geetools_VH/sds_extract.py

# -*- coding: utf-8 -*-
"""
Created on Thu Mar  1 14:32:08 2018

@author: z5030440

Main code to extract shorelines from Landsat imagery
"""
# Preamble

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 functions
import functions.utils as utils
import functions.sds as sds

np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
ee.Initialize()

#%% Select images

# parameters
plot_bool = False # if you want the plots
prob_high = 99.9 # upper probability to clip and rescale pixel intensity
min_contour_points = 100 # minimum number of points contained in each water line
output_epsg = 28356 # GDA94 / MGA Zone 56
cloud_threshold = 0.8

# select collection
input_col = ee.ImageCollection('LANDSAT/LC08/C01/T1_RT_TOA')

# location (Narrabeen-Collaroy beach)
rect_narra = [[[151.3473129272461,-33.69035274454718],
              [151.2820816040039,-33.68206818063878], 
              [151.27281188964844,-33.74775138989556],
              [151.3425064086914,-33.75231878701767],
              [151.3473129272461,-33.69035274454718]]];
#rect_narra = [[[151.301454, -33.700754],
#               [151.311453, -33.702075], 
#               [151.307237, -33.739761],
#               [151.294220, -33.736329],
#               [151.301454, -33.700754]]];
# Dates
start_date = '2016-01-01'
end_date = '2016-12-31'
# filter by location
flt_col = input_col.filterBounds(ee.Geometry.Polygon(rect_narra)).filterDate(start_date, end_date)

n_img = flt_col.size().getInfo()
print('Number of images covering Narrabeen:', n_img)
im_all = flt_col.getInfo().get('features')

#%% Extract shorelines
metadata = {'timestamp':[],
            'date_acquired':[],
            'cloud_cover':[],
            'geom_rmse_model':[],
            'gcp_model':[],
            'quality':[],
            'sun_azimuth':[],
            'sun_elevation':[]}
skipped_images = np.zeros((n_img,1)).astype(bool)
output_wl = []
# loop through all images
for i in range(n_img):
    # find each image in ee database
    im = ee.Image(im_all[i].get('id')) 
    # load image as np.array
    im_pan, im_ms, im_cloud, crs, meta = sds.read_eeimage(im, rect_narra, plot_bool)
    # if 100% cloud
    if sum(sum(im_cloud.astype(int)))/(im_cloud.shape[0]*im_cloud.shape[1]) > cloud_threshold:
        skipped_images[i] = True
        continue
    # store metadata of each image in dict
    metadata['timestamp'].append(meta['timestamp'])
    metadata['date_acquired'].append(meta['date_acquired'])
    metadata['cloud_cover'].append(sum(sum(im_cloud.astype(int)))/(im_cloud.shape[0]*im_cloud.shape[1]))
    metadata['geom_rmse_model'].append(meta['geom_rmse_model'])
    metadata['gcp_model'].append(meta['gcp_model'])
    metadata['quality'].append(meta['quality'])
    metadata['sun_azimuth'].append(meta['sun_azimuth'])
    metadata['sun_elevation'].append(meta['sun_elevation'])
    # rescale intensities
    im_ms = sds.rescale_image_intensity(im_ms, im_cloud, prob_high, plot_bool)
    im_pan = sds.rescale_image_intensity(im_pan, im_cloud, prob_high, plot_bool)
    # pansharpen rgb image
    im_ms_ps = sds.pansharpen(im_ms[:,:,[0,1,2]], im_pan, im_cloud, 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], im_cloud, plot_bool)
    # edge detection
    wl_pix = sds.find_wl_contours(im_ndwi, im_cloud, min_contour_points, plot_bool)
    # 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)
    
    output_wl.append(wl)
    print(i)
   
# generate datetimes
#fmt = '%Y-%m-%d %H:%M:%S %Z%z'
#au_tz = pytz.timezone('Australia/Sydney')
dt = [];
t = metadata['timestamp']
for k in range(len(t)): dt.append(datetime.fromtimestamp(t[k]/1000, tz=pytz.utc)) 

# save outputs
data = metadata.copy()
data.update({'dt':dt})
data.update({'contours':output_wl})  

with open('data_2016.pkl', 'wb') as f:
    pickle.dump(data, f) 
#%% Load data

#with open('data_2016.pkl', 'rb') as f:
#    data = pickle.load(f)
    

# load backgroud image
i = 0
im = ee.Image(im_all[i].get('id')) 
im_pan, im_ms, im_cloud, crs, meta = sds.read_eeimage(im, rect_narra, plot_bool)
im_ms = sds.rescale_image_intensity(im_ms, im_cloud, prob_high, plot_bool)
im_pan = sds.rescale_image_intensity(im_pan, im_cloud, prob_high, plot_bool)
im_ms_ps = sds.pansharpen(im_ms[:,:,[0,1,2]], im_pan, im_cloud, plot_bool)

plt.figure()
plt.imshow(im_ms_ps[:,:,[2,1,0]])
plt.axis('image')
plt.title('2016 shorelines')

n = len(data['cloud_cover'])
idx_best = []
# remove overlapping images, based on cloud cover
for i in range(n):
    date_im = data['date_acquired'][i]
    idx = np.isin(data['date_acquired'], date_im)
    best = np.where(idx)[0][np.argmin(np.array(data['cloud_cover'])[idx])]
    if ~np.isin(best, idx_best):
        idx_best.append(best)

point_narra = np.array([342500, 6266990])
plt.figure()
plt.axis('equal')
plt.grid()
cmap = cm.get_cmap('jet')
colours = cmap(np.linspace(0, 1, num=len(idx_best)))
for i, idx in enumerate(idx_best):
    for j in range(len(data['contours'][i])):
        if np.any(np.linalg.norm(data['contours'][i][j][:,[0,1]] - point_narra, axis=1) < 200):
            plt.plot(data['contours'][i][j][:,0], data['contours'][i][j][:,1],
                     label=str(data['date_acquired'][i]),
                     linewidth=2, color=colours[i,:])
            
plt.legend()
plt.show()