269 lines
12 KiB
Python
269 lines
12 KiB
Python
"""Tests for rendering module (colormaps, tif_to_png)."""
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import numpy as np
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import rasterio
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from rasterio.transform import from_bounds
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import pytest
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from pathlib import Path
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def _make_test_tif(tmp_path, data=None, size=50):
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"""Create a small test GeoTIFF and return its path."""
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if data is None:
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rng = np.random.default_rng(42)
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data = rng.normal(0, 1, (size, size)).astype(np.float32)
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transform = from_bounds(660000, 6700000, 661000, 6701000, size, size)
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tif_file = tmp_path / "test_vis.tif"
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with rasterio.open(
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tif_file, 'w', driver='GTiff', height=size, width=size,
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count=1, dtype='float32', crs='EPSG:2154', transform=transform,
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compress='lzw'
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) as dst:
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dst.write(data, 1)
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return tif_file
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class TestColormaps:
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def test_colormaps_dict_exists(self):
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from lidar_pipeline.rendering import COLORMAPS
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assert isinstance(COLORMAPS, dict)
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def test_all_viz_steps_have_colormaps(self):
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"""Every VIZ_STEPS entry should have a corresponding COLORMAPS entry or render correctly."""
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from lidar_pipeline.pipeline import VIZ_STEPS
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from lidar_pipeline.rendering import COLORMAPS
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# Some viz names differ from colormap keys
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name_map = {
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'pos_open': 'positive_openness',
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'neg_open': 'negative_openness',
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'hillshade': 'hillshade_multi',
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}
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# Images RGB (fonds IGN, relief orienté) — pas de colormap
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from lidar_pipeline.rendering import RGB_KEYWORDS
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skip = set(RGB_KEYWORDS)
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for name, _ in VIZ_STEPS:
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if name in skip:
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continue
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cmap_key = name_map.get(name, name)
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assert cmap_key in COLORMAPS, f"Missing colormap for: {name} (looked as {cmap_key})"
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def test_colormap_has_required_keys(self):
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"""Each colormap entry must have cmap, title, legend, description."""
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from lidar_pipeline.rendering import COLORMAPS
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required = {'cmap', 'title', 'legend', 'description'}
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for name, entry in COLORMAPS.items():
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missing = required - set(entry.keys())
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assert not missing, f"Colormap '{name}' missing keys: {missing}"
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class TestTifToPng:
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def test_converts_tif_to_webp(self, tmp_path):
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from lidar_pipeline.rendering import tif_to_png
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tif_file = _make_test_tif(tmp_path)
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result = tif_to_png(tif_file, tmp_path, 5.0)
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assert result is not None
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assert result.exists()
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assert result.suffix == '.avif'
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def test_removes_source_tif(self, tmp_path):
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from lidar_pipeline.rendering import tif_to_png
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tif_file = _make_test_tif(tmp_path)
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assert tif_file.exists()
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tif_to_png(tif_file, tmp_path, 5.0)
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assert not tif_file.exists(), "Source TIF should be deleted after conversion"
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def test_webp_has_content(self, tmp_path):
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from lidar_pipeline.rendering import tif_to_png
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tif_file = _make_test_tif(tmp_path)
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result = tif_to_png(tif_file, tmp_path, 5.0)
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assert result.stat().st_size > 1000 # Must be a real image
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class TestApplyColormap:
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def test_symmetric_mode(self, tmp_path):
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from lidar_pipeline.rendering import COLORMAPS, tif_to_png
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# LRM uses symmetric mode
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data = np.random.default_rng(42).normal(0, 0.5, (50, 50)).astype(np.float32)
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tif_file = _make_test_tif(tmp_path, data)
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result = tif_to_png(tif_file, tmp_path, 5.0)
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assert result is not None
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assert result.exists()
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def test_percentile_mode(self, tmp_path):
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from lidar_pipeline.rendering import tif_to_png
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# Most visualizations use percentile mode
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data = np.random.default_rng(42).normal(50, 10, (50, 50)).astype(np.float32)
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tif_file = _make_test_tif(tmp_path, data)
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result = tif_to_png(tif_file, tmp_path, 5.0)
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assert result is not None
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assert result.exists()
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def test_knots_mode_fixed_transfer(self):
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"""L'étalonnage quantile figé (ondelette) : même valeur → même couleur.
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Les nœuds sont constants (pas de percentile local) : clamp aux
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extrêmes, médiane (1.0) → 0.5, transfert monotone.
