Supprimer 'sds_extract.py'

sds_extract.py

@@ -1,111 +0,0 @@
-# -*- coding: utf-8 -*-
-
-#==========================================================#
-# Extract shorelines from Landsat images
-#==========================================================#
-
-# Initial settings
-import ee
-import matplotlib.pyplot as plt
-import matplotlib.cm as cm
-import numpy as np
-import pandas as pd
-from datetime import datetime
-import pickle
-import pdb
-import pytz
-
-
-# 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.morphology as morphology
-import skimage.measure as measure
-
-# my 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'] = False
-plt.rcParams['figure.max_open_warning'] = 100
-ee.Initialize()
-
-# parameters
-cloud_thresh = 0.5      # threshold for cloud cover
-plot_bool = True      # if you want the plots
-min_contour_points = 100# minimum number of points contained in each water line
-output_epsg = 28356     # GDA94 / MGA Zone 56
-buffer_size = 10        # radius of disk for buffer (sand classif parameter)
-min_beach_size = 50     # number of pixels in a beach (sand classif parameter)
-
-# select collection
-satname = 'L8'
-input_col = ee.ImageCollection('LANDSAT/LC08/C01/T1_RT_TOA') # Landsat 8 Tier 1 TOA
-
-# location (Narrabeen-Collaroy beach)
-polygon = [[[151.3473129272461,-33.69035274454718],
-            [151.2820816040039,-33.68206818063878], 
-            [151.27281188964844,-33.74775138989556],
-            [151.3425064086914,-33.75231878701767],
-            [151.3473129272461,-33.69035274454718]]];
-
-# dates
-start_date = '2013-01-01'
-end_date = '2018-12-31'
-
-# filter by location and date
-flt_col = input_col.filterBounds(ee.Geometry.Polygon(polygon)).filterDate(start_date, end_date)
-
-n_img = flt_col.size().getInfo()
-print('Number of images covering the polygon:', n_img)
-im_all = flt_col.getInfo().get('features')
-
-i = 0 # first image
-    
-# find image in ee database
-im = ee.Image(im_all[i].get('id')) 
-
-# load image as np.array
-im_pan, im_ms, cloud_mask, crs, meta = sds.read_eeimage(im, polygon, satname, plot_bool)
-
-# mask -inf or nan values on the image 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)
-cloud_cover = sum(sum(cloud_mask.astype(int)))/(cloud_mask.shape[0]*cloud_mask.shape[1])
-print('Cloud cover : ' + str(int(round(100*cloud_cover))) + ' %')
-
-# 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) 
-
-# calculate NDWI
-im_ndwi = sds.nd_index(im_ms_ps[:,:,3], im_ms_ps[:,:,1], cloud_mask, plot_bool)
-
-# edge detection
-wl_pix = sds.find_wl_contours(im_ndwi, cloud_mask, min_contour_points, plot_bool)
-
-plt.figure()
-plt.imshow(im_ms_ps[:,:,[2,1,0]])
-for i,contour in enumerate(wl_pix): plt.plot(contour[:, 1], contour[:, 0], linewidth=2)
-plt.axis('image')
-plt.title('Detected water lines')
-plt.show()
-
-# convert from pixels to world coordinates
-wl_coords = sds.convert_pix2world(wl_pix, crs['crs_15m'])
-
-# convert to output epsg spatial reference
-wl = sds.convert_epsg(wl_coords, crs['epsg_code'], output_epsg)
-
-# classify sand pixels with Kmeans
-#im_sand = sds.classify_sand_unsupervised(im_ms_ps, im_pan, cloud_mask, wl_pix, buffer_size, min_beach_size, plot_bool)
-
-# classify image in 4 classes (sand, whitewater, water, other) with NN classifier
-im_classif = sds.classify_image_NN(im_ms_ps, im_pan, cloud_mask, plot_bool)