finalised new shoreline detection method

if buffer_size = False, then classify the entire image

.gitignore

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+*.pyc
+*.mat
+*.tif
+*.png
+*.mp4
+*.gif
+*.jpg

data/L7/NARRA/NARRA_epsgcode.pkl

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+<EFBFBD>M<>.

data/L8/BROULEE/BROULEE_epsgcode.pkl

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+<EFBFBD>M<>.

data/L8/DUCK/DUCK_epsgcode.pkl

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+<EFBFBD>Mj.

data/L8/MURI/MURI_epsgcode.pkl

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+<EFBFBD>M\.

data/L8/MURI2/MURI2_epsgcode.pkl

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+<EFBFBD>M\.

data/L8/NARRA/NARRA_epsgcode.pkl

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+<EFBFBD>M<>.

data/L8/NARRA_all/NARRA_all_epsgcode.pkl

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+<EFBFBD>M<>.

data/L8/OLDBAR/OLDBAR_epsgcode.pkl

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+<EFBFBD>M<>.

data/L8/OLDBAR_inlet/OLDBAR_inlet_epsgcode.pkl

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+<EFBFBD>M<>.

data/L8/SANDMOTOR/SANDMOTOR_epsgcode.pkl

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+<EFBFBD>Mw.

data/L8/TAIRUA/TAIRUA_epsgcode.pkl

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+<EFBFBD>M<>.

download_images_L7.py

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+# -*- coding: utf-8 -*-
+
+#==========================================================#
+# Download L7 images of a given area between given dates
+#==========================================================#
+
+# Initial settings
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+import pdb
+import ee
+
+# other modules
+from osgeo import gdal, ogr, osr
+from urllib.request import urlretrieve
+import zipfile
+from datetime import datetime
+import pytz
+import pickle
+
+# 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.measure as measure
+
+# import own modules
+import functions.utils as utils
+
+np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
+ee.Initialize()
+
+def download_tif(image, polygon, bandsId, filepath):
+    """downloads tif image (region and bands) from the ee server and stores it in a temp file"""
+    url = ee.data.makeDownloadUrl(ee.data.getDownloadId({
+        'image': image.serialize(),
+        'region': polygon,
+        'bands': bandsId,
+        'filePerBand': 'false',
+        'name': 'data',
+        }))
+    local_zip, headers = urlretrieve(url)
+    with zipfile.ZipFile(local_zip) as local_zipfile:
+        return local_zipfile.extract('data.tif', filepath)
+    
+# select collection
+input_col = ee.ImageCollection('LANDSAT/LE07/C01/T1_RT_TOA')
+# location (Narrabeen-Collaroy beach)
+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')
+
+satname = 'L7'
+sitename = 'NARRA'
+suffix = '.tif'
+filepath = os.path.join(os.getcwd(), 'data', satname, sitename)
+filepath_pan = os.path.join(filepath, 'pan')
+filepath_ms = os.path.join(filepath, 'ms')
+
+all_names_pan = []
+all_names_ms = []
+timestamps = []
+# 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_dic = im.getInfo()
+    im_bands = im_dic.get('bands')
+    im_date = im_dic['properties']['DATE_ACQUIRED']
+    t = im_dic['properties']['system:time_start']
+    im_timestamp = datetime.fromtimestamp(t/1000, tz=pytz.utc)
+    timestamps.append(im_timestamp)
+    im_epsg = int(im_dic['bands'][0]['crs'][5:])
+    
+    # delete dimensions key from dictionnary, otherwise the entire image is extracted
+    for j in range(len(im_bands)): del im_bands[j]['dimensions']
+    pan_band = [im_bands[7]]
+    ms_bands = [im_bands[0], im_bands[1], im_bands[2], im_bands[3], im_bands[4], im_bands[9]]
+
+    filename_pan = satname + '_' + sitename + '_' + im_date + '_pan' + suffix
+    filename_ms = satname + '_' + sitename + '_' + im_date +  '_ms' + suffix
+
+    print(i)
+    if any(filename_pan in _ for _ in all_names_pan):
+        filename_pan = satname + '_' + sitename + '_' + im_date + '_pan' + '_r' + suffix 
+        filename_ms = satname + '_' + sitename + '_' + im_date + '_ms' + '_r' + suffix 
+    all_names_pan.append(filename_pan)
+    
+    local_data_pan = download_tif(im, rect_narra, pan_band, filepath_pan)
+    os.rename(local_data_pan, os.path.join(filepath_pan, filename_pan))
+    local_data_ms = download_tif(im, rect_narra, ms_bands, filepath_ms)
+    os.rename(local_data_ms, os.path.join(filepath_ms, filename_ms))
+    
+    
+with open(os.path.join(filepath, sitename + '_timestamps' + '.pkl'), 'wb') as f:
+    pickle.dump(timestamps, f)  
+with open(os.path.join(filepath, sitename + '_epsgcode' + '.pkl'), 'wb') as f:
+    pickle.dump(im_epsg, f)      
+    

download_images_L8.py

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+# -*- coding: utf-8 -*-
+
+#==========================================================#
+# Download L8 images of a given area between given dates
+#==========================================================#
+
+# Initial settings
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+import pdb
+import ee
+
+# other modules
+from osgeo import gdal, ogr, osr
+from urllib.request import urlretrieve
+import zipfile
+from datetime import datetime
+import pytz
+import pickle
+
+# 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.measure as measure
+
+# import own modules
+import functions.utils as utils
+
+np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
+ee.Initialize()
+
+def download_tif(image, polygon, bandsId, filepath):
+    """downloads tif image (region and bands) from the ee server and stores it in a temp file"""
+    url = ee.data.makeDownloadUrl(ee.data.getDownloadId({
+        'image': image.serialize(),
+        'region': polygon,
+        'bands': bandsId,
+        'filePerBand': 'false',
+        'name': 'data',
+        }))
+    local_zip, headers = urlretrieve(url)
+    with zipfile.ZipFile(local_zip) as local_zipfile:
+        return local_zipfile.extract('data.tif', filepath)
+    
+# select collection
+input_col = ee.ImageCollection('LANDSAT/LC08/C01/T1_RT_TOA')
+# Location (Narrabeen all)
+#polygon = [[[151.3473129272461,-33.69035274454718],
+#            [151.2820816040039,-33.68206818063878], 
+#            [151.27281188964844,-33.74775138989556],
+#            [151.3425064086914,-33.75231878701767],
+#            [151.3473129272461,-33.69035274454718]]];
+# location (Narrabeen-Collaroy beach)
+#polygon = [[[151.301454, -33.700754],
+#               [151.311453, -33.702075], 
+#               [151.307237, -33.739761],
+#               [151.294220, -33.736329],
+#               [151.301454, -33.700754]]];
+# location (Oldbar beach)
+#polygon = [[[152.664508, -31.896163],
+#               [152.665827, -31.897112], 
+#               [152.631516, -31.924846],
+#               [152.629285, -31.923362],
+#               [152.664508, -31.896163]]]
+# location (Oldbar inlet)
+#polygon = [[[152.676283, -31.866784],
+#   [152.709174, -31.869993], 
+#   [152.678229, -31.892082],
+#   [152.670366, -31.886360],
+#   [152.676283, -31.866784]]];
+# Location (Sand Engine)
+#polygon = [[[4.171742, 52.070455], 
+#            [4.223708, 52.069576],
+#            [4.220808, 52.025293],
+#            [4.147749, 52.028861],
+#            [4.171742, 52.070455]]];
+# Location (Tairua)
+#polygon = [[[175.852115, -36.985414], 
+#            [175.872797, -36.985145],
+#            [175.873738, -37.000039],
+#            [175.853956, -36.998749],
+#            [175.852115, -36.985414]]];  
+# Location (Duck)
+#polygon = [[[-75.766220, 36.195928], 
+#            [-75.748282, 36.196401],
+#            [-75.738851, 36.173974],
+#            [-75.763546, 36.174249],
+#            [-75.766220, 36.195928]]];
+# Location (Broulee Island)
+#polygon = [[[150.173557, -35.847138], 
+#            [150.196164, -35.848064],
+#            [150.195143, -35.869967],
+#            [150.172779, -35.861760],
+#            [150.173557, -35.847138]]];
+# Location (Rarotonga, Muri lagoon)
+polygon = [[[-159.732071, -21.241348], 
+            [-159.719820, -21.242892],
+            [-159.720006, -21.261134],
+            [-159.731592, -21.258875],
+            [-159.732071, -21.241348]]];
+
+# dates
+start_date = '2013-01-01'
+end_date = '2019-01-01'
+# filter by location
+flt_col = input_col.filterBounds(ee.Geometry.Polygon(polygon)).filterDate(start_date, end_date)
+
+n_img = flt_col.size().getInfo()
+print('Number of images covering the area:', n_img)
+im_all = flt_col.getInfo().get('features')
+
+satname = 'L8'
+#sitename = 'NARRA_all'
+#sitename = 'NARRA'
+#sitename = 'OLDBAR'
+#sitename = 'SANDMOTOR'
+#sitename = 'TAIRUA'
+#sitename = 'DUCK'
+#sitename = 'BROULEE'
+sitename = 'MURI'
+
+
+suffix = '.tif'
+filepath = os.path.join(os.getcwd(), 'data', satname, sitename)
+filepath_pan = os.path.join(filepath, 'pan')
+filepath_ms = os.path.join(filepath, 'ms')
+
+all_names_pan = []
+all_names_ms = []
+timestamps = []
+acc_georef = []
+# 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_dic = im.getInfo()
+    im_bands = im_dic.get('bands')
+    im_date = im_dic['properties']['DATE_ACQUIRED']
+    t = im_dic['properties']['system:time_start']
+    im_timestamp = datetime.fromtimestamp(t/1000, tz=pytz.utc)
+    timestamps.append(im_timestamp)
+    im_epsg = int(im_dic['bands'][0]['crs'][5:])
+    try:
+        acc_georef.append(im_dic['properties']['GEOMETRIC_RMSE_MODEL'])
+    except:
+        acc_georef.append(10)   
+        print('No geometric rmse model property')
+    
+    # delete dimensions key from dictionnary, otherwise the entire image is extracted
+    for j in range(len(im_bands)): del im_bands[j]['dimensions']
+    pan_band = [im_bands[7]]
+    ms_bands = [im_bands[1], im_bands[2], im_bands[3], im_bands[4], im_bands[5], im_bands[11]]
+
+    filename_pan = satname + '_' + sitename + '_' + im_date + '_pan' + suffix
+    filename_ms = satname + '_' + sitename + '_' + im_date +  '_ms' + suffix
+
+    print(i)
+    if any(filename_pan in _ for _ in all_names_pan):
+        filename_pan = satname + '_' + sitename + '_' + im_date + '_pan' + '_r' + suffix 
+        filename_ms = satname + '_' + sitename + '_' + im_date + '_ms' + '_r' + suffix 
+    all_names_pan.append(filename_pan)
+    
+    local_data_pan = download_tif(im, polygon, pan_band, filepath_pan)
+    os.rename(local_data_pan, os.path.join(filepath_pan, filename_pan))
+    local_data_ms = download_tif(im, polygon, ms_bands, filepath_ms)
+    os.rename(local_data_ms, os.path.join(filepath_ms, filename_ms))
+    
+    
+with open(os.path.join(filepath, sitename + '_timestamps' + '.pkl'), 'wb') as f:
+    pickle.dump(timestamps, f)  
+with open(os.path.join(filepath, sitename + '_epsgcode' + '.pkl'), 'wb') as f:
+    pickle.dump(im_epsg, f)      
+with open(os.path.join(filepath, sitename + '_accuracy_georef' + '.pkl'), 'wb') as f:
+    pickle.dump(acc_georef, f)  

draw_refpoints.py

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+# -*- coding: utf-8 -*-
+
+#==========================================================#
+# Draw reference points on satellite image
+#==========================================================#
+
+# Preamble
+import os
+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()
+
+# collection
+input_col = ee.ImageCollection('LANDSAT/LC08/C01/T1_RT_TOA')
+
+# location (Narrabeen-Collaroy beach)
+#polygon = [[[151.301454, -33.700754],
+#               [151.311453, -33.702075], 
+#               [151.307237, -33.739761],
+#               [151.294220, -33.736329],
+#               [151.301454, -33.700754]]];
+# location (Oldbar shoreline)
+#polygon = [[[152.664508, -31.896163],
+#               [152.665827, -31.897112], 
+#               [152.631516, -31.924846],
+#               [152.629285, -31.923362],
+#               [152.664508, -31.896163]]];
+# location (Oldbar inlet)
+polygon = [[[152.676283, -31.866784],
+   [152.709174, -31.869993], 
+   [152.678229, -31.892082],
+   [152.670366, -31.886360],
+   [152.676283, -31.866784]]];
+
+# dates
+start_date = '2017-01-30'
+end_date = '2017-02-02'
+#start_date = '2017-01-30'
+#end_date = '2018-02-02'
+# filter by location
+flt_col = input_col.filterBounds(ee.Geometry.Polygon(polygon)).filterDate(start_date, end_date)
+
+n_img = flt_col.size().getInfo()
+print('Number of images covering the area:', n_img)
+im_all = flt_col.getInfo().get('features')
+
+satname = 'L8'
+#sitename = 'NARRA'
+sitename = 'OLDBAR_inlet'
+
+# 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 = 0.8
+
+# find image in ee database
+im = ee.Image(im_all[0].get('id')) 
+# load image as np.array
+im_pan, im_ms, im_cloud, crs, meta = sds.read_eeimage(im, polygon, satname, plot_bool)
+
+# 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)
+
+plt.figure()
+plt.imshow(im_ms_ps[:,:,[2,1,0]])
+plt.show()
+
+pts = ginput(n=50, timeout=1000, show_clicks=True)
+points = np.array(pts)
+plt.plot(points[:,0], points[:,1], 'ko')
+plt.show()
+
+pts_coords = sds.convert_pix2world(points[:,[1,0]], crs['crs_15m'])
+pts = sds.convert_epsg(pts_coords, crs['epsg_code'], output_epsg)
+
+filepath = os.path.join(os.getcwd(), 'data', satname, sitename)
+with open(os.path.join(filepath, sitename + '_refpoints2.pkl'), 'wb') as f:
+    pickle.dump(pts, f)

extract_shorelines_test.py

@@ -0,0 +1,322 @@
+# -*- coding: utf-8 -*-
+
+#==========================================================#
+# Extract shorelines from Landsat images
+#==========================================================#
+
+# Initial settings
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+import ee
+import pdb
+
+# other modules
+from osgeo import gdal, ogr, osr
+import pickle
+import matplotlib.cm as cm
+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.measure as measure
+import skimage.morphology as morphology
+
+# machine learning modules
+from sklearn.model_selection import train_test_split
+from sklearn.neural_network import MLPClassifier
+from sklearn.preprocessing import StandardScaler, Normalizer 
+from sklearn.externals import joblib
+
+# import own modules
+import functions.utils as utils
+import functions.sds as sds
+
+# some settings
+np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
+plt.rcParams['axes.grid'] = True
+plt.rcParams['figure.max_open_warning'] = 100
+ee.Initialize()
+
+# parameters
+cloud_thresh = 0.3      # threshold for cloud cover
+plot_bool = False      # if you want the plots
+min_contour_points = 100# minimum number of points contained in each water line
+output_epsg = 28356     # GDA94 / MGA Zone 56
+buffer_size = 10        # radius (in pixels) of disk for buffer (pixel classification)
+min_beach_size = 30     # number of pixels in a beach (pixel classification)
+
+# load metadata (timestamps and epsg code) for the collection
+satname = 'L8'
+sitename = 'NARRA'
+#sitename = 'OLDBAR'
+
+# Load metadata
+filepath = os.path.join(os.getcwd(), 'data', satname, sitename)
+with open(os.path.join(filepath, sitename + '_timestamps' + '.pkl'), 'rb') as f:
+    timestamps = pickle.load(f)
+with open(os.path.join(filepath, sitename + '_accuracy_georef' + '.pkl'), 'rb') as f:
+    acc_georef = pickle.load(f) 
+with open(os.path.join(filepath, sitename + '_epsgcode' + '.pkl'), 'rb') as f:
+    input_epsg = pickle.load(f)
+with open(os.path.join(filepath, sitename + '_refpoints' + '.pkl'), 'rb') as f:
+    refpoints = pickle.load(f)
+# sort timestamps and georef accuracy (dowloaded images are sorted by date in directory)
+timestamps_sorted = sorted(timestamps)
+idx_sorted = sorted(range(len(timestamps)), key=timestamps.__getitem__)
+acc_georef_sorted = [acc_georef[j] for j in idx_sorted]
+
+# path to images
+file_path_pan = os.path.join(os.getcwd(), 'data', satname, sitename, 'pan')
+file_path_ms = os.path.join(os.getcwd(), 'data', satname, sitename, 'ms')
+file_names_pan = os.listdir(file_path_pan)
+file_names_ms = os.listdir(file_path_ms)
+N = len(file_names_pan)
+
+# initialise some variables
+cloud_cover_ts = []
+date_acquired_ts = []
+acc_georef_ts = []
+idx_skipped = []
+idx_nocloud = []
+t = []
+shorelines = []
+
+#%%
+for i in [20]:#range(N):
+    # read pan image
+    fn_pan = os.path.join(file_path_pan, file_names_pan[i])
+    data = gdal.Open(fn_pan, gdal.GA_ReadOnly)
+    georef = np.array(data.GetGeoTransform())
+    bands = [data.GetRasterBand(i + 1).ReadAsArray() for i in range(data.RasterCount)]
+    im_pan = np.stack(bands, 2)[:,:,0]
+    nrows = im_pan.shape[0]
+    ncols = im_pan.shape[1]
+    
+    # read ms image
+    fn_ms = os.path.join(file_path_ms, file_names_ms[i])
+    data = gdal.Open(fn_ms, gdal.GA_ReadOnly)
+    bands = [data.GetRasterBand(i + 1).ReadAsArray() for i in range(data.RasterCount)]
+    im_ms = np.stack(bands, 2)
+    
+    # cloud mask
+    im_qa = im_ms[:,:,5]
+    cloud_mask = sds.create_cloud_mask(im_qa, satname, plot_bool)
+    cloud_mask = transform.resize(cloud_mask, (im_pan.shape[0], im_pan.shape[1]),
+                                order=0, preserve_range=True, 
+                                mode='constant').astype('bool_')    
+    # resize the image using bilinear interpolation (order 1)
+    im_ms = transform.resize(im_ms,(im_pan.shape[0], im_pan.shape[1]),
+                             order=1, preserve_range=True, mode='constant')
+    
+    # check if -inf or nan values and add to cloud mask
+    im_inf = np.isin(im_ms[:,:,0], -np.inf)
+    im_nan = np.isnan(im_ms[:,:,0])
+    cloud_mask = np.logical_or(np.logical_or(cloud_mask, im_inf), im_nan)
+    
+    # calculate cloud cover and skip image if too high
+    cloud_cover = sum(sum(cloud_mask.astype(int)))/(cloud_mask.shape[0]*cloud_mask.shape[1])
+    if cloud_cover > cloud_thresh:
+        print('skip ' + str(i) + ' - cloudy (' + str(cloud_cover) + ')')
+        idx_skipped.append(i)
+        continue
+    idx_nocloud.append(i)
+    
+    # check if image for that date already exists and choose the best in terms of cloud cover and georeferencing
+    if file_names_pan[i][len(satname)+1+len(sitename)+1:len(satname)+1+len(sitename)+1+10] in date_acquired_ts:
+        
+        # find the index of the image that is repeated
+        idx_samedate = utils.find_indices(date_acquired_ts, lambda e : e == file_names_pan[i][9:19])
+        idx_samedate = idx_samedate[0]
+        print('cloud cover ' + str(cloud_cover) + ' - ' + str(cloud_cover_ts[idx_samedate]))
+        print('acc georef ' + str(acc_georef_sorted[i]) + ' - ' + str(acc_georef_ts[idx_samedate]))
+        
+        # keep image with less cloud cover or best georeferencing accuracy
+        if cloud_cover < cloud_cover_ts[idx_samedate] - 0.01: 
+            skip = False
+        elif acc_georef_sorted[i] < acc_georef_ts[idx_samedate]:
+            skip = False
+        else:
+            skip = True
+            
+        if skip:
+            print('skip ' + str(i) + ' - repeated')
+            idx_skipped.append(i)
+            continue
+        else:
+            del shorelines[idx_samedate]
+            del t[idx_samedate]
+            del cloud_cover_ts[idx_samedate]
+            del date_acquired_ts[idx_samedate]
+            del acc_georef_ts[idx_samedate]
+            print('keep ' + str(i) + ' - deleted ' + str(idx_samedate))
+            
+    # pansharpen rgb image
+    im_ms_ps = sds.pansharpen(im_ms[:,:,[0,1,2]], im_pan, cloud_mask, plot_bool)
+    # rescale pansharpened RGB for visualisation
+    im_display = sds.rescale_image_intensity(im_ms_ps[:,:,[2,1,0]], cloud_mask, 100, False)
+    # 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) 
+    
+    # classify image in 4 classes (sand, whitewater, water, other) with NN classifier
+    im_classif, im_labels = sds.classify_image_NN(im_ms_ps, im_pan, cloud_mask, min_beach_size, True)
+    
+    t.append(timestamps_sorted[i])
+    cloud_cover_ts.append(cloud_cover)
+    acc_georef_ts.append(acc_georef_sorted[i])
+    date_acquired_ts.append(file_names_pan[i][9:19])
+    
+    # labels
+    im_sand = im_classif == 1
+    im_swash = im_classif == 2
+    im_water = im_classif == 3
+    vec_sand = im_sand.reshape(ncols*nrows)
+    vec_water = im_water.reshape(ncols*nrows)
+    vec_swash = im_swash.reshape(ncols*nrows)
+    
+    # calculate indices and stack into a vector
+    im_ndwi = sds.nd_index(im_ms_ps[:,:,3], im_ms_ps[:,:,1], cloud_mask, plot_bool)
+    im_ndmwi = sds.nd_index(im_ms_ps[:,:,4], im_ms_ps[:,:,1], cloud_mask, plot_bool)
+    im_nir = im_ms_ps[:,:,3]
+    im_swir = im_ms_ps[:,:,4]
+    im_ind = np.stack((im_ndwi, im_ndmwi), axis=-1)
+    vec_ind = im_ind.reshape(nrows*ncols,2)
+    
+    # remove noise and only keep the sand belonging to large beaches
+    morphology.remove_small_objects(im_sand, min_size=50, connectivity=2, in_place=True)
+    # create a buffer around beach
+    buffer_size = 7
+    se = morphology.disk(buffer_size)
+    im_buffer = morphology.binary_dilation(im_sand, se)
+    vec_buffer = im_buffer.reshape(nrows*ncols)
+    
+    # display buffer
+    im = np.copy(im_display)
+    im[~im_buffer,0] = 1
+    im[~im_buffer,1] = 1
+    im[~im_buffer,2] = 1
+    
+    im2 = np.copy(im_ndmwi)
+    im2[~im_buffer] = np.nan
+    
+    plt.figure()
+    ax1 = plt.subplot(121)
+    plt.imshow(im)
+    plt.axis('off')
+    plt.title('RGB')
+    ax2 = plt.subplot(122, sharex=ax1, sharey=ax1)
+    plt.imshow(im2, cmap='seismic')
+    plt.colorbar()
+    plt.axis('off')
+    plt.title('Water Index')  
+    plt.tight_layout()   
+    plt.draw()
+    
+    # select water/sand/swash pixels that are within the buffer
+    int_water = vec_ind[np.logical_and(vec_buffer,vec_water),:]
+    int_sand = vec_ind[np.logical_and(vec_buffer,vec_sand),:]
+    int_swash = vec_ind[np.logical_and(vec_buffer,vec_swash),:]
+    
+    # append sand and water
+    int_all = np.append(int_water,int_sand, axis=0)
+    t_ndwi = filters.threshold_otsu(int_all[:,0])
+    t_ndmwi = filters.threshold_otsu(int_all[:,1])
+    
+    
+    fig, ax = plt.subplots(2,1, sharex=True)
+    vals = ax[0].hist(int_water[:,0], bins=100, label='water')
+    ax[0].hist(int_sand[:,0], bins=100, alpha=0.5, label='sand')
+    ax[0].hist(int_swash[:,0], bins=100, alpha=0.5, label='swash')
+    ax[0].plot([t_ndwi, t_ndwi], [0, np.max(vals[0])], 'r-')
+    ax[0].legend()
+    ax[0].set_title('Water Index NIR-G')
+    vals = ax[1].hist(int_water[:,1], bins=100, label='water')
+    ax[1].hist(int_sand[:,1], bins=100, alpha=0.5, label='sand')
+    ax[1].hist(int_swash[:,1], bins=100, alpha=0.5, label='swash')
+    ax[1].plot([t_ndmwi, t_ndmwi], [0, np.max(vals[0])], 'r-')
+    ax[1].legend()
+    ax[1].set_title('Modified Water Index SWIR-G')
+    plt.draw()
+    
+    im_ndwi_buffer = np.copy(im_ndwi)
+    im_ndwi_buffer[~im_buffer] = np.nan
+    
+    contours1 = measure.find_contours(im_ndwi_buffer, t_ndwi)
+
+    im_ndmwi_buffer = np.copy(im_ndmwi)
+    im_ndmwi_buffer[~im_buffer] = np.nan
+    
+    contours2 = measure.find_contours(im_ndmwi_buffer, t_ndmwi)
+    
+    plt.figure()
+    ax1 = plt.subplot(1,3,1)
+    im = np.copy(im_display)
+    # define colours for plot
+    colours = np.array([[1,128/255,0/255],[204/255,1,1],[0,0,204/255]])
+    for k in range(0,im_labels.shape[2]):
+        im[im_labels[:,:,k],0] = colours[k,0]
+        im[im_labels[:,:,k],1] = colours[k,1]
+        im[im_labels[:,:,k],2] = colours[k,2]
+    plt.imshow(im)
+    for i,contour in enumerate(contours2): plt.plot(contour[:, 1], contour[:, 0], linewidth=3, color='k')
+    plt.tight_layout()
+    plt.grid(False)
+    plt.draw()
+    
+    plt.subplot(1,3,2, sharex=ax1, sharey=ax1)
+    plt.imshow(im_display)
+    for i,contour in enumerate(contours2): plt.plot(contour[:, 1], contour[:, 0], linewidth=3, color='k')
+    plt.tight_layout()
+    plt.grid(False)
+    plt.draw()
+    
+    plt.subplot(1,3,3, sharex=ax1, sharey=ax1)
+    plt.imshow(im_ndmwi, cmap='seismic')
+    plt.colorbar()
+    for i,contour in enumerate(contours2): plt.plot(contour[:, 1], contour[:, 0], linewidth=3, color='k')
+    plt.tight_layout()
+    plt.grid(False)
+    plt.draw()
+    
+    
+    # plot of all the indices
+    plt.figure()
+    ax1 = plt.subplot(1,5,1)
+    plt.imshow(im_display)
+    plt.xticks([])
+    plt.yticks([])
+    plt.axis('off')
+    plt.title('RGB')
+    plt.subplot(1,5,2, sharex=ax1, sharey=ax1)
+    plt.imshow(im_ndwi, cmap='seismic')
+    plt.xticks([])
+    plt.yticks([])
+    plt.axis('off')
+    plt.title('NDWI')
+    plt.subplot(1,5,3, sharex=ax1, sharey=ax1)
+    plt.imshow(im_ndmwi, cmap='seismic')
+    plt.xticks([])
+    plt.yticks([])
+    plt.axis('off')
+    plt.title('NDMWI')
+    plt.subplot(1,5,4, sharex=ax1, sharey=ax1)
+    plt.imshow(im_nir, cmap='seismic')
+    plt.xticks([])
+    plt.yticks([])
+    plt.axis('off')
+    plt.title('NIR')
+    plt.subplot(1,5,5, sharex=ax1, sharey=ax1)
+    plt.imshow(im_swir, cmap='seismic')
+    plt.xticks([])
+    plt.yticks([])
+    plt.axis('off')
+    plt.title('SWIR')
+    
+    
+    
+    
+    
+    

functions/sds.py

@@ -10,6 +10,8 @@ Created on Thu Mar  1 11:20:35 2018
 # Initial settings
 import numpy as np
 import matplotlib.pyplot as plt
+import matplotlib.patches as mpatches
+from matplotlib import gridspec
 import pdb
 import ee
 
@@ -25,6 +27,12 @@ import skimage.exposure as exposure
 import skimage.transform as transform
 import sklearn.decomposition as decomposition
 import skimage.measure as measure
+import skimage.morphology as morphology
+
+# machine learning modules
+from sklearn.cluster import KMeans
+from sklearn.neural_network import MLPClassifier
+from sklearn.externals import joblib
 
 
 # import own modules
@@ -79,7 +87,7 @@ def load_image(image, polygon, bandsId):
     bands = [dataset.GetRasterBand(i + 1).ReadAsArray() for i in range(dataset.RasterCount)]
     return np.stack(bands, 2), georef
 
-def create_cloud_mask(im_qa):
+def create_cloud_mask(im_qa, satname, plot_bool):
     """
     Creates a cloud mask from the image containing the QA band information
     
@@ -89,6 +97,10 @@ def create_cloud_mask(im_qa):
     -----------
         im_qa: np.ndarray
             Image containing the QA band
+        satname: string
+            short name for the satellite (L8, L7, S2)
+        plot_bool: boolean
+            True if plot is wanted
             
     Returns:
     -----------
@@ -97,15 +109,27 @@ def create_cloud_mask(im_qa):
     """
     
     # convert QA bits
-    cloud_values = [2800, 2804, 2808, 2812, 6896, 6900, 6904, 6908]
+    if satname == 'L8':
+        cloud_values = [2800, 2804, 2808, 2812, 6896, 6900, 6904, 6908]
+    elif satname == 'L7':
+        cloud_values = [752, 756, 760, 764]
+        
     cloud_mask = np.isin(im_qa, cloud_values)
+    # remove isolated cloud pixels (there are some in the swash and they cause problems)
+    if sum(sum(cloud_mask)) > 0:
+        morphology.remove_small_objects(cloud_mask, min_size=10, connectivity=1, in_place=True)
+    
+    if plot_bool:
+        plt.figure()
+        plt.imshow(cloud_mask, cmap='gray')
+        plt.draw()
     
