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825 lines
34 KiB
Python
825 lines
34 KiB
Python
"""This module contains all the functions needed for extracting satellite-derived shorelines (SDS)
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Author: Kilian Vos, Water Research Laboratory, University of New South Wales
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"""
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# load modules
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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 pdb
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# image processing modules
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import skimage.filters as filters
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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.externals import joblib
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from shapely.geometry import LineString
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# other modules
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import matplotlib.patches as mpatches
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import matplotlib.lines as mlines
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import matplotlib.cm as cm
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from matplotlib import gridspec
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from pylab import ginput
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import pickle
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# own modules
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from coastsat import SDS_tools, SDS_preprocess
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np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
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###################################################################################################
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# IMAGE CLASSIFICATION FUNCTIONS
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###################################################################################################
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def calculate_features(im_ms, cloud_mask, im_bool):
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"""
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Calculates a range of features on the image that are used for the supervised classification.
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The features include spectral normalized-difference indices and standard deviation of the image.
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KV WRL 2018
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Arguments:
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-----------
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im_ms: np.array
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RGB + downsampled NIR and SWIR
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cloud_mask: np.array
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2D cloud mask with True where cloud pixels are
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im_bool: np.array
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2D array of boolean indicating where on the image to calculate the features
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Returns: -----------
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features: np.array
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matrix containing each feature (columns) calculated for all
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the pixels (rows) indicated in im_bool
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"""
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# add all the multispectral bands
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features = np.expand_dims(im_ms[im_bool,0],axis=1)
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for k in range(1,im_ms.shape[2]):
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feature = np.expand_dims(im_ms[im_bool,k],axis=1)
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features = np.append(features, feature, axis=-1)
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# NIR-G
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im_NIRG = SDS_tools.nd_index(im_ms[:,:,3], im_ms[:,:,1], cloud_mask)
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features = np.append(features, np.expand_dims(im_NIRG[im_bool],axis=1), axis=-1)
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# SWIR-G
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im_SWIRG = SDS_tools.nd_index(im_ms[:,:,4], im_ms[:,:,1], cloud_mask)
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features = np.append(features, np.expand_dims(im_SWIRG[im_bool],axis=1), axis=-1)
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# NIR-R
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im_NIRR = SDS_tools.nd_index(im_ms[:,:,3], im_ms[:,:,2], cloud_mask)
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features = np.append(features, np.expand_dims(im_NIRR[im_bool],axis=1), axis=-1)
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# SWIR-NIR
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im_SWIRNIR = SDS_tools.nd_index(im_ms[:,:,4], im_ms[:,:,3], cloud_mask)
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features = np.append(features, np.expand_dims(im_SWIRNIR[im_bool],axis=1), axis=-1)
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# B-R
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im_BR = SDS_tools.nd_index(im_ms[:,:,0], im_ms[:,:,2], cloud_mask)
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features = np.append(features, np.expand_dims(im_BR[im_bool],axis=1), axis=-1)
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# calculate standard deviation of individual bands
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for k in range(im_ms.shape[2]):
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im_std = SDS_tools.image_std(im_ms[:,:,k], 1)
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features = np.append(features, np.expand_dims(im_std[im_bool],axis=1), axis=-1)
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# calculate standard deviation of the spectral indices
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im_std = SDS_tools.image_std(im_NIRG, 1)
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features = np.append(features, np.expand_dims(im_std[im_bool],axis=1), axis=-1)
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im_std = SDS_tools.image_std(im_SWIRG, 1)
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features = np.append(features, np.expand_dims(im_std[im_bool],axis=1), axis=-1)
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im_std = SDS_tools.image_std(im_NIRR, 1)
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features = np.append(features, np.expand_dims(im_std[im_bool],axis=1), axis=-1)
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im_std = SDS_tools.image_std(im_SWIRNIR, 1)
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features = np.append(features, np.expand_dims(im_std[im_bool],axis=1), axis=-1)
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im_std = SDS_tools.image_std(im_BR, 1)
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features = np.append(features, np.expand_dims(im_std[im_bool],axis=1), axis=-1)
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return features
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def classify_image_NN(im_ms, im_extra, cloud_mask, min_beach_area, clf):
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"""
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Classifies every pixel in the image in one of 4 classes:
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- sand --> label = 1
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- whitewater (breaking waves and swash) --> label = 2
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- water --> label = 3
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- other (vegetation, buildings, rocks...) --> label = 0
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The classifier is a Neural Network previously trained.
