| """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): |
| """ |
| Map the normalised prediction (0-1) to colormap positions. Display only; values are unchanged. |
| |
| ``Default``: the full scale, 0-1 (= 0-500 Pa single cells, 0-50 Pa spheroids). |
| ``Range``: the window [clip_min, clip_max] is stretched over the whole colormap; values below the window |
| are shown as the lowest colour and values above it **saturate at the highest colour**. (Earlier versions set |
| values above the window to 0, which displayed the strongest stresses as zero.) |
| ``clamp_only`` is kept for backward compatibility and ignored. |
| """ |
| h = np.asarray(heatmap, dtype=np.float32) |
| if mode != "Range": |
| return np.clip(h, 0, 1).astype(np.float32) |
| vmin, vmax = float(clip_min), float(clip_max) |
| if vmax > vmin: |
| return np.clip((h - vmin) / (vmax - vmin), 0, 1).astype(np.float32) |
| return np.clip(h, 0, 1).astype(np.float32) |
|
|