     #cloud_shadow_values = [2976, 2980, 2984, 2988, 3008, 3012, 3016, 3020]
     #cloud_shadow_mask = np.isin(im_qa, cloud_shadow_values)
     
     return cloud_mask
 
-def read_eeimage(im, polygon, plot_bool):
+def read_eeimage(im, polygon, sat_name, plot_bool):
     """
     Read an ee.Image() object and returns the panchromatic band, multispectral bands (B, G, R, NIR, SWIR)
     and a cloud mask. All outputs are at 15m resolution (bilinear interpolation for the multispectral bands)
@@ -160,7 +184,7 @@ def read_eeimage(im, polygon, plot_bool):
     qa_band = [im_bands[11]]
     im_qa, crs_qa = load_image(im, polygon, qa_band)
     im_qa = im_qa[:,:,0]
-    im_cloud = create_cloud_mask(im_qa)
+    im_cloud = create_cloud_mask(im_qa, sat_name, plot_bool)
     im_cloud = transform.resize(im_cloud, (im_pan.shape[0], im_pan.shape[1]),
                                 order=0, preserve_range=True, mode='constant').astype('bool_')
     
@@ -379,10 +403,6 @@ def pansharpen(im_ms, im_pan, cloud_mask, plot_bool):
     vec_pcs[:,0] = hist_match(vec_pan, vec_pcs[:,0])
     vec_ms_ps = pca.inverse_transform(vec_pcs)
     
-    # normalise between 0 and 1
-    for i in range(vec_pcs.shape[1]):
-        vec_ms_ps[:,i] = np.divide(vec_ms_ps[:,i] - np.min(vec_ms_ps[:,i]),
-                         np.max(vec_ms_ps[:,i]) - np.min(vec_ms_ps[:,i]))
     # reshape vector into image
     vec_ms_ps_full = np.ones((len(vec_mask), im_ms.shape[2])) * np.nan
     vec_ms_ps_full[~vec_mask,:] = vec_ms_ps
@@ -391,11 +411,11 @@ def pansharpen(im_ms, im_pan, cloud_mask, plot_bool):
     if plot_bool:
         plt.figure()
         ax1 = plt.subplot(121)
-        plt.imshow(im_ms[:,:,[2,1,0]])
+        plt.imshow(rescale_image_intensity(im_ms[:,:,[2,1,0]], cloud_mask, 99.9, False))
         plt.axis('off')
         plt.title('Original')
         ax2 = plt.subplot(122, sharex=ax1, sharey=ax1)
-        plt.imshow(im_ms_ps[:,:,[2,1,0]])
+        plt.imshow(rescale_image_intensity(im_ms_ps[:,:,[2,1,0]], cloud_mask, 99.9, False))
         plt.axis('off')
         plt.title('Pansharpened')
         plt.show()
@@ -442,7 +462,8 @@ def nd_index(im1, im2, cloud_mask, plot_bool):
 
 def find_wl_contours(im_ndwi, cloud_mask, min_contour_points, plot_bool):
     """
-    Computes normalised difference index on 2 images (2D), given a cloud mask (2D)
+    Finds the water line by thresholding the Normalized Difference Water Index and applying the Marching 
+    Squares Algorithm
     
     KV WRL 2018
 
@@ -588,3 +609,307 @@ def convert_epsg(points, epsg_in, epsg_out):
         raise
         
     return points_converted
+
+def classify_sand_unsupervised(im_ms_ps, im_pan, cloud_mask, wl_pix, buffer_size, min_beach_size, plot_bool):
+    """
+    Classifies sand pixels using an unsupervised algorithm (Kmeans)
+    Set buffer size to False if you want to classify the entire image,
+    otherwise buffer size defines the buffer around the shoreline in which 
+    pixels are considered for classification.
+    This classification is not robust and is only used to train a supervised algorithm
+    
+    KV WRL 2018
+
+    Arguments:
+    -----------
+        im_ms_ps: np.ndarray
+            Pansharpened RGB + downsampled NIR and SWIR
+        im_pan:
+            Panchromatic band
+        cloud_mask: np.ndarray
+            2D cloud mask with True where cloud pixels are
+        wl_pix: list of np.ndarray
+            list of arrays containig the pixel coordinates of the water line
+        buffer_size: int or False
+            radius of the disk used to create a buffer around the water line
+            when False, the entire image is considered for kmeans
+        min_beach_size: int
+            minimum number of connected pixels belonging to a single beach
+        plot_bool: boolean
+            True if plot is wanted
+                
+    Returns:    -----------
+        im_sand: np.ndarray
+            2D binary image containing True where sand pixels are located
+
+    """     
+    # reshape the 2D images into vectors
+    vec_ms_ps = im_ms_ps.reshape(im_ms_ps.shape[0] * im_ms_ps.shape[1], im_ms_ps.shape[2])
+    vec_pan = im_pan.reshape(im_pan.shape[0]*im_pan.shape[1])
+    vec_mask = cloud_mask.reshape(im_ms_ps.shape[0] * im_ms_ps.shape[1])
+    # add B,G,R,NIR and pan bands to the vector of features
+    vec_features = np.zeros((vec_ms_ps.shape[0], 5))
+    vec_features[:,[0,1,2,3]] = vec_ms_ps[:,[0,1,2,3]]
+    vec_features[:,4] = vec_pan
+    
+    if buffer_size:
+        # create binary image with ones where the detected water lines is
+        im_buffer = np.zeros((im_ms_ps.shape[0], im_ms_ps.shape[1]))
+        for i, contour in enumerate(wl_pix):
+                indices = [(int(_[0]), int(_[1])) for _ in list(np.round(contour))]
+                for j, idx in enumerate(indices):
+                    im_buffer[idx] = 1
+        # perform a dilation on the binary image
+        se = morphology.disk(buffer_size)
+        im_buffer = morphology.binary_dilation(im_buffer, se)
+        vec_buffer = (im_buffer == 1).reshape(im_ms_ps.shape[0] * im_ms_ps.shape[1])
+    else:
+        vec_buffer = np.ones((vec_pan.shape[0]))
+    # add cloud mask to buffer
+    vec_buffer= np.logical_and(vec_buffer, ~vec_mask)
+    # perform kmeans (6 clusters)
+    kmeans = KMeans(n_clusters=6, random_state=0).fit(vec_features[vec_buffer,:])
+    labels = np.ones((len(vec_mask))) * np.nan
+    labels[vec_buffer] = kmeans.labels_
+    im_labels = labels.reshape(im_ms_ps.shape[0], im_ms_ps.shape[1])
+    # find the class with maximum reflection in the B,G,R,Pan
+    im_sand = im_labels == np.argmax(np.mean(kmeans.cluster_centers_[:,[0,1,2,4]], axis=1))
+    im_sand = morphology.remove_small_objects(im_sand, min_size=min_beach_size, connectivity=2)
+    im_sand = morphology.binary_erosion(im_sand, morphology.disk(1))
+#    im_sand = morphology.binary_dilation(im_sand, morphology.disk(1))
+    
+    if plot_bool:
+        im = np.copy(rescale_image_intensity(im_ms_ps[:,:,[2,1,0]], cloud_mask, 99.9, False))
+        im[im_sand,0] = 0
+        im[im_sand,1] = 0
+        im[im_sand,2] = 1
+        plt.figure()
+        plt.imshow(im)
+        plt.axis('image')
+        plt.title('Sand classification')
+        plt.show()
+    
+    return im_sand
+
+def classify_image_NN(im_ms_ps, im_pan, cloud_mask, min_beach_size, plot_bool):
+    """
+    Classifies every pixel in the image in one of 4 classes:
+        - sand                                          --> label = 1
+        - whitewater (breaking waves and swash)         --> label = 2
+        - water                                         --> label = 3
+        - other (vegetation, buildings, rocks...)       --> label = 0
+    
+    The classifier is a Neural Network, trained with 7000 pixels for the class SAND and 1500 pixels for
+    each of the other classes. This is because the class of interest for my application is SAND and I 
+    wanted to minimize the classification error for that class
+    
+    KV WRL 2018
+
+    Arguments:
+    -----------
+        im_ms_ps: np.ndarray
+            Pansharpened RGB + downsampled NIR and SWIR
+        im_pan:
+            Panchromatic band
+        cloud_mask: np.ndarray
+            2D cloud mask with True where cloud pixels are
+        plot_bool: boolean
+            True if plot is wanted
+                
+    Returns:    -----------
+        im_classif: np.ndarray
+            2D image containing labels
+        im_labels: np.ndarray of booleans
+            3D image containing a boolean image for each class (im_classif == label)
+
+    """     
+    
+    # load classifier
+    clf = joblib.load('functions/NeuralNet_classif.pkl')
+    
+    # calculate features
+    n_features = 10
+    im_features = np.zeros((im_ms_ps.shape[0], im_ms_ps.shape[1], n_features))
+    im_features[:,:,[0,1,2,3,4]] = im_ms_ps
+    im_features[:,:,5] = im_pan
+    im_features[:,:,6] = nd_index(im_ms_ps[:,:,3], im_ms_ps[:,:,1], cloud_mask, False) # (NIR-G)
+    im_features[:,:,7] = nd_index(im_ms_ps[:,:,3], im_ms_ps[:,:,2], cloud_mask, False) # ND(NIR-R)
+    im_features[:,:,8] = nd_index(im_ms_ps[:,:,0], im_ms_ps[:,:,2], cloud_mask, False) # ND(B-R)
+    im_features[:,:,9] = nd_index(im_ms_ps[:,:,4], im_ms_ps[:,:,1], cloud_mask, False) # ND(SWIR-G)
+    # remove NaNs and clouds
+    vec_features = im_features.reshape((im_ms_ps.shape[0] * im_ms_ps.shape[1], n_features))
+    vec_cloud = cloud_mask.reshape(cloud_mask.shape[0]*cloud_mask.shape[1])
+    vec_nan = np.any(np.isnan(vec_features), axis=1)
+    vec_mask = np.logical_or(vec_cloud, vec_nan)
+    vec_features = vec_features[~vec_mask, :]
+    # predict with NN classifier
+    labels = clf.predict(vec_features)
+    # recompose image
+    vec_classif = np.zeros((cloud_mask.shape[0]*cloud_mask.shape[1])) 
+    vec_classif[~vec_mask] = labels
+    im_classif = vec_classif.reshape((im_ms_ps.shape[0], im_ms_ps.shape[1]))
+
+    # labels
+    im_sand = im_classif == 1
+    im_sand = morphology.remove_small_objects(im_sand, min_size=min_beach_size, connectivity=2)
+    im_swash = im_classif == 2
+    im_water = im_classif == 3
+    im_labels = np.stack((im_sand,im_swash,im_water), axis=-1)  
+    
+    if plot_bool:
+        # display on top of pansharpened RGB
+        im_display = rescale_image_intensity(im_ms_ps[:,:,[2,1,0]], cloud_mask, 99.9, False)
+        im = np.copy(im_display)
+        # define colours for plot
+        colours = np.array([[1,128/255,0/255],[204/255,1,1],[0,0,204/255]])
+        for k in range(0,im_labels.shape[2]):
+            im[im_labels[:,:,k],0] = colours[k,0]
+            im[im_labels[:,:,k],1] = colours[k,1]
+            im[im_labels[:,:,k],2] = colours[k,2]
+             
+        plt.figure()
+        ax1 = plt.subplot(121)
+        plt.imshow(im_display)
+        plt.axis('off')
+        plt.title('Image')
+        ax2 = plt.subplot(122, sharex=ax1, sharey=ax1)
+        plt.imshow(im)
+        plt.axis('off')
+        plt.title('NN classifier')  
+        mng = plt.get_current_fig_manager()                                         
+        mng.window.showMaximized()
+        plt.tight_layout()   
+        plt.draw()
+    
+    return im_classif, im_labels
+
+def find_wl_contours2(im_ms_ps, im_labels, cloud_mask, buffer_size, plot_bool):
+    """
+    New mthod for extracting shorelines (more robust)
+    
+    KV WRL 2018
+
+    Arguments:
+    -----------
+        im_ms_ps: np.ndarray
+            Pansharpened RGB + downsampled NIR and SWIR
+        im_labels: np.ndarray
+            3D image containing a boolean image for each class in the order (sand, swash, water)
+        cloud_mask: np.ndarray
+            2D cloud mask with True where cloud pixels are
+        buffer_size: int
+            size of the buffer around the sandy beach
+        plot_bool: boolean
+            True if plot is wanted
+                
+    Returns:    -----------
+        contours_wi: list of np.arrays 
+            contains the (row,column) coordinates of the contour lines extracted with the Water Index
+        contours_mwi: list of np.arrays 
+            contains the (row,column) coordinates of the contour lines extracted with the Modified Water Index
+
+    """  
+    
+    nrows = cloud_mask.shape[0]
+    ncols = cloud_mask.shape[1]
+    im_display = rescale_image_intensity(im_ms_ps[:,:,[2,1,0]], cloud_mask, 99.9, False)
+
+    # calculate Normalized Difference Modified Water Index (SWIR - G)
+    im_mwi = nd_index(im_ms_ps[:,:,4], im_ms_ps[:,:,1], cloud_mask, False)
+    # calculate Normalized Difference Modified Water Index (NIR - G)
+    im_wi = nd_index(im_ms_ps[:,:,3], im_ms_ps[:,:,1], cloud_mask, False)
+    # stack indices together
+    im_ind = np.stack((im_wi, im_mwi), axis=-1)
+    vec_ind = im_ind.reshape(nrows*ncols,2)
+    
+    # process labels
+    vec_sand = im_labels[:,:,0].reshape(ncols*nrows)
+    vec_swash = im_labels[:,:,1].reshape(ncols*nrows)
+    vec_water = im_labels[:,:,2].reshape(ncols*nrows)
+    
+    # create a buffer around the sandy beach
+    se = morphology.disk(buffer_size)
+    im_buffer = morphology.binary_dilation(im_labels[:,:,0], se)
+    vec_buffer = im_buffer.reshape(nrows*ncols)
+    
+    # select water/sand/swash pixels that are within the buffer
+    int_water = vec_ind[np.logical_and(vec_buffer,vec_water),:]
+    int_sand = vec_ind[np.logical_and(vec_buffer,vec_sand),:]
+    int_swash = vec_ind[np.logical_and(vec_buffer,vec_swash),:]
+
+    # threshold the sand/water intensities 
+    int_all = np.append(int_water,int_sand, axis=0)
+    t_mwi = filters.threshold_otsu(int_all[:,0])
+    t_wi = filters.threshold_otsu(int_all[:,1])
+    
+    # find contour with MS algorithm
+    im_wi_buffer = np.copy(im_wi)
+    im_wi_buffer[~im_buffer] = np.nan
+    im_mwi_buffer = np.copy(im_mwi)
+    im_mwi_buffer[~im_buffer] = np.nan
+    contours_wi = measure.find_contours(im_wi_buffer, t_wi)
+    contours_mwi = measure.find_contours(im_mwi, t_mwi) # WARNING (on entire image)
+    
+    if plot_bool:
+        
+        im = np.copy(im_display)
+        # define colours for plot
+        colours = np.array([[1,128/255,0/255],[204/255,1,1],[0,0,204/255]])
+        for k in range(0,im_labels.shape[2]):
+            im[im_labels[:,:,k],0] = colours[k,0]
+            im[im_labels[:,:,k],1] = colours[k,1]
+            im[im_labels[:,:,k],2] = colours[k,2]
+        
+        fig = plt.figure()
+        gs = gridspec.GridSpec(3, 3, height_ratios=[1, 1, 3])
+        
+        ax1 = fig.add_subplot(gs[0,:]) 
+        vals = plt.hist(int_water[:,0], bins=100, label='water')
+        plt.hist(int_sand[:,0], bins=100, alpha=0.5, label='sand')
+        plt.hist(int_swash[:,0], bins=100, alpha=0.5, label='swash')
+        plt.plot([t_wi, t_wi], [0, np.max(vals[0])], 'r-')
+        plt.legend()
+        plt.title('Water Index NIR-G')
+        
+        ax2 = fig.add_subplot(gs[1,:], sharex=ax1)
+        vals = plt.hist(int_water[:,1], bins=100, label='water')
+        plt.hist(int_sand[:,1], bins=100, alpha=0.5, label='sand')
+        plt.hist(int_swash[:,1], bins=100, alpha=0.5, label='swash')
+        plt.plot([t_mwi, t_mwi], [0, np.max(vals[0])], 'r-')
+        plt.legend()
+        plt.title('Modified Water Index SWIR-G')        
+        
+        ax3 = fig.add_subplot(gs[2,0])
+        plt.imshow(im)
+        for i,contour in enumerate(contours_mwi): plt.plot(contour[:, 1], contour[:, 0], linewidth=3, color='k')
+        for i,contour in enumerate(contours_wi): plt.plot(contour[:, 1], contour[:, 0], linestyle='--', linewidth=1, color='w')
+        plt.grid(False)
+        plt.xticks([])
+        plt.yticks([])
+
+        ax4 = fig.add_subplot(gs[2,1], sharex=ax3, sharey=ax3)
+        plt.imshow(im_display)
+        for i,contour in enumerate(contours_mwi): plt.plot(contour[:, 1], contour[:, 0], linewidth=3, color='k')
+        for i,contour in enumerate(contours_wi): plt.plot(contour[:, 1], contour[:, 0], linestyle='--', linewidth=1, color='w')
+        plt.grid(False)     
+        plt.xticks([])
+        plt.yticks([])
+        
+        ax5 = fig.add_subplot(gs[2,2], sharex=ax3, sharey=ax3)
+        plt.imshow(im_mwi, cmap='seismic')
+        for i,contour in enumerate(contours_mwi): plt.plot(contour[:, 1], contour[:, 0], linewidth=3, color='k')
+        for i,contour in enumerate(contours_wi): plt.plot(contour[:, 1], contour[:, 0], linestyle='--', linewidth=1, color='w')
+        plt.grid(False)
+        plt.xticks([])
+        plt.yticks([])
+        
+        
+#        plt.gcf().set_size_inches(17.99,7.55)
+        mng = plt.get_current_fig_manager()                                         
+        mng.window.showMaximized()
+        plt.gcf().set_tight_layout(True)
+        plt.draw()
+
+    
+    return contours_wi, contours_mwi
+    

