# -*- 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 = 7 # 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 = [] idx_keep = [] #%% 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 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, plot_bool) idx_keep.append(i) 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, 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])