commit 647ef2192e5f1a70dc432925708c508dc470f0e4
Author: kvos <voskilian@gmail.com>
Date:   Fri Mar 16 14:27:58 2018 +1100

    initial shoreline mapping
    
    Loads images from the Google Earth Engine database and maps shorelines using the marching squares algorithm

diff --git a/geocheck.py b/geocheck.py
new file mode 100644
index 0000000..b39d3d8
--- /dev/null
+++ b/geocheck.py
@@ -0,0 +1,221 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Mar  1 14:32:08 2018
+
+@author: z5030440
+
+Main code to extract shorelines from Landsat imagery
+"""
+# Preamble
+
+import ee
+from IPython import display
+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
+
+from shapely.geometry import Polygon
+
+from osgeo import gdal
+from osgeo import osr
+import tempfile
+import urllib
+from urllib.request import urlretrieve
+import zipfile
+
+# my modules
+from utils import *
+# from sds import *
+
+np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
+ee.Initialize()
+plot_bool = True # if you want the plots
+
+def download_tif(image, 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(),
+        '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, bandsId): 
+    """loads an ee.Image() as a np.array. e.Image() is retrieved from the EE database."""   
+    local_tif_filename = download_tif(image, bandsId)
+    dataset = gdal.Open(local_tif_filename, gdal.GA_ReadOnly)
+    bands = [dataset.GetRasterBand(i + 1).ReadAsArray() for i in range(dataset.RasterCount)]
+    return np.stack(bands, 2), dataset
+
+
+
+im = ee.Image('LANDSAT/LC08/C01/T1_RT_TOA/LC08_089083_20130411')
+
+lon = [151.2820816040039, 151.3425064086914]
+lat = [-33.68206818063878, -33.74775138989556]
+polygon = [[lon[0], lat[0]], [lon[1], lat[0]], [lon[1], lat[1]], [lon[0], lat[1]]];
+             
+# get image metadata into dictionnary
+im_dic = im.getInfo()
+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']
+pan_band = [im_bands[7]]
+ms_bands = [im_bands[1], im_bands[2], im_bands[3]]
+im_full, dataset_full = load_image(im, ms_bands)
+
+plt.figure()
+plt.imshow(np.clip(im_full[:,:,[2,1,0]] * 3, 0, 1))
+plt.show()
+#%%
+
+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)
+    """
+    
+    local_tif_filename = download_tif(image, polygon, bandsId)
+    dataset = gdal.Open(local_tif_filename, gdal.GA_ReadOnly)
+    bands = [dataset.GetRasterBand(i + 1).ReadAsArray() for i in range(dataset.RasterCount)]
+    return np.stack(bands, 2), dataset
+
+
+for i in range(len(im_bands)): del im_bands[i]['dimensions']
+ms_bands = [im_bands[1], im_bands[2], im_bands[3]]
+
+im_cropped, dataset_cropped = load_image(im, polygon, ms_bands)
+
+plt.figure()
+plt.imshow(np.clip(im_cropped[:,:,[2,1,0]] * 3, 0, 1))
+plt.show()
+
+#%%
+crs_full  = dataset_full.GetGeoTransform()
+crs_cropped  = dataset_cropped.GetGeoTransform()
+scale = crs_full[1]
+ul_full = np.array([crs_full[0], crs_full[3]])
+ul_cropped = np.array([crs_cropped[0], crs_cropped[3]])
+
+delta = np.abs(ul_full - ul_cropped)/scale
+
+u0 = delta[0].astype('int')
+v0 = delta[1].astype('int')
+
+im_full[v0,u0,:]
+im_cropped[0,0,:]
+
+lrx = ul_cropped[0] + (dataset_cropped.RasterXSize * scale)
+lry = ul_cropped[1] + (dataset_cropped.RasterYSize * (-scale))
+
+lr_cropped = np.array([lrx, lry])
+
+delta = np.abs(ul_full - lr_cropped)/scale
+u1 = delta[0].astype('int')
+v1 = delta[1].astype('int')
+