functions/sds_old1.py

@@ -0,0 +1,685 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Mar  1 11:20:35 2018
+
+@author: z5030440
+"""
+
+"""This script contains the functions needed for satellite derived shoreline (SDS) extraction"""
+
+# Initial settings
+import numpy as np
+import matplotlib.pyplot as plt
+import pdb
+import ee
+
+# other modules
+from osgeo import gdal, ogr, osr
+import tempfile
+from urllib.request import urlretrieve
+import zipfile
+
+# 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.measure as measure
+import skimage.morphology as morphology
+from sklearn.cluster import KMeans
+
+
+# import own modules
+from functions.utils import *
+
+
+# Download from ee server function
+
+def download_tif(image, polygon, bandsId):
+    """downloads tif image (region and bands) from the ee server and stores it in a temp file"""
+    url = ee.data.makeDownloadUrl(ee.data.getDownloadId({
+        'image': image.serialize(),
+        'region': polygon,
+        'bands': bandsId,
+        'filePerBand': 'false',
+        'name': 'data',
+        }))
+    local_zip, headers = urlretrieve(url)
+    with zipfile.ZipFile(local_zip) as local_zipfile:
+        return local_zipfile.extract('data.tif', tempfile.mkdtemp())
+
+def load_image(image, polygon, bandsId): 
+    """
+    Loads an ee.Image() as a np.array. e.Image() is retrieved from the EE database.
+    The geographic area and bands to select can be specified
+    
+    KV WRL 2018
+    
+    Arguments:
+    -----------
+        image: ee.Image()
+            image objec from the EE database
+        polygon: list
+            coordinates of the points creating a polygon. Each point is a list with 2 values
+        bandsId: list
+            bands to select, each band is a dictionnary in the list containing the following keys:
+            crs, crs_transform, data_type and id. NOTE: you have to remove the key dimensions, otherwise
+            the entire image is retrieved.
+            
+    Returns:
+    -----------
+        image_array : np.ndarray
+            An array containing the image (2D if one band, otherwise 3D)
+        georef : np.ndarray
+            6 element vector containing the crs_parameters 
+            [X_ul_corner Xscale Xshear Y_ul_corner Yshear Yscale]
+    """
+    
+    local_tif_filename = download_tif(image, polygon, bandsId)
+    dataset = gdal.Open(local_tif_filename, gdal.GA_ReadOnly)
+    georef = np.array(dataset.GetGeoTransform())
+    bands = [dataset.GetRasterBand(i + 1).ReadAsArray() for i in range(dataset.RasterCount)]
+    return np.stack(bands, 2), georef
+
+def create_cloud_mask(im_qa, satname, plot_bool):
+    """
+    Creates a cloud mask from the image containing the QA band information
+    
+    KV WRL 2018
+    
+    Arguments:
+    -----------
+        im_qa: np.ndarray
+            Image containing the QA band
+        satname: string
+            short name for the satellite (L8, L7, S2)
+        plot_bool: boolean
+            True if plot is wanted
+            
+    Returns:
+    -----------
+        cloud_mask : np.ndarray of booleans
+            A boolean array with True where the cloud are present
+    """
+    
+    # convert QA bits
+    if satname == 'L8':
+        cloud_values = [2800, 2804, 2808, 2812, 6896, 6900, 6904, 6908]
+    elif satname == 'L7':
+        cloud_values = [752, 756, 760, 764]
+        
+    cloud_mask = np.isin(im_qa, cloud_values)
+    # remove isolated cloud pixels (there are some in the swash and they cause problems)
+    if sum(sum(cloud_mask)) > 0:
+        morphology.remove_small_objects(cloud_mask, min_size=10, connectivity=1, in_place=True)
+    
+    if plot_bool:
+        plt.figure()
+        plt.imshow(cloud_mask, cmap='gray')
+        plt.draw()
+    
+    #cloud_shadow_values = [2976, 2980, 2984, 2988, 3008, 3012, 3016, 3020]
+    #cloud_shadow_mask = np.isin(im_qa, cloud_shadow_values)
+    
+    return cloud_mask
+
+def read_eeimage(im, polygon, sat_name, plot_bool):
+    """
+    Read an ee.Image() object and returns the panchromatic band, multispectral bands (B, G, R, NIR, SWIR)
+    and a cloud mask. All outputs are at 15m resolution (bilinear interpolation for the multispectral bands)
+    
+    KV WRL 2018
+
+    Arguments:
+    -----------
+        im: ee.Image()
+            Image to read from the Google Earth Engine database
+        plot_bool: boolean
+            True if plot is wanted
+        
+    Returns:
+    -----------
+        im_pan: np.ndarray (2D)
+            The panchromatic band (15m)
+        im_ms: np.ndarray (3D)
+            The multispectral bands interpolated at 15m
+        im_cloud: np.ndarray (2D)
+            The cloud mask at 15m
+        crs_params: list
+            EPSG code and affine transformation parameters
+    """   
+
+    im_dic = im.getInfo()
+    # save metadata
+    im_meta = im_dic.get('properties')
+    meta = {'timestamp':im_meta['system:time_start'],
+            'date_acquired':im_meta['DATE_ACQUIRED'],
+            'geom_rmse_model':im_meta['GEOMETRIC_RMSE_MODEL'],
+            'gcp_model':im_meta['GROUND_CONTROL_POINTS_MODEL'],
+            'quality':im_meta['IMAGE_QUALITY_OLI'],
+            'sun_azimuth':im_meta['SUN_AZIMUTH'],
+            'sun_elevation':im_meta['SUN_ELEVATION']}
+    
+    im_bands = im_dic.get('bands')
+    
+    # delete dimensions key from dictionnary, otherwise the entire image is extracted
+    for i in range(len(im_bands)): del im_bands[i]['dimensions']
+    
+    # load panchromatic band
+    pan_band = [im_bands[7]]
+    im_pan, crs_pan = load_image(im, polygon, pan_band)
+    im_pan = im_pan[:,:,0]
+    
+    # load the multispectral bands (B2,B3,B4,B5,B6) = (blue,green,red,nir,swir1)
+    ms_bands = [im_bands[1], im_bands[2], im_bands[3], im_bands[4], im_bands[5]]
+    im_ms_30m, crs_ms = load_image(im, polygon, ms_bands)
+        
+    # create cloud mask
+    qa_band = [im_bands[11]]
+    im_qa, crs_qa = load_image(im, polygon, qa_band)
+    im_qa = im_qa[:,:,0]
+    im_cloud = create_cloud_mask(im_qa, sat_name, plot_bool)
+    im_cloud = transform.resize(im_cloud, (im_pan.shape[0], im_pan.shape[1]),
+                                order=0, preserve_range=True, mode='constant').astype('bool_')
+    
+    # resize the image using bilinear interpolation (order 1)
+    im_ms = transform.resize(im_ms_30m,(im_pan.shape[0], im_pan.shape[1]),
+                             order=1, preserve_range=True, mode='constant')
+    
+    # check if -inf values (means out of image) and add to cloud mask
+    im_inf = np.isin(im_ms[:,:,0], -np.inf)
+    im_nan = np.isnan(im_ms[:,:,0])
+    im_cloud = np.logical_or(np.logical_or(im_cloud, im_inf), im_nan)
+
+    # get the crs parameters for the image at 15m and 30m resolution
+    crs = {'crs_15m':crs_pan, 'crs_30m':crs_ms, 'epsg_code':int(pan_band[0]['crs'][5:])}
+
+    if plot_bool:
+        
+        # if there are -inf in the image, set them to 0 before plotting
+        if sum(sum(np.isin(im_ms_30m[:,:,0], -np.inf).astype(int))) > 0:
+            idx = np.isin(im_ms_30m[:,:,0], -np.inf)
+            im_ms_30m[idx,0] = 0; im_ms_30m[idx,1] = 0; im_ms_30m[idx,2] = 0; 
+            im_ms_30m[idx,3] = 0; im_ms_30m[idx,4] = 0
+
+        plt.figure()
+        
+        plt.subplot(221)
+        plt.imshow(im_pan, cmap='gray')
+        plt.title('PANCHROMATIC')
+        
+        plt.subplot(222)
+        plt.imshow(im_ms_30m[:,:,[2,1,0]])
+        plt.title('RGB')
+
+
+        plt.subplot(223)
+        plt.imshow(im_ms_30m[:,:,3], cmap='gray')
+        plt.title('NIR')
+        
+        plt.subplot(224)
+        plt.imshow(im_ms_30m[:,:,4], cmap='gray')
+        plt.title('SWIR')
+        
+        plt.show()
+    
+    return im_pan, im_ms, im_cloud, crs, meta
+
+
+def rescale_image_intensity(im, cloud_mask, prob_high, plot_bool):
+    """
+    Rescales the intensity of an image (multispectral or single band) by applying
+    a cloud mask and clipping the prob_high upper percentile. This functions allows
+    to stretch the contrast of an image.
+    
+    KV WRL 2018
+
+    Arguments:
+    -----------
+        im: np.ndarray
+            Image to rescale, can be 3D (multispectral) or 2D (single band)
+        cloud_mask: np.ndarray
+            2D cloud mask with True where cloud pixels are
+        prob_high: float
+            probability of exceedence used to calculate the upper percentile
+        plot_bool: boolean
+            True if plot is wanted
+        
+    Returns:
+    -----------
+        im_adj: np.ndarray
+            The rescaled image
+    """
+    prc_low = 0 # lower percentile
+    vec_mask = cloud_mask.reshape(im.shape[0] * im.shape[1])
+    
+    if plot_bool:
+        plt.figure()
+        
+    if len(im.shape) > 2:
+        vec =  im.reshape(im.shape[0] * im.shape[1], im.shape[2])    
+        vec_adj = np.ones((len(vec_mask), im.shape[2])) * np.nan
+
+        for i in range(im.shape[2]):
+            prc_high = np.percentile(vec[~vec_mask, i], prob_high)
+            vec_rescaled = exposure.rescale_intensity(vec[~vec_mask, i], in_range=(prc_low, prc_high))
+            vec_adj[~vec_mask,i] = vec_rescaled
+            
+            if plot_bool:
+                plt.subplot(np.floor(im.shape[2]/2) + 1, np.floor(im.shape[2]/2), i+1)
+                plt.hist(vec[~vec_mask, i], bins=200, label='original')
+                plt.hist(vec_rescaled, bins=200, alpha=0.5, label='rescaled')
+                plt.legend()
+                plt.title('Band' + str(i+1))
+                plt.show()
+        
+        im_adj = vec_adj.reshape(im.shape[0], im.shape[1], im.shape[2])
+        
+        if plot_bool:
+            plt.figure()
+            ax1 = plt.subplot(121)
+            plt.imshow(im[:,:,[2,1,0]])
+            plt.axis('off')
+            plt.title('Original')
+            ax2 = plt.subplot(122, sharex=ax1, sharey=ax1)
+            plt.imshow(im_adj[:,:,[2,1,0]])
+            plt.axis('off')
+            plt.title('Rescaled')
+            plt.show()
+            
+    else:
+        vec =  im.reshape(im.shape[0] * im.shape[1])
+        vec_adj = np.ones(len(vec_mask)) * np.nan
+        prc_high = np.percentile(vec[~vec_mask], prob_high)
+        vec_rescaled = exposure.rescale_intensity(vec[~vec_mask], in_range=(prc_low, prc_high))
+        vec_adj[~vec_mask] = vec_rescaled
+        
+        if plot_bool:
+            plt.hist(vec[~vec_mask], bins=200, label='original')
+            plt.hist(vec_rescaled, bins=200, alpha=0.5, label='rescaled')
+            plt.legend()
+            plt.title('Single band')
+            plt.show()
+        
+        im_adj = vec_adj.reshape(im.shape[0], im.shape[1])
+        
+        if plot_bool:
+            plt.figure()
+            ax1 = plt.subplot(121)
+            plt.imshow(im, cmap='gray')
+            plt.axis('off')
+            plt.title('Original')
+            ax2 = plt.subplot(122, sharex=ax1, sharey=ax1)
+            plt.imshow(im_adj, cmap='gray')
+            plt.axis('off')
+            plt.title('Rescaled')
+            plt.show()
+
+    return im_adj
+    
+    
+def hist_match(source, template):
+    """
+    Adjust the pixel values of a grayscale image such that its histogram
+    matches that of a target image
+
+    Arguments:
+    -----------
+        source: np.ndarray
+            Image to transform; the histogram is computed over the flattened
+            array
+        template: np.ndarray
+            Template image; can have different dimensions to source
+    Returns:
+    -----------
+        matched: np.ndarray
+            The transformed output image
+    """
+
+    oldshape = source.shape
+    source = source.ravel()
+    template = template.ravel()
+
+    # get the set of unique pixel values and their corresponding indices and
+    # counts
+    s_values, bin_idx, s_counts = np.unique(source, return_inverse=True,
+                                            return_counts=True)
+    t_values, t_counts = np.unique(template, return_counts=True)
+
+    # take the cumsum of the counts and normalize by the number of pixels to
+    # get the empirical cumulative distribution functions for the source and
+    # template images (maps pixel value --> quantile)
+    s_quantiles = np.cumsum(s_counts).astype(np.float64)
+    s_quantiles /= s_quantiles[-1]
+    t_quantiles = np.cumsum(t_counts).astype(np.float64)
+    t_quantiles /= t_quantiles[-1]
+
+    # interpolate linearly to find the pixel values in the template image
+    # that correspond most closely to the quantiles in the source image
+    interp_t_values = np.interp(s_quantiles, t_quantiles, t_values)
+
+    return interp_t_values[bin_idx].reshape(oldshape)
+
+def pansharpen(im_ms, im_pan, cloud_mask, plot_bool):
+    """
+    Pansharpens a multispectral image (3D), using the panchromatic band (2D)
+    and a cloud mask
+    
+    KV WRL 2018
+
+    Arguments:
+    -----------
+        im_ms: np.ndarray
+            Multispectral image to pansharpen (3D)
+        im_pan: np.ndarray
+            Panchromatic band (2D)
+        cloud_mask: np.ndarray
+            2D cloud mask with True where cloud pixels are
+        plot_bool: boolean
+            True if plot is wanted
+        
+    Returns:
+    -----------
+        im_ms_ps: np.ndarray
+            Pansharpened multisoectral image (3D)
+    """
+    
+    # reshape image into vector and apply cloud mask
+    vec = im_ms.reshape(im_ms.shape[0] * im_ms.shape[1], im_ms.shape[2])
+    vec_mask = cloud_mask.reshape(im_ms.shape[0] * im_ms.shape[1])
+    vec = vec[~vec_mask, :]
+    # apply PCA to RGB bands
+    pca = decomposition.PCA()
+    vec_pcs = pca.fit_transform(vec)
+    # replace 1st PC with pan band (after matching histograms)
+    vec_pan = im_pan.reshape(im_pan.shape[0] * im_pan.shape[1])
+    vec_pan = vec_pan[~vec_mask]
+    vec_pcs[:,0] = hist_match(vec_pan, vec_pcs[:,0])
+    vec_ms_ps = pca.inverse_transform(vec_pcs)
+    
+    # normalise between 0 and 1
+    for i in range(vec_pcs.shape[1]):
+        vec_ms_ps[:,i] = np.divide(vec_ms_ps[:,i] - np.min(vec_ms_ps[:,i]),
+                         np.max(vec_ms_ps[:,i]) - np.min(vec_ms_ps[:,i]))
+    # reshape vector into image
+    vec_ms_ps_full = np.ones((len(vec_mask), im_ms.shape[2])) * np.nan
+    vec_ms_ps_full[~vec_mask,:] = vec_ms_ps
+    im_ms_ps = vec_ms_ps_full.reshape(im_ms.shape[0], im_ms.shape[1], im_ms.shape[2])
+    
+    if plot_bool:
+        plt.figure()
+        ax1 = plt.subplot(121)
+        plt.imshow(im_ms[:,:,[2,1,0]])
+        plt.axis('off')
+        plt.title('Original')
+        ax2 = plt.subplot(122, sharex=ax1, sharey=ax1)
+        plt.imshow(im_ms_ps[:,:,[2,1,0]])
+        plt.axis('off')
+        plt.title('Pansharpened')
+        plt.show()
+
+    return im_ms_ps
+    
+def nd_index(im1, im2, cloud_mask, plot_bool):
+    """
+    Computes normalised difference index on 2 images (2D), given a cloud mask (2D)
+    
+    KV WRL 2018
+
+    Arguments:
+    -----------
+        im1, im2: np.ndarray
+            Images (2D) with which to calculate the ND index
+        cloud_mask: np.ndarray
+            2D cloud mask with True where cloud pixels are
+        plot_bool: boolean
+            True if plot is wanted
+        
+    Returns:    -----------
+        im_nd: np.ndarray
+
+            Image (2D) containing the ND index
+    """
+    vec_mask = cloud_mask.reshape(im1.shape[0] * im1.shape[1])
+    vec_nd = np.ones(len(vec_mask)) * np.nan
+    vec1 = im1.reshape(im1.shape[0] * im1.shape[1])
+    vec2 = im2.reshape(im2.shape[0] * im2.shape[1])
+    temp = np.divide(vec1[~vec_mask] - vec2[~vec_mask],
+                     vec1[~vec_mask] + vec2[~vec_mask])
+    vec_nd[~vec_mask] = temp
+    im_nd = vec_nd.reshape(im1.shape[0], im1.shape[1])
+    
+    if plot_bool:
+        plt.figure()
+        plt.imshow(im_nd, cmap='seismic')
+        plt.colorbar()
+        plt.title('Normalised index')
+        plt.show()
+    
+    return im_nd
+
+def find_wl_contours(im_ndwi, cloud_mask, min_contour_points, plot_bool):
+    """
+    Computes normalised difference index on 2 images (2D), given a cloud mask (2D)
+    
+    KV WRL 2018
+
+    Arguments:
+    -----------
+        im_ndwi: np.ndarray
+            Image (2D) with the NDWI (water index)
+        cloud_mask: np.ndarray
+            2D cloud mask with True where cloud pixels are
+        min_contour_points: int
+            minimum number of points in each contour line
+        plot_bool: boolean
+            True if plot is wanted
+                
+    Returns:    -----------
+        contours_wl: list of np.arrays 
+            contains the (row,column) coordinates of the contour lines
+
+    """  
+    
+    # reshape image to vector
+    vec_ndwi = im_ndwi.reshape(im_ndwi.shape[0] * im_ndwi.shape[1])
+    vec_mask = cloud_mask.reshape(cloud_mask.shape[0] * cloud_mask.shape[1])
+    vec = vec_ndwi[~vec_mask]
+    # apply otsu's threshold
+    t_otsu = filters.threshold_otsu(vec)
+    # use Marching Squares algorithm to detect contours on ndwi image
+    contours = measure.find_contours(im_ndwi, t_otsu)
+    # filter water lines
+    contours_wl = []
+    for i, contour in enumerate(contours):
+        # remove contour points that are around clouds (nan values)
+        if np.any(np.isnan(contour)):
+            index_nan = np.where(np.isnan(contour))[0]
+            contour = np.delete(contour, index_nan, axis=0) 
+        # remove contours that have only few points (less than min_contour_points)
+        if contour.shape[0] > min_contour_points:
+            contours_wl.append(contour)
+        
+    if plot_bool:
+        # plot otsu's histogram segmentation
+        plt.figure()
+        vals = plt.hist(vec, bins=200)
+        plt.plot([t_otsu, t_otsu],[0, np.max(vals[0])], 'r-', label='Otsu threshold')
+        plt.legend()
+        plt.show()
+        
+        # plot the water line contours on top of water index
+        plt.figure()
+        plt.imshow(im_ndwi, cmap='seismic')
+        plt.colorbar()
+        for i,contour in enumerate(contours_wl): plt.plot(contour[:, 1], contour[:, 0], linewidth=3, color='k')
+        plt.axis('image')
+        plt.title('Detected water lines')
+        plt.show()
+    
+    return contours_wl
+
+def convert_pix2world(points, crs_vec):
+    """
+    Converts pixel coordinates (row,columns) to world projected coordinates
+    performing an affine transformation
+    
+    KV WRL 2018
+
+    Arguments:
+    -----------
+        points: np.ndarray or list of np.ndarray
+            array with 2 columns (rows first and columns second)
+        crs_vec: np.ndarray
+            vector of 6 elements [Xtr, Xscale, Xshear, Ytr, Yshear, Yscale]
+                
+    Returns:    -----------
+        points_converted: np.ndarray or list of np.ndarray 
+            converted coordinates, first columns with X and second column with Y
+        
+    """
+    
+    # make affine transformation matrix
+    aff_mat = np.array([[crs_vec[1], crs_vec[2], crs_vec[0]],
+                       [crs_vec[4], crs_vec[5], crs_vec[3]],
+                       [0, 0, 1]])
+    # create affine transformation
+    tform = transform.AffineTransform(aff_mat)
+
+    if type(points) is list:
+        points_converted = []
+        # iterate over the list
+        for i, arr in enumerate(points): 
+            tmp = arr[:,[1,0]]
+            points_converted.append(tform(tmp))
+            
+    elif type(points) is np.ndarray:
+        tmp = points[:,[1,0]]
+        points_converted = tform(tmp)
+        
+    else:
+        print('invalid input type')
+        raise
+        
+    return points_converted
+
+def convert_epsg(points, epsg_in, epsg_out):
+    """
+    Converts from one spatial reference to another using the epsg codes
+    
+    KV WRL 2018
+
+    Arguments:
+    -----------
+        points: np.ndarray or list of np.ndarray
+            array with 2 columns (rows first and columns second)
+        epsg_in: int
+            epsg code of the spatial reference in which the input is
+        epsg_out: int
+            epsg code of the spatial reference in which the output will be            
+                
+    Returns:    -----------
+        points_converted: np.ndarray or list of np.ndarray 
+            converted coordinates
+        
+    """
+    
+    # define input and output spatial references
+    inSpatialRef = osr.SpatialReference()
+    inSpatialRef.ImportFromEPSG(epsg_in)
+    outSpatialRef = osr.SpatialReference()
+    outSpatialRef.ImportFromEPSG(epsg_out)
+    # create a coordinates transform
+    coordTransform = osr.CoordinateTransformation(inSpatialRef, outSpatialRef)
+    # transform points
+    if type(points) is list:
+        points_converted = []
+        # iterate over the list
+        for i, arr in enumerate(points): 
+            points_converted.append(np.array(coordTransform.TransformPoints(arr)))
+            
+    elif type(points) is np.ndarray:
+        points_converted = np.array(coordTransform.TransformPoints(points))
+        
+    else:
+        print('invalid input type')
+        raise
+        
+    return points_converted
+
+def classify_sand_unsupervised(im_ms_ps, im_pan, cloud_mask, wl_pix, buffer_size, min_beach_size, plot_bool):
+    """
+    Classifies sand pixels using an unsupervised algorithm (Kmeans)
+    Set buffer size to False if you 
+    
+    KV WRL 2018
+
+    Arguments:
+    -----------
+        im_ms_ps: np.ndarray
+            Pansharpened RGB + downsampled NIR and SWIR
+        im_pan:
+            Panchromatic band
+        cloud_mask: np.ndarray
+            2D cloud mask with True where cloud pixels are
+        wl_pix: list of np.ndarray
+            list of arrays containig the pixel coordinates of the water line
+        buffer_size: int or False
+            radius of the disk used to create a buffer around the water line
+            when False, the entire image is considered for kmeans
+        min_beach_size: int
+            minimum number of connected pixels belonging to a single beach
+        plot_bool: boolean
+            True if plot is wanted
+                
+    Returns:    -----------
+        im_sand: np.ndarray
+            2D binary image containing True where sand pixels are located
+
+    """     
+    # reshape the 2D images into vectors
+    vec_ms_ps = im_ms_ps.reshape(im_ms_ps.shape[0] * im_ms_ps.shape[1], im_ms_ps.shape[2])
+    vec_pan = im_pan.reshape(im_pan.shape[0]*im_pan.shape[1])
+    vec_mask = cloud_mask.reshape(im_ms_ps.shape[0] * im_ms_ps.shape[1])
+    # add B,G,R,NIR and pan bands to the vector of features
+    vec_features = np.zeros((vec_ms_ps.shape[0], 5))
+    vec_features[:,[0,1,2,3]] = vec_ms_ps[:,[0,1,2,3]]
+    vec_features[:,4] = vec_pan
+    
+    if buffer_size:
+        # create binary image with ones where the detected water lines is
+        im_buffer = np.zeros((im_ms_ps.shape[0], im_ms_ps.shape[1]))
+        for i, contour in enumerate(wl_pix):
+                indices = [(int(_[0]), int(_[1])) for _ in list(np.round(contour))]
+                for j, idx in enumerate(indices):
+                    im_buffer[idx] = 1
+        # perform a dilation on the binary image
+        se = morphology.disk(buffer_size)
+        im_buffer = morphology.binary_dilation(im_buffer, se)
+        vec_buffer = (im_buffer == 1).reshape(im_ms_ps.shape[0] * im_ms_ps.shape[1])
+    else:
+        vec_buffer = np.ones((vec_pan.shape[0]))
+    # add cloud mask to buffer
+    vec_buffer= np.logical_and(vec_buffer, ~vec_mask)
+    # perform kmeans (6 clusters)
+    kmeans = KMeans(n_clusters=6, random_state=0).fit(vec_features[vec_buffer,:])
+    labels = np.ones((len(vec_mask))) * np.nan
+    labels[vec_buffer] = kmeans.labels_
+    im_labels = labels.reshape(im_ms_ps.shape[0], im_ms_ps.shape[1])
+    # find the class with maximum reflection in the B,G,R,Pan
+    im_sand = im_labels == np.argmax(np.mean(kmeans.cluster_centers_[:,[0,1,2,4]], axis=1))
+    im_sand = morphology.remove_small_objects(im_sand, min_size=min_beach_size, connectivity=2)
+#    im_sand = morphology.binary_dilation(im_sand, morphology.disk(1))
+    
+    if plot_bool:
+        im = np.copy(im_ms_ps)
+        im[im_sand,0] = 0
+        im[im_sand,1] = 0
+        im[im_sand,2] = 1
+        plt.figure()
+        plt.imshow(im[:,:,[2,1,0]])
+        plt.axis('image')
+        plt.title('Sand classification')
+        plt.show()
+    
+    return im_sand

