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231135a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 | from __future__ import annotations
import csv
from pathlib import Path
import cv2
import numpy as np
import yaml
from bat_tracker.pipeline import run_pipeline
def _write_video(path: Path, frames: list[np.ndarray], fps: int = 10) -> None:
height, width = frames[0].shape
writer = cv2.VideoWriter(
str(path),
cv2.VideoWriter_fourcc(*"mp4v"),
float(fps),
(width, height),
)
assert writer.isOpened(), f"could not open writer for {path}"
for frame in frames:
writer.write(cv2.cvtColor(frame, cv2.COLOR_GRAY2BGR))
writer.release()
def _read_tracks(path: Path) -> list[dict[str, str]]:
with path.open(newline="", encoding="utf-8") as handle:
return list(csv.DictReader(handle))
def _normalize_tracks(rows: list[dict[str, str]]) -> list[tuple[str, ...]]:
keys = [
"track_id",
"frame",
"time_sec",
"x",
"y",
"vx",
"vy",
"bbox_x1",
"bbox_y1",
"bbox_x2",
"bbox_y2",
"area",
]
return [tuple(row[key] for key in keys) for row in rows]
def _filter_rows_to_frame_range(
rows: list[dict[str, str]],
*,
start_frame: int,
end_frame: int,
) -> list[dict[str, str]]:
return [row for row in rows if start_frame <= int(row["frame"]) <= end_frame]
def _base_config() -> dict:
return {
"background": {
"sample_frames": 20,
"uniform_sampling": True,
},
"detection": {
"blur_kernel": 1,
"threshold_mode": "fixed",
"diff_threshold": 10,
"morph_open": 1,
"morph_close": 1,
"min_area": 20,
"max_area": 5000,
"max_global_intensity_shift": -1.0,
"max_foreground_ratio": -1.0,
"max_detections_per_frame": 0,
"temporal_burst_min_detections": 0,
"temporal_burst_window_frames": 0,
"temporal_burst_trigger_frames": 0,
"temporal_burst_cooldown_frames": 0,
},
"tracking": {
"max_distance": 30,
"max_missed": 2,
"min_track_length": 1,
"min_track_displacement": 0.0,
"min_track_path_length": 0.0,
"min_track_straightness": 0.0,
"min_track_duration_sec": 0.0,
"auto_merge_suggested": False,
"require_start_or_end_in_valid_region": False,
"valid_region_gate_dilate_px": 0,
},
"valid_region": {
"enabled": False,
},
"output": {
"progress_enabled": False,
"export_track_clips": False,
},
}
def _make_prefix_clip_case(tmp_path: Path) -> tuple[Path, Path]:
full_frames: list[np.ndarray] = []
clip_frames: list[np.ndarray] = []
for idx in range(40):
frame = np.zeros((48, 64), dtype=np.uint8)
if idx < 12:
x0 = 18 + idx
cv2.rectangle(frame, (x0, 18), (x0 + 18, 32), 220, -1)
full_frames.append(frame)
if idx < 12:
clip_frames.append(frame.copy())
full_path = tmp_path / "full.mp4"
clip_path = tmp_path / "clip.mp4"
_write_video(full_path, full_frames)
_write_video(clip_path, clip_frames)
return full_path, clip_path
def test_clip_can_reuse_full_background_for_prefix_reproducibility(tmp_path: Path) -> None:
full_path, clip_path = _make_prefix_clip_case(tmp_path)
cfg = _base_config()
cfg_path = tmp_path / "cfg.yaml"
cfg_path.write_text(yaml.safe_dump(cfg), encoding="utf-8")
out_full = tmp_path / "out_full"
out_clip = tmp_path / "out_clip"
out_clip_reused = tmp_path / "out_clip_reused"
run_pipeline(str(full_path), str(out_full), str(cfg_path))
run_pipeline(str(clip_path), str(out_clip), str(cfg_path))
cfg_reused = _base_config()
cfg_reused["background"]["input_image"] = str(out_full / "background.png")
cfg_reused_path = tmp_path / "cfg_reused.yaml"
