def apply_spatial_filter(self, collection, kernel, threshold):
kernel = ee.Kernel.fixed(len(kernel[0]), len(kernel), kernel,
-(len(kernel[0]) / 2), -(len(kernel) / 2),
False)
new_collection = ImageCollection([])
for year in self.__years:
image = Image(
collection.filter(ee.Filter.eq('year',
year)).first()).unmask(None)
filename = "{0}_{1}".format(self.__collection_prefix, str(year))
roi = image.geometry()
filtered_image = image.convolve(kernel).gte(threshold)
filtered_image = filtered_image.clip(roi).set(
'system:index',
filename).set('year', year).set('system:footprint', roi)
new_collection = new_collection.merge(
ImageCollection(filtered_image))
final_name = filename
final_image = filtered_image.updateMask(filtered_image.eq(1))
self.add_image_in_batch(final_name, {
"image": final_image,
"year": int(year),
"geometry": roi
})
return new_collection
def run(self):
features = self._feature_collection.get_features()
for year in self._years:
for feature in features:
path = feature.get('PATH')
row = feature.get('ROW')
geometry = ee.Geometry.MultiPolygon(feature.get('GEOMETRY'))
neighbors = self.get_neighbors(features, int(path), int(row))
neighbors_paths = map(lambda x: x[0], neighbors)
neighbors_rows = map(lambda x: x[1], neighbors)
neighbors_geometry = ee.Geometry.MultiPolygon(map(lambda x: x[2], neighbors))
image_collection = self._collection \
.filter(ee.Filter.inList('WRS_PATH', ee.List(neighbors_paths))) \
.filter(ee.Filter.inList('WRS_ROW', ee.List(neighbors_rows)))
final_image = Image()
for period in self._periods:
period_dates = feature.get(period)
images_by_period = ImageCollection(image_collection).filter_by_period(year, period_dates, self._offset)
if self._clip_geometry:
images_by_period = images_by_period.clip_geometry()
if self._apply_mask:
images_by_period = images_by_period.apply_qamask()
if self._bands:
images_by_period = images_by_period.apply_bands(self._bands)
image_reduced = Image(images_by_period.apply_reducers(self._reducers))
final_image = final_image.addBands(
image_reduced.rename(image_reduced.bandNames().map(lambda band: ee.String(period).cat('_').cat(band)))
)
if settings.GENERATION_EXTRA_BANDS:
final_image = final_image.addBands(Image(final_image).get_bands(settings.GENERATION_EXTRA_BANDS))
final_name = "{0}_{1}_{2}".format(settings.COLLECTION_PREFIX, "{0}{1}".format(path, row), str(year))
final_image = final_image.select(settings.GENERATION_VARIABLES + settings.GENERATION_EXTRA_VARIABLES).set('year', year)
self.add_image_in_batch(final_name,
{"image": final_image, "year": int(year), "path": int(path), "row": int(row), "geometry": geometry,
'neighbors': neighbors_geometry})
def apply_temporal_filter(self, collection, offset, initial_threshold):
collection = collection.toList(settings.MAX_IMAGES)
new_collection = ImageCollection([])
for index in xrange(len(self.__years)):
left, center, right, threshold = self.__break_list(
collection, index, offset, 0,
len(self.__years) - 1, initial_threshold)
year = self.__years[index]
roi = center.geometry()
filename = "{0}_{1}".format(self.__collection_prefix, str(year))
center = center.unmask(None).eq(self.__class_of_reference)
left = ImageCollection(left)
right = ImageCollection(right)
sides = ImageCollection(left.merge(right)).map(lambda image: Image(
image).eq(self.__class_of_reference)).sum()
mask = center.add(sides.eq(0)).neq(2)
image = center.add(sides).gte(threshold + 1)
filtered_image = Image(center.add(image)).updateMask(mask).gte(1)
filtered_image = filtered_image.clip(roi).set(
'system:index',
filename).set('year', year).set('system:footprint', roi)
new_collection = new_collection.merge(
ImageCollection(filtered_image))
final_name = filename
final_image = filtered_image
self.add_image_in_batch(final_name, {
"image": final_image,
"year": int(year),
"geometry": roi
})
return new_collection
def apply_smoothing(self):
collection = self
time_field = 'system:time_start'
join = ee.Join.saveAll(matchesKey='images')
diff_filter = ee.Filter.maxDifference(difference=1000 * 60 * 60 * 24 *
17,
leftField=time_field,
rightField=time_field)
three_neighbor_join = join.apply(primary=collection,
secondary=collection,
condition=diff_filter)
smoothed = ImageCollection(
three_neighbor_join.map(lambda image: Image(image).addBands(
