def ee_pre_processing(request):
request = request.get_json()
# Variables
collection = request.get('collection')
startDate = ee.Date(request.get('start'))
stopDate = ee.Date(request.get('end'))
scale = request.get('scale')
# Bands
bands = ee_collection_specifics.ee_bands(collection)
# Get composite
image = ee_collection_specifics.Composite(collection)(startDate, stopDate)
image = image.select(bands)
# Normalize images
if ee_collection_specifics.normalize(collection):
# Get min man values for each band
values = min_max_values(image, collection, scale)
# Normalize images
image = normalize_ee_images(image, collection, values)
else:
values = {}
return json.dumps({'bands_min_max': values, 'composite': image.serialize()})
def create_collection(self, scale=None):
years = np.arange(self.start_year, self.stop_year + 1)
dic = ee_collection_specifics.date_range(self.instrument)
years_range = list(map(list, list(dic.values())))
collections = list(dic.keys())
image_list = []
for year in years:
n = 0
in_range = False
for sub_years in years_range:
if year in sub_years:
in_range = True
break
n = +1
if not in_range:
raise ValueError(f'Year out of range.')
collection = collections[n]
if scale:
scale = scale
else:
scale = ee_collection_specifics.scales(collection)
# Image Collection
image_collection = ee_collection_specifics.ee_collections(
collection)
# Image Visualization parameters
vis = ee_collection_specifics.ee_vis(collection)
step_range = ee_collection_specifics.step_range(collection)
startDate = ee.Date(str(year + step_range[0]) + '-12-31')
stopDate = ee.Date(str(year + step_range[1]) + '-12-31')
image = ee_collection_specifics.Composite(collection)(
image_collection, startDate, stopDate, scale, self.polygon)
# convert image to an RGB visualization
image_list.append(self.visFun(image, vis))
return image_list
def read_datasets(self):
## Composite
image = ee_collection_specifics.Composite(self.collection)(self.image_collection, self.startDate, self.stopDate, self.geom)
## Calculate normalized Difference
if self.normDiff_bands:
for n, normDiff_band in enumerate(self.normDiff_bands):
image_nd = image.normalizedDifference(normDiff_band)
## Concatenate images into one multi-band image
if n == 0:
image = ee.Image.cat([image.select(self.bands), image_nd])
else:
image = ee.Image.cat([image, image_nd])
else:
image = image.select(self.bands)
# Choose the scale
image = image.reproject(crs='EPSG:4326', scale=self.scale)
# Download images as tif
download_image_tif(image, 'data.zip', scale = self.scale, region = self.region)
# Load data
directory = "./data/"
files = sorted(f for f in os.listdir(directory) if f.endswith('.' + 'tif'))
data = load_tif_bands(directory, files)
# Remove data folders and files
file="data.zip"
## If file exists, delete it ##
if os.path.isfile(file):
os.remove(file)
else: ## Show an error ##
print("Error: %s file not found" % file)
## Try to remove tree; if failed show an error using try...except on screen
folder = "./data"
try:
shutil.rmtree(folder)
except OSError as e:
print ("Error: %s - %s." % (e.filename, e.strerror))
return data
def read_datasets(self):
## Composite
image = ee_collection_specifics.Composite(self.collection)(self.image_collection, self.startDate, self.stopDate, self.geom)
# Choose the scale
image = image.reproject(crs='EPSG:4326', scale=self.scale)
## Select the bands
if self.normDiff_bands:
for n, normDiff_band in enumerate(self.normDiff_bands):
image_nd = image.normalizedDifference(normDiff_band)
## Concatenate images into one multi-band image
if n == 0:
image_nd = image_nd.rename(self.normDiff_bands_names[0])
image = ee.Image.cat([image.select(self.bands), image_nd])
else:
image_nd = image_nd.rename(self.normDiff_bands_names[1])
image = ee.Image.cat([image, image_nd])
else:
image = image.select(self.bands)
# get the lat lon
latlon = ee.Image.pixelLonLat().addBands([image])
# apply reducer to list
latlon = latlon.reduceRegion(
reducer=ee.Reducer.toList(),
geometry=self.region,
maxPixels=1e8,
scale=self.scale);
# get list of bands
bands = list(latlon.getInfo().keys())
bands.remove('latitude'); bands.remove('longitude')
nbands = len(bands)
# get data into three different arrays
lats = np.array((ee.Array(latlon.get("latitude")).getInfo()))
lons = np.array((ee.Array(latlon.get("longitude")).getInfo()))
# get the unique coordinates
uniqueLats = np.unique(lats)
uniqueLons = np.unique(lons)
# get number of columns and rows from coordinates
ncols = len(uniqueLons)
nrows = len(uniqueLats)
# determine pixelsizes
ys = uniqueLats[1] - uniqueLats[0]
xs = uniqueLons[1] - uniqueLons[0]
# create an array with dimensions of image
arr = np.zeros([nrows, ncols, nbands], np.float32) #-9999
for n, band in enumerate(bands):
data = np.array((ee.Array(latlon.get(band)).getInfo()))
# fill the array with values
counter =0
for y in range(0,len(arr),1):
for x in range(0,len(arr[0]),1):
if lats[counter] == uniqueLats[y] and lons[counter] == uniqueLons[x] and counter < len(lats)-1:
counter+=1
arr[len(uniqueLats)-1-y,x,n] = data[counter] # we start from lower left corner
return arr
def read_composite(self):
## Composite
self.image = ee_collection_specifics.Composite(self.collection)(self.image_collection, self.startDate, self.stopDate, self.geom, self.scale)
self.image = self.image.visualize(**self.vis)