forked from kilianv/CoastSat_WRL
implemented sand classification
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# -*- coding: utf-8 -*-
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#==========================================================#
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# Extract shorelines from Landsat images
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#==========================================================#
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# Initial settings
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import ee
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import matplotlib.pyplot as plt
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import matplotlib.cm as cm
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import numpy as np
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import pandas as pd
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from datetime import datetime
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import pickle
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import pdb
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import pytz
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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.morphology as morphology
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import skimage.measure as measure
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# machine learning modules
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from sklearn.cluster import KMeans
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# my 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'] = False
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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 = 0.5 # threshold for cloud cover
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plot_bool = True # 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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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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# select collection
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satname = 'L8'
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input_col = ee.ImageCollection('LANDSAT/LC08/C01/T1_RT_TOA') # Landsat 8 Tier 1 TOA
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# location (Narrabeen-Collaroy beach)
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polygon = [[[151.3473129272461,-33.69035274454718],
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[151.2820816040039,-33.68206818063878],
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[151.27281188964844,-33.74775138989556],
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[151.3425064086914,-33.75231878701767],
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[151.3473129272461,-33.69035274454718]]];
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# dates
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start_date = '2013-01-01'
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end_date = '2018-12-31'
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# filter by location and date
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flt_col = input_col.filterBounds(ee.Geometry.Polygon(polygon)).filterDate(start_date, end_date)
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n_img = flt_col.size().getInfo()
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print('Number of images covering the polygon:', n_img)
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im_all = flt_col.getInfo().get('features')
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i = 0 # first image
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# find image in ee database
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im = ee.Image(im_all[i].get('id'))
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# load image as np.array
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im_pan, im_ms, im_cloud, crs, meta = sds.read_eeimage(im, polygon, satname, plot_bool)
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# rescale intensities
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im_ms = sds.rescale_image_intensity(im_ms, im_cloud, prob_high, plot_bool)
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im_pan = sds.rescale_image_intensity(im_pan, im_cloud, 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, im_cloud, 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], im_cloud, plot_bool)
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# edge detection
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wl_pix = sds.find_wl_contours(im_ndwi, im_cloud, 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, crs['crs_15m'])
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# convert to output epsg spatial reference
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wl = sds.convert_epsg(wl_coords, crs['epsg_code'], output_epsg)
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# classify sand pixels
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im_sand = sds.classify_sand_unsupervised(im_ms_ps, im_pan, im_cloud, wl_pix, buffer_size, min_beach_size, plot_bool)
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#%%
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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('Detected water lines')
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plt.show()
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vec = im_ms_ps.reshape(im_ms_ps.shape[0] * im_ms_ps.shape[1], im_ms_ps.shape[2])
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vec_pan = im_pan.reshape(im_pan.shape[0]*im_pan.shape[1])
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features = np.zeros((len(vec), 5))
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features[:,[0,1,2,3]] = vec[:,[0,1,2,3]]
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features[:,4] = vec_pan
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vec_mask = im_cloud.reshape(im_ms_ps.shape[0] * im_ms_ps.shape[1])
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# create buffer
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im_buffer = np.zeros((im_ms_ps.shape[0], im_ms_ps.shape[1]))
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for i, contour in enumerate(wl_pix):
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indices = [(int(_[0]), int(_[1])) for _ in list(np.round(contour))]
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for j, idx in enumerate(indices):
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im_buffer[idx] = 1
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plt.figure()
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plt.imshow(im_buffer)
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plt.draw()
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se = morphology.disk(buffer_size)
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im_buffer = morphology.binary_dilation(im_buffer, se)
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plt.figure()
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plt.imshow(im_buffer)
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plt.draw()
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vec_buffer = (im_buffer == 1).reshape(im_ms_ps.shape[0] * im_ms_ps.shape[1])
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vec_buffer= np.logical_and(vec_buffer, ~vec_mask)
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#vec_buffer = np.ravel_multi_index(z,(im_ms_ps.shape[0], im_ms_ps.shape[1]))
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kmeans = KMeans(n_clusters=6, random_state=0).fit(vec[vec_buffer,:])
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labels = kmeans.labels_
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labels_full = np.ones((len(vec_mask))) * np.nan
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labels_full[vec_buffer] = labels
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im_labels = labels_full.reshape(im_ms_ps.shape[0], im_ms_ps.shape[1])
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plt.figure()
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plt.imshow(im_labels)
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plt.axis('equal')
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plt.draw()
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utils.compare_images(im_labels, im_pan)
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plt.figure()
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for i in range(6): plt.plot(kmeans.cluster_centers_[i,:])
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plt.draw()
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im_sand = im_labels == np.argmax(np.mean(kmeans.cluster_centers_[:,[0,1,2,4]], axis=1))
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im_sand2 = morphology.remove_small_objects(im_sand, min_size=min_beach_size, connectivity=2)
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im_sand3 = morphology.binary_dilation(im_sand2, morphology.disk(1))
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plt.figure()
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plt.imshow(im_sand3)
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plt.draw()
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im_ms_ps[im_sand3,0] = 0
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im_ms_ps[im_sand3,1] = 0
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im_ms_ps[im_sand3,2] = 1
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plt.figure()
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plt.imshow(im_ms_ps[:,:,[2,1,0]])
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plt.axis('image')
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plt.title('Sand classification')
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plt.show()
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#%%
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