| """Shared utility for computing sky-colour histograms from a panorama image.""" |
|
|
| import numpy as np |
| import torch |
|
|
|
|
| def compute_sky_histogram(sky_image_np: np.ndarray, |
| hist_range: tuple = (-1, 1), |
| bins: int = 100, |
| skip_bins: int = 10) -> np.ndarray: |
| """Compute a normalised colour histogram from a masked sky image. |
| |
| Parameters |
| ---------- |
| sky_image_np : np.ndarray |
| Sky image array of shape ``(C, H, W)`` (single image, no batch dim). |
| Pixel values should lie within *hist_range*. |
| hist_range : tuple |
| ``(min, max)`` passed to ``np.histogram``. |
| bins : int |
| Number of histogram bins. |
| skip_bins : int |
| Number of leading bins to discard (removes near-zero / masked pixels). |
| |
| Returns |
| ------- |
| np.ndarray |
| Concatenated normalised histogram of shape ``((bins - skip_bins) * C,)``. |
| """ |
| channel_histograms = [] |
| for channel in sky_image_np: |
| histo = np.histogram(channel.flatten(), bins=bins, range=hist_range)[0] |
| histo = histo[skip_bins:] |
| if histo.sum() != 0: |
| histo = histo / histo.sum() |
| channel_histograms.append(histo) |
| return np.concatenate(channel_histograms) |
|
|
