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