diff --git a/classify_sand.py b/classify_sand.py deleted file mode 100644 index b5349be..0000000 --- a/classify_sand.py +++ /dev/null @@ -1,181 +0,0 @@ -# -*- coding: utf-8 -*- - -#==========================================================# -# Extract shorelines from Landsat images -#==========================================================# - -# Initial settings -import ee -import matplotlib.pyplot as plt -import matplotlib.cm as cm -import numpy as np -import pandas as pd -from datetime import datetime -import pickle -import pdb -import pytz - - -# image processing modules -import skimage.filters as filters -import skimage.exposure as exposure -import skimage.transform as transform -import sklearn.decomposition as decomposition -import skimage.morphology as morphology -import skimage.measure as measure - -# machine learning modules -from sklearn.cluster import KMeans - -# my modules -import functions.utils as utils -import functions.sds as sds - -# some settings -np.seterr(all='ignore') # raise/ignore divisions by 0 and nans -plt.rcParams['axes.grid'] = False -plt.rcParams['figure.max_open_warning'] = 100 -ee.Initialize() - -# parameters -cloud_thresh = 0.5 # threshold for cloud cover -plot_bool = True # if you want the plots -prob_high = 99.9 # upper probability to clip and rescale pixel intensity -min_contour_points = 100# minimum number of points contained in each water line -output_epsg = 28356 # GDA94 / MGA Zone 56 -buffer_size = 10 # radius (in pixels) of disk for buffer (pixel classification) -min_beach_size = 50 # number of pixels in a beach (pixel classification) - -# select collection -satname = 'L8' -input_col = ee.ImageCollection('LANDSAT/LC08/C01/T1_RT_TOA') # Landsat 8 Tier 1 TOA - -# location (Narrabeen-Collaroy beach) -polygon = [[[151.3473129272461,-33.69035274454718], - [151.2820816040039,-33.68206818063878], - [151.27281188964844,-33.74775138989556], - [151.3425064086914,-33.75231878701767], - [151.3473129272461,-33.69035274454718]]]; - -# dates -start_date = '2013-01-01' -end_date = '2018-12-31' - -# filter by location and date -flt_col = input_col.filterBounds(ee.Geometry.Polygon(polygon)).filterDate(start_date, end_date) - -n_img = flt_col.size().getInfo() -print('Number of images covering the polygon:', n_img) -im_all = flt_col.getInfo().get('features') - -i = 0 # first image - -# find image in ee database -im = ee.Image(im_all[i].get('id')) - -# load image as np.array -im_pan, im_ms, im_cloud, crs, meta = sds.read_eeimage(im, polygon, satname, plot_bool) - -# rescale intensities -im_ms = sds.rescale_image_intensity(im_ms, im_cloud, prob_high, plot_bool) -im_pan = sds.rescale_image_intensity(im_pan, im_cloud, prob_high, plot_bool) - -# pansharpen rgb image -im_ms_ps = sds.pansharpen(im_ms[:,:,[0,1,2]], im_pan, im_cloud, plot_bool) - -# add down-sized bands for NIR and SWIR (since pansharpening is not possible) -im_ms_ps = np.append(im_ms_ps, im_ms[:,:,[3,4]], axis=2) - -# calculate NDWI -im_ndwi = sds.nd_index(im_ms_ps[:,:,3], im_ms_ps[:,:,1], im_cloud, plot_bool) - -# edge detection -wl_pix = sds.find_wl_contours(im_ndwi, im_cloud, min_contour_points, plot_bool) - -# convert from pixels to world coordinates -wl_coords = sds.convert_pix2world(wl_pix, crs['crs_15m']) - -# convert to output epsg spatial reference -wl = sds.convert_epsg(wl_coords, crs['epsg_code'], output_epsg) - -# classify sand pixels -im_sand = sds.classify_sand_unsupervised(im_ms_ps, im_pan, im_cloud, wl_pix, buffer_size, min_beach_size, plot_bool) - -#%% -plt.figure() -plt.imshow(im_ms_ps[:,:,[2,1,0]]) -for i,contour in enumerate(wl_pix): plt.plot(contour[:, 1], contour[:, 0], linewidth=2) -plt.axis('image') -plt.title('Detected water lines') -plt.show() - -vec = im_ms_ps.reshape(im_ms_ps.shape[0] * im_ms_ps.shape[1], im_ms_ps.shape[2]) -vec_pan = im_pan.reshape(im_pan.shape[0]*im_pan.shape[1]) -features = np.zeros((len(vec), 5)) -features[:,[0,1,2,3]] = vec[:,[0,1,2,3]] -features[:,4] = vec_pan -vec_mask = im_cloud.reshape(im_ms_ps.shape[0] * im_ms_ps.shape[1]) - -# create buffer -im_buffer = np.zeros((im_ms_ps.shape[0], im_ms_ps.shape[1])) -for i, contour in enumerate(wl_pix): - indices = [(int(_[0]), int(_[1])) for _ in list(np.round(contour))] - for j, idx in enumerate(indices): - im_buffer[idx] = 1 - - -plt.figure() -plt.imshow(im_buffer) -plt.draw() - -se = morphology.disk(buffer_size) -im_buffer = morphology.binary_dilation(im_buffer, se) - -plt.figure() -plt.imshow(im_buffer) -plt.draw() - -vec_buffer = (im_buffer == 1).reshape(im_ms_ps.shape[0] * im_ms_ps.shape[1]) - -vec_buffer= np.logical_and(vec_buffer, ~vec_mask) - -#vec_buffer = np.ravel_multi_index(z,(im_ms_ps.shape[0], im_ms_ps.shape[1])) - - -kmeans = KMeans(n_clusters=6, random_state=0).fit(vec[vec_buffer,:]) -labels = kmeans.labels_ -labels_full = np.ones((len(vec_mask))) * np.nan -labels_full[vec_buffer] = labels -im_labels = labels_full.reshape(im_ms_ps.shape[0], im_ms_ps.shape[1]) - -plt.figure() -plt.imshow(im_labels) -plt.axis('equal') -plt.draw() - -utils.compare_images(im_labels, im_pan) - -plt.figure() -for i in range(6): plt.plot(kmeans.cluster_centers_[i,:]) -plt.draw() - -im_sand = im_labels == np.argmax(np.mean(kmeans.cluster_centers_[:,[0,1,2,4]], axis=1)) - -im_sand2 = morphology.remove_small_objects(im_sand, min_size=min_beach_size, connectivity=2) -im_sand3 = morphology.binary_dilation(im_sand2, morphology.disk(1)) -plt.figure() -plt.imshow(im_sand3) -plt.draw() - -im_ms_ps[im_sand3,0] = 0 -im_ms_ps[im_sand3,1] = 0 -im_ms_ps[im_sand3,2] = 1 - - -plt.figure() -plt.imshow(im_ms_ps[:,:,[2,1,0]]) -plt.axis('image') -plt.title('Sand classification') -plt.show() - -#%% diff --git a/data/L8/NARRA_all/NARRA_all_accuracy_georef.pkl b/data/L8/NARRA_all/NARRA_all_accuracy_georef.pkl new file mode 100644 index 0000000..76567ec Binary files /dev/null and b/data/L8/NARRA_all/NARRA_all_accuracy_georef.pkl differ diff --git a/data/L8/NARRA_all/NARRA_all_clf.pkl