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KV WRL 2018
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Arguments:
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-----------
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im_ms: np.array
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Pansharpened RGB + downsampled NIR and SWIR
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im_extra:
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only used for Landsat 7 and 8 where im_extra is the panchromatic band
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cloud_mask: np.array
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2D cloud mask with True where cloud pixels are
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min_beach_area: int
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minimum number of pixels that have to be connected to belong to the SAND class
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clf: classifier
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Returns: -----------
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im_classif: np.array
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2D image containing labels
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im_labels: np.array of booleans
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3D image containing a boolean image for each class (im_classif == label)
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"""
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# calculate features
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vec_features = calculate_features(im_ms, cloud_mask, np.ones(cloud_mask.shape).astype(bool))
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vec_features[np.isnan(vec_features)] = 1e-9 # NaN values are create when std is too close to 0
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# remove NaNs and cloudy pixels
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vec_cloud = cloud_mask.reshape(cloud_mask.shape[0]*cloud_mask.shape[1])
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vec_nan = np.any(np.isnan(vec_features), axis=1)
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vec_mask = np.logical_or(vec_cloud, vec_nan)
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vec_features = vec_features[~vec_mask, :]
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# classify pixels
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labels = clf.predict(vec_features)
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# recompose image
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vec_classif = np.nan*np.ones((cloud_mask.shape[0]*cloud_mask.shape[1]))
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vec_classif[~vec_mask] = labels
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im_classif = vec_classif.reshape((cloud_mask.shape[0], cloud_mask.shape[1]))
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# create a stack of boolean images for each label
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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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# remove small patches of sand or water that could be around the image (usually noise)
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im_sand = morphology.remove_small_objects(im_sand, min_size=min_beach_area, connectivity=2)
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im_water = morphology.remove_small_objects(im_water, min_size=min_beach_area, connectivity=2)
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im_labels = np.stack((im_sand,im_swash,im_water), axis=-1)
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return im_classif, im_labels
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###################################################################################################
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# CONTOUR MAPPING FUNCTIONS
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###################################################################################################
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def find_wl_contours1(im_ndwi, cloud_mask, im_ref_buffer):
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"""
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Traditional method for shorelien detection.
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Finds the water line by thresholding the Normalized Difference Water Index and applying
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the Marching Squares Algorithm to contour the iso-value corresponding to the threshold.
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KV WRL 2018
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Arguments:
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-----------
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im_ndwi: np.ndarray
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Image (2D) with the NDWI (water index)
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cloud_mask: np.ndarray
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2D cloud mask with True where cloud pixels are
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im_ref_buffer: np.array
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Binary image containing a buffer around the reference shoreline
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Returns: -----------
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contours_wl: list of np.arrays
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contains the (row,column) coordinates of the contour lines
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"""
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# reshape image to vector
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vec_ndwi = im_ndwi.reshape(im_ndwi.shape[0] * im_ndwi.shape[1])
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vec_mask = cloud_mask.reshape(cloud_mask.shape[0] * cloud_mask.shape[1])
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vec = vec_ndwi[~vec_mask]
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# apply otsu's threshold
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vec = vec[~np.isnan(vec)]
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t_otsu = filters.threshold_otsu(vec)
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# use Marching Squares algorithm to detect contours on ndwi image
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im_ndwi_buffer = np.copy(im_ndwi)
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im_ndwi_buffer[~im_ref_buffer] = np.nan
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contours = measure.find_contours(im_ndwi_buffer, t_otsu)
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# remove contours that contain NaNs (due to cloud pixels in the contour)
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contours_nonans = []
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for k in range(len(contours)):
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if np.any(np.isnan(contours[k])):
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index_nan = np.where(np.isnan(contours[k]))[0]
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contours_temp = np.delete(contours[k], index_nan, axis=0)
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if len(contours_temp) > 1:
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contours_nonans.append(contours_temp)
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else:
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contours_nonans.append(contours[k])
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contours = contours_nonans
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return contours
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def find_wl_contours2(im_ms, im_labels, cloud_mask, buffer_size, im_ref_buffer):
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"""
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New robust method for extracting shorelines. Incorporates the classification component to
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refine the treshold and make it specific to the sand/water interface.
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KV WRL 2018
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Arguments:
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-----------
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im_ms: np.array
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RGB + downsampled NIR and SWIR
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im_labels: np.array
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3D image containing a boolean image for each class in the order (sand, swash, water)
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cloud_mask: np.array
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2D cloud mask with True where cloud pixels are
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buffer_size: int
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size of the buffer around the sandy beach over which the pixels are considered in the
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thresholding algorithm.
