forked from kilianv/CoastSat_WRL
Supprimer 'read_images.py'
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#==========================================================#
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#==========================================================#
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# Extract shorelines from Landsat images
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#==========================================================#
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#==========================================================#
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#==========================================================#
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# Initial settings
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#==========================================================#
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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 other 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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#==========================================================#
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# Parameters
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#==========================================================#
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sitename = 'NARRA'
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cloud_thresh = 0.7 # threshold for cloud cover
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plot_bool = False # if you want the plots
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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 # maximum distance from reference point
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min_length_wl = 200 # minimum length of shoreline LineString to be kept
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output = dict([])
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#==========================================================#
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# Metadata
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#==========================================================#
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filepath = os.path.join(os.getcwd(), 'data', sitename)
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with open(os.path.join(filepath, sitename + '_metadata' + '.pkl'), 'rb') as f:
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metadata = pickle.load(f)
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#%%
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#==========================================================#
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# Read S2 images
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#==========================================================#
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satname = 'S2'
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dates = metadata[satname]['dates']
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input_epsg = metadata[satname]['epsg']
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# path to images
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filepath10 = os.path.join(os.getcwd(), 'data', sitename, satname, '10m')
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filenames10 = os.listdir(filepath10)
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filepath20 = os.path.join(os.getcwd(), 'data', sitename, satname, '20m')
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filenames20 = os.listdir(filepath20)
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filepath60 = os.path.join(os.getcwd(), 'data', sitename, satname, '60m')
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filenames60 = os.listdir(filepath60)
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if (not len(filenames10) == len(filenames20)) or (not len(filenames20) == len(filenames60)):
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raise 'error: not the same amount of files for 10, 20 and 60 m'
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N = len(filenames10)
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# initialise variables
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cloud_cover_ts = []
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acc_georef_ts = []
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date_acquired_ts = []
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filename_ts = []
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satname_ts = []
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timestamp = []
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shorelines = []
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idx_skipped = []
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spacing = '=========================================================='
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msg = ' %s\n %s\n %s' % (spacing, satname, spacing)
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print(msg)
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for i in range(N):
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# read 10m bands
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fn = os.path.join(filepath10, filenames10[i])
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data = gdal.Open(fn, 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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im10 = np.stack(bands, 2)
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im10 = im10/10000 # TOA scaled to 10000
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# if image is only zeros, skip it
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if sum(sum(sum(im10))) < 1:
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print('skip ' + str(i) + ' - no data')
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idx_skipped.append(i)
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continue
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nrows = im10.shape[0]
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ncols = im10.shape[1]
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# read 20m band (SWIR1)
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fn = os.path.join(filepath20, filenames20[i])
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data = gdal.Open(fn, gdal.GA_ReadOnly)
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bands = [data.GetRasterBand(k + 1).ReadAsArray() for k in range(data.RasterCount)]
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im20 = np.stack(bands, 2)
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im20 = im20[:,:,0]
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im20 = im20/10000 # TOA scaled to 10000
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im_swir = transform.resize(im20, (nrows, ncols), order=1, preserve_range=True, mode='constant')
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im_swir = np.expand_dims(im_swir, axis=2)
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# append down-sampled swir band to the 10m bands
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im_ms = np.append(im10, im_swir, axis=2)
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# read 60m band (QA)
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fn = os.path.join(filepath60, filenames60[i])
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data = gdal.Open(fn, gdal.GA_ReadOnly)
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bands = [data.GetRasterBand(k + 1).ReadAsArray() for k in range(data.RasterCount)]
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im60 = np.stack(bands, 2)
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im_qa = im60[:,:,0]
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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,(nrows, ncols), order=0, preserve_range=True, mode='constant')
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# check if -inf or nan values on any band and add to cloud mask
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for k in range(im_ms.shape[2]):
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im_inf = np.isin(im_ms[:,:,k], -np.inf)
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im_nan = np.isnan(im_ms[:,:,k])
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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 if above threshold, skip it
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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(np.round(cloud_cover*100).astype(int)) + '%)')
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idx_skipped.append(i)
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continue
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# rescale image intensity for display purposes
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im_display = sds.rescale_image_intensity(im_ms[:,:,[2,1,0]], cloud_mask, 99.9, False)
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# plot rgb image
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plt.figure()
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plt.subplot(121)
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plt.axis('off')
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plt.imshow(im_display)
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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_nopan(im_ms, cloud_mask, min_beach_size, plot_bool)
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plt.subplot(122)
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plt.axis('off')
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im = np.copy(im_display)
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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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# store the data
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cloud_cover_ts.append(cloud_cover)
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acc_georef_ts.append(metadata[satname]['acc_georef'][i])
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filename_ts.append(filenames10[i])
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satname_ts.append(satname)
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date_acquired_ts.append(filenames10[i][:10])
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timestamp.append(metadata[satname]['dates'][i])
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# store in output structure
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output[satname] = {'dates':timestamp, 'idx_skipped':idx_skipped,
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'metadata':{'filenames':filename_ts, 'satname':satname_ts, 'cloud_cover':cloud_cover_ts,
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'acc_georef':acc_georef_ts}}
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# save output
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#with open(os.path.join(filepath, sitename + '_output' + satname + '.pkl'), 'wb') as f:
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# pickle.dump(output, f)
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