b/data/L8/NARRA_all/NARRA_all_clf.pkl new file mode 100644 index 0000000..e69de29 diff --git a/data/L8/NARRA_all/NARRA_all_epsgcode.pkl b/data/L8/NARRA_all/NARRA_all_epsgcode.pkl new file mode 100644 index 0000000..a8a7a3d --- /dev/null +++ b/data/L8/NARRA_all/NARRA_all_epsgcode.pkl @@ -0,0 +1 @@ +M. \ No newline at end of file diff --git a/data/L8/NARRA_all/NARRA_all_sand_idxskip.pkl b/data/L8/NARRA_all/NARRA_all_sand_idxskip.pkl new file mode 100644 index 0000000..8f7024d Binary files /dev/null and b/data/L8/NARRA_all/NARRA_all_sand_idxskip.pkl differ diff --git a/data/L8/NARRA_all/NARRA_all_sand_train.pkl b/data/L8/NARRA_all/NARRA_all_sand_train.pkl new file mode 100644 index 0000000..4cde94e Binary files /dev/null and b/data/L8/NARRA_all/NARRA_all_sand_train.pkl differ diff --git a/data/L8/NARRA_all/NARRA_all_skipped_sandclassif.pkl b/data/L8/NARRA_all/NARRA_all_skipped_sandclassif.pkl new file mode 100644 index 0000000..01ad1f0 Binary files /dev/null and b/data/L8/NARRA_all/NARRA_all_skipped_sandclassif.pkl differ diff --git a/data/L8/NARRA_all/NARRA_all_timestamps.pkl b/data/L8/NARRA_all/NARRA_all_timestamps.pkl new file mode 100644 index 0000000..681a00c Binary files /dev/null and b/data/L8/NARRA_all/NARRA_all_timestamps.pkl differ diff --git a/data/L8/NARRA_all/NARRA_all_train_neg.pkl b/data/L8/NARRA_all/NARRA_all_train_neg.pkl new file mode 100644 index 0000000..b1af9de Binary files /dev/null and b/data/L8/NARRA_all/NARRA_all_train_neg.pkl differ diff --git a/data/L8/NARRA_all/NARRA_all_train_pos.pkl b/data/L8/NARRA_all/NARRA_all_train_pos.pkl new file mode 100644 index 0000000..feb767f Binary files /dev/null and b/data/L8/NARRA_all/NARRA_all_train_pos.pkl differ diff --git a/data/L8/NARRA_all/NARRA_all_training_data_wnans.pkl b/data/L8/NARRA_all/NARRA_all_training_data_wnans.pkl new file mode 100644 index 0000000..9647616 Binary files /dev/null and b/data/L8/NARRA_all/NARRA_all_training_data_wnans.pkl differ diff --git a/download_images_L8.py b/download_images_L8.py index 805cd07..afc418e 100644 --- a/download_images_L8.py +++ b/download_images_L8.py @@ -47,12 +47,18 @@ def download_tif(image, polygon, bandsId, filepath): # select collection input_col = ee.ImageCollection('LANDSAT/LC08/C01/T1_RT_TOA') +# Location (Narrabeen all) +polygon = [[[151.3473129272461,-33.69035274454718], + [151.2820816040039,-33.68206818063878], + [151.27281188964844,-33.74775138989556], + [151.3425064086914,-33.75231878701767], + [151.3473129272461,-33.69035274454718]]]; # location (Narrabeen-Collaroy beach) -polygon = [[[151.301454, -33.700754], - [151.311453, -33.702075], - [151.307237, -33.739761], - [151.294220, -33.736329], - [151.301454, -33.700754]]]; +#polygon = [[[151.301454, -33.700754], +# [151.311453, -33.702075], +# [151.307237, -33.739761], +# [151.294220, -33.736329], +# [151.301454, -33.700754]]]; # location (Oldbar beach) #polygon = [[[152.664508, -31.896163], # [152.665827, -31.897112], @@ -77,7 +83,8 @@ print('Number of images covering the area:', n_img) im_all = flt_col.getInfo().get('features') satname = 'L8' -sitename = 'NARRA' +sitename = 'NARRA_all' +#sitename = 'NARRA' #sitename = 'OLDBAR' suffix = '.tif' filepath = os.path.join(os.getcwd(), 'data', satname, sitename) @@ -115,15 +122,15 @@ for i in range(n_img): filename_ms = satname + '_' + sitename + '_' + im_date + '_ms' + '_r' + suffix all_names_pan.append(filename_pan) -# local_data_pan = download_tif(im, polygon, pan_band, filepath_pan) -# os.rename(local_data_pan, os.path.join(filepath_pan, filename_pan)) -# local_data_ms = download_tif(im, polygon, ms_bands, filepath_ms) -# os.rename(local_data_ms, os.path.join(filepath_ms, filename_ms)) + local_data_pan = download_tif(im, polygon, pan_band, filepath_pan) + os.rename(local_data_pan, os.path.join(filepath_pan, filename_pan)) + local_data_ms = download_tif(im, polygon, ms_bands, filepath_ms) + os.rename(local_data_ms, os.path.join(filepath_ms, filename_ms)) -#with open(os.path.join(filepath, sitename + '_timestamps' + '.pkl'), 'wb') as f: -# pickle.dump(timestamps, f) -#with open(os.path.join(filepath, sitename + '_epsgcode' + '.pkl'), 'wb') as f: -# pickle.dump(im_epsg, f) +with open(os.path.join(filepath, sitename + '_timestamps' + '.pkl'), 'wb') as f: + pickle.dump(timestamps, f) +with open(os.path.join(filepath, sitename + '_epsgcode' + '.pkl'), 'wb') as f: + pickle.dump(im_epsg, f) with open(os.path.join(filepath, sitename + '_accuracy_georef' + '.pkl'), 'wb') as f: pickle.dump(acc_georef, f) \ No newline at end of file diff --git a/oldbar/L8OLDBAR_inlet2_shorelines.kml b/old/oldbar/L8OLDBAR_inlet2_shorelines.kml similarity index 100% rename from oldbar/L8OLDBAR_inlet2_shorelines.kml rename to old/oldbar/L8OLDBAR_inlet2_shorelines.kml diff --git a/oldbar/L8OLDBAR_inlet_shorelines.kml b/old/oldbar/L8OLDBAR_inlet_shorelines.kml similarity index 100% rename from oldbar/L8OLDBAR_inlet_shorelines.kml rename to old/oldbar/L8OLDBAR_inlet_shorelines.kml diff --git a/oldbar/L8OLDBAR_shorelines.kml b/old/oldbar/L8OLDBAR_shorelines.kml similarity index 100% rename from oldbar/L8OLDBAR_shorelines.kml rename to old/oldbar/L8OLDBAR_shorelines.kml diff --git a/oldbar/time_coverage_oldbar.py b/old/oldbar/time_coverage_oldbar.py similarity index 100% rename from oldbar/time_coverage_oldbar.py rename to old/oldbar/time_coverage_oldbar.py diff --git a/oldcodes/box_head.py b/old/oldcodes/box_head.py similarity index 100% rename from oldcodes/box_head.py rename to old/oldcodes/box_head.py diff --git a/old/oldcodes/classify_sand_test.py b/old/oldcodes/classify_sand_test.py new file mode 100644 index 0000000..43c4c0c --- /dev/null +++ b/old/oldcodes/classify_sand_test.py @@ -0,0 +1,476 @@ +# -*- coding: utf-8 -*- + +#==========================================================# +# Extract shorelines from Landsat images +#==========================================================# + +# Initial settings +import