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import shutil
from pathlib import Path
os.environ.setdefault("KMP_DUPLICATE_LIB_OK", "TRUE")
import numpy as np
import pandas as pd
import pytest
import torch
from PIL import Image
from rtnls_fundusprep.cli import _run_preprocessing
from vascx_models.config import AppConfig
from vascx_models.disc_circles import generate_disc_circles
from vascx_models.inference import (
run_fovea_detection,
run_quality_estimation,
run_segmentation_disc,
run_segmentation_vessels_and_av,
)
from vascx_models.runtime import configure_runtime_environment
from vascx_models.utils import batch_create_overlays
from vascx_models.vessel_widths import (
measure_vessel_widths_between_disc_circle_pair,
resolve_vessel_width_circle_pair,
)
pytestmark = [pytest.mark.e2e, pytest.mark.slow]
REPO_ROOT = Path(__file__).resolve().parents[1]
SAMPLE_IMAGE = REPO_ROOT / "samples" / "fundus" / "original" / "DRIVE_22.png"
EXPECTED_VESSEL_WIDTH_COLUMNS = [
"image_id",
"inner_circle",
"outer_circle",
"inner_circle_radius_px",
"outer_circle_radius_px",
"connection_index",
"sample_index",
"x",
"y",
"width_px",
"x_start",
"y_start",
"x_end",
"y_end",
"vessel_type",
]
def _require_e2e_opt_in() -> None:
if os.environ.get("VASCX_RUN_E2E") != "1":
pytest.skip("Set VASCX_RUN_E2E=1 to run real-model end-to-end tests")
def _device_or_skip(device_name: str) -> torch.device:
if device_name == "cpu":
return torch.device("cpu")
if device_name == "cuda":
if not torch.cuda.is_available():
pytest.skip("CUDA is not available in this environment")
return torch.device("cuda:0")
if device_name == "mps":
if not torch.backends.mps.is_available():
pytest.skip("MPS is not available in this environment")
return torch.device("mps")
raise AssertionError(f"Unsupported device name: {device_name}")
def _prepare_single_image_input(tmp_path: Path) -> tuple[str, Path, Path]:
input_dir = tmp_path / "input"
input_dir.mkdir()
image_path = input_dir / SAMPLE_IMAGE.name
shutil.copy2(SAMPLE_IMAGE, image_path)
return SAMPLE_IMAGE.stem, image_path, input_dir
def _assert_nonempty_mask(path: Path) -> None:
assert path.exists()
assert np.any(np.array(Image.open(path)) > 0)
@pytest.mark.parametrize("device_name", ["cpu", "cuda", "mps"])
def test_single_image_pipeline_smoke(tmp_path: Path, device_name: str) -> None:
_require_e2e_opt_in()
configure_runtime_environment()
device = _device_or_skip(device_name)
app_config = AppConfig()
image_id, image_path, input_dir = _prepare_single_image_input(tmp_path)
output_dir = tmp_path / "output"
output_dir.mkdir()
preprocessed_rgb_dir = output_dir / "preprocessed_rgb"
av_dir = output_dir / "artery_vein"
vessels_dir = output_dir / "vessels"
disc_dir = output_dir / "disc"
disc_circles_dir = output_dir / "disc_circles"
overlay_dir = output_dir / "overlays"
preprocessed_rgb_dir.mkdir()
av_dir.mkdir()
vessels_dir.mkdir()
disc_dir.mkdir()
overlay_dir.mkdir()
bounds_path = output_dir / "bounds.csv"
quality_path = output_dir / "quality.csv"
fovea_path = output_dir / "fovea.csv"
disc_geometry_path = output_dir / "disc_geometry.csv"
vessel_widths_path = output_dir / "vessel_widths.csv"
_run_preprocessing(
files=[image_path],
ids=[image_id],
rgb_path=preprocessed_rgb_dir,
bounds_path=bounds_path,
n_jobs=1,
)
preprocessed_image_path = preprocessed_rgb_dir / f"{image_id}.png"