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im_ref_buffer: np.array
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Binary image containing a buffer around the reference shoreline
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Returns: -----------
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contours_wi: list of np.arrays
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contains the (row,column) coordinates of the contour lines extracted from the
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NDWI (Normalized Difference Water Index) image
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contours_mwi: list of np.arrays
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contains the (row,column) coordinates of the contour lines extracted from the
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MNDWI (Modified Normalized Difference Water Index) image
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"""
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nrows = cloud_mask.shape[0]
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ncols = cloud_mask.shape[1]
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# calculate Normalized Difference Modified Water Index (SWIR - G)
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im_mwi = SDS_tools.nd_index(im_ms[:,:,4], im_ms[:,:,1], cloud_mask)
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# calculate Normalized Difference Modified Water Index (NIR - G)
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im_wi = SDS_tools.nd_index(im_ms[:,:,3], im_ms[:,:,1], cloud_mask)
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# stack indices together
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im_ind = np.stack((im_wi, im_mwi), axis=-1)
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vec_ind = im_ind.reshape(nrows*ncols,2)
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# reshape labels into vectors
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vec_sand = im_labels[:,:,0].reshape(ncols*nrows)
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vec_water = im_labels[:,:,2].reshape(ncols*nrows)
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# create a buffer around the sandy beach
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se = morphology.disk(buffer_size)
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im_buffer = morphology.binary_dilation(im_labels[:,:,0], se)
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vec_buffer = im_buffer.reshape(nrows*ncols)
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# select water/sand/swash pixels that are within the buffer
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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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# make sure both classes have the same number of pixels before thresholding
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if len(int_water) > 0 and len(int_sand) > 0:
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if np.argmin([int_sand.shape[0],int_water.shape[0]]) == 1:
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int_sand = int_sand[np.random.choice(int_sand.shape[0],int_water.shape[0], replace=False),:]
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else:
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int_water = int_water[np.random.choice(int_water.shape[0],int_sand.shape[0], replace=False),:]
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# threshold the sand/water intensities
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int_all = np.append(int_water,int_sand, axis=0)
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t_mwi = filters.threshold_otsu(int_all[:,0])
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t_wi = filters.threshold_otsu(int_all[:,1])
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# find contour with MS algorithm
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im_wi_buffer = np.copy(im_wi)
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im_wi_buffer[~im_ref_buffer] = np.nan
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im_mwi_buffer = np.copy(im_mwi)
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im_mwi_buffer[~im_ref_buffer] = np.nan
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contours_wi = measure.find_contours(im_wi_buffer, t_wi)
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contours_mwi = measure.find_contours(im_mwi_buffer, t_mwi)
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# remove contour points that are NaNs (around clouds)
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contours = contours_wi
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contours_nonans = []
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for k in range(len(contours)):
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if np.any(np.isnan(contours[k])):
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index_nan = np.where(np.isnan(contours[k]))[0]
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contours_temp = np.delete(contours[k], index_nan, axis=0)
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if len(contours_temp) > 1:
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contours_nonans.append(contours_temp)
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else:
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contours_nonans.append(contours[k])
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contours_wi = contours_nonans
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# repeat for MNDWI contours
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contours = contours_mwi
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contours_nonans = []
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for k in range(len(contours)):
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if np.any(np.isnan(contours[k])):
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index_nan = np.where(np.isnan(contours[k]))[0]
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contours_temp = np.delete(contours[k], index_nan, axis=0)
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if len(contours_temp) > 1:
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contours_nonans.append(contours_temp)
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else:
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contours_nonans.append(contours[k])
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contours_mwi = contours_nonans
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return contours_wi, contours_mwi
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###################################################################################################
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# SHORELINE PROCESSING FUNCTIONS
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###################################################################################################
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def create_shoreline_buffer(im_shape, georef, image_epsg, pixel_size, settings):
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"""
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Creates a buffer around the reference shoreline. The size of the buffer is given by
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settings['max_dist_ref'].
