def main(polygon, dates, sat_list, sitename, vidType, rate):
# filepath where data will be stored
filepath_data = os.path.join(os.getcwd(), 'data')
# put all the inputs into a dictionnary
inputs = {
'polygon': polygon,
'dates': dates,
'sat_list': sat_list,
'sitename': sitename,
'filepath': filepath_data,
}
#%% 2. Retrieve images
### retrieve satellite images from GEE
metadata = SDS_download.retrieve_images(inputs)
##
### if you have already downloaded the images, just load the metadata file
metadata = SDS_download.get_metadata(inputs)
##
###%% 3. Batch island contour detection
##
### settings for the sand contour mapping
settings = {
# general parameters:
'cloud_thresh': 0.5, # threshold on maximum cloud cover
'output_epsg':
3857, # epsg code of spatial reference system desired for the output
# quality control:
'check_detection_sand_poly':
True, # if True, uses sand polygon for detection and shows user for validation
'save_figure':
True, # if True, saves a figure showing the mapped shoreline for each image
# add the inputs defined previously
'inputs': inputs,
# [ONLY FOR ADVANCED USERS] shoreline detection parameters:
'min_beach_area':
50, # minimum area (in metres^2) for an object to be labelled as a beach
'buffer_size':
100, # radius (in metres) of the buffer around sandy pixels considered in the shoreline detection
'min_length_sl':
500, # minimum length (in metres) of shoreline perimeter to be valid
'cloud_mask_issue':
False, # switch this parameter to True if sand pixels are masked (in black) on many images
'sand_color':
'default', # 'default', 'dark' (for grey/black sand beaches) or 'bright' (for white sand beaches)
}
##
### [OPTIONAL] preprocess images (cloud masking, pansharpening/down-sampling)
SDS_preprocess.save_jpg(metadata, settings, vidType)
videoName = os.path.join(filepath_data, sitename, sitename + '.avi')
imageFolder = os.path.join(filepath_data, sitename, 'jpg_files',
'preprocessed')
makeVideo(imageFolder, videoName, rate)
sitename = 'BRT'
# filepath where data will be stored
filepath_data = os.path.join(os.getcwd(), 'data')
# put all the inputs into a dictionnary
inputs = {
'polygon': polygon,
'dates': dates,
'sat_list': sat_list,
'sitename': sitename,
'filepath': filepath_data
}
# before downloading the images, check how many images are available for your inputs
SDS_download.check_images_available(inputs)
#%% 2. Retrieve images
# # only uncomment this line if you want Landsat Tier 2 images (not suitable for time-series analysis)
# # inputs['include_T2'] = True
# retrieve satellite images from GEE
metadata = SDS_download.retrieve_images(inputs)
# if you have already downloaded the images, just load the metadata file
metadata = SDS_download.get_metadata(inputs)
#%% 3. Batch shoreline detection
# settings for the shoreline extraction
# filepath where data will be stored
filepath_data = os.path.join(os.getcwd(), 'data')
# put all the inputs into a dictionnary
inputs = {
'polygon': polygon,
'dates': dates,
'sat_list': sat_list,
'sitename': sitename,
'filepath': filepath_data
}
#%% 2. Retrieve images
# retrieve satellite images from GEE
metadata = SDS_download.retrieve_images(inputs)
# if you have already downloaded the images, just load the metadata file
metadata = SDS_download.get_metadata(inputs)
#%% 3. Batch shoreline detection
# settings for the shoreline extraction
settings = {
# general parameters:
'cloud_thresh': 0.5, # threshold on maximum cloud cover
'output_epsg':
28356, # epsg code of spatial reference system desired for the output
# quality control:
'check_detection':
True, # if True, shows each shoreline detection to the user for validation