df_quality = run_quality_estimation([preprocessed_image_path], ids=[image_id], device=device)
df_quality.to_csv(quality_path)
run_segmentation_vessels_and_av(
rgb_paths=[preprocessed_image_path],
ids=[image_id],
av_path=av_dir,
vessels_path=vessels_dir,
artery_color=app_config.overlay.colors.artery,
vein_color=app_config.overlay.colors.vein,
vessel_color=app_config.overlay.colors.vessel,
device=device,
)
run_segmentation_disc(
rgb_paths=[preprocessed_image_path],
ids=[image_id],
output_path=disc_dir,
disc_color=app_config.overlay.colors.disc,
device=device,
)
df_disc_geometry = generate_disc_circles(
disc_dir=disc_dir,
circle_output_dir=disc_circles_dir,
circles=app_config.overlay.circles,
measurements_path=disc_geometry_path,
)
inner_circle, outer_circle = resolve_vessel_width_circle_pair(
app_config.overlay.circles,
inner_circle_name=app_config.vessel_widths.inner_circle,
outer_circle_name=app_config.vessel_widths.outer_circle,
)
df_vessel_widths = measure_vessel_widths_between_disc_circle_pair(
vessels_dir=vessels_dir,
av_dir=av_dir,
disc_geometry_path=disc_geometry_path,
inner_circle=inner_circle,
outer_circle=outer_circle,
output_path=vessel_widths_path,
samples_per_connection=app_config.vessel_widths.samples_per_connection,
)
df_fovea = run_fovea_detection([preprocessed_image_path], ids=[image_id], device=device)
df_fovea.to_csv(fovea_path)
batch_create_overlays(
rgb_dir=preprocessed_rgb_dir,
output_dir=overlay_dir,
av_dir=av_dir,
disc_dir=disc_dir,
vessels_dir=vessels_dir,
circle_dirs={
circle.name: disc_circles_dir / circle.name
for circle in app_config.overlay.circles
},
vessel_width_data=df_vessel_widths,
fovea_data={
index: (row["x_fovea"], row["y_fovea"])
for index, row in df_fovea.iterrows()
},
overlay_config=app_config.overlay,
)
assert df_quality.index.tolist() == [image_id]
assert df_quality.columns.tolist() == ["q1", "q2", "q3"]
assert np.isfinite(df_quality.to_numpy()).all()
assert quality_path.exists()
assert bounds_path.exists()
assert preprocessed_image_path.exists()
_assert_nonempty_mask(av_dir / f"{image_id}.png")
_assert_nonempty_mask(vessels_dir / f"{image_id}.png")
_assert_nonempty_mask(disc_dir / f"{image_id}.png")
assert df_disc_geometry.index.tolist() == [image_id]
assert float(df_disc_geometry.loc[image_id, "disc_radius_px"]) > 0.0
assert disc_geometry_path.exists()
for circle in app_config.overlay.circles:
_assert_nonempty_mask(disc_circles_dir / circle.name / f"{image_id}.png")
assert vessel_widths_path.exists()
df_vessel_widths_disk = pd.read_csv(vessel_widths_path)
assert df_vessel_widths_disk.columns.tolist() == EXPECTED_VESSEL_WIDTH_COLUMNS
assert df_vessel_widths.columns.tolist() == EXPECTED_VESSEL_WIDTH_COLUMNS
if not df_vessel_widths.empty:
assert df_vessel_widths["image_id"].eq(image_id).all()
assert df_vessel_widths["vessel_type"].isin(["artery", "vein"]).all()
assert (df_vessel_widths["width_px"] > 0).all()
assert df_fovea.index.tolist() == [image_id]
assert df_fovea.columns.tolist() == ["x_fovea", "y_fovea"]
assert np.isfinite(df_fovea.to_numpy()).all()
assert fovea_path.exists()
overlay_path = overlay_dir / f"{image_id}.png"
assert overlay_path.exists()
assert Image.open(overlay_path).size == Image.open(preprocessed_image_path).size
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