scripts/_diag_report77_fix.py

"""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()