From ec290ab3232c74d524bdbf3ce79eda3c19d4f2aa Mon Sep 17 00:00:00 2001 From: kvos Date: Wed, 28 Mar 2018 17:42:21 +1100 Subject: [PATCH] shoreline comparison --- compare_shorelines.py | 222 ++++++++++++++++++++++++++++++++ compare_shorelines2.py | 47 +++++++ data/data_2016.pkl | Bin 0 -> 506207 bytes data/data_gt_l8.pkl | Bin 0 -> 616802 bytes data/idx_nogt.pkl | Bin 0 -> 210 bytes data/narra_beach.pkl | Bin 0 -> 520 bytes data/wl_final.pkl | Bin 0 -> 58880652 bytes data/wl_l8_comparison.pkl | Bin 0 -> 365411 bytes data_2016.pkl | Bin 1458361 -> 0 bytes functions/utils.py | 4 + get_coordinates.py | 96 ++++++++++++++ plot_cloud_cover.py | 119 +++++++++++++++++ sds_extract.py | 77 +++++++++-- sds_extract_test.py | 260 ++++++++++++++++++++++++++++++++++++++ time_coverage.py | 136 ++++++++++++++++++++ 15 files changed, 948 insertions(+), 13 deletions(-) create mode 100644 compare_shorelines.py create mode 100644 compare_shorelines2.py create mode 100644 data/data_2016.pkl create mode 100644 data/data_gt_l8.pkl create mode 100644 data/idx_nogt.pkl create mode 100644 data/narra_beach.pkl create mode 100644 data/wl_final.pkl create mode 100644 data/wl_l8_comparison.pkl delete mode 100644 data_2016.pkl create mode 100644 get_coordinates.py create mode 100644 plot_cloud_cover.py create mode 100644 sds_extract_test.py create mode 100644 time_coverage.py diff --git a/compare_shorelines.py b/compare_shorelines.py new file mode 100644 index 0000000..629a702 --- /dev/null +++ b/compare_shorelines.py @@ -0,0 +1,222 @@ +# -*- coding: utf-8 -*- + +# 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 +import scipy.io as sio +import scipy.interpolate +import os + +# 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() + +au_tz = pytz.timezone('Australia/Sydney') + +#%% +# load SDS shorelines +with open('data\data_gt_l8.pkl', 'rb') as f: + data = pickle.load(f) + +# 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)) + +# remove overlapping images, keep the one with lowest cloud_cover +n = len(data['cloud_cover']) +idx_worst = [] +for i in range(n): + date_im = data['date_acquired'][i] + idx_double = np.isin(data['date_acquired'], date_im) + if sum(idx_double.astype(int)) > 1: + idx_worst.append(np.where(idx_double)[0][np.argmax(np.array(data['cloud_cover'])[idx_double])]) +dt_sat = [] +new_meta = {'contours':[], + 'cloud_cover':[], + 'geom_rmse_model':[], + 'gcp_model':[], + 'quality':[], + 'sun_azimuth':[], + 'sun_elevation':[]} +for i in range(n): + if not np.isin(i,idx_worst): + dt_sat.append(data['dt'][i].astimezone(au_tz)) + new_meta['contours'].append(data['contours'][i]) + new_meta['cloud_cover'].append(data['cloud_cover'][i]) + new_meta['geom_rmse_model'].append(data['geom_rmse_model'][i]) + new_meta['gcp_model'].append(data['gcp_model'][i]) + new_meta['quality'].append(data['quality'][i]) + new_meta['sun_azimuth'].append(data['sun_azimuth'][i]) + new_meta['sun_elevation'].append(data['sun_elevation'][i]) + +# calculate difference between days +diff_days = [ [(x - _).days for _ in dt_quad] for x in dt_sat] +day_thresh = 15 +idx_close = [utils.find_indices(_, lambda e: abs(e) < day_thresh) for _ in diff_days] + +# put everything in a dictionnary and save it +wl_comp = [] +for i in range(len(dt_sat)): + wl_comp.append({'sat dt': dt_sat[i], + 'quad dt': [dt_quad[_] for _ in idx_close[i]], + 'days diff': [diff_days[i][_] for _ in idx_close[i]], + 'contours': new_meta['contours'][i], + 'cloud_cover': new_meta['cloud_cover'][i], + 'geom_rmse_model': new_meta['geom_rmse_model'][i], + 'gcp_model': new_meta['gcp_model'][i], + 'quality': new_meta['quality'][i], + 'sun_azimuth': new_meta['sun_azimuth'][i], + 'sun_elevation': new_meta['sun_elevation'][i]}) + +with open('wl_l8_comparison.pkl', 'wb') as f: + pickle.dump(wl_comp, f) + +#%% +with