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Port the SAR3D background gate, CLEAN and object boxes from Multi_depthnorm_clean

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- BG_GATE_ENABLE: frame peak over its median background (sar3d_background); the
  GUI scales the frame with bg_gate_gain and its cloud threshold.
- SAR_CLEAN_ENABLE: CLEAN of a one-frame aperture on a 4x10x4 cm search grid over
  the whole scene; objects in their own scale (sar3d_objects).
- SAR_MARK_ENABLE: boxes by class around object groups (sar3d_marks), with the
  legend under the views.
- SAR_VOXEL_M as three steps X/Y/Z with the notebook defaults 0.025/0.02/0.02.
- GPU backend: image/download/load/project API, per-plane block reduction for the
  maximum, depth gate without FMA; sar3d host code built with -ffp-contract=off.
- References regenerated from notebook cells 1 and 5, with new CLEAN scenes and a
  synthetic box-rule case; docs updated.
This commit is contained in:
Ayzen
2026-10-16 12:08:57 +03:00
parent 9a9b8ce5c7
commit b88d704126
26 changed files with 1895 additions and 407 deletions
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data_acq_and_processing/tests/generate_backprojection_reference.py
+131 -25
View File
@@ -1,6 +1,6 @@
"""Generate SAR3D/GPR references from notebook cells 1/14 and Horns coherent BP.
"""Generate SAR3D/GPR references from notebook cells 1/5 and Horns coherent BP.
Requires the original, untracked Multi_antenna_demonstrate_depthnorm.ipynb; a missing notebook
Requires the original, untracked Multi_depthnorm_clean.ipynb; a missing notebook
is an error. NumPy/SciPy execute its function/class ASTs unchanged. Only parameter
values are replaced; plotting imports and the interactive top-level runs are
excluded. No production SAR implementation or hand-transcribed oracle is used.
@@ -16,6 +16,7 @@ import hashlib
import json
from pathlib import Path
import tempfile
from types import SimpleNamespace
import numpy as np
import scipy
@@ -24,7 +25,7 @@ ROOT = Path(__file__).resolve().parents[2]
REFERENCE = Path(__file__).with_name("sar3d_reference.npz")
def reference_functions(source_path: Path, parameters: dict, cells: tuple[int, ...] = (1, 14)) -> tuple[dict, dict]:
def reference_functions(source_path: Path, parameters: dict, cells: tuple[int, ...] = (1, 5)) -> tuple[dict, dict]:
contents = source_path.read_text(encoding="utf-8")
sources = [(str(i), "".join(json.loads(contents)["cells"][i]["source"])) for i in cells] if cells else [("script", contents)]
namespace = {"__name__": __name__}
@@ -56,8 +57,9 @@ def scene(f_start=1e9, f_stop=4e9, points=201) -> dict:
tx=[[0, -.4, 0, 0], [1, .45, .2, .05]],
rx=[[0, -.2, .1, 0], [1, .3, .35, .02]], f_start=f_start, f_stop=f_stop,
min_depth=1., max_depth=3., speed=.4, pitch=13., ground=.3,
voxel=.4, radius=2, power=1.5, compensation=True, normalize=True,
voxel=[.4, .4, .4], radius=2, power=1.5, compensation=True, normalize=True,
equalize=True, subtract_mean=False, mean_count=1, motion="int_minus",
bg_gate=True, clean=False,
)
# Match the float32 settings/geometry accepted by the native fixture.
for key in ("tx", "rx"):
@@ -79,6 +81,45 @@ def scene(f_start=1e9, f_stop=4e9, points=201) -> dict:
return request
# Point reflectors (X, height Y, Z, amplitude) at heights of 0, 0.6 and 0.25 m.
REFLECTORS = [(.3, 0., 2.2, 1.), (-.8, .6, 2.6, .5), (1.3, .25, 1.9, .7)]
def point_scene(targets: list[tuple[float, float, float, float]], noise: float, seed: int, **settings) -> dict:
"""Settings of one static frame of point reflectors (X, height Y, Z, amplitude) in noise, for CLEAN.
Three Tx and three Rx at different heights resolve Y in a single frame. The voxel steps
keep the notebook's proportions, 2.5 : 2 : 2.
