forked from kilianv/CoastSat_WRL
You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
229 lines
9.3 KiB
Python
229 lines
9.3 KiB
Python
# -*- coding: utf-8 -*-
|
|
|
|
#==========================================================#
|
|
# Run Neural Network on image to extract sandy pixels
|
|
#==========================================================#
|
|
|
|
# Initial settings
|
|
import os
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
import matplotlib.patches as mpatches
|
|
import matplotlib.lines as mlines
|
|
from matplotlib import gridspec
|
|
from datetime import datetime, timedelta
|
|
import pytz
|
|
import ee
|
|
import pdb
|
|
import time
|
|
import pandas as pd
|
|
# 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
|
|
from scipy import ndimage
|
|
import imageio
|
|
|
|
|
|
# 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 = 0.2 # threshold for cloud cover
|
|
plot_bool = False # if you want the plots
|
|
prob_high = 100 # upper probability to clip and rescale pixel intensity
|
|
min_contour_points = 100# minimum number of points contained in each water line
|
|
output_epsg = 28356 # GDA94 / MGA Zone 56
|
|
buffer_size = 10 # radius (in pixels) of disk for buffer (pixel classification)
|
|
min_beach_size = 10 # number of pixels in a beach (pixel classification)
|
|
|
|
# load metadata (timestamps and epsg code) for the collection
|
|
satname = 'L8'
|
|
#sitename = 'NARRA_all'
|
|
#sitename = 'NARRA'
|
|
#sitename = 'OLDBAR'
|
|
#sitename = 'OLDBAR_inlet'
|
|
#sitename = 'SANDMOTOR'
|
|
#sitename = 'TAIRUA'
|
|
#sitename = 'DUCK'
|
|
#sitename = 'BROULEE'
|
|
sitename = 'MURI'
|
|
|
|
|
|
# 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)
|
|
timestamps_sorted = sorted(timestamps)
|
|
daysall = (datetime(2019,1,1,tzinfo=pytz.utc) - datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds()
|
|
# 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
|
|
idx_skipped = []
|
|
idx_nocloud = []
|
|
n_features = 10
|
|
train_pos = np.nan*np.ones((1,n_features))
|
|
train_neg = np.nan*np.ones((1,n_features))
|
|
columns = ('B','G','R','NIR','SWIR','Pan','WI','VI','BR', 'mWI', 'class')
|
|
|
|
#%%
|
|
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 k in range(data.RasterCount)]
|
|
im_pan = np.stack(bands, 2)[:,:,0]
|
|
nrow = im_pan.shape[0]
|
|
ncol = 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 k 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)
|
|
# skip if cloud cover is more than the threshold
|
|
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(np.round(cloud_cover*100).astype(int)) + '%)')
|
|
idx_skipped.append(i)
|
|
continue
|
|
idx_nocloud.append(i)
|
|
|
|
# pansharpen rgb image
|
|
im_ms_ps = sds.pansharpen(im_ms[:,:,[0,1,2]], im_pan, cloud_mask, plot_bool)
|
|
# 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)
|
|
|
|
im_classif, im_labels = sds.classify_image_NN(im_ms_ps, im_pan, cloud_mask, min_beach_size, plot_bool)
|
|
|
|
# if there are no sand pixels, skip the image (maybe later change the detection method with old method)
|
|
if sum(sum(im_labels[:,:,0])) == 0 :
|
|
print('skip ' + str(i) + ' - no sand')
|
|
idx_skipped.append(i)
|
|
continue
|
|
|
|
contours_wi, contours_mwi = sds.find_wl_contours2(im_ms_ps, im_labels, cloud_mask, buffer_size, False)
|
|
|
|
|
|
im_display = sds.rescale_image_intensity(im_ms_ps[:,:,[2,1,0]], cloud_mask, 100, False)
|
|
im = np.copy(im_display)
|
|
# define colours for plot
|
|
colours = np.array([[1,128/255,0/255],[204/255,1,1],[0,0,204/255]])
|
|
for k in range(0,im_labels.shape[2]):
|
|
im[im_labels[:,:,k],0] = colours[k,0]