ee +import matplotlib.pyplot as plt +import matplotlib.cm as cm +import numpy as np +import pandas as pd +from datetime import datetime +import pickle +import pdb +import pytz +from pylab import ginput +from osgeo import gdal + + + +# image processing modules +import skimage.filters as filters +import skimage.exposure as exposure +import skimage.transform as transform +import sklearn.decomposition as decomposition +import skimage.morphology as morphology +import skimage.measure as measure +import skimage.color as color +import skimage.feature as feature +# machine learning modules +from sklearn.cluster import KMeans + +# my modules +import functions.utils as utils +import functions.sds as sds + +# some settings +np.seterr(all='ignore') # raise/ignore divisions by 0 and nans +plt.rcParams['axes.grid'] = False +plt.rcParams['figure.max_open_warning'] = 100 +ee.Initialize() + +# parameters +cloud_thresh = 0.5 # threshold for cloud cover +plot_bool = False # if you want the plots +prob_high = 99.9 # upper probability to clip and rescale pixel intensity +min_contour_points = 100# minimum number of points contained in each water line +output_epsg = 28356 # GDA94 / MGA Zone 56 +buffer_size = 10 # radius (in pixels) of disk for buffer (pixel classification) +min_beach_size = 50 # number of pixels in a beach (pixel classification) + +# select collection +satname = 'L8' +input_col = ee.ImageCollection('LANDSAT/LC08/C01/T1_RT_TOA') # Landsat 8 Tier 1 TOA + +# location (Narrabeen-Collaroy beach) +polygon = [[[151.3473129272461,-33.69035274454718], + [151.2820816040039,-33.68206818063878], + [151.27281188964844,-33.74775138989556], + [151.3425064086914,-33.75231878701767], + [151.3473129272461,-33.69035274454718]]]; + +# dates +start_date = '2013-01-01' +end_date = '2018-12-31' + +# filter by location and date +flt_col = input_col.filterBounds(ee.Geometry.Polygon(polygon)).filterDate(start_date, end_date) + +n_img = flt_col.size().getInfo() +print('Number of images covering the polygon:', n_img) +im_all = flt_col.getInfo().get('features') + +i = 0 # first image + +# find image in ee database +im = ee.Image(im_all[i].get('id')) + +# load image as np.array +im_pan, im_ms, im_cloud, crs, meta = sds.read_eeimage(im, polygon, satname, plot_bool) + +# rescale intensities +im_ms = sds.rescale_image_intensity(im_ms, im_cloud, prob_high, plot_bool) +im_pan = sds.rescale_image_intensity(im_pan, im_cloud, prob_high, plot_bool) + +# pansharpen rgb image +im_ms_ps = sds.pansharpen(im_ms[:,:,[0,1,2]], im_pan, im_cloud, plot_bool) + +# add down-sized bands for NIR and SWIR (since pansharpening is not possible) +im_ms_ps = np.append(im_ms_ps, im_ms[:,:,[3,4]], axis=2) + +# calculate NDWI +im_ndwi = sds.nd_index(im_ms_ps[:,:,3], im_ms_ps[:,:,1], im_cloud, plot_bool) + +# edge detection +wl_pix = sds.find_wl_contours(im_ndwi, im_cloud, min_contour_points, plot_bool) + +# convert from pixels to world coordinates +wl_coords = sds.convert_pix2world(wl_pix, crs['crs_15m']) + +# convert to output epsg spatial reference +wl = sds.convert_epsg(wl_coords, crs['epsg_code'], output_epsg) + +# classify sand pixels +im_sand = sds.classify_sand_unsupervised(im_ms_ps, im_pan, im_cloud, wl_pix, buffer_size, min_beach_size, plot_bool) + +#pt_in = np.array(ginput(n=1, timeout=1000)) +#if pt_in[0][0] < im_ms_ps.shape[1]/2: +win = np.ones((3,3)) + +im_features = np.zeros((sum(sum(im_sand)), 20)) +im_features[:,[0,1,2,3,4]] = im_ms_ps[im_sand,:] # B G R NIR SWIR +im_features[:,5] = im_pan[im_sand] # Pan +im_features[:,6] = im_ndwi[im_sand] # NDWI +im_features[:,7] = sds.nd_index(im_ms_ps[:,:,3], im_ms_ps[:,:,2], im_cloud, False)[im_sand] # NDVI +im_features[:,8] = sds.nd_index(im_ms_ps[:,:,0], im_ms_ps[:,:,2], im_cloud, False)[im_sand] # ND Blue - Red +for i in range(9): + im_features[:,i+9] = ndimage.generic_filter(im_features[:,i], np.std, footprint = win) + + +im_ms_ps[im_sand,:] + +im_grey = color.rgb2grey(im_ms_ps[:,:,[2,1,0]]) +plt.figure() +plt.imshow(im_grey, cmap='gray') +plt.draw() + +counts, bins = np.histogram(im_grey[~im_cloud], bins=255) +im_grey_d = np.digitize(im_grey, bins=bins) - 1 + +from scipy import ndimage + +varianceMatrix1 = ndimage.generic_filter(im_grey_d, np.max, footprint = np.ones((3,3))) +varianceMatrix2 = ndimage.generic_filter(im_grey_d, np.min, footprint = np.ones((3,3))) +varianceMatrix = varianceMatrix1 - varianceMatrix2 +im_grey = color.rgb2grey(im_ms_ps[:,:,[2,1,0]]) +plt.figure() +plt.imshow(varianceMatrix, cmap='gray') +plt.draw() +#%% +data = gdal.Open('l8_test.tif', gdal.GA_ReadOnly) +bands = [data.GetRasterBand(i + 1).ReadAsArray() for i in range(data.RasterCount)] +im = np.stack(bands, 2) + +im_test = im[:,:,3] + +plt.figure() +plt.imshow(im_test, cmap='gray') +plt.axis('image') +plt.draw() + +im_stats = np.zeros((im_test.shape[0], im_test.shape[1], 6)) +winsize = 5 +prop_names = ('contrast', 'dissimilarity', 'homogeneity', 'energy', 'correlation', 'ASM') +for i in range(im_test.shape[0]): + print(int(np.round(100*i/im_test.shape[0]))) + for j in range(im_test.shape[1]): + #windows needs to fit completely in image + if i <2 or j <2: + continue + if i > (im_test.shape[0] - 3) or j > (im_test.shape[1] - 3): + continue + + #Calculate GLCM on a 3x3 window + glcm_window = im_test[i-2: i+3, j-2 : j+3] + glcm = feature.greycomatrix(glcm_window, [1], [0], symmetric = True, normed = True ) + + #Calculate contrast and replace center pixel + for k in range(6): im_stats[i,j,k] = feature.greycoprops(glcm, prop_names[k]) + +plt.figure() +for i in range(6): + plt.subplot(2,3,i+1) + plt.imshow(im_stats[:,:,i], cmap='jet') + plt.axis('image') + plt.title(prop_names[i]) + plt.draw() + +pixel_loc = [200, 200] +im_stats[pixel_loc[0], pixel_loc[1], 3] +#%% + +for i in range(im_grey_d.shape[0]): + print(int(np.round(100*i/im_grey_d.shape[0]))) + for j in range(im_grey_d.shape[1]): + #windows needs to fit completely in image + if i <1 or j <1: + continue + if i > (im_grey_d.shape[0] - 1) or j > (im_grey_d.shape[0] - 1): + continue + + #Calculate GLCM on a 3x3 window + glcm_window = im_grey_d[i-1: i+1, j-1 : j+1] + glcm = feature.greycomatrix(glcm_window, [1,2], [0], levels=256, symmetric = True, normed = True ) + + #Calculate contrast and replace center pixel + im_stats[i,j,0] = feature.greycoprops(glcm, 'contrast') + im_stats[i,j,1] = feature.greycoprops(glcm, 'dissimilarity') + im_stats[i,j,2] = feature.greycoprops(glcm, 'homogeneity') + im_stats[i,j,3] = feature.greycoprops(glcm, 'energy') + im_stats[i,j,4] = feature.greycoprops(glcm, 'correlation') + im_stats[i,j,5] = feature.greycoprops(glcm, 'ASM') + +plt.figure() +for i in range(6): + plt.subplot(2,3,i+1) + plt.imshow(im_stats[:,:,i], cmap='jet') + plt.axis('image') + plt.draw() +#%% +from multiprocessing import Pool +from itertools import product + +N = 10000000 +pool = Pool() #defaults to number of available CPU's +a = np.ones((N)) +b = np.ones((N))*2 +out = np.zeros((N)) + +t = time.time() +for i in range(len(a)): + out[i] = a[i]*b[i] +elapsed = time.time() - t +print(elapsed) + +def fun(a,b): + return a*b + +chunksize = 20 #this may take some guessing ... take a look at the docs to decide +for ind, res in enumerate(pool.map(fun, range(N)), chunksize): + output.flat[ind] = res +#%% + +import gdal, osr +import numpy as np +from scipy.interpolate import RectBivariateSpline +from numpy.lib.stride_tricks import as_strided as ast +import dask.array as da +from joblib import Parallel, delayed, cpu_count +import os +from skimage.feature import greycomatrix, greycoprops + +def im_resize(im,Nx,Ny): + ''' + resize array by bivariate spline interpolation + ''' + ny, nx = np.shape(im) + xx = np.linspace(0,nx,Nx) + yy = np.linspace(0,ny,Ny) + + try: + im = da.from_array(im, chunks=1000) #dask implementation + except: + pass + + newKernel = RectBivariateSpline(np.r_[:ny],np.r_[:nx],im) + return newKernel(yy,xx) + +def p_me(Z, win): + ''' + loop to calculate greycoprops + ''' + try: + glcm = greycomatrix(Z, [5], [0], 256, symmetric=True, normed=True) + cont = greycoprops(glcm, 'contrast') + diss = greycoprops(glcm, 'dissimilarity') + homo = greycoprops(glcm, 'homogeneity') + eng = greycoprops(glcm, 'energy') + corr = greycoprops(glcm, 'correlation') + ASM = greycoprops(glcm, 'ASM') + return (cont, diss, homo, eng, corr, ASM) + except: + return (0,0,0,0,0,0) + +def norm_shape(shap): + ''' + Normalize numpy array shapes so they're always expressed as a tuple, + even for one-dimensional shapes. + ''' + try: + i = int(shap) + return (i,) + except TypeError: + # shape was not a number + pass + + try: + t = tuple(shap) + return t + except TypeError: + # shape was not iterable + pass + + raise TypeError('shape must be an int, or a tuple of ints') + +def sliding_window(a, ws, ss = None, flatten = True): + ''' + Source: http://www.johnvinyard.com/blog/?p=268#more-268 + Parameters: + a - an n-dimensional numpy array + ws - an int (a is 1D) or tuple (a is 2D or greater) representing the size + of each dimension of the window + ss - an int (a is 1D) or tuple (a is 2D or greater) representing the + amount to slide the window in each dimension. If not specified, it + defaults to ws. + flatten - if True, all slices are flattened, otherwise, there is an + extra dimension for each dimension of the input. + + Returns + an array containing each n-dimensional window from a + ''' + if None is ss: + # ss was not provided. the windows will not overlap in any direction. + ss = ws + ws = norm_shape(ws) + ss = norm_shape(ss) + # convert ws, ss, and a.shape to numpy arrays + ws = np.array(ws) + ss = np.array(ss) + shap = np.array(a.shape) + # ensure that ws, ss, and a.shape all have the same number of dimensions + ls = [len(shap),len(ws),len(ss)] + if 1 != len(set(ls)): + raise ValueError(\ + 'a.shape, ws and ss must all have the same length. They were %s' % str(ls)) + + # ensure that ws is smaller than a in every dimension + if np.any(ws > shap): + raise ValueError(\ + 'ws cannot be larger than a in any dimension.\ + a.shape was %s and ws was %s' % (str(a.shape),str(ws))) + + # how many slices will there be in each dimension? + newshape = norm_shape(((shap - ws) // ss) + 1) + + + # the shape of the strided array will be the number of slices in each dimension + # plus the shape of the window (tuple addition) + newshape += norm_shape(ws) + + + # the strides tuple will be the array's strides multiplied by step size, plus + # the array's strides (tuple addition) + newstrides = norm_shape(np.array(a.strides) * ss) + a.strides + a = ast(a,shape = newshape,strides = newstrides) + if not flatten: + return a + # Collapse strided so that it has one more dimension than the window. I.e., + # the new array is a flat list of slices. + meat = len(ws) if ws.shape else 0 + firstdim = (np.product(newshape[:-meat]),) if ws.shape else () + dim = firstdim + (newshape[-meat:]) + # remove any dimensions with size 1 + dim = filter(lambda i : i != 1,dim) + return a.reshape(dim), newshape + +#Stuff to change +win = 3 +meter = str(win/4) +merge = im_grey_d +Z,ind = sliding_window(merge,(win,win),(1,1)) + +Ny, Nx = np.shape(merge) + +w = Parallel(n_jobs = cpu_count(), verbose=0)(delayed(p_me)(Z[k]) for k in xrange(len(Z))) + +cont = [a[0] for a in w] +diss = [a[1] for a in w] +homo = [a[2] for a in w] +eng = [a[3] for a in w] +corr = [a[4] for a in w] +ASM = [a[5] for a in w] + + +#Reshape to match number of windows +plt_cont = np.reshape(cont , ( ind[0], ind[1] ) ) +plt_diss = np.reshape(diss , ( ind[0], ind[1] ) ) +plt_homo = np.reshape(homo , ( ind[0], ind[1] ) ) +plt_eng = np.reshape(eng , ( ind[0], ind[1] ) ) +plt_corr = np.reshape(corr , ( ind[0], ind[1] ) ) +plt_ASM = np.reshape(ASM , ( ind[0], ind[1] ) ) +del cont, diss, homo, eng, corr, ASM + +#Resize Images to receive texture and define filenames +contrast = im_resize(plt_cont,Nx,Ny) +contrast[merge==0]=np.nan +dissimilarity = im_resize(plt_diss,Nx,Ny) +dissimilarity[merge==0]=np.nan +homogeneity = im_resize(plt_homo,Nx,Ny) +homogeneity[merge==0]=np.nan +energy = im_resize(plt_eng,Nx,Ny) +energy[merge==0]=np.nan +correlation = im_resize(plt_corr,Nx,Ny) +correlation[merge==0]=np.nan +ASM = im_resize(plt_ASM,Nx,Ny) +ASM[merge==0]=np.nan + +#%% +plt.figure() +plt.imshow(im_ms_ps[:,:,[2,1,0]]) +for i,contour in enumerate(wl_pix): plt.plot(contour[:, 1], contour[:, 0], linewidth=2) +plt.axis('image') +plt.title('Detected water lines') +plt.show() + +vec = im_ms_ps.reshape(im_ms_ps.shape[0] * im_ms_ps.shape[1], im_ms_ps.shape[2]) +vec_pan = im_pan.reshape(im_pan.shape[0]*im_pan.shape[1]) +features = np.zeros((len(vec), 5)) +features[:,[0,1,2,3]] = vec[:,[0,1,2,3]] +features[:,4] = vec_pan +vec_mask = im_cloud.reshape(im_ms_ps.shape[0] * im_ms_ps.shape[1]) + +# create buffer +im_buffer = np.zeros((im_ms_ps.shape[0], im_ms_ps.shape[1])) +for i, contour in enumerate(wl_pix): + indices = [(int(_[0]), int(_[1])) for _ in list(np.round(contour))] + for j, idx in enumerate(indices): + im_buffer[idx] = 1 + + +plt.figure() +plt.imshow(im_buffer) +plt.draw() + +se = morphology.disk(buffer_size) +im_buffer = morphology.binary_dilation(im_buffer, se) + +plt.figure() +plt.imshow(im_buffer) +plt.draw() + +vec_buffer = (im_buffer == 1).reshape(im_ms_ps.shape[0] * im_ms_ps.shape[1]) + +vec_buffer= np.logical_and(vec_buffer, ~vec_mask) + +#vec_buffer = np.ravel_multi_index(z,(im_ms_ps.shape[0], im_ms_ps.shape[1])) + + +kmeans = KMeans(n_clusters=6, random_state=0).fit(vec[vec_buffer,:]) +labels = kmeans.labels_ +labels_full = np.ones((len(vec_mask))) * np.nan +labels_full[vec_buffer] = labels +im_labels = labels_full.reshape(im_ms_ps.shape[0], im_ms_ps.shape[1]) + +plt.figure() +plt.imshow(im_labels) +plt.axis('equal') +plt.draw() + +utils.compare_images(im_labels, im_pan) + +plt.figure() +for i in range(6): plt.plot(kmeans.cluster_centers_[i,:]) +plt.draw() + +im_sand = im_labels == np.argmax(np.mean(kmeans.cluster_centers_[:,[0,1,2,4]], axis=1)) + +im_sand2 = morphology.remove_small_objects(im_sand, min_size=min_beach_size, connectivity=2) +im_sand3 = morphology.binary_dilation(im_sand2, morphology.disk(1)) +plt.figure() +plt.imshow(im_sand3) +plt.draw() + +im_ms_ps[im_sand3,0] = 0 +im_ms_ps[im_sand3,1] = 0 +im_ms_ps[im_sand3,2] = 1 + + +plt.figure() +plt.imshow(im_ms_ps[:,:,[2,1,0]]) +plt.axis('image') +plt.title('Sand classification') +plt.show() + +#%% diff --git a/oldcodes/compare_shorelines.py b/old/oldcodes/compare_shorelines.py similarity index 100% rename from oldcodes/compare_shorelines.py rename to old/oldcodes/compare_shorelines.py diff --git a/oldcodes/compare_shorelines2.py b/old/oldcodes/compare_shorelines2.py similarity index 100% rename from oldcodes/compare_shorelines2.py rename to old/oldcodes/compare_shorelines2.py diff --git a/oldcodes/geocheck.py b/old/oldcodes/geocheck.py similarity index 100% rename from oldcodes/geocheck.py rename to old/oldcodes/geocheck.py diff --git a/oldcodes/get_coordinates.py b/old/oldcodes/get_coordinates.py similarity index 100% rename from oldcodes/get_coordinates.py rename to old/oldcodes/get_coordinates.py diff --git a/old/oldcodes/l8_calendar_dates.py b/old/oldcodes/l8_calendar_dates.py new file mode 100644 index 0000000..bd20841 --- /dev/null +++ b/old/oldcodes/l8_calendar_dates.py @@ -0,0 +1,23 @@ +# -*- coding: utf-8 -*- + + +from datetime import datetime, timedelta +import pytz +import csv +import pandas as pd +au_tz = pytz.timezone('Australia/Sydney') + +dt1 = datetime(2018, 4, 17, tzinfo= au_tz) + +dt = [] +dt.append(dt1) +for i in range(1,100): + dt1 = dt[i-1] + dt.append(dt1 + timedelta(days=16)) + + +dtstr = [_.strftime('%d %b %Y') for _ in dt] + +df = pd.DataFrame(dtstr) +df.to_csv('L7_NARRA_dates.csv', index=False, header=False) + diff --git a/oldcodes/plot_cloud_cover.py b/old/oldcodes/plot_cloud_cover.py similarity index 100% rename from oldcodes/plot_cloud_cover.py rename to old/oldcodes/plot_cloud_cover.py diff --git a/oldcodes/sds_extract.py b/old/oldcodes/sds_extract.py similarity index 100% rename from oldcodes/sds_extract.py rename to old/oldcodes/sds_extract.py diff --git a/oldcodes/sds_extract_allcode.py b/old/oldcodes/sds_extract_allcode.py similarity index 100% rename from oldcodes/sds_extract_allcode.py rename to old/oldcodes/sds_extract_allcode.py diff --git a/oldcodes/sds_extract_test.py b/old/oldcodes/sds_extract_test.py similarity index 100% rename from oldcodes/sds_extract_test.py rename to old/oldcodes/sds_extract_test.py diff --git a/oldcodes/time_coverage.py b/old/oldcodes/time_coverage.py similarity index 100% rename from oldcodes/time_coverage.py rename to old/oldcodes/time_coverage.py diff --git a/olddata/NARRA_output.pkl b/old/olddata/NARRA_output.pkl similarity index 100% rename from olddata/NARRA_output.pkl rename to old/olddata/NARRA_output.pkl diff --git a/olddata/data_2016.pkl b/old/olddata/data_2016.pkl similarity index 100% rename from olddata/data_2016.pkl rename to old/olddata/data_2016.pkl diff --git a/olddata/data_gt_l8.pkl b/old/olddata/data_gt_l8.pkl similarity index 100% rename from olddata/data_gt_l8.pkl rename to old/olddata/data_gt_l8.pkl diff --git a/olddata/idx_nogt.pkl b/old/olddata/idx_nogt.pkl similarity index 100% rename from olddata/idx_nogt.pkl rename to old/olddata/idx_nogt.pkl diff --git a/olddata/wl_final.pkl b/old/olddata/wl_final.pkl similarity index 100% rename from olddata/wl_final.pkl rename to old/olddata/wl_final.pkl diff --git a/olddata/wl_l8_comparison.pkl b/old/olddata/wl_l8_comparison.pkl similarity index 100% rename from olddata/wl_l8_comparison.pkl rename to old/olddata/wl_l8_comparison.pkl diff --git a/sand_classification/NN_all.pkl b/sand_classification/NN_all.pkl new file mode 100644 index 0000000..4984a7a Binary files /dev/null and b/sand_classification/NN_all.pkl differ diff --git