functions/sds_old2.py

@@ -0,0 +1,883 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Mar  1 11:20:35 2018
+
+@author: z5030440
+"""
+
+"""This script contains the functions needed for satellite derived shoreline (SDS) extraction"""
+
+# Initial settings
+import numpy as np
+import matplotlib.pyplot as plt
+import pdb
+import ee
+
+# other modules
+from osgeo import gdal, ogr, osr
+import tempfile
+from urllib.request import urlretrieve
+import zipfile
+
+# 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.measure as measure
+import skimage.morphology as morphology
+
+# machine learning modules
+from sklearn.cluster import KMeans
+from sklearn.neural_network import MLPClassifier
+from sklearn.externals import joblib
+
+
+# import own modules
+from functions.utils import *
+
+
+# Download from ee server function
+
+def download_tif(image, polygon, bandsId):
+    """downloads tif image (region and bands) from the ee server and stores it in a temp file"""
+    url = ee.data.makeDownloadUrl(ee.data.getDownloadId({
+        'image': image.serialize(),
+        'region': polygon,
+        'bands': bandsId,
+        'filePerBand': 'false',
+        'name': 'data',
+        }))
+    local_zip, headers = urlretrieve(url)
+    with zipfile.ZipFile(local_zip) as local_zipfile:
+        return local_zipfile.extract('data.tif', tempfile.mkdtemp())
+
+def load_image(image, polygon, bandsId): 
+    """
+    Loads an ee.Image() as a np.array. e.Image() is retrieved from the EE database.
+    The geographic area and bands to select can be specified
+    
+    KV WRL 2018
+    
+    Arguments:
+    -----------
+        image: ee.Image()
+            image objec from the EE database
+        polygon: list
+            coordinates of the points creating a polygon. Each point is a list with 2 values
+        bandsId: list
+            bands to select, each band is a dictionnary in the list containing the following keys:
+            crs, crs_transform, data_type and id. NOTE: you have to remove the key dimensions, otherwise
+            the entire image is retrieved.
+            
+    Returns:
+    -----------
+        image_array : np.ndarray
+            An array containing the image (2D if one band, otherwise 3D)
+        georef : np.ndarray
+            6 element vector containing the crs_parameters 
+            [X_ul_corner Xscale Xshear Y_ul_corner Yshear Yscale]
+    """
+    
+    local_tif_filename = download_tif(image, polygon, bandsId)
+    dataset = gdal.Open(local_tif_filename, gdal.GA_ReadOnly)
+    georef = np.array(dataset.GetGeoTransform())
+    bands = [dataset.GetRasterBand(i + 1).ReadAsArray() for i in range(dataset.RasterCount)]
+    return np.stack(bands, 2), georef
+
+def create_cloud_mask(im_qa, satname, plot_bool):
+    """
+    Creates a cloud mask from the image containing the QA band information
+    
+    KV WRL 2018
+    
+    Arguments:
+    -----------
+        im_qa: np.ndarray
+            Image containing the QA band
+        satname: string
+            short name for the satellite (L8, L7, S2)
+        plot_bool: boolean
+            True if plot is wanted
+            
+    Returns:
+    -----------
+        cloud_mask : np.ndarray of booleans
+            A boolean array with True where the cloud are present
+    """
+    
+    # convert QA bits
+    if satname == 'L8':
+        cloud_values = [2800, 2804, 2808, 2812, 6896, 6900, 6904, 6908]
+    elif satname == 'L7':
+        cloud_values = [752, 756, 760, 764]
+        
+    cloud_mask = np.isin(im_qa, cloud_values)
+    # remove isolated cloud pixels (there are some in the swash and they cause problems)
+    if sum(sum(cloud_mask)) > 0:
+        morphology.remove_small_objects(cloud_mask, min_size=10, connectivity=1, in_place=True)
+    
+    if plot_bool:
+        plt.figure()
+        plt.imshow(cloud_mask, cmap='gray')
+        plt.draw()
+    
+    #cloud_shadow_values = [2976, 2980, 2984, 2988, 3008, 3012, 3016, 3020]
+    #cloud_shadow_mask = np.isin(im_qa, cloud_shadow_values)
+    
+    return cloud_mask
+
+def read_eeimage(im, polygon, sat_name, plot_bool):
+    """
+    Read an ee.Image() object and returns the panchromatic band, multispectral bands (B, G, R, NIR, SWIR)
+    and a cloud mask. All outputs are at 15m resolution (bilinear interpolation for the multispectral bands)
+    
+    KV WRL 2018
+
+    Arguments:
+    -----------
+        im: ee.Image()
+            Image to read from the Google Earth Engine database
+        plot_bool: boolean
+            True if plot is wanted
+        
+    Returns:
+    -----------
+        im_pan: np.ndarray (2D)
+            The panchromatic band (15m)
+        im_ms: np.ndarray (3D)
+            The multispectral bands interpolated at 15m
+        im_cloud: np.ndarray (2D)
+            The cloud mask at 15m
+        crs_params: list
+            EPSG code and affine transformation parameters
+    """   
+
+    im_dic = im.getInfo()
+    # save metadata
+    im_meta = im_dic.get('properties')
+    meta = {'timestamp':im_meta['system:time_start'],
+            'date_acquired':im_meta['DATE_ACQUIRED'],
+            'geom_rmse_model':im_meta['GEOMETRIC_RMSE_MODEL'],
+            'gcp_model':im_meta['GROUND_CONTROL_POINTS_MODEL'],
+            'quality':im_meta['IMAGE_QUALITY_OLI'],
+            'sun_azimuth':im_meta['SUN_AZIMUTH'],
+            'sun_elevation':im_meta['SUN_ELEVATION']}
+    
+    im_bands = im_dic.get('bands')
+    
+    # delete dimensions key from dictionnary, otherwise the entire image is extracted
+    for i in range(len(im_bands)): del im_bands[i]['dimensions']
+    
+    # load panchromatic band
+    pan_band = [im_bands[7]]
+    im_pan, crs_pan = load_image(im, polygon, pan_band)
+    im_pan = im_pan[:,:,0]
+    
+    # load the multispectral bands (B2,B3,B4,B5,B6) = (blue,green,red,nir,swir1)
+    ms_bands = [im_bands[1], im_bands[2], im_bands[3], im_bands[4], im_bands[5]]
+    im_ms_30m, crs_ms = load_image(im, polygon, ms_bands)
+        
+    # create cloud mask
+    qa_band = [im_bands[11]]
+    im_qa, crs_qa = load_image(im, polygon, qa_band)
+    im_qa = im_qa[:,:,0]
+    im_cloud = create_cloud_mask(im_qa, sat_name, plot_bool)
+    im_cloud = transform.resize(im_cloud, (im_pan.shape[0], im_pan.shape[1]),
+                                order=0, preserve_range=True, mode='constant').astype('bool_')
+    
+    # resize the image using bilinear interpolation (order 1)
+    im_ms = transform.resize(im_ms_30m,(im_pan.shape[0], im_pan.shape[1]),
+                             order=1, preserve_range=True, mode='constant')
+    
+    # check if -inf values (means out of image) and add to cloud mask
+    im_inf = np.isin(im_ms[:,:,0], -np.inf)
+    im_nan = np.isnan(im_ms[:,:,0])
+    im_cloud = np.logical_or(np.logical_or(im_cloud, im_inf), im_nan)
+
+    # get the crs parameters for the image at 15m and 30m resolution
+    crs = {'crs_15m':crs_pan, 'crs_30m':crs_ms, 'epsg_code':int(pan_band[0]['crs'][5:])}
+
+    if plot_bool:
+        
+        # if there are -inf in the image, set them to 0 before plotting
+        if sum(sum(np.isin(im_ms_30m[:,:,0], -np.inf).astype(int))) > 0:
+            idx = np.isin(im_ms_30m[:,:,0], -np.inf)
+            im_ms_30m[idx,0] = 0; im_ms_30m[idx,1] = 0; im_ms_30m[idx,2] = 0; 
+            im_ms_30m[idx,3] = 0; im_ms_30m[idx,4] = 0
+
+        plt.figure()
+        
+        plt.subplot(221)
+        plt.imshow(im_pan, cmap='gray')
+        plt.title('PANCHROMATIC')
+        
+        plt.subplot(222)
+        plt.imshow(im_ms_30m[:,:,[2,1,0]])
+        plt.title('RGB')
+
+
+        plt.subplot(223)
+        plt.imshow(im_ms_30m[:,:,3], cmap='gray')
+        plt.title('NIR')
+        
+        plt.subplot(224)
+        plt.imshow(im_ms_30m[:,:,4], cmap='gray')
+        plt.title('SWIR')
+        
+        plt.show()
+    
+    return im_pan, im_ms, im_cloud, crs, meta
+
+
+def rescale_image_intensity(im, cloud_mask, prob_high, plot_bool):
+    """
+    Rescales the intensity of an image (multispectral or single band) by applying
+    a cloud mask and clipping the prob_high upper percentile. This functions allows
+    to stretch the contrast of an image.
+    
+    KV WRL 2018
+
+    Arguments:
+    -----------
+        im: np.ndarray
+            Image to rescale, can be 3D (multispectral) or 2D (single band)
+        cloud_mask: np.ndarray
+            2D cloud mask with True where cloud pixels are
+        prob_high: float
+            probability of exceedence used to calculate the upper percentile
+        plot_bool: boolean
+            True if plot is wanted
+        
+    Returns:
+    -----------
+        im_adj: np.ndarray
+            The rescaled image
+    """
+    prc_low = 0 # lower percentile
+    vec_mask = cloud_mask.reshape(im.shape[0] * im.shape[1])
+    
+    if plot_bool:
+        plt.figure()
+        
+    if len(im.shape) > 2:
+        vec =  im.reshape(im.shape[0] * im.shape[1], im.shape[2])    
+        vec_adj = np.ones((len(vec_mask), im.shape[2])) * np.nan
+
+        for i in range(im.shape[2]):
+            prc_high = np.percentile(vec[~vec_mask, i], prob_high)
+            vec_rescaled = exposure.rescale_intensity(vec[~vec_mask, i], in_range=(prc_low, prc_high))
+            vec_adj[~vec_mask,i] = vec_rescaled
+            
+            if plot_bool:
+                plt.subplot(np.floor(im.shape[2]/2) + 1, np.floor(im.shape[2]/2), i+1)
+                plt.hist(vec[~vec_mask, i], bins=200, label='original')
+                plt.hist(vec_rescaled, bins=200, alpha=0.5, label='rescaled')
+                plt.legend()
+                plt.title('Band' + str(i+1))
+                plt.show()
+        
+        im_adj = vec_adj.reshape(im.shape[0], im.shape[1], im.shape[2])
+        
+        if plot_bool:
+            plt.figure()
+            ax1 = plt.subplot(121)
+            plt.imshow(im[:,:,[2,1,0]])
+            plt.axis('off')
+            plt.title('Original')
+            ax2 = plt.subplot(122, sharex=ax1, sharey=ax1)
+            plt.imshow(im_adj[:,:,[2,1,0]])
+            plt.axis('off')
+            plt.title('Rescaled')
+            plt.show()
+            
+    else:
+        vec =  im.reshape(im.shape[0] * im.shape[1])
+        vec_adj = np.ones(len(vec_mask)) * np.nan
+        prc_high = np.percentile(vec[~vec_mask], prob_high)
+        vec_rescaled = exposure.rescale_intensity(vec[~vec_mask], in_range=(prc_low, prc_high))
+        vec_adj[~vec_mask] = vec_rescaled
+        
+        if plot_bool:
+            plt.hist(vec[~vec_mask], bins=200, label='original')
+            plt.hist(vec_rescaled, bins=200, alpha=0.5, label='rescaled')
+            plt.legend()
+            plt.title('Single band')
+            plt.show()
+        
+        im_adj = vec_adj.reshape(im.shape[0], im.shape[1])
+        
+        if plot_bool:
+            plt.figure()
+            ax1 = plt.subplot(121)
+            plt.imshow(im, cmap='gray')
+            plt.axis('off')
+            plt.title('Original')
+            ax2 = plt.subplot(122, sharex=ax1, sharey=ax1)
+            plt.imshow(im_adj, cmap='gray')
+            plt.axis('off')
+            plt.title('Rescaled')
+            plt.show()
+
+    return im_adj
+    
+    
+def hist_match(source, template):
+    """
+    Adjust the pixel values of a grayscale image such that its histogram
+    matches that of a target image
+
+    Arguments:
+    -----------
+        source: np.ndarray
+            Image to transform; the histogram is computed over the flattened
+            array
+        template: np.ndarray
+            Template image; can have different dimensions to source
+    Returns:
+    -----------
+        matched: np.ndarray
+            The transformed output image
+    """
+
+    oldshape = source.shape
+    source = source.ravel()
+    template = template.ravel()
+
+    # get the set of unique pixel values and their corresponding indices and
+    # counts
+    s_values, bin_idx, s_counts = np.unique(source, return_inverse=True,
+                                            return_counts=True)
+    t_values, t_counts = np.unique(template, return_counts=True)
+
+    # take the cumsum of the counts and normalize by the number of pixels to
+    # get the empirical cumulative distribution functions for the source and
+    # template images (maps pixel value --> quantile)
+    s_quantiles = np.cumsum(s_counts).astype(np.float64)
+    s_quantiles /= s_quantiles[-1]
+    t_quantiles = np.cumsum(t_counts).astype(np.float64)
+    t_quantiles /= t_quantiles[-1]
+
+    # interpolate linearly to find the pixel values in the template image
+    # that correspond most closely to the quantiles in the source image
+    interp_t_values = np.interp(s_quantiles, t_quantiles, t_values)
+
+    return interp_t_values[bin_idx].reshape(oldshape)
+
+def pansharpen(im_ms, im_pan, cloud_mask, plot_bool):
+    """
+    Pansharpens a multispectral image (3D), using the panchromatic band (2D)
+    and a cloud mask
+    
+    KV WRL 2018
+
+    Arguments:
+    -----------
+        im_ms: np.ndarray
+            Multispectral image to pansharpen (3D)
+        im_pan: np.ndarray
+            Panchromatic band (2D)
+        cloud_mask: np.ndarray
+            2D cloud mask with True where cloud pixels are
+        plot_bool: boolean
+            True if plot is wanted
+        
+    Returns:
+    -----------
+        im_ms_ps: np.ndarray
+            Pansharpened multisoectral image (3D)
+    """
+    
+    # reshape image into vector and apply cloud mask
+    vec = im_ms.reshape(im_ms.shape[0] * im_ms.shape[1], im_ms.shape[2])
+    vec_mask = cloud_mask.reshape(im_ms.shape[0] * im_ms.shape[1])
+    vec = vec[~vec_mask, :]
+    # apply PCA to RGB bands
+    pca = decomposition.PCA()
+    vec_pcs = pca.fit_transform(vec)
+    # replace 1st PC with pan band (after matching histograms)
+    vec_pan = im_pan.reshape(im_pan.shape[0] * im_pan.shape[1])
+    vec_pan = vec_pan[~vec_mask]
+    vec_pcs[:,0] = hist_match(vec_pan, vec_pcs[:,0])
+    vec_ms_ps = pca.inverse_transform(vec_pcs)
+    
+    # reshape vector into image
+    vec_ms_ps_full = np.ones((len(vec_mask), im_ms.shape[2])) * np.nan
+    vec_ms_ps_full[~vec_mask,:] = vec_ms_ps
+    im_ms_ps = vec_ms_ps_full.reshape(im_ms.shape[0], im_ms.shape[1], im_ms.shape[2])
+    
+    if plot_bool:
+        plt.figure()
+        ax1 = plt.subplot(121)
+        plt.imshow(rescale_image_intensity(im_ms[:,:,[2,1,0]], cloud_mask, 99.9, False))
+        plt.axis('off')
+        plt.title('Original')
+        ax2 = plt.subplot(122, sharex=ax1, sharey=ax1)
+        plt.imshow(rescale_image_intensity(im_ms_ps[:,:,[2,1,0]], cloud_mask, 99.9, False))
+        plt.axis('off')
+        plt.title('Pansharpened')
+        plt.show()
+
+    return im_ms_ps
+    
+def nd_index(im1, im2, cloud_mask, plot_bool):
+    """
+    Computes normalised difference index on 2 images (2D), given a cloud mask (2D)
+    
+    KV WRL 2018
+
+    Arguments:
+    -----------
+        im1, im2: np.ndarray
+            Images (2D) with which to calculate the ND index
+        cloud_mask: np.ndarray
+            2D cloud mask with True where cloud pixels are
+        plot_bool: boolean
+            True if plot is wanted
+        
+    Returns:    -----------
+        im_nd: np.ndarray
+
+            Image (2D) containing the ND index
+    """
+    vec_mask = cloud_mask.reshape(im1.shape[0] * im1.shape[1])
+    vec_nd = np.ones(len(vec_mask)) * np.nan
+    vec1 = im1.reshape(im1.shape[0] * im1.shape[1])
+    vec2 = im2.reshape(im2.shape[0] * im2.shape[1])
+    temp = np.divide(vec1[~vec_mask] - vec2[~vec_mask],
+                     vec1[~vec_mask] + vec2[~vec_mask])
+    vec_nd[~vec_mask] = temp
+    im_nd = vec_nd.reshape(im1.shape[0], im1.shape[1])
+    
+    if plot_bool:
+        plt.figure()
+        plt.imshow(im_nd, cmap='seismic')
+        plt.colorbar()
+        plt.title('Normalised index')
+        plt.show()
+    
+    return im_nd
+
+def find_wl_contours(im_ndwi, cloud_mask, min_contour_points, plot_bool):
+    """
+    Finds the water line by thresholding the Normalized Difference Water Index and applying the Marching 
+    Squares Algorithm
+    
+    KV WRL 2018
+
+    Arguments:
+    -----------
+        im_ndwi: np.ndarray
+            Image (2D) with the NDWI (water index)
+        cloud_mask: np.ndarray
+            2D cloud mask with True where cloud pixels are
+        min_contour_points: int
+            minimum number of points in each contour line
+        plot_bool: boolean
+            True if plot is wanted
+                
+    Returns:    -----------
+        contours_wl: list of np.arrays 
+            contains the (row,column) coordinates of the contour lines
+
+    """  
+    
+    # reshape image to vector
+    vec_ndwi = im_ndwi.reshape(im_ndwi.shape[0] * im_ndwi.shape[1])
+    vec_mask = cloud_mask.reshape(cloud_mask.shape[0] * cloud_mask.shape[1])
+    vec = vec_ndwi[~vec_mask]
+    # apply otsu's threshold
+    t_otsu = filters.threshold_otsu(vec)
+    # use Marching Squares algorithm to detect contours on ndwi image
+    contours = measure.find_contours(im_ndwi, t_otsu)
+    # filter water lines
+    contours_wl = []
+    for i, contour in enumerate(contours):
+        # remove contour points that are around clouds (nan values)
+        if np.any(np.isnan(contour)):
+            index_nan = np.where(np.isnan(contour))[0]
+            contour = np.delete(contour, index_nan, axis=0) 
+        # remove contours that have only few points (less than min_contour_points)
+        if contour.shape[0] > min_contour_points:
+            contours_wl.append(contour)
+        
+    if plot_bool:
+        # plot otsu's histogram segmentation
+        plt.figure()
+        vals = plt.hist(vec, bins=200)
+        plt.plot([t_otsu, t_otsu],[0, np.max(vals[0])], 'r-', label='Otsu threshold')
+        plt.legend()
+        plt.show()
+        
+        # plot the water line contours on top of water index
+        plt.figure()
+        plt.imshow(im_ndwi, cmap='seismic')
+        plt.colorbar()
+        for i,contour in enumerate(contours_wl): plt.plot(contour[:, 1], contour[:, 0], linewidth=3, color='k')
+        plt.axis('image')
+        plt.title('Detected water lines')
+        plt.show()
+    
+    return contours_wl
+
+def convert_pix2world(points, crs_vec):
+    """
+    Converts pixel coordinates (row,columns) to world projected coordinates
+    performing an affine transformation
+    
+    KV WRL 2018
+
+    Arguments:
+    -----------
+        points: np.ndarray or list of np.ndarray
+            array with 2 columns (rows first and columns second)
+        crs_vec: np.ndarray
+            vector of 6 elements [Xtr, Xscale, Xshear, Ytr, Yshear, Yscale]
+                
+    Returns:    -----------
+        points_converted: np.ndarray or list of np.ndarray 
+            converted coordinates, first columns with X and second column with Y
+        
+    """
+    
+    # make affine transformation matrix
+    aff_mat = np.array([[crs_vec[1], crs_vec[2], crs_vec[0]],
+                       [crs_vec[4], crs_vec[5], crs_vec[3]],
+                       [0, 0, 1]])
+    # create affine transformation
+    tform = transform.AffineTransform(aff_mat)
+
+    if type(points) is list:
+        points_converted = []
+        # iterate over the list
+        for i, arr in enumerate(points): 
+            tmp = arr[:,[1,0]]
+            points_converted.append(tform(tmp))
+            
+    elif type(points) is np.ndarray:
+        tmp = points[:,[1,0]]
+        points_converted = tform(tmp)
+        
+    else:
+        print('invalid input type')
+        raise
+        
+    return points_converted
+
+def convert_epsg(points, epsg_in, epsg_out):
+    """
+    Converts from one spatial reference to another using the epsg codes
+    
+    KV WRL 2018
+
+    Arguments:
+    -----------
+        points: np.ndarray or list of np.ndarray
+            array with 2 columns (rows first and columns second)
+        epsg_in: int
+            epsg code of the spatial reference in which the input is
+        epsg_out: int
+            epsg code of the spatial reference in which the output will be            
+                
+    Returns:    -----------
+        points_converted: np.ndarray or list of np.ndarray 
+            converted coordinates
+        
+    """
+    
+    # define input and output spatial references
+    inSpatialRef = osr.SpatialReference()
+    inSpatialRef.ImportFromEPSG(epsg_in)
+    outSpatialRef = osr.SpatialReference()
+    outSpatialRef.ImportFromEPSG(epsg_out)
+    # create a coordinates transform
+    coordTransform = osr.CoordinateTransformation(inSpatialRef, outSpatialRef)
+    # transform points
+    if type(points) is list:
+        points_converted = []
+        # iterate over the list
+        for i, arr in enumerate(points): 
+            points_converted.append(np.array(coordTransform.TransformPoints(arr)))
+            
+    elif type(points) is np.ndarray:
+        points_converted = np.array(coordTransform.TransformPoints(points))
+        
+    else:
+        print('invalid input type')
+        raise
+        
+    return points_converted
+
+def classify_sand_unsupervised(im_ms_ps, im_pan, cloud_mask, wl_pix, buffer_size, min_beach_size, plot_bool):
+    """
+    Classifies sand pixels using an unsupervised algorithm (Kmeans)
+    Set buffer size to False if you want to classify the entire image,
+    otherwise buffer size defines the buffer around the shoreline in which 
+    pixels are considered for classification.
+    This classification is not robust and is only used to train a supervised algorithm
+    
+    KV WRL 2018
+
+    Arguments:
+    -----------
+        im_ms_ps: np.ndarray
+            Pansharpened RGB + downsampled NIR and SWIR
+        im_pan:
+            Panchromatic band
+        cloud_mask: np.ndarray
+            2D cloud mask with True where cloud pixels are
+        wl_pix: list of np.ndarray
+            list of arrays containig the pixel coordinates of the water line
+        buffer_size: int or False
+            radius of the disk used to create a buffer around the water line
+            when False, the entire image is considered for kmeans
+        min_beach_size: int
+            minimum number of connected pixels belonging to a single beach
+        plot_bool: boolean
+            True if plot is wanted
+                
+    Returns:    -----------
+        im_sand: np.ndarray
+            2D binary image containing True where sand pixels are located
+
+    """     
+    # reshape the 2D images into vectors
+    vec_ms_ps = im_ms_ps.reshape(im_ms_ps.shape[0] * im_ms_ps.shape[1], im_ms_ps.shape[2])
+    vec_pan = im_pan.reshape(im_pan.shape[0]*im_pan.shape[1])
+    vec_mask = cloud_mask.reshape(im_ms_ps.shape[0] * im_ms_ps.shape[1])
+    # add B,G,R,NIR and pan bands to the vector of features
+    vec_features = np.zeros((vec_ms_ps.shape[0], 5))
+    vec_features[:,[0,1,2,3]] = vec_ms_ps[:,[0,1,2,3]]
+    vec_features[:,4] = vec_pan
+    
+    if buffer_size:
+        # create binary image with ones where the detected water lines is
+        im_buffer = np.zeros((im_ms_ps.shape[0], im_ms_ps.shape[1]))
+        for i, contour in enumerate(wl_pix):
+                indices = [(int(_[0]), int(_[1])) for _ in list(np.round(contour))]
+                for j, idx in enumerate(indices):
+                    im_buffer[idx] = 1
+        # perform a dilation on the binary image
+        se = morphology.disk(buffer_size)
+        im_buffer = morphology.binary_dilation(im_buffer, se)
+        vec_buffer = (im_buffer == 1).reshape(im_ms_ps.shape[0] * im_ms_ps.shape[1])
+    else:
+        vec_buffer = np.ones((vec_pan.shape[0]))
+    # add cloud mask to buffer
+    vec_buffer= np.logical_and(vec_buffer, ~vec_mask)
+    # perform kmeans (6 clusters)
+    kmeans = KMeans(n_clusters=6, random_state=0).fit(vec_features[vec_buffer,:])
+    labels = np.ones((len(vec_mask))) * np.nan
+    labels[vec_buffer] = kmeans.labels_
+    im_labels = labels.reshape(im_ms_ps.shape[0], im_ms_ps.shape[1])
+    # find the class with maximum reflection in the B,G,R,Pan
+    im_sand = im_labels == np.argmax(np.mean(kmeans.cluster_centers_[:,[0,1,2,4]], axis=1))
+    im_sand = morphology.remove_small_objects(im_sand, min_size=min_beach_size, connectivity=2)
+    im_sand = morphology.binary_erosion(im_sand, morphology.disk(1))
+#    im_sand = morphology.binary_dilation(im_sand, morphology.disk(1))
+    
+    if plot_bool:
+        im = np.copy(rescale_image_intensity(im_ms_ps[:,:,[2,1,0]], cloud_mask, 99.9, False))
+        im[im_sand,0] = 0
+        im[im_sand,1] = 0
+        im[im_sand,2] = 1
+        plt.figure()
+        plt.imshow(im)
+        plt.axis('image')
+        plt.title('Sand classification')
+        plt.show()
+    
+    return im_sand
+
+def classify_image_NN(im_ms_ps, im_pan, cloud_mask, min_beach_size, plot_bool):
+    """
+    Classifies every pixel in the image in one of 4 classes:
+        - sand                                          --> label = 1
+        - whitewater (breaking waves and swash)         --> label = 2
+        - water                                         --> label = 3
+        - other (vegetation, buildings, rocks...)       --> label = 0
+    
+    The classifier is a Neural Network, trained with 7000 pixels for the class SAND and 1500 pixels for
+    each of the other classes. This is because the class of interest for my application is SAND and I 
+    wanted to minimize the classification error for that class
+    
+    KV WRL 2018
+
+    Arguments:
+    -----------
+        im_ms_ps: np.ndarray
+            Pansharpened RGB + downsampled NIR and SWIR
+        im_pan:
+            Panchromatic band
+        cloud_mask: np.ndarray
+            2D cloud mask with True where cloud pixels are
+        plot_bool: boolean
+            True if plot is wanted
+                
+    Returns:    -----------
+        im_classif: np.ndarray
+            2D image containing labels
+        im_labels: np.ndarray of booleans
+            3D image containing a boolean image for each class (im_classif == label)
+
+    """     
+    
+    # load classifier
+    clf = joblib.load('functions/NeuralNet_classif.pkl')
+    
+    # calculate features
+    n_features = 10
+    im_features = np.zeros((im_ms_ps.shape[0], im_ms_ps.shape[1], n_features))
+    im_features[:,:,[0,1,2,3,4]] = im_ms_ps
+    im_features[:,:,5] = im_pan
+    im_features[:,:,6] = nd_index(im_ms_ps[:,:,3], im_ms_ps[:,:,1], cloud_mask, False) # (NIR-G)
+    im_features[:,:,7] = nd_index(im_ms_ps[:,:,3], im_ms_ps[:,:,2], cloud_mask, False) # ND(NIR-R)
+    im_features[:,:,8] = nd_index(im_ms_ps[:,:,0], im_ms_ps[:,:,2], cloud_mask, False) # ND(B-R)
+    im_features[:,:,9] = nd_index(im_ms_ps[:,:,4], im_ms_ps[:,:,1], cloud_mask, False) # ND(SWIR-G)
+    # remove NaNs and clouds
+    vec_features = im_features.reshape((im_ms_ps.shape[0] * im_ms_ps.shape[1], n_features))
+    vec_cloud = cloud_mask.reshape(cloud_mask.shape[0]*cloud_mask.shape[1])
+    vec_nan = np.any(np.isnan(vec_features), axis=1)
+    vec_mask = np.logical_or(vec_cloud, vec_nan)
+    vec_features = vec_features[~vec_mask, :]
+    # predict with NN classifier
+    labels = clf.predict(vec_features)
+    # recompose image
+    vec_classif = np.zeros((cloud_mask.shape[0]*cloud_mask.shape[1])) 
+    vec_classif[~vec_mask] = labels
+    im_classif = vec_classif.reshape((im_ms_ps.shape[0], im_ms_ps.shape[1]))
+
+    # labels
+    im_sand = im_classif == 1
+    im_sand = morphology.remove_small_objects(im_sand, min_size=min_beach_size, connectivity=2)
+    im_swash = im_classif == 2
+    im_water = im_classif == 3
+    im_labels = np.stack((im_sand,im_swash,im_water), axis=-1)  
+    
+    if plot_bool:
+        # display on top of pansharpened RGB
+        im_display = rescale_image_intensity(im_ms_ps[:,:,[2,1,0]], cloud_mask, 99.9, False)
+        im = np.copy(im_display)
+        # define colours for plot
+        colours = np.array([[1,128/255,0/255],[204/255,1,1],[0,0,204/255]])
+        for k in range(0,im_labels.shape[2]):
+            im[im_labels[:,:,k],0] = colours[k,0]
+            im[im_labels[:,:,k],1] = colours[k,1]
+            im[im_labels[:,:,k],2] = colours[k,2]
+             
+        plt.figure()
+        ax1 = plt.subplot(121)
+        plt.imshow(im_display)
+        plt.axis('off')
+        plt.title('Image')
+        ax2 = plt.subplot(122, sharex=ax1, sharey=ax1)
+        plt.imshow(im)
+        plt.axis('off')
+        plt.title('NN classifier')  
+        mng = plt.get_current_fig_manager()                                         
+        mng.window.showMaximized()
+        plt.tight_layout()   
+        plt.draw()
+    
+    return im_classif, im_labels
+
+def find_wl_contours2(im_ms_ps, im_labels, cloud_mask, buffer_size, plot_bool):
+    """
+    New mthod for extracting shorelines (more robust)
+    
+    KV WRL 2018
+
+    Arguments:
+    -----------
+        im_ms_ps: np.ndarray
+            Pansharpened RGB + downsampled NIR and SWIR
+        im_labels: np.ndarray
+            3D image containing a boolean image for each class in the order (sand, swash, water)
+        cloud_mask: np.ndarray
+            2D cloud mask with True where cloud pixels are
+        buffer_size: int
+            size of the buffer around the sandy beach
+        plot_bool: boolean
+            True if plot is wanted
+                
+    Returns:    -----------
+        contours_wi: list of np.arrays 
+            contains the (row,column) coordinates of the contour lines extracted with the Water Index
+        contours_mwi: list of np.arrays 
+            contains the (row,column) coordinates of the contour lines extracted with the Modified Water Index
+
+    """  
+    
+    nrows = cloud_mask.shape[0]
+    ncols = cloud_mask.shape[1]
+    im_display = rescale_image_intensity(im_ms_ps[:,:,[2,1,0]], cloud_mask, 99.9, False)
+
+    # calculate Normalized Difference Modified Water Index (SWIR - G)
+    im_mwi = nd_index(im_ms_ps[:,:,4], im_ms_ps[:,:,1], cloud_mask, False)
+    # calculate Normalized Difference Modified Water Index (NIR - G)
+    im_wi = nd_index(im_ms_ps[:,:,3], im_ms_ps[:,:,1], cloud_mask, False)
+    # stack indices together
+    im_ind = np.stack((im_wi, im_mwi), axis=-1)
+    vec_ind = im_ind.reshape(nrows*ncols,2)
+    
+    # process labels
+    vec_sand = im_labels[:,:,0].reshape(ncols*nrows)
+    vec_swash = im_labels[:,:,1].reshape(ncols*nrows)
+    vec_water = im_labels[:,:,2].reshape(ncols*nrows)
+    
+    # create a buffer around the sandy beach
+    se = morphology.disk(buffer_size)
+    im_buffer = morphology.binary_dilation(im_labels[:,:,0], se)
+    vec_buffer = im_buffer.reshape(nrows*ncols)
+    
+    # select water/sand/swash pixels that are within the buffer
+    int_water = vec_ind[np.logical_and(vec_buffer,vec_water),:]
+    int_sand = vec_ind[np.logical_and(vec_buffer,vec_sand),:]
+    int_swash = vec_ind[np.logical_and(vec_buffer,vec_swash),:]
+
+    # threshold the sand/water intensities 
+    int_all = np.append(int_water,int_sand, axis=0)
+    t_mwi = filters.threshold_otsu(int_all[:,0])
+    t_wi = filters.threshold_otsu(int_all[:,1])
+    
+    # find contour with MS algorithm
+    im_wi_buffer = np.copy(im_wi)
+    im_wi_buffer[~im_buffer] = np.nan
+    im_mwi_buffer = np.copy(im_mwi)
+    im_mwi_buffer[~im_buffer] = np.nan
+    contours_wi = measure.find_contours(im_wi_buffer, t_wi)
+    contours_mwi = measure.find_contours(im_mwi_buffer, t_mwi)
+    
+    if plot_bool:
+        
+        im = np.copy(im_display)
+        # define colours for plot
+        colours = np.array([[1,128/255,0/255],[204/255,1,1],[0,0,204/255]])
+        for k in range(0,im_labels.shape[2]):
+            im[im_labels[:,:,k],0] = colours[k,0]
+            im[im_labels[:,:,k],1] = colours[k,1]
+            im[im_labels[:,:,k],2] = colours[k,2]
+        plt.figure()
+        plt.imshow(im)
+        for i,contour in enumerate(contours_wi): plt.plot(contour[:, 1], contour[:, 0], linewidth=3, color='k')
+        for i,contour in enumerate(contours_mwi): plt.plot(contour[:, 1], contour[:, 0], linewidth=3, color='g')
+        plt.draw()
+        
+        fig, ax = plt.subplots(2,1, sharex=True)
+        vals = ax[0].hist(int_water[:,0], bins=100, label='water')
+        ax[0].hist(int_sand[:,0], bins=100, alpha=0.5, label='sand')
+        ax[0].hist(int_swash[:,0], bins=100, alpha=0.5, label='swash')
+        ax[0].plot([t_wi, t_wi], [0, np.max(vals[0])], 'r-')
+        ax[0].legend()
+        ax[0].set_title('Water Index NIR-G')
+        vals = ax[1].hist(int_water[:,1], bins=100, label='water')
+        ax[1].hist(int_sand[:,1], bins=100, alpha=0.5, label='sand')
+        ax[1].hist(int_swash[:,1], bins=100, alpha=0.5, label='swash')
+        ax[1].plot([t_mwi, t_mwi], [0, np.max(vals[0])], 'r-')
+        ax[1].legend()
+        ax[1].set_title('Modified Water Index SWIR-G')
+        plt.draw()
+        
+    
+    return contours_wi, contours_mwi
+    

functions/utils.py

@@ -9,7 +9,6 @@ Contains all the utilities, convenience functions and small functions that do si
 
 import matplotlib.pyplot as plt
 import numpy as np
-import datetime
 import pdb
 
 
@@ -56,3 +55,15 @@ def compare_images(im1, im2):
 def find_indices(lst, condition):
     "imitation of MATLAB find function"
     return [i for i, elem in enumerate(lst) if condition(elem)]
+
+def reject_outliers(data, m=2):
+    "rejects outliers in a numpy array"
+    return data[abs(data - np.mean(data)) < m * np.std(data)]
+
+def duplicates_dict(lst):
+    "return duplicates and indices"
+    # nested function
+    def duplicates(lst, item):
+            return [i for i, x in enumerate(lst) if x == item]
+        
+    return dict((x, duplicates(lst, x)) for x in set(lst) if lst.count(x) > 1)

functions/xyz2spz/convert2matfile.py

@@ -0,0 +1,39 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Apr  5 16:19:31 2018
+
+@author: z5030440
+"""
+
+d_gt = {'arr':sl_gt}
+d_sds = {'arr':sl_sds}
+
+sio.savemat('sl_gt.mat', mdict=d_gt)
+sio.savemat('sl_sds.mat', mdict=d_sds)
+
+#%%
+
+herror = sio.loadmat('hor_error.mat')
+
+diff_p = (herror['gt_av'] - herror['sds_av'])[0,:]
+
+f = plt.figure()
+plt.subplot(3,1,1)
+plt.bar(np.linspace(1,len(zav),len(zav)), herror['p_rmse'][0])
+plt.ylabel('rmse [m]')
+plt.xticks([])
+plt.title('Horizontal cross-shore error')
+
+plt.subplot(3,1,2)
+plt.bar(np.linspace(1,len(zav),len(zav)), herror['p_mean'][0], color=orange)
+plt.ylabel('mean [m]')
+plt.xticks([])
+
+plt.subplot(3,1,3)
+plt.bar(np.linspace(1,len(zav),len(zav)), herror['p_std'][0], color='g')
+plt.ylabel('std [m]')
+plt.xlabel('comparison #')
+plt.grid(False)
+plt.grid(axis='y')
+f.subplots_adjust(hspace=0.2)
+plt.draw()

functions/xyz2spz/convert2spz.m

@@ -0,0 +1,65 @@
+close all
+clear
+clc
+
+addpath(genpath('C:\Users\z5030440\Documents\GitHub\geetools\functions\xyz2spz'))
+
+sl_gt = load('sl_gt.mat')
+sl_sds = load('sl_sds.mat')
+
+sl_sds = sl_sds.arr
+sl_gt = sl_gt.arr
+for i = 1:length(sl_sds)
+    sds.x = sl_sds{i}(:,1)
+    sds.y = sl_sds{i}(:,2)
+    sds.z = zeros(length(sl_sds{i}(:,1)),1)
+    
+    gt.x = sl_gt{i}(:,1)
+    gt.y = sl_gt{i}(:,2)
+    gt.z = zeros(length(sl_gt{i}(:,1)),1)
+    
+    outsds = xyz2spz(sds,'NARRA')
+    outgt = xyz2spz(gt,'NARRA')
+    
+    figure
+    hold on
+    grid on
+    box on
+    plot(outsds.s, outsds.p, 'b-', 'linewidth',2)
+    plot(outgt.s, outgt.p, 'r-', 'linewidth',2)
+    xlabel('s [m]')
+    ylabel('p [m]')
+    title('Horizontal comparison in spz coordinates')
+    legend({'SDS', 'contour at tide level'})
+    
+    xq = nanmin(outsds.s):10:nanmax(outsds.s)
+    [gt_s idx_gt] = unique(outgt.s)
+    gt_p = outgt.p(idx_gt)
+    gt_p_int = interp1(gt_s, gt_p, xq)
+    [sds_s idx_sds] = unique(outsds.s)
+    sds_p = outsds.p(idx_sds)
+    sds_p_int = interp1(sds_s, sds_p, xq)
+    diff_p = sds_p_int - gt_p_int;
+    
+    sds_av(i) = median(sds_p_int(~(sds_p_int > median(sds_p_int) + 2*std(sds_p_int) | sds_p_int < median(sds_p_int) - 2*std(sds_p_int))))
+    gt_p_int(isnan(gt_p_int)) = []
+    gt_av(i) = median(gt_p_int(~(gt_p_int > median(gt_p_int) + 2*std(gt_p_int) | gt_p_int < median(gt_p_int) - 2*std(gt_p_int))))
+    
+    
+    idx_nan = isnan(diff_p)
+    diff_p(idx_nan) = []
+    xq(idx_nan) = []    
+    idx_outlier = diff_p > median(diff_p) + 2*std(diff_p) | diff_p < median(diff_p) - 2*std(diff_p)
+    diff_p(idx_outlier) = []
+    xq(idx_outlier) = []  
+    
+    p_rmse(i) = sqrt(mean(diff_p.^2))
+    p_mean(i) = mean(diff_p)
+    p_std(i) = std(diff_p)
+    p_q90(i) = quantile(abs(diff_p), 0.9)
+    
+end
+
+clearvars -except sds_av gt_av p_rmse p_mean p_std p_q90
+
+save 'hor_error.mat'

functions/xyz2spz/muller.m

@@ -0,0 +1,213 @@
+function [res,fval,it] = muller (f,Z0,itmax,ztol,ftol,option)
+% MULLER find a zero of a real or complex function Y = F(Z)
+% 
+% Syntax:
+%
+% RES = MULLER (F,Z0) find the zero of a complex or real function 
+% 'F' (either an anonymous function or .m function) using three initial 
+% guesses contained in the vector Z0. Muller takes the function F and
+% evaluetes it at each initial point using feval. F doesn't need to be
+% vectorized.
+% The initial guesses can be real or complex numbers close to the zero, 
+% bracketing the zero is not necessary. Parameters ITMAX, ZTOL and
+% FTOL are set by default to 1000, 1e-5 and 1e-5, respectively.
+%
+% RES = MULLER (F,Z0,ITMAX) the maximum number of iterations is set
+% equal to ITMAX. ZTOL and FTOL are set by default with the values mentioned
+% above.
+%
+% RES = MULLER (F,Z0,ITMAX,ZTOL) ZTOL is used as a stopping
+% criterion. If the absolute difference between the values of Z found in
+% the two latest iterations is less than ZTOL, the program is stopped. FTOL
+% is set by default with the value mentioned above.
+%
+% RES = MULLER (F,Z0,ITMAX,ZTOL,FTOL) FTOL is used as a stopping
+% criterion. If the value of the function F at the Z found in the last
+% iteration is less than FTOL, the program is stopped.
+%
+% RES = MULLER (F,Z0,ITMAX,ZTOL,FTOL,'both') indicate that both
+% criteria ZTOL and FTOL must be satisfied simultaneously. By default, 
+% MULLER stops if one of the two criteria is fulfilled.
+%
+% [RES,FVAL] = MULLER (F,Z0,...) return the value of the function 
+% F at the Z found in the last iteration.
+%
+% [RES,FVAL,IT] = MULLER (F,Z0,...) return the number of iterations
+% used to find the zero.
+%
+% Example 1:
+% myf = @(x) (x-1)^3; 
+% 
+% muller(myf,[0 0.1 0.2],[],[],[],'both')
+% ans = 
+%    1.0000 + 0.0000i
+%
+% Example 2:
+% 
+% [res,fval,it] = muller2('cosh',[0 0.1 0.2],[],[],[],'both')
+% 
+% res = 
+%   0.0000 + 1.5708i
+%
+% fval = 
+%   5.5845e-012 + 3.0132e-012i
+% 
+% it =
+%   5
+%
+% Method taken from:
+% Numerical Recipes: The art of scientific computing
+% W.H. Press; B.P. Flannery; S.A. Teukolsky; W.T. Vetterling
+% 1986
+%
+% Thanks to John D'Errico for his helpfull review.
+%
+% Written by Daniel H. Cortes
+% MAE Department, West Virginia University
+% March, 2008.
+%
+
+
+%=================================================
+% Checking proper values of the input parameters
+%=================================================
+
+
+if nargin > 6
+    error ('Too many arguments.')
+elseif nargin < 2
+    error('Too few arguments.')
+end
+
+if nargin < 6
+    opt = 1;
+elseif ischar(option) == 1
+    if size(option,2) == 4
+        if sum(option == 'both') == 4
+            opt = 2;
+        else
+            error ('Option parameter must be *both*.')
+        end
+    else
+        error ('Option parameter must be *both*.')
+    end
+else
+    error ('Option parameter must be a character array (string).')
+end
+
+
+if nargin < 5
+    ftol = 1e-5;
+elseif isnumeric(ftol) ~= 1
+    error ('FTOL must be a numeric argument.')
+elseif isempty(ftol) == 1
+    ftol = 1e-5;
+elseif  size(ftol,1) ~= 1 || size(ftol,2) ~= 1
+    error ('FTOL cannot be an array')
+end
+
+
+if nargin < 4
+    ztol = 1e-5;
+elseif isnumeric(ztol) ~= 1
+    error ('ZTOL must be a numeric argument.')
+elseif isempty(ztol) == 1
+    ztol = 1e-5;
+elseif  size(ztol,1) ~= 1 || size(ztol,2) ~= 1
+    error ('ZTOL cannot be an array.')
+end
+
+
+if nargin < 3
+    itmax = 1000;
+elseif isnumeric(itmax) ~= 1
+    error ('ITMAX must be a numeric argument.')
+elseif isempty(itmax) == 1
+    itmax = 1000;
+elseif  size(itmax,1) ~= 1 || size(itmax,2) ~= 1
+    error ('ITMAX cannot be an array.')
+end
+
+
+if isnumeric(Z0) ~= 1
+    error ('Z0 must be a vector of three numeric arguments.')
+elseif isempty(Z0) == 1 || length(Z0) ~= 3 || min(size(Z0)) ~= 1
+    error ('Z0 must be a vector of length 3 of either complex or real arguments.')
+end
+
+if Z0(1)==Z0(2) || Z0(1)==Z0(3) || Z0(2)==Z0(3)
+    error('The initial guesses must be different')
+end
+
+%=============================
+% Begining of Muller's method
+%=============================
+
+z0 = Z0(1);
+z1 = Z0(2);
+z2 = Z0(3);
+
+y0 = feval ( f, z0);
+y1 = feval ( f, z1);
+y2 = feval ( f, z2);
+
+for it = 1:itmax
+    q = (z2 - z1)/(z1 - z0);
+    A = q*y2 - q*(1+q)*y1 + q^2*y0;
+    B = (2*q + 1)*y2 - (1 + q)^2*y1 + q^2*y0;
+    C = (1 + q)*y2;
+
+    if ( A ~= 0 )
+
+        disc = B^2 - 4*A*C;
+
+        den1 = ( B + sqrt ( disc ) );
+        den2 = ( B - sqrt ( disc ) );
+
+        if ( abs ( den1 ) < abs ( den2 ) )
+            z3 = z2 - (z2 - z1)*(2*C/den2);
+        else
+            z3 = z2 - (z2 - z1)*(2*C/den1);
+        end
+
+    elseif ( B ~= 0 )
+        z3 = z2 - (z2 - z1)*(2*C/B);
+    else
+        warning('Muller Method failed to find a root. Last iteration result used as an output. Result may not be accurate')
+        res = z2;
+        fval = y2;
+        return
+    end
+
+    y3 = feval ( f, z3);
+    
+    if opt == 1
+        if ( abs (z3 - z2) < ztol || abs ( y3 ) < ftol )
+            res = z3;
+            fval = y3;
+            return
+        end
+    else
+        if ( abs (z3 - z2) < ztol && abs ( y3 ) < ftol )
+            res = z3;
+            fval = y3;
+            return
+        end
+    end
+    
+    z0 = z1;
+    z1 = z2;
+    z2 = z3;
+
+    y0 = y1;
+    y1 = y2;
+    y2 = y3;
+
+end
+
+res = z2;
+fval = y2;
+%warning('Maximum number of iterations reached. Result may not be accurate')
+
+
+

