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286 lines
11 KiB
Python
286 lines
11 KiB
Python
#==========================================================#
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# Create a classifier for satellite images
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#==========================================================#
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# load modules
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import os
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import pickle
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import warnings
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import numpy as np
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import matplotlib.cm as cm
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warnings.filterwarnings("ignore")
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import matplotlib.pyplot as plt
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from pylab import ginput
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import SDS_download, SDS_preprocess, SDS_shoreline, SDS_tools, SDS_classification
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filepath_sites = os.path.join(os.getcwd(), 'polygons')
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sites = os.listdir(filepath_sites)
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for site in sites:
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polygon = SDS_tools.coords_from_kml(os.path.join(filepath_sites,site))
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# load Sentinel-2 images
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inputs = {
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'polygon': polygon,
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'dates': ['2016-10-01', '2016-11-01'],
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'sat_list': ['S2'],
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'sitename': site[:site.find('.')]
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}
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satname = inputs['sat_list'][0]
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metadata = SDS_download.get_images(inputs)
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metadata = SDS_download.remove_cloudy_images(metadata,inputs,0.2)
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filepath = os.path.join(os.getcwd(), 'data', inputs['sitename'])
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with open(os.path.join(filepath, inputs['sitename'] + '_metadata_' + satname + '.pkl'), 'wb') as f:
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pickle.dump(metadata, f)
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#with open(os.path.join(filepath, inputs['sitename'] + '_metadata_' + satname + '.pkl'), 'rb') as f:
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# metadata = pickle.load(f)
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# settings needed to run the shoreline extraction
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settings = {
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# general parameters:
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'cloud_thresh': 0.1, # threshold on maximum cloud cover
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'output_epsg': 28356, # epsg code of spatial reference system desired for the output
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# shoreline detection parameters:
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'min_beach_size': 20, # minimum number of connected pixels for a beach
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'buffer_size': 7, # radius (in pixels) of disk for buffer around sandy pixels
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'min_length_sl': 200, # minimum length of shoreline perimeter to be kept
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'max_dist_ref': 100, # max distance (in meters) allowed from a reference shoreline
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# quality control:
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'check_detection': True, # if True, shows each shoreline detection and lets the user
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# decide which ones are correct and which ones are false due to
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# the presence of clouds
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# also add the inputs
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'inputs': inputs
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}
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# preprocess images (cloud masking, pansharpening/down-sampling)
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SDS_preprocess.preprocess_all_images(metadata, settings)
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training_data = dict([])
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training_data['sand'] = dict([])
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training_data['swash'] = dict([])
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training_data['water'] = dict([])
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training_data['land'] = dict([])
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# read images
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filepath = SDS_tools.get_filepath(inputs,satname)
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filenames = metadata[satname]['filenames']
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for i in range(len(filenames)):
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fn = SDS_tools.get_filenames(filenames[i],filepath,satname)
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im_ms, georef, cloud_mask, im20, imQA = SDS_preprocess.preprocess_single(fn,satname)
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nrow = im_ms.shape[0]
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ncol = im_ms.shape[1]
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im_RGB = SDS_preprocess.rescale_image_intensity(im_ms[:,:,[2,1,0]], cloud_mask, 99.9)
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plt.figure()
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mng = plt.get_current_fig_manager()
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mng.window.showMaximized()
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plt.imshow(im_RGB)
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plt.axis('off')
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# Digitize sandy pixels
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plt.title('Digitize SAND pixels', fontweight='bold', fontsize=15)
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pt = ginput(n=1000, timeout=100000, show_clicks=True)
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if len(pt) > 0:
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pt = np.round(pt).astype(int)
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im_sand = np.zeros((nrow,ncol))
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for k in range(len(pt)):
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im_sand[pt[k,1],pt[k,0]] = 1
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im_RGB[pt[k,1],pt[k,0],0] = 1
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im_RGB[pt[k,1],pt[k,0],1] = 0
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im_RGB[pt[k,1],pt[k,0],2] = 0
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im_sand = im_sand.astype(bool)
