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CoastSat_WRL
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Code to download and read sentinel 2 images

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master
kvos 7 years ago
parent 62f5c5330f
commit f8e1397412
2 changed files with 35 additions and 665 deletions
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274
download_images.py
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@ -35,19 +35,13 @@ ee.Initialize()
#==========================================================#
## 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]]];
# location (Tairua beach)
sitename = 'TAIRUA'
polygon = [[[175.835574, -36.982022],
[175.888220, -36.980680],
[175.893527, -37.029610],
[175.833444, -37.031767],
[175.835574, -36.982022]]];
sitename = 'NARRA'
polygon = [[[151.301454, -33.700754],
[151.311453, -33.702075],
[151.307237, -33.739761],
[151.294220, -33.736329],
[151.301454, -33.700754]]];
# initialise metadata dictionnary (stores timestamps and georefencing accuracy of each image)
metadata = dict([])
@ -58,232 +52,6 @@ try:
except:
print('directory already exists')
#%%
#==========================================================#
#==========================================================#
# L5
#==========================================================#
#==========================================================#
# define filenames for images
suffix = '.tif'
filepath = os.path.join(os.getcwd(), 'data', sitename, 'L5', '30m')
try:
os.makedirs(filepath)
except:
print('directory already exists')
#==========================================================#
# Select L5 collection
#==========================================================#
satname = 'L5'
input_col = ee.ImageCollection('LANDSAT/LT05/C01/T1_TOA')
# filter by location
flt_col = input_col.filterBounds(ee.Geometry.Polygon(polygon))
n_img = flt_col.size().getInfo()
print('Number of images covering ' + sitename, n_img)
im_all = flt_col.getInfo().get('features')
#==========================================================#
# Main loop trough images
#==========================================================#
timestamps = []
acc_georef = []
all_names = []
for i in range(n_img):
# find each image in ee database
im = ee.Image(im_all[i].get('id'))
im_dic = im.getInfo()
im_bands = im_dic.get('bands')
t = im_dic['properties']['system:time_start']
im_timestamp = datetime.fromtimestamp(t/1000, tz=pytz.utc)
timestamps.append(im_timestamp)
im_date = im_timestamp.strftime('%Y-%m-%d-%H-%M-%S')
im_epsg = int(im_dic['bands'][0]['crs'][5:])
try:
acc_georef.append(im_dic['properties']['GEOMETRIC_RMSE_MODEL'])
except:
acc_georef.append(12)
print('No geometric rmse model property')
# delete dimensions key from dictionnary, otherwise the entire image is extracted
for j in range(len(im_bands)): del im_bands[j]['dimensions']
# bands for L5
ms_bands = [im_bands[0], im_bands[1], im_bands[2], im_bands[3], im_bands[4], im_bands[7]]
# filenames
filename = im_date + '_' + satname + '_' + sitename + suffix
print(i)
if any(filename in _ for _ in all_names):
filename = im_date + '_' + satname + '_' + sitename + '_dup' + suffix
all_names.append(filename)
local_data = sds.download_tif(im, polygon, ms_bands, filepath)
os.rename(local_data, os.path.join(filepath, filename))
