geetools_VH/read_images.py

# -*- coding: utf-8 -*-

#==========================================================#
# Extract shorelines from Landsat images
#==========================================================#

# 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

# some settings
np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
plt.rcParams['axes.grid'] = True
plt.rcParams['figure.max_open_warning'] = 100
ee.Initialize()

# parameters
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

# load metadata (timestamps and epsg code) for the collection
satname = 'L8'
#sitename = 'NARRA'
sitename = 'OLDBAR'
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) # sort timestamps since images are sorted in directory
with open(os.path.join(filepath, sitename + '_epsgcode' + '.pkl'), 'rb') as f:
    input_epsg = pickle.load(f)
with open(os.path.join(filepath, sitename + '_refpoints' + '.pkl'), 'rb') as f:
    refpoints = pickle.load(f)

# path to images
file_path_pan = os.path.join(os.getcwd(), 'data', satname, sitename, 'pan')
file_path_ms = os.path.join(os.getcwd(), 'data', satname, sitename, 'ms')
file_names_pan = os.listdir(file_path_pan)
file_names_ms = os.listdir(file_path_ms)
N = len(file_names_pan)

# initialise some variables
cloud_cover_ts = []
date_acquired_ts = []
idx_skipped = []
idx_nocloud = []
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, satname, plot_bool)
    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_cover = sum(sum(cloud_mask.astype(int)))/(cloud_mask.shape[0]*cloud_mask.shape[1])
    if cloud_cover > cloud_thresh:
        print('skipped cloud ' + str(i))
        idx_skipped.append(i)
        continue
    idx_nocloud.append(i)
    # check if image for that date is already present and keep the one with less clouds
    if file_names_pan[i][len(satname)+1+len(sitename)+1:len(satname)+1+len(sitename)+1+10] in date_acquired_ts:
        idx_samedate = utils.find_indices(date_acquired_ts, lambda e : e == file_names_pan[i][9:19])
        idx_samedate = idx_samedate[0]
        print(str(cloud_cover) + ' - ' + str(cloud_cover_ts[idx_samedate]))
        if cloud_cover >= cloud_cover_ts[idx_samedate]:
            print('skipped double ' + str(i))
            idx_skipped.append(i)
            continue
        else:
            del shorelines[idx_samedate]
            del t[idx_samedate]
            del cloud_cover_ts[idx_samedate]
            del date_acquired_ts[idx_samedate]
            print('deleted ' + str(idx_samedate))
            
    # 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)
    
    # plot a figure to select the correct water line and discard cloudy images
    plt.figure()
    cmap = cm.get_cmap('jet')
    plt.subplot(121)
    plt.imshow(im_ms_ps[:,:,[2,1,0]])
    for j,contour in enumerate(wl_pix):
        colours = cmap(np.linspace(0, 1, num=len(wl_pix)))
        plt.plot(contour[:, 1], contour[:, 0], linewidth=2, color=colours[j,:])
    plt.axis('image')
    plt.title(file_names_pan[i])
    plt.subplot(122)
    centroids = []
    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.plot(refpoints[:,0], refpoints[:,1], 'k.')
    plt.axis('equal')
    plt.title(file_names_pan[i])
    mng = plt.get_current_fig_manager()                                         
    mng.window.showMaximized()
    plt.tight_layout()   
    plt.draw()
    # click on the left image to discard, otherwise on the closest centroid in the right image
    pt_in = np.array(ginput(n=1, timeout=1000))
    if pt_in[0][0] < 10000:
        print('skipped manual ' + str(i))
        idx_skipped.append(i)
        continue
    # get contour that was selected (clock closest to centroid)
    dist_centroid = [np.linalg.norm(_ - pt_in) for _ in centroids]
    shorelines.append(wl[np.argmin(dist_centroid)])
    t.append(timestamps_sorted[i])
    cloud_cover_ts.append(cloud_cover)
    date_acquired_ts.append(file_names_pan[i][9:19])
    
    
#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, 'cloud_cover':cloud_cover_ts}

with open(os.path.join(filepath, sitename + '_output' + '.pkl'), 'wb') as f:
    pickle.dump(output, f)
    
with open(os.path.join(filepath, sitename + '_skipped' + '.pkl'), 'wb') as f:
    pickle.dump(idx_skipped, f)

with open(os.path.join(filepath, sitename + '_idxnocloud' + '.pkl'), 'wb') as f:
    pickle.dump(idx_nocloud, f)