a/sand_classification/NN_new.pkl b/sand_classification/NN_new.pkl new file mode 100644 index 0000000..4e93726 Binary files /dev/null and b/sand_classification/NN_new.pkl differ diff --git a/sand_classification/wekaNN/NNdefaultparams.txt b/sand_classification/wekaNN/NNdefaultparams.txt new file mode 100644 index 0000000..315a902 --- /dev/null +++ b/sand_classification/wekaNN/NNdefaultparams.txt @@ -0,0 +1,26 @@ +Time taken to build model: 44.94 seconds + +=== Stratified cross-validation === +=== Summary === + +Correctly Classified Instances 79136 99.2799 % +Incorrectly Classified Instances 574 0.7201 % +Kappa statistic 0.9838 +Mean absolute error 0.0105 +Root mean squared error 0.0781 +Relative absolute error 2.3774 % +Root relative squared error 16.5717 % +Total Number of Instances 79710 + +=== Detailed Accuracy By Class === + + TP Rate FP Rate Precision Recall F-Measure MCC ROC Area PRC Area Class + 0.995 0.011 0.994 0.995 0.995 0.984 0.998 0.999 0 + 0.989 0.005 0.990 0.989 0.989 0.984 0.998 0.997 1 +Weighted Avg. 0.993 0.009 0.993 0.993 0.993 0.984 0.998 0.998 + +=== Confusion Matrix === + + a b <-- classified as + 52960 270 | a = 0 + 304 26176 | b = 1 \ No newline at end of file diff --git a/sand_classification/wekaNN/training_data.csv b/sand_classification/wekaNN/training_data.csv new file mode 100644 index 0000000..c55c03b --- /dev/null +++ b/sand_classification/wekaNN/training_data.csv @@ -0,0 +1,79711 @@ +B,G,R,NIR,SWIR,Pan,WI,VI,BR,SAND +0.482885409,0.57190048,0.532847465,0.528955343,0.396551604,0.585131288,-0.039010682,-0.003665578,-0.049188184,1 +0.502498313,0.596984505,0.608401522,0.558515143,0.451061072,0.655806124,-0.033292405,-0.042750592,-0.095331015,1 +0.59463259,0.716977013,0.730143788,0.542236642,0.510471976,0.83541292,-0.138769437,-0.147681574,-0.102289866,1 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+0.987412437,0.957654412,0.815201026,0.406742668,0.088714833,1,-0.403776695,-0.334269377,0.095534298,0 +0.91602548,0.919273813,0.761294536,0.318041144,0.073518995,0.996036232,-0.48591724,-0.410672416,0.09224891,0 +0.635212224,0.640570446,0.545890356,0.20958056,0.05039122,0.624546468,-0.506956862,-0.445165776,0.075625834,0 diff --git a/sand_create_train.py b/sand_create_train.py new file mode 100644 index 0000000..827ddc7 --- /dev/null +++ b/sand_create_train.py @@ -0,0 +1,245 @@ +# -*- coding: utf-8 -*- + +#==========================================================# +# Create a training data +#==========================================================# + +# Initial settings +import os +import numpy as np +import matplotlib.pyplot as plt +import ee +import pdb +import time +import pandas as pd +# other modules +from osgeo import gdal, ogr, osr +import pickle +import matplotlib.cm as cm +from pylab import ginput + +# image processing modules +import skimage.filters as filters +import skimage.exposure as exposure +import skimage.transform as transform +import sklearn.decomposition as decomposition +import skimage.measure as measure +import skimage.morphology as morphology +from scipy import ndimage + +# machine learning modules +from sklearn.model_selection import train_test_split +from sklearn.neural_network import MLPClassifier +from sklearn.preprocessing import StandardScaler, Normalizer +from sklearn.externals import joblib + +# import own modules +import functions.utils as utils +import functions.sds as sds + +# some settings +np.seterr(all='ignore') # raise/ignore divisions by 0 and nans +plt.rcParams['axes.grid'] = True +plt.rcParams['figure.max_open_warning'] = 100 +ee.Initialize() + +# parameters +cloud_thresh = 0.5 # threshold for cloud cover +plot_bool = False # if you want the plots +prob_high = 99.9 # upper probability to clip and rescale pixel intensity +min_contour_points = 100# minimum number of points contained in each water line +output_epsg = 28356 # GDA94 / MGA Zone 56 +buffer_size = 10 # radius (in pixels) of disk for buffer (pixel classification) +min_beach_size = 50 # number of pixels in a beach (pixel classification) + +# load metadata (timestamps and epsg code) for the collection +satname = 'L8' +sitename = 'NARRA_all' +#sitename = 'NARRA' +#sitename = 'OLDBAR' + +# Load metadata +filepath = os.path.join(os.getcwd(), 'data', satname, sitename) + +# path to images +file_path_pan = os.path.join(os.getcwd(), 'data', satname, sitename, 'pan') +file_path_ms = os.path.join(os.getcwd(), 'data', satname, sitename, 'ms') +file_names_pan = os.listdir(file_path_pan) +file_names_ms = os.listdir(file_path_ms) +N = len(file_names_pan) + +# initialise some variables +idx_skipped = [] +idx_nocloud = [] +n_features = 10 +train_pos = np.nan*np.ones((1,n_features)) +train_neg = np.nan*np.ones((1,n_features)) +columns = ('B','G','R','NIR','SWIR','Pan','WI','VI','BR', 'mWI', 'SAND') + +#%% +for i in range(N): + # read pan image + fn_pan = os.path.join(file_path_pan, file_names_pan[i]) + data = gdal.Open(fn_pan, gdal.GA_ReadOnly) + georef = np.array(data.GetGeoTransform()) + bands = [data.GetRasterBand(i + 1).ReadAsArray() for i in range(data.RasterCount)] + im_pan = np.stack(bands, 2)[:,:,0] + # read ms image + fn_ms = os.path.join(file_path_ms, file_names_ms[i]) + data = gdal.Open(fn_ms, gdal.GA_ReadOnly) + bands = [data.GetRasterBand(i + 1).ReadAsArray() for i in range(data.RasterCount)] + im_ms = np.stack(bands, 2) + # cloud mask + im_qa = im_ms[:,:,5] + cloud_mask = sds.create_cloud_mask(im_qa, satname, plot_bool) + cloud_mask = transform.resize(cloud_mask, (im_pan.shape[0], im_pan.shape[1]), + order=0, preserve_range=True, + mode='constant').astype('bool_') + # resize the image using bilinear interpolation (order 1) + im_ms = transform.resize(im_ms,(im_pan.shape[0], im_pan.shape[1]), + order=1, preserve_range=True, mode='constant') + # check if -inf or nan values and add to cloud mask + im_inf = np.isin(im_ms[:,:,0], -np.inf) + im_nan = np.isnan(im_ms[:,:,0]) + cloud_mask = np.logical_or(np.logical_or(cloud_mask, im_inf), im_nan) + # skip if cloud cover is more than the threshold + cloud_cover = sum(sum(cloud_mask.astype(int)))/(cloud_mask.shape[0]*cloud_mask.shape[1]) + if cloud_cover > cloud_thresh: + print('skip ' + str(i) + ' - cloudy (' + str(cloud_cover) + ')') + idx_skipped.append(i) + continue + idx_nocloud.append(i) + + # rescale intensities + im_ms = sds.rescale_image_intensity(im_ms, cloud_mask, prob_high, plot_bool) + im_pan = sds.rescale_image_intensity(im_pan, cloud_mask, prob_high, plot_bool) + # pansharpen rgb image + im_ms_ps = sds.pansharpen(im_ms[:,:,[0,1,2]], im_pan, cloud_mask, plot_bool) + nrow = im_ms_ps.shape[0] + ncol = im_ms_ps.shape[1] + # add down-sized bands for NIR and SWIR (since pansharpening is not possible) + im_ms_ps = np.append(im_ms_ps, im_ms[:,:,[3,4]], axis=2) + # calculate NDWI + im_ndwi = sds.nd_index(im_ms_ps[:,:,3], im_ms_ps[:,:,1], cloud_mask, plot_bool) + # detect edges + wl_pix = sds.find_wl_contours(im_ndwi, cloud_mask, min_contour_points, plot_bool) + # classify sand pixels with Kmeans + im_sand = sds.classify_sand_unsupervised(im_ms_ps, im_pan, cloud_mask, wl_pix, buffer_size, min_beach_size, plot_bool) + + # plot a figure to manually select which images to keep + im = np.copy(im_ms_ps) + im[im_sand,0] = 0 + im[im_sand,1] = 0 + im[im_sand,2] = 1 + plt.figure() + plt.imshow(im[:,:,[2,1,0]]) + plt.axis('image') + plt.title('Sand classification') + plt.show() + mng = plt.get_current_fig_manager() + mng.window.showMaximized() + plt.tight_layout() + plt.draw() + + + + # click a point + # top-left quadrant: keep classif as pos and click somewhere for neg + # bottom-left: keep classif as neg + # any right quadrant: discard image + pt_in = np.array(ginput(n=1, timeout=1000)) + if pt_in[0][0] < im_ms_ps.shape[1]/2: + + im_features = np.zeros((im_ms_ps.shape[0], im_ms_ps.shape[1], n_features)) + im_features[:,:,[0,1,2,3,4]] = im_ms_ps + im_features[:,:,5] = im_pan + im_features[:,:,6] = sds.nd_index(im_ms_ps[:,:,3], im_ms_ps[:,:,1], cloud_mask, False) # (NIR-G) + im_features[:,:,7] = sds.nd_index(im_ms_ps[:,:,3], im_ms_ps[:,:,2], cloud_mask, False) # (NIR-R) + im_features[:,:,8] = sds.nd_index(im_ms_ps[:,:,0], im_ms_ps[:,:,2], cloud_mask, False) # (B-R) + im_features[:,:,9] = sds.nd_index(im_ms_ps[:,:,4], im_ms_ps[:,:,1], cloud_mask, False) # (SWIR-G) + # win = np.ones((3,3)) + # im_features[:,:,9] = ndimage.generic_filter(im_features[:,:,5], np.std, footprint=win) + # im_features[:,:,10] = ndimage.generic_filter(im_features[:,:,5], np.max, footprint=win) - ndimage.generic_filter(im_features[:,:,5], np.min, footprint=win) + + if pt_in[0][1] < im_ms_ps.shape[0]/2: + # positive examples + vec_pos = im_features[im_sand,:] + train_pos = np.append(train_pos, vec_pos, axis=0) + + # click where negative examples are + pt_neg = np.round(np.array(ginput(n=1, timeout=1000))[0]) + radius = int(round(np.sqrt(sum(sum(im_sand))))) + idx_rows = np.linspace(0,radius-1,radius).astype(int) + int(pt_neg[1]) + idx_cols = np.linspace(0,radius-1,radius).astype(int) + int(pt_neg[0]) + xx, yy = np.meshgrid(idx_rows,idx_cols, indexing='ij') + row_neg = xx.reshape(radius*radius) + col_neg = yy.reshape(radius*radius) + im_nosand = np.zeros((nrow,ncol)).astype(bool) + for i in range(len(row_neg)): + im_nosand[row_neg[i],col_neg[i]] = True + im_ms_ps[row_neg[i],col_neg[i],0] = 1 + im_ms_ps[row_neg[i],col_neg[i],1] = 1 + im_ms_ps[row_neg[i],col_neg[i],2] = 0 + plt.imshow(im_ms_ps[:,:,[2,1,0]]) + plt.draw() + + # negative examples + vec_neg = im_features[im_nosand,:] + train_neg = np.append(train_neg, vec_neg, axis=0) + + else: + # negative examples + vec_neg = im_features[im_sand,:] + train_neg = np.append(train_neg, vec_neg, axis=0) + + else: + print('skip ' + str(i)) + idx_skipped.append(i) + +# format data +train_pos = train_pos[1:,:] +train_neg = train_neg[1:,:] +n_pos = len(train_pos) +n_neg = len(train_neg) +training_data = np.zeros((n_pos+n_neg, n_features+1)) +training_data[:n_pos,:n_features] = train_pos +training_data[n_pos:n_pos+n_neg,:n_features] = train_neg +training_data[:n_pos,n_features] = np.ones((n_pos)) +df_train = pd.DataFrame(training_data, columns=columns) +df_train.dropna(axis=0, how='any', inplace=True) +sand_train = np.array(df_train) + +# save data +#with open(os.path.join(filepath, sitename + '_sand_idxskip' + '.pkl'), 'wb') as f: +# pickle.dump(idx_skipped, f) +#with open(os.path.join(filepath, sitename + '_sand_train' + '.pkl'), 'wb') as f: +# pickle.dump(sand_train, f) +#df_train.to_csv('training_data.csv') + +#%% Train neural network on data + +# load training data +with open(os.path.join(filepath, sitename + '_sand_train' + '.pkl'), 'rb') as f: + sand_train = pickle.load(f) + +n_features = sand_train.shape[1] - 1 +X = sand_train[:,0:n_features] +y = sand_train[:,n_features] + +# divide in train and test +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0) + +#scaler = StandardScaler() +#scaler.fit(X_train) +#X_train = scaler.transform(X_train) +#X_test = scaler.transform(X_test) + +# run NN on train dat and evaluate on test data +clf = MLPClassifier() +clf.fit(X_train,y_train) +clf.score(X_test,y_test) + +# save NN model +joblib.dump(clf, os.path.join(os.getcwd(), 'sand_classification', 'NN_small.pkl')) + + diff --git a/sand_runNN.py b/sand_runNN.py new file mode 100644 index 0000000..55deaac --- /dev/null +++ b/sand_runNN.py @@ -0,0 +1,176 @@ +# -*- coding: utf-8 -*- + +#==========================================================# +# Run Neural Network on image to extract sandy pixels +#==========================================================# + +# Initial settings +import os +import numpy as np +import matplotlib.pyplot as plt +import ee +import pdb +import time +import pandas as pd +# other modules +from osgeo import gdal, ogr, osr +import pickle +import matplotlib.cm as cm +from pylab import ginput + +# image processing modules +import skimage.filters as filters +import skimage.exposure as exposure +import skimage.transform as transform +import sklearn.decomposition as decomposition +import skimage.measure as measure +import skimage.morphology as morphology +from scipy import ndimage + +# machine learning modules +from sklearn.model_selection import train_test_split +from sklearn.neural_network import MLPClassifier +from sklearn.preprocessing import StandardScaler, Normalizer +from sklearn.externals import joblib + +# import own modules +import functions.utils as utils +import functions.sds as sds + +# some settings +np.seterr(all='ignore') # raise/ignore divisions by 0 and nans +plt.rcParams['axes.grid'] = True +plt.rcParams['figure.max_open_warning'] = 100 +ee.Initialize() + +# parameters +cloud_thresh = 0.5 # threshold for cloud cover +plot_bool = False # if you want the plots +prob_high = 100 # upper probability to clip and rescale pixel intensity +min_contour_points = 100# minimum number of points contained in each water line +output_epsg = 28356 # GDA94 / MGA Zone 56 +buffer_size = 10 # radius (in pixels) of disk for buffer (pixel classification) +min_beach_size = 50 # number of pixels in a beach (pixel classification) + +# load metadata (timestamps and epsg code) for the collection +satname = 'L8' +sitename = 'NARRA_all' +#sitename = 'NARRA' +#sitename = 'OLDBAR' + +# Load metadata +filepath = os.path.join(os.getcwd(), 'data', satname, sitename) + +# path to images +file_path_pan = os.path.join(os.getcwd(), 'data', satname, sitename, 'pan') +file_path_ms = os.path.join(os.getcwd(), 'data', satname, sitename, 'ms') +file_names_pan = os.listdir(file_path_pan) +file_names_ms = os.listdir(file_path_ms) +N = len(file_names_pan) + +# initialise some variables +idx_skipped = [] +idx_nocloud = [] +n_features = 10 +train_pos = np.nan*np.ones((1,n_features)) +train_neg = np.nan*np.ones((1,n_features)) +columns = ('B','G','R','NIR','SWIR','Pan','WI','VI','BR', 'mWI', 'SAND') + +clf = joblib.load(os.path.join(os.getcwd(), 'sand_classification', 'NN_new.pkl')) +#%% +for i in range(N): + # read pan image + fn_pan = os.path.join(file_path_pan, file_names_pan[i]) + data = gdal.Open(fn_pan, gdal.GA_ReadOnly) + georef = np.array(data.GetGeoTransform()) + bands = [data.GetRasterBand(i + 1).ReadAsArray() for i in range(data.RasterCount)] + im_pan = np.stack(bands, 2)[:,:,0] + # read ms image + fn_ms = os.path.join(file_path_ms, file_names_ms[i]) + data = gdal.Open(fn_ms, gdal.GA_ReadOnly) + bands = [data.GetRasterBand(i + 1).ReadAsArray() for i in range(data.RasterCount)] + im_ms = np.stack(bands, 2) + # cloud mask + im_qa = im_ms[:,:,5] + cloud_mask = sds.create_cloud_mask(im_qa, satname, plot_bool) + cloud_mask = transform.resize(cloud_mask, (im_pan.shape[0], im_pan.shape[1]), + order=0, preserve_range=True, + mode='constant').astype('bool_') + # resize the image using bilinear interpolation (order 1) + im_ms = transform.resize(im_ms,(im_pan.shape[0], im_pan.shape[1]), + order=1, preserve_range=True, mode='constant') + # check if -inf or nan values and add to cloud mask + im_inf = np.isin(im_ms[:,:,0], -np.inf) + im_nan = np.isnan(im_ms[:,:,0]) + cloud_mask = np.logical_or(np.logical_or(cloud_mask, im_inf), im_nan) + # skip if cloud cover is more than the threshold + cloud_cover = sum(sum(cloud_mask.astype(int)))/(cloud_mask.shape[0]*cloud_mask.shape[1]) + if cloud_cover > cloud_thresh: + print('skip ' + str(i) + ' - cloudy (' + str(np.round(cloud_cover*100).astype(int)) + '%)') + idx_skipped.append(i) + continue + idx_nocloud.append(i) + + # rescale intensities + im_ms = sds.rescale_image_intensity(im_ms, cloud_mask, prob_high, plot_bool) + im_pan = sds.rescale_image_intensity(im_pan, cloud_mask, prob_high, plot_bool) + # pansharpen rgb image + im_ms_ps = sds.pansharpen(im_ms[:,:,[0,1,2]], im_pan, cloud_mask, plot_bool) + nrow = im_ms_ps.shape[0] + ncol = im_ms_ps.shape[1] + # add down-sized bands for NIR and SWIR (since pansharpening is not possible) + im_ms_ps = np.append(im_ms_ps, im_ms[:,:,[3,4]], axis=2) + # calculate NDWI + im_ndwi = sds.nd_index(im_ms_ps[:,:,3], im_ms_ps[:,:,1], cloud_mask, plot_bool) + # detect edges + wl_pix = sds.find_wl_contours(im_ndwi, cloud_mask, min_contour_points, plot_bool) + # classify sand pixels with Kmeans + im_sand = sds.classify_sand_unsupervised(im_ms_ps, im_pan, cloud_mask, wl_pix, buffer_size, min_beach_size, plot_bool) + + # calculate features + im_features = np.zeros((im_ms_ps.shape[0], im_ms_ps.shape[1], n_features)) + im_features[:,:,[0,1,2,3,4]] = im_ms_ps + im_features[:,:,5] = im_pan + im_features[:,:,6] = im_ndwi + im_features[:,:,7] = sds.nd_index(im_ms_ps[:,:,3], im_ms_ps[:,:,2], cloud_mask, False) # ND(NIR-R) + im_features[:,:,8] = sds.nd_index(im_ms_ps[:,:,0], im_ms_ps[:,:,2], cloud_mask, False) # ND(B-R) + im_features[:,:,9] = sds.nd_index(im_ms_ps[:,:,4], im_ms_ps[:,:,1], cloud_mask, False) # (SWIR-G) + # remove NaNs and clouds + vec = im_features.reshape((im_ms_ps.shape[0] * im_ms_ps.shape[1], n_features)) + vec_cloud = cloud_mask.reshape(cloud_mask.shape[0]*cloud_mask.shape[1]) + vec_nan = np.any(np.isnan(vec), axis=1) + vec_mask = np.logical_or(vec_cloud, vec_nan) + vec = vec[~vec_mask, :] + # predict with NN + y = clf.predict(vec) + # recompose image + vec_new = np.zeros((cloud_mask.shape[0]*cloud_mask.shape[1])) + vec_new[~vec_mask] = y + im_classif = vec_new.reshape((im_ms_ps.shape[0], im_ms_ps.shape[1])) + im_classif = im_classif.astype(bool) + im_classif = morphology.remove_small_objects(im_classif, min_size=min_beach_size, connectivity=2) + + # make comparison plot between NN and Kmeans + im1 = np.copy(im_ms_ps) + im1[im_classif,0] = 0 + im1[im_classif,1] = 0 + im1[im_classif,2] = 1 + + im2 = np.copy(im_ms_ps) + im2[im_sand,0] = 0 + im2[im_sand,1] = 0 + im2[im_sand,2] = 1 + + plt.figure() + ax1 = plt.subplot(121) + plt.imshow(im1[:,:,[2,1,0]]) + plt.axis('off') + 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