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# MaxPool-based downsampling for coarse labels
"""Downsample binary masks preserving thin positives.
We use area-based resize on float32 masks followed by a >0 threshold.
This emulates block-wise max pooling: any positive in the source region
produces a positive in the target pixel.
"""
import numpy as np
def downsample_label_maxpool(mask: np.ndarray, out_h: int, out_w: int) -> np.ndarray:
"""
Args:
mask: HxW binary (0/1) numpy array
out_h, out_w: target size
Returns:
H'xW' binary array via max-pooling-like downsample
"""
assert mask.ndim == 2
# Convert to float32 so area resize yields fractional averages > 0 if any positive present
import cv2
m = mask.astype(np.float32)
r = cv2.resize(m, (out_w, out_h), interpolation=cv2.INTER_AREA)
out = (r > 0.0).astype(np.uint8)
return out
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