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KV WRL 2018
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Arguments:
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-----------
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im_shape: np.array
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size of the image (rows,columns)
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georef: np.array
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vector of 6 elements [Xtr, Xscale, Xshear, Ytr, Yshear, Yscale]
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image_epsg: int
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spatial reference system of the image from which the contours were extracted
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pixel_size: int
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size of the pixel in metres (15 for Landsat, 10 for Sentinel-2)
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settings: dict
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contains the following fields:
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output_epsg: int
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output spatial reference system
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reference_shoreline: np.array
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coordinates of the reference shoreline
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max_dist_ref: int
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maximum distance from the reference shoreline in metres
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Returns: -----------
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im_buffer: np.array
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binary image, True where the buffer is, False otherwise
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"""
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# initialise the image buffer
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im_buffer = np.ones(im_shape).astype(bool)
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if 'reference_shoreline' in settings.keys():
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# convert reference shoreline to pixel coordinates
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ref_sl = settings['reference_shoreline']
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ref_sl_conv = SDS_tools.convert_epsg(ref_sl, settings['output_epsg'],image_epsg)[:,:-1]
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ref_sl_pix = SDS_tools.convert_world2pix(ref_sl_conv, georef)
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ref_sl_pix_rounded = np.round(ref_sl_pix).astype(int)
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# create binary image of the reference shoreline (1 where the shoreline is 0 otherwise)
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im_binary = np.zeros(im_shape)
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for j in range(len(ref_sl_pix_rounded)):
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im_binary[ref_sl_pix_rounded[j,1], ref_sl_pix_rounded[j,0]] = 1
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im_binary = im_binary.astype(bool)
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# dilate the binary image to create a buffer around the reference shoreline
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max_dist_ref_pixels = np.ceil(settings['max_dist_ref']/pixel_size)
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se = morphology.disk(max_dist_ref_pixels)
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im_buffer = morphology.binary_dilation(im_binary, se)
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return im_buffer
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def process_shoreline(contours, cloud_mask, georef, image_epsg, settings):
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"""
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Converts the contours from image coordinates to world coordinates. This function also removes
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the contours that are too small to be a shoreline (based on the parameter
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settings['min_length_sl'])
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KV WRL 2018
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Arguments:
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-----------
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contours: np.array or list of np.array
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image contours as detected by the function find_contours
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cloud_mask: np.array
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2D cloud mask with True where cloud pixels are
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georef: np.array
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vector of 6 elements [Xtr, Xscale, Xshear, Ytr, Yshear, Yscale]
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image_epsg: int
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spatial reference system of the image from which the contours were extracted
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settings: dict
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contains the following fields:
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output_epsg: int
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output spatial reference system
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min_length_sl: float
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minimum length of shoreline perimeter to be kept (in meters)
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Returns:
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-----------
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shoreline: np.array
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array of points with the X and Y coordinates of the shoreline
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"""
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# convert pixel coordinates to world coordinates
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contours_world = SDS_tools.convert_pix2world(contours, georef)
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# convert world coordinates to desired spatial reference system
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contours_epsg = SDS_tools.convert_epsg(contours_world, image_epsg, settings['output_epsg'])
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# remove contours that have a perimeter < min_length_sl (provided in settings dict)
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# this enables to remove the very small contours that do not correspond to the shoreline
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contours_long = []
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for l, wl in enumerate(contours_epsg):
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coords = [(wl[k,0], wl[k,1]) for k in range(len(wl))]
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a = LineString(coords) # shapely LineString structure
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if a.length >= settings['min_length_sl']:
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contours_long.append(wl)
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# format points into np.array
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x_points = np.array([])
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y_points = np.array([])
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for k in range(len(contours_long)):
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x_points = np.append(x_points,contours_long[k][:,0])
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y_points = np.append(y_points,contours_long[k][:,1])
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contours_array = np.transpose(np.array([x_points,y_points]))
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shoreline = contours_array
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# now remove any shoreline points that are attached to cloud pixels
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if sum(sum(cloud_mask)) > 0:
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# get the coordinates of the cloud pixels
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idx_cloud = np.where(cloud_mask)
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idx_cloud = np.array([(idx_cloud[0][k], idx_cloud[1][k]) for k in range(len(idx_cloud[0]))])
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# convert to world coordinates and same epsg as the shoreline points
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coords_cloud = SDS_tools.convert_epsg(SDS_tools.convert_pix2world(idx_cloud, georef),
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image_epsg, settings['output_epsg'])[:,:-1]
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# only keep the shoreline points that are at least 30m from any cloud pixel
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idx_keep = np.ones(len(shoreline)).astype(bool)
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for k in range(len(shoreline)):
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if np.any(np.linalg.norm(shoreline[k,:] - coords_cloud, axis=1) < 30):
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|
idx_keep[k] = False
|
|
shoreline = shoreline[idx_keep]
|
|
|
|
return shoreline
|
|
|
|
def show_detection(im_ms, cloud_mask, im_labels, shoreline,image_epsg, georef,
|
|
settings, date, satname):
|
|
"""
|
|
Shows the detected shoreline to the user for visual quality control. The user can select "keep"
|
|
if the shoreline detection is correct or "skip" if it is incorrect.
