326)
|
||||
#
|
||||
##kml.newlinestring(name="beach",
|
||||
## coords = [(_[0], _[1]) for _ in pts_narra])
|
||||
##kml.save("narra.kml")
|
||||
|
||||
|
||||
#%%
|
||||
|
||||
#with open('data_gt15d_0_31.pkl', 'rb') as f:
|
||||
# data1 = pickle.load(f)
|
||||
#with open('data_gt15d_32_56.pkl', 'rb') as f:
|
||||
# data2 = pickle.load(f)
|
||||
#with open('data_gt15d_99_193.pkl', 'rb') as f:
|
||||
# data3 = pickle.load(f)
|
||||
#
|
||||
#data = []
|
||||
#data = data1.copy()
|
||||
#for k,cat in enumerate(data.keys()):
|
||||
# for j in range(len(data2[cat])):
|
||||
# data[cat].append(data2[cat][j])
|
||||
# for j in range(len(data3[cat])):
|
||||
# data[cat].append(data3[cat][j])
|
||||
#
|
||||
#
|
||||
#with open('data_gt_l8.pkl', 'wb') as f:
|
||||
# pickle.dump(data, f)
|
||||
@ -0,0 +1,136 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Tue Mar 20 16:15:51 2018
|
||||
|
||||
@author: z5030440
|
||||
"""
|
||||
|
||||
import scipy.io as sio
|
||||
import os
|
||||
import ee
|
||||
import matplotlib.pyplot as plt
|
||||
import matplotlib.dates as mdates
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from datetime import datetime, timedelta
|
||||
import pickle
|
||||
import pdb
|
||||
import pytz
|
||||
|
||||
|
||||
# image processing modules
|
||||
import skimage.filters as filters
|
||||
import skimage.exposure as exposure
|
||||
import skimage.transform as transform
|
||||
import skimage.morphology as morphology
|
||||
import skimage.measure as measure
|
||||
import sklearn.decomposition as decomposition
|
||||
from scipy import spatial
|
||||
# my functions
|
||||
import functions.utils as utils
|
||||
import functions.sds as sds
|
||||
#plt.rcParams['axes.grid'] = True
|
||||
au_tz = pytz.timezone('Australia/Sydney')
|
||||
|
||||
# load quadbike dates and convert from datenum to datetime
|
||||
suffix = '.mat'
|
||||
dir_name = os.getcwd()
|
||||
file_name = 'data\quadbike_dates'
|
||||
file_path = os.path.join(dir_name, file_name + suffix)
|
||||
quad_dates = sio.loadmat(file_path)['dates']
|
||||
dt_quad = []
|
||||
for i in range(quad_dates.shape[0]):
|
||||
dt_quad.append(datetime(quad_dates[i,0], quad_dates[i,1], quad_dates[i,2], tzinfo=au_tz))
|
||||
|
||||
# load satellite datetimes (in UTC) and convert to AEST time
|
||||
input_col = ee.ImageCollection('LANDSAT/LC08/C01/T1_RT_TOA')
|
||||
# location (Narrabeen-Collaroy beach)
|
||||
rect_narra = [[[151.3473129272461,-33.69035274454718],
|
||||
[151.2820816040039,-33.68206818063878],
|
||||
[151.27281188964844,-33.74775138989556],
|
||||
[151.3425064086914,-33.75231878701767],
|
||||
[151.3473129272461,-33.69035274454718]]];
|
||||
flt_col = input_col.filterBounds(ee.Geometry.Polygon(rect_narra))
|
||||
n_img = flt_col.size().getInfo()
|
||||
print('Number of images covering Narrabeen:', n_img)
|
||||
im_all = flt_col.getInfo().get('features')
|
||||
|
||||
# extract datetimes from image metadata
|