# sort timestamps and georef accuracy (dowloaded images are sorted by date in directory)
timestamps_sorted = sorted(timestamps)
idx_sorted = sorted(range(len(timestamps)), key=timestamps.__getitem__)
acc_georef_sorted = [acc_georef[j] for j in idx_sorted]
metadata[satname] = {'dates':timestamps_sorted, 'acc_georef':acc_georef_sorted, 'epsg':im_epsg}
#%%
#==========================================================#
#==========================================================#
# L7&L8
#==========================================================#
#==========================================================#
# define filenames for images
suffix = '.tif'
filepath = os.path.join(os.getcwd(), 'data', sitename, 'L7&L8')
filepath_pan = os.path.join(filepath, 'pan')
filepath_ms = os.path.join(filepath, 'ms')
try:
os.makedirs(filepath_pan)
os.makedirs(filepath_ms)
except:
print('directory already exists')
#==========================================================#
# Select L7 collection
#==========================================================#
satname = 'L7'
input_col = ee.ImageCollection('LANDSAT/LE07/C01/T1_RT_TOA')
# filter by location
flt_col = input_col.filterBounds(ee.Geometry.Polygon(polygon))
n_img = flt_col.size().getInfo()
print('Number of images covering ' + sitename, n_img)
im_all = flt_col.getInfo().get('features')
#==========================================================#
# Main loop trough images
#==========================================================#
timestamps = []
acc_georef = []
all_names = []
for i in range(n_img):
# find each image in ee database
im = ee.Image(im_all[i].get('id'))
im_dic = im.getInfo()
im_bands = im_dic.get('bands')
t = im_dic['properties']['system:time_start']
im_timestamp = datetime.fromtimestamp(t/1000, tz=pytz.utc)
timestamps.append(im_timestamp)
im_date = im_timestamp.strftime('%Y-%m-%d-%H-%M-%S')
im_epsg = int(im_dic['bands'][0]['crs'][5:])
try:
acc_georef.append(im_dic['properties']['GEOMETRIC_RMSE_MODEL'])
except:
acc_georef.append(12)
print('No geometric rmse model property')
# delete dimensions key from dictionnary, otherwise the entire image is extracted
for j in range(len(im_bands)): del im_bands[j]['dimensions']
# bands for L7
pan_band = [im_bands[8]]
ms_bands = [im_bands[0], im_bands[1], im_bands[2], im_bands[3], im_bands[4], im_bands[9]]
# filenames
filename_pan = im_date + '_' + satname + '_' + sitename + '_pan' + suffix
filename_ms = im_date + '_' + satname + '_' + sitename + '_ms' + suffix
print(i)
if any(filename_pan in _ for _ in all_names):
filename_pan = im_date + '_' + satname + '_' + sitename + '_pan' + '_dup' + suffix
filename_ms = im_date + '_' + satname + '_' + sitename + '_ms' + '_dup' + suffix
all_names.append(filename_pan)
local_data_pan = sds.download_tif(im, polygon, pan_band, filepath_pan)
os.rename(local_data_pan, os.path.join(filepath_pan, filename_pan))
local_data_ms = sds.download_tif(im, polygon, ms_bands, filepath_ms)
os.rename(local_data_ms, os.path.join(filepath_ms, filename_ms))
#==========================================================#
# Select L8 collection
#==========================================================#
satname = 'L8'
input_col = ee.ImageCollection('LANDSAT/LC08/C01/T1_RT_TOA')
# filter by location
flt_col = input_col.filterBounds(ee.Geometry.Polygon(polygon))
n_img = flt_col.size().getInfo()
print('Number of images covering Narrabeen:', n_img)