functions/xyz2spz/sort_back.m

@@ -0,0 +1,87 @@
+function out = sort_back( data, ind, dim )
+% SORT_BACK sort back data to original order
+% ind is the indexes obtained from sorting
+% dim is the sorted dimension of the data (assumed to be 1 if not specified)
+% Ex:
+% y = randn(3,4,2);
+% [y,ind] = sort(y,2);
+% do stuff with sorted y...
+% y2 = sort_back( y, ind, 2 );
+% 
+% Works on arrays of any dimension
+% Also works on cellstrings (vectors)
+% 
+% C = {'hello' 'yes' 'no' 'goodbye'};
+% [C,ind] = sort(C);
+% C2 = sort_back(C,ind);
+%
+% See also SORT
+
+%Author Ivar Eskerud Smith
+
+if size(ind)~=size(data)
+    error('Different size of indexes and input data');
+end
+
+if iscell(data)
+    if ~any(size(data)==1)
+        error('Only vectors are supported in cell sorting/back-sorting');
+    end
+    out=cell(size(data));
+    out(ind) = data;
+    return;
+end
+
+if ~isnumeric(data) || ~isnumeric(ind)
+    error('Inputs have to be numeric or cell');
+end
+
+n=ndims(ind);
+if ~exist('dim','var')
+    dim=1;
+end
+if dim>n
+    error('Specified sorted dimension must be within array bounds');
+end
+
+%shift array so that the sorted dim is the first dimension
+if dim~=1
+    sortInd=1:1:n;sortInd(1)=dim;sortInd(dim)=1;
+    data = permute(data,sortInd);
+    ind = permute(ind,sortInd);
+end
+inds = repmat({1},1,n);inds{1}=':';
+if ~issorted( data(inds{:}) )
+    warning('The input data is not sorted along the specified dimension');
+end
+
+s = size(ind);
+nData = numel(data);
+inds = repmat({1},1,n);
+inds(1:2)={':',':'};
+shiftSize = s(1)*s(2);
+out=nan(size(data));
+
+%loop all 2d arrays within nd-array
+for k=1:prod(s(3:end))
+    tmpdata = data(inds{:});
+    tmpind  = ind(inds{:});
+
+    %data is shifted so that the sorted dim = 1
+    for i=1:numel(tmpdata(1,:))
+        out(tmpind(:,i),i) = tmpdata(:,i);
+    end
+
+    if n>2
+        %shift to next 2d array within nd-array
+        shiftInds = mod((1:nData)-shiftSize-1,nData)+1;
+        out=reshape(out(shiftInds),s);
+        data=reshape(data(shiftInds),s);
+        ind=reshape(ind(shiftInds),s);
+    end
+end
+
+%permute back to original order
+sortInd=1:1:ndims(out);sortInd(1)=dim;sortInd(dim)=1;
+out = permute(out,sortInd);
+

functions/xyz2spz/xyz2spz.m

@@ -0,0 +1,117 @@
+function out = xyz2spz(xyz_data,site)
+
+%function out = xyz2spz(xyz_data,site)
+%
+%Function to transform (x,y,z) coordinates on an embayed beach to alongshore - cross-shore
+%coordinates (s,p,z) using the log spiral, given by the equation  
+%r = r0*exp(A*theta). A = cot(alpha).
+%
+%xyz_data is a structure containing:
+%
+%xyz_data.x
+%xyz_data.y
+%xyz_data.z
+%
+%site is the name of the structure generated from the MALT graphical user interface 
+%
+%Refer to paper
+%
+%Harley, M.D. and Turner,I.L. (2007) A simple data transformation technique
+%for pre-processing survey data at embayed beaches, Coast. Eng.,
+%doi:10.1016/j.coastaleng.2007.07.001, in press.
+%
+%Created by Mitch Harley
+%8th August, 2005
+%Last Modified 4th April, 2012
+
+%----------------------------------------------------------------
+%LOAD LOGSPIRAL-FIT PARAMETERS
+
+eval(['load ' site ';'])
+eval(['site = ' site ';'])
+
+%Define origin and A of log spiral
+origin = site.origin;
+alph = site.alpha;
+A = cot(alph*pi/180);
+r0_origin = site.r0_origin;
+
+%-----------------------------------------------------------------
+
+%DO TRANSFORMATION
+
+
+%Points need to be sorted prior to analysis %MDH 4/4/2012
+aa = [xyz_data.x xyz_data.y xyz_data.z];
+[sorted_points,Isort] = sortrows(aa);
+
+%Convert xyz coordinates to polar coordinates
+r = sqrt((sorted_points(:,1) - origin(1)).^2+(sorted_points(:,2) - origin(2)).^2);
+theta = unwrap(atan2((sorted_points(:,2)-origin(2)),(sorted_points(:,1)-origin(1))) );
+
+
+%Find constants delta and kappa
+delta = pi/2+acot(A)-theta; %From Equation 5
+kappa = r./(r0_origin*sin(pi/2-acot(A))); %From Equation 6
+
+
+%Find theta_s by solving implicitly using fzero function
+for i = 1:length(theta);
+    %Use muller function in case any complex solutions
+    theta_s(i,1) = muller(@(x) (x-(1/A)*log(kappa(i)*sin(delta(i)+x))),[theta(i)-pi/8 theta(i) theta(i)+pi/8]);%From Equation 6
+end
+
+%plot(theta_s*180/pi)
+
+%Find r_s
+r_s = r0_origin*exp(A*theta_s);%From Equation 1
+
+%Find s
+lamda = r0_origin*sec(acot(A));%From Equation 8
+start_point =  0; %Can be changed to make a more suitable start point
+s = lamda*(exp(A*theta_s)-exp(A*start_point));%From Equation 8
+
+%Find p
+p = r.*sin(theta-theta_s)./sin(pi/2-acot(A)); %From Equation 9
+
+%Convert any complex numbers to real numbers
+p = real(p);
+s = real(s);
+
+%Sort back points to get the right indices %MDH 4/4/2012
+p = sort_back(p,Isort);
+s = sort_back(s,Isort);
+
+%-----------------------------------------------------------------
+%POST-PROCESS DATA
+%s data
+
+if site.reverse_s ==0
+    s = s - site.startpoint;%Make minimum s == 0
+elseif site.reverse_s ==1
+    s = -(s - site.startpoint);
+end
+
+%p data
+if site.subtract_res ==1 %Add switch for user to subtract residuals or not - MDH 19/5/2010
+[MIN,L] = min(site.boundary.s);
+I = find(s<=MIN);
+p(I) = p(I) - site.boundary.p(L);
+[MAX,L] = max(site.boundary.s);
+I = find(s>=MAX);
+p(I) = p(I) - site.boundary.p(L);
+I = find(s>MIN&s<MAX);
+p(I) = p(I) - interp1(site.boundary.s,site.boundary.p,s(I));%Subtract logspiral errors from p data
+end
+
+
+if site.alpha<0
+    p = -p;
+end
+
+%-----------------------------------------------------------------
+
+out.s = s;
+out.p = p;
+out.z = xyz_data.z;
+

make_gif.py

@@ -0,0 +1,166 @@
+# -*- coding: utf-8 -*-
+
+#==========================================================#
+# Make a gif of the satellite images
+#==========================================================#
+
+# Initial settings
+import os
+import numpy as np
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+import matplotlib.animation as manimation
+import ee
+import pdb
+
+# other modules
+from osgeo import gdal, ogr, osr
+import pickle
+import matplotlib.cm as cm
+from pylab import ginput
+import imageio
+
+# 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.measure as measure
+
+# import own modules
+import functions.utils as utils
+import functions.sds as sds
+
+# some settings
+np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
+plt.rcParams['axes.grid'] = True
+plt.rcParams['figure.max_open_warning'] = 100
+ee.Initialize()
+
+# parameters
+cloud_thresh = 0.5      # threshold for cloud cover
+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
+
+# load metadata (timestamps and epsg code) for the collection
+satname = 'L8'
+#sitename = 'NARRA'
+sitename = 'OLDBAR_inlet'
+filepath = os.path.join(os.getcwd(), 'data', satname, sitename)
+with open(os.path.join(filepath, sitename + '_timestamps' + '.pkl'), 'rb') as f:
+    timestamps = pickle.load(f)
+timestamps_sorted = sorted(timestamps) # sort timestamps since images are sorted in directory
+with open(os.path.join(filepath, sitename + '_epsgcode' + '.pkl'), 'rb') as f:
+    input_epsg = pickle.load(f)
+with open(os.path.join(filepath, sitename + '_refpoints2' + '.pkl'), 'rb') as f:
+    refpoints = pickle.load(f)
+
+# path to images
+file_path_pan = os.path.join(os.getcwd(), 'data', satname, sitename, 'pan')
+file_path_ms = os.path.join(os.getcwd(), 'data', satname, sitename, 'ms')
+file_names_pan = os.listdir(file_path_pan)
+file_names_ms = os.listdir(file_path_ms)
+N = len(file_names_pan)
+
+# initialise some variables
+cloud_cover_ts = []
+date_acquired_ts = []
+idx_skipped = []
+idx_nocloud = []
+
+t = []
+shorelines = []
+
+with open(os.path.join(filepath, sitename + '_idxnocloud' + '.pkl'), 'rb') as f:
+    idx_nocloud = pickle.load(f)
+    
+for i in idx_nocloud:
+    # read pan image
+    fn_pan = os.path.join(file_path_pan, file_names_pan[i])
+    data = gdal.Open(fn_pan, gdal.GA_ReadOnly)
+    georef = np.array(data.GetGeoTransform())
+    bands = [data.GetRasterBand(i + 1).ReadAsArray() for k in range(data.RasterCount)]
+    im_pan = np.stack(bands, 2)[:,:,0]
+    # read ms image
+    fn_ms = os.path.join(file_path_ms, file_names_ms[i])
+    data = gdal.Open(fn_ms, gdal.GA_ReadOnly)
+    bands = [data.GetRasterBand(i + 1).ReadAsArray() for k in range(data.RasterCount)]
+    im_ms = np.stack(bands, 2)
+    # cloud mask
+    im_qa = im_ms[:,:,5]
+    cloud_mask = sds.create_cloud_mask(im_qa, satname, plot_bool)
+    cloud_mask = transform.resize(cloud_mask, (im_pan.shape[0], im_pan.shape[1]),
+                                order=0, preserve_range=True, 
+                                mode='constant').astype('bool_')    
+    # resize the image using bilinear interpolation (order 1)
+    im_ms = transform.resize(im_ms,(im_pan.shape[0], im_pan.shape[1]),
+                             order=1, preserve_range=True, mode='constant')
+    # check if -inf or nan values and add to cloud mask
+    im_inf = np.isin(im_ms[:,:,0], -np.inf)
+    im_nan = np.isnan(im_ms[:,:,0])
+    cloud_mask = np.logical_or(np.logical_or(cloud_mask, im_inf), im_nan)
+    cloud_cover = sum(sum(cloud_mask.astype(int)))/(cloud_mask.shape[0]*cloud_mask.shape[1])
+    if cloud_cover > cloud_thresh:
+        print('skipped cloud ' + str(i))
+        idx_skipped.append(i)
+        continue
+#        idx_nocloud.append(i)
+    # check if image for that date is already present and keep the one with less clouds
+    if file_names_pan[i][len(satname)+1+len(sitename)+1:len(satname)+1+len(sitename)+1+10] in date_acquired_ts:
+        idx_samedate = utils.find_indices(date_acquired_ts, lambda e : e == file_names_pan[i][9:19])
+        idx_samedate = idx_samedate[0]
+        print(str(cloud_cover) + ' - ' + str(cloud_cover_ts[idx_samedate]))
+        if cloud_cover >= cloud_cover_ts[idx_samedate]:
+            print('skipped double ' + str(i))
+            idx_skipped.append(i)
+            continue
+        else:
+            del shorelines[idx_samedate]
+            del t[idx_samedate]
+            del cloud_cover_ts[idx_samedate]
+            del date_acquired_ts[idx_samedate]
+            print('deleted ' + str(idx_samedate))
+            
+    # rescale intensities
+    im_ms = sds.rescale_image_intensity(im_ms, cloud_mask, prob_high, plot_bool)
+    im_pan = sds.rescale_image_intensity(im_pan, cloud_mask, prob_high, plot_bool)
+    # pansharpen rgb image
+    im_ms_ps = sds.pansharpen(im_ms[:,:,[0,1,2]], im_pan, cloud_mask, 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], cloud_mask, plot_bool)
+    # detect edges
+    wl_pix = sds.find_wl_contours(im_ndwi, cloud_mask, min_contour_points, plot_bool)
+    # convert from pixels to world coordinates
+    wl_coords = sds.convert_pix2world(wl_pix, georef)
+    # convert to output epsg spatial reference
+    wl = sds.convert_epsg(wl_coords, input_epsg, output_epsg)
+    
+    # save images as png for video
+    fig = plt.figure()
+    plt.grid(False)
+    plt.imshow(im_ms_ps[:,:,[2,1,0]], animated=True)
+    mng = plt.get_current_fig_manager()                                         
+    mng.window.showMaximized()
+    plt.title(file_names_pan[i][len(satname)+1+len(sitename)+1:len(satname)+1+len(sitename)+1+10])  
+    plt.xticks([])
+    plt.yticks([])
+    plt.axis('equal')
+    plt.tight_layout() 
+    plt.draw()
+    plt.savefig(os.path.join(filepath,
+        'plots', file_names_pan[i][len(satname)+1+len(sitename)+1:len(satname)+1+len(sitename)+1+10] + '.png'),
+        dpi = 300)
+    plt.close()
+    
+# create gif
+images = []
+filenames = os.listdir(os.path.join(filepath, 'plots'))
+with imageio.get_writer('movie.gif', mode='I', duration=0.2) as writer:
+    for filename in filenames:
+        image = imageio.imread(os.path.join(filepath,'plots',filename))
+        writer.append_data(image)

make_gif_classified.py

@@ -0,0 +1,213 @@
+# -*- coding: utf-8 -*-
+
+#==========================================================#
+# Run Neural Network on image to extract sandy pixels
+#==========================================================#
+
+# Initial settings
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatches
+from matplotlib import gridspec
+from datetime import datetime, timedelta
+import pytz
+import ee
+import pdb
+import time
+import pandas as pd
+# other modules
+from osgeo import gdal, ogr, osr
+import pickle
+import matplotlib.cm as cm
+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.measure as measure
+import skimage.morphology as morphology
+from scipy import ndimage
+import imageio
+
+
+# machine learning modules
+from sklearn.model_selection import train_test_split
+from sklearn.neural_network import MLPClassifier
+from sklearn.preprocessing import StandardScaler, Normalizer 
+from sklearn.externals import joblib
+
+# import own modules
+import functions.utils as utils
+import functions.sds as sds
+
+# some settings
+np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
+plt.rcParams['axes.grid'] = True
+plt.rcParams['figure.max_open_warning'] = 100
+ee.Initialize()
+
+# parameters
+cloud_thresh = 0.3      # threshold for cloud cover
+plot_bool = False      # if you want the plots
+prob_high = 100        # 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
+buffer_size = 10        # radius (in pixels) of disk for buffer (pixel classification)
+min_beach_size = 20     # number of pixels in a beach (pixel classification)
+
+# load metadata (timestamps and epsg code) for the collection
+satname = 'L8'
+#sitename = 'NARRA_all'
+#sitename = 'NARRA'
+#sitename = 'OLDBAR'
+#sitename = 'OLDBAR_inlet'
+#sitename = 'SANDMOTOR'
+sitename = 'TAIRUA'
+#sitename = 'DUCK'
+
+# Load metadata
+filepath = os.path.join(os.getcwd(), 'data', satname, sitename)
+with open(os.path.join(filepath, sitename + '_timestamps' + '.pkl'), 'rb') as f:
+    timestamps = pickle.load(f)
+timestamps_sorted = sorted(timestamps)
+daysall = (datetime(2019,1,1,tzinfo=pytz.utc) - datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds()
+# path to images
+file_path_pan = os.path.join(os.getcwd(), 'data', satname, sitename, 'pan')
+file_path_ms = os.path.join(os.getcwd(), 'data', satname, sitename, 'ms')
+file_names_pan = os.listdir(file_path_pan)
+file_names_ms = os.listdir(file_path_ms)
+N = len(file_names_pan)
+
+# initialise some variables
+idx_skipped = []
+idx_nocloud = []
+n_features = 10
+train_pos = np.nan*np.ones((1,n_features))
+train_neg = np.nan*np.ones((1,n_features))
+columns = ('B','G','R','NIR','SWIR','Pan','WI','VI','BR', 'mWI', 'class')
+
+#%%
+for i in range(N):
+    # read pan image
+    fn_pan = os.path.join(file_path_pan, file_names_pan[i])
+    data = gdal.Open(fn_pan, gdal.GA_ReadOnly)
+    georef = np.array(data.GetGeoTransform())
+    bands = [data.GetRasterBand(i + 1).ReadAsArray() for k in range(data.RasterCount)]
+    im_pan = np.stack(bands, 2)[:,:,0]
+    nrow = im_pan.shape[0]
+    ncol = im_pan.shape[1]
+    # read ms image
+    fn_ms = os.path.join(file_path_ms, file_names_ms[i])
+    data = gdal.Open(fn_ms, gdal.GA_ReadOnly)
+    bands = [data.GetRasterBand(i + 1).ReadAsArray() for k in range(data.RasterCount)]
+    im_ms = np.stack(bands, 2)
+    # cloud mask
+    im_qa = im_ms[:,:,5]
+    cloud_mask = sds.create_cloud_mask(im_qa, satname, plot_bool)
+    cloud_mask = transform.resize(cloud_mask, (im_pan.shape[0], im_pan.shape[1]),
+                                order=0, preserve_range=True, 
+                                mode='constant').astype('bool_')    
+    # resize the image using bilinear interpolation (order 1)
+    im_ms = transform.resize(im_ms,(im_pan.shape[0], im_pan.shape[1]),
+                             order=1, preserve_range=True, mode='constant')
+    # check if -inf or nan values and add to cloud mask
+    im_inf = np.isin(im_ms[:,:,0], -np.inf)
+    im_nan = np.isnan(im_ms[:,:,0])
+    cloud_mask = np.logical_or(np.logical_or(cloud_mask, im_inf), im_nan)
+    # skip if cloud cover is more than the threshold
+    cloud_cover = sum(sum(cloud_mask.astype(int)))/(cloud_mask.shape[0]*cloud_mask.shape[1])
+    if cloud_cover > cloud_thresh:
+        print('skip ' + str(i) + ' - cloudy (' + str(np.round(cloud_cover*100).astype(int)) + '%)')
+        idx_skipped.append(i)
+        continue
+    idx_nocloud.append(i)
+            
+    # pansharpen rgb image
+    im_ms_ps = sds.pansharpen(im_ms[:,:,[0,1,2]], im_pan, cloud_mask, 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) 
+    
+    im_classif, im_labels = sds.classify_image_NN(im_ms_ps, im_pan, cloud_mask, min_beach_size, plot_bool)
+    
+    im_display = sds.rescale_image_intensity(im_ms_ps[:,:,[2,1,0]], cloud_mask, 100, False)
+    im = np.copy(im_display)
+    # define colours for plot
+    colours = np.array([[1,128/255,0/255],[204/255,1,1],[0,0,204/255]])
+    for k in range(0,im_labels.shape[2]):
+        im[im_labels[:,:,k],0] = colours[k,0]
+        im[im_labels[:,:,k],1] = colours[k,1]
+        im[im_labels[:,:,k],2] = colours[k,2]
+    
+     
+#    fig = plt.figure()
+#    plt.suptitle(date_im, fontsize=17, fontweight='bold')
+#    ax1 = plt.subplot(121)
+#    plt.imshow(im_display)
+#    plt.axis('off')
+#    ax2 = plt.subplot(122, sharex=ax1, sharey=ax1)
+#    plt.imshow(im)
+#    plt.axis('off')
+#    plt.gcf().set_size_inches(17.99,7.55)
+#    plt.tight_layout() 
+#    orange_patch = mpatches.Patch(color=[1,128/255,0/255], label='sand')
+#    white_patch = mpatches.Patch(color=[204/255,1,1], label='swash/whitewater')
+#    blue_patch = mpatches.Patch(color=[0,0,204/255], label='water')
+#    plt.legend(handles=[orange_patch,white_patch,blue_patch], bbox_to_anchor=(0.95, 0.2))
+#    plt.draw()
+    
+    date_im = timestamps_sorted[i].strftime('%d %b %Y')
+    daysnow = (timestamps_sorted[i] - datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds()
+    
+    fig = plt.figure()
+    gs = gridspec.GridSpec(2, 2, height_ratios=[1, 20]) 
+
+    ax1 = fig.add_subplot(gs[0,:]) 
+    plt.plot(0,0,'ko',daysall,0,'ko')
+    plt.plot([0,daysall],[0,0],'k-')
+    plt.plot(daysnow,0,'ro')
+    plt.text(0,0.05,'2013')
+    plt.text(daysall,0.05,'2019')
+    plt.plot((datetime(2014,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0,'ko',markersize=3)
+    plt.plot((datetime(2015,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0,'ko',markersize=3)
+    plt.plot((datetime(2016,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0,'ko',markersize=3)
+    plt.plot((datetime(2017,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0,'ko',markersize=3)
+    plt.plot((datetime(2018,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0,'ko',markersize=3)
+    plt.text((datetime(2014,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0.05,'2014')
+    plt.text((datetime(2015,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0.05,'2015')
+    plt.text((datetime(2016,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0.05,'2016')
+    plt.text((datetime(2017,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0.05,'2017')
+    plt.text((datetime(2018,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0.05,'2018')
+
+    plt.axis('off')
+
+    ax2 = fig.add_subplot(gs[1,0])
+    plt.imshow(im_display)
+    plt.axis('off')
+    plt.title(date_im, fontsize=17, fontweight='bold')
+
+    ax3 = fig.add_subplot(gs[1,1])
+    plt.imshow(im)
+    plt.axis('off')
+    orange_patch = mpatches.Patch(color=[1,128/255,0/255], label='sand')
+    white_patch = mpatches.Patch(color=[204/255,1,1], label='swash/whitewater')
+    blue_patch = mpatches.Patch(color=[0,0,204/255], label='water')
+    plt.legend(handles=[orange_patch,white_patch,blue_patch], bbox_to_anchor=(0.95, 0.2))
+    
+    plt.gcf().set_size_inches(17.99,7.55)
+    plt.gcf().set_tight_layout(True)
+
+    plt.draw()
+    plt.savefig(os.path.join(filepath,'plots_classif', file_names_pan[i][len(satname)+1+len(sitename)+1:len(satname)+1+len(sitename)+1+10] + '.jpg'), dpi = 300)
+    plt.close()
+    
+# create gif
+images = []
+filenames = os.listdir(os.path.join(filepath, 'plots_classif'))
+with imageio.get_writer(sitename + '.gif', mode='I', duration=0.4) as writer:
+    for filename in filenames:
+        image = imageio.imread(os.path.join(filepath,'plots_classif',filename))
+        writer.append_data(image)
+    

make_gif_classified_sl.py

@@ -0,0 +1,228 @@
+# -*- coding: utf-8 -*-
+
+#==========================================================#
+# Run Neural Network on image to extract sandy pixels
+#==========================================================#
+
+# Initial settings
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatches
+import matplotlib.lines as mlines
+from matplotlib import gridspec
+from datetime import datetime, timedelta
+import pytz
+import ee
+import pdb
+import time
+import pandas as pd
+# other modules
+from osgeo import gdal, ogr, osr
+import pickle
+import matplotlib.cm as cm
+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.measure as measure
+import skimage.morphology as morphology
+from scipy import ndimage
+import imageio
+
+
+# machine learning modules
+from sklearn.model_selection import train_test_split
+from sklearn.neural_network import MLPClassifier
+from sklearn.preprocessing import StandardScaler, Normalizer 
+from sklearn.externals import joblib
+
+# import own modules
+import functions.utils as utils
+import functions.sds as sds
+
+# some settings
+np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
+plt.rcParams['axes.grid'] = True
+plt.rcParams['figure.max_open_warning'] = 100
+ee.Initialize()
+
+# parameters
+cloud_thresh = 0.2      # threshold for cloud cover
+plot_bool = False      # if you want the plots
+prob_high = 100        # 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
+buffer_size = 10        # radius (in pixels) of disk for buffer (pixel classification)
+min_beach_size = 10     # number of pixels in a beach (pixel classification)
+
+# load metadata (timestamps and epsg code) for the collection
+satname = 'L8'
+#sitename = 'NARRA_all'
+#sitename = 'NARRA'
+#sitename = 'OLDBAR'
+#sitename = 'OLDBAR_inlet'
+#sitename = 'SANDMOTOR'
+#sitename = 'TAIRUA'
+#sitename = 'DUCK'
+#sitename = 'BROULEE'
+sitename = 'MURI'
+
+
+# Load metadata
+filepath = os.path.join(os.getcwd(), 'data', satname, sitename)
+with open(os.path.join(filepath, sitename + '_timestamps' + '.pkl'), 'rb') as f:
+    timestamps = pickle.load(f)
+timestamps_sorted = sorted(timestamps)
+daysall = (datetime(2019,1,1,tzinfo=pytz.utc) - datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds()
+# path to images
+file_path_pan = os.path.join(os.getcwd(), 'data', satname, sitename, 'pan')
+file_path_ms = os.path.join(os.getcwd(), 'data', satname, sitename, 'ms')
+file_names_pan = os.listdir(file_path_pan)
+file_names_ms = os.listdir(file_path_ms)
+N = len(file_names_pan)
+
+# initialise some variables
+idx_skipped = []
+idx_nocloud = []
+n_features = 10
+train_pos = np.nan*np.ones((1,n_features))
+train_neg = np.nan*np.ones((1,n_features))
+columns = ('B','G','R','NIR','SWIR','Pan','WI','VI','BR', 'mWI', 'class')
+
+#%%
+for i in range(N):
+    # read pan image
+    fn_pan = os.path.join(file_path_pan, file_names_pan[i])
+    data = gdal.Open(fn_pan, gdal.GA_ReadOnly)
+    georef = np.array(data.GetGeoTransform())
+    bands = [data.GetRasterBand(i + 1).ReadAsArray() for k in range(data.RasterCount)]
+    im_pan = np.stack(bands, 2)[:,:,0]
+    nrow = im_pan.shape[0]
+    ncol = im_pan.shape[1]
+    # read ms image
+    fn_ms = os.path.join(file_path_ms, file_names_ms[i])
+    data = gdal.Open(fn_ms, gdal.GA_ReadOnly)
+    bands = [data.GetRasterBand(i + 1).ReadAsArray() for k in range(data.RasterCount)]
+    im_ms = np.stack(bands, 2)
+    # cloud mask
+    im_qa = im_ms[:,:,5]
+    cloud_mask = sds.create_cloud_mask(im_qa, satname, plot_bool)
+    cloud_mask = transform.resize(cloud_mask, (im_pan.shape[0], im_pan.shape[1]),
+                                order=0, preserve_range=True, 
+                                mode='constant').astype('bool_')    
+    # resize the image using bilinear interpolation (order 1)
+    im_ms = transform.resize(im_ms,(im_pan.shape[0], im_pan.shape[1]),
+                             order=1, preserve_range=True, mode='constant')
+    # check if -inf or nan values and add to cloud mask
+    im_inf = np.isin(im_ms[:,:,0], -np.inf)
+    im_nan = np.isnan(im_ms[:,:,0])
+    cloud_mask = np.logical_or(np.logical_or(cloud_mask, im_inf), im_nan)
+    # skip if cloud cover is more than the threshold
+    cloud_cover = sum(sum(cloud_mask.astype(int)))/(cloud_mask.shape[0]*cloud_mask.shape[1])
+    if cloud_cover > cloud_thresh:
+        print('skip ' + str(i) + ' - cloudy (' + str(np.round(cloud_cover*100).astype(int)) + '%)')
+        idx_skipped.append(i)
+        continue
+    idx_nocloud.append(i)
+            
+    # pansharpen rgb image
+    im_ms_ps = sds.pansharpen(im_ms[:,:,[0,1,2]], im_pan, cloud_mask, 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) 
+    
+    im_classif, im_labels = sds.classify_image_NN(im_ms_ps, im_pan, cloud_mask, min_beach_size, plot_bool)
+        
+    # if there are no sand pixels, skip the image (maybe later change the detection method with old method)
+    if sum(sum(im_labels[:,:,0])) == 0 :
+        print('skip ' + str(i) + ' - no sand')
+        idx_skipped.append(i)
+        continue
+    
+    contours_wi, contours_mwi = sds.find_wl_contours2(im_ms_ps, im_labels, cloud_mask, buffer_size, False)
+    
+    
+    im_display = sds.rescale_image_intensity(im_ms_ps[:,:,[2,1,0]], cloud_mask, 100, False)
+    im = np.copy(im_display)
+    # define colours for plot
+    colours = np.array([[1,128/255,0/255],[204/255,1,1],[0,0,204/255]])
+    for k in range(0,im_labels.shape[2]):
+        im[im_labels[:,:,k],0] = colours[k,0]
+        im[im_labels[:,:,k],1] = colours[k,1]
+        im[im_labels[:,:,k],2] = colours[k,2]
+    
+     
+#    fig = plt.figure()
+#    plt.suptitle(date_im, fontsize=17, fontweight='bold')
+#    ax1 = plt.subplot(121)
+#    plt.imshow(im_display)
+#    plt.axis('off')
+#    ax2 = plt.subplot(122, sharex=ax1, sharey=ax1)
+#    plt.imshow(im)
+#    plt.axis('off')
+#    plt.gcf().set_size_inches(17.99,7.55)
+#    plt.tight_layout() 
+#    orange_patch = mpatches.Patch(color=[1,128/255,0/255], label='sand')
+#    white_patch = mpatches.Patch(color=[204/255,1,1], label='swash/whitewater')
+#    blue_patch = mpatches.Patch(color=[0,0,204/255], label='water')
+#    plt.legend(handles=[orange_patch,white_patch,blue_patch], bbox_to_anchor=(0.95, 0.2))
+#    plt.draw()
+    
+    date_im = timestamps_sorted[i].strftime('%d %b %Y')
+    daysnow = (timestamps_sorted[i] - datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds()
+    
+    fig = plt.figure()
+    gs = gridspec.GridSpec(2, 2, height_ratios=[1, 20]) 
+
+    ax1 = fig.add_subplot(gs[0,:]) 
+    plt.plot(0,0,'ko',daysall,0,'ko')
+    plt.plot([0,daysall],[0,0],'k-')
+    plt.plot(daysnow,0,'ro')
+    plt.text(0,0.05,'2013')
+    plt.text(daysall,0.05,'2019')
+    plt.plot((datetime(2014,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0,'ko',markersize=3)
+    plt.plot((datetime(2015,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0,'ko',markersize=3)
+    plt.plot((datetime(2016,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0,'ko',markersize=3)
+    plt.plot((datetime(2017,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0,'ko',markersize=3)
+    plt.plot((datetime(2018,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0,'ko',markersize=3)
+    plt.text((datetime(2014,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0.05,'2014')
+    plt.text((datetime(2015,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0.05,'2015')
+    plt.text((datetime(2016,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0.05,'2016')
+    plt.text((datetime(2017,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0.05,'2017')
+    plt.text((datetime(2018,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0.05,'2018')
+
+    plt.axis('off')
+
+    ax2 = fig.add_subplot(gs[1,0])
+    plt.imshow(im_display)
+    plt.axis('off')
+    plt.title(date_im, fontsize=17, fontweight='bold')
+
+    ax3 = fig.add_subplot(gs[1,1])
+    plt.imshow(im)
+    for l,contour in enumerate(contours_mwi): plt.plot(contour[:, 1], contour[:, 0], linewidth=2, color='k', linestyle='--')
+    plt.axis('off')
+    orange_patch = mpatches.Patch(color=[1,128/255,0/255], label='sand')
+    white_patch = mpatches.Patch(color=[204/255,1,1], label='swash/whitewater')
+    blue_patch = mpatches.Patch(color=[0,0,204/255], label='water')
+    black_line = mlines.Line2D([],[],color='k',linestyle='-', label='shoreline')
+    plt.legend(handles=[orange_patch,white_patch,blue_patch, black_line], bbox_to_anchor=(0.95, 0.2))
+    
+    plt.gcf().set_size_inches(17.99,7.55)
+    plt.gcf().set_tight_layout(True)
+
+    plt.draw()
+    plt.savefig(os.path.join(filepath,'plots_classif', file_names_pan[i][len(satname)+1+len(sitename)+1:len(satname)+1+len(sitename)+1+10] + '.jpg'), dpi = 300)
+    plt.close()
+    
+# create gif
+images = []
+filenames = os.listdir(os.path.join(filepath, 'plots_classif'))
+with imageio.get_writer(sitename + '.gif', mode='I', duration=0.4) as writer:
+    for filename in filenames:
+        image = imageio.imread(os.path.join(filepath,'plots_classif',filename))
+        writer.append_data(image)
+    

