forked from kilianv/CoastSat_WRL
new implementation
Kilian Vos
7 years ago
parent
ec290ab323
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eecdb485fc
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# -*- coding: utf-8 -*-
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"""
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Created on Tue Mar 27 17:12:35 2018
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@author: Kilian
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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 pdb
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import ee
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# other modules
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from osgeo import gdal, ogr, osr
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from urllib.request import urlretrieve
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import zipfile
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from datetime import datetime
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import pytz
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import pickle
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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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from functions.utils import *
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np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
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ee.Initialize()
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def download_tif(image, polygon, bandsId, filepath):
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"""downloads tif image (region and bands) from the ee server and stores it in a temp file"""
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url = ee.data.makeDownloadUrl(ee.data.getDownloadId({
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'image': image.serialize(),
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'region': polygon,
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'bands': bandsId,
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'filePerBand': 'false',
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'name': 'data',
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}))
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local_zip, headers = urlretrieve(url)
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with zipfile.ZipFile(local_zip) as local_zipfile:
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return local_zipfile.extract('data.tif', filepath)
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# select collection
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input_col = ee.ImageCollection('LANDSAT/LC08/C01/T1_RT_TOA')
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# location (Narrabeen-Collaroy beach)
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rect_narra = [[[151.301454, -33.700754],
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[151.311453, -33.702075],
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[151.307237, -33.739761],
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[151.294220, -33.736329],
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[151.301454, -33.700754]]];
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# dates
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start_date = '2016-01-01'
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end_date = '2016-12-31'
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# filter by location
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flt_col = input_col.filterBounds(ee.Geometry.Polygon(rect_narra)).filterDate(start_date, end_date)
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n_img = flt_col.size().getInfo()
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print('Number of images covering Narrabeen:', n_img)
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im_all = flt_col.getInfo().get('features')
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satname = 'L8'
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sitename = 'NARRA'
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suffix = '.tif'
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filepath = os.path.join(os.getcwd(), 'data', satname, sitename)
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filepath_pan = os.path.join(filepath, 'pan')
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filepath_ms = os.path.join(filepath, 'ms')
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all_names_pan = []
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all_names_ms = []
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timestamps = []
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# loop through all images
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for i in range(n_img):
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# find each image in ee database
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im = ee.Image(im_all[i].get('id'))
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im_dic = im.getInfo()
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im_bands = im_dic.get('bands')
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im_date = im_dic['properties']['DATE_ACQUIRED']
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t = im_dic['properties']['system:time_start']
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im_timestamp = datetime.fromtimestamp(t/1000, tz=pytz.utc)
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timestamps.append(im_timestamp)
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im_epsg = int(im_dic['bands'][0]['crs'][5:])
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# delete dimensions key from dictionnary, otherwise the entire image is extracted
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for j in range(len(im_bands)): del im_bands[j]['dimensions']
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pan_band = [im_bands[7]]
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ms_bands = [im_bands[1], im_bands[2], im_bands[3], im_bands[4], im_bands[5], im_bands[11]]
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filename_pan = satname + '_' + sitename + '_' + im_date + '_pan' + suffix
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filename_ms = satname + '_' + sitename + '_' + im_date + '_ms' + suffix
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print(i)
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if any(filename_pan in _ for _ in all_names_pan):
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filename_pan = satname + '_' + sitename + '_' + im_date + '_pan' + '_r' + suffix
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filename_ms = satname + '_' + sitename + '_' + im_date + '_ms' + '_r' + suffix
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all_names_pan.append(filename_pan)
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# local_data_pan = download_tif(im, rect_narra, pan_band, filepath_pan)
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# os.rename(local_data_pan, os.path.join(filepath_pan, filename_pan))
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# local_data_ms = download_tif(im, rect_narra, ms_bands, filepath_ms)
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# os.rename(local_data_ms, os.path.join(filepath_ms, filename_ms))
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with open(os.path.join(filepath, sitename + '_timestamps' + '.pkl'), 'wb') as f:
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pickle.dump(timestamps, f)
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with open(os.path.join(filepath, sitename + '_epsgcode' + '.pkl'), 'wb') as f:
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pickle.dump(im_epsg, f)
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@ -0,0 +1,140 @@
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# -*- coding: utf-8 -*-
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"""
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Created on Tue Mar 27 17:12:35 2018
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@author: Kilian
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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 as sds
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np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
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ee.Initialize()
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# initial settings
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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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satname = 'L8'
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sitename = 'NARRA'
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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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timestamps_sorted = sorted(timestamps)
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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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file_path_pan = os.path.join(os.getcwd(), 'data', 'L8', 'NARRA', 'pan')
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file_path_ms = os.path.join(os.getcwd(), 'data', 'L8', 'NARRA', '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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idx_high_cloud = []
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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 i 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 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)
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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_content = sum(sum(cloud_mask.astype(int)))/(cloud_mask.shape[0]*cloud_mask.shape[1])
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if cloud_content > cloud_thresh:
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print('skipped ' + str(i))
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idx_high_cloud.append(i)
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continue
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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, 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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# 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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# plt.figure()
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# plt.imshow(im_ms_ps[:,:,[2,1,0]])
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# for i,contour in enumerate(wl_pix): plt.plot(contour[:, 1], contour[:, 0], linewidth=2)
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# plt.axis('image')
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# plt.title(file_names_pan[i])
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# plt.show()
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plt.figure()
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centroids = []
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cmap = cm.get_cmap('jet')
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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.axis('equal')
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plt.title(file_names_pan[i])
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plt.draw()
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pt_in = np.array(ginput(1))
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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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#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}
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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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