# -*- 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 math
import matplotlib.pyplot as plt
import numpy as np
import pdb

# 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
from utils import *
from sds import *

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

# 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

# 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]]];
# Dates
start_date = '2016-01-01'
end_date = '2016-12-01'
# 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')
output = []
# 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 = read_eeimage(im, rect_narra, plot_bool)
    # rescale intensities
    im_ms = rescale_image_intensity(im_ms, im_cloud, prob_high, plot_bool)
    im_pan = rescale_image_intensity(im_pan, im_cloud, prob_high, plot_bool)
    # pansharpen rgb image
    im_ms_ps = 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 = nd_index(im_ms_ps[:,:,3], im_ms_ps[:,:,1], im_cloud, plot_bool)
    # edge detection
    wl_pix = find_wl_contours(im_ndwi, im_cloud, min_contour_points, True)
    # convert from pixels to world coordinates
    wl_coords = convert_pix2world(wl_pix, crs['crs_15m'])
    output.append(wl_coords)