make_gif_classified_sl_MURI.py

@@ -0,0 +1,193 @@
+# -*- coding: utf-8 -*-
+
+#==========================================================#
+# Run Neural Network on image to extract sandy pixels
+#==========================================================#
+
+# Initial settings
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatches
+import matplotlib.lines as mlines
+from matplotlib import gridspec
+from datetime import datetime, timedelta
+import pytz
+import ee
+import pdb
+import time
+import pandas as pd
+# other modules
+from osgeo import gdal, ogr, osr
+import pickle
+import matplotlib.cm as cm
+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.measure as measure
+import skimage.morphology as morphology
+from scipy import ndimage
+import imageio
+
+
+# machine learning modules
+from sklearn.model_selection import train_test_split
+from sklearn.neural_network import MLPClassifier
+from sklearn.preprocessing import StandardScaler, Normalizer 
+from sklearn.externals import joblib
+
+# import own modules
+import functions.utils as utils
+import functions.sds as sds
+
+# some settings
+np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
+plt.rcParams['axes.grid'] = True
+plt.rcParams['figure.max_open_warning'] = 100
+ee.Initialize()
+
+# parameters
+cloud_thresh = 0.5      # threshold for cloud cover
+plot_bool = False      # if you want the plots
+prob_high = 100        # upper probability to clip and rescale pixel intensity
+min_contour_points = 30# minimum number of points contained in each water line
+output_epsg = 28356     # GDA94 / MGA Zone 56
+buffer_size = 10        # radius (in pixels) of disk for buffer (pixel classification)
+min_beach_size = 10     # number of pixels in a beach (pixel classification)
+
+# load metadata (timestamps and epsg code) for the collection
+satname = 'L8'
+#sitename = 'NARRA_all'
+#sitename = 'NARRA'
+#sitename = 'OLDBAR'
+#sitename = 'OLDBAR_inlet'
+#sitename = 'SANDMOTOR'
+#sitename = 'TAIRUA'
+#sitename = 'DUCK'
+#sitename = 'BROULEE'
+sitename = 'MURI2'
+
+
+# Load metadata
+filepath = os.path.join(os.getcwd(), 'data', satname, sitename)
+with open(os.path.join(filepath, sitename + '_timestamps' + '.pkl'), 'rb') as f:
+    timestamps = pickle.load(f)
+timestamps_sorted = sorted(timestamps)
+daysall = (datetime(2019,1,1,tzinfo=pytz.utc) - datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds()
+# path to images
+file_path_pan = os.path.join(os.getcwd(), 'data', satname, sitename, 'pan')
+file_path_ms = os.path.join(os.getcwd(), 'data', satname, sitename, 'ms')
+file_names_pan = os.listdir(file_path_pan)
+file_names_ms = os.listdir(file_path_ms)
+N = len(file_names_pan)
+
+# initialise some variables
+idx_skipped = []
+idx_nocloud = []
+n_features = 10
+train_pos = np.nan*np.ones((1,n_features))
+train_neg = np.nan*np.ones((1,n_features))
+columns = ('B','G','R','NIR','SWIR','Pan','WI','VI','BR', 'mWI', 'class')
+
+#%%
+for i in range(N):
+    # read pan image
+    fn_pan = os.path.join(file_path_pan, file_names_pan[i])
+    data = gdal.Open(fn_pan, gdal.GA_ReadOnly)
+    georef = np.array(data.GetGeoTransform())
+    bands = [data.GetRasterBand(i + 1).ReadAsArray() for k in range(data.RasterCount)]
+    im_pan = np.stack(bands, 2)[:,:,0]
+    nrow = im_pan.shape[0]
+    ncol = im_pan.shape[1]
+    # read ms image
+    fn_ms = os.path.join(file_path_ms, file_names_ms[i])
+    data = gdal.Open(fn_ms, gdal.GA_ReadOnly)
+    bands = [data.GetRasterBand(i + 1).ReadAsArray() for k in range(data.RasterCount)]
+    im_ms = np.stack(bands, 2)
+    # cloud mask
+    im_qa = im_ms[:,:,5]
+    cloud_mask = sds.create_cloud_mask(im_qa, satname, plot_bool)
+    cloud_mask = transform.resize(cloud_mask, (im_pan.shape[0], im_pan.shape[1]),
+                                order=0, preserve_range=True, 
+                                mode='constant').astype('bool_')    
+    # resize the image using bilinear interpolation (order 1)
+    im_ms = transform.resize(im_ms,(im_pan.shape[0], im_pan.shape[1]),
+                             order=1, preserve_range=True, mode='constant')
+    # check if -inf or nan values and add to cloud mask
+    im_inf = np.isin(im_ms[:,:,0], -np.inf)
+    im_nan = np.isnan(im_ms[:,:,0])
+    cloud_mask = np.logical_or(np.logical_or(cloud_mask, im_inf), im_nan)
+    # skip if cloud cover is more than the threshold
+    cloud_cover = sum(sum(cloud_mask.astype(int)))/(cloud_mask.shape[0]*cloud_mask.shape[1])
+    if cloud_cover > cloud_thresh:
+        print('skip ' + str(i) + ' - cloudy (' + str(np.round(cloud_cover*100).astype(int)) + '%)')
+        idx_skipped.append(i)
+        continue
+    idx_nocloud.append(i)
+            
+    # pansharpen rgb image
+    im_ms_ps = sds.pansharpen(im_ms[:,:,[0,1,2]], im_pan, cloud_mask, 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) 
+    
+    # extract shorelines (old method)
+    im_ndwi = sds.nd_index(im_ms_ps[:,:,3], im_ms_ps[:,:,1], cloud_mask, plot_bool)
+    wl_pix = sds.find_wl_contours(im_ndwi, cloud_mask, min_contour_points, plot_bool)
+    
+    im_display = sds.rescale_image_intensity(im_ms_ps[:,:,[2,1,0]], cloud_mask, 100, False)
+
+    date_im = timestamps_sorted[i].strftime('%d %b %Y')
+    daysnow = (timestamps_sorted[i] - datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds()
+
+    fig = plt.figure()
+    gs = gridspec.GridSpec(2, 2, height_ratios=[1, 20]) 
+
+    ax1 = fig.add_subplot(gs[0,:]) 
+    plt.plot(0,0,'ko',daysall,0,'ko')
+    plt.plot([0,daysall],[0,0],'k-')
+    plt.plot(daysnow,0,'ro')
+    plt.text(0,0.05,'2013')
+    plt.text(daysall,0.05,'2019')
+    plt.plot((datetime(2014,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0,'ko',markersize=3)
+    plt.plot((datetime(2015,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0,'ko',markersize=3)
+    plt.plot((datetime(2016,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0,'ko',markersize=3)
+    plt.plot((datetime(2017,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0,'ko',markersize=3)
+    plt.plot((datetime(2018,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0,'ko',markersize=3)
+    plt.text((datetime(2014,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0.05,'2014')
+    plt.text((datetime(2015,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0.05,'2015')
+    plt.text((datetime(2016,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0.05,'2016')
+    plt.text((datetime(2017,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0.05,'2017')
+    plt.text((datetime(2018,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0.05,'2018')
+    plt.axis('off')
+
+#    ax2 = fig.add_subplot(gs[1,0])
+#    plt.imshow(im_display)
+#    plt.axis('off')
+#    plt.title(date_im, fontsize=17, fontweight='bold')
+
+    ax3 = fig.add_subplot(gs[1,:])
+    plt.imshow(im_display)
+    for l,contour in enumerate(wl_pix): plt.plot(contour[:, 1], contour[:, 0], linewidth=2, color='k', linestyle='--')
+    plt.title(date_im, fontsize=17, fontweight='bold')
+    plt.axis('off')
+    
+    plt.gcf().set_size_inches(5.34,9.18)
+    plt.gcf().set_tight_layout(True)
+
+    plt.draw()
+    
+    plt.savefig(os.path.join(filepath,'plots_classif', file_names_pan[i][len(satname)+1+len(sitename)+1:len(satname)+1+len(sitename)+1+10] + '.jpg'), dpi = 300)
+    plt.close()
+    
+# create gif
+images = []
+filenames = os.listdir(os.path.join(filepath, 'plots_classif'))
+with imageio.get_writer(sitename + '_final.gif', mode='I', duration=0.6) as writer:
+    for filename in filenames:
+        image = imageio.imread(os.path.join(filepath,'plots_classif',filename))
+        writer.append_data(image)
+    

make_gif_classified_sl_NARRA.py

@@ -0,0 +1,227 @@
+# -*- coding: utf-8 -*-
+
+#==========================================================#
+# Run Neural Network on image to extract sandy pixels
+#==========================================================#
+
+# Initial settings
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatches
+import matplotlib.lines as mlines
+from matplotlib import gridspec
+from datetime import datetime, timedelta
+import pytz
+import ee
+import pdb
+import time
+import pandas as pd
+# other modules
+from osgeo import gdal, ogr, osr
+import pickle
+import matplotlib.cm as cm
+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.measure as measure
+import skimage.morphology as morphology
+from scipy import ndimage
+import imageio
+
+
+# machine learning modules
+from sklearn.model_selection import train_test_split
+from sklearn.neural_network import MLPClassifier
+from sklearn.preprocessing import StandardScaler, Normalizer 
+from sklearn.externals import joblib
+
+# import own modules
+import functions.utils as utils
+import functions.sds as sds
+
+# some settings
+np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
+plt.rcParams['axes.grid'] = True
+plt.rcParams['figure.max_open_warning'] = 100
+ee.Initialize()
+
+# parameters
+cloud_thresh = 0.2      # threshold for cloud cover
+plot_bool = False      # if you want the plots
+prob_high = 100        # 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
+buffer_size = 10        # radius (in pixels) of disk for buffer (pixel classification)
+min_beach_size = 20     # number of pixels in a beach (pixel classification)
+
+# load metadata (timestamps and epsg code) for the collection
+satname = 'L8'
+#sitename = 'NARRA_all'
+sitename = 'NARRA'
+#sitename = 'OLDBAR'
+#sitename = 'OLDBAR_inlet'
+#sitename = 'SANDMOTOR'
+#sitename = 'TAIRUA'
+#sitename = 'DUCK'
+#sitename = 'BROULEE'
+
+# Load metadata
+filepath = os.path.join(os.getcwd(), 'data', satname, sitename)
+with open(os.path.join(filepath, sitename + '_timestamps' + '.pkl'), 'rb') as f:
+    timestamps = pickle.load(f)
+timestamps_sorted = sorted(timestamps)
+daysall = (datetime(2019,1,1,tzinfo=pytz.utc) - datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds()
+# path to images
+file_path_pan = os.path.join(os.getcwd(), 'data', satname, sitename, 'pan')
+file_path_ms = os.path.join(os.getcwd(), 'data', satname, sitename, 'ms')
+file_names_pan = os.listdir(file_path_pan)
+file_names_ms = os.listdir(file_path_ms)
+N = len(file_names_pan)
+
+# initialise some variables
+idx_skipped = []
+idx_nocloud = []
+n_features = 10
+train_pos = np.nan*np.ones((1,n_features))
+train_neg = np.nan*np.ones((1,n_features))
+columns = ('B','G','R','NIR','SWIR','Pan','WI','VI','BR', 'mWI', 'class')
+
+#%%
+for i in range(N):
+    # read pan image
+    fn_pan = os.path.join(file_path_pan, file_names_pan[i])
+    data = gdal.Open(fn_pan, gdal.GA_ReadOnly)
+    georef = np.array(data.GetGeoTransform())
+    bands = [data.GetRasterBand(i + 1).ReadAsArray() for k in range(data.RasterCount)]
+    im_pan = np.stack(bands, 2)[:,:,0]
+    nrow = im_pan.shape[0]
+    ncol = im_pan.shape[1]
+    # read ms image
+    fn_ms = os.path.join(file_path_ms, file_names_ms[i])
+    data = gdal.Open(fn_ms, gdal.GA_ReadOnly)
+    bands = [data.GetRasterBand(i + 1).ReadAsArray() for k in range(data.RasterCount)]
+    im_ms = np.stack(bands, 2)
+    # cloud mask
+    im_qa = im_ms[:,:,5]
+    cloud_mask = sds.create_cloud_mask(im_qa, satname, plot_bool)
+    cloud_mask = transform.resize(cloud_mask, (im_pan.shape[0], im_pan.shape[1]),
+                                order=0, preserve_range=True, 
+                                mode='constant').astype('bool_')    
+    # resize the image using bilinear interpolation (order 1)
+    im_ms = transform.resize(im_ms,(im_pan.shape[0], im_pan.shape[1]),
+                             order=1, preserve_range=True, mode='constant')
+    # check if -inf or nan values and add to cloud mask
+    im_inf = np.isin(im_ms[:,:,0], -np.inf)
+    im_nan = np.isnan(im_ms[:,:,0])
+    cloud_mask = np.logical_or(np.logical_or(cloud_mask, im_inf), im_nan)
+    # skip if cloud cover is more than the threshold
+    cloud_cover = sum(sum(cloud_mask.astype(int)))/(cloud_mask.shape[0]*cloud_mask.shape[1])
+    if cloud_cover > cloud_thresh:
+        print('skip ' + str(i) + ' - cloudy (' + str(np.round(cloud_cover*100).astype(int)) + '%)')
+        idx_skipped.append(i)
+        continue
+    idx_nocloud.append(i)
+            
+    # pansharpen rgb image
+    im_ms_ps = sds.pansharpen(im_ms[:,:,[0,1,2]], im_pan, cloud_mask, 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) 
+    
+    im_classif, im_labels = sds.classify_image_NN(im_ms_ps, im_pan, cloud_mask, min_beach_size, plot_bool)
+        
+    # if there are no sand pixels, skip the image (maybe later change the detection method with old method)
+    if sum(sum(im_labels[:,:,0])) == 0 :
+        print('skip ' + str(i) + ' - no sand')
+        idx_skipped.append(i)
+        continue
+    
+    contours_wi, contours_mwi = sds.find_wl_contours2(im_ms_ps, im_labels, cloud_mask, buffer_size, False)
+    
+    
+    im_display = sds.rescale_image_intensity(im_ms_ps[:,:,[2,1,0]], cloud_mask, 100, False)
+    im = np.copy(im_display)
+    # define colours for plot
+    colours = np.array([[1,128/255,0/255],[204/255,1,1],[0,0,204/255]])
+    for k in range(0,im_labels.shape[2]):
+        im[im_labels[:,:,k],0] = colours[k,0]
+        im[im_labels[:,:,k],1] = colours[k,1]
+        im[im_labels[:,:,k],2] = colours[k,2]
+    
+     
+#    fig = plt.figure()
+#    plt.suptitle(date_im, fontsize=17, fontweight='bold')
+#    ax1 = plt.subplot(121)
+#    plt.imshow(im_display)
+#    plt.axis('off')
+#    ax2 = plt.subplot(122, sharex=ax1, sharey=ax1)
+#    plt.imshow(im)
+#    plt.axis('off')
+#    plt.gcf().set_size_inches(17.99,7.55)
+#    plt.tight_layout() 
+#    orange_patch = mpatches.Patch(color=[1,128/255,0/255], label='sand')
+#    white_patch = mpatches.Patch(color=[204/255,1,1], label='swash/whitewater')
+#    blue_patch = mpatches.Patch(color=[0,0,204/255], label='water')
+#    plt.legend(handles=[orange_patch,white_patch,blue_patch], bbox_to_anchor=(0.95, 0.2))
+#    plt.draw()
+    
+    date_im = timestamps_sorted[i].strftime('%d %b %Y')
+    daysnow = (timestamps_sorted[i] - datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds()
+    
+    fig = plt.figure()
+    gs = gridspec.GridSpec(2, 2, height_ratios=[1, 20]) 
+
+    ax1 = fig.add_subplot(gs[0,:]) 
+    plt.plot(0,0,'ko',daysall,0,'ko')
+    plt.plot([0,daysall],[0,0],'k-')
+    plt.plot(daysnow,0,'ro')
+    plt.text(0,0.05,'2013')
+    plt.text(daysall,0.05,'2019')
+    plt.plot((datetime(2014,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0,'ko',markersize=3)
+    plt.plot((datetime(2015,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0,'ko',markersize=3)
+    plt.plot((datetime(2016,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0,'ko',markersize=3)
+    plt.plot((datetime(2017,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0,'ko',markersize=3)
+    plt.plot((datetime(2018,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0,'ko',markersize=3)
+    plt.text((datetime(2014,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0.05,'2014')
+    plt.text((datetime(2015,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0.05,'2015')
+    plt.text((datetime(2016,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0.05,'2016')
+    plt.text((datetime(2017,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0.05,'2017')
+    plt.text((datetime(2018,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0.05,'2018')
+
+    plt.axis('off')
+
+#    ax2 = fig.add_subplot(gs[1,0])
+#    plt.imshow(im_display)
+#    plt.axis('off')
+#    plt.title(date_im, fontsize=17, fontweight='bold')
+
+    ax3 = fig.add_subplot(gs[1,:])
+    plt.imshow(im)
+    for l,contour in enumerate(contours_mwi): plt.plot(contour[:, 1], contour[:, 0], linewidth=2, color='k', linestyle='--')
+    plt.axis('off')
+    orange_patch = mpatches.Patch(color=[1,128/255,0/255], label='sand')
+    white_patch = mpatches.Patch(color=[204/255,1,1], label='swash/whitewater')
+    blue_patch = mpatches.Patch(color=[0,0,204/255], label='water')
+    black_line = mlines.Line2D([],[],color='k',linestyle='--', label='shoreline')
+    plt.legend(handles=[orange_patch,white_patch,blue_patch, black_line], bbox_to_anchor=(0.6, 0.6))
+    plt.title(date_im, fontsize=17, fontweight='bold')
+    
+    plt.gcf().set_size_inches(5.34,9.18)
+    plt.gcf().set_tight_layout(True)
+
+    plt.draw()
+    plt.savefig(os.path.join(filepath,'plots_classif', file_names_pan[i][len(satname)+1+len(sitename)+1:len(satname)+1+len(sitename)+1+10] + '.jpg'), dpi = 300)
+    plt.close()
+    
+# create gif
+images = []
+filenames = os.listdir(os.path.join(filepath, 'plots_classif'))
+with imageio.get_writer(sitename + '.gif', mode='I', duration=0.4) as writer:
+    for filename in filenames:
+        image = imageio.imread(os.path.join(filepath,'plots_classif',filename))
+        writer.append_data(image)
+    

make_gif_classified_sl_NARRA_Chris.py

@@ -0,0 +1,229 @@
+# -*- coding: utf-8 -*-
+
+#==========================================================#
+# Run Neural Network on image to extract sandy pixels
+#==========================================================#
+
+# Initial settings
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatches
+import matplotlib.lines as mlines
+from matplotlib import gridspec
+from datetime import datetime, timedelta
+import pytz
+import ee
+import pdb
+import time
+import pandas as pd
+# other modules
+from osgeo import gdal, ogr, osr
+import pickle
+import matplotlib.cm as cm
+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.measure as measure
+import skimage.morphology as morphology
+from scipy import ndimage
+import imageio
+
+
+# machine learning modules
+from sklearn.model_selection import train_test_split
+from sklearn.neural_network import MLPClassifier
+from sklearn.preprocessing import StandardScaler, Normalizer 
+from sklearn.externals import joblib
+
+# import own modules
+import functions.utils as utils
+import functions.sds as sds
+
+# some settings
+np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
+plt.rcParams['axes.grid'] = True
+plt.rcParams['figure.max_open_warning'] = 100
+ee.Initialize()
+
+# parameters
+cloud_thresh = 0.2      # threshold for cloud cover
+plot_bool = False      # if you want the plots
+prob_high = 100        # 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
+buffer_size = 10        # radius (in pixels) of disk for buffer (pixel classification)
+min_beach_size = 50     # number of pixels in a beach (pixel classification)
+
+# load metadata (timestamps and epsg code) for the collection
+satname = 'L8'
+sitename = 'NARRA_all'
+#sitename = 'NARRA'
+#sitename = 'OLDBAR'
+#sitename = 'OLDBAR_inlet'
+#sitename = 'SANDMOTOR'
+#sitename = 'TAIRUA'
+#sitename = 'DUCK'
+#sitename = 'BROULEE'
+
+# Load metadata
+filepath = os.path.join(os.getcwd(), 'data', satname, sitename)
+with open(os.path.join(filepath, sitename + '_timestamps' + '.pkl'), 'rb') as f:
+    timestamps = pickle.load(f)
+timestamps_sorted = sorted(timestamps)
+daysall = (datetime(2019,1,1,tzinfo=pytz.utc) - datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds()
+# path to images
+file_path_pan = os.path.join(os.getcwd(), 'data', satname, sitename, 'pan')
+file_path_ms = os.path.join(os.getcwd(), 'data', satname, sitename, 'ms')
+file_names_pan = os.listdir(file_path_pan)
+file_names_ms = os.listdir(file_path_ms)
+N = len(file_names_pan)
+
+# initialise some variables
+idx_skipped = []
+idx_nocloud = []
+n_features = 10
+train_pos = np.nan*np.ones((1,n_features))
+train_neg = np.nan*np.ones((1,n_features))
+columns = ('B','G','R','NIR','SWIR','Pan','WI','VI','BR', 'mWI', 'class')
+
+#%%
+for i in range(1):
+    i = 156 # open (96 close)
+    # read pan image
+    fn_pan = os.path.join(file_path_pan, file_names_pan[i])
+    data = gdal.Open(fn_pan, gdal.GA_ReadOnly)
+    georef = np.array(data.GetGeoTransform())
+    bands = [data.GetRasterBand(i + 1).ReadAsArray() for k in range(data.RasterCount)]
+    im_pan = np.stack(bands, 2)[:,:,0]
+    nrow = im_pan.shape[0]
+    ncol = im_pan.shape[1]
+    # read ms image
+    fn_ms = os.path.join(file_path_ms, file_names_ms[i])
+    data = gdal.Open(fn_ms, gdal.GA_ReadOnly)
+    bands = [data.GetRasterBand(i + 1).ReadAsArray() for k in range(data.RasterCount)]
+    im_ms = np.stack(bands, 2)
+    # cloud mask
+    im_qa = im_ms[:,:,5]
+    cloud_mask = sds.create_cloud_mask(im_qa, satname, plot_bool)
+    cloud_mask = transform.resize(cloud_mask, (im_pan.shape[0], im_pan.shape[1]),
+                                order=0, preserve_range=True, 
+                                mode='constant').astype('bool_')    
+    # resize the image using bilinear interpolation (order 1)
+    im_ms = transform.resize(im_ms,(im_pan.shape[0], im_pan.shape[1]),
+                             order=1, preserve_range=True, mode='constant')
+    # check if -inf or nan values and add to cloud mask
+    im_inf = np.isin(im_ms[:,:,0], -np.inf)
+    im_nan = np.isnan(im_ms[:,:,0])
+    cloud_mask = np.logical_or(np.logical_or(cloud_mask, im_inf), im_nan)
+    # skip if cloud cover is more than the threshold
+    cloud_cover = sum(sum(cloud_mask.astype(int)))/(cloud_mask.shape[0]*cloud_mask.shape[1])
+    if cloud_cover > cloud_thresh:
+        print('skip ' + str(i) + ' - cloudy (' + str(np.round(cloud_cover*100).astype(int)) + '%)')
+        idx_skipped.append(i)
+        continue
+    idx_nocloud.append(i)
+            
+    # pansharpen rgb image
+    im_ms_ps = sds.pansharpen(im_ms[:,:,[0,1,2]], im_pan, cloud_mask, 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) 
+    
+    im_classif, im_labels = sds.classify_image_NN(im_ms_ps, im_pan, cloud_mask, min_beach_size, plot_bool)
+        
+    # if there are no sand pixels, skip the image (maybe later change the detection method with old method)
+    if sum(sum(im_labels[:,:,0])) == 0 :
+        print('skip ' + str(i) + ' - no sand')
+        idx_skipped.append(i)
+        continue
+    
+    contours_wi, contours_mwi = sds.find_wl_contours2(im_ms_ps, im_labels, cloud_mask, buffer_size, False)
+    
+    
+    im_display = sds.rescale_image_intensity(im_ms_ps[:,:,[2,1,0]], cloud_mask, 100, False)
+    im = np.copy(im_display)
+    # define colours for plot
+    colours = np.array([[1,128/255,0/255],[0,0,204/255],[0,0,204/255]])
+    
+    for k in range(0,im_labels.shape[2]):
+        im[im_labels[:,:,k],0] = colours[k,0]
+        im[im_labels[:,:,k],1] = colours[k,1]
+        im[im_labels[:,:,k],2] = colours[k,2]
+    
+     
+#    fig = plt.figure()
+#    plt.suptitle(date_im, fontsize=17, fontweight='bold')
+#    ax1 = plt.subplot(121)
+#    plt.imshow(im_display)
+#    plt.axis('off')
+#    ax2 = plt.subplot(122, sharex=ax1, sharey=ax1)
+#    plt.imshow(im)
+#    plt.axis('off')
+#    plt.gcf().set_size_inches(17.99,7.55)
+#    plt.tight_layout() 
+#    orange_patch = mpatches.Patch(color=[1,128/255,0/255], label='sand')
+#    white_patch = mpatches.Patch(color=[204/255,1,1], label='swash/whitewater')
+#    blue_patch = mpatches.Patch(color=[0,0,204/255], label='water')
+#    plt.legend(handles=[orange_patch,white_patch,blue_patch], bbox_to_anchor=(0.95, 0.2))
+#    plt.draw()
+    
+    date_im = timestamps_sorted[i].strftime('%d %b %Y')
+    daysnow = (timestamps_sorted[i] - datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds()
+    
+    fig = plt.figure()
+    gs = gridspec.GridSpec(2, 2, height_ratios=[1, 20]) 
+
+    ax1 = fig.add_subplot(gs[0,:]) 
+    plt.plot(0,0,'ko',daysall,0,'ko')
+    plt.plot([0,daysall],[0,0],'k-')
+    plt.plot(daysnow,0,'ro')
+    plt.text(0,0.05,'2013')
+    plt.text(daysall,0.05,'2019')
+    plt.plot((datetime(2014,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0,'ko',markersize=3)
+    plt.plot((datetime(2015,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0,'ko',markersize=3)
+    plt.plot((datetime(2016,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0,'ko',markersize=3)
+    plt.plot((datetime(2017,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0,'ko',markersize=3)
+    plt.plot((datetime(2018,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0,'ko',markersize=3)
+    plt.text((datetime(2014,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0.05,'2014')
+    plt.text((datetime(2015,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0.05,'2015')
+    plt.text((datetime(2016,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0.05,'2016')
+    plt.text((datetime(2017,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0.05,'2017')
+    plt.text((datetime(2018,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0.05,'2018')
+
+    plt.axis('off')
+
+    ax2 = fig.add_subplot(gs[1,0])
+    plt.imshow(im_display)
+    plt.axis('off')
+    plt.title(date_im, fontsize=17, fontweight='bold')
+
+    ax3 = fig.add_subplot(gs[1,1], sharex=ax2, sharey=ax2)
+    plt.imshow(im)
+    for l,contour in enumerate(contours_mwi): plt.plot(contour[:, 1], contour[:, 0], linewidth=2, color='k', linestyle='--')
+    plt.axis('off')
+    orange_patch = mpatches.Patch(color=[1,128/255,0/255], label='sand')
+    blue_patch = mpatches.Patch(color=[0,0,204/255], label='water')
+    black_line = mlines.Line2D([],[],color='k',linestyle='--', label='water line')
+    plt.legend(handles=[orange_patch,blue_patch, black_line], bbox_to_anchor=(0.6, 0.6))
+#    plt.title(date_im, fontsize=17, fontweight='bold')
+    
+    plt.gcf().set_size_inches(11.38,  7.51)
+    plt.gcf().set_tight_layout(True)
+
+    plt.draw()
+    
+#    plt.savefig(os.path.join(filepath,'plots_classif', file_names_pan[i][len(satname)+1+len(sitename)+1:len(satname)+1+len(sitename)+1+10] + '.jpg'), dpi = 300)
+#    plt.close()
+    
+# create gif
+#images = []
+#filenames = os.listdir(os.path.join(filepath, 'plots_classif'))
+#with imageio.get_writer(sitename + '.gif', mode='I', duration=0.4) as writer:
+#    for filename in filenames:
+#        image = imageio.imread(os.path.join(filepath,'plots_classif',filename))
+#        writer.append_data(image)
+    