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features = SDS_classification.calculate_features(im_ms, cloud_mask, im_sand)
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else:
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im_sand = np.zeros((nrow,ncol)).astype(bool)
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features = []
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training_data['sand'][filenames[i]] = {'pixels':im_sand,'features':features}
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# Digitize swash pixels
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plt.title('Digitize SWASH pixels', fontweight='bold', fontsize=15)
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plt.draw()
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pt = ginput(n=1000, timeout=100000, show_clicks=True)
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if len(pt) > 0:
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pt = np.round(pt).astype(int)
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im_swash = np.zeros((nrow,ncol))
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for k in range(len(pt)):
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im_swash[pt[k,1],pt[k,0]] = 1
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im_RGB[pt[k,1],pt[k,0],0] = 0
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im_RGB[pt[k,1],pt[k,0],1] = 1
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im_RGB[pt[k,1],pt[k,0],2] = 0
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im_swash = im_swash.astype(bool)
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features = SDS_classification.calculate_features(im_ms, cloud_mask, im_swash)
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else:
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im_swash = np.zeros((nrow,ncol)).astype(bool)
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features = []
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training_data['swash'][filenames[i]] = {'pixels':im_swash,'features':features}
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# Digitize rectangle containig water pixels
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plt.title('Click 2 points to draw a rectange in the WATER', fontweight='bold', fontsize=15)
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plt.draw()
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pt = ginput(n=2, timeout=100000, show_clicks=True)
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if len(pt) > 0:
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pt = np.round(pt).astype(int)
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idx_row = np.arange(np.min(pt[:,1]),np.max(pt[:,1])+1,1)
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idx_col = np.arange(np.min(pt[:,0]),np.max(pt[:,0])+1,1)
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xx, yy = np.meshgrid(idx_row,idx_col, indexing='ij')
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rows = xx.reshape(xx.shape[0]*xx.shape[1])
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cols = yy.reshape(yy.shape[0]*yy.shape[1])
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im_water = np.zeros((nrow,ncol)).astype(bool)
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for k in range(len(rows)):
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im_water[rows[k],cols[k]] = 1
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im_RGB[rows[k],cols[k],0] = 0
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im_RGB[rows[k],cols[k],1] = 0
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im_RGB[rows[k],cols[k],2] = 1
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im_water = im_water.astype(bool)
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features = SDS_classification.calculate_features(im_ms, cloud_mask, im_water)
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else:
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im_water = np.zeros((nrow,ncol)).astype(bool)
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features = []
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training_data['water'][filenames[i]] = {'pixels':im_water,'features':features}
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# Digitize rectangle containig land pixels
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plt.title('Click 2 points to draw a rectange in the LAND', fontweight='bold', fontsize=15)
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plt.draw()
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pt = ginput(n=2, timeout=100000, show_clicks=True)
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plt.close()
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if len(pt) > 0:
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pt = np.round(pt).astype(int)
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idx_row = np.arange(np.min(pt[:,1]),np.max(pt[:,1])+1,1)
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idx_col = np.arange(np.min(pt[:,0]),np.max(pt[:,0])+1,1)
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xx, yy = np.meshgrid(idx_row,idx_col, indexing='ij')
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rows = xx.reshape(xx.shape[0]*xx.shape[1])
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cols = yy.reshape(yy.shape[0]*yy.shape[1])
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im_land = np.zeros((nrow,ncol)).astype(bool)
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for k in range(len(rows)):
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im_land[rows[k],cols[k]] = 1
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im_RGB[rows[k],cols[k],0] = 1
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im_RGB[rows[k],cols[k],1] = 1
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im_RGB[rows[k],cols[k],2] = 0
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im_land = im_land.astype(bool)
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features = SDS_classification.calculate_features(im_ms, cloud_mask, im_land)
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else:
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im_land = np.zeros((nrow,ncol)).astype(bool)
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features = []
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training_data['land'][filenames[i]] = {'pixels':im_land,'features':features}
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plt.figure()
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plt.title('Classified image')
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plt.imshow(im_RGB)
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# save training data for each site
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filepath = os.path.join(os.getcwd(), 'data', inputs['sitename'])
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with open(os.path.join(filepath, inputs['sitename'] + '_training_' + satname + '.pkl'), 'wb') as f:
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pickle.dump(training_data, f)
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#%%
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## load Landsat 5 images
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#inputs = {
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# 'polygon': polygon,
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# 'dates': ['1987-01-01', '1988-01-01'],
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# 'sat_list': ['L5'],
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# 'sitename': site[:site.find('.')]