"""
request = dict(
tx=[[0, -.6, 0., 0.], [1, 0., .35, .02], [2, .6, .1, 0.]],
rx=[[0, -.3, .2, 0.], [1, .3, .45, .03], [2, .9, 0., .01]],
min_depth=1., max_depth=3.5, speed=0., pitch=13., ground=1., voxel=[.0625, .05, .05], radius=0,
clean=True, previous=[],
)
request.update(settings)
for key in ("tx", "rx"):
request[key] = np.asarray(request[key], dtype=np.float32).astype(float).tolist()
frequency = np.linspace(1e9, 4e9, 201, dtype=np.float32)
cos_p, sin_p = np.cos(np.radians(request["pitch"])), np.sin(np.radians(request["pitch"]))
random = np.random.default_rng(seed)
traces = []
for tx in request["tx"]:
for rx in request["rx"]:
samples = noise * (random.standard_normal(frequency.size) + 1j * random.standard_normal(frequency.size))
for x, height, z, amplitude in targets:
# Scene to radar axes, as sar_scene_to_radar_yz.
y0 = height - request["ground"]
point = np.array([x, y0 * cos_p + z * sin_p, -y0 * sin_p + z * cos_p])
path = np.linalg.norm(point - tx[1:]) + np.linalg.norm(point - rx[1:])
samples += amplitude * np.exp(-2j * np.pi * frequency.astype(float) * path / 3e8)
samples = samples.astype(np.complex64)
traces.append(dict(input=int(rx[0]), output=int(tx[0]), start=10_000_000_000, end=10_165_000_000,
frequency=frequency.tolist(), s21=np.column_stack((samples.real, samples.imag)).tolist()))
request["latest"] = dict(id=1, timestamp=10_165_000_000, traces=traces)
return request
def write_dataset(directory: Path, request: dict) -> list[Path]:
"""Supply the notebook's original config_profile/meta/NPY input contract."""
profile = {"gpr": {}, "run": {"combos": []}}
@@ -115,6 +156,8 @@ def reference(notebook: Path, request: dict, directory: Path) -> tuple[dict, dic
snapshots = write_dataset(directory, request)
center = snapshots[-1]
parameters = dict(
SAR_Y_RANGE_M=(request.get("y_min", -.5), request.get("y_max", 2.)),
BG_GATE_ENABLE=request["bg_gate"], SAR_CLEAN_ENABLE=request["clean"], SAR_MARK_ENABLE=True,
INPUT_IDX=[int(row[0]) for row in request["rx"]],
OUTPUT_IDX=[int(row[0]) for row in request["tx"]],
F_START=request["f_start"], F_STOP=request["f_stop"],
@@ -122,7 +165,7 @@ def reference(notebook: Path, request: dict, directory: Path) -> tuple[dict, dic
PAIR_NORM_DEPTH_MIN=request["min_depth"], PAIR_NORM_DEPTH_MAX=request["max_depth"],
SPEED_M_S=request["speed"], LOOK_ANGLE_DEG=request["pitch"],
BP_GROUND_DEPTH_M=request["ground"], COMP_RANGE_POWER=request["power"],
SAR_VOXEL_M=(request["voxel"],) * 3, SAR_FRAME_RADIUS=request["radius"],
SAR_VOXEL_M=tuple(request["voxel"]), SAR_FRAME_RADIUS=request["radius"],
SAR_USE_COMPENSATION=request["compensation"], SAR_NORMALIZE_EACH_FRAME=request["normalize"],
PAIR_DEPTH_EQUALIZE=request["equalize"],
SAR_SCAN_ROOT=directory, DATA_PATH=center, BG_PATH=directory, v=3e8,
@@ -150,31 +193,76 @@ def reference(notebook: Path, request: dict, directory: Path) -> tuple[dict, dic
motion_phase_sign=ns["MOTION_PHASE_SIGN"],
)
timestamps, _ = ns["compute_pair_timestamps_for_snapshot"](center, config)
grid = ns["sar_build_full_region_grid"](config, y_range_m=(request.get("y_min", -.5), request.get("y_max", 2.)))
for axis in ("x", "z"):
size = request.get(f"{axis}_size", 0.)
grid = ns["sar_build_full_region_grid"](config)
for index, axis in ((0, "x"), (2, "z")):
size, step = request.get(f"{axis}_size", 0.), request["voxel"][index]
if size:
center_axis = .5 * (grid[axis][0] + grid[axis][-1])
grid[axis] = np.linspace(center_axis - size/2, center_axis + size/2, max(round(size/request["voxel"])+1, 2))
grid[axis] = np.linspace(center_axis - size/2, center_axis + size/2, max(round(size/step)+1, 2))
grid["XX"], grid["ZZ"] = np.meshgrid(grid["x"], grid["z"])
ns["x_min"], ns["x_max"] = grid["x"][0], grid["x"][-1]
result = ns["sar_compute_volume"](center, None, config, timestamps, grid=grid, verbose=False)
volume, grid = result["volume"], result["grid"]
iy, iz, ix = ns["sar_global_max_index"](volume, grid)
from scipy.signal import peak_widths
widths = []
for axis, peak_index, profile in (("x", ix, volume[iy, iz, :]), ("y", iy, volume[:, iz, ix]), ("z", iz, volume[iy, :, ix])):
profile = np.asarray(profile, dtype=np.float64)
width, *_ = peak_widths(profile, [peak_index], prominence_data=(np.array([profile[peak_index]]),
np.array([0], dtype=np.intp), np.array([len(profile)-1], dtype=np.intp)))
widths.append(max(float(width[0]), 1.) * (grid[axis][1]-grid[axis][0]))
