forked from kilianv/CoastSat_WRL
You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
140 lines
5.1 KiB
Python
140 lines
5.1 KiB
Python
# -*- coding: utf-8 -*-
|
|
"""
|
|
Created on Tue Mar 27 17:12:35 2018
|
|
|
|
@author: Kilian
|
|
"""
|
|
|
|
# Initial settings
|
|
import os
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
import ee
|
|
import pdb
|
|
|
|
# other modules
|
|
from osgeo import gdal, ogr, osr
|
|
import pickle
|
|
import matplotlib.cm as cm
|
|
from pylab import ginput
|
|
|
|
# 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.measure as measure
|
|
|
|
|
|
# import own modules
|
|
import functions.utils as utils
|
|
import functions.sds as sds
|
|
|
|
np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
|
|
ee.Initialize()
|
|
|
|
# initial settings
|
|
cloud_thresh = 0.5 # threshold for cloud cover
|
|
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
|
|
|
|
satname = 'L8'
|
|
sitename = 'NARRA'
|
|
filepath = os.path.join(os.getcwd(), 'data', satname, sitename)
|
|
with open(os.path.join(filepath, sitename + '_timestamps' + '.pkl'), 'rb') as f:
|
|
timestamps = pickle.load(f)
|
|
timestamps_sorted = sorted(timestamps)
|
|
with open(os.path.join(filepath, sitename + '_epsgcode' + '.pkl'), 'rb') as f:
|
|
input_epsg = pickle.load(f)
|
|
|
|
|
|
file_path_pan = os.path.join(os.getcwd(), 'data', 'L8', 'NARRA', 'pan')
|
|
file_path_ms = os.path.join(os.getcwd(), 'data', 'L8', 'NARRA', 'ms')
|
|
file_names_pan = os.listdir(file_path_pan)
|
|
file_names_ms = os.listdir(file_path_ms)
|
|
N = len(file_names_pan)
|
|
idx_high_cloud = []
|
|
t = []
|
|
shorelines = []
|
|
|
|
for i in range(N):
|
|
# read pan image
|
|
fn_pan = os.path.join(file_path_pan, file_names_pan[i])
|
|
data = gdal.Open(fn_pan, gdal.GA_ReadOnly)
|
|
georef = np.array(data.GetGeoTransform())
|
|
bands = [data.GetRasterBand(i + 1).ReadAsArray() for i in range(data.RasterCount)]
|
|
im_pan = np.stack(bands, 2)[:,:,0]
|
|
# read ms image
|
|
fn_ms = os.path.join(file_path_ms, file_names_ms[i])
|
|
data = gdal.Open(fn_ms, gdal.GA_ReadOnly)
|
|
bands = [data.GetRasterBand(i + 1).ReadAsArray() for i in range(data.RasterCount)]
|
|
im_ms = np.stack(bands, 2)
|
|
# cloud mask
|
|
im_qa = im_ms[:,:,5]
|
|
cloud_mask = sds.create_cloud_mask(im_qa)
|
|
cloud_mask = transform.resize(cloud_mask, (im_pan.shape[0], im_pan.shape[1]),
|
|
order=0, preserve_range=True,
|
|
mode='constant').astype('bool_')
|
|
# resize the image using bilinear interpolation (order 1)
|
|
im_ms = transform.resize(im_ms,(im_pan.shape[0], im_pan.shape[1]),
|
|
order=1, preserve_range=True, mode='constant')
|
|
# check if -inf or nan values and add to cloud mask
|
|
im_inf = np.isin(im_ms[:,:,0], -np.inf)
|
|
im_nan = np.isnan(im_ms[:,:,0])
|
|
cloud_mask = np.logical_or(np.logical_or(cloud_mask, im_inf), im_nan)
|
|
cloud_content = sum(sum(cloud_mask.astype(int)))/(cloud_mask.shape[0]*cloud_mask.shape[1])
|
|
if cloud_content > cloud_thresh:
|
|
print('skipped ' + str(i))
|
|
idx_high_cloud.append(i)
|
|
continue
|
|
# rescale intensities
|
|
im_ms = sds.rescale_image_intensity(im_ms, cloud_mask, prob_high, plot_bool)
|
|
im_pan = sds.rescale_image_intensity(im_pan, cloud_mask, prob_high, plot_bool)
|
|
# pansharpen rgb image
|
|
im_ms_ps = sds.pansharpen(im_ms[:,:,[0,1,2]], im_pan, cloud_mask, 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], cloud_mask, plot_bool)
|
|
# detect edges
|
|
wl_pix = sds.find_wl_contours(im_ndwi, cloud_mask, min_contour_points, plot_bool)
|
|
# convert from pixels to world coordinates
|
|
wl_coords = sds.convert_pix2world(wl_pix, georef)
|
|
# convert to output epsg spatial reference
|
|
wl = sds.convert_epsg(wl_coords, input_epsg, output_epsg)
|
|
|
|
# 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(file_names_pan[i])
|
|
# plt.show()
|
|
|
|
plt.figure()
|
|
centroids = []
|
|
cmap = cm.get_cmap('jet')
|
|
for j,contour in enumerate(wl):
|
|
colours = cmap(np.linspace(0, 1, num=len(wl)))
|
|
centroids.append([np.mean(contour[:, 0]),np.mean(contour[:, 1])])
|
|
plt.plot(contour[:, 0], contour[:, 1], linewidth=2, color=colours[j,:])
|
|
plt.plot(np.mean(contour[:, 0]), np.mean(contour[:, 1]), 'o', color=colours[j,:])
|
|
plt.axis('equal')
|
|
plt.title(file_names_pan[i])
|
|
plt.draw()
|
|
pt_in = np.array(ginput(1))
|
|
dist_centroid = [np.linalg.norm(_ - pt_in) for _ in centroids]
|
|
shorelines.append(wl[np.argmin(dist_centroid)])
|
|
t.append(timestamps_sorted[i])
|
|
|
|
#plt.figure()
|
|
#plt.axis('equal')
|
|
#for j in range(len(shorelines)):
|
|
# plt.plot(shorelines[j][:,0], shorelines[j][:,1])
|
|
#plt.draw()
|
|
|
|
output = {'t':t, 'shorelines':shorelines}
|
|
|
|
with open(os.path.join(filepath, sitename + '_output' + '.pkl'), 'wb') as f:
|
|
pickle.dump(output, f) |