|
|
|
|
KV WRL 2018
|
|
|
|
Arguments:
|
|
-----------
|
|
im_ms: np.array
|
|
RGB + downsampled NIR and SWIR
|
|
cloud_mask: np.array
|
|
2D cloud mask with True where cloud pixels are
|
|
im_labels: np.array
|
|
3D image containing a boolean image for each class in the order (sand, swash, water)
|
|
shoreline: np.array
|
|
array of points with the X and Y coordinates of the shoreline
|
|
image_epsg: int
|
|
spatial reference system of the image from which the contours were extracted
|
|
georef: np.array
|
|
vector of 6 elements [Xtr, Xscale, Xshear, Ytr, Yshear, Yscale]
|
|
settings: dict
|
|
contains the following fields:
|
|
date: string
|
|
date at which the image was taken
|
|
satname: string
|
|
indicates the satname (L5,L7,L8 or S2)
|
|
|
|
Returns:
|
|
-----------
|
|
skip_image: boolean
|
|
True if the user wants to skip the image, False otherwise.
|
|
|
|
"""
|
|
|
|
sitename = settings['inputs']['sitename']
|
|
filepath_data = settings['inputs']['filepath']
|
|
# subfolder where the .jpg file is stored if the user accepts the shoreline detection
|
|
filepath = os.path.join(filepath_data, sitename, 'jpg_files', 'detection')
|
|
|
|
im_RGB = SDS_preprocess.rescale_image_intensity(im_ms[:,:,[2,1,0]], cloud_mask, 99.9)
|
|
|
|
# compute classified image
|
|
im_class = np.copy(im_RGB)
|
|
cmap = cm.get_cmap('tab20c')
|
|
colorpalette = cmap(np.arange(0,13,1))
|
|
colours = np.zeros((3,4))
|
|
colours[0,:] = colorpalette[5]
|
|
colours[1,:] = np.array([204/255,1,1,1])
|
|
colours[2,:] = np.array([0,91/255,1,1])
|
|
for k in range(0,im_labels.shape[2]):
|
|
im_class[im_labels[:,:,k],0] = colours[k,0]
|
|
im_class[im_labels[:,:,k],1] = colours[k,1]
|
|
im_class[im_labels[:,:,k],2] = colours[k,2]
|
|
|
|
# compute MNDWI grayscale image
|
|
im_mwi = SDS_tools.nd_index(im_ms[:,:,4], im_ms[:,:,1], cloud_mask)
|
|
|
|
# transform world coordinates of shoreline into pixel coordinates
|
|
# use try/except in case there are no coordinates to be transformed (shoreline = [])
|
|
try:
|
|
sl_pix = SDS_tools.convert_world2pix(SDS_tools.convert_epsg(shoreline,
|
|
settings['output_epsg'],
|
|
image_epsg)[:,[0,1]], georef)
|
|
except:
|
|
# if try fails, just add nan into the shoreline vector so the next parts can still run
|
|
sl_pix = np.array([[np.nan, np.nan],[np.nan, np.nan]])
|
|
|
|
if plt.get_fignums():
|
|
# get open figure if it exists
|
|
fig = plt.gcf()
|
|
ax1 = fig.axes[0]
|
|
ax2 = fig.axes[1]
|
|
ax3 = fig.axes[2]
|
|
else:
|
|
# else create a new figure
|
|
fig = plt.figure()
|
|
fig.set_size_inches([12.53, 9.3])
|
|
mng = plt.get_current_fig_manager()
|
|
mng.window.showMaximized()
|
|
|
|
# according to the image shape, decide whether it is better to have the images
|
|
# in vertical subplots or horizontal subplots
|
|
if im_RGB.shape[1] > 2*im_RGB.shape[0]:
|
|
# vertical subplots
|
|
gs = gridspec.GridSpec(3, 1)
|
|
gs.update(bottom=0.03, top=0.97, left=0.03, right=0.97)
|
|
ax1 = fig.add_subplot(gs[0,0])
|
|
ax2 = fig.add_subplot(gs[1,0])
|
|
ax3 = fig.add_subplot(gs[2,0])
|
|
else:
|
|
# horizontal subplots
|
|
gs = gridspec.GridSpec(1, 3)
|
|
gs.update(bottom=0.05, top=0.95, left=0.05, right=0.95)
|
|
ax1 = fig.add_subplot(gs[0,0])
|
|
ax2 = fig.add_subplot(gs[0,1])
|
|
ax3 = fig.add_subplot(gs[0,2])
|
|
|
|
# change the color of nans to either black (0.0) or white (1.0) or somewhere in between
|
|
nan_color = 1.0
|
|
im_RGB = np.where(np.isnan(im_RGB), nan_color, im_RGB)
