geetools_VH/sds.py

# -*- 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