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520 lines
17 KiB
Python
520 lines
17 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Created on Thu Mar 1 11:20:35 2018
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@author: z5030440
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"""
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"""This script contains the functions needed for satellite derived shoreline (SDS) extraction"""
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# Initial settings
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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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import ee
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# other modules
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from osgeo import gdal
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import tempfile
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from urllib.request import urlretrieve
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import zipfile
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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 own modules
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from utils import *
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# Download from ee server function
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def download_tif(image, polygon, bandsId):
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"""downloads tif image (region and bands) from the ee server and stores it in a temp file"""
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url = ee.data.makeDownloadUrl(ee.data.getDownloadId({
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'image': image.serialize(),
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'region': polygon,
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'bands': bandsId,
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'filePerBand': 'false',
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'name': 'data',
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}))
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local_zip, headers = urlretrieve(url)
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with zipfile.ZipFile(local_zip) as local_zipfile:
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return local_zipfile.extract('data.tif', tempfile.mkdtemp())
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def load_image(image, polygon, bandsId):
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"""
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Loads an ee.Image() as a np.array. e.Image() is retrieved from the EE database.
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The geographic area and bands to select can be specified
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KV WRL 2018
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Arguments:
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-----------
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image: ee.Image()
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image objec from the EE database
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polygon: list
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coordinates of the points creating a polygon. Each point is a list with 2 values
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bandsId: list
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bands to select, each band is a dictionnary in the list containing the following keys:
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crs, crs_transform, data_type and id. NOTE: you have to remove the key dimensions, otherwise
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the entire image is retrieved.
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Returns:
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-----------
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image_array : np.ndarray
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An array containing the image (2D if one band, otherwise 3D)
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georef : np.ndarray
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6 element vector containing the crs_parameters
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[X_ul_corner Xscale Xshear Y_ul_corner Yshear Yscale]
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"""
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local_tif_filename = download_tif(image, polygon, bandsId)
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dataset = gdal.Open(local_tif_filename, gdal.GA_ReadOnly)
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georef = np.array(dataset.GetGeoTransform())
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bands = [dataset.GetRasterBand(i + 1).ReadAsArray() for i in range(dataset.RasterCount)]
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return np.stack(bands, 2), georef
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def create_cloud_mask(im_qa):
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"""
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Creates a cloud mask from the image containing the QA band information
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KV WRL 2018
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Arguments:
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-----------
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im_qa: np.ndarray
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Image containing the QA band
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Returns:
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-----------
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cloud_mask : np.ndarray of booleans
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A boolean array with True where the cloud are present
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"""
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# convert QA bits
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cloud_values = [2800, 2804, 2808, 2812, 6896, 6900, 6904, 6908]
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cloud_mask = np.isin(im_qa, cloud_values)
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#cloud_shadow_values = [2976, 2980, 2984, 2988, 3008, 3012, 3016, 3020]
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#cloud_shadow_mask = np.isin(im_qa, cloud_shadow_values)
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return cloud_mask
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def read_eeimage(im, polygon, plot_bool):
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"""
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Read an ee.Image() object and returns the panchromatic band, multispectral bands (B, G, R, NIR, SWIR)
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and a cloud mask. All outputs are at 15m resolution (bilinear interpolation for the multispectral bands)
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KV WRL 2018
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Arguments:
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-----------
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im: ee.Image()
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Image to read from the Google Earth Engine database
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plot_bool: boolean
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True if plot is wanted
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Returns:
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-----------
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im_pan: np.ndarray (2D)
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The panchromatic band (15m)
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im_ms: np.ndarray (3D)
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The multispectral bands interpolated at 15m
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im_cloud: np.ndarray (2D)
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The cloud mask at 15m
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crs_params: list
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EPSG code and affine transformation parameters
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"""
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im_dic = im.getInfo()
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im_bands = im_dic.get('bands')
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# delete dimensions key from dictionnary, otherwise the entire image is extracted
