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from pathlib import Path
from typing import Optional
import numpy as np
import rasterio as rio
from rasterio.profiles import Profile
from .model_utils import channel_norm
def get_patch(
input_array: np.ndarray,
index: tuple,
no_data_value: Optional[int] = 0,
) -> tuple[Optional[np.ndarray], Optional[tuple[int, int, int, int]]]:
"""Extract a patch from a 3D array and normalize it. If the patch is entirely nodata, return None.
If the patch contains nodata, try to move patches to reduce nodata regions in patches.
"""
assert input_array.ndim == 3, "Input array must have 3 dimensions"
top, bottom, left, right = index
patch = input_array[:, top:bottom, left:right].astype(np.float32)
if patch.sum() == 0:
return None, None
if no_data_value is None:
if np.all(patch == no_data_value):
return None, None
if np.any(patch == 0):
max_bottom, max_right = input_array.shape[1:3]
if np.any(patch[:, 0, :]) or np.any(patch[:, -1, :]):
while not np.any(patch[:, 0, :]) and bottom < max_bottom: # check top row
patch = patch[:, 1:, :]
top += 1
bottom += 1
while not np.any(patch[:, -1, :]) and top > 0:
patch = patch[:, :-1, :]
bottom -= 1
top -= 1
# Both sides are not zero-filled
if np.any(patch[:, :, 0]) or np.any(patch[:, :, -1]):
while not np.any(patch[:, :, 0]) and right < max_right: # check left column
patch = patch[:, :, 1:]
left += 1
right += 1
while not np.any(patch[:, :, -1]) and left > 0: # check right column
patch = patch[:, :, :-1]
right -= 1
left -= 1
patch = input_array[:, top:bottom, left:right].astype(np.float32)
index = (top, bottom, left, right)
# trim index bottom and right to match patch shape
index = (top, top + patch.shape[1], left, left + patch.shape[2])
return channel_norm(patch, no_data_value), index
def mask_prediction(
scene: np.ndarray, pred_tracker_np: np.ndarray, no_data_value: int = 0
) -> np.ndarray:
"""Create a no data mask from a raster scene."""
assert scene.ndim == 3, "Scene must have 3 dimensions"
assert pred_tracker_np.ndim == 3, "Prediction tracker must have 3 dimensions"
assert (
scene.shape[1:] == pred_tracker_np.shape[1:]
), "Scene and prediction tracker must have the same shape"
mask = np.all(scene != no_data_value, axis=0).astype(np.uint8)
pred_tracker_np *= mask
return pred_tracker_np
def make_patch_indexes(
array_width: int,
array_height: int,
patch_size: int = 1000,
patch_overlap: int = 300,
) -> list[tuple[int, int, int, int]]:
"""Create a list of patch indexes for a given shape and patch size."""
assert patch_size > patch_overlap, "Patch size must be greater than patch overlap"
assert patch_overlap >= 0, "Patch overlap must be greater than or equal to 0"
assert patch_size > 0, "Patch size must be greater than 0"
assert (
patch_size <= array_width
), "Patch size must be less than or equal to array width"
assert (
patch_size <= array_height
), "Patch size must be less than or equal to array height"
stride = patch_size - patch_overlap
max_bottom = array_height - patch_size
max_right = array_width - patch_size
patch_indexes = []
for top in range(0, array_height, stride):
if top > max_bottom:
top = max_bottom
bottom = top + patch_size
for left in range(0, array_width, stride):
if left > max_right:
left = max_right
right = left + patch_size
patch_indexes.append((top, bottom, left, right))
return patch_indexes
def save_prediction(
output_path: Path, export_profile: Profile, pred_tracker_np: np.ndarray
) -> None:
with rio.open(output_path, "w", **export_profile) as dst:
dst.write(pred_tracker_np)
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