ImageCollection.fromImages(image.get('images')).mean(), None,
True)))
return smoothed
def run(self):
for year in self._years:
for feature in self._feature_collection.get_features():
path = feature.get('PATH')
row = feature.get('ROW')
geometry = ee.Geometry.MultiPolygon(feature.get('geometry'))
image_collection = self._collection.map(lambda image: image.clip(geometry))
images = []
for period in self._periods:
period_dates = feature.get(period)
images_by_period = ImageCollection(image_collection).filter_by_period(year, period_dates, self._offset)
images_by_period = images_by_period.apply_smoothing()
if self._clip_geometry:
images_by_period = images_by_period.clip_geometry()
if self._apply_mask:
images_by_period = images_by_period.apply_qamask()
if self._bands:
images_by_period = images_by_period.apply_bands(self._bands)
image_reduced = Image(images_by_period.apply_reducers(self._reducers))
image_reduced = image_reduced.rename(image_reduced.bandNames().map(
lambda band: ee.String(period).cat('_').cat(band))
)
images.append(image_reduced)
final_image = Image.cat(images)
if settings.GENERATION_EXTRA_BANDS:
final_image = final_image.addBands(Image(final_image).get_bands(settings.GENERATION_EXTRA_BANDS))
final_name = "{0}_{1}_{2}".format(settings.COLLECTION_PREFIX, "{0}{1}".format(path, row), str(year))
final_image = final_image.clip(geometry).select(settings.GENERATION_VARIABLES + settings.GENERATION_EXTRA_VARIABLES).set('year',
year).set(
'system:footprint', geometry)
self.add_image_in_batch(final_name,
{"image": final_image, "year": int(year), "path": int(path), "row": int(row), "geometry": geometry})
def apply_bands(self, bands):
return self.map(
lambda image: Band.rescale(Image(image).get_bands(bands)))
def clip_geometry(self):
return self.map(lambda image: Image(image).clip(image.geometry(
).buffer(settings.EXPORT_BUFFER)))
def apply_qamask(self):
return self.map(
lambda image: image.updateMask(Image(image).get_qamask()))
def apply_brdf(self):
return self.map(lambda image: Image(image).apply_brdf())
def run(self):
for year in self._years:
for feature in self._feature_collection.get_features():
path = feature.get('PATH')
row = feature.get('ROW')
geometry = ee.Geometry.MultiPolygon(feature.get('GEOMETRY'))
image_collection = self._collection.filter(ee.Filter.eq('WRS_PATH', int(path))).filter(ee.Filter.eq('WRS_ROW', int(row)))
for period in self._periods:
images_by_period = ImageCollection(image_collection).filter_by_period(year, feature.get(period), self._offset)
if self._apply_brdf:
images_by_period = images_by_period.apply_brdf()
if self._clip_geometry:
images_by_period = images_by_period.clip_geometry()
if self._apply_mask:
images_by_period = images_by_period.apply_qamask()
if self._bands:
images_by_period = images_by_period.apply_bands(self._bands)
if self._reducers:
image_reduced = Image(ImageCollection(images_by_period).apply_reducers(self._reducers))
image_reduced = image_reduced.rename(image_reduced.bandNames().map(
lambda band: ee.String(period).cat('_').cat(band))
)
final_name = "{0}_{1}_{2}".format(settings.COLLECTION_PREFIX, "{0}{1}".format(path, row), str(year))
final_image = image_reduced.clip(geometry).select(settings.GENERATION_VARIABLES + settings.GENERATION_EXTRA_VARIABLES).set('year', year).set('system:footprint', geometry)
self.add_image_in_batch(final_name, {"image": final_image,
"year": int(year),
"path": int(path),
"row": int(row),
"geometry": geometry})
else:
images_by_period = images_by_period.map(
lambda image: Image(image).rename(Image(image).bandNames().map(
lambda band: ee.String(period).cat('_').cat(band))
)
)
images_by_period = images_by_period.toList(settings.MAX_IMAGES)
for i in xrange(images_by_period.size().getInfo()):
image = Image(images_by_period.get(i))
date = image.date().format('yyyyMMdd').getInfo()
final_name = "{0}_{1}_{2}".format(settings.COLLECTION_PREFIX, "{0}{1}".format(path, row), str(date))
final_image = Image(image).clip(geometry).select(settings.GENERATION_VARIABLES + settings.GENERATION_EXTRA_VARIABLES).set('year', year).set('system:footprint', geometry)
self.add_image_in_batch(final_name, {"image": final_image,
"year": int(year),
"path": int(path),
"row": int(row),
"geometry": geometry})