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for i in range(len(im_bands)): del im_bands[i]['dimensions']
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# load panchromatic band
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pan_band = [im_bands[7]]
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im_pan, crs_pan = load_image(im, polygon, pan_band)
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im_pan = im_pan[:,:,0]
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# load the multispectral bands (B2,B3,B4,B5,B6) = (blue,green,red,nir,swir1)
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ms_bands = [im_bands[1], im_bands[2], im_bands[3], im_bands[4], im_bands[5]]
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im_ms_30m, crs_ms = load_image(im, polygon, ms_bands)
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# create cloud mask
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qa_band = [im_bands[11]]
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im_qa, crs_qa = load_image(im, polygon, qa_band)
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im_qa = im_qa[:,:,0]
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im_cloud = create_cloud_mask(im_qa)
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im_cloud = transform.resize(im_cloud, (im_pan.shape[0], im_pan.shape[1]),
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order=0, preserve_range=True, mode='constant').astype('bool_')
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if plot_bool:
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plt.figure()
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plt.subplot(221)
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plt.imshow(im_pan, cmap='gray')
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plt.title('PANCHROMATIC')
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plt.subplot(222)
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plt.imshow(im_ms_30m[:,:,[2,1,0]])
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plt.title('RGB')
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plt.subplot(223)
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plt.imshow(im_ms_30m[:,:,3], cmap='gray')
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plt.title('NIR')
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plt.subplot(224)
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plt.imshow(im_ms_30m[:,:,4], cmap='gray')
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plt.title('SWIR')
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plt.show()
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# resize the image using bilinear interpolation (order 1)
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im_ms = transform.resize(im_ms_30m,(im_pan.shape[0], im_pan.shape[1]),
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order=1, preserve_range=True, mode='constant')
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# get the crs parameters for the image at 15m and 30m resolution
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crs = {'crs_15m':crs_pan, 'crs_30m':crs_ms, 'epsg_code':pan_band[0]['crs']}
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return im_pan, im_ms, im_cloud, crs
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def rescale_image_intensity(im, cloud_mask, prob_high, plot_bool):
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"""
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Rescales the intensity of an image (multispectral or single band) by applying
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a cloud mask and clipping the prob_high upper percentile. This functions allows
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to stretch the contrast of an image.
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KV WRL 2018
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Arguments:
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-----------
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im: np.ndarray
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Image to rescale, can be 3D (multispectral) or 2D (single band)
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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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prob_high: float
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probability of exceedence used to calculate the upper percentile
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plot_bool: boolean
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True if plot is wanted
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Returns:
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-----------
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im_adj: np.ndarray
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The rescaled image
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"""
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prc_low = 0 # lower percentile
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vec_mask = cloud_mask.reshape(im.shape[0] * im.shape[1])
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if plot_bool:
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plt.figure()
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if len(im.shape) > 2:
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vec = im.reshape(im.shape[0] * im.shape[1], im.shape[2])
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vec_adj = np.ones((len(vec_mask), im.shape[2])) * np.nan
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for i in range(im.shape[2]):
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prc_high = np.percentile(vec[~vec_mask, i], prob_high)
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vec_rescaled = exposure.rescale_intensity(vec[~vec_mask, i], in_range=(prc_low, prc_high))
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vec_adj[~vec_mask,i] = vec_rescaled
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if plot_bool:
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plt.subplot(np.floor(im.shape[2]/2) + 1, np.floor(im.shape[2]/2), i+1)
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plt.hist(vec[~vec_mask, i], bins=200, label='original')
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plt.hist(vec_rescaled, bins=200, alpha=0.5, label='rescaled')
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plt.legend()
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plt.title('Band' + str(i+1))
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plt.show()
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im_adj = vec_adj.reshape(im.shape[0], im.shape[1], im.shape[2])
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if plot_bool:
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plt.figure()
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ax1 = plt.subplot(121)
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plt.imshow(im[:,:,[2,1,0]])
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plt.axis('off')
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plt.title('Original')
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ax2 = plt.subplot(122, sharex=ax1, sharey=ax1)
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plt.imshow(im_adj[:,:,[2,1,0]])
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plt.axis('off')
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plt.title('Rescaled')
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plt.show()
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else:
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vec = im.reshape(im.shape[0] * im.shape[1])
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vec_adj = np.ones(len(vec_mask)) * np.nan
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prc_high = np.percentile(vec[~vec_mask], prob_high)
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vec_rescaled = exposure.rescale_intensity(vec[~vec_mask], in_range=(prc_low, prc_high))
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vec_adj[~vec_mask] = vec_rescaled
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if plot_bool:
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plt.hist(vec[~vec_mask], bins=200, label='original')
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plt.hist(vec_rescaled, bins=200, alpha=0.5, label='rescaled')
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plt.legend()
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plt.title('Single band')