|
|
im[im_labels[:,:,k],1] = colours[k,1]
|
|
im[im_labels[:,:,k],2] = colours[k,2]
|
|
|
|
|
|
# fig = plt.figure()
|
|
# plt.suptitle(date_im, fontsize=17, fontweight='bold')
|
|
# ax1 = plt.subplot(121)
|
|
# plt.imshow(im_display)
|
|
# plt.axis('off')
|
|
# ax2 = plt.subplot(122, sharex=ax1, sharey=ax1)
|
|
# plt.imshow(im)
|
|
# plt.axis('off')
|
|
# plt.gcf().set_size_inches(17.99,7.55)
|
|
# plt.tight_layout()
|
|
# orange_patch = mpatches.Patch(color=[1,128/255,0/255], label='sand')
|
|
# white_patch = mpatches.Patch(color=[204/255,1,1], label='swash/whitewater')
|
|
# blue_patch = mpatches.Patch(color=[0,0,204/255], label='water')
|
|
# plt.legend(handles=[orange_patch,white_patch,blue_patch], bbox_to_anchor=(0.95, 0.2))
|
|
# plt.draw()
|
|
|
|
date_im = timestamps_sorted[i].strftime('%d %b %Y')
|
|
daysnow = (timestamps_sorted[i] - datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds()
|
|
|
|
fig = plt.figure()
|
|
gs = gridspec.GridSpec(2, 2, height_ratios=[1, 20])
|
|
|
|
ax1 = fig.add_subplot(gs[0,:])
|
|
plt.plot(0,0,'ko',daysall,0,'ko')
|
|
plt.plot([0,daysall],[0,0],'k-')
|
|
plt.plot(daysnow,0,'ro')
|
|
plt.text(0,0.05,'2013')
|
|
plt.text(daysall,0.05,'2019')
|
|
plt.plot((datetime(2014,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0,'ko',markersize=3)
|
|
plt.plot((datetime(2015,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0,'ko',markersize=3)
|
|
plt.plot((datetime(2016,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0,'ko',markersize=3)
|
|
plt.plot((datetime(2017,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0,'ko',markersize=3)
|
|
plt.plot((datetime(2018,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0,'ko',markersize=3)
|
|
plt.text((datetime(2014,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0.05,'2014')
|
|
plt.text((datetime(2015,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0.05,'2015')
|
|
plt.text((datetime(2016,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0.05,'2016')
|
|
plt.text((datetime(2017,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0.05,'2017')
|
|
plt.text((datetime(2018,1,1,tzinfo=pytz.utc)- datetime(2013,1,1,tzinfo=pytz.utc)).total_seconds(),0.05,'2018')
|
|
|
|
plt.axis('off')
|
|
|
|
ax2 = fig.add_subplot(gs[1,0])
|
|
plt.imshow(im_display)
|
|
plt.axis('off')
|
|
plt.title(date_im, fontsize=17, fontweight='bold')
|
|
|
|
ax3 = fig.add_subplot(gs[1,1])
|
|
plt.imshow(im)
|
|
for l,contour in enumerate(contours_mwi): plt.plot(contour[:, 1], contour[:, 0], linewidth=2, color='k', linestyle='--')
|
|
plt.axis('off')
|
|
orange_patch = mpatches.Patch(color=[1,128/255,0/255], label='sand')
|
|
white_patch = mpatches.Patch(color=[204/255,1,1], label='swash/whitewater')
|
|
blue_patch = mpatches.Patch(color=[0,0,204/255], label='water')
|
|
black_line = mlines.Line2D([],[],color='k',linestyle='-', label='shoreline')
|
|
plt.legend(handles=[orange_patch,white_patch,blue_patch, black_line], bbox_to_anchor=(0.95, 0.2))
|
|
|
|
plt.gcf().set_size_inches(17.99,7.55)
|
|
plt.gcf().set_tight_layout(True)
|
|
|
|
plt.draw()
|
|
plt.savefig(os.path.join(filepath,'plots_classif', file_names_pan[i][len(satname)+1+len(sitename)+1:len(satname)+1+len(sitename)+1+10] + '.jpg'), dpi = 300)
|
|
plt.close()
|
|
|
|
# create gif
|
|
images = []
|
|
filenames = os.listdir(os.path.join(filepath, 'plots_classif'))
|
|
with imageio.get_writer(sitename + '.gif', mode='I', duration=0.4) as writer:
|
|
for filename in filenames:
|
|
image = imageio.imread(os.path.join(filepath,'plots_classif',filename))
|
|
writer.append_data(image)
|
|
|