+im_cropped2 = im_full[v0:v1,u0:u1,:]
+
+#%%
+crs_full  = dataset_full.GetGeoTransform()
+source = osr.SpatialReference()
+source.ImportFromWkt(dataset_full.GetProjection())
+
+target = osr.SpatialReference()
+target.ImportFromEPSG(4326)
+
+transform = osr.CoordinateTransformation(source, target)
+
+transform.TransformPoint(ulx, uly)
+
+#%%
+crs_cropped  = dataset_cropped.GetGeoTransform()
+ulx = crs_cropped[0]
+uly = crs_cropped[3]
+source = osr.SpatialReference()
+source.ImportFromWkt(dataset_cropped.GetProjection())
+
+target = osr.SpatialReference()
+target.ImportFromEPSG(4326)
+
+transform = osr.CoordinateTransformation(source, target)
+
+transform.TransformPoint(lrx, lry)
+
+
+
+#%%
+source = osr.SpatialReference()
+source.ImportFromEPSG(4326)
+
+target = osr.SpatialReference()
+target.ImportFromEPSG(32656)
+
+coords = transform.TransformPoint(151.2820816040039, -33.68206818063878)
+coords[0] - ulx
+coords[1] - uly
+#%%
+x_ul_full = ms_bands[0]['crs_transform'][2]
+y_ul_full = ms_bands[0]['crs_transform'][5]
+scale = ms_bands[0]['crs_transform'][0]
+
+x_ul_cropped = np.array([340756.105840223, 346357.851288875, 346474.839525944, 340877.362938763])
+y_ul_cropped = np.array([-3728229.45372866, -3728137.91775723, -3735421.58347927,  -3735513.20696522])
+
+dx = abs(x_ul_full - x_ul_cropped)
+dy = abs(y_ul_full - y_ul_cropped)
+
+u_coord = np.round(dx/scale).astype('int')
+v_coord = np.round(dy/scale).astype('int')
+
+im_cropped2 = im_full[np.min(v_coord):np.max(v_coord), np.min(u_coord):np.max(u_coord),:]
+
+plt.figure()
+plt.imshow(np.clip(im_cropped2[:,:,[2,1,0]] * 3, 0, 1), cmap='gray')
+plt.show()
+
+sum(sum(sum(np.equal(im_cropped,im_cropped2).astype('int')-1)))
+
diff --git a/sds.py b/sds.py
new file mode 100644
index 0000000..799e409
--- /dev/null
+++ b/sds.py
@@ -0,0 +1,519 @@
+# -*- 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
+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 own modules
+from 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):
+    """
+    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
+            
+    Returns:
+    -----------
+        cloud_mask : np.ndarray of booleans
+            A boolean array with True where the cloud are present
+    """
+    
+    # convert QA bits
+    cloud_values = [2800, 2804, 2808, 2812, 6896, 6900, 6904, 6908]
+    cloud_mask = np.isin(im_qa, cloud_values)
+    
+    #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):
+    """
+    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()
+    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)
+    im_cloud = transform.resize(im_cloud, (im_pan.shape[0], im_pan.shape[1]),
+                                order=0, preserve_range=True, mode='constant').astype('bool_')
+    if plot_bool:
+
+        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()
+    
+    # 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')
+    
+    # get the crs parameters for the image at 15m and 30m resolution
+    crs = {'crs_15m':crs_pan, 'crs_30m':crs_ms, 'epsg_code':pan_band[0]['crs']}
+    
+    return im_pan, im_ms, im_cloud, crs
+    
+
+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
diff --git a/sds_extract.py b/sds_extract.py
new file mode 100644
index 0000000..95c1bfc
--- /dev/null
+++ b/sds_extract.py
@@ -0,0 +1,200 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Mar  1 14:32:08 2018
