scripts/preprocess_step2_qc_plots.py

#!/usr/bin/env python3
"""
preprocess_step2_qc_plots.py
NEST3D QC plots for visual inspection of PLY files.

For each sample, generates a 3-by-2 grid:
  Row 1: Top view    (X,Y) - labels | RGB
  Row 2: Side view   (X,Z) - labels | RGB
  Row 3: Front view  (Y,Z) - labels | RGB
Stats box shows point counts per class.

Usage:
  python preprocess_step2_qc_plots.py --data-dir /path/to/reconstructions --version original
  python preprocess_step2_qc_plots.py --data-dir /path/to/reconstructions --version corrected
  python preprocess_step2_qc_plots.py --data-dir /path/to/reconstructions --version corrected --samples sample001 sample002

Output: <data-dir>/sampleXXX/sampleXXX_qc_{version}.png
"""

import argparse
import numpy as np
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from plyfile import PlyData
from pathlib import Path

MAX_PLOT_PTS = 150_000

CLASS_COLORS = {
    0:   np.array([0.2, 0.7, 0.2]),
    1:   np.array([0.6, 0.3, 0.1]),
    2:   np.array([0.9, 0.1, 0.1]),
    255: np.array([0.7, 0.7, 0.7]),
}
LABEL_NAMES = {0:"grass(0)", 1:"tree(1)", 2:"nest(2)", 255:"ignore(255)"}

def load_ply(path):
    ply = PlyData.read(str(path))
    v = ply["vertex"]
    xyz = np.stack([v["x"],v["y"],v["z"]], axis=1).astype(np.float32)
    rgb = np.stack([v["red"],v["green"],v["blue"]], axis=1).astype(np.float32)/255.0
    lbl = np.array(v["scalar_Classification"], dtype=np.int32)
    return xyz, rgb, lbl

def subsample(xyz, rgb, lbl, n=MAX_PLOT_PTS):
    if len(xyz) <= n:
        return xyz, rgb, lbl
    idx = np.random.default_rng(42).choice(len(xyz), n, replace=False)
    return xyz[idx], rgb[idx], lbl[idx]

def make_label_colors(lbl):
    c = np.zeros((len(lbl),3), np.float32)
    for k,col in CLASS_COLORS.items():
        c[lbl==k] = col
    return c

def scatter2d(ax, a, b, colors, s, title, xl, yl):
    ax.scatter(a, b, c=colors, s=s, linewidths=0, rasterized=True)
    ax.set_title(title, fontsize=8, pad=3)
    ax.set_xlabel(xl, fontsize=7)
    ax.set_ylabel(yl, fontsize=7)
    ax.tick_params(labelsize=6)
    ax.set_aspect("equal")

def make_plot(sample_id, ply_path, version):
    out_png = ply_path.parent / f"{sample_id}_qc_{version}.png"
    if out_png.exists():
        print(f"[SKIP] {sample_id} ({version})")
        return

    print(f"[PLOT] {sample_id} ({version}) ...", end=" ", flush=True)
    try:
        xyz, rgb, lbl = load_ply(ply_path)
    except Exception as e:
        print(f"ERROR: {e}")
        return

    total = len(lbl)
    xyz_s, rgb_s, lbl_s = subsample(xyz, rgb, lbl)
    lbl_colors = make_label_colors(lbl_s)
    dot = max(0.2, min(1.5, 80_000/len(xyz_s)))

    unique, counts = np.unique(lbl, return_counts=True)
    stats = dict(zip(unique.tolist(), counts.tolist()))
    nest_pct = 100*stats.get(2,0)/total if total>0 else 0

    stats_lines = [f"Total: {total:,}", ""]
    for k in [0,1,2,255]:
        c = stats.get(k,0)
        stats_lines.append(f"{LABEL_NAMES[k]}: {c:,} ({100*c/total:.1f}%)")
    stats_lines += ["", f"Nest ~{nest_pct:.2f}%"]
    stats_lines.append(f"X extent: {xyz[:,0].max()-xyz[:,0].min():.1f}m")
    stats_lines.append(f"Y extent: {xyz[:,1].max()-xyz[:,1].min():.1f}m")
    stats_lines.append(f"Z extent: {xyz[:,2].max()-xyz[:,2].min():.1f}m")

    fig, axes = plt.subplots(3, 2, figsize=(12, 15))
    fig.suptitle(f"{sample_id} [{version}]", fontsize=14, fontweight="bold", y=0.98)

    views = [
        (0,1,"X (m)","Y (m)","Top view"),
        (0,2,"X (m)","Z (m)","Side view"),
        (1,2,"Y (m)","Z (m)","Front view"),
    ]
    for row,(hi,vi,xl,yl,vname) in enumerate(views):
        scatter2d