|
|
im_class = np.where(np.isnan(im_class), 1.0, im_class)
|
|
|
|
# create image 1 (RGB)
|
|
ax1.imshow(im_RGB)
|
|
ax1.plot(sl_pix[:,0], sl_pix[:,1], 'k.', markersize=3)
|
|
ax1.axis('off')
|
|
ax1.set_title(sitename, fontweight='bold', fontsize=16)
|
|
|
|
# create image 2 (classification)
|
|
ax2.imshow(im_class)
|
|
ax2.plot(sl_pix[:,0], sl_pix[:,1], 'k.', markersize=3)
|
|
ax2.axis('off')
|
|
orange_patch = mpatches.Patch(color=colours[0,:], label='sand')
|
|
white_patch = mpatches.Patch(color=colours[1,:], label='whitewater')
|
|
blue_patch = mpatches.Patch(color=colours[2,:], label='water')
|
|
black_line = mlines.Line2D([],[],color='k',linestyle='-', label='shoreline')
|
|
ax2.legend(handles=[orange_patch,white_patch,blue_patch, black_line],
|
|
bbox_to_anchor=(1, 0.5), fontsize=10)
|
|
ax2.set_title(date, fontweight='bold', fontsize=16)
|
|
|
|
# create image 3 (MNDWI)
|
|
ax3.imshow(im_mwi, cmap='bwr')
|
|
ax3.plot(sl_pix[:,0], sl_pix[:,1], 'k.', markersize=3)
|
|
ax3.axis('off')
|
|
ax3.set_title(satname, fontweight='bold', fontsize=16)
|
|
|
|
# additional options
|
|
# ax1.set_anchor('W')
|
|
# ax2.set_anchor('W')
|
|
# cb = plt.colorbar()
|
|
# cb.ax.tick_params(labelsize=10)
|
|
# cb.set_label('MNDWI values')
|
|
# ax3.set_anchor('W')
|
|
|
|
# if check_detection is True, let user manually accept/reject the images
|
|
skip_image = False
|
|
if settings['check_detection']:
|
|
|
|
# set a key event to accept/reject the detections (see https://stackoverflow.com/a/15033071)
|
|
# this variable needs to be immuatable so we can access it after the keypress event
|
|
key_event = {}
|
|
def press(event):
|
|
# store what key was pressed in the dictionary
|
|
key_event['pressed'] = event.key
|
|
# let the user press a key, right arrow to keep the image, left arrow to skip it
|
|
# to break the loop the user can press 'escape'
|
|
while True:
|
|
btn_keep = plt.text(1.1, 0.9, 'keep ⇨', size=12, ha="right", va="top",
|
|
transform=ax1.transAxes,
|
|
bbox=dict(boxstyle="square", ec='k',fc='w'))
|
|
btn_skip = plt.text(-0.1, 0.9, '⇦ skip', size=12, ha="left", va="top",
|
|
transform=ax1.transAxes,
|
|
bbox=dict(boxstyle="square", ec='k',fc='w'))
|
|
btn_esc = plt.text(0.5, 0, '<esc> to quit', size=12, ha="center", va="top",
|
|
transform=ax1.transAxes,
|
|
bbox=dict(boxstyle="square", ec='k',fc='w'))
|
|
plt.draw()
|
|
fig.canvas.mpl_connect('key_press_event', press)
|
|
plt.waitforbuttonpress()
|
|
# after button is pressed, remove the buttons
|
|
btn_skip.remove()
|
|
btn_keep.remove()
|
|
btn_esc.remove()
|
|
|
|
# keep/skip image according to the pressed key, 'escape' to break the loop
|
|
if key_event.get('pressed') == 'right':
|
|
skip_image = False
|
|
break
|
|
elif key_event.get('pressed') == 'left':
|
|
skip_image = True
|
|
break
|
|
elif key_event.get('pressed') == 'escape':
|
|
plt.close()
|
|
raise StopIteration('User cancelled checking shoreline detection')
|
|
else:
|
|
plt.waitforbuttonpress()
|
|
|
|
# if save_figure is True, save a .jpg under /jpg_files/detection
|
|
if settings['save_figure'] and not skip_image:
|
|
fig.savefig(os.path.join(filepath, date + '_' + satname + '.jpg'), dpi=150)
|
|
|
|
# Don't close the figure window, but remove all axes and settings, ready for next plot
|
|
for ax in fig.axes:
|
|
ax.clear()
|
|
|
|
return skip_image
|
|
|
|
|
|
def extract_shorelines(metadata, settings):
|
|
"""
|
|
Extracts shorelines from satellite images.
