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213 lines
8.6 KiB
Python
213 lines
8.6 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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# 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 own modules
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import functions.utils as utils
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import functions.sds_old1 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 = 0.5 # threshold for cloud cover
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plot_bool = False # if you want the plots
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prob_high = 99.9 # upper probability to clip and rescale pixel intensity
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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 = 10 # radius (in pixels) of disk for buffer (pixel classification)
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min_beach_size = 50 # number of pixels in a beach (pixel classification)
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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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# 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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idx_skipped = []
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idx_nocloud = []
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t = []
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shorelines = []
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for i in range(N):
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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(i + 1).ReadAsArray() for k in range(data.RasterCount)]
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im_pan = np.stack(bands, 2)[:,:,0]
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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(i + 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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cloud_cover = sum(sum(cloud_mask.astype(int)))/(cloud_mask.shape[0]*cloud_mask.shape[1])
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if cloud_cover > cloud_thresh:
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print('skip ' + str(i) + ' - cloudy (' + str(cloud_cover) + ')')
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idx_skipped.append(i)
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continue
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idx_nocloud.append(i)
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# check if image for that date is already present
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if file_names_pan[i][len(satname)+1+len(sitename)+1:len(satname)+1+len(sitename)+1+10] in date_acquired_ts:
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# find the index of the image that is repeated
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idx_samedate = utils.find_indices(date_acquired_ts, lambda e : e == file_names_pan[i][9:19])
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idx_samedate = idx_samedate[0]
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print('cloud cover ' + str(cloud_cover) + ' - ' + str(cloud_cover_ts[idx_samedate]))
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print('acc georef ' + str(acc_georef_sorted[i]) + ' - ' + str(acc_georef_ts[idx_samedate]))
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# keep image with less cloud cover or best georeferencing accuracy
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if cloud_cover < cloud_cover_ts[idx_samedate] - 0.01:
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skip = False
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elif acc_georef_sorted[i] < acc_georef_ts[idx_samedate]:
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skip = False
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else:
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skip = True
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if skip:
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print('skip ' + str(i) + ' - repeated')
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idx_skipped.append(i)
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continue
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else:
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del shorelines[idx_samedate]
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del t[idx_samedate]
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del cloud_cover_ts[idx_samedate]
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del date_acquired_ts[idx_samedate]
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del acc_georef_ts[idx_samedate]
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print('keep ' + str(i) + ' - deleted ' + str(idx_samedate))
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# rescale intensities
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im_ms = sds.rescale_image_intensity(im_ms, cloud_mask, prob_high, plot_bool)
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im_pan = sds.rescale_image_intensity(im_pan, cloud_mask, prob_high, plot_bool)
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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, True)
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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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# calculate NDWI
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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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# detect edges
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wl_pix = sds.find_wl_contours(im_ndwi, cloud_mask, min_contour_points, True)
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# convert from pixels to world coordinates
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wl_coords = sds.convert_pix2world(wl_pix, 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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# classify sand pixels
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# im_sand = sds.classify_sand_unsupervised(im_ms_ps, im_pan, cloud_mask, wl_pix, False, min_beach_size, True)
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# plot a figure to select the correct water line and discard cloudy images
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plt.figure()
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cmap = cm.get_cmap('jet')
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plt.subplot(121)
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plt.imshow(im_ms_ps[:,:,[2,1,0]])
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for j,contour in enumerate(wl_pix):
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colours = cmap(np.linspace(0, 1, num=len(wl_pix)))
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plt.plot(contour[:, 1], contour[:, 0], linewidth=2, color=colours[j,:])
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plt.axis('image')
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plt.title(file_names_pan[i])
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plt.subplot(122)
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centroids = []
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for j,contour in enumerate(wl):
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colours = cmap(np.linspace(0, 1, num=len(wl)))
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centroids.append([np.mean(contour[:, 0]),np.mean(contour[:, 1])])
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plt.plot(contour[:, 0], contour[:, 1], linewidth=2, color=colours[j,:])
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plt.plot(np.mean(contour[:, 0]), np.mean(contour[:, 1]), 'o', color=colours[j,:])
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plt.plot(refpoints[:,0], refpoints[:,1], 'k.')
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plt.axis('equal')
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plt.title(file_names_pan[i])
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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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# click on the left image to discard, otherwise on the closest centroid in the right image
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pt_in = np.array(ginput(n=1, timeout=1000))
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if pt_in[0][0] < 10000:
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print('skip ' + str(i) + ' - manual')
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idx_skipped.append(i)
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continue
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# get contour that was selected (click closest to centroid)
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dist_centroid = [np.linalg.norm(_ - pt_in) for _ in centroids]
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shorelines.append(wl[np.argmin(dist_centroid)])
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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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#plt.figure()
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#plt.axis('equal')
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#for j in range(len(shorelines)):
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# plt.plot(shorelines[j][:,0], shorelines[j][:,1])
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#plt.draw()
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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' + '.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' + '.pkl'), 'wb') as f:
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# pickle.dump(idx_skipped, f)
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#
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#with open(os.path.join(filepath, sitename + '_idxnocloud' + '.pkl'), 'wb') as f:
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# pickle.dump(idx_nocloud, f) |