Spaces:
Running
Running
File size: 3,711 Bytes
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 | """Live-like ablation matching job 68 / report 77 path for 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,
ai_deep_learning_method,
analyze_change_regions,
get_detection_max_size,
normalize_radiometry,
preprocess_image,
recover_chromatic_roof_construction,
recover_dark_roof_construction,
split_weakly_bridged_change_blobs,
strip_alignment_edge_ribbons_from_mask,
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)
# Match job_runner: large max_size keeps native 1429x1180
ms = 20000
b0 = preprocess_image(b_pil, max_size=ms)
a0 = preprocess_image(a_pil, max_size=ms)
ncc = float(_alignment_ncc(b0, a0))
ok = ncc >= 0.55
print("shape", b0.shape, "ncc", round(ncc, 4), "ok", ok)
b_chr, a_chr = b0.copy(), a0.copy()
b, a = normalize_radiometry(b0, a0)
m, dbg = ai_deep_learning_method(b, a, sensitivity=0.45, registration_ok=ok)
print("dl thr", dbg.get("threshold_score"), "model", dbg.get("model_changed_px"),
"combined", dbg.get("combined_changed_px"))
def px(x):
return int(np.sum(x > 127))
print("after_dl", px(m))
m = strip_transient_from_mask(m, b, a)
print("transient", px(m))
m = strip_shadow_only_from_mask(m, b, a, registration_ok=ok)
print("shadow_only", px(m))
m = strip_shadow_fragments_from_mask(m, b, a, registration_ok=ok)
print("fragments", px(m))
m = strip_alignment_edge_ribbons_from_mask(m)
print("ribbons", px(m))
m = strip_parking_cluster_from_mask(m, b, a)
m = strip_weak_seasonal_veg_from_mask(m, b, a)
m = recover_chromatic_roof_construction(m, b_chr, a_chr, registration_ok=ok)
print("chroma", px(m))
m = recover_dark_roof_construction(m, b_chr, a_chr, registration_ok=ok)
print("dark", px(m))
m = strip_shadow_fragments_from_mask(m, b, a, registration_ok=False)
m = strip_alignment_edge_ribbons_from_mask(m)
m = split_weakly_bridged_change_blobs(m)
print("final", px(m), f"{100 * px(m) / m.size:.2f}%")
regs = analyze_change_regions(
m, a_chr, min_area=150, use_ensemble=False,
before_img=b_chr, registration_ok=False,
)
print("regions", len(regs))
for r in regs[:12]:
w, h = r["bbox"][2], r["bbox"][3]
print(
f" #{r['id']} area={r['area']} {w}x{h} "
f"fill={r.get('fill_ratio')} verts={len(r.get('polygon') or [])} "
f"{r.get('object_type')}"
)
out = ROOT / "data/delhi_cd/friday_drone_report_fix/report77_fix"
out.mkdir(parents=True, exist_ok=True)
overlay = visualize_changes(b_chr, a_chr, m, regions=regs, shape_mode="polygon")
cv2.imwrite(str(out / "overlay.png"), cv2.cvtColor(overlay, cv2.COLOR_RGB2BGR))
print("wrote", out / "overlay.png")
print("COMPARE report75=171544/29 report77=150915/24 target<<both")
if __name__ == "__main__":
main()
|