|
|
|
|
KV WRL 2018
|
|
|
|
Arguments:
|
|
-----------
|
|
metadata: dict
|
|
contains all the information about the satellite images that were downloaded
|
|
|
|
settings: dict
|
|
contains the following fields:
|
|
sitename: str
|
|
String containig the name of the site
|
|
cloud_mask_issue: boolean
|
|
True if there is an issue with the cloud mask and sand pixels are being masked on the images
|
|
buffer_size: int
|
|
size of the buffer (m) around the sandy beach over which the pixels are considered in the
|
|
thresholding algorithm
|
|
min_beach_area: int
|
|
minimum allowable object area (in metres^2) for the class 'sand'
|
|
cloud_thresh: float
|
|
value between 0 and 1 defining the maximum percentage of cloud cover allowed in the images
|
|
output_epsg: int
|
|
output spatial reference system as EPSG code
|
|
check_detection: boolean
|
|
True to show each invidual detection and let the user validate the mapped shoreline
|
|
|
|
Returns:
|
|
-----------
|
|
output: dict
|
|
contains the extracted shorelines and corresponding dates.
|
|
|
|
"""
|
|
|
|
sitename = settings['inputs']['sitename']
|
|
filepath_data = settings['inputs']['filepath']
|
|
# initialise output structure
|
|
output = dict([])
|
|
# create a subfolder to store the .jpg images showing the detection
|
|
filepath_jpg = os.path.join(filepath_data, sitename, 'jpg_files', 'detection')
|
|
if not os.path.exists(filepath_jpg):
|
|
os.makedirs(filepath_jpg)
|
|
# close all open figures
|
|
plt.close('all')
|
|
|
|
print('Mapping shorelines:')
|
|
|
|
# loop through satellite list
|
|
for satname in metadata.keys():
|
|
|
|
# get images
|
|
filepath = SDS_tools.get_filepath(settings['inputs'],satname)
|
|
filenames = metadata[satname]['filenames']
|
|
|
|
# initialise the output variables
|
|
output_timestamp = [] # datetime at which the image was acquired (UTC time)
|
|
output_shoreline = [] # vector of shoreline points
|
|
output_filename = [] # filename of the images from which the shorelines where derived
|
|
output_cloudcover = [] # cloud cover of the images
|
|
output_geoaccuracy = []# georeferencing accuracy of the images
|
|
output_idxkeep = [] # index that were kept during the analysis (cloudy images are skipped)
|
|
|
|
# load classifiers and
|
|
if satname in ['L5','L7','L8']:
|
|
pixel_size = 15
|
|
if settings['sand_color'] == 'dark':
|
|
clf = joblib.load(os.path.join(os.getcwd(), 'classifiers', 'NN_4classes_Landsat_dark.pkl'))
|
|
elif settings['sand_color'] == 'bright':
|
|
clf = joblib.load(os.path.join(os.getcwd(), 'classifiers', 'NN_4classes_Landsat_bright.pkl'))
|
|
else:
|
|
clf = joblib.load(os.path.join(os.getcwd(), 'classifiers', 'NN_4classes_Landsat.pkl'))
|
|
|
|
elif satname == 'S2':
|
|
pixel_size = 10
|
|
clf = joblib.load(os.path.join(os.getcwd(), 'classifiers', 'NN_4classes_S2.pkl'))
|
|
|
|
# convert settings['min_beach_area'] and settings['buffer_size'] from metres to pixels
|
|
buffer_size_pixels = np.ceil(settings['buffer_size']/pixel_size)
|
|
min_beach_area_pixels = np.ceil(settings['min_beach_area']/pixel_size**2)
|
|
|
|
# loop through the images
|
|
for i in range(len(filenames)):
|
|
|
|
print('\r%s: %d%%' % (satname,int(((i+1)/len(filenames))*100)), end='')
|
|
|
|
# get image filename
|
|
fn = SDS_tools.get_filenames(filenames[i],filepath, satname)
|
|
# preprocess image (cloud mask + pansharpening/downsampling)
|
|
im_ms, georef, cloud_mask, im_extra, im_QA, im_nodata = SDS_preprocess.preprocess_single(fn, satname, settings['cloud_mask_issue'])
|
|
# get image spatial reference system (epsg code) from metadata dict
|
|
image_epsg = metadata[satname]['epsg'][i]
|
|
# define an advanced cloud mask (for L7 it takes into account the fact that diagonal
|
|
# bands of no data are not clouds)
|
|
if not satname == 'L7' or sum(sum(im_nodata)) == 0 or sum(sum(im_nodata)) > 0.5*im_nodata.size:
|
|
cloud_mask_adv = cloud_mask