im_all = flt_col.getInfo().get('features')
#==========================================================#
# Main loop trough images
#==========================================================#
for i in range(n_img):
# find each image in ee database
im = ee.Image(im_all[i].get('id'))
im_dic = im.getInfo()
im_bands = im_dic.get('bands')
t = im_dic['properties']['system:time_start']
im_timestamp = datetime.fromtimestamp(t/1000, tz=pytz.utc)
timestamps.append(im_timestamp)
im_date = im_timestamp.strftime('%Y-%m-%d-%H-%M-%S')
im_epsg = int(im_dic['bands'][0]['crs'][5:])
try:
acc_georef.append(im_dic['properties']['GEOMETRIC_RMSE_MODEL'])
except:
acc_georef.append(12)
print('No geometric rmse model property')
# delete dimensions key from dictionnary, otherwise the entire image is extracted
for j in range(len(im_bands)): del im_bands[j]['dimensions']
# bands for L8
pan_band = [im_bands[7]]
ms_bands = [im_bands[1], im_bands[2], im_bands[3], im_bands[4], im_bands[5], im_bands[11]]
# filenames
filename_pan = im_date + '_' + satname + '_' + sitename + '_pan' + suffix
filename_ms = im_date + '_' + satname + '_' + sitename + '_ms' + suffix
print(i)
if any(filename_pan in _ for _ in all_names):
filename_pan = im_date + '_' + satname + '_' + sitename + '_pan' + '_dup' + suffix
filename_ms = im_date + '_' + satname + '_' + sitename + '_ms' + '_dup' + suffix
all_names.append(filename_pan)
local_data_pan = sds.download_tif(im, polygon, pan_band, filepath_pan)
os.rename(local_data_pan, os.path.join(filepath_pan, filename_pan))
local_data_ms = sds.download_tif(im, polygon, ms_bands, filepath_ms)
os.rename(local_data_ms, os.path.join(filepath_ms, filename_ms))
# sort timestamps and georef accuracy (dowloaded images are sorted by date in directory)
timestamps_sorted = sorted(timestamps)
idx_sorted = sorted(range(len(timestamps)), key=timestamps.__getitem__)
acc_georef_sorted = [acc_georef[j] for j in idx_sorted]
metadata[satname] = {'dates':timestamps_sorted, 'acc_georef':acc_georef_sorted, 'epsg':im_epsg}
#%%
#==========================================================#
#==========================================================#
@ -323,6 +91,7 @@ im_all = flt_col.getInfo().get('features')
timestamps = []
acc_georef = []
all_names = []
im_epsg = []
for i in range(n_img):
# find each image in ee database
@ -333,15 +102,6 @@ for i in range(n_img):
t = im_dic['properties']['system:time_start']
im_timestamp = datetime.fromtimestamp(t/1000, tz=pytz.utc)
im_date = im_timestamp.strftime('%Y-%m-%d-%H-%M-%S')
timestamps.append(im_timestamp)
im_epsg = int(im_dic['bands'][0]['crs'][5:])
try:
if im_dic['properties']['GEOMETRIC_QUALITY_FLAG'] == 'PASSED':
acc_georef.append(1)
else:
acc_georef.append(0)
except:
acc_georef.append(0)
# delete dimensions key from dictionnary, otherwise the entire image is extracted
for j in range(len(im_bands)): del im_bands[j]['dimensions']
@ -358,9 +118,7 @@ for i in range(n_img):
print(i)
if any(filename10 in _ for _ in all_names):
filename10 = im_date + '_' + satname + '_' + sitename + '_' + '10m' + '_dup' + suffix
filename20 = im_date + '_' + satname + '_' + sitename + '_' + '20m' + '_dup' + suffix
filename60 = im_date + '_' + satname + '_' + sitename + '_' + '60m' + '_dup' + suffix
continue
all_names.append(filename10)
local_data = sds.download_tif(im, polygon, bands10, filepath)