old/oldbar/time_coverage_oldbar.py

@@ -0,0 +1,66 @@
+# -*- coding: utf-8 -*-
+
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+import pdb
+import ee
+
+import matplotlib.dates as mdates
+import matplotlib.cm as cm
+from datetime import datetime, timedelta
+import pickle
+import pytz
+import scipy.io as sio
+import scipy.interpolate as interpolate
+import statsmodels.api as sm
+import skimage.measure as measure
+
+
+# my functions
+import functions.utils as utils
+import functions.sds as sds
+
+# some settings
+np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
+plt.rcParams['axes.grid'] = True
+plt.rcParams['figure.max_open_warning'] = 100
+
+au_tz = pytz.timezone('Australia/Sydney')
+
+# load timestamps from satellite images
+satname = 'L8'
+sitename = 'OLDBAR'
+filepath = os.path.join(os.getcwd(), 'data', satname, sitename)
+with open(os.path.join(filepath, sitename + '_output' + '.pkl'), 'rb') as f:
+    output = pickle.load(f)
+dates_l8 = output['t']
+# convert to AEST
+dates_l8 = [_.astimezone(au_tz) for _ in dates_l8]
+# get the satellite shorelines
+sl = output['shorelines']
+
+# load narrabeen beach points (manually digitized)
+with open(os.path.join(os.getcwd(), 'olddata', 'oldbar_beach' + '.pkl'), 'rb') as f:
+    narrabeach = pickle.load(f)
+    
+
+dist_thresh = 250
+frac_smooth = 1./12
+plt.figure()
+plt.axis('equal')
+for i in range(1):
+    # select point of sds that are close to the manually digitized points
+    idx_beach = [np.min(np.linalg.norm(sl[i][k,:] - narrabeach, axis=1)) < dist_thresh for k in range(sl[i].shape[0])]
+    plt.plot(sl[i][:,0], sl[i][:,1])
+    plt.plot(sl[i][idx_beach,0], sl[i][idx_beach,1])
+
+
+    # smooth (LOWESS) satellite shoreline    
+    sl_smooth = sm.nonparametric.lowess(sl[i][idx_beach,0],sl[i][idx_beach,1], frac=frac_smooth, it = 10)
+    sl_smooth = sl_smooth[:,[1,0]]
+    
+    plt.plot(sl_smooth[:,0], sl_smooth[:,1])
+
+    
+plt.draw()   

old/oldcodes/box_head.py

@@ -0,0 +1,80 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Mar 19 14:44:57 2018
+
+@author: z5030440
+
+Main code to extract shorelines from Landsat imagery
+"""
+# Preamble
+
+import ee
+import math
+import matplotlib.pyplot as plt
+import numpy as np
+import pdb
+
+# 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 modules
+# my functions
+from functions.utils import *
+from functions.sds import *
+
+np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
+ee.Initialize()
+
+# parameters
+plot_bool = True # 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
+
+# select collection
+input_col = ee.ImageCollection('LANDSAT/LC08/C01/T1_TOA')
+
+# location (Narrabeen-Collaroy beach)
+rect_narra = [[[151.317395,-33.494601],
+              [151.388635,-33.495174], 
+              [151.363624,-33.565184],
+              [151.305228,-33.563299],
+              [151.317395,-33.494601]]];
+
+# filter by location
+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')
+output = []
+# loop through all images
+# find each image in ee database
+i = 2
+im = ee.Image(im_all[i].get('id')) 
+# load image as np.array
+im_pan, im_ms, im_cloud, crs = read_eeimage(im, rect_narra, plot_bool)
+# rescale intensities
+im_ms = rescale_image_intensity(im_ms, im_cloud, prob_high, plot_bool)
+im_pan = rescale_image_intensity(im_pan, im_cloud, prob_high, plot_bool)
+# pansharpen rgb image
+im_ms_ps = 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 = nd_index(im_ms_ps[:,:,3], im_ms_ps[:,:,1], im_cloud, plot_bool)
+# edge detection
+wl_pix = find_wl_contours(im_ndwi, im_cloud, min_contour_points, True)
+# convert from pixels to world coordinates
+wl_coords = convert_pix2world(wl_pix, crs['crs_15m'])
+output.append(wl_coords)
+
+plt.figure()
+plt.imshow(im_ms_ps[:,:,[2,1,0]])
+plt.axis('off')
+plt.title('RGB at 15m')
+plt.show()

old/oldcodes/classify_sand_test.py

@@ -0,0 +1,476 @@
+# -*- coding: utf-8 -*-
+
+#==========================================================#
+# Extract shorelines from Landsat images
+#==========================================================#
+
+# Initial settings
+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
+from osgeo import gdal
+
+
+
+# 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
+import skimage.color as color
+import skimage.feature as feature
+# machine learning modules
+from sklearn.cluster import KMeans
+
+# my modules
+import functions.utils as utils
+import functions.sds as sds
+
+# some settings
+np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
+plt.rcParams['axes.grid'] = False
+plt.rcParams['figure.max_open_warning'] = 100
+ee.Initialize()
+
+# parameters
+cloud_thresh = 0.5      # threshold for cloud cover
+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
+buffer_size = 10        # radius (in pixels) of disk for buffer (pixel classification)
+min_beach_size = 50     # number of pixels in a beach (pixel classification)
+
+# select collection
+satname = 'L8'
+input_col = ee.ImageCollection('LANDSAT/LC08/C01/T1_RT_TOA') # Landsat 8 Tier 1 TOA
+
+# location (Narrabeen-Collaroy beach)
+polygon = [[[151.3473129272461,-33.69035274454718],
+            [151.2820816040039,-33.68206818063878], 
+            [151.27281188964844,-33.74775138989556],
+            [151.3425064086914,-33.75231878701767],
+            [151.3473129272461,-33.69035274454718]]];
+
+# dates
+start_date = '2013-01-01'
+end_date = '2018-12-31'
+
+# filter by location and date
+flt_col = input_col.filterBounds(ee.Geometry.Polygon(polygon)).filterDate(start_date, end_date)
+
+n_img = flt_col.size().getInfo()
+print('Number of images covering the polygon:', n_img)
+im_all = flt_col.getInfo().get('features')
+
+i = 0 # first image
+
+# find 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, polygon, satname, plot_bool)
+
+# 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)
+
+# classify sand pixels
+im_sand = sds.classify_sand_unsupervised(im_ms_ps, im_pan, im_cloud, wl_pix, buffer_size, min_beach_size, plot_bool)
+
+#pt_in = np.array(ginput(n=1, timeout=1000))
+#if pt_in[0][0] < im_ms_ps.shape[1]/2:
+win = np.ones((3,3))
+
+im_features = np.zeros((sum(sum(im_sand)), 20))
+im_features[:,[0,1,2,3,4]] = im_ms_ps[im_sand,:] # B G R NIR SWIR
+im_features[:,5] = im_pan[im_sand] # Pan
+im_features[:,6] = im_ndwi[im_sand] # NDWI
+im_features[:,7] = sds.nd_index(im_ms_ps[:,:,3], im_ms_ps[:,:,2], im_cloud, False)[im_sand] # NDVI
+im_features[:,8] = sds.nd_index(im_ms_ps[:,:,0], im_ms_ps[:,:,2], im_cloud, False)[im_sand] # ND Blue - Red
+for i in range(9):
+    im_features[:,i+9] = ndimage.generic_filter(im_features[:,i], np.std, footprint = win)
+     
+    
+im_ms_ps[im_sand,:]
+ 
+im_grey = color.rgb2grey(im_ms_ps[:,:,[2,1,0]])
+plt.figure()
+plt.imshow(im_grey, cmap='gray')
+plt.draw()
+
+counts, bins = np.histogram(im_grey[~im_cloud], bins=255)
+im_grey_d = np.digitize(im_grey, bins=bins) - 1
+
+from scipy import ndimage
+
+varianceMatrix1 = ndimage.generic_filter(im_grey_d, np.max, footprint = np.ones((3,3)))
+varianceMatrix2 = ndimage.generic_filter(im_grey_d, np.min, footprint = np.ones((3,3)))
+varianceMatrix = varianceMatrix1 - varianceMatrix2
+im_grey = color.rgb2grey(im_ms_ps[:,:,[2,1,0]])
+plt.figure()
+plt.imshow(varianceMatrix, cmap='gray')
+plt.draw()
+#%%
+data = gdal.Open('l8_test.tif', gdal.GA_ReadOnly)
+bands = [data.GetRasterBand(i + 1).ReadAsArray() for i in range(data.RasterCount)]
+im = np.stack(bands, 2)
+
+im_test = im[:,:,3]
+
+plt.figure()
+plt.imshow(im_test, cmap='gray')
+plt.axis('image')
+plt.draw()
+
+im_stats = np.zeros((im_test.shape[0], im_test.shape[1], 6))
+winsize = 5
+prop_names = ('contrast', 'dissimilarity', 'homogeneity', 'energy', 'correlation', 'ASM')
+for i in range(im_test.shape[0]):
+    print(int(np.round(100*i/im_test.shape[0])))
+    for j in range(im_test.shape[1]):
+        #windows needs to fit completely in image
+        if i <2 or j <2:
+            continue
+        if i > (im_test.shape[0] - 3) or j > (im_test.shape[1] - 3):
+            continue
+
+        #Calculate GLCM on a 3x3 window
+        glcm_window = im_test[i-2: i+3, j-2 : j+3]
+        glcm = feature.greycomatrix(glcm_window, [1], [0],  symmetric = True, normed = True )
+
+        #Calculate contrast and replace center pixel
+        for k in range(6): im_stats[i,j,k] = feature.greycoprops(glcm, prop_names[k])
+
+plt.figure()    
+for i in range(6):
+    plt.subplot(2,3,i+1)
+    plt.imshow(im_stats[:,:,i], cmap='jet')
+    plt.axis('image')
+    plt.title(prop_names[i])
+    plt.draw()
+    
+pixel_loc = [200, 200]
+im_stats[pixel_loc[0], pixel_loc[1], 3]
+#%%
+
+for i in range(im_grey_d.shape[0]):
+    print(int(np.round(100*i/im_grey_d.shape[0])))
+    for j in range(im_grey_d.shape[1]):
+        #windows needs to fit completely in image
+        if i <1 or j <1:
+            continue
+        if i > (im_grey_d.shape[0] - 1) or j > (im_grey_d.shape[0] - 1):
+            continue
+
+        #Calculate GLCM on a 3x3 window
+        glcm_window = im_grey_d[i-1: i+1, j-1 : j+1]
+        glcm = feature.greycomatrix(glcm_window, [1,2], [0], levels=256,  symmetric = True, normed = True )
+
+        #Calculate contrast and replace center pixel
+        im_stats[i,j,0] = feature.greycoprops(glcm, 'contrast')
+        im_stats[i,j,1] = feature.greycoprops(glcm, 'dissimilarity')
+        im_stats[i,j,2] = feature.greycoprops(glcm, 'homogeneity')
+        im_stats[i,j,3] = feature.greycoprops(glcm, 'energy')
+        im_stats[i,j,4] = feature.greycoprops(glcm, 'correlation')
+        im_stats[i,j,5] = feature.greycoprops(glcm, 'ASM')
+
+plt.figure()    
+for i in range(6):
+    plt.subplot(2,3,i+1)
+    plt.imshow(im_stats[:,:,i], cmap='jet')
+    plt.axis('image')
+    plt.draw()
+#%%
+from multiprocessing import Pool
+from itertools import product
+
+N = 10000000
+pool = Pool() #defaults to number of available CPU's
+a = np.ones((N))
+b = np.ones((N))*2
+out = np.zeros((N))
+
+t = time.time()
+for i in range(len(a)):
+    out[i] = a[i]*b[i]
+elapsed = time.time() - t
+print(elapsed)
+
+def fun(a,b):
+    return a*b
+
+chunksize = 20 #this may take some guessing ... take a look at the docs to decide
+for ind, res in enumerate(pool.map(fun, range(N)), chunksize):
+    output.flat[ind] = res
+#%%
+
+import gdal, osr
+import numpy as np
+from scipy.interpolate import RectBivariateSpline
+from numpy.lib.stride_tricks import as_strided as ast
+import dask.array as da
+from joblib import Parallel, delayed, cpu_count
+import os
+from skimage.feature import greycomatrix, greycoprops
+
+def im_resize(im,Nx,Ny):
+    '''
+    resize array by bivariate spline interpolation
+    '''
+    ny, nx = np.shape(im)
+    xx = np.linspace(0,nx,Nx)
+    yy = np.linspace(0,ny,Ny)
+
+    try:
+        im = da.from_array(im, chunks=1000)   #dask implementation
+    except:
+        pass
+
+    newKernel = RectBivariateSpline(np.r_[:ny],np.r_[:nx],im)
+    return newKernel(yy,xx)
+
+def p_me(Z, win):
+    '''
+    loop to calculate greycoprops
+    '''
+    try:
+        glcm = greycomatrix(Z, [5], [0], 256, symmetric=True, normed=True)
+        cont = greycoprops(glcm, 'contrast')
+        diss = greycoprops(glcm, 'dissimilarity')
+        homo = greycoprops(glcm, 'homogeneity')
+        eng = greycoprops(glcm, 'energy')
+        corr = greycoprops(glcm, 'correlation')
+        ASM = greycoprops(glcm, 'ASM')
+        return (cont, diss, homo, eng, corr, ASM)
+    except:
+        return (0,0,0,0,0,0)
+
+def norm_shape(shap):
+   '''
+   Normalize numpy array shapes so they're always expressed as a tuple,
+   even for one-dimensional shapes.
+   '''
+   try:
+      i = int(shap)
+      return (i,)
+   except TypeError:
+      # shape was not a number
+      pass
+
+   try:
+      t = tuple(shap)
+      return t
+   except TypeError:
+      # shape was not iterable
+      pass
+
+   raise TypeError('shape must be an int, or a tuple of ints')
+
+def sliding_window(a, ws, ss = None, flatten = True):
+    '''
+    Source: http://www.johnvinyard.com/blog/?p=268#more-268
+    Parameters:
+        a  - an n-dimensional numpy array
+        ws - an int (a is 1D) or tuple (a is 2D or greater) representing the size 
+             of each dimension of the window
+        ss - an int (a is 1D) or tuple (a is 2D or greater) representing the 
+             amount to slide the window in each dimension. If not specified, it
+             defaults to ws.
+        flatten - if True, all slices are flattened, otherwise, there is an 
+                  extra dimension for each dimension of the input.
+
+    Returns
+        an array containing each n-dimensional window from a
+    '''      
+    if None is ss:
+        # ss was not provided. the windows will not overlap in any direction.
+        ss = ws
+    ws = norm_shape(ws)
+    ss = norm_shape(ss)
+    # convert ws, ss, and a.shape to numpy arrays
+    ws = np.array(ws)
+    ss = np.array(ss)
+    shap = np.array(a.shape)
+    # ensure that ws, ss, and a.shape all have the same number of dimensions
+    ls = [len(shap),len(ws),len(ss)]
+    if 1 != len(set(ls)):
+        raise ValueError(\
+        'a.shape, ws and ss must all have the same length. They were %s' % str(ls))
+
+    # ensure that ws is smaller than a in every dimension
+    if np.any(ws > shap):
+        raise ValueError(\
+        'ws cannot be larger than a in any dimension.\
+     a.shape was %s and ws was %s' % (str(a.shape),str(ws)))
+
+    # how many slices will there be in each dimension?
+    newshape = norm_shape(((shap - ws) // ss) + 1)
+
+
+    # the shape of the strided array will be the number of slices in each dimension
+    # plus the shape of the window (tuple addition)
+    newshape += norm_shape(ws)
+
+
+    # the strides tuple will be the array's strides multiplied by step size, plus
+    # the array's strides (tuple addition)
+    newstrides = norm_shape(np.array(a.strides) * ss) + a.strides
+    a = ast(a,shape = newshape,strides = newstrides)
+    if not flatten:
+        return a
+    # Collapse strided so that it has one more dimension than the window.  I.e.,
+    # the new array is a flat list of slices.
+    meat = len(ws) if ws.shape else 0
+    firstdim = (np.product(newshape[:-meat]),) if ws.shape else ()
+    dim = firstdim + (newshape[-meat:])
+    # remove any dimensions with size 1
+    dim = filter(lambda i : i != 1,dim)
+    return a.reshape(dim), newshape
+
+#Stuff to change
+win = 3
+meter = str(win/4)
+merge = im_grey_d
+Z,ind = sliding_window(merge,(win,win),(1,1))
+
+Ny, Nx = np.shape(merge)
+
+w = Parallel(n_jobs = cpu_count(), verbose=0)(delayed(p_me)(Z[k]) for k in xrange(len(Z)))
+
+cont = [a[0] for a in w]
+diss = [a[1] for a in w]
+homo = [a[2] for a in w]
+eng  = [a[3] for a in w]
+corr = [a[4] for a in w]
+ASM  = [a[5] for a in w]
+
+
+#Reshape to match number of windows
+plt_cont = np.reshape(cont , ( ind[0], ind[1] ) )
+plt_diss = np.reshape(diss , ( ind[0], ind[1] ) )
+plt_homo = np.reshape(homo , ( ind[0], ind[1] ) )
+plt_eng = np.reshape(eng , ( ind[0], ind[1] ) )
+plt_corr = np.reshape(corr , ( ind[0], ind[1] ) )
+plt_ASM =  np.reshape(ASM , ( ind[0], ind[1] ) )
+del cont, diss, homo, eng, corr, ASM
+
+#Resize Images to receive texture and define filenames
+contrast = im_resize(plt_cont,Nx,Ny)
+contrast[merge==0]=np.nan
+dissimilarity = im_resize(plt_diss,Nx,Ny)
+dissimilarity[merge==0]=np.nan    
+homogeneity = im_resize(plt_homo,Nx,Ny)
+homogeneity[merge==0]=np.nan
+energy = im_resize(plt_eng,Nx,Ny)
+energy[merge==0]=np.nan
+correlation = im_resize(plt_corr,Nx,Ny)
+correlation[merge==0]=np.nan
+ASM = im_resize(plt_ASM,Nx,Ny)
+ASM[merge==0]=np.nan
+
+#%%
+plt.figure()
+plt.imshow(im_ms_ps[:,:,[2,1,0]])
+for i,contour in enumerate(wl_pix): plt.plot(contour[:, 1], contour[:, 0], linewidth=2)
+plt.axis('image')
+plt.title('Detected water lines')
+plt.show()
+
+vec = im_ms_ps.reshape(im_ms_ps.shape[0] * im_ms_ps.shape[1], im_ms_ps.shape[2])
+vec_pan = im_pan.reshape(im_pan.shape[0]*im_pan.shape[1])
+features = np.zeros((len(vec), 5))
+features[:,[0,1,2,3]] = vec[:,[0,1,2,3]]
+features[:,4] = vec_pan
+vec_mask = im_cloud.reshape(im_ms_ps.shape[0] * im_ms_ps.shape[1])
+
+# create buffer
+im_buffer = np.zeros((im_ms_ps.shape[0], im_ms_ps.shape[1]))
+for i, contour in enumerate(wl_pix):
+        indices = [(int(_[0]), int(_[1])) for _ in list(np.round(contour))]
+        for j, idx in enumerate(indices):
+            im_buffer[idx] = 1
+        
+        
+plt.figure()
+plt.imshow(im_buffer)
+plt.draw()
+
+se = morphology.disk(buffer_size)
+im_buffer = morphology.binary_dilation(im_buffer, se)
+
+plt.figure()
+plt.imshow(im_buffer)
+plt.draw()
+
+vec_buffer = (im_buffer == 1).reshape(im_ms_ps.shape[0] * im_ms_ps.shape[1])
+
+vec_buffer= np.logical_and(vec_buffer, ~vec_mask)
+
+#vec_buffer = np.ravel_multi_index(z,(im_ms_ps.shape[0], im_ms_ps.shape[1]))
+
+
+kmeans = KMeans(n_clusters=6, random_state=0).fit(vec[vec_buffer,:])
+labels = kmeans.labels_
+labels_full = np.ones((len(vec_mask))) * np.nan
+labels_full[vec_buffer] = labels
+im_labels = labels_full.reshape(im_ms_ps.shape[0], im_ms_ps.shape[1])
+
+plt.figure()
+plt.imshow(im_labels)
+plt.axis('equal')
+plt.draw()
+
+utils.compare_images(im_labels, im_pan)
+
+plt.figure()
+for i in range(6): plt.plot(kmeans.cluster_centers_[i,:])
+plt.draw()
+
+im_sand = im_labels == np.argmax(np.mean(kmeans.cluster_centers_[:,[0,1,2,4]], axis=1))
+
+im_sand2 = morphology.remove_small_objects(im_sand, min_size=min_beach_size, connectivity=2)
+im_sand3 = morphology.binary_dilation(im_sand2, morphology.disk(1))
+plt.figure()
+plt.imshow(im_sand3)
+plt.draw()
+
+im_ms_ps[im_sand3,0] = 0
+im_ms_ps[im_sand3,1] = 0
+im_ms_ps[im_sand3,2] = 1
+
+
+plt.figure()
+plt.imshow(im_ms_ps[:,:,[2,1,0]])
+plt.axis('image')
+plt.title('Sand classification')
+plt.show()
+
+#%%

old/oldcodes/get_coordinates.py

@@ -36,7 +36,7 @@ ee.Initialize()
 #%% Select images
 
 # parameters
-plot_bool = False # if you want the plots
+plot_bool = True # 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
@@ -44,23 +44,29 @@ cloud_threshold = 0.8
 
 # select collection
 input_col = ee.ImageCollection('LANDSAT/LC08/C01/T1_RT_TOA')
-
+satname = 'L8'
 # 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]]];
+#rect_narra = [[[151.3473129272461,-33.69035274454718],
+#              [151.2820816040039,-33.68206818063878], 
+#              [151.27281188964844,-33.74775138989556],
+#              [151.3425064086914,-33.75231878701767],
+#              [151.3473129272461,-33.69035274454718]]];
                
 #rect_narra = [[[151.301454, -33.700754],
 #               [151.311453, -33.702075], 
 #               [151.307237, -33.739761],
 #               [151.294220, -33.736329],
 #               [151.301454, -33.700754]]];
+# location (Oldbar NSW)
+rect_narra = [[[152.578395, -31.841216],
+               [152.777281, -31.842523], 
+               [152.738086, -32.028773],
+               [152.557812, -32.004663],
+               [152.578395, -31.841216]]];
 
 # Dates
-start_date = '2016-01-01'
-end_date = '2016-12-31'
+start_date = '2018-01-18'
+end_date = '2018-01-20'
 # filter by location
 flt_col = input_col.filterBounds(ee.Geometry.Polygon(rect_narra)).filterDate(start_date, end_date)
 
@@ -71,7 +77,7 @@ im_all = flt_col.getInfo().get('features')
 # find each image in ee database
 im = ee.Image(im_all[0].get('id')) 
 # load image as np.array
-im_pan, im_ms, im_cloud, crs, meta = sds.read_eeimage(im, rect_narra, plot_bool)
+im_pan, im_ms, im_cloud, crs, meta = sds.read_eeimage(im, rect_narra, satname, plot_bool)
 
 # rescale intensities
 im_ms = sds.rescale_image_intensity(im_ms, im_cloud, prob_high, plot_bool)
@@ -84,7 +90,7 @@ plt.figure()
 plt.imshow(im_ms_ps[:,:,[2,1,0]])
 plt.show()
 
-pts = ginput(15)
+pts = ginput(n=20, timeout=1000, show_clicks=True)
 points = np.array(pts)
 plt.plot(points[:,0], points[:,1], 'ko')
 plt.show()
@@ -92,5 +98,13 @@ plt.show()
 pts_coords = sds.convert_pix2world(points[:,[1,0]], crs['crs_15m'])
 pts = sds.convert_epsg(pts_coords, crs['epsg_code'], output_epsg)
 
-with open('data/narra_beach.pkl', 'wb') as f:
-    pickle.dump(pts, f)
+with open('olddata/oldbar_beach.pkl', 'wb') as f:
+    pickle.dump(pts, f)
+
+
+#pts_wgs84 = sds.convert_epsg(pts_coords, crs['epsg_code'], 4326)
+#
+#import simplekml
+#kml = simplekml.Kml()
+#kml.new(name='test', coords=pts_wgs84)
+#kml.save("test.kml")

old/oldcodes/l8_calendar_dates.py

@@ -0,0 +1,23 @@
+# -*- coding: utf-8 -*-
+
+
+from datetime import datetime, timedelta
+import pytz
+import csv
+import pandas as pd
+au_tz = pytz.timezone('Australia/Sydney')
+
+dt1 = datetime(2018, 4, 17, tzinfo= au_tz)
+
+dt = []
+dt.append(dt1)
+for i in range(1,100):
+    dt1 = dt[i-1]
+    dt.append(dt1 + timedelta(days=16))
+
+
+dtstr = [_.strftime('%d %b %Y') for _ in dt]
+
+df = pd.DataFrame(dtstr)
+df.to_csv('L7_NARRA_dates.csv', index=False, header=False)
+

process_shoreline.py

@@ -0,0 +1,88 @@
+# -*- coding: utf-8 -*-
+
+#==========================================================#
+# Process shorelines (clipping and smoothing)
+#==========================================================#
+
+# Initial settings
+
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+import pdb
+import ee
+
+import matplotlib.dates as mdates
+import matplotlib.cm as cm
+import matplotlib.colors as mcolor
+from datetime import datetime, timedelta
+import pickle
+import pytz
+import scipy.io as sio
+import scipy.interpolate as interpolate
+import statsmodels.api as sm
+import skimage.measure as measure
+import simplekml
+
+# my functions
+import functions.utils as utils
+import functions.sds as sds
+
+# some settings
+np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
+plt.rcParams['axes.grid'] = True
+plt.rcParams['figure.max_open_warning'] = 100
+
+au_tz = pytz.timezone('Australia/Sydney')
+au_epsg = 28356
+# load the satellite-derived shorelines
+satname = 'L8'
+sitename = 'OLDBAR'
+filepath = os.path.join(os.getcwd(), 'data', satname, sitename)
+with open(os.path.join(filepath, sitename + '_output' + '.pkl'), 'rb') as f:
+    output = pickle.load(f)
+
+sl = output['shorelines']
+dates_sl = output['t']
+# convert to AEST
+dates_sl = [_.astimezone(au_tz) for _ in dates_sl]
+
+# load the reference shoreline points
+with open(os.path.join(os.getcwd(), 'data', satname, sitename, sitename + '_refpoints.pkl'), 'rb') as f:
+    refpoints = pickle.load(f)
+    
+dist_thresh = 200 
+frac_smooth = 1./15
+plt.figure()
+plt.axis('equal')
+cmap = cm.get_cmap('brg')
+colours = cmap(np.linspace(0, 1, num=len(sl)))
+kml = simplekml.Kml()
+for i in range(len(sl)):
+    # select points of SDS that are close to the manually digitized points
+    idx_ref = [np.min(np.linalg.norm(sl[i][k,:] - refpoints, axis=1)) < dist_thresh for k in range(sl[i].shape[0])]
+    # smooth (LOWESS) satellite shoreline    
+    sl_smooth = sm.nonparametric.lowess(sl[i][idx_ref,0],sl[i][idx_ref,1], frac=frac_smooth, it = 10)
+    sl_smooth = sl_smooth[:,[1,0]]
+#    sl_smooth = sl[i][idx_ref,:]
+    
+#    plt.plot(sl[i][idx_ref,0],sl[i][idx_ref,1], 'k-')
+    plt.plot(sl_smooth[:,0], sl_smooth[:,1], color=colours[i,:], label=dates_sl[i].strftime('%d-%b-%Y'))
+    
+    # convert to wgs84 (epsg = 4326)
+    sl_wgs84 = sds.convert_epsg(sl_smooth, 28356, 4326)
+    # save in kml file
+    ln = kml.newlinestring(name=dates_sl[i].strftime('%d-%b-%Y'))
+    ln.coords = sl_wgs84
+    ln.style.labelstyle.color = mcolor.rgb2hex(colours[i,:3])
+    ln.style.linestyle.color = mcolor.rgb2hex(colours[i,:3])
+    
+plt.legend(ncol=3)
+plt.xlabel('Eastings [m]')
+plt.ylabel('Northings [m]')
+plt.title('Oldbar inlet (South)')
+plt.draw()
+
+kml.save(satname + sitename + '_shorelines.kml')
+
+