cfg_reused_path.write_text(yaml.safe_dump(cfg_reused), encoding="utf-8")
run_pipeline(str(clip_path), str(out_clip_reused), str(cfg_reused_path))
full_tracks = _read_tracks(out_full / "tracks.csv")
clip_tracks = _read_tracks(out_clip / "tracks.csv")
reused_tracks = _read_tracks(out_clip_reused / "tracks.csv")
assert _normalize_tracks(clip_tracks) != _normalize_tracks(full_tracks)
assert _normalize_tracks(reused_tracks) == _normalize_tracks(full_tracks)
def test_precomputed_valid_region_mask_is_loaded_verbatim(tmp_path: Path) -> None:
_, clip_path = _make_prefix_clip_case(tmp_path)
mask = np.zeros((48, 64), dtype=np.uint8)
mask[:, 20:44] = 255
mask_path = tmp_path / "mask.png"
cv2.imwrite(str(mask_path), mask)
cfg = _base_config()
cfg["valid_region"] = {
"enabled": True,
"input_mask": str(mask_path),
"apply_to_detection": False,
}
cfg_path = tmp_path / "cfg_mask.yaml"
cfg_path.write_text(yaml.safe_dump(cfg), encoding="utf-8")
out_dir = tmp_path / "out_mask"
meta = run_pipeline(str(clip_path), str(out_dir), str(cfg_path))
exported_mask = cv2.imread(str(out_dir / "valid_region" / "mask.png"), cv2.IMREAD_GRAYSCALE)
assert exported_mask is not None
assert np.array_equal(exported_mask, mask)
assert meta["valid_region"]["method"] == "input_mask"
assert meta["valid_region"]["input_mask"] == str(mask_path.resolve())
def test_prefix_context_window_makes_full_and_clip_match(tmp_path: Path) -> None:
full_path, clip_path = _make_prefix_clip_case(tmp_path)
cfg = _base_config()
cfg["background"]["context_start_sec"] = 0.0
cfg["background"]["context_duration_sec"] = 1.2
cfg_path = tmp_path / "cfg_prefix.yaml"
cfg_path.write_text(yaml.safe_dump(cfg), encoding="utf-8")
out_full = tmp_path / "out_full_prefix"
out_clip = tmp_path / "out_clip_prefix"
run_pipeline(str(full_path), str(out_full), str(cfg_path))
run_pipeline(str(clip_path), str(out_clip), str(cfg_path))
full_tracks = _read_tracks(out_full / "tracks.csv")
clip_tracks = _read_tracks(out_clip / "tracks.csv")
full_prefix_tracks = _filter_rows_to_frame_range(full_tracks, start_frame=0, end_frame=11)
assert _normalize_tracks(full_prefix_tracks) == _normalize_tracks(clip_tracks)
def test_valid_region_can_use_dedicated_context_without_changing_detection_background(tmp_path: Path) -> None:
full_path, _ = _make_prefix_clip_case(tmp_path)
cfg = _base_config()
cfg["background"]["context_start_sec"] = 0.0
cfg["background"]["context_duration_sec"] = -1.0
cfg["valid_region"] = {
"enabled": True,
"method": "horizontal_illumination_profile",
"apply_to_detection": False,
"context_start_sec": 0.0,
"context_duration_sec": 1.2,
"blur_kernel_size": 31,
"profile_smooth_window": 9,
"threshold_ratio": 0.4,
"safety_margin": 0,
"min_region_width_ratio": 0.2,
}
cfg_path = tmp_path / "cfg_vr_context.yaml"
cfg_path.write_text(yaml.safe_dump(cfg), encoding="utf-8")
out_dir = tmp_path / "out_vr_context"
meta = run_pipeline(str(full_path), str(out_dir), str(cfg_path))
assert meta["background"]["context_duration_sec"] == -1.0
assert meta["valid_region"]["enabled"] is True
mask = cv2.imread(str(out_dir / "valid_region" / "mask.png"), cv2.IMREAD_GRAYSCALE)
assert mask is not None
assert int(np.count_nonzero(mask)) > 0
gate_overlay = out_dir / "valid_region" / "gate_overlay.png"
assert gate_overlay.exists()
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