@ -372,12 +130,24 @@ for i in range(n_img):
local_data = sds.download_tif(im, polygon, bands60, filepath)
os.rename(local_data, os.path.join(filepath, '60m', filename60))
timestamps.append(im_timestamp)
im_epsg.append(int(im_dic['bands'][0]['crs'][5:]))
try:
if im_dic['properties']['GEOMETRIC_QUALITY_FLAG'] == 'PASSED':
acc_georef.append(1)
else:
acc_georef.append(0)
except:
acc_georef.append(0)
# sort timestamps and georef accuracy (dowloaded images are sorted by date in directory)
timestamps_sorted = sorted(timestamps)
idx_sorted = sorted(range(len(timestamps)), key=timestamps.__getitem__)
acc_georef_sorted = [acc_georef[j] for j in idx_sorted]
im_epsg_sorted = [im_epsg[j] for j in idx_sorted]
metadata[satname] = {'dates':timestamps_sorted, 'acc_georef':acc_georef_sorted, 'epsg':im_epsg}
metadata[satname] = {'dates':timestamps_sorted, 'acc_georef':acc_georef_sorted, 'epsg':im_epsg_sorted}

422
read_images.py
Unescape Escape View File

@ -59,8 +59,6 @@ buffer_size = 7 # radius (in pixels) of disk for buffer (pixel classific
min_beach_size = 20 # number of pixels in a beach (pixel classification)
dist_ref = 100 # maximum distance from reference point
min_length_wl = 200 # minimum length of shoreline LineString to be kept
manual_bool = True # to manually check images
output = dict([])
@ -72,7 +70,6 @@ filepath = os.path.join(os.getcwd(), 'data', sitename)
with open(os.path.join(filepath, sitename + '_metadata' + '.pkl'), 'rb') as f:
metadata = pickle.load(f)
#%%
#==========================================================#
# Read S2 images
@ -80,7 +77,7 @@ with open(os.path.join(filepath, sitename + '_metadata' + '.pkl'), 'rb') as f:
satname = 'S2'
dates = metadata[satname]['dates']
input_epsg = 32756 # metadata[satname]['epsg']
input_epsg = metadata[satname]['epsg']
# path to images
filepath10 = os.path.join(os.getcwd(), 'data', sitename, satname, '10m')
@ -163,427 +160,30 @@ for i in range(N):
# rescale image intensity for display purposes
im_display = sds.rescale_image_intensity(im_ms[:,:,[2,1,0]], cloud_mask, 99.9, False)
# classify image in 4 classes (sand, whitewater, water, other) with NN classifier
im_classif, im_labels = sds.classify_image_NN_nopan(im_ms, cloud_mask, min_beach_size, plot_bool)
# if there aren't any sandy pixels
if sum(sum(im_labels[:,:,0])) == 0 :
# use global threshold
im_ndwi = sds.nd_index(im_ms[:,:,4], im_ms[:,:,1], cloud_mask, plot_bool)
contours = sds.find_wl_contours(im_ndwi, cloud_mask, plot_bool)
else:
# use specific threhsold
contours_wi, contours_mwi = sds.find_wl_contours2(im_ms, im_labels, cloud_mask, buffer_size, plot_bool)
# convert from pixels to world coordinates
wl_coords = sds.convert_pix2world(contours_mwi, georef)
# convert to output epsg spatial reference
wl = sds.convert_epsg(wl_coords, input_epsg, output_epsg)
# remove contour lines that have a perimeter < min_length_wl
wl_good = []
for l, wls in enumerate(wl):
coords = [(wls[k,0], wls[k,1]) for k in range(len(wls))]
a = LineString(coords) # shapely LineString structure
if a.length >= min_length_wl:
wl_good.append(wls)
# format points and only select the ones close to the refpoints
x_points = np.array([])
y_points = np.array([])
for k in range(len(wl_good)):
x_points = np.append(x_points,wl_good[k][:,0])
y_points = np.append(y_points,wl_good[k][:,1])
wl_good = np.transpose(np.array([x_points,y_points]))
