def prepare_images(self):
x_data = []
y_data = []
for img, lbl, extent in zip(self.chunks_array, self.chunks_label, self.extents):
if self.removal_function(lbl, self.image_size, self.threshold):
x_data.append(Raster.from_array(img, img.pixel, extent))
y_data.append(Raster.from_array(lbl, img.pixel, extent))
return UnetData(x_data, y_data)
def test_split_image_to_cnn(self):
px = Pixel(1.0, 1.0)
epsg_4326 = Crs("epsg:4326")
image_array = np.array([[*list(range(1, 8))], [*list(range(2, 9))],
[*list(range(3, 10))], [*list(range(4, 11))],
[*list(range(5, 12))], [*list(range(10, 17))],
[*list(range(20, 27))]]).reshape(7, 7, 1)
image = Raster.from_array(image_array,
extent=Extent.from_coordinates(
[0, 0, 7.0, 7.0], epsg_4326),
pixel=px)
target_result = [
Raster.from_array(image_array[0:5, 0:5],
extent=Extent.from_coordinates([0, 0, 1, 1],
crs=epsg_4326),
pixel=px),
Raster.from_array(image_array[0:5, 1:6],
extent=Extent.from_coordinates([0, 0, 1, 1],
crs=epsg_4326),
pixel=px),
Raster.from_array(image_array[0:5, 2:7],
extent=Extent.from_coordinates([0, 0, 1, 1],
crs=epsg_4326),
pixel=px),
Raster.from_array(image_array[1:6, 0:5],
extent=Extent.from_coordinates([0, 0, 1, 1],
crs=epsg_4326),
pixel=px),
Raster.from_array(image_array[1:6, 1:6],
extent=Extent.from_coordinates([0, 0, 1, 1],
crs=epsg_4326),
pixel=px),
Raster.from_array(image_array[1:6, 2:7],
extent=Extent.from_coordinates([0, 0, 1, 1],
crs=epsg_4326),
pixel=px),
Raster.from_array(image_array[2:7, 0:5],
extent=Extent.from_coordinates([0, 0, 1, 1],
crs=epsg_4326),
pixel=px),
Raster.from_array(image_array[2:7, 1:6],
extent=Extent.from_coordinates([0, 0, 1, 1],
crs=epsg_4326),
pixel=px),
Raster.from_array(image_array[2:7, 2:7],
extent=Extent.from_coordinates([0, 0, 1, 1],
crs=epsg_4326),
pixel=px)
]
function_result = split_images_to_cnn(image=image, window_size=5)
self.assertEqual(target_result, function_result)
def predict(self, x, threshold) -> Raster:
if not self.is_trained:
raise AttributeError("Model is not trained yet")
predicted = self.model.predict(np.array([x]))
filtered = (predicted > threshold).astype(np.uint8)
raster = Raster.from_array(filtered[0, :, :, :],
pixel=x.pixel,
extent=x.extent)
return raster