# What the processor publishes: the frame volume, or the CLEAN display when CLEAN found
# objects, and the peak it marks; the background gain stays to the GUI.
ns["sar_prepare_display"](result, None, timestamps)
objects = result["clean_objects"] or []
volume, grid = result["display_volume"] if objects else result["volume"], result["grid"]
iy, iz, ix = result["display_max_idx"]
expected = {axis: grid[axis] for axis in "xyz"}
expected["target_width"] = np.asarray(widths)
expected.update(front=volume.max(axis=1), side=volume.max(axis=2), top=volume.max(axis=0),
peak=np.array([result["level"], grid["x"][ix], grid["y"][iy], grid["z"][iz]]))
if not objects:
# Width of the response through the marked maximum; objects are marked instead.
from scipy.signal import peak_widths
widths = []
for axis, peak_index, profile in (("x", ix, volume[iy, iz, :]), ("y", iy, volume[:, iz, ix]),
("z", iz, volume[iy, :, ix])):
profile = np.asarray(profile, dtype=np.float64)
width, *_ = peak_widths(profile, [peak_index], prominence_data=(np.array([profile[peak_index]]),
np.array([0], dtype=np.intp), np.array([len(profile)-1], dtype=np.intp)))
widths.append(max(float(width[0]), 1.) * (grid[axis][1]-grid[axis][0]))
expected["target_width"] = np.asarray(widths)
if request["bg_gate"]:
expected["background"] = np.array([result["bg_ratio"], ns["BG_GATE_3D_SHOW"], ns["BG_GATE_3D_FULL"]])
if result["clean_info"] is not None:
expected["clean"] = np.array([result["clean_info"]["steps"], result["clean_info"]["residual_ratio"]])
if objects:
kinds = {"low": 0, "ground": 1, "up": 2, "edge": 3}
expected["objects"] = np.array([[item["x"], item["y"], item["z"], item["level_bg"]] for item in objects])
expected["marks"] = np.array([[kinds[group["kind"]], *group["box"]] for group in result["mark_groups"]])
return expected, hashes
def marks_reference(notebook: Path) -> tuple[dict, np.ndarray]:
"""sar_mark_groups on synthetic CLEAN objects, each of which tests one rule of the boxes.
Returns the fixture's "marks" request and the expected rows [class, X0, X1, Y0, Y1, Z0, Z1].
"""
scene = dict(x=[-2.6, 2.9], y=[-.5, 2.], pitch=13., ground=1., min_depth=1., max_depth=3.5)
objects = [ # [x, y, z, y_low, y_high, level], strongest first
[0., 0., 2., -.1, .3, 60.], # low
[1., .05, 2.2, -.1, .6, 55.], # centre low, top above SAR_MARK_LOW_TOP_M: ground
[-1., .3, 2.4, .1, .5, 50.], # ground
[-.2, .45, 3., .25, .65, 45.], # up while its weak member (last) stays out of the centre
[-1.6, 0., 1.3, -.1, .3, 40.], # a chain of three: the ends are 1.4 m apart, still one box
[-.9, .1, 1.3, -.1, .35, 35.],
[-.2, .05, 1.3, -.15, .3, 30.],
[-2.5, .2, 2., 0., .4, 28.], # near the X minimum: edge
[2.75, .2, 2., 0., .4, 26.], # near the X maximum: edge
[2.2, .1, 3.2, -.1, .3, 24.], # near MAX_DEPTH: edge
[2.2, 0., .85, -.2, .2, 22.], # near MIN_DEPTH: edge
[1.2, 1.7, 2.8, 1.5, 1.9, 20.], # near the top: edge
[.2, -.4, 3.1, -.5, -.25, 15.], # weak member of the "up" box, below 40 % of its top
]
ns, _ = reference_functions(notebook, dict(BP_GROUND_DEPTH_M=scene["ground"], MIN_DEPTH=scene["min_depth"],
MAX_DEPTH=scene["max_depth"]))
groups = ns["sar_mark_groups"](
[dict(zip(("x", "y", "z", "y_lo", "y_hi", "level_bg"), row)) for row in objects],
{axis: np.asarray(scene[axis]) for axis in "xy"}, SimpleNamespace(look_angle_deg=scene["pitch"]))
kinds = {"low": 0, "ground": 1, "up": 2, "edge": 3}
return dict(mode="marks", objects=objects, **scene), np.array([[kinds[g["kind"]], *g["box"]] for g in groups])
def previous_mean(request: dict) -> dict:
samples = {}
if request["subtract_mean"] and request["mean_count"]:
@@ -258,17 +346,32 @@ def generate(notebook: Path, output: Path) -> None:
cases = [
dict(compensation=True, normalize=True),
dict(compensation=True, normalize=False, equalize=False, speed=-.2, pitch=-7., ground=.5),
