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76 lines
2.2 KiB
Python
76 lines
2.2 KiB
Python
7 years ago
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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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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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# 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 = 'L7'
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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(filepath, 'pan')
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file_path_ms = os.path.join(filepath, '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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