| """Display utilities for heatmaps and colormaps.""" |
| import numpy as np |
| import cv2 |
|
|
| from config.constants import COLORMAPS, COLORMAP_N_SAMPLES |
|
|
|
|
| def cv_colormap_to_plotly_colorscale(colormap_name, n_samples=None): |
| """Build a Plotly colorscale from OpenCV colormap so UI matches download/PDF exactly.""" |
| n = n_samples or COLORMAP_N_SAMPLES |
| cv2_cmap = COLORMAPS.get(colormap_name, cv2.COLORMAP_JET) |
| gradient = np.linspace(0, 255, n, dtype=np.uint8).reshape(1, -1) |
| rgb = cv2.applyColorMap(gradient, cv2_cmap) |
| rgb = cv2.cvtColor(rgb, cv2.COLOR_BGR2RGB) |
| scale = [] |
| for i in range(n): |
| r, g, b = rgb[0, i] |
| scale.append([i / (n - 1), f"rgb({r},{g},{b})"]) |
| return scale |
|
|
|
|
| def is_display_range_remapped(display_mode, clip_min, clip_max): |
| """ |
| True when the UI uses Range mode with a clip window other than the full [0, 1] |
| (colorbar tick labels map normalized 0–1 to that interval). |
| """ |
| if display_mode != "Range": |
| return False |
| lo, hi = float(clip_min), float(clip_max) |
| return hi > lo and not (lo == 0.0 and hi == 1.0) |
|
|
|
|
| def apply_display_scale(heatmap, mode, clip_min=0, clip_max=1, clamp_only=False): |
| """ |
| Apply display scaling. Display only—does not change underlying values. |
| |
| Supports modes used by the app: ``Default`` (clip to [0, 1]) and ``Range`` |
| (window to [clip_min, clip_max]). Unknown ``mode`` is treated like ``Default``. |
| |
| Parameters |
| ---------- |
| clamp_only : bool |
| For ``mode == "Range"`` when ``clip_max > clip_min``: |
| |
| - **False** (default in the app for Range): pixels outside ``[clip_min, clip_max]`` are set |
| to 0; values inside are linearly mapped to ``[0, 1]`` for the colormap. |
| - **True**: values are clamped to ``[clip_min, clip_max]`` without rescaling to ``[0, 1]``. |
| """ |
| if mode != "Range": |
| return np.clip(heatmap, 0, 1).astype(np.float32) |
| |
| vmin, vmax = float(clip_min), float(clip_max) |
| if vmax > vmin: |
| h = heatmap.astype(np.float32) |
| if clamp_only: |
| return np.clip(h, vmin, vmax).astype(np.float32) |
| mask = (h >= vmin) & (h <= vmax) |
| out = np.zeros_like(h) |
| out[mask] = (h[mask] - vmin) / (vmax - vmin) |
| return np.clip(out, 0, 1).astype(np.float32) |
| return np.clip(heatmap, 0, 1).astype(np.float32) |
|
|
