forked from kilianv/CoastSat_WRL
testing the NN classifier
kvos
7 years ago
parent
0a78e3c539
commit
220e5557f7
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# -*- coding: utf-8 -*-
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#==========================================================#
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# Extract shorelines from Landsat images
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#==========================================================#
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# Initial settings
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import os
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import numpy as np
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import matplotlib.pyplot as plt
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import ee
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import pdb
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# other modules
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from osgeo import gdal, ogr, osr
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import pickle
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import matplotlib.cm as cm
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from pylab import ginput
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# image processing modules
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import skimage.filters as filters
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import skimage.exposure as exposure
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import skimage.transform as transform
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import sklearn.decomposition as decomposition
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import skimage.measure as measure
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import skimage.morphology as morphology
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# machine learning modules
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from sklearn.model_selection import train_test_split
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from sklearn.neural_network import MLPClassifier
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from sklearn.preprocessing import StandardScaler, Normalizer
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from sklearn.externals import joblib
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# import own modules
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import functions.utils as utils
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import functions.sds as sds
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# some settings
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np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
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plt.rcParams['axes.grid'] = True
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plt.rcParams['figure.max_open_warning'] = 100
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ee.Initialize()
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# parameters
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cloud_thresh = 0.5 # threshold for cloud cover
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plot_bool = False # if you want the plots
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min_contour_points = 100# minimum number of points contained in each water line
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output_epsg = 28356 # GDA94 / MGA Zone 56
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buffer_size = 10 # radius (in pixels) of disk for buffer (pixel classification)
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min_beach_size = 50 # number of pixels in a beach (pixel classification)
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# load metadata (timestamps and epsg code) for the collection
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satname = 'L8'
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sitename = 'NARRA'
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#sitename = 'OLDBAR'
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# Load metadata
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filepath = os.path.join(os.getcwd(), 'data', satname, sitename)
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with open(os.path.join(filepath, sitename + '_timestamps' + '.pkl'), 'rb') as f:
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timestamps = pickle.load(f)
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with open(os.path.join(filepath, sitename + '_accuracy_georef' + '.pkl'), 'rb') as f:
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acc_georef = pickle.load(f)
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with open(os.path.join(filepath, sitename + '_epsgcode' + '.pkl'), 'rb') as f:
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input_epsg = pickle.load(f)
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with open(os.path.join(filepath, sitename + '_refpoints' + '.pkl'), 'rb') as f:
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refpoints = pickle.load(f)
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# sort timestamps and georef accuracy (dowloaded images are sorted by date in directory)
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timestamps_sorted = sorted(timestamps)
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idx_sorted = sorted(range(len(timestamps)), key=timestamps.__getitem__)
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acc_georef_sorted = [acc_georef[j] for j in idx_sorted]
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# path to images
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file_path_pan = os.path.join(os.getcwd(), 'data', satname, sitename, 'pan')
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file_path_ms = os.path.join(os.getcwd(), 'data', satname, sitename, 'ms')
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file_names_pan = os.listdir(file_path_pan)
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file_names_ms = os.listdir(file_path_ms)
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N = len(file_names_pan)
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# initialise some variables
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cloud_cover_ts = []
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date_acquired_ts = []
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acc_georef_ts = []
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idx_skipped = []
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idx_nocloud = []
