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d70361b | 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 | """Ablate report-75 improvements: ribbon strip + soft DL close on before6/after6."""
from __future__ import annotations
import sys
from pathlib import Path
import cv2
import numpy as np
from dotenv import load_dotenv
from PIL import Image
ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT))
load_dotenv(ROOT / ".env", override=True)
from app.detection_engine import ( # noqa: E402
_alignment_ncc,
_is_alignment_edge_ribbon,
ai_deep_learning_method,
analyze_change_regions,
get_detection_max_size,
normalize_radiometry,
preprocess_image,
recover_chromatic_roof_construction,
recover_dark_roof_construction,
strip_alignment_edge_ribbons_from_mask,
split_weakly_bridged_change_blobs,
strip_parking_cluster_from_mask,
strip_shadow_fragments_from_mask,
strip_shadow_only_from_mask,
strip_transient_from_mask,
strip_weak_seasonal_veg_from_mask,
visualize_changes,
)
def main():
before_p = ROOT / "data/library_sources/central_delhi/Images/before6.tif"
after_p = ROOT / "data/library_sources/central_delhi/Images/after6.tif"
b_pil = Image.open(before_p).convert("RGB")
a_pil = Image.open(after_p).convert("RGB")
if a_pil.size != b_pil.size:
a_pil = a_pil.resize(b_pil.size, Image.Resampling.LANCZOS)
ms = get_detection_max_size()
b0 = preprocess_image(b_pil, max_size=ms)
a0 = preprocess_image(a_pil, max_size=ms)
ncc = float(_alignment_ncc(b0, a0))
registration_ok = ncc >= 0.55
print("ncc", round(ncc, 4), "registration_ok", registration_ok, "ms", ms)
b_chr, a_chr = b0.copy(), a0.copy()
b, a = normalize_radiometry(b0, a0)
# Match UI job: sensitivity 0.45
change_mask, debug = ai_deep_learning_method(
b, a, sensitivity=0.45, registration_ok=registration_ok
)
print("dl", {k: debug.get(k) for k in (
"threshold_score", "model_changed_px", "combined_changed_px")})
def count(m):
return int(np.sum(m > 127))
steps = [("raw_clean+chroma", change_mask.copy())]
change_mask = strip_transient_from_mask(change_mask, b, a)
steps.append(("transient", change_mask.copy()))
change_mask = strip_shadow_only_from_mask(
change_mask, b, a, registration_ok=registration_ok)
steps.append(("shadow_only", change_mask.copy()))
change_mask = strip_shadow_fragments_from_mask(
change_mask, b, a, registration_ok=registration_ok)
steps.append(("fragments", change_mask.copy()))
change_mask = strip_alignment_edge_ribbons_from_mask(change_mask)
steps.append(("ribbons", change_mask.copy()))
change_mask = strip_parking_cluster_from_mask(change_mask, b, a)
change_mask = strip_weak_seasonal_veg_from_mask(change_mask, b, a)
change_mask = recover_chromatic_roof_construction(change_mask, b_chr, a_chr)
change_mask = recover_dark_roof_construction(
change_mask, b_chr, a_chr, registration_ok=registration_ok)
steps.append(("recover", change_mask.copy()))
change_mask = strip_shadow_fragments_from_mask(
change_mask, b, a, registration_ok=False)
change_mask = strip_alignment_edge_ribbons_from_mask(change_mask)
change_mask = split_weakly_bridged_change_blobs(change_mask)
steps.append(("final", change_mask.copy()))
prev = None
for name, m in steps:
px = count(m)
delta = None if prev is None else px - prev
print(f"{name:18s} px={px:7d} delta={delta} pct={100*px/m.size:.2f}%")
prev = px
regs = analyze_change_regions(
change_mask, a_chr, min_area=150, use_ensemble=False,
before_img=b_chr, registration_ok=False,
)
print("regions", len(regs), "top", [
(r["id"], r["area"], r["bbox"][2], r["bbox"][3],
round(max(r["bbox"][2], r["bbox"][3]) / max(min(r["bbox"][2], r["bbox"][3]), 1), 1),
_is_alignment_edge_ribbon(r["area"], r["bbox"][2], r["bbox"][3], r.get("fill_ratio")))
for r in regs[:15]
])
out = ROOT / "data/delhi_cd/friday_drone_report_fix/report75_improve"
out.mkdir(parents=True, exist_ok=True)
overlay = visualize_changes(
b_chr, a_chr, change_mask, regions=regs, shape_mode="polygon")
cv2.imwrite(str(out / "overlay_bgr.png"), cv2.cvtColor(overlay, cv2.COLOR_RGB2BGR))
print("wrote", out / "overlay_bgr.png")
if __name__ == "__main__":
main()
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