||||
dt_sat = [_['properties']['system:time_start'] for _ in im_all]
|
||||
dt_sat = [datetime.fromtimestamp(_/1000, tz=pytz.utc) for _ in dt_sat]
|
||||
dt_sat = [_.astimezone(au_tz) for _ in dt_sat]
|
||||
# calculate days difference
|
||||
diff_days = [ [(x - _).days for _ in dt_quad] for x in dt_sat]
|
||||
day_thresh = 15
|
||||
idx = [utils.find_indices(_, lambda e: abs(e) < day_thresh) for _ in diff_days]
|
||||
|
||||
dt_diff = []
|
||||
idx_nogt = []
|
||||
for i in range(n_img):
|
||||
if not idx[i]:
|
||||
idx_nogt.append(i)
|
||||
continue
|
||||
dt_diff.append({"sat dt": dt_sat[i],
|
||||
"quad dt": [dt_quad[_] for _ in idx[i]],
|
||||
"days diff": [diff_days[i][_] for _ in idx[i]] })
|
||||
|
||||
with open('idx_nogt.pkl', 'wb') as f:
|
||||
pickle.dump(idx_nogt, f)
|
||||
|
||||
|
||||
#%%
|
||||
dates_sat = mdates.date2num(dt_sat)
|
||||
dates_quad = mdates.date2num(dt_quad)
|
||||
plt.figure()
|
||||
plt.plot_date(dates_sat, np.zeros((n_img,1)))
|
||||
plt.plot_date(dates_quad, np.ones((len(dates_quad),1)))
|
||||
plt.show()
|
||||
|
||||
data = pd.read_pickle('data_2016.pkl')
|
||||
|
||||
dt_sat = [_.astimezone(au_tz) for _ in data['dt']]
|
||||
|
||||
[ (_ - dt_sat[0]).days for _ in dt_quad]
|
||||
|
||||
|
||||
|
||||
dn_sat = []
|
||||
for i in range(len(dt_sat)): dn_sat.append(dt_sat[i].toordinal())
|
||||
dn_sat = np.array(dn_sat)
|
||||
dn_sur = []
|
||||
for i in range(len(dt_survey)): dn_sur.append(dt_survey[i].toordinal())
|
||||
dn_sur = np.array(dn_sur)
|
||||
|
||||
distances = np.zeros((len(dn_sat),4)).astype('int32')
|
||||
indexes = np.zeros((len(dn_sat),2)).astype('int32')
|
||||
for i in range(len(dn_sat)):
|
||||
distances[i,0] = np.sort(abs(dn_sat[i] - dn_sur))[0]
|
||||
distances[i,1] = np.sort(abs(dn_sat[i] - dn_sur))[1]
|
||||
distances[i,2] = dt_sat[i].year
|
||||
distances[i,3] = dt_sat[i].month
|
||||
indexes[i,0] = np.where(abs(dn_sat[i] - dn_sur) == np.sort(abs(dn_sat[i] - dn_sur))[0])[0][0]
|
||||
indexes[i,1] = np.where(abs(dn_sat[i] - dn_sur) == np.sort(abs(dn_sat[i] - dn_sur))[1])[0][0]
|
||||
|
||||
|
||||
years = [2013, 2014, 2015, 2016]
|
||||
months = mdates.MonthLocator()
|
||||
days = mdates.DayLocator()
|
||||
month_fmt = mdates.DateFormatter('%b')
|
||||
f, ax = plt.subplots(4, 1)
|
||||
for i, ca in enumerate(ax):
|
||||
ca.xaxis.set_major_locator(months)
|
||||
ca.xaxis.set_major_formatter(month_fmt)
|
||||
ca.xaxis.set_minor_locator(days)
|
||||
ca.set_ylabel(str(years[i]))
|
||||
for j in range(len(dt_sat)):
|
||||
if dt_sat[j].year == years[i]:
|
||||
ca.plot(dt_sat[j],0, 'bo', markerfacecolor='b')
|
||||
#f.subplots_adjust(hspace=0)
|
||||
#plt.setp([a.get_xticklabels() for a in f.axes[:-1]], visible=False)
|
||||
|
||||
|
||||
plt.plot(dt_survey, np.zeros([len(dt_survey),1]), 'bo')
|
||||
plt.plot(dt_sat, np.ones([len(dt_sat),1]), 'ro')
|
||||
plt.yticks([])
|
||||
plt.show()
|
||||
|
||||
Loading…
Reference in New Issue