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# }
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#metadata = SDS_download.get_images(inputs)
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#
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## load Landsat 7 images
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#inputs = {
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# 'polygon': polygon,
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# 'dates': ['2001-01-01', '2002-01-01'],
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# 'sat_list': ['L7'],
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# 'sitename': site[:site.find('.')]
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# }
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#metadata = SDS_download.get_images(inputs)
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#
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## load Landsat 8 images
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#inputs = {
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# 'polygon': polygon,
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# 'dates': ['2014-01-01', '2015-01-01'],
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# 'sat_list': ['L8'],
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# 'sitename': site[:site.find('.')]
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# }
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#metadata = SDS_download.get_images(inputs)
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#%% clean the Landsat collections
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#import ee
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#from datetime import datetime, timedelta
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#import pytz
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#import copy
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#ee.Initialize()
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#site = sites[0]
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#dates = ['2017-12-01', '2017-12-25']
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#polygon = SDS_tools.coords_from_kml(os.path.join(filepath_sites,site))
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## Landsat collection
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#input_col = ee.ImageCollection('LANDSAT/LC08/C01/T1_RT_TOA')
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## filter by location and dates
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#flt_col = input_col.filterBounds(ee.Geometry.Polygon(polygon)).filterDate(inputs['dates'][0],inputs['dates'][1])
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## get all images in the filtered collection
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#im_all = flt_col.getInfo().get('features')
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#cloud_cover = [_['properties']['CLOUD_COVER'] for _ in im_all]
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#if np.any([_ > 90 for _ in cloud_cover]):
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# idx_delete = np.where([_ > 90 for _ in cloud_cover])[0]
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# im_all_cloud = [x for k,x in enumerate(im_all) if k not in idx_delete]
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#%% clean the S2 collection
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#import ee
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#from datetime import datetime, timedelta
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#import pytz
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#import copy
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#ee.Initialize()
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## Sentinel2 collection
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#input_col = ee.ImageCollection('COPERNICUS/S2')
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## filter by location and dates
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#flt_col = input_col.filterBounds(ee.Geometry.Polygon(polygon)).filterDate(inputs['dates'][0],inputs['dates'][1])
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## get all images in the filtered collection
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#im_all = flt_col.getInfo().get('features')
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#
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## remove duplicates (there are many in S2 collection)
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## timestamps
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#timestamps = [datetime.fromtimestamp(_['properties']['system:time_start']/1000, tz=pytz.utc) for _ in im_all]
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## utm zones
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#utm_zones = np.array([int(_['bands'][0]['crs'][5:]) for _ in im_all])
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#utm_zone_selected = np.max(np.unique(utm_zones))
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#idx_all = np.arange(0,len(im_all),1)
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#idx_covered = np.ones(len(im_all)).astype(bool)
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#idx_delete = []
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#i = 0
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#while 1:
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# same_time = np.abs([(timestamps[i]-_).total_seconds() for _ in timestamps]) < 60*60*24
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# idx_same_time = np.where(same_time)[0]
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# same_utm = utm_zones == utm_zone_selected
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# idx_temp = np.where([same_time[j] == True and same_utm[j] == False for j in idx_all])[0]
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# idx_keep = idx_same_time[[_ not in idx_temp for _ in idx_same_time ]]
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# if len(idx_keep) > 2: # if more than 2 images with same date and same utm, drop the last one
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# idx_temp = np.append(idx_temp,idx_keep[-1])
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# for j in idx_temp:
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# idx_delete.append(j)
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# idx_covered[idx_same_time] = False
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# if np.any(idx_covered):
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# i = np.where(idx_covered)[0][0]
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# else:
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# break
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#im_all_updated = [x for k,x in enumerate(im_all) if k not in idx_delete]
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#
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## remove very cloudy images (>90% cloud)
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#cloud_cover = [_['properties']['CLOUDY_PIXEL_PERCENTAGE'] for _ in im_all_updated]
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#if np.any([_ > 90 for _ in cloud_cover]):
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# idx_delete = np.where([_ > 90 for _ in cloud_cover])[0]
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# im_all_cloud = [x for k,x in enumerate(im_all_updated) if k not in idx_delete]
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