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261 lines
8.3 KiB
Python
261 lines
8.3 KiB
Python
7 years ago
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# -*- coding: utf-8 -*-
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"""
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Created on Thu Mar 1 14:32:08 2018
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@author: z5030440
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Main code to extract shorelines from Landsat imagery
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"""
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# Preamble
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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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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.morphology as morphology
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import skimage.measure as measure
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# my functions
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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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#%% Select images
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# parameters
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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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cloud_threshold = 0.7
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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 = [[[151.3473129272461,-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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with open('data/narra_beach.pkl', 'rb') as f:
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pts_beach = pickle.load(f)
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with open('data/idx_nogt.pkl', 'rb') as f:
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idx_nogt = pickle.load(f)
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idx_nogt = np.array(idx_nogt)
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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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#%% Extract shorelines
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metadata = {'timestamp':[],
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'date_acquired':[],
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'cloud_cover':[],
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'geom_rmse_model':[],
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'gcp_model':[],
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'quality':[],
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'sun_azimuth':[],
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'sun_elevation':[]}
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skipped_images = np.zeros((n_img,1)).astype(bool)
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output_wl = []
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# loop through all images
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for i in range(n_img):
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if np.isin(i, idx_nogt):
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continue
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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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# load image as np.array
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im_pan, im_ms, im_cloud, crs, meta = sds.read_eeimage(im, rect_narra, plot_bool)
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# if clouds -> skip the image
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if sum(sum(im_cloud.astype(int)))/(im_cloud.shape[0]*im_cloud.shape[1]) > cloud_threshold:
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skipped_images[i] = True
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continue
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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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# find contour closest to narrabeen beach
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sum_dist = np.zeros(len(wl))
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for k,contour in enumerate(wl):
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min_dist = np.zeros(len(pts_beach))
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for j,pt in enumerate(pts_beach):
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min_dist[j] = np.min(np.linalg.norm(contour - pt, axis=1))
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sum_dist[k] = np.sum(min_dist)/len(min_dist)
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try:
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wl_beach = wl[np.argmin(sum_dist)]
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# plt.figure()
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# plt.axis('equal')
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# plt.plot(pts_beach[:,0], pts_beach[:,1], 'ko')
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# plt.plot(wl_beach[:,0], wl_beach[:,1], 'r')
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# plt.show()
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except:
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wl_beach = []
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plt.figure()
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plt.imshow(im_ms_ps[:,:,[2,1,0]])
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for k,contour in enumerate(wl_pix): plt.plot(contour[:, 1], contour[:, 0], linewidth=2)
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if len(wl_beach) > 0:
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plt.plot(wl_pix[np.argmin(sum_dist)][:,1], wl_pix[np.argmin(sum_dist)][:,0], linewidth=3, color='w')
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plt.axis('image')
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plt.title('im ' + str(i) + ' : ' + datetime.strftime(datetime
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.fromtimestamp(meta['timestamp']/1000, tz=pytz.utc)
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.astimezone(pytz.timezone('Australia/Sydney')), '%Y-%m-%d %H:%M:%S %Z%z'))
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plt.show()
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# manually validate shoreline detection
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input_pt = np.array(ginput(1))
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if input_pt[0,1] > 300:
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skipped_images[i] = True
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continue
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# store metadata of each image in dict
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metadata['timestamp'].append(meta['timestamp'])
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metadata['date_acquired'].append(meta['date_acquired'])
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metadata['cloud_cover'].append(sum(sum(im_cloud.astype(int)))/(im_cloud.shape[0]*im_cloud.shape[1]))
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metadata['geom_rmse_model'].append(meta['geom_rmse_model'])
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metadata['gcp_model'].append(meta['gcp_model'])
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metadata['quality'].append(meta['quality'])
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metadata['sun_azimuth'].append(meta['sun_azimuth'])
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metadata['sun_elevation'].append(meta['sun_elevation'])
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# store water lines
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output_wl.append(wl_beach)
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print(i)
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# generate datetimes
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#fmt = '%Y-%m-%d %H:%M:%S %Z%z'
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#au_tz = pytz.timezone('Australia/Sydney')
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dt = [];
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t = metadata['timestamp']
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for k in range(len(t)): dt.append(datetime.fromtimestamp(t[k]/1000, tz=pytz.utc))
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# save outputs
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data = metadata.copy()
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data.update({'dt':dt})
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data.update({'contours':output_wl})
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with open('data_gt15d_32_56.pkl', 'wb') as f:
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pickle.dump(data, f)
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#%% Load data
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##with open('data_2016.pkl', 'rb') as f:
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## data = pickle.load(f)
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#
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#
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## load backgroud image
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#i = 0
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#im = ee.Image(im_all[i].get('id'))
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#im_pan, im_ms, im_cloud, crs, meta = sds.read_eeimage(im, rect_narra, plot_bool)
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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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#im_ms_ps = sds.pansharpen(im_ms[:,:,[0,1,2]], im_pan, im_cloud, 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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#plt.axis('image')
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#plt.title('2016 shorelines')
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#
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#n = len(data['cloud_cover'])
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#idx_best = []
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## remove overlapping images, based on cloud cover
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#for i in range(n):
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# date_im = data['date_acquired'][i]
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# idx = np.isin(data['date_acquired'], date_im)
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# best = np.where(idx)[0][np.argmin(np.array(data['cloud_cover'])[idx])]
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# if ~np.isin(best, idx_best):
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# idx_best.append(best)
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#
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#point_narra = np.array([342500, 6266990])
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#plt.figure()
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#plt.axis('equal')
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#plt.grid()
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#cmap = cm.get_cmap('jet')
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#colours = cmap(np.linspace(0, 1, num=len(idx_best)))
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#for i, idx in enumerate(idx_best):
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# for j in range(len(data['contours'][i])):
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# if np.any(np.linalg.norm(data['contours'][i][j][:,[0,1]] - point_narra, axis=1) < 200):
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# plt.plot(data['contours'][i][j][:,0], data['contours'][i][j][:,1],
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# label=str(data['date_acquired'][i]),
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# linewidth=2, color=colours[i,:])
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#
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#plt.legend()
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#plt.show()
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#
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#pts_narra = sds.convert_epsg(pts_narra, output_epsg, 4326)
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#
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##kml.newlinestring(name="beach",
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## coords = [(_[0], _[1]) for _ in pts_narra])
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##kml.save("narra.kml")
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#%%
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#with open('data_gt15d_0_31.pkl', 'rb') as f:
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# data1 = pickle.load(f)
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#with open('data_gt15d_32_56.pkl', 'rb') as f:
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# data2 = pickle.load(f)
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#with open('data_gt15d_99_193.pkl', 'rb') as f:
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# data3 = pickle.load(f)
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#
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#data = []
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#data = data1.copy()
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#for k,cat in enumerate(data.keys()):
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# for j in range(len(data2[cat])):
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# data[cat].append(data2[cat][j])
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# for j in range(len(data3[cat])):
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# data[cat].append(data3[cat][j])
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#
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#
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#with open('data_gt_l8.pkl', 'wb') as f:
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# pickle.dump(data, f)
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