"""Tests for rendering module (colormaps, tif_to_png).""" import numpy as np import rasterio from rasterio.transform import from_bounds import pytest from pathlib import Path def _make_test_tif(tmp_path, data=None, size=50): """Create a small test GeoTIFF and return its path.""" if data is None: rng = np.random.default_rng(42) data = rng.normal(0, 1, (size, size)).astype(np.float32) transform = from_bounds(660000, 6700000, 661000, 6701000, size, size) tif_file = tmp_path / "test_vis.tif" with rasterio.open( tif_file, 'w', driver='GTiff', height=size, width=size, count=1, dtype='float32', crs='EPSG:2154', transform=transform, compress='lzw' ) as dst: dst.write(data, 1) return tif_file class TestColormaps: def test_colormaps_dict_exists(self): from lidar_pipeline.rendering import COLORMAPS assert isinstance(COLORMAPS, dict) def test_all_viz_steps_have_colormaps(self): """Every VIZ_STEPS entry should have a corresponding COLORMAPS entry or render correctly.""" from lidar_pipeline.pipeline import VIZ_STEPS from lidar_pipeline.rendering import COLORMAPS # Some viz names differ from colormap keys name_map = { 'pos_open': 'positive_openness', 'neg_open': 'negative_openness', } # IGN overlays (ortho, topo) are RGB images — no colormap needed skip = {'ortho', 'topo'} for name, _ in VIZ_STEPS: if name in skip: continue cmap_key = name_map.get(name, name) assert cmap_key in COLORMAPS, f"Missing colormap for: {name} (looked as {cmap_key})" def test_colormap_has_required_keys(self): """Each colormap entry must have cmap, title, legend, description.""" from lidar_pipeline.rendering import COLORMAPS required = {'cmap', 'title', 'legend', 'description'} for name, entry in COLORMAPS.items(): missing = required - set(entry.keys()) assert not missing, f"Colormap '{name}' missing keys: {missing}" class TestTifToPng: def test_converts_tif_to_webp(self, tmp_path): from lidar_pipeline.rendering import tif_to_png tif_file = _make_test_tif(tmp_path) result = tif_to_png(tif_file, tmp_path, 5.0) assert result is not None assert result.exists() assert result.suffix == '.avif' def test_removes_source_tif(self, tmp_path): from lidar_pipeline.rendering import tif_to_png tif_file = _make_test_tif(tmp_path) assert tif_file.exists() tif_to_png(tif_file, tmp_path, 5.0) assert not tif_file.exists(), "Source TIF should be deleted after conversion" def test_webp_has_content(self, tmp_path): from lidar_pipeline.rendering import tif_to_png tif_file = _make_test_tif(tmp_path) result = tif_to_png(tif_file, tmp_path, 5.0) assert result.stat().st_size > 1000 # Must be a real image class TestApplyColormap: def test_symmetric_mode(self, tmp_path): from lidar_pipeline.rendering import COLORMAPS, tif_to_png # LRM uses symmetric mode data = np.random.default_rng(42).normal(0, 0.5, (50, 50)).astype(np.float32) tif_file = _make_test_tif(tmp_path, data) result = tif_to_png(tif_file, tmp_path, 5.0) assert result is not None assert result.exists() def test_percentile_mode(self, tmp_path): from lidar_pipeline.rendering import tif_to_png # Most visualizations use percentile mode data = np.random.default_rng(42).normal(50, 10, (50, 50)).astype(np.float32) tif_file = _make_test_tif(tmp_path, data) result = tif_to_png(tif_file, tmp_path, 5.0) assert result is not None assert result.exists() def test_knots_mode_fixed_transfer(self): """L'étalonnage quantile figé (ondelette) : même valeur → même couleur. Les nœuds sont constants (pas de percentile local) : clamp aux extrêmes, médiane (1.0) → 0.5, transfert monotone. """ from lidar_pipeline.rendering import COLORMAPS, _apply_colormap info = COLORMAPS['wavelet'] kv, kt = info['knots'][0.5] assert kv[6] == 1.0 and kt[6] == 0.5 # nœud médian vals = np.array([0.05, kv[0], 1.0, kv[-1], 50.0], dtype=float) out, cmap, *_ = _apply_colormap(vals, 'x_wavelet.tif', resolution=0.5) assert cmap == 'inferno' assert out[0] == 0.0 and out[1] == 0.01 # clamp bas assert abs(out[2] - 0.5) < 1e-9 # médiane → 0.5 assert out[3] == 0.995 and out[4] == 1.0 # nœud haut, puis clamp au-delà # Monotonie (pas d'inversion de teinte) s = np.sort(np.random.default_rng(1).uniform(0.2, 3.0, 500)) o, *_ = _apply_colormap(s, 'x_wavelet.tif', resolution=0.5) assert np.all(np.diff(o) >= -1e-12) # Les nœuds 0.2 m diffèrent de ceux 0.5 m (calibrations distinctes) kv02, _ = info['knots'][0.2] assert kv02 != kv class TestTifToCrop: """Conversion TIF → dalle cartographique (tif_to_crop).""" @staticmethod def _write_named_tif(tmp_path, name, arr): transform = from_bounds(660000, 6700000, 661000, 6701000, arr.shape[1], arr.shape[0]) tif_file = tmp_path / name with rasterio.open( tif_file, 'w', driver='GTiff', height=arr.shape[0], width=arr.shape[1], count=1, dtype='float32', crs='EPSG:2154', transform=transform, nodata=float('nan'), compress='lzw' ) as dst: dst.write(arr.astype('float32'), 1) return tif_file def test_nodata_renders_black(self, tmp_path): """Le nodata restant est rendu en noir (comportement historique). Les trous du MNT sont comblés en amont (interpolation dans create_dtm_fast, tous modes) ; ce qui reste en nodata doit rester visible en noir sur la dalle plutôt qu'inventé au rendu. """ from PIL import Image as PILImage from lidar_pipeline.rendering import tif_to_crop data = np.random.default_rng(7).normal(50, 10, (40, 40)).astype(np.float32) data[15:25, 15:25] = np.nan tif_file = self._write_named_tif(tmp_path, "LHD_test_slope.tif", data) # WebP lossless : l'encodeur AVIF de l'image « saigne » légèrement les # bords du noir même en lossless — on teste la logique nodata→noir, # pas les artefacts du codec. out = tif_to_crop(tif_file, tmp_path, 5.0, keep_tif=True, quality=100, output_format='webp') assert out is not None and out.exists() rgb = np.asarray(PILImage.open(str(out)).convert('RGB')) hole = rgb[15:25, 15:25, :] assert np.all(hole < 10), "le nodata doit être rendu en noir" def test_without_nodata(self, tmp_path): """Un TIF sans nodata est converti sans crash, taille préservée.""" from PIL import Image as PILImage from lidar_pipeline.rendering import tif_to_crop data = np.random.default_rng(7).normal(50, 10, (40, 40)).astype(np.float32) tif_file = self._write_named_tif(tmp_path, "LHD_test_slope.tif", data) out = tif_to_crop(tif_file, tmp_path, 5.0) assert out is not None and out.exists() img = PILImage.open(str(out)) assert img.size == (40, 40)