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"""
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from lidar_pipeline.rendering import COLORMAPS, _apply_colormap
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info = COLORMAPS['wavelet']
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kv, kt = info['knots'][0.5]
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assert kv[6] == 1.0 and kt[6] == 0.5 # nœud médian
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vals = np.array([0.05, kv[0], 1.0, kv[-1], 50.0], dtype=float)
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out, cmap, *_ = _apply_colormap(vals, 'x_wavelet.tif', resolution=0.5)
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assert cmap == 'inferno'
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assert out[0] == 0.0 and out[1] == 0.01 # clamp bas
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assert abs(out[2] - 0.5) < 1e-9 # médiane → 0.5
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assert out[3] == 0.995 and out[4] == 1.0 # nœud haut, puis clamp au-delà
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# Monotonie (pas d'inversion de teinte)
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s = np.sort(np.random.default_rng(1).uniform(0.2, 3.0, 500))
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o, *_ = _apply_colormap(s, 'x_wavelet.tif', resolution=0.5)
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assert np.all(np.diff(o) >= -1e-12)
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# Les nœuds 0.2 m diffèrent de ceux 0.5 m (calibrations distinctes)
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kv02, _ = info['knots'][0.2]
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assert kv02 != kv
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class TestTifToCrop:
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"""Conversion TIF → dalle cartographique (tif_to_crop)."""
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@staticmethod
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def _write_named_tif(tmp_path, name, arr):
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transform = from_bounds(660000, 6700000, 661000, 6701000, arr.shape[1], arr.shape[0])
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tif_file = tmp_path / name
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with rasterio.open(
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tif_file, 'w', driver='GTiff', height=arr.shape[0], width=arr.shape[1],
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count=1, dtype='float32', crs='EPSG:2154', transform=transform,
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nodata=float('nan'), compress='lzw'
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) as dst:
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dst.write(arr.astype('float32'), 1)
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return tif_file
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def test_nodata_renders_black(self, tmp_path):
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"""Le nodata restant est rendu en noir (comportement historique).
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Les trous du MNT sont comblés en amont (interpolation dans
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create_dtm_fast, tous modes) ; ce qui reste en nodata doit rester
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visible en noir sur la dalle plutôt qu'inventé au rendu.
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"""
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from PIL import Image as PILImage
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from lidar_pipeline.rendering import tif_to_crop
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data = np.random.default_rng(7).normal(50, 10, (40, 40)).astype(np.float32)
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data[15:25, 15:25] = np.nan
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tif_file = self._write_named_tif(tmp_path, "LHD_test_slope.tif", data)
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# WebP lossless : l'encodeur AVIF de l'image « saigne » légèrement les
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# bords du noir même en lossless — on teste la logique nodata→noir,
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# pas les artefacts du codec.
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out = tif_to_crop(tif_file, tmp_path, 5.0, keep_tif=True,
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quality=100, output_format='webp')
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assert out is not None and out.exists()
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rgb = np.asarray(PILImage.open(str(out)).convert('RGB'))
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hole = rgb[15:25, 15:25, :]
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assert np.all(hole < 10), "le nodata doit être rendu en noir"
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def test_without_nodata(self, tmp_path):
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"""Un TIF sans nodata est converti sans crash, taille préservée."""
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from PIL import Image as PILImage
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from lidar_pipeline.rendering import tif_to_crop
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data = np.random.default_rng(7).normal(50, 10, (40, 40)).astype(np.float32)
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tif_file = self._write_named_tif(tmp_path, "LHD_test_slope.tif", data)
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out = tif_to_crop(tif_file, tmp_path, 5.0)
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assert out is not None and out.exists()
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img = PILImage.open(str(out))
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assert img.size == (40, 40)
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class TestCoreTileWindow:
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"""Recadrage des TIF à bande de raccord sur la dalle nominale 1 km."""
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def _write_tif(self, tmp_path, name, bounds, size):
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transform = from_bounds(*bounds, size, size)
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tif_file = tmp_path / name
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with rasterio.open(
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tif_file, 'w', driver='GTiff', height=size, width=size,
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count=1, dtype='float32', crs='EPSG:2154', transform=transform,
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) as dst:
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dst.write(np.zeros((size, size), dtype=np.float32), 1)
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return tif_file
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def test_window_on_buffered_tif(self, tmp_path):
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from lidar_pipeline.rendering import _core_tile_window
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tif = self._write_tif(tmp_path, "LHD_FXX_0638_6628_PTS_LAMB93_IGN69_slope.tif",
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(637900, 6626900, 639100, 6628100), 1200)
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with rasterio.open(tif) as src:
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win = _core_tile_window(tif, src)
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assert win is not None
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assert (win.width, win.height) == (1000, 1000)
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assert (win.col_off, win.row_off) == (100, 100)
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wt = src.window_transform(win)
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assert abs(wt.c - 638000.0) < 1e-6
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assert abs(wt.f - 6628000.0) < 1e-6
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def test_no_window_without_overflow(self, tmp_path):
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"""TIF historique sur les bornes d'en-tête (~999,99 m) : rien à recadrer."""