+
+@author: z5030440
+
+Main code to extract shorelines from Landsat imagery
+"""
+# Preamble
+
+import ee
+from IPython import display
+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
+# my modules
+from utils import *
+from sds1 import *
+
+np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
+ee.Initialize()
+plot_bool = True
+
+input_col = ee.ImageCollection('LANDSAT/LC08/C01/T1_RT_TOA')
+# filter collection on location (Narrabeen-Collaroy rectangle)
+rect_narra = [[[151.3473129272461,-33.69035274454718],
+              [151.2820816040039,-33.68206818063878], 
+              [151.27281188964844,-33.74775138989556],
+              [151.3425064086914,-33.75231878701767],
+              [151.3473129272461,-33.69035274454718]]];
+flt_col = input_col.filterBounds(ee.Geometry.Polygon(rect_narra))
+n_img = flt_col.size().getInfo()
+print('Number of images covering Narrabeen:', n_img)
+
+# select the most recent image of the filtered collection
+im = ee.Image(flt_col.sort('SENSING_TIME',False).first())
+im_dic = im.getInfo()
+image_prop = im_dic.get('properties')
+im_bands = im_dic.get('bands')
+for i in range(len(im_bands)): del im_bands[i]['dimensions'] # delete the dimensions key
+
+# download the panchromatic band (B8) and QA band (Q11)
+pan_band = [im_bands[7]]
+im_pan = load_image(im, rect_narra, pan_band)
+im_pan = im_pan[:,:,0]
+size_pan = im_pan.shape
+vec_pan = im_pan.reshape(size_pan[0] * size_pan[1])
+
+qa_band = [im_bands[11]]
+im_qa = load_image(im, rect_narra, qa_band)
+im_qa = im_qa[:,:,0]
+
+# download the other 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 = load_image(im, rect_narra, ms_bands)
+size_ms = im_ms.shape
+vec_ms = im_ms.reshape(size_ms[0] * size_ms[1], size_ms[2])
+
+# create cloud mask
+im_cloud = create_cloud_mask(im_qa)
+im_cloud_res = transform.resize(im_cloud, (size_pan[0], size_pan[1]), order=0, preserve_range=True).astype('bool_')
+vec_cloud = im_cloud.reshape(im_cloud.shape[0] * im_cloud.shape[1])
+vec_cloud_res = im_cloud_res.reshape(size_pan[0] * size_pan[1])
+
+# Plot the RGB image and cloud masks
+plt.figure()
+ax1 = plt.subplot(121)
+plt.imshow(im_ms[:,:,[2,1,0]])
+plt.title('RGB')
+ax2 = plt.subplot(122)
+plt.imshow(im_cloud, cmap='gray')
+plt.title('Cloud mask')
+#ax3 = plt.subplot(133, sharex=ax1, sharey=ax1)
+#plt.imshow(im_cloud_shadow)
+#plt.title('Cloud mask shadow')
+plt.show()
+
+# Resize multispectral bands (30m) to the size of the pan band (15m) using bilinear interpolation
+im_ms_res = transform.resize(im_ms,(size_pan[0], size_pan[1]), order=1, preserve_range=True, mode='constant')
+# Rescale image intensity between 0 and 1
+prob_high = 99.9
+im_ms_adj = rescale_image_intensity(im_ms_res, im_cloud_res, prob_high, plot_bool)
+im_pan_adj = rescale_image_intensity(im_pan, im_cloud_res, prob_high, plot_bool)
+
+# Plot adjusted images
+plt.figure()
+plt.subplot(131)
+plt.imshow(im_pan_adj, cmap='gray')
+plt.title('PANCHROMATIC (15 m pixel)')
+
+plt.subplot(132)
+plt.imshow(im_ms_adj[:,:,[2,1,0]])
+plt.title('RGB (30 m pixel)')
+plt.show()
+
+plt.subplot(133)
+plt.imshow(im_ms_adj[:,:,[3,1,0]])
+plt.title('NIR-GB (30 m pixel)')
+plt.show()
+
+#%% Pansharpening
+
+im_ms_ps = pansharpen(im_ms_adj[:,:,[0,1,2]], im_pan_adj, im_cloud_res)
+# Add resized bands for NIR and SWIR
+im_ms_ps = np.append(im_ms_ps, im_ms_adj[:,:,[3,4]], axis=2)
+
+# Plot adjusted images