|
|
else:
|
|
cloud_mask_adv = np.logical_xor(cloud_mask, im_nodata)
|
|
|
|
# calculate cloud cover
|
|
cloud_cover = np.divide(sum(sum(cloud_mask_adv.astype(int))),
|
|
(cloud_mask.shape[0]*cloud_mask.shape[1]))
|
|
# skip image if cloud cover is above threshold
|
|
if cloud_cover > settings['cloud_thresh']:
|
|
continue
|
|
|
|
# calculate a buffer around the reference shoreline (if any has been digitised)
|
|
im_ref_buffer = create_shoreline_buffer(cloud_mask.shape, georef, image_epsg,
|
|
pixel_size, settings)
|
|
|
|
# when running the automated mode, skip image if cloudy pixels are found in the shoreline buffer
|
|
if not settings['check_detection'] and 'reference_shoreline' in settings.keys():
|
|
if sum(sum(np.logical_and(im_ref_buffer, cloud_mask_adv))) > 0:
|
|
continue
|
|
|
|
# classify image in 4 classes (sand, whitewater, water, other) with NN classifier
|
|
im_classif, im_labels = classify_image_NN(im_ms, im_extra, cloud_mask,
|
|
min_beach_area_pixels, clf)
|
|
|
|
# there are two options to map the contours:
|
|
# if there are pixels in the 'sand' class --> use find_wl_contours2 (enhanced)
|
|
# otherwise use find_wl_contours2 (traditional)
|
|
try: # use try/except structure for long runs
|
|
if sum(sum(im_labels[:,:,0])) == 0 :
|
|
# compute MNDWI image (SWIR-G)
|
|
im_mndwi = SDS_tools.nd_index(im_ms[:,:,4], im_ms[:,:,1], cloud_mask)
|
|
# find water contours on MNDWI grayscale image
|
|
contours_mwi = find_wl_contours1(im_mndwi, cloud_mask, im_ref_buffer)
|
|
else:
|
|
# use classification to refine threshold and extract the sand/water interface
|
|
contours_wi, contours_mwi = find_wl_contours2(im_ms, im_labels,
|
|
cloud_mask, buffer_size_pixels, im_ref_buffer)
|
|
except:
|
|
print('Could not map shoreline for this image: ' + filenames[i])
|
|
continue
|
|
|
|
# process the water contours into a shoreline
|
|
shoreline = process_shoreline(contours_mwi, cloud_mask, georef, image_epsg, settings)
|
|
|
|
# visualise the mapped shorelines, there are two options:
|
|
# if settings['check_detection'] = True, shows the detection to the user for accept/reject
|
|
# if settings['save_figure'] = True, saves a figure for each mapped shoreline
|
|
if settings['check_detection'] or settings['save_figure']:
|
|
date = filenames[i][:19]
|
|
skip_image = show_detection(im_ms, cloud_mask, im_labels, shoreline,
|
|
image_epsg, georef, settings, date, satname)
|
|
# if the user decides to skip the image, continue and do not save the mapped shoreline
|
|
if skip_image:
|
|
continue
|
|
|
|
# append to output variables
|
|
output_timestamp.append(metadata[satname]['dates'][i])
|
|
output_shoreline.append(shoreline)
|
|
output_filename.append(filenames[i])
|
|
output_cloudcover.append(cloud_cover)
|
|
output_geoaccuracy.append(metadata[satname]['acc_georef'][i])
|
|
output_idxkeep.append(i)
|
|
|
|
# create dictionnary of output
|
|
output[satname] = {
|
|
'dates': output_timestamp,
|
|
'shorelines': output_shoreline,
|
|
'filename': output_filename,
|
|
'cloud_cover': output_cloudcover,
|
|
'geoaccuracy': output_geoaccuracy,
|
|
'idx': output_idxkeep
|
|
}
|
|
print('')
|
|
|
|
# Close figure window if still open
|
|
if plt.get_fignums():
|
|
plt.close()
|
|
|
|
# change the format to have one list sorted by date with all the shorelines (easier to use)
|
|
output = SDS_tools.merge_output(output)
|
|
|
|
# save outputput structure as output.pkl
|
|
filepath = os.path.join(filepath_data, sitename)
|
|
with open(os.path.join(filepath, sitename + '_output.pkl'), 'wb') as f:
|
|
pickle.dump(output, f)
|
|
|
|
# save output into a gdb.GeoDataFrame
|
|
gdf = SDS_tools.output_to_gdf(output)
|
|
# set projection
|
|
gdf.crs = {'init':'epsg:'+str(settings['output_epsg'])}
|
|
# save as geojson
|
|
gdf.to_file(os.path.join(filepath, sitename + '_output.geojson'), driver='GeoJSON', encoding='utf-8')
|
|
|
|
return output
|