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from lidar_pipeline.rendering import _core_tile_window
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tif = self._write_tif(tmp_path, "LHD_FXX_0638_6628_PTS_LAMB93_IGN69_slope.tif",
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(638000, 6627000, 638999.99, 6627999.99), 5000)
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with rasterio.open(tif) as src:
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assert _core_tile_window(tif, src) is None
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def test_no_window_for_non_lhd_name(self, tmp_path):
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from lidar_pipeline.rendering import _core_tile_window
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tif = self._write_tif(tmp_path, "test_vis.tif",
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(637900, 6626900, 639100, 6628100), 1200)
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with rasterio.open(tif) as src:
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assert _core_tile_window(tif, src) is None
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class TestDensiteSolCrop:
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"""Densité de points : WebP sans perte en niveaux de gris, sous-tuiles
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écrites depuis l'image d'origine (un seul encodage)."""
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def test_lossless_gray_levels_and_subtiles(self, tmp_path):
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from PIL import Image as PILImage
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from lidar_pipeline.rendering import tif_to_crop
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base = "LHD_FXX_0660_6701_PTS_LAMB93_IGN69"
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vis = tmp_path / "visualisations" / f"{base}_r0p2"
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vis.mkdir(parents=True)
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levels = np.tile(np.arange(16, dtype=np.float32).repeat(4), (64, 1)) # 64 × 64
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tif = TestTifToCrop._write_named_tif(vis, f"{base}_densite_sol.tif", levels)
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out = tif_to_crop(tif, vis, 0.2, output_format='avif', subtiles_dir=tmp_path)
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assert out is not None and out.suffix == ".webp"
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# WebP n'a pas de mode gris : relu en RGB à canaux égaux
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rgb = np.asarray(PILImage.open(str(out)).convert("RGB")).astype(int)
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assert (rgb[..., 0] == rgb[..., 1]).all() and (rgb[..., 1] == rgb[..., 2]).all()
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img = PILImage.fromarray(rgb[..., 0].astype(np.uint8))
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row = np.asarray(img)[0, ::4].astype(int)
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assert len(set(row.tolist())) == 16 and np.all(np.diff(row) > 0)
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# Sous-tuiles 2 × 2 (0,2 m/px) en WebP sans perte, identiques à la dalle
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q = tmp_path / "index_subtiles" / f"{base}_r0p2_densite_sol_0_1.webp"
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assert q.exists()
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assert np.array_equal(np.asarray(PILImage.open(str(q)).convert("L")), np.asarray(img)[:32, :32])
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assert (tmp_path / "index_subtiles" / f"{base}_r0p2_densite_sol_0_1_mid.webp").exists()
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def test_relief_subtiles_encoded_from_source(self, tmp_path):
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"""Relief : sous-tuiles AVIF écrites par tif_to_crop, plus récentes que la dalle."""
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import pytest
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from lidar_pipeline.rendering import tif_to_crop
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base = "LHD_FXX_0660_6701_PTS_LAMB93_IGN69"
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vis = tmp_path / "visualisations" / f"{base}_r0p2"
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vis.mkdir(parents=True)
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rgb = np.random.default_rng(1).integers(0, 255, (3, 64, 64)).astype('uint8')
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tif = vis / f"{base}_relief_oriente.tif"
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with rasterio.open(tif, 'w', driver='GTiff', height=64, width=64, count=3,
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dtype='uint8', crs='EPSG:2154',
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transform=from_bounds(660000, 6700000, 661000, 6701000, 64, 64)) as dst:
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dst.write(rgb)
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try:
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out = tif_to_crop(tif, vis, 0.2, output_format='avif', subtiles_dir=tmp_path)
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except Exception:
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pytest.skip("encodeur AVIF indisponible")
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sub = tmp_path / "index_subtiles"
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quads = sorted(sub.glob(f"{base}_r0p2_relief_oriente_?_?.avif"))
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assert len(quads) == 4
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assert all(q.stat().st_mtime_ns >= out.stat().st_mtime_ns for q in quads)
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