+plt.figure()
+plt.subplot(121)
+plt.imshow(im_ms_adj[:,:,[2,1,0]])
+plt.title('Original RGB')
+plt.show()
+
+plt.subplot(122)
+plt.imshow(im_ms_ps[:,:,[2,1,0]])
+plt.title('Pansharpened RGB')
+plt.show()
+
+plt.figure()
+plt.subplot(121)
+plt.imshow(im_ms_adj[:,:,[3,1,0]])
+plt.title('Original NIR-GB')
+plt.show()
+
+plt.subplot(122)
+plt.imshow(im_ms_ps[:,:,[3,1,0]])
+plt.title('Pansharpened NIR-GB')
+plt.show()
+
+im_ndwi_nir = normalized_difference(im_ms_ps[:,:,3], im_ms_ps[:,:,1], im_cloud_res, plot_bool)
+vec_ndwi_nir = im_ndwi_nir.reshape(size_pan[0] * size_pan[1])
+
+ndwi_nir = vec_ndwi_nir[~vec_cloud_res]
+
+t_otsu = filters.threshold_otsu(ndwi_nir)
+t_min = filters.threshold_minimum(ndwi_nir)
+t_mean = filters.threshold_mean(ndwi_nir)
+t_li = filters.threshold_li(ndwi_nir)
+# try all thresholding algorithms
+
+plt.figure()
+plt.hist(ndwi_nir, bins=300)
+plt.plot([t_otsu, t_otsu],[0, 15000], 'r-', label='Otsu threshold')
+#plt.plot([t_min, t_min],[0, 15000], 'g-', label='min')
+#plt.plot([t_mean, t_mean],[0, 15000], 'y-', label='mean')
+#plt.plot([t_li, t_li],[0, 15000], 'm-', label='li')
+plt.legend()
+plt.show()
+
+plt.figure()
+plt.imshow(im_ndwi_nir > t_otsu, cmap='gray')
+plt.title('Binary image')
+plt.show()
+
+im_bin = im_ndwi_nir > t_otsu
+im_open = morphology.binary_opening(im_bin,morphology.disk(1))
+im_close = morphology.binary_closing(im_open,morphology.disk(1))
+im_bin_coast_in = im_close ^ morphology.erosion(im_close,morphology.disk(1))
+im_bin_sl_in = morphology.remove_small_objects(im_bin_coast_in,100,8)
+
+plt.figure()
+plt.subplot(121)
+plt.imshow(im_close, cmap='gray')
+plt.title('morphological closing')
+plt.subplot(122)
+plt.imshow(im_bin_sl_in, cmap='gray')
+plt.title('Water mark')
+plt.show()
+
+
+im_bin_coast_out = morphology.dilation(im_close,morphology.disk(1)) ^ im_close
+im_bin_sl_out = morphology.remove_small_objects(im_bin_coast_out,100,8)
+
+
+# Plot shorelines on top of RGB image
+im_rgb_sl = np.copy(im_ms_ps[:,:,[2,1,0]])
+
+im_rgb_sl[im_bin_sl_in,0] = 0
+im_rgb_sl[im_bin_sl_in,1] = 1
+im_rgb_sl[im_bin_sl_in,2] = 1
+
+im_rgb_sl[im_bin_sl_out,0] = 1
+im_rgb_sl[im_bin_sl_out,1] = 0
+im_rgb_sl[im_bin_sl_out,2] = 1
+
+plt.figure()
+plt.imshow(im_rgb_sl)
+plt.title('Pansharpened RGB')
+plt.show()
+
+
+
+
diff --git a/sds_extract_collection.py b/sds_extract_collection.py
new file mode 100644
index 0000000..e89292e
--- /dev/null
+++ b/sds_extract_collection.py
@@ -0,0 +1,76 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Mar  1 14:32:08 2018
+
+@author: z5030440
+
+Main code to extract shorelines from Landsat imagery
+"""
+# Preamble
+
+import ee
+import 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
+from utils import *
+from sds import *
+
+np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
+ee.Initialize()
+
+# 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
+
+# select collection
+input_col = ee.ImageCollection('LANDSAT/LC08/C01/T1_RT_TOA')
+
+# location (Narrabeen-Collaroy beach)
+rect_narra = [[[151.3473129272461,-33.69035274454718],
+              [151.2820816040039,-33.68206818063878], 
+              [151.27281188964844,-33.74775138989556],
+              [151.3425064086914,-33.75231878701767],
+              [151.3473129272461,-33.69035274454718]]];
+# Dates
+start_date = '2016-01-01'
+end_date = '2016-12-01'
+# 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')