temp = np.zeros((len(wl_good))).astype(bool)
for k in range(len(refpoints)):
temp = np.logical_or(np.linalg.norm(wl_good - refpoints[k,[0,1]], axis=1) < dist_ref, temp)
wl_final = wl_good[temp]
# plot output
# plot rgb image
plt.figure()
im = np.copy(im_display)
colours = np.array([[1,128/255,0/255],[204/255,1,1],[0,0,204/255]])
for k in range(0,im_labels.shape[2]):
im[im_labels[:,:,k],0] = colours[k,0]
im[im_labels[:,:,k],1] = colours[k,1]
im[im_labels[:,:,k],2] = colours[k,2]
plt.imshow(im)
for k,contour in enumerate(contours_mwi): plt.plot(contour[:, 1], contour[:, 0], linewidth=2, color='k', linestyle='--')
plt.title(satname + ' ' + metadata[satname]['dates'][i].strftime('%Y-%m-%d') + ' acc : ' + str(metadata[satname]['acc_georef'][i]) + ' m' )
plt.draw()
pt_in = np.array(ginput(n=1, timeout=1000))
plt.close()
# if image is rejected, skip it
if pt_in[0][1] > nrows/2:
print('skip ' + str(i) + ' - rejected')
idx_skipped.append(i)
continue
# if accepted, store the data
cloud_cover_ts.append(cloud_cover)
acc_georef_ts.append(metadata[satname]['acc_georef'][i])
filename_ts.append(filenames10[i])
satname_ts.append(satname)
date_acquired_ts.append(filenames10[i][:10])
timestamp.append(metadata[satname]['dates'][i])
shorelines.append(wl_final)
# store in output structure
output[satname] = {'dates':timestamp, 'shorelines':shorelines, 'idx_skipped':idx_skipped,
'metadata':{'filenames':filename_ts, 'satname':satname_ts, 'cloud_cover':cloud_cover_ts,
'acc_georef':acc_georef_ts}}
del idx_skipped
#%%
#==========================================================#
# Read L7&L8 images
#==========================================================#
satname = 'L8'
dates = metadata[satname]['dates']
input_epsg = 32656 # metadata[satname]['epsg']
# path to images
filepath_pan = os.path.join(os.getcwd(), 'data', sitename, 'L7&L8', 'pan')
filepath_ms = os.path.join(os.getcwd(), 'data', sitename, 'L7&L8', 'ms')
filenames_pan = os.listdir(filepath_pan)
filenames_ms = os.listdir(filepath_ms)
if (not len(filenames_pan) == len(filenames_ms)):
raise 'error: not the same amount of files for pan and ms'
N = len(filenames_pan)
# initialise variables
cloud_cover_ts = []
acc_georef_ts = []
date_acquired_ts = []
filename_ts = []
satname_ts = []
timestamp = []
shorelines = []
idx_skipped = []
spacing = '=========================================================='
msg = ' %s\n %s\n %s' % (spacing, satname, spacing)
print(msg)
for i in range(N):
# get satellite name
sat = filenames_pan[i][20:22]
# read pan image
fn_pan = os.path.join(filepath_pan, filenames_pan[i])
data = gdal.Open(fn_pan, gdal.GA_ReadOnly)
georef = np.array(data.GetGeoTransform())
bands = [data.GetRasterBand(k + 1).ReadAsArray() for k in range(data.RasterCount)]
im_pan = np.stack(bands, 2)[:,:,0]
nrows = im_pan.shape[0]
ncols = im_pan.shape[1]
# read ms image
fn_ms = os.path.join(filepath_ms, filenames_ms[i])
data = gdal.Open(fn_ms, gdal.GA_ReadOnly)
bands = [data.GetRasterBand(k + 1).ReadAsArray() for k 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, sat, plot_bool)
cloud_mask = transform.resize(cloud_mask, (nrows, ncols), order=0, preserve_range=True, mode='constant').astype('bool_')
# resize the image using bilinear interpolation (order 1)
im_ms = im_ms[:,:,:5]
im_ms = transform.resize(im_ms,(nrows, ncols), order=1, preserve_range=True, mode='constant')