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plt.show()
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im_adj = vec_adj.reshape(im.shape[0], im.shape[1])
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if plot_bool:
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plt.figure()
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ax1 = plt.subplot(121)
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plt.imshow(im, cmap='gray')
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plt.axis('off')
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plt.title('Original')
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ax2 = plt.subplot(122, sharex=ax1, sharey=ax1)
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plt.imshow(im_adj, cmap='gray')
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plt.axis('off')
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plt.title('Rescaled')
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plt.show()
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return im_adj
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def hist_match(source, template):
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"""
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Adjust the pixel values of a grayscale image such that its histogram
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matches that of a target image
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Arguments:
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-----------
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source: np.ndarray
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Image to transform; the histogram is computed over the flattened
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array
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template: np.ndarray
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Template image; can have different dimensions to source
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Returns:
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-----------
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matched: np.ndarray
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The transformed output image
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"""
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oldshape = source.shape
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source = source.ravel()
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template = template.ravel()
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# get the set of unique pixel values and their corresponding indices and
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# counts
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s_values, bin_idx, s_counts = np.unique(source, return_inverse=True,
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return_counts=True)
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t_values, t_counts = np.unique(template, return_counts=True)
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# take the cumsum of the counts and normalize by the number of pixels to
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# get the empirical cumulative distribution functions for the source and
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# template images (maps pixel value --> quantile)
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s_quantiles = np.cumsum(s_counts).astype(np.float64)
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s_quantiles /= s_quantiles[-1]
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t_quantiles = np.cumsum(t_counts).astype(np.float64)
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t_quantiles /= t_quantiles[-1]
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# interpolate linearly to find the pixel values in the template image
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# that correspond most closely to the quantiles in the source image
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interp_t_values = np.interp(s_quantiles, t_quantiles, t_values)
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return interp_t_values[bin_idx].reshape(oldshape)
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def pansharpen(im_ms, im_pan, cloud_mask, plot_bool):
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"""
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Pansharpens a multispectral image (3D), using the panchromatic band (2D)
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and a cloud mask
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KV WRL 2018
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Arguments:
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-----------
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im_ms: np.ndarray
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Multispectral image to pansharpen (3D)
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im_pan: np.ndarray
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Panchromatic band (2D)
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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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plot_bool: boolean
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True if plot is wanted
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Returns:
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-----------
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im_ms_ps: np.ndarray
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Pansharpened multisoectral image (3D)
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"""
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# reshape image into vector and apply cloud mask
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vec = im_ms.reshape(im_ms.shape[0] * im_ms.shape[1], im_ms.shape[2])
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vec_mask = cloud_mask.reshape(im_ms.shape[0] * im_ms.shape[1])
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vec = vec[~vec_mask, :]
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# apply PCA to RGB bands
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pca = decomposition.PCA()
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vec_pcs = pca.fit_transform(vec)
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# replace 1st PC with pan band (after matching histograms)
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vec_pan = im_pan.reshape(im_pan.shape[0] * im_pan.shape[1])
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vec_pan = vec_pan[~vec_mask]
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vec_pcs[:,0] = hist_match(vec_pan, vec_pcs[:,0])
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vec_ms_ps = pca.inverse_transform(vec_pcs)
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# normalise between 0 and 1
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for i in range(vec_pcs.shape[1]):
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vec_ms_ps[:,i] = np.divide(vec_ms_ps[:,i] - np.min(vec_ms_ps[:,i]),
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np.max(vec_ms_ps[:,i]) - np.min(vec_ms_ps[:,i]))
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# reshape vector into image
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vec_ms_ps_full = np.ones((len(vec_mask), im_ms.shape[2])) * np.nan
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vec_ms_ps_full[~vec_mask,:] = vec_ms_ps
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im_ms_ps = vec_ms_ps_full.reshape(im_ms.shape[0], im_ms.shape[1], im_ms.shape[2])
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if plot_bool:
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plt.figure()
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ax1 = plt.subplot(121)
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plt.imshow(im_ms[:,:,[2,1,0]])
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plt.axis('off')
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plt.title('Original')
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ax2 = plt.subplot(122, sharex=ax1, sharey=ax1)
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plt.imshow(im_ms_ps[:,:,[2,1,0]])
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plt.axis('off')
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plt.title('Pansharpened')
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plt.show()
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return im_ms_ps
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def nd_index(im1, im2, cloud_mask, plot_bool):
