# -*- 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
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
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, im_cloud, crs, meta = sds.read_eeimage(im, polygon, satname, plot_bool)

# 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)

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
im_sand = sds.classify_sand_unsupervised(im_ms_ps, im_pan, im_cloud, wl_pix, buffer_size, min_beach_size, plot_bool)