open('data\wl_l8_comparison.pkl', 'rb') as f: + wl = pickle.load(f) +# load quadbike dates and convert from datenum to datetime +suffix = '.mat' +dir_name = os.getcwd() +subfolder_name = 'data\quadbike_surveys' +file_path = os.path.join(dir_name, subfolder_name) +file_names = os.listdir(file_path) + +for i in range(len(file_names)): + fn_mat = os.path.join(file_path, file_names[i]) + years = int(file_names[i][6:10]) + months = int(file_names[i][11:13]) + days = int(file_names[i][14:16]) + for j in range(len(wl)): + if wl[j]['quad dt'][0] == datetime(years, months, days, tzinfo=au_tz): + quad_mat = sio.loadmat(fn_mat) + wl[j].update({'quad_data':{'x':quad_mat['x'], + 'y':quad_mat['y'], + 'z':quad_mat['z'], + 'dt': datetime(years, months, days, tzinfo=au_tz)}}) +with open('data\wl_final.pkl', 'wb') as f: + pickle.dump(wl, f) +#%% +with open('data\wl_final.pkl', 'rb') as f: + wl = pickle.load(f) + +i = 0 +x = wl[i]['quad_data']['x'] +y = wl[i]['quad_data']['y'] +z = wl[i]['quad_data']['z'] +x = x.reshape(x.shape[0] * x.shape[1]) +y = y.reshape(y.shape[0] * y.shape[1]) +z = z.reshape(z.shape[0] * z.shape[1]) +idx_nan = np.isnan(z) + +x_nan = x[idx_nan] +y_nan = y[idx_nan] +z_nan = z[idx_nan] + +x_nonan = x[~idx_nan] +y_nonan = y[~idx_nan] +z_nonan = z[~idx_nan] + +xs = x_nonan[::10] +ys = y_nonan[::10] +zs = z_nonan[::10] + +xq = wl[i]['contours'][:,0] +yq = wl[i]['contours'][:,1] + +# cut xq around xs +np.min(xs) +np.max(xs) +np.min(ys) +np.max(ys) + +idx_x = np.logical_and(xq < np.max(xs), xq > np.min(xs)) +idx_y = np.logical_and(yq < np.max(ys), yq > np.min(ys)) +idx_in = np.logical_and(idx_x, idx_y) + +xq = xq[idx_in] +yq = yq[idx_in] + +for i in range(len(xq)): + idx_x = np.logical_and(xs < xq[i] + 10, xs > xq[i] - 10) + idx_y = np.logical_and(ys < yq[i] + 10, ys > yq[i] - 10) + xint = xs[idx_x] + yint = ys[idx_y] + +f = interpolate.interp2d(xs, ys, zs, kind='linear') +zq = f(xq,yq) + +plt.figure() +plt.grid() +plt.scatter(xs, ys, s=10, c=zs, marker='o', cmap=cm.get_cmap('jet'), + label='quad data') +plt.plot(xq,yq,'r-o', markersize=5, label='SDS') +plt.axis('equal') +plt.legend() +plt.colorbar(label='mAHD') +plt.xlabel('Eastings [m]') +plt.ylabel('Northings [m]') +plt.show() + +plt.figure() +plt.plot(zq[:,0]) +plt.show() + + +plt.figure() +plt.grid() +plt.scatter(x_nonan, y_nonan, s=10, c=z_nonan, marker='o', cmap=cm.get_cmap('jet'), + label='quad data') +#plt.plot(x_nan, y_nan, 'k.', label='nans') +plt.plot(xq,yq,'r-o', markersize=5, label='SDS') +plt.axis('equal') +plt.legend() +plt.colorbar(label='mAHD') +plt.xlabel('Eastings [m]') +plt.ylabel('Northings [m]') +plt.show() + + +z2 = scipy.interpolate.griddata([x, y], z, [xq, yq], method='linear') + +f_interp = scipy.interpolate.interp2d(x1,y1,z1, kind='linear') + + +sio.savemat('shoreline1.mat', {'x':xq, 'y':yq}) + +from scipy import interpolate +x = np.arange(-5.01, 5.01, 0.01) +y = np.arange(-5.01, 5.01, 0.01) +xx, yy = np.meshgrid(x, y) +z = np.sin(xx**2+yy**2) +f = interpolate.interp2d(x, y, z, kind='cubic') + +xnew = np.arange(-5.01, 5.01, 1e-2) +ynew = np.arange(-5.01, 5.01, 1e-2) +znew = f(xnew, ynew) +plt.plot(x, z[:, 0], 'ro-', xnew, znew[:, 0], 'b-') +plt.show() \ No newline at end of file diff --git a/compare_shorelines2.py b/compare_shorelines2.py new file mode 100644 index 0000000..b38ba1a --- /dev/null +++ b/compare_shorelines2.py @@ -0,0 +1,47 @@ +# -*- coding: utf-8 -*- + +# 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 +import scipy.io as sio +import scipy.interpolate +import os + +# 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() + +with open('data\wl_final.pkl', 'rb') as f: + wl = pickle.load(f) + +i = 0 +x = wl[i]['quad_data']['x'] +y = wl[i]['quad_data']['y'] +z = wl[i]['quad_data']['z'] +x = 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