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249 lines
9.4 KiB
Python
249 lines
9.4 KiB
Python
# -*- coding: utf-8 -*-
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#==========================================================#
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# Create a training data
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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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import time
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import pandas as pd
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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 skimage.morphology as morphology
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from scipy import ndimage
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import random
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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 = 0.3 # threshold for cloud cover
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plot_bool = False # if you want the plots
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prob_high = 100 # 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_all'
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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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# 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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idx_skipped = []
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idx_nocloud = []
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n_features = 10
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train_pos = np.nan*np.ones((1,n_features))
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train_neg = np.nan*np.ones((1,n_features))
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train_other = np.nan*np.ones((1,n_features))
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train_water = np.nan*np.ones((1,n_features))
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columns = ('B','G','R','NIR','SWIR','Pan','WI','VI','BR', 'mWI', 'class')
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#%%
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date_acquired_ts = []
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fig = plt.figure()
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for i in range(N):
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plt.close(fig)
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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 i in range(data.RasterCount)]
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im_pan = np.stack(bands, 2)[:,:,0]
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nrow = im_pan.shape[0]
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ncol = 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(i + 1).ReadAsArray() for i 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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# skip if cloud cover is more than the threshold
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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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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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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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# 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, plot_bool)
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if not wl_pix:
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idx_skipped.append(i)
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continue
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# classify sand pixels with Kmeans
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im_sandKmeans = sds.classify_sand_unsupervised(im_ms_ps, im_pan, cloud_mask, wl_pix, buffer_size, min_beach_size, plot_bool)
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# plot a figure to manually select which images to keep
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im = np.copy(sds.rescale_image_intensity(im_ms_ps[:,:,[2,1,0]], cloud_mask, 100, False))
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im[im_sandKmeans,0] = 0
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im[im_sandKmeans,1] = 0
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im[im_sandKmeans,2] = 1
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# select water pixels on image
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pt_water = np.array([140, 70])
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lenrow = 20
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lencol = 20
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idx_row = np.linspace(0,lenrow-1,lenrow).astype(int) + int(pt_water[0])
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idx_col = np.linspace(0,lencol-1,lencol).astype(int) + int(pt_water[1])
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xx, yy = np.meshgrid(idx_row,idx_col, indexing='ij')
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row_water = xx.reshape(lenrow*lencol)
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col_water = yy.reshape(lenrow*lencol)
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im_water = np.zeros((nrow,ncol)).astype(bool)
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for k in range(len(row_water)):
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im_water[row_water[k],col_water[k]] = True
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im[row_water[k],col_water[k],0] = 0
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im[row_water[k],col_water[k],1] = 1
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im[row_water[k],col_water[k],2] = 1
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# select other pixels on image (vegetation + buildings)
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pt_other = np.array([random.randint(150,235), 7])
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lenrow = 40
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lencol = 15
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idx_row = np.linspace(0,lenrow-1,lenrow).astype(int) + int(pt_other[0])
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idx_col = np.linspace(0,lencol-1,lencol).astype(int) + int(pt_other[1])
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xx, yy = np.meshgrid(idx_row,idx_col, indexing='ij')
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row_other = xx.reshape(lenrow*lencol)
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col_other = yy.reshape(lenrow*lencol)
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im_other = np.zeros((nrow,ncol)).astype(bool)
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for k in range(len(row_other)):
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im_other[row_other[k],col_other[k]] = True
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im[row_other[k],col_other[k],0] = 1
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im[row_other[k],col_other[k],1] = 1
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im[row_other[k],col_other[k],2] = 0
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# plot image
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fig = plt.figure()
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plt.imshow(im)
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plt.axis('image')
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plt.title('Sand classification')
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plt.draw()
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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 a point
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# top-left quadrant: sand
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# bottom-left quadrant: swash
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# any right quadrant: discard image
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pt_in = np.array(ginput(n=1, timeout=1000))
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if pt_in[0][0] < im_ms_ps.shape[1]/2:
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# calculate features
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im_features = np.zeros((im_ms_ps.shape[0], im_ms_ps.shape[1], n_features))
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im_features[:,:,[0,1,2,3,4]] = im_ms_ps
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im_features[:,:,5] = im_pan
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im_features[:,:,6] = sds.nd_index(im_ms_ps[:,:,3], im_ms_ps[:,:,1], cloud_mask, False) # (NIR-G)
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im_features[:,:,7] = sds.nd_index(im_ms_ps[:,:,3], im_ms_ps[:,:,2], cloud_mask, False) # (NIR-R)
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im_features[:,:,8] = sds.nd_index(im_ms_ps[:,:,0], im_ms_ps[:,:,2], cloud_mask, False) # (B-R)
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im_features[:,:,9] = sds.nd_index(im_ms_ps[:,:,4], im_ms_ps[:,:,1], cloud_mask, False) # (SWIR-G)
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# win = np.ones((3,3))
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# im_features[:,:,9] = ndimage.generic_filter(im_features[:,:,5], np.std, footprint=win)
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# im_features[:,:,10] = ndimage.generic_filter(im_features[:,:,5], np.max, footprint=win) - ndimage.generic_filter(im_features[:,:,5], np.min, footprint=win)
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# positive examples
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# fill training data
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vec_other = im_features[im_other,:]
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train_other = np.append(train_other, vec_other, axis=0)
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vec_water = im_features[im_water,:]
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train_water = np.append(train_water, vec_water, axis=0)
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if pt_in[0][1] < im_ms_ps.shape[0]/2:
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# sand examples
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vec_pos = im_features[im_sandKmeans,:]
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train_pos = np.append(train_pos, vec_pos, axis=0)
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else:
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# swash examples
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vec_neg = im_features[im_sandKmeans,:]
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train_neg = np.append(train_neg, vec_neg, axis=0)
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else:
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print('skip ' + str(i))
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idx_skipped.append(i)
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continue
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date_acquired_ts.append(file_names_pan[i][9:19])
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# format data
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train_pos = train_pos[1:,:]
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train_neg = train_neg[1:,:]
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train_water = train_water[1:,:]
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train_other = train_other[1:,:]
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# save data
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#with open(os.path.join(os.getcwd(), 'sand_classification', sitename + '_sand.pkl'), 'wb') as f:
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# pickle.dump(train_pos, f)
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#with open(os.path.join(os.getcwd(), 'sand_classification', sitename + '_swash.pkl'), 'wb') as f:
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# pickle.dump(train_neg, f)
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#with open(os.path.join(os.getcwd(), 'sand_classification', sitename + '_water.pkl'), 'wb') as f:
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# pickle.dump(train_water, f)
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#with open(os.path.join(os.getcwd(), 'sand_classification', sitename + '_other.pkl'), 'wb') as f:
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# pickle.dump(train_other, f)
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