+output = []
+# loop through all images
+for i in range(n_img):
+    # find each image in ee database
+    im = ee.Image(im_all[i].get('id')) 
+    # load image as np.array
+    im_pan, im_ms, im_cloud, crs = 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)
+
diff --git a/shoreline_extraction.py b/shoreline_extraction.py
new file mode 100644
index 0000000..9ef6659
--- /dev/null
+++ b/shoreline_extraction.py
@@ -0,0 +1,359 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Feb 21 18:05:01 2018
+
+@author: z5030440
+"""
+
+#%% Initial settings
+
+# import packages
+import ee
+from IPython import display
+import math
+import matplotlib.pyplot as plt
+import numpy as np
+from osgeo import gdal
+import tempfile
+import tensorflow as tf
+import urllib
+from urllib.request import urlretrieve
+import zipfile
+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 scripts
+from GEEImageFunctions import *
+
+np.seterr(all='ignore') # raise divisions by 0 and nans
+ee.Initialize()
+
+# Load image collection and filter it based on location (Narrabeen)
+
+input_col = ee.ImageCollection('LANDSAT/LC08/C01/T1_RT_TOA')
+#n_img = input_col.size().getInfo()
+#print('Number of images in collection:', n_img)
+
+# filter based on location (Narrabeen-Collaroy)
+rect_narra = [[[151.3473129272461,-33.69035274454718],
+              [151.2820816040039,-33.68206818063878], 
+              [151.27281188964844,-33.74775138989556],
+              [151.3425064086914,-33.75231878701767],
+              [151.3473129272461,-33.69035274454718]]]; 
+              
+flt_col = input_col.filterBounds(ee.Geometry.Polygon(rect_narra))
+n_img = flt_col.size().getInfo()
+print('Number of images covering Narrabeen:', n_img)
+
+# Select the most recent image and download it
+
+im = ee.Image(flt_col.sort('SENSING_TIME',False).first())
+im_dic = im.getInfo()
+image_prop = im_dic.get('properties')
+im_bands = im_dic.get('bands')
+for i in range(len(im_bands)): del im_bands[i]['dimensions'] # delete the dimensions key
+
+# download the panchromatic band (B8)
+pan_band = [im_bands[7]]
+im_pan = load_image(im, rect_narra, pan_band)
+im_pan = im_pan[:,:,0]
+size_pan = im_pan.shape
+vec_pan = im_pan.reshape(size_pan[0] * size_pan[1])
+# download the QA band (BQA)
+qa_band = [im_bands[11]]
+im_qa = load_image(im, rect_narra, qa_band)
+im_qa = im_qa[:,:,0]
+
+# convert QA bits
+cloud_values = [2800, 2804, 2808, 2812, 6896, 6900, 6904, 6908]
+cloud_shadow_values = [2976, 2980, 2984, 2988, 3008, 3012, 3016, 3020]
+
+# Create cloud mask (resized to be applied to the Pan band)
+im_cloud = np.isin(im_qa, cloud_values)
+im_cloud_shadow = np.isin(im_qa, cloud_shadow_values)
+im_cloud_res = transform.resize(im_cloud,(im_pan.shape[0], im_pan.shape[1]), order=0, preserve_range=True).astype('bool_')
+vec_cloud = im_cloud.reshape(im_cloud.shape[0] * im_cloud.shape[1])
+vec_cloud_res = im_cloud_res.reshape(size_pan[0] * size_pan[1])
+
+
+# download the other 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 = load_image(im, rect_narra, ms_bands)
+size_ms = im_ms.shape
+vec_ms = im_ms.reshape(size_ms[0] * size_ms[1], size_ms[2])
+
+# Plot the RGB image and cloud masks
+plt.figure()
+ax1 = plt.subplot(121)
+plt.imshow(im_ms[:,:,[2,1,0]])
+plt.title('RGB')
+ax2 = plt.subplot(122)
+plt.imshow(im_cloud, cmap='gray')
+plt.title('Cloud mask')
+#ax3 = plt.subplot(133, sharex=ax1, sharey=ax1)