dict(compensation=False, normalize=True, f_start=1.3e9, f_stop=3.7e9),
# Three different voxel steps: each axis keeps its own.
dict(compensation=False, normalize=True, f_start=1.3e9, f_stop=3.7e9, voxel=[.45, .4, .35]),
dict(compensation=False, normalize=False, speed=0., pitch=0., radius=0),
dict(compensation=True, normalize=True, subtract_mean=True, mean_count=1),
dict(compensation=True, normalize=True, x_size=3., z_size=2., y_min=-.2, y_max=1.4),
# CLEAN stops below its thresholds; the background gate hides the residual.
point_scene(REFLECTORS, noise=.02, seed=1),
# CLEAN stops at SAR_CLEAN_MAX_STEPS; without the gate the residual stays visible.
point_scene(REFLECTORS, noise=.01, seed=3, bg_gate=False),
# The stop at the background of each range (SAR_CLEAN_STOP_RANGE_BG) adds two steps to
# those of the stop at the background of the whole volume.
point_scene([(.2, 0., 2., 1.), (-.5, .1, 4.4, .08)], noise=.01, seed=4, max_depth=5.),
# A narrowed frame: CLEAN still searches the whole scene, so the reflector at X = -0.8 m
# just outside the frame is subtracted instead of its sidelobe becoming an object.
point_scene(REFLECTORS, noise=.02, seed=19, x_size=1.6, z_size=.8),
]
arrays, configurations = {}, []
for index, settings in enumerate(cases):
request = scene()
request.update(settings)
for key in ("min_depth", "max_depth", "speed", "pitch", "ground", "voxel", "power"):
request[key] = float(np.float32(request[key]))
# Match the float32 live settings of the native fixture.
for key in ("min_depth", "max_depth", "speed", "pitch", "ground", "power",
"x_size", "z_size", "y_min", "y_max"):
if key in request:
request[key] = float(np.float32(request[key]))
request["voxel"] = [float(np.float32(step)) for step in request["voxel"]]
if index == 1:
# A separate, unequal capture window per output instead of interleaving.
for member in [*request["previous"], request["latest"]]:
@@ -288,10 +391,13 @@ def generate(notebook: Path, output: Path) -> None:
))
arrays.update({f"case_{index}_{key}": value for key, value in expected.items()})
arrays["cases"] = np.array(json.dumps(configurations, separators=(",", ":")))
marks_request, arrays["marks"] = marks_reference(notebook)
arrays["marks_request"] = np.array(json.dumps(marks_request))
arrays["provenance"] = np.array(json.dumps(dict(
notebook=notebook.name, cell_source_sha256=hashes, cells=[1, 14], cases=len(cases),
notebook=notebook.name, cell_source_sha256=hashes, cells=[1, 5], cases=len(cases),
numpy=np.__version__, scipy=scipy.__version__,
method="Unchanged notebook AST functions; per-frame NumPy previous-mean subtraction; max projections of sar_compute_volume output",
method="Unchanged notebook AST functions; per-frame NumPy previous-mean subtraction; "
"max projections of the sar_prepare_display volume, its background, CLEAN objects and marks",
), sort_keys=True))
output.parent.mkdir(parents=True, exist_ok=True)
np.savez_compressed(output, **arrays)
@@ -300,7 +406,7 @@ def generate(notebook: Path, output: Path) -> None:
if __name__ == "__main__":
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--notebook", type=Path, default=ROOT / "Multi_antenna_demonstrate_depthnorm.ipynb")
parser.add_argument("--notebook", type=Path, default=ROOT / "Multi_depthnorm_clean.ipynb")
parser.add_argument("--output", type=Path, default=REFERENCE)
parser.add_argument("--gpr-script", type=Path, default=ROOT / "Horns_motion_4x4_ground.py")
parser.add_argument("--gpr-output", type=Path, default=REFERENCE.with_name("gpr_reference.npz"))