# check if -inf or nan values on any band and add to cloud mask
for k in range(im_ms.shape[2]+1):
if k == 5:
im_inf = np.isin(im_pan, -np.inf)
im_nan = np.isnan(im_pan)
else:
im_inf = np.isin(im_ms[:,:,k], -np.inf)
im_nan = np.isnan(im_ms[:,:,k])
cloud_mask = np.logical_or(np.logical_or(cloud_mask, im_inf), im_nan)
# calculate cloud cover and skip image if above 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
# Pansharpen image (different for L8 and L7)
if sat == 'L7':
# pansharpen (Green, Red, NIR) and downsample Blue and SWIR1
im_ms_ps = sds.pansharpen(im_ms[:,:,[1,2,3]], im_pan, cloud_mask, plot_bool)
im_ms_ps = np.append(im_ms[:,:,[0]], im_ms_ps, axis=2)
im_ms_ps = np.append(im_ms_ps, im_ms[:,:,[4]], axis=2)
im_display = sds.rescale_image_intensity(im_ms[:,:,[2,1,0]], cloud_mask, 99.9, False)
elif sat == 'L8':
# pansharpen RGB image and downsample NIR and SWIR1
im_ms_ps = sds.pansharpen(im_ms[:,:,[0,1,2]], im_pan, cloud_mask, plot_bool)
im_ms_ps = np.append(im_ms_ps, im_ms[:,:,[3,4]], axis=2)
im_display = sds.rescale_image_intensity(im_ms_ps[:,:,[2,1,0]], cloud_mask, 99.9, False)
# classify image in 4 classes (sand, whitewater, water, other) with NN classifier
im_classif, im_labels = sds.classify_image_NN(im_ms_ps, im_pan, cloud_mask, min_beach_size, plot_bool)
# if there aren't any sandy pixels
if sum(sum(im_labels[:,:,0])) == 0 :
# use global threshold
im_ndwi = sds.nd_index(im_ms_ps[:,:,4], im_ms_ps[:,:,1], cloud_mask, plot_bool)
contours = sds.find_wl_contours(im_ndwi, cloud_mask, plot_bool)
else:
# use specific threhsold
contours_wi, contours_mwi = sds.find_wl_contours2(im_ms_ps, im_labels, cloud_mask, buffer_size, plot_bool)
# convert from pixels to world coordinates
wl_coords = sds.convert_pix2world(contours_mwi, georef)
# convert to output epsg spatial reference
wl = sds.convert_epsg(wl_coords, input_epsg, output_epsg)
# remove contour lines that have a perimeter < min_length_wl
wl_good = []
for l, wls in enumerate(wl):
coords = [(wls[k,0], wls[k,1]) for k in range(len(wls))]
a = LineString(coords) # shapely LineString structure
if a.length >= min_length_wl:
wl_good.append(wls)
# format points and only select the ones close to the refpoints
x_points = np.array([])
y_points = np.array([])
for k in range(len(wl_good)):
x_points = np.append(x_points,wl_good[k][:,0])
y_points = np.append(y_points,wl_good[k][:,1])
wl_good = np.transpose(np.array([x_points,y_points]))
temp = np.zeros((len(wl_good))).astype(bool)
for k in range(len(refpoints)):
temp = np.logical_or(np.linalg.norm(wl_good - refpoints[k,[0,1]], axis=1) < dist_ref, temp)
wl_final = wl_good[temp]
# plot output
plt.figure()
plt.subplot(121)
im = np.copy(im_display)
colours = np.array([[1,128/255,0/255],[204/255,1,1],[0,0,204/255]])
for k in range(0,im_labels.shape[2]):
im[im_labels[:,:,k],0] = colours[k,0]
im[im_labels[:,:,k],1] = colours[k,1]
im[im_labels[:,:,k],2] = colours[k,2]
plt.imshow(im)
for k,contour in enumerate(contours_mwi): plt.plot(contour[:, 1], contour[:, 0], linewidth=2, color='k', linestyle='--')
plt.title(sat + ' ' + metadata[satname]['dates'][i].strftime('%Y-%m-%d') + ' acc : ' + str(metadata[satname]['acc_georef'][i]) + ' m' )
pt_in = np.array(ginput(n=1, timeout=1000))
plt.close()
# if image is rejected, skip it
if pt_in[0][1] > nrows/2:
print('skip ' + str(i) + ' - rejected')
idx_skipped.append(i)