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t = []
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shorelines = []
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#%%
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for i in range(1):
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i = 0
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# read pan image
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fn_pan = os.path.join(file_path_pan, file_names_pan[i])
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data = gdal.Open(fn_pan, gdal.GA_ReadOnly)
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georef = np.array(data.GetGeoTransform())
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bands = [data.GetRasterBand(i + 1).ReadAsArray() for i in range(data.RasterCount)]
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im_pan = np.stack(bands, 2)[:,:,0]
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nrows = im_pan.shape[0]
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ncols = im_pan.shape[1]
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# read ms image
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fn_ms = os.path.join(file_path_ms, file_names_ms[i])
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data = gdal.Open(fn_ms, gdal.GA_ReadOnly)
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bands = [data.GetRasterBand(i + 1).ReadAsArray() for i in range(data.RasterCount)]
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im_ms = np.stack(bands, 2)
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# cloud mask
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im_qa = im_ms[:,:,5]
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cloud_mask = sds.create_cloud_mask(im_qa, satname, plot_bool)
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cloud_mask = transform.resize(cloud_mask, (im_pan.shape[0], im_pan.shape[1]),
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order=0, preserve_range=True,
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mode='constant').astype('bool_')
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# resize the image using bilinear interpolation (order 1)
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im_ms = transform.resize(im_ms,(im_pan.shape[0], im_pan.shape[1]),
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order=1, preserve_range=True, mode='constant')
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# check if -inf or nan values and add to cloud mask
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im_inf = np.isin(im_ms[:,:,0], -np.inf)
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im_nan = np.isnan(im_ms[:,:,0])
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cloud_mask = np.logical_or(np.logical_or(cloud_mask, im_inf), im_nan)
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# calculate cloud cover and skip image if too high
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cloud_cover = sum(sum(cloud_mask.astype(int)))/(cloud_mask.shape[0]*cloud_mask.shape[1])
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if cloud_cover > cloud_thresh:
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print('skip ' + str(i) + ' - cloudy (' + str(cloud_cover) + ')')
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idx_skipped.append(i)
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continue
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idx_nocloud.append(i)
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# check if image for that date is already present
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if file_names_pan[i][len(satname)+1+len(sitename)+1:len(satname)+1+len(sitename)+1+10] in date_acquired_ts:
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# find the index of the image that is repeated
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idx_samedate = utils.find_indices(date_acquired_ts, lambda e : e == file_names_pan[i][9:19])
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idx_samedate = idx_samedate[0]
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print('cloud cover ' + str(cloud_cover) + ' - ' + str(cloud_cover_ts[idx_samedate]))
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print('acc georef ' + str(acc_georef_sorted[i]) + ' - ' + str(acc_georef_ts[idx_samedate]))
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# keep image with less cloud cover or best georeferencing accuracy
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if cloud_cover < cloud_cover_ts[idx_samedate] - 0.01:
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skip = False
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elif acc_georef_sorted[i] < acc_georef_ts[idx_samedate]:
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skip = False
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else:
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skip = True
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if skip:
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print('skip ' + str(i) + ' - repeated')
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idx_skipped.append(i)
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continue
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else:
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del shorelines[idx_samedate]
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del t[idx_samedate]
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del cloud_cover_ts[idx_samedate]
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del date_acquired_ts[idx_samedate]
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del acc_georef_ts[idx_samedate]
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print('keep ' + str(i) + ' - deleted ' + str(idx_samedate))
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# pansharpen rgb image
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im_ms_ps = sds.pansharpen(im_ms[:,:,[0,1,2]], im_pan, cloud_mask, plot_bool)
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im_display = sds.rescale_image_intensity(im_ms_ps[:,:,[2,1,0]], cloud_mask, 100, False)
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# add down-sized bands for NIR and SWIR (since pansharpening is not possible)
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im_ms_ps = np.append(im_ms_ps, im_ms[:,:,[3,4]], axis=2)
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# classify image in 4 classes (sand, whitewater, water, other) with NN classifier
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im_classif = sds.classify_image_NN(im_ms_ps, im_pan, cloud_mask, True)
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# labels