read_images.py

@@ -0,0 +1,213 @@
+# -*- coding: utf-8 -*-
+
+#==========================================================#
+# Extract shorelines from Landsat images
+#==========================================================#
+
+# Initial settings
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+import ee
+import pdb
+
+# other modules
+from osgeo import gdal, ogr, osr
+import pickle
+import matplotlib.cm as cm
+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.measure as measure
+
+# import own modules
+import functions.utils as utils
+import functions.sds_old1 as sds
+
+# some settings
+np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
+plt.rcParams['axes.grid'] = True
+plt.rcParams['figure.max_open_warning'] = 100
+ee.Initialize()
+
+# parameters
+cloud_thresh = 0.5      # threshold for cloud cover
+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
+buffer_size = 10        # radius (in pixels) of disk for buffer (pixel classification)
+min_beach_size = 50     # number of pixels in a beach (pixel classification)
+
+# load metadata (timestamps and epsg code) for the collection
+satname = 'L8'
+sitename = 'NARRA'
+#sitename = 'OLDBAR'
+
+# Load metadata
+filepath = os.path.join(os.getcwd(), 'data', satname, sitename)
+with open(os.path.join(filepath, sitename + '_timestamps' + '.pkl'), 'rb') as f:
+    timestamps = pickle.load(f)
+with open(os.path.join(filepath, sitename + '_accuracy_georef' + '.pkl'), 'rb') as f:
+    acc_georef = pickle.load(f) 
+with open(os.path.join(filepath, sitename + '_epsgcode' + '.pkl'), 'rb') as f:
+    input_epsg = pickle.load(f)
+with open(os.path.join(filepath, sitename + '_refpoints' + '.pkl'), 'rb') as f:
+    refpoints = pickle.load(f)
+# sort timestamps and georef accuracy (dowloaded images are sorted by date in directory)
+timestamps_sorted = sorted(timestamps)
+idx_sorted = sorted(range(len(timestamps)), key=timestamps.__getitem__)
+acc_georef_sorted = [acc_georef[j] for j in idx_sorted]
+
+# path to images
+file_path_pan = os.path.join(os.getcwd(), 'data', satname, sitename, 'pan')
+file_path_ms = os.path.join(os.getcwd(), 'data', satname, sitename, 'ms')
+file_names_pan = os.listdir(file_path_pan)
+file_names_ms = os.listdir(file_path_ms)
+N = len(file_names_pan)
+
+# initialise some variables
+cloud_cover_ts = []
+date_acquired_ts = []
+acc_georef_ts = []
+idx_skipped = []
+idx_nocloud = []
+t = []
+shorelines = []
+
+for i in range(N):
+    # read pan image
+    fn_pan = os.path.join(file_path_pan, file_names_pan[i])
+    data = gdal.Open(fn_pan, gdal.GA_ReadOnly)
+    georef = np.array(data.GetGeoTransform())
+    bands = [data.GetRasterBand(i + 1).ReadAsArray() for k in range(data.RasterCount)]
+    im_pan = np.stack(bands, 2)[:,:,0]
+    # read ms image
+    fn_ms = os.path.join(file_path_ms, file_names_ms[i])
+    data = gdal.Open(fn_ms, gdal.GA_ReadOnly)
+    bands = [data.GetRasterBand(i + 1).ReadAsArray() for k in range(data.RasterCount)]
+    im_ms = np.stack(bands, 2)
+    # cloud mask
+    im_qa = im_ms[:,:,5]
+    cloud_mask = sds.create_cloud_mask(im_qa, satname, plot_bool)
+    cloud_mask = transform.resize(cloud_mask, (im_pan.shape[0], im_pan.shape[1]),
+                                order=0, preserve_range=True, 
+                                mode='constant').astype('bool_')    
+    # resize the image using bilinear interpolation (order 1)
+    im_ms = transform.resize(im_ms,(im_pan.shape[0], im_pan.shape[1]),
+                             order=1, preserve_range=True, mode='constant')
+    # check if -inf or nan values and add to cloud mask
+    im_inf = np.isin(im_ms[:,:,0], -np.inf)
+    im_nan = np.isnan(im_ms[:,:,0])
+    cloud_mask = np.logical_or(np.logical_or(cloud_mask, im_inf), im_nan)
+    cloud_cover = sum(sum(cloud_mask.astype(int)))/(cloud_mask.shape[0]*cloud_mask.shape[1])
+    if cloud_cover > cloud_thresh:
+        print('skip ' + str(i) + ' - cloudy (' + str(cloud_cover) + ')')
+        idx_skipped.append(i)
+        continue
+    idx_nocloud.append(i)
+    # check if image for that date is already present
+    if file_names_pan[i][len(satname)+1+len(sitename)+1:len(satname)+1+len(sitename)+1+10] in date_acquired_ts:
+        # find the index of the image that is repeated
+        idx_samedate = utils.find_indices(date_acquired_ts, lambda e : e == file_names_pan[i][9:19])
+        idx_samedate = idx_samedate[0]
+        print('cloud cover ' + str(cloud_cover) + ' - ' + str(cloud_cover_ts[idx_samedate]))
+        print('acc georef ' + str(acc_georef_sorted[i]) + ' - ' + str(acc_georef_ts[idx_samedate]))
+        # keep image with less cloud cover or best georeferencing accuracy
+        if cloud_cover < cloud_cover_ts[idx_samedate] - 0.01: 
+            skip = False
+        elif acc_georef_sorted[i] < acc_georef_ts[idx_samedate]:
+            skip = False
+        else:
+            skip = True
+        if skip:
+            print('skip ' + str(i) + ' - repeated')
+            idx_skipped.append(i)
+            continue
+        else:
+            del shorelines[idx_samedate]
+            del t[idx_samedate]
+            del cloud_cover_ts[idx_samedate]
+            del date_acquired_ts[idx_samedate]
+            del acc_georef_ts[idx_samedate]
+            print('keep ' + str(i) + ' - deleted ' + str(idx_samedate))
+            
+    # rescale intensities
+    im_ms = sds.rescale_image_intensity(im_ms, cloud_mask, prob_high, plot_bool)
+    im_pan = sds.rescale_image_intensity(im_pan, cloud_mask, prob_high, plot_bool)
+    # pansharpen rgb image
+    im_ms_ps = sds.pansharpen(im_ms[:,:,[0,1,2]], im_pan, cloud_mask, True)
+    # 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], cloud_mask, plot_bool)
+    # detect edges
+    wl_pix = sds.find_wl_contours(im_ndwi, cloud_mask, min_contour_points, True)
+    # convert from pixels to world coordinates
+    wl_coords = sds.convert_pix2world(wl_pix, georef)
+    # convert to output epsg spatial reference
+    wl = sds.convert_epsg(wl_coords, input_epsg, output_epsg)
+    
+    # classify sand pixels
+#    im_sand = sds.classify_sand_unsupervised(im_ms_ps, im_pan, cloud_mask, wl_pix, False, min_beach_size, True)
+    
+    # plot a figure to select the correct water line and discard cloudy images
+    plt.figure()
+    cmap = cm.get_cmap('jet')
+    plt.subplot(121)
+    plt.imshow(im_ms_ps[:,:,[2,1,0]])
+    for j,contour in enumerate(wl_pix):
+        colours = cmap(np.linspace(0, 1, num=len(wl_pix)))
+        plt.plot(contour[:, 1], contour[:, 0], linewidth=2, color=colours[j,:])
+    plt.axis('image')
+    plt.title(file_names_pan[i])
+    plt.subplot(122)
+    centroids = []
+    for j,contour in enumerate(wl):
+        colours = cmap(np.linspace(0, 1, num=len(wl)))
+        centroids.append([np.mean(contour[:, 0]),np.mean(contour[:, 1])])
+        plt.plot(contour[:, 0], contour[:, 1], linewidth=2, color=colours[j,:])
+        plt.plot(np.mean(contour[:, 0]), np.mean(contour[:, 1]), 'o', color=colours[j,:])
+    plt.plot(refpoints[:,0], refpoints[:,1], 'k.')
+    plt.axis('equal')
+    plt.title(file_names_pan[i])
+    mng = plt.get_current_fig_manager()                                         
+    mng.window.showMaximized()
+    plt.tight_layout()   
+    plt.draw()
+    # click on the left image to discard, otherwise on the closest centroid in the right image
+    pt_in = np.array(ginput(n=1, timeout=1000))
+    if pt_in[0][0] < 10000:
+        print('skip ' + str(i) + ' - manual')
+        idx_skipped.append(i)
+        continue
+    # get contour that was selected (click closest to centroid)
+    dist_centroid = [np.linalg.norm(_ - pt_in) for _ in centroids]
+    shorelines.append(wl[np.argmin(dist_centroid)])
+    
+    t.append(timestamps_sorted[i])
+    cloud_cover_ts.append(cloud_cover)
+    acc_georef_ts.append(acc_georef_sorted[i])
+    date_acquired_ts.append(file_names_pan[i][9:19])
+    
+    
+#plt.figure()
+#plt.axis('equal')
+#for j in range(len(shorelines)):
+#    plt.plot(shorelines[j][:,0], shorelines[j][:,1])
+#plt.draw()
+    
+output = {'t':t, 'shorelines':shorelines, 'cloud_cover':cloud_cover_ts, 'acc_georef':acc_georef_ts}
+
+#with open(os.path.join(filepath, sitename + '_output' + '.pkl'), 'wb') as f:
+#    pickle.dump(output, f)
+#    
+#with open(os.path.join(filepath, sitename + '_skipped' + '.pkl'), 'wb') as f:
+#    pickle.dump(idx_skipped, f)
+#
+#with open(os.path.join(filepath, sitename + '_idxnocloud' + '.pkl'), 'wb') as f:
+#    pickle.dump(idx_nocloud, f) 

read_images2.py

@@ -0,0 +1,265 @@
+# -*- coding: utf-8 -*-
+
+#==========================================================#
+# Extract shorelines from Landsat images
+#==========================================================#
+
+# Initial settings
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+import ee
+import pdb
+
+# other modules
+from osgeo import gdal, ogr, osr
+import pickle
+import matplotlib.cm as cm
+from pylab import ginput
+from shapely.geometry import LineString
+
+# 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.measure as measure
+import skimage.morphology as morphology
+
+# machine learning modules
+from sklearn.model_selection import train_test_split
+from sklearn.neural_network import MLPClassifier
+from sklearn.preprocessing import StandardScaler, Normalizer 
+from sklearn.externals import joblib
+
+# import own modules
+import functions.utils as utils
+import functions.sds as sds
+
+# some settings
+np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
+plt.rcParams['axes.grid'] = True
+plt.rcParams['figure.max_open_warning'] = 100
+ee.Initialize()
+
+# parameters
+cloud_thresh = 0.2      # threshold for cloud cover
+plot_bool = False       # if you want the plots
+min_contour_points = 100# minimum number of points contained in each water line
+output_epsg = 28356     # GDA94 / MGA Zone 56
+buffer_size = 7         # radius (in pixels) of disk for buffer (pixel classification)
+min_beach_size = 20     # number of pixels in a beach (pixel classification)
+dist_ref = 100
+min_length_wl = 300
+manual = False
+
+# load metadata (timestamps and epsg code) for the collection
+satname = 'L8'
+sitename = 'NARRA'
+#sitename = 'OLDBAR'
+
+# Load metadata
+filepath = os.path.join(os.getcwd(), 'data', satname, sitename)
+with open(os.path.join(filepath, sitename + '_timestamps' + '.pkl'), 'rb') as f:
+    timestamps = pickle.load(f)
+with open(os.path.join(filepath, sitename + '_accuracy_georef' + '.pkl'), 'rb') as f:
+    acc_georef = pickle.load(f) 
+with open(os.path.join(filepath, sitename + '_epsgcode' + '.pkl'), 'rb') as f:
+    input_epsg = pickle.load(f)
+with open(os.path.join(filepath, sitename + '_refpoints' + '.pkl'), 'rb') as f:
+    refpoints = pickle.load(f)
+try:
+    with open(os.path.join(filepath, sitename + '_skipped_new' + '.pkl'), 'rb') as f:
+        idx_skipped = pickle.load(f)
+except:
+    idx_skipped = []
+    manual = True
+            
+# sort timestamps and georef accuracy (dowloaded images are sorted by date in directory)
+timestamps_sorted = sorted(timestamps)
+idx_sorted = sorted(range(len(timestamps)), key=timestamps.__getitem__)
+acc_georef_sorted = [acc_georef[j] for j in idx_sorted]
+
+# path to images
+file_path_pan = os.path.join(os.getcwd(), 'data', satname, sitename, 'pan')
+file_path_ms = os.path.join(os.getcwd(), 'data', satname, sitename, 'ms')
+file_names_pan = os.listdir(file_path_pan)
+file_names_ms = os.listdir(file_path_ms)
+N = len(file_names_pan)
+
+# initialise some variables
+cloud_cover_ts = []
+date_acquired_ts = []
+acc_georef_ts = []
+t = []
+shorelines = []
+
+#%%
+for i in range(N):
+    if ~manual:
+        if i in idx_skipped:
+            continue
+        
+    # read pan image
+    fn_pan = os.path.join(file_path_pan, file_names_pan[i])
+    data = gdal.Open(fn_pan, gdal.GA_ReadOnly)
+    georef = np.array(data.GetGeoTransform())
+    bands = [data.GetRasterBand(k + 1).ReadAsArray() for k in range(data.RasterCount)]
+    im_pan = np.stack(bands, 2)[:,:,0]
+    nrows = im_pan.shape[0]
+    ncols = im_pan.shape[1]
+    
+    # read ms image
+    fn_ms = os.path.join(file_path_ms, file_names_ms[i])
+    data = gdal.Open(fn_ms, gdal.GA_ReadOnly)
+    bands = [data.GetRasterBand(k + 1).ReadAsArray() for k in range(data.RasterCount)]
+    im_ms = np.stack(bands, 2)
+    
+    # cloud mask
+    im_qa = im_ms[:,:,5]
+    cloud_mask = sds.create_cloud_mask(im_qa, satname, plot_bool)
+    cloud_mask = transform.resize(cloud_mask, (im_pan.shape[0], im_pan.shape[1]),
+                                order=0, preserve_range=True, 
+                                mode='constant').astype('bool_')    
+    # resize the image using bilinear interpolation (order 1)
+    im_ms = transform.resize(im_ms,(im_pan.shape[0], im_pan.shape[1]),
+                             order=1, preserve_range=True, mode='constant')
+    
+    # check if -inf or nan values and add to cloud mask
+    im_inf = np.isin(im_ms[:,:,0], -np.inf)
+    im_nan = np.isnan(im_ms[:,:,0])
+    cloud_mask = np.logical_or(np.logical_or(cloud_mask, im_inf), im_nan)
+    
+    # calculate cloud cover and skip image if too high
+    cloud_cover = sum(sum(cloud_mask.astype(int)))/(cloud_mask.shape[0]*cloud_mask.shape[1])
+    if manual:
+        if cloud_cover > cloud_thresh:
+            print('skip ' + str(i) + ' - cloudy (' + str(np.round(cloud_cover*100).astype(int)) + '%)')
+            idx_skipped.append(i)
+            continue
+            
+    # pansharpen rgb image
+    im_ms_ps = sds.pansharpen(im_ms[:,:,[0,1,2]], im_pan, cloud_mask, plot_bool)
+    # rescale pansharpened RGB for visualisation
+    im_display = sds.rescale_image_intensity(im_ms_ps[:,:,[2,1,0]], cloud_mask, 100, False)
+    # 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) 
+    
+    # classify image in 4 classes (sand, whitewater, water, other) with NN classifier
+    im_classif, im_labels = sds.classify_image_NN(im_ms_ps, im_pan, cloud_mask, min_beach_size, plot_bool)
+
+#    # manually validate classification
+#    pt_in = np.array(ginput(n=1, timeout=1000))
+#    if pt_in[0][1] > nrows/2:
+#        print('skip ' + str(i) + ' - wrong classification')
+#        idx_skipped.append(i)
+#        continue
+    
+    # if there are no sand pixels, skip the image (maybe later change the detection method with old method)
+    if manual:
+        if sum(sum(im_labels[:,:,0])) == 0 :
+            print('skip ' + str(i) + ' - no sand')
+            idx_skipped.append(i)
+            continue
+    
+    # extract shorelines (new method)
+    contours_wi, contours_mwi = sds.find_wl_contours2(im_ms_ps, im_labels, cloud_mask, buffer_size, plot_bool)
+    
+    plt.figure()
+    im = np.copy(im_display)
+    # define colours for plot
+    colours = np.array([[1,128/255,0/255],[204/255,1,1],[0,0,204/255]])
+    for k in range(0,im_labels.shape[2]):
+        im[im_labels[:,:,k],0] = colours[k,0]
+        im[im_labels[:,:,k],1] = colours[k,1]
+        im[im_labels[:,:,k],2] = colours[k,2]
+    plt.imshow(im)
+    for k,contour in enumerate(contours_mwi): plt.plot(contour[:, 1], contour[:, 0], linewidth=2, color='k', linestyle='--')
+    mng = plt.get_current_fig_manager()                                         
+    mng.window.showMaximized()
+    plt.tight_layout()
+    plt.draw()
+    
+    # manually validate detection
+    if manual:
+        pt_in = np.array(ginput(n=1, timeout=1000))
+        plt.close()
+        if pt_in[0][1] > nrows/2:
+            print('skip ' + str(i) + ' - wrong detection')
+            idx_skipped.append(i)
+            continue
+    else:
+        plt.close()
+    
+    # remove contour points that are around clouds (nan values)
+    for k, contour in enumerate(contours_mwi):
+        if np.any(np.isnan(contour)):
+            index_nan = np.where(np.isnan(contour))[0]
+            contour = np.delete(contour, index_nan, axis=0) 
+            
+    # convert from pixels to world coordinates
+    wl_coords = sds.convert_pix2world(contours_mwi, georef)
+    # convert to output epsg spatial reference
+    wl = sds.convert_epsg(wl_coords, input_epsg, output_epsg)
+        
+    # remove contours that have a perimeter < min_length_wl  as usually they are not shoreline
+    wl_good = []
+    for l, wls in enumerate(wl):
+        coords = [(wls[k,0], wls[k,1]) for k in range(len(wls))]
+        a = LineString(coords) # shapely LineString structure
+        if a.length >= min_length_wl:
+            wl_good.append(wls)
+    
+    # pre-process points (list of arrays to single array of points)
+    x_points = np.array([])
+    y_points = np.array([])
+    for k in range(len(wl_good)):
+        x_points = np.append(x_points,wl_good[k][:,0])
+        y_points = np.append(y_points,wl_good[k][:,1])
+    wl_good = np.transpose(np.array([x_points,y_points]))
+    
+    # only select points around Narrabeen beach (refpoints given)
+    temp = np.zeros((len(wl_good))).astype(bool)
+    for k in range(len(refpoints)): 
+        temp = np.logical_or(np.linalg.norm(wl_good - refpoints[k,[0,1]], axis=1) < dist_ref, temp) 
+    wl_final = wl_good[temp]
+    
+#    plt.figure()
+#    plt.axis('equal')
+#    plt.plot(wl_final[:,0],wl_final[:,1],'k.')
+#    plt.draw()            
+            
+    # save data
+    shorelines.append(wl_final)
+    t.append(timestamps_sorted[i])
+    cloud_cover_ts.append(cloud_cover)
+    acc_georef_ts.append(acc_georef_sorted[i])
+    date_acquired_ts.append(file_names_pan[i][9:19])
+
+
+output = {'t':t, 'shorelines':shorelines, 'cloud_cover':cloud_cover_ts, 'acc_georef':acc_georef_ts}
+
+
+#with open(os.path.join(filepath, sitename + '_output_new' + '.pkl'), 'wb') as f:
+#    pickle.dump(output, f)
+#    
+#with open(os.path.join(filepath, sitename + '_skipped_new' + '.pkl'), 'wb') as f:
+#    pickle.dump(idx_skipped, f)
+    
+#    plt.figure()
+#    plt.axis('equal')
+#    plt.plot(refpoints[:,0], refpoints[:,1], 'ko')
+#    plt.plot(all_points[temp,0], all_points[temp,1], 'go')
+#    plt.plot(all_points[~temp,0], all_points[~temp,1], 'ro')
+#    plt.draw()
+    
+    # extract shorelines (old method)
+#    im_ndwi = sds.nd_index(im_ms_ps[:,:,3], im_ms_ps[:,:,1], cloud_mask, plot_bool)
+#    wl_pix = sds.find_wl_contours(im_ndwi, cloud_mask, min_contour_points, plot_bool)
+
+#    plt.figure()
+#    plt.imshow(im_display)
+#    for k,contour in enumerate(contours_mwi): plt.plot(contour[:, 1], contour[:, 0], linewidth=3, color='k')
+#    for k,contour in enumerate(wl_pix): plt.plot(contour[:, 1], contour[:, 0], linestyle='--', linewidth=1, color='w')
+#    plt.draw()
+    

read_images_old.py

@@ -0,0 +1,284 @@
+# -*- coding: utf-8 -*-
+
+#==========================================================#
+# Extract shorelines from Landsat images
+#==========================================================#
+
+# Initial settings
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+import ee
+import pdb
+
+# other modules
+from osgeo import gdal, ogr, osr
+import pickle
+import matplotlib.cm as cm
+from pylab import ginput
+from shapely.geometry import LineString
+
+# 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.measure as measure
+import skimage.morphology as morphology
+
+# machine learning modules
+from sklearn.model_selection import train_test_split
+from sklearn.neural_network import MLPClassifier
+from sklearn.preprocessing import StandardScaler, Normalizer 
+from sklearn.externals import joblib
+
+# import own modules
+import functions.utils as utils
+import functions.sds as sds
+
+# some settings
+np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
+plt.rcParams['axes.grid'] = True
+plt.rcParams['figure.max_open_warning'] = 100
+ee.Initialize()
+
+# parameters
+cloud_thresh = 0.5      # threshold for cloud cover
+plot_bool = False      # if you want the plots
+min_contour_points = 100# minimum number of points contained in each water line
+output_epsg = 28356     # GDA94 / MGA Zone 56
+buffer_size = 7        # radius (in pixels) of disk for buffer (pixel classification)
+min_beach_size = 20     # number of pixels in a beach (pixel classification)
+dist_ref = 100
+min_length_wl = 300
+
+# load metadata (timestamps and epsg code) for the collection
+satname = 'L8'
+sitename = 'NARRA'
+#sitename = 'OLDBAR'
+
+# Load metadata
+filepath = os.path.join(os.getcwd(), 'data', satname, sitename)
+with open(os.path.join(filepath, sitename + '_timestamps' + '.pkl'), 'rb') as f:
+    timestamps = pickle.load(f)
+with open(os.path.join(filepath, sitename + '_accuracy_georef' + '.pkl'), 'rb') as f:
+    acc_georef = pickle.load(f) 
+with open(os.path.join(filepath, sitename + '_epsgcode' + '.pkl'), 'rb') as f:
+    input_epsg = pickle.load(f)
+with open(os.path.join(filepath, sitename + '_refpoints2' + '.pkl'), 'rb') as f:
+    refpoints = pickle.load(f)
+# sort timestamps and georef accuracy (dowloaded images are sorted by date in directory)
+timestamps_sorted = sorted(timestamps)
+idx_sorted = sorted(range(len(timestamps)), key=timestamps.__getitem__)
+acc_georef_sorted = [acc_georef[j] for j in idx_sorted]
+
+# path to images
+file_path_pan = os.path.join(os.getcwd(), 'data', satname, sitename, 'pan')
+file_path_ms = os.path.join(os.getcwd(), 'data', satname, sitename, 'ms')
+file_names_pan = os.listdir(file_path_pan)
+file_names_ms = os.listdir(file_path_ms)
+N = len(file_names_pan)
+
+# initialise some variables
+cloud_cover_ts = []
+date_acquired_ts = []
+acc_georef_ts = []
+idx_skipped = []
+idx_nocloud = []
+t = []
+shorelines = []
+
+#%%
+for i in range(N):
+    
+    # read pan image
+    fn_pan = os.path.join(file_path_pan, file_names_pan[i])
+    data = gdal.Open(fn_pan, gdal.GA_ReadOnly)
+    georef = np.array(data.GetGeoTransform())
+    bands = [data.GetRasterBand(i + 1).ReadAsArray() for k in range(data.RasterCount)]
+    im_pan = np.stack(bands, 2)[:,:,0]
+    nrows = im_pan.shape[0]
+    ncols = im_pan.shape[1]
+    
+    # read ms image
+    fn_ms = os.path.join(file_path_ms, file_names_ms[i])
+    data = gdal.Open(fn_ms, gdal.GA_ReadOnly)
+    bands = [data.GetRasterBand(i + 1).ReadAsArray() for k in range(data.RasterCount)]
+    im_ms = np.stack(bands, 2)
+    
+    # cloud mask
+    im_qa = im_ms[:,:,5]
+    cloud_mask = sds.create_cloud_mask(im_qa, satname, plot_bool)
+    cloud_mask = transform.resize(cloud_mask, (im_pan.shape[0], im_pan.shape[1]),
+                                order=0, preserve_range=True, 
+                                mode='constant').astype('bool_')    
+    # resize the image using bilinear interpolation (order 1)
+    im_ms = transform.resize(im_ms,(im_pan.shape[0], im_pan.shape[1]),
+                             order=1, preserve_range=True, mode='constant')
+    
+    # check if -inf or nan values and add to cloud mask
+    im_inf = np.isin(im_ms[:,:,0], -np.inf)
+    im_nan = np.isnan(im_ms[:,:,0])
+    cloud_mask = np.logical_or(np.logical_or(cloud_mask, im_inf), im_nan)
+    
+    # calculate cloud cover and skip image if too high
+    cloud_cover = sum(sum(cloud_mask.astype(int)))/(cloud_mask.shape[0]*cloud_mask.shape[1])
+    if cloud_cover > cloud_thresh:
+        print('skip ' + str(i) + ' - cloudy (' + str(np.round(cloud_cover*100).astype(int)) + '%)')
+        idx_skipped.append(i)
+        continue
+    idx_nocloud.append(i)
+            
+    # pansharpen rgb image
+    im_ms_ps = sds.pansharpen(im_ms[:,:,[0,1,2]], im_pan, cloud_mask, plot_bool)
+    # rescale pansharpened RGB for visualisation
+    im_display = sds.rescale_image_intensity(im_ms_ps[:,:,[2,1,0]], cloud_mask, 100, False)
+    # 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) 
+    
+    # classify image in 4 classes (sand, whitewater, water, other) with NN classifier
+    im_classif, im_labels = sds.classify_image_NN(im_ms_ps, im_pan, cloud_mask, min_beach_size, plot_bool)
+
+#    # manually validate classification
+#    pt_in = np.array(ginput(n=1, timeout=1000))
+#    if pt_in[0][1] > nrows/2:
+#        print('skip ' + str(i) + ' - wrong classification')
+#        idx_skipped.append(i)
+#        continue
+    
+    # if there are no sand pixels, skip the image (maybe later change the detection method with old method)
+    if sum(sum(im_labels[:,:,0])) == 0 :
+        print('skip ' + str(i) + ' - no sand')
+        idx_skipped.append(i)
+        continue
+    
+    # extract shorelines (new method)
+    contours_wi, contours_mwi = sds.find_wl_contours2(im_ms_ps, im_labels, cloud_mask, buffer_size, plot_bool)
+    
+    plt.figure()
+    im = np.copy(im_display)
+    # define colours for plot
+    colours = np.array([[1,128/255,0/255],[204/255,1,1],[0,0,204/255]])
+    for k in range(0,im_labels.shape[2]):
+        im[im_labels[:,:,k],0] = colours[k,0]
+        im[im_labels[:,:,k],1] = colours[k,1]
+        im[im_labels[:,:,k],2] = colours[k,2]
+    plt.imshow(im)
+    for k,contour in enumerate(contours_mwi): plt.plot(contour[:, 1], contour[:, 0], linewidth=2, color='k', linestyle='--')
+    mng = plt.get_current_fig_manager()                                         
+    mng.window.showMaximized()
+    plt.tight_layout()
+    plt.draw()
+
+    
+    # manually validate detection
+    pt_in = np.array(ginput(n=1, timeout=1000))
+    if pt_in[0][1] > nrows/2:
+        print('skip ' + str(i) + ' - wrong detection')
+        idx_skipped.append(i)
+        continue
+    
+    # remove contour points that are around clouds (nan values)
+    for k, contour in enumerate(contours_mwi):
+        if np.any(np.isnan(contour)):
+            index_nan = np.where(np.isnan(contour))[0]
+            contour = np.delete(contour, index_nan, axis=0) 
+            
+    # convert from pixels to world coordinates
+    wl_coords = sds.convert_pix2world(contours_mwi, georef)
+    # convert to output epsg spatial reference
+    wl = sds.convert_epsg(wl_coords, input_epsg, output_epsg)
+        
+    # remove contours that have a perimeter < min_length_wl  as usually they are not shoreline
+    wl_good = []
+    for l, wls in enumerate(wl):
+        coords = [(wls[k,0], wls[k,1]) for k in range(len(wls))]
+        a = LineString(coords) # shapely LineString structure
+        if a.length >= min_length_wl:
+            wl_good.append(wls)
+    
+    # pre-process points (list of arrays to single array of points)
+    x_points = np.array([])
+    y_points = np.array([])
+    for k in range(len(wl_good)):
+        x_points = np.append(x_points,wl_good[k][:,0])
+        y_points = np.append(y_points,wl_good[k][:,1])
+    wl_good = np.transpose(np.array([x_points,y_points]))
+    
+    # only select points around Narrabeen beach (refpoints given)
+    temp = np.zeros((len(wl_good))).astype(bool)
+    for k in range(len(refpoints)): 
+        temp = np.logical_or(np.linalg.norm(wl_good - refpoints[k,[0,1]], axis=1) < dist_ref, temp) 
+    wl_final = wl_good[temp]
+    
+    plt.figure()
+    plt.axis('equal')
+    plt.plot(wl_final[:,0],wl_final[:,1],'k.')
+    plt.draw()
+      
+    
+    # check if image for that date already exists and choose the best in terms of cloud cover and georeferencing
+    if file_names_pan[i][len(satname)+1+len(sitename)+1:len(satname)+1+len(sitename)+1+10] in date_acquired_ts:
+        
+        # find the index of the image that is repeated
+        idx_samedate = utils.find_indices(date_acquired_ts, lambda e : e == file_names_pan[i][9:19])
+        idx_samedate = idx_samedate[0]
+#        print('cloud cover ' + str(cloud_cover) + ' - ' + str(cloud_cover_ts[idx_samedate]))
+#        print('acc georef ' + str(acc_georef_sorted[i]) + ' - ' + str(acc_georef_ts[idx_samedate]))
+        
+        # keep image with less cloud cover or best georeferencing accuracy
+        if cloud_cover < cloud_cover_ts[idx_samedate] - 0.01: 
+            skip = False
+        elif acc_georef_sorted[i] < acc_georef_ts[idx_samedate]:
+            skip = False
+        else:
+            skip = True
+            
+        if skip:
+            print('skip ' + str(i) + ' - repeated')
+            idx_skipped.append(i)
+            continue
+        else:
+            del shorelines[idx_samedate]
+            del t[idx_samedate]
+            del cloud_cover_ts[idx_samedate]
+            del date_acquired_ts[idx_samedate]
+            del acc_georef_ts[idx_samedate]
+            print('keep ' + str(i) + ' - deleted ' + str(idx_samedate))
+            
+            
+    # save data
+    shorelines.append(wl_final)
+    t.append(timestamps_sorted[i])
+    cloud_cover_ts.append(cloud_cover)
+    acc_georef_ts.append(acc_georef_sorted[i])
+    date_acquired_ts.append(file_names_pan[i][9:19])
+    
+output = {'t':t, 'shorelines':shorelines, 'cloud_cover':cloud_cover_ts, 'acc_georef':acc_georef_ts}
+
+#with open(os.path.join(filepath, sitename + '_output2' + '.pkl'), 'wb') as f:
+#    pickle.dump(output, f)
+#    
+#with open(os.path.join(filepath, sitename + '_skipped2' + '.pkl'), 'wb') as f:
+#    pickle.dump(idx_skipped, f)
+#
+#with open(os.path.join(filepath, sitename + '_idxnocloud2' + '.pkl'), 'wb') as f:
+#    pickle.dump(idx_nocloud, f) 
+    
+#    plt.figure()
+#    plt.axis('equal')
+#    plt.plot(refpoints[:,0], refpoints[:,1], 'ko')
+#    plt.plot(all_points[temp,0], all_points[temp,1], 'go')
+#    plt.plot(all_points[~temp,0], all_points[~temp,1], 'ro')
+#    plt.draw()
+    
+    # extract shorelines (old method)
+#    im_ndwi = sds.nd_index(im_ms_ps[:,:,3], im_ms_ps[:,:,1], cloud_mask, plot_bool)
+#    wl_pix = sds.find_wl_contours(im_ndwi, cloud_mask, min_contour_points, plot_bool)
+
+#    plt.figure()
+#    plt.imshow(im_display)
+#    for i,contour in enumerate(contours_mwi): plt.plot(contour[:, 1], contour[:, 0], linewidth=3, color='k')
+#    for i,contour in enumerate(wl_pix): plt.plot(contour[:, 1], contour[:, 0], linestyle='--', linewidth=1, color='w')
+#    plt.draw()
+