continue
# if accepted, store the data
cloud_cover_ts.append(cloud_cover)
acc_georef_ts.append(metadata[satname]['acc_georef'][i])
filename_ts.append(filenames_pan[i])
satname_ts.append(sat)
date_acquired_ts.append(filenames_pan[i][:10])
timestamp.append(metadata[satname]['dates'][i])
shorelines.append(wl_final)
# store in output structure
output[satname] = {'dates':timestamp, 'shorelines':shorelines, 'idx_skipped':idx_skipped,
'metadata':{'filenames':filename_ts, 'satname':satname_ts, 'cloud_cover':cloud_cover_ts,
'acc_georef':acc_georef_ts}}
del idx_skipped
#%%
#==========================================================#
# Read L5 images
#==========================================================#
satname = 'L5'
dates = metadata[satname]['dates']
input_epsg = 32656 # metadata[satname]['epsg']
# path to images
filepath_img = os.path.join(os.getcwd(), 'data', sitename, satname, '30m')
filenames = os.listdir(filepath_img)
N = len(filenames)
# initialise variables
cloud_cover_ts = []
acc_georef_ts = []
date_acquired_ts = []
filename_ts = []
satname_ts = []
timestamp = []
shorelines = []
idx_skipped = []
spacing = '=========================================================='
msg = ' %s\n %s\n %s' % (spacing, satname, spacing)
print(msg)
for i in range(N):
# read ms image
fn = os.path.join(filepath_img, filenames[i])
data = gdal.Open(fn, gdal.GA_ReadOnly)
georef = np.array(data.GetGeoTransform())
bands = [data.GetRasterBand(k + 1).ReadAsArray() for k in range(data.RasterCount)]
im_ms = np.stack(bands, 2)
# down-sample to half hte original pixel size
nrows = im_ms.shape[0]*2
ncols = im_ms.shape[1]*2
# cloud mask
im_qa = im_ms[:,:,5]
im_ms = im_ms[:,:,:-1]
cloud_mask = sds.create_cloud_mask(im_qa, satname, plot_bool)
cloud_mask = transform.resize(cloud_mask, (nrows, ncols), order=0, preserve_range=True, mode='constant').astype('bool_')
# resize the image using bilinear interpolation (order 1)
im_ms = transform.resize(im_ms,(nrows, ncols), order=1, preserve_range=True, mode='constant')
# adjust georef vector (scale becomes 15m and origin is adjusted to the center of new corner pixel)
georef[1] = 15
georef[5] = -15
georef[0] = georef[0] + 7.5
georef[3] = georef[3] - 7.5
# check if -inf or nan values on any band and add to cloud mask
for k in range(im_ms.shape[2]):
im_inf = np.isin(im_ms[:,:,k], -np.inf)
im_nan = np.isnan(im_ms[:,:,k])
cloud_mask = np.logical_or(np.logical_or(cloud_mask, im_inf), im_nan)
# calculate cloud cover and skip image if above 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
# rescale image intensity for display purposes
im_display = sds.rescale_image_intensity(im_ms[:,:,[2,1,0]], cloud_mask, 99.9, False)
plt.axis('off')
plt.imshow(im_display)
# classify image in 4 classes (sand, whitewater, water, other) with NN classifier
im_classif, im_labels = sds.classify_image_NN_nopan(im_ms, cloud_mask, min_beach_size, plot_bool)
# if there aren't any sandy pixels
if sum(sum(im_labels[:,:,0])) == 0 :
# use global threshold
im_ndwi = sds.nd_index(im_ms[:,:,4], im_ms[:,:,1], cloud_mask, plot_bool)
contours = sds.find_wl_contours(im_ndwi, cloud_mask, plot_bool)
else:
# use specific threhsold
contours_wi, contours_mwi = sds.find_wl_contours2(im_ms, im_labels, cloud_mask, buffer_size, plot_bool)
# convert from pixels to world coordinates
wl_coords = sds.convert_pix2world(contours_mwi, georef)