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im_sand = im_classif == 1
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im_swash = im_classif == 2
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im_water = im_classif == 3
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vec_sand = im_sand.reshape(ncols*nrows)
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vec_water = im_water.reshape(ncols*nrows)
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vec_swash = im_swash.reshape(ncols*nrows)
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t.append(timestamps_sorted[i])
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cloud_cover_ts.append(cloud_cover)
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acc_georef_ts.append(acc_georef_sorted[i])
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date_acquired_ts.append(file_names_pan[i][9:19])
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# calculate indices
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im_ndwi = sds.nd_index(im_ms_ps[:,:,3], im_ms_ps[:,:,1], cloud_mask, plot_bool)
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im_ndmwi = sds.nd_index(im_ms_ps[:,:,4], im_ms_ps[:,:,1], cloud_mask, plot_bool)
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im_nir = im_ms_ps[:,:,3]
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im_swir = im_ms_ps[:,:,4]
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im_ind = np.stack((im_ndwi, im_ndmwi), axis=-1)
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vec_ind = im_ind.reshape(nrows*ncols,2)
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# keep only beach
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morphology.remove_small_objects(im_sand, min_size=50, connectivity=2, in_place=True)
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# buffer around beach
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buffer_size = 7
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se = morphology.disk(buffer_size)
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im_buffer = morphology.binary_dilation(im_sand, se)
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vec_buffer = im_buffer.reshape(nrows*ncols)
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im = np.copy(im_display)
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im[~im_buffer,0] = 0
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im[~im_buffer,1] = 0
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im[~im_buffer,2] = 0
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plt.figure()
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plt.imshow(im)
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plt.draw()
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int_water = vec_ind[np.logical_and(vec_buffer,vec_water),:]
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int_sand = vec_ind[np.logical_and(vec_buffer,vec_sand),:]
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int_swash = vec_ind[np.logical_and(vec_buffer,vec_swash),:]
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fig, ax = plt.subplots(2,1, sharex=True)
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ax[0].hist(int_water[:,0], bins=100, label='water')
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ax[0].hist(int_sand[:,0], bins=100, alpha=0.5, label='sand')
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ax[0].hist(int_swash[:,0], bins=100, alpha=0.5, label='swash')
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ax[0].legend()
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ax[0].set_title('Water Index NIR-G')
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ax[1].hist(int_water[:,1], bins=100, label='water')
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ax[1].hist(int_sand[:,1], bins=100, alpha=0.5, label='sand')
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ax[1].hist(int_swash[:,1], bins=100, alpha=0.5, label='swash')
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ax[1].legend()
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ax[1].set_title('Modified Water Index SWIR-G')
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plt.draw()
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int_all = np.append(int_water,int_sand, axis=0)
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t1 = filters.threshold_otsu(int_all[:,0])
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t2 = filters.threshold_otsu(int_all[:,1])
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contours1 = measure.find_contours(im_ndwi, t1)
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contours2 = measure.find_contours(im_ndmwi, t1)
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plt.figure()
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plt.imshow(im_display)
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for i,contour in enumerate(contours1): plt.plot(contour[:, 1], contour[:, 0], linewidth=3, color='c')
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for i,contour in enumerate(contours2): plt.plot(contour[:, 1], contour[:, 0], linewidth=3, color='m')
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plt.draw()
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plt.figure()
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ax1 = plt.subplot(1,5,1)
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plt.imshow(im_display)
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plt.xticks([])
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plt.yticks([])
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plt.axis('off')
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plt.title('RGB')
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plt.subplot(1,5,2, sharex=ax1, sharey=ax1)
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plt.imshow(im_ndwi, cmap='seismic')
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plt.xticks([])
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plt.yticks([])
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plt.axis('off')
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plt.title('NDWI')
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plt.subplot(1,5,3, sharex=ax1, sharey=ax1)
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plt.imshow(im_ndmwi, cmap='seismic')
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plt.xticks([])
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plt.yticks([])
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plt.axis('off')
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plt.title('NDMWI')
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plt.subplot(1,5,4, sharex=ax1, sharey=ax1)
