def calculate(image: Image, L: float = 0., H: float = 1., E: float = 0.5, clip: float = 0.) -> Image:
bcet_image = np.zeros(image.shape)
for i in tqdm(range(image.band_count), total=image.band_count, desc='Calculating bands'):
x = image[i].pixels
l0 = np.ma.min(x)
h0 = np.ma.max(x)
e = np.ma.mean(x)
l = l0 + (clip * (h0-l0))
h = h0 - (clip * (h0-l0))
L = L
H = H
E = E
s = np.ma.mean(x**2)
b = (h**2 * (E - L) - s * (H - L) + l**2 * (H - E)) / (2 * (h * (E - L) - e * (H - L) + l * (H - E)))
a = (H - L) / ((h - l) * (h + l - 2 * b))
c = L - a * (l - b)**2
bcet_image[:, :, i] = a * (x - b)**2 + c
bcet_image[bcet_image > H] = H
bcet_image[bcet_image < L] = L
return Image(bcet_image, image.geotransform, image.projection)
def image():
import numpy as np
from remotesensing.image import Image, Geotransform
return Image(
pixels=np.zeros((10, 10, 2)),
geotransform=Geotransform(10, 10, 2, 2, 0, 0),
projection='')
def calculate(image: Image, k: float = 0.6) -> Image:
x = np.amin(image.pixels, axis=2)
dds_image = np.copy(image.pixels)
for i in range(image.band_count):
dds_image[:, :, i] -= (k * x)
return Image(dds_image, image.geotransform, image.projection)
def load_from_dataset(self, image_dataset: gdal.Dataset) -> Image:
geo_transform = self._load_geotransform(image_dataset)
projection = image_dataset.GetProjection()
pixels = image_dataset.ReadAsArray()
if pixels.ndim > 2:
pixels = pixels.transpose(1, 2, 0)
return Image(pixels, geo_transform, projection)
def calibrate_landsat(self, image: Image, band_list: List[str]) -> Image:
if image.band_count == 1:
calibrated_image = self.calibrate_landsat_band(image.pixels, 0)
else:
calibrated_image = np.zeros(image.shape)
for i, band_name in enumerate(band_list):
band_array = image.pixels[:, :, i]
calibrated_image[:, :, i] = self.calibrate_landsat_band(band_array, i)
return Image(calibrated_image, image.geotransform, image.projection, band_labels={i+1: band for i, band in enumerate(band_list)})
def apply_model(self, image):
"""
:type image: image.Image
:rtype: image.Image
"""
""" Run the trained model on an image """
if not self.trained:
raise UserWarning(
"Model needs to be trained before it can be tested.")
features = image.pixels.reshape(image.width * image.height,
image.band_count)
features[np.isnan(features)] = 0
results_list = self.model.predict(features)
results_image = results_list.reshape(image.width, image.height)
results_image[np.isnan(image[0].pixels)] = 0.
return Image(results_image, self.image.geotransform,
self.image.projection)
def load_from_dataset_and_clip(self, image_dataset: gdal.Dataset,
extent: GeoPolygon) -> Image:
geo_transform = self._load_geotransform(image_dataset)
pixel_polygon = extent.to_pixel(geo_transform)
bounds = [int(bound) for bound in pixel_polygon.polygon.bounds]
pixels = image_dataset.ReadAsArray(bounds[0], bounds[1],
bounds[2] - bounds[0],
bounds[3] - bounds[1])
subset_geo_transform = geo_transform.subset(x=bounds[0], y=bounds[1])
pixel_polygon = extent.to_pixel(subset_geo_transform)
if pixels.ndim > 2:
pixels = pixels.transpose(1, 2, 0)
return Image(pixels, subset_geo_transform, image_dataset.GetProjection())\
.clip_with(pixel_polygon, mask_value=0)
def calculate(cls, low_resolution_image: Image, pan_image: Image) -> Image:
pan_smooth = cls._smooth_image(pan_image.pixels)
if low_resolution_image.band_count > 2:
pansharpened = np.zeros((
pan_image.shape[0],
pan_image.shape[1],
low_resolution_image.shape[2]))
for b in tqdm(range(pansharpened.shape[2]), total=pansharpened.shape[2], desc="Fusing images"):
pansharpened[:, :, b] = cls._fuse_images(
low_resolution_image=low_resolution_image[b].pixels,
pan_image=pan_image.pixels,
smoothed_pan_image=pan_smooth)
else:
pansharpened = cls._fuse_images(
low_resolution_image=low_resolution_image.pixels,
pan_image=pan_image.pixels,
smoothed_pan_image=pan_smooth)
return Image(pansharpened, pan_image.geotransform, pan_image.projection)