# convert to output epsg spatial reference
wl = sds.convert_epsg(wl_coords, input_epsg, output_epsg)
# remove contour lines that have a perimeter < min_length_wl
wl_good = []
for l, wls in enumerate(wl):
coords = [(wls[k,0], wls[k,1]) for k in range(len(wls))]
a = LineString(coords) # shapely LineString structure
if a.length >= min_length_wl:
wl_good.append(wls)
# format points and only select the ones close to the refpoints
x_points = np.array([])
y_points = np.array([])
for k in range(len(wl_good)):
x_points = np.append(x_points,wl_good[k][:,0])
y_points = np.append(y_points,wl_good[k][:,1])
wl_good = np.transpose(np.array([x_points,y_points]))
temp = np.zeros((len(wl_good))).astype(bool)
for k in range(len(refpoints)):
temp = np.logical_or(np.linalg.norm(wl_good - refpoints[k,[0,1]], axis=1) < dist_ref, temp)
wl_final = wl_good[temp]
# plot output
plt.figure()
plt.subplot(121)
im = np.copy(im_display)
colours = np.array([[1,128/255,0/255],[204/255,1,1],[0,0,204/255]])
for k in range(0,im_labels.shape[2]):
im[im_labels[:,:,k],0] = colours[k,0]
im[im_labels[:,:,k],1] = colours[k,1]
im[im_labels[:,:,k],2] = colours[k,2]
plt.imshow(im)
for k,contour in enumerate(contours_mwi): plt.plot(contour[:, 1], contour[:, 0], linewidth=2, color='k', linestyle='--')
plt.title(satname + ' ' + metadata[satname]['dates'][i].strftime('%Y-%m-%d') + ' acc : ' + str(metadata[satname]['acc_georef'][i]) + ' m' )
plt.subplot(122)
plt.axis('equal')
plt.axis('off')
plt.plot(refpoints[:,0], refpoints[:,1], 'k.')
plt.plot(wl_final[:,0], wl_final[:,1], 'r.')
mng = plt.get_current_fig_manager()
mng.window.showMaximized()
plt.tight_layout()
plt.draw()
pt_in = np.array(ginput(n=1, timeout=1000))
plt.close()
# if image is rejected, skip it
if pt_in[0][1] > nrows/2:
print('skip ' + str(i) + ' - rejected')
idx_skipped.append(i)
continue
# if accepted, store the data
# store the data
cloud_cover_ts.append(cloud_cover)
acc_georef_ts.append(metadata[satname]['acc_georef'][i])
filename_ts.append(filenames[i])
filename_ts.append(filenames10[i])
satname_ts.append(satname)
date_acquired_ts.append(filenames[i][:10])
date_acquired_ts.append(filenames10[i][:10])
timestamp.append(metadata[satname]['dates'][i])
shorelines.append(wl_final)
# store in output structure
output[satname] = {'dates':timestamp, 'shorelines':shorelines, 'idx_skipped':idx_skipped,
output[satname] = {'dates':timestamp, 'idx_skipped':idx_skipped,
'metadata':{'filenames':filename_ts, 'satname':satname_ts, 'cloud_cover':cloud_cover_ts,
'acc_georef':acc_georef_ts}}
del idx_skipped
# save output
#with open(os.path.join(filepath, sitename + '_output' + satname + '.pkl'), 'wb') as f:
# pickle.dump(output, f)
#==========================================================#
#==========================================================#
#==========================================================#
#==========================================================#
#%%
# save output
with open(os.path.join(filepath, sitename + '_output' + '.pkl'), 'wb') as f:
pickle.dump(output, f)
# save idx_skipped
#idx_skipped = dict([])
#for satname in list(output.keys()):
# idx_skipped[satname] = output[satname]['idx_skipped']
#with open(os.path.join(filepath, sitename + '_idxskipped' + '.pkl'), 'wb') as f:
# pickle.dump(idx_skipped, f)
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