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222 lines
6.4 KiB
Python
222 lines
6.4 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Created on Thu Mar 1 14:32:08 2018
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@author: z5030440
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Main code to extract shorelines from Landsat imagery
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"""
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# Preamble
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import ee
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from IPython import display
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import math
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import matplotlib.pyplot as plt
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import numpy as np
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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.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.morphology as morphology
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import skimage.measure as measure
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from shapely.geometry import Polygon
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from osgeo import gdal
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from osgeo import osr
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import tempfile
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import urllib
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from urllib.request import urlretrieve
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import zipfile
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# my modules
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from utils import *
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# from sds import *
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np.seterr(all='ignore') # raise/ignore divisions by 0 and nans
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ee.Initialize()
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plot_bool = True # if you want the plots
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def download_tif(image, 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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'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, bandsId):
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"""loads an ee.Image() as a np.array. e.Image() is retrieved from the EE database."""
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local_tif_filename = download_tif(image, bandsId)
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dataset = gdal.Open(local_tif_filename, gdal.GA_ReadOnly)
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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), dataset
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im = ee.Image('LANDSAT/LC08/C01/T1_RT_TOA/LC08_089083_20130411')
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lon = [151.2820816040039, 151.3425064086914]
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lat = [-33.68206818063878, -33.74775138989556]
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polygon = [[lon[0], lat[0]], [lon[1], lat[0]], [lon[1], lat[1]], [lon[0], lat[1]]];
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# get image metadata into dictionnary
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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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pan_band = [im_bands[7]]
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ms_bands = [im_bands[1], im_bands[2], im_bands[3]]
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im_full, dataset_full = load_image(im, ms_bands)
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plt.figure()
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plt.imshow(np.clip(im_full[:,:,[2,1,0]] * 3, 0, 1))
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plt.show()
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#%%
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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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"""
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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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bands = [dataset.GetRasterBand(i + 1).ReadAsArray() for i in range(dataset.RasterCount)]
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return np.stack(bands, 2), dataset
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for i in range(len(im_bands)): del im_bands[i]['dimensions']
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ms_bands = [im_bands[1], im_bands[2], im_bands[3]]
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im_cropped, dataset_cropped = load_image(im, polygon, ms_bands)
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plt.figure()
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plt.imshow(np.clip(im_cropped[:,:,[2,1,0]] * 3, 0, 1))
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plt.show()
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#%%
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crs_full = dataset_full.GetGeoTransform()
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crs_cropped = dataset_cropped.GetGeoTransform()
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scale = crs_full[1]
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ul_full = np.array([crs_full[0], crs_full[3]])
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ul_cropped = np.array([crs_cropped[0], crs_cropped[3]])
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delta = np.abs(ul_full - ul_cropped)/scale
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u0 = delta[0].astype('int')
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v0 = delta[1].astype('int')
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im_full[v0,u0,:]
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im_cropped[0,0,:]
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lrx = ul_cropped[0] + (dataset_cropped.RasterXSize * scale)
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lry = ul_cropped[1] + (dataset_cropped.RasterYSize * (-scale))
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lr_cropped = np.array([lrx, lry])
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delta = np.abs(ul_full - lr_cropped)/scale
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u1 = delta[0].astype('int')
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v1 = delta[1].astype('int')
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im_cropped2 = im_full[v0:v1,u0:u1,:]
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#%%
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crs_full = dataset_full.GetGeoTransform()
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source = osr.SpatialReference()
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source.ImportFromWkt(dataset_full.GetProjection())
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target = osr.SpatialReference()
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target.ImportFromEPSG(4326)
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transform = osr.CoordinateTransformation(source, target)
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transform.TransformPoint(ulx, uly)
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#%%
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crs_cropped = dataset_cropped.GetGeoTransform()
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ulx = crs_cropped[0]
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uly = crs_cropped[3]
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source = osr.SpatialReference()
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source.ImportFromWkt(dataset_cropped.GetProjection())
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target = osr.SpatialReference()
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target.ImportFromEPSG(4326)
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transform = osr.CoordinateTransformation(source, target)
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transform.TransformPoint(lrx, lry)
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#%%
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source = osr.SpatialReference()
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source.ImportFromEPSG(4326)
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target = osr.SpatialReference()
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target.ImportFromEPSG(32656)
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coords = transform.TransformPoint(151.2820816040039, -33.68206818063878)
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coords[0] - ulx
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coords[1] - uly
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#%%
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x_ul_full = ms_bands[0]['crs_transform'][2]
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y_ul_full = ms_bands[0]['crs_transform'][5]
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scale = ms_bands[0]['crs_transform'][0]
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x_ul_cropped = np.array([340756.105840223, 346357.851288875, 346474.839525944, 340877.362938763])
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y_ul_cropped = np.array([-3728229.45372866, -3728137.91775723, -3735421.58347927, -3735513.20696522])
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dx = abs(x_ul_full - x_ul_cropped)
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dy = abs(y_ul_full - y_ul_cropped)
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u_coord = np.round(dx/scale).astype('int')
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v_coord = np.round(dy/scale).astype('int')
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im_cropped2 = im_full[np.min(v_coord):np.max(v_coord), np.min(u_coord):np.max(u_coord),:]
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plt.figure()
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plt.imshow(np.clip(im_cropped2[:,:,[2,1,0]] * 3, 0, 1), cmap='gray')
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plt.show()
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sum(sum(sum(np.equal(im_cropped,im_cropped2).astype('int')-1)))
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