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"""
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Computes normalised difference index on 2 images (2D), given a cloud mask (2D)
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KV WRL 2018
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Arguments:
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-----------
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im1, im2: np.ndarray
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Images (2D) with which to calculate the ND 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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plot_bool: boolean
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True if plot is wanted
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Returns: -----------
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im_nd: np.ndarray
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Image (2D) containing the ND index
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"""
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vec_mask = cloud_mask.reshape(im1.shape[0] * im1.shape[1])
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vec_nd = np.ones(len(vec_mask)) * np.nan
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vec1 = im1.reshape(im1.shape[0] * im1.shape[1])
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vec2 = im2.reshape(im2.shape[0] * im2.shape[1])
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temp = np.divide(vec1[~vec_mask] - vec2[~vec_mask],
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vec1[~vec_mask] + vec2[~vec_mask])
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vec_nd[~vec_mask] = temp
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im_nd = vec_nd.reshape(im1.shape[0], im1.shape[1])
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if plot_bool:
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plt.figure()
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plt.imshow(im_nd, cmap='seismic')
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plt.colorbar()
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plt.title('Normalised index')
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plt.show()
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return im_nd
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def find_wl_contours(im_ndwi, cloud_mask, min_contour_points, plot_bool):
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"""
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Computes normalised difference index on 2 images (2D), given a cloud mask (2D)
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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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min_contour_points: int
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minimum number of points in each contour line
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plot_bool: boolean
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True if plot is wanted
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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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t_otsu = filters.threshold_otsu(vec)
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# use Marching Squares algorithm to detect contours on ndwi image
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contours = measure.find_contours(im_ndwi, t_otsu)
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# filter water lines
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contours_wl = []
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for i, contour in enumerate(contours):
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# remove contour points that are around clouds (nan values)
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if np.any(np.isnan(contour)):
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index_nan = np.where(np.isnan(contour))[0]
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contour = np.delete(contour, index_nan, axis=0)
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# remove contours that have only few points (less than min_contour_points)
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if contour.shape[0] > min_contour_points:
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contours_wl.append(contour)
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if plot_bool:
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# plot otsu's histogram segmentation
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plt.figure()
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vals = plt.hist(vec, bins=200)
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plt.plot([t_otsu, t_otsu],[0, np.max(vals[0])], 'r-', label='Otsu threshold')
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plt.legend()
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plt.show()
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# plot the water line contours on top of water index
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plt.figure()
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plt.imshow(im_ndwi, cmap='seismic')
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plt.colorbar()
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for i,contour in enumerate(contours_wl): plt.plot(contour[:, 1], contour[:, 0], linewidth=3, color='k')
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plt.axis('image')
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plt.title('Detected water lines')
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plt.show()
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return contours_wl
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def convert_pix2world(points, crs_vec):
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"""
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Converts pixel coordinates (row,columns) to world projected coordinates
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performing an affine transformation
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KV WRL 2018
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Arguments:
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-----------
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points: np.ndarray or list of np.ndarray
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array with 2 columns (rows first and columns second)
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crs_vec: np.ndarray
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vector of 6 elements [Xtr, Xscale, Xshear, Ytr, Yshear, Yscale]
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Returns: -----------
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points_converted: np.ndarray or list of np.ndarray
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converted coordinates, first columns with X and second column with Y
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"""
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# make affine transformation matrix
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aff_mat = np.array([[crs_vec[1], crs_vec[2], crs_vec[0]],
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[crs_vec[4], crs_vec[5], crs_vec[3]],
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[0, 0, 1]])
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# create affine transformation
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tform = transform.AffineTransform(aff_mat)
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if type(points) is list:
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points_converted = []
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# iterate over the list
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for i, arr in enumerate(points):
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tmp = arr[:,[1,0]]
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points_converted.append(tform(tmp))
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elif type(points) is np.ndarray:
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tmp = points[:,[1,0]]
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points_converted = tform(tmp)
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else:
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print('invalid input type')
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raise
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return points_converted
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