+#plt.imshow(im_cloud_shadow)
+#plt.title('Cloud mask shadow')
+plt.show()
+
+# Resize multispectral bands (30m) to the size of the pan band (15m) using bilinear interpolation
+im_ms_res = transform.resize(im_ms,(size_pan[0], size_pan[1]), order=1, preserve_range=True, mode='constant')
+vec_ms_res = im_ms_res.reshape(size_pan[0] * size_pan[1], size_ms[2])
+
+# Adjust intensities (set cloud pixels to 0 intensity)
+cloud_value = np.nan
+
+prc_low = 0 # lower percentile
+prob_high = 99.9 # upper percentile probability to clip
+
+# Rescale intensities between 0 and 1
+vec_ms_adj = np.ones((len(vec_cloud_res),size_ms[2])) * np.nan
+for i in range(im_ms.shape[2]):
+    prc_high = np.percentile(vec_ms_res[~vec_cloud_res,i], prob_high)
+    vec_rescaled = exposure.rescale_intensity(vec_ms_res[~vec_cloud_res,i], in_range=(prc_low,prc_high))
+    plt.figure()
+    plt.hist(vec_rescaled, bins = 300)
+    plt.show()
+    vec_ms_adj[~vec_cloud_res,i] = vec_rescaled
+
+    
+im_ms_adj = vec_ms_adj.reshape(size_pan[0], size_pan[1], size_ms[2])
+
+# same for the pan band
+vec_pan_adj = np.ones(len(vec_cloud_res)) * np.nan
+prc_high = np.percentile(vec_pan[~vec_cloud_res],prob_high)
+vec_rescaled = exposure.rescale_intensity(vec_pan[~vec_cloud_res], in_range=(prc_low,prc_high))
+plt.figure()
+plt.hist(vec_rescaled, bins = 300)
+plt.show()
+vec_pan_adj[~vec_cloud_res] = vec_rescaled
+im_pan_adj = vec_pan_adj.reshape(size_pan[0], size_pan[1])
+
+# Plot adjusted images
+plt.figure()
+plt.subplot(131)
+plt.imshow(im_pan_adj, cmap='gray')
+plt.title('PANCHROMATIC (15 m pixel)')
+
+plt.subplot(132)
+plt.imshow(im_ms_adj[:,:,[2,1,0]])
+plt.title('RGB (30 m pixel)')
+plt.show()
+
+plt.subplot(133)
+plt.imshow(im_ms_adj[:,:,[3,1,0]])
+plt.title('NIR-GB (30 m pixel)')
+plt.show()
+
+
+#%% Pansharpening (PCA)
+# Run PCA on selected bands
+
+sel_bands = [0,1,2]
+temp = vec_ms_adj[:,sel_bands]
+vec_ms_adj_nocloud = temp[~vec_cloud_res,:]
+pca = decomposition.PCA()
+vec_pcs = pca.fit_transform(vec_ms_adj_nocloud)
+vec_pcs_all = np.ones((len(vec_cloud_res),len(sel_bands))) * np.nan
+vec_pcs_all[~vec_cloud_res,:] = vec_pcs
+im_pcs = vec_pcs_all.reshape(size_pan[0], size_pan[1], vec_pcs.shape[1])
+
+plt.figure()
+plt.subplot(221)
+plt.imshow(im_pcs[:,:,0], cmap='gray')
+plt.title('Component 1')
+plt.subplot(222)
+plt.imshow(im_pcs[:,:,1], cmap='gray')
+plt.title('Component 2')
+plt.subplot(223)
+plt.imshow(im_pcs[:,:,2], cmap='gray')
+plt.title('Component 3')
+plt.show()
+
+# Compare the Pan image with the 1st Principal component
+compare_images(im_pan_adj,im_pcs[:,:,0])
+intensity_histogram(im_pan_adj)
+intensity_histogram(im_pcs[:,:,0])
+
+# Match histogram of the pan image with the 1st principal component and replace the 1st component
+vec_pcs[:,0] = hist_match(vec_pan_adj[~vec_cloud_res], 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]))
+
+vec_ms_ps_all = np.ones((len(vec_cloud_res),len(sel_bands))) * np.nan
+vec_ms_ps_all[~vec_cloud_res,:] = vec_ms_ps
+im_ms_ps = vec_ms_ps_all.reshape(size_pan[0], size_pan[1], len(sel_bands))
+vec_ms_ps_all = np.append(vec_ms_ps_all, vec_ms_adj[:,[3,4]], axis=1)
+im_ms_ps = np.append(im_ms_ps, im_ms_adj[:,:,[3,4]], axis=2)
+
+# Plot adjusted images
+plt.figure()
+plt.subplot(121)
+plt.imshow(im_ms_adj[:,:,[2,1,0]])
+plt.title('Original RGB')
+plt.show()
+
+plt.subplot(122)
