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