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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 | """Balanced weak-align path for before6/after6 (post report 78 under-detect)."""
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,
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():
b_pil = Image.open(
ROOT / "data/library_sources/central_delhi/Images/before6.tif").convert("RGB")
a_pil = Image.open(
ROOT / "data/library_sources/central_delhi/Images/after6.tif").convert("RGB")
if a_pil.size != b_pil.size:
a_pil = a_pil.resize(b_pil.size, Image.Resampling.LANCZOS)
b0 = preprocess_image(b_pil, max_size=20000)
a0 = preprocess_image(a_pil, max_size=20000)
ncc = float(_alignment_ncc(b0, a0))
ok = ncc >= 0.55
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("thr", dbg.get("threshold_score"), "combined", dbg.get("combined_changed_px"))
def px(x):
return int(np.sum(x > 127))
m = strip_transient_from_mask(m, b, a, registration_ok=ok)
m = strip_shadow_only_from_mask(m, b, a, registration_ok=ok)
m = strip_shadow_fragments_from_mask(m, b, a, registration_ok=ok)
m = strip_alignment_edge_ribbons_from_mask(m)
m = strip_parking_cluster_from_mask(m, b, a)
m = strip_weak_seasonal_veg_from_mask(m, b, a)
print("pre_recover", px(m))
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), "top", [r["area"] for r in regs[:8]])
out = ROOT / "data/delhi_cd/friday_drone_report_fix/report78_rebalance"
out.mkdir(parents=True, exist_ok=True)
ov = visualize_changes(b_chr, a_chr, m, regions=regs, shape_mode="polygon")
cv2.imwrite(str(out / "overlay.png"), cv2.cvtColor(ov, cv2.COLOR_RGB2BGR))
print("target: between 78=55k/10 and 77=151k/24; prefer ~100-130k with majors")
if __name__ == "__main__":
main()
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