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plt.imshow(im_nir, cmap='seismic')
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plt.xticks([])
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plt.yticks([])
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plt.axis('off')
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plt.title('NIR')
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plt.subplot(1,5,5, sharex=ax1, sharey=ax1)
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plt.imshow(im_swir, cmap='seismic')
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plt.xticks([])
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plt.yticks([])
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plt.axis('off')
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plt.title('SWIR')
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fig, (ax1,ax2,ax3,ax4) = plt.subplots(4,2, figsize = (8,6))
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ax1[0].set_title('Probability density function')
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ax1[1].set_title('Cumulative distribution')
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im = im_ndwi
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t1 = filters.threshold_otsu(im)
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vals = ax1[0].hist(im.reshape(nrows*ncols), bins=300)
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ax1[0].plot([t1, t1],[0, np.max(vals[0])], 'r-', label='Otsu threshold')
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vals = ax1[1].hist(im.reshape(nrows*ncols), bins=300, cumulative=True, histtype='step')
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ax1[1].plot([t1, t1],[0, np.max(vals[0])], 'r-', label='Otsu threshold')
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ax1[0].set_ylabel('NDWI')
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im = im_ndmwi
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t1 = filters.threshold_otsu(im)
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vals = ax2[0].hist(im.reshape(nrows*ncols), bins=300)
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ax2[0].plot([t1, t1],[0, np.max(vals[0])], 'r-', label='Otsu threshold')
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vals = ax2[1].hist(im.reshape(nrows*ncols), bins=300, cumulative=True, histtype='step')
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ax2[1].plot([t1, t1],[0, np.max(vals[0])], 'r-', label='Otsu threshold')
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ax2[0].set_ylabel('NDMWI')
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im = im_nir
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t1 = filters.threshold_otsu(im)
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vals = ax3[0].hist(im.reshape(nrows*ncols), bins=300)
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ax3[0].plot([t1, t1],[0, np.max(vals[0])], 'r-', label='Otsu threshold')
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vals = ax3[1].hist(im.reshape(nrows*ncols), bins=300, cumulative=True, histtype='step')
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ax3[1].plot([t1, t1],[0, np.max(vals[0])], 'r-', label='Otsu threshold')
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ax3[0].set_ylabel('NIR')
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im = im_swir
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t1 = filters.threshold_otsu(im)
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vals = ax4[0].hist(im.reshape(nrows*ncols), bins=300)
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ax4[0].plot([t1, t1],[0, np.max(vals[0])], 'r-', label='Otsu threshold')
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vals = ax4[1].hist(im.reshape(nrows*ncols), bins=300, cumulative=True, histtype='step')
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ax4[1].plot([t1, t1],[0, np.max(vals[0])], 'r-', label='Otsu threshold')
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ax4[0].set_ylabel('SWIR')
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plt.draw()
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#%%
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# calculate NDWI
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im_ndwi = sds.nd_index(im_ms_ps[:,:,3], im_ms_ps[:,:,1], cloud_mask, plot_bool)
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# detect edges
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wl_pix = sds.find_wl_contours(im_ndwi, cloud_mask, min_contour_points, True)
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# convert from pixels to world coordinates
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wl_coords = sds.convert_pix2world(wl_pix, georef)
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# convert to output epsg spatial reference
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wl = sds.convert_epsg(wl_coords, input_epsg, output_epsg)
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# classify sand pixels
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im_sand = sds.classify_sand_unsupervised(im_ms_ps, im_pan, cloud_mask, wl_pix, False, min_beach_size, plot_bool)
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|
|
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|
# plot a figure to select the correct water line and discard cloudy images
|
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|
plt.figure()
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|
cmap = cm.get_cmap('jet')
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|
plt.subplot(121)
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|
plt.imshow(im_ms_ps[:,:,[2,1,0]])
|
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|
for j,contour in enumerate(wl_pix):
|
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|
colours = cmap(np.linspace(0, 1, num=len(wl_pix)))
|
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|
plt.plot(contour[:, 1], contour[:, 0], linewidth=2, color=colours[j,:])
|
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|
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('skip ' + str(i) + ' - manual')
|
||||||
|
idx_skipped.append(i)
|
||||||
|
continue
|
||||||
|
# get contour that was selected (click closest to centroid)
|
||||||
|
dist_centroid = [np.linalg.norm(_ - pt_in) for _ in centroids]
|
||||||
|
shorelines.append(wl[np.argmin(dist_centroid)])
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
# plot all shorelines
|
||||||
|
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, 'acc_georef':acc_georef_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)
|
||||||
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Reference in New Issue