+plt.imshow(im_ms_ps[:,:,[2,1,0]])
+plt.title('Pansharpened RGB')
+plt.show()
+
+plt.figure()
+plt.subplot(121)
+plt.imshow(im_ms_adj[:,:,[3,1,0]])
+plt.title('Original NIR-GB')
+plt.show()
+
+plt.subplot(122)
+plt.imshow(im_ms_ps[:,:,[3,1,0]])
+plt.title('Pansharpened NIR-GB')
+plt.show()
+#%% Compute Normalized Difference Water Index (NDWI)
+
+# With NIR
+vec_ndwi_nir = np.ones(len(vec_cloud_res)) * np.nan
+temp = np.divide(vec_ms_ps_all[~vec_cloud_res,3] - vec_ms_ps_all[~vec_cloud_res,1],
+                         vec_ms_ps_all[~vec_cloud_res,3] + vec_ms_ps_all[~vec_cloud_res,1])
+vec_ndwi_nir[~vec_cloud_res] = temp
+im_ndwi_nir = vec_ndwi_nir.reshape(size_pan[0], size_pan[1])
+
+# With SWIR_1
+vec_ndwi_swir = np.ones(len(vec_cloud_res)) * np.nan
+temp = np.divide(vec_ms_ps_all[~vec_cloud_res,4] - vec_ms_ps_all[~vec_cloud_res,1],
+                         vec_ms_ps_all[~vec_cloud_res,4] + vec_ms_ps_all[~vec_cloud_res,1])
+vec_ndwi_swir[~vec_cloud_res] = temp
+im_ndwi_swir = vec_ndwi_swir.reshape(size_pan[0], size_pan[1])
+
+plt.figure()
+ax1 = plt.subplot(211)
+plt.hist(vec_ndwi_nir[~vec_cloud_res], bins=300, label='NIR')
+plt.hist(vec_ndwi_swir[~vec_cloud_res], bins=300, label='SWIR', alpha=0.5)
+plt.legend()
+ax2 = plt.subplot(212, sharex=ax1)
+plt.hist(vec_ndwi_nir[~vec_cloud_res], bins=300, cumulative=True, histtype='step', label='NIR')
+plt.hist(vec_ndwi_swir[~vec_cloud_res], bins=300, cumulative=True, histtype='step', label='SWIR')
+plt.legend()
+plt.show()
+
+compare_images(im_ndwi_nir,im_ndwi_swir)
+
+plt.figure()
+plt.imshow(im_ndwi_nir, cmap='seismic')
+plt.title('Water Index')
+plt.colorbar()
+plt.show()
+
+#%% Extract shorelines (NIR)
+
+ndwi_nir = vec_ndwi_nir[~vec_cloud_res]
+
+t_otsu = filters.threshold_otsu(ndwi_nir)
+t_min = filters.threshold_minimum(ndwi_nir)
+t_mean = filters.threshold_mean(ndwi_nir)
+t_li = filters.threshold_li(ndwi_nir)
+# try all thresholding algorithms
+
+plt.figure()
+plt.hist(ndwi_nir, bins=300)
+plt.plot([t_otsu, t_otsu],[0, 15000], 'r-', label='Otsu threshold')
+#plt.plot([t_min, t_min],[0, 15000], 'g-', label='min')
+#plt.plot([t_mean, t_mean],[0, 15000], 'y-', label='mean')
+#plt.plot([t_li, t_li],[0, 15000], 'm-', label='li')
+plt.legend()
+plt.show()
+
+plt.figure()
+plt.imshow(im_ndwi_nir > t_otsu, cmap='gray')
+plt.title('Binary image')
+plt.show()
+
+im_bin = im_ndwi_nir > t_otsu
+im_open = morphology.binary_opening(im_bin,morphology.disk(1))
+im_close = morphology.binary_closing(im_open,morphology.disk(1))
+im_bin_coast_in = im_close ^ morphology.erosion(im_close,morphology.disk(1))
+im_bin_sl_in = morphology.remove_small_objects(im_bin_coast_in,100,8)
+
+compare_images(im_close,im_bin_sl_in)
+
+plt.figure()
+plt.subplot(121)
+plt.imshow(im_close, cmap='gray')
+plt.title('morphological closing')
+plt.subplot(122)
+plt.imshow(im_bin_sl_in, cmap='gray')
+plt.title('Water mark')
+plt.show()
+
+
+im_bin_coast_out = morphology.dilation(im_close,morphology.disk(1)) ^ im_close
+im_bin_sl_out = morphology.remove_small_objects(im_bin_coast_out,100,8)
+
+
+# Plot shorelines on top of RGB image
+im_rgb_sl = np.copy(im_ms_ps[:,:,[2,1,0]])
+
+im_rgb_sl[im_bin_sl_in,0] = 0
+im_rgb_sl[im_bin_sl_in,1] = 1
+im_rgb_sl[im_bin_sl_in,2] = 1
+
+im_rgb_sl[im_bin_sl_out,0] = 1
+im_rgb_sl[im_bin_sl_out,1] = 0
+im_rgb_sl[im_bin_sl_out,2] = 1
+
+plt.figure()
+plt.imshow(im_rgb_sl)
+plt.title('Pansharpened RGB')
+plt.show()
+
+#%% Extract shorelines SWIR
+
+ndwi_swir = vec_ndwi_swir[~vec_cloud_res]
+t_otsu = filters.threshold_otsu(ndwi_swir)
+
+plt.figure()
+plt.hist(ndwi_swir, bins=300)
+plt.plot([t_otsu, t_otsu],[0, 15000], 'r-', label='Otsu threshold')
+#plt.plot([t_min, t_min],[0, 15000], 'g-', label='min')
+#plt.plot([t_mean, t_mean],[0, 15000], 'y-', label='mean')
+#plt.plot([t_li, t_li],[0, 15000], 'm-', label='li')
+plt.legend()
+plt.show()
+
+plt.figure()
+plt.imshow(im_ndwi_swir > t_otsu, cmap='gray')
+plt.title('Binary image')
+plt.show()
+
+im_bin = im_ndwi_swir > t_otsu
+im_open = morphology.binary_opening(im_bin,morphology.disk(1))
+im_close = morphology.binary_closing(im_open,morphology.disk(1))
+im_bin_coast_in = im_close ^ morphology.erosion(im_close,morphology.disk(1))
+im_bin_sl_in = morphology.remove_small_objects(im_bin_coast_in,100,8)
+
+compare_images(im_close,im_bin_sl_in)
+
+
+im_bin_coast_out = morphology.dilation(im_close,morphology.disk(1)) ^ im_close
+im_bin_sl_out = morphology.remove_small_objects(im_bin_coast_out,100,8)
+
+
+# Plot shorelines on top of RGB image
+im_rgb_sl = np.copy(im_ms_ps[:,:,[2,1,0]])
+
+im_rgb_sl[im_bin_sl_in,0] = 0
+im_rgb_sl[im_bin_sl_in,1] = 1
+im_rgb_sl[im_bin_sl_in,2] = 1
+
+im_rgb_sl[im_bin_sl_out,0] = 1
+im_rgb_sl[im_bin_sl_out,1] = 0
+im_rgb_sl[im_bin_sl_out,2] = 1
+
+plt.figure()
+plt.imshow(im_rgb_sl)
+plt.title('Pansharpened RGB')
+plt.show()
\ No newline at end of file
diff --git a/utils.py b/utils.py
new file mode 100644
index 0000000..c809a18
--- /dev/null
+++ b/utils.py
@@ -0,0 +1,54 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Mar  1 11:30:31 2018
+
+@author: z5030440
+
+Contains all the utilities, convenience functions and small functions that do simple things
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+
+def ecdf(x):
+    """convenience function for computing the empirical CDF"""
+    vals, counts = np.unique(x, return_counts=True)
+    ecdf = np.cumsum(counts).astype(np.float64)
+    ecdf /= ecdf[-1]
+    return vals, ecdf
+
+def intensity_histogram(image):
+    """plots histogram and cumulative distribution of the pixel intensities in an image"""
+    imSize = image.shape
+    if len(imSize) == 2:
+        im = image[:,:].reshape(imSize[0] * imSize[1])
+        im = im[~np.isnan(im)] 
+        fig, (ax1, ax2) = plt.subplots(2,1, sharex=True, figsize = (8,6))
+        ax1.hist(im, bins=300)
+        ax1.set_title('Probability density function')
+        ax2.hist(im, bins=300, cumulative=True, histtype='step')
+        ax2.set_title('Cumulative distribution')
+        plt.show()
+    
+    else:       
+        for i in range(imSize[2]):
+            im = image[:,:,i].reshape(imSize[0] * imSize[1])
+            im = im[~np.isnan(im)] 
+            fig, (ax1, ax2) = plt.subplots(2,1, sharex=True, figsize = (8,6))
+            ax1.hist(im, bins=300)
+            ax1.set_title('Probability density function')
+            ax2.hist(im, bins=300, cumulative=True, histtype='step')
+            ax2.set_title('Cumulative distribution')
+            plt.show()
+            
+def compare_images(im1, im2):
+    """plots 2 images next to each other, sharing the axis"""
+    plt.figure()
+    ax1 = plt.subplot(121)
+    plt.imshow(im1, cmap='gray')
+    ax2 = plt.subplot(122, sharex=ax1, sharey=ax1)
+    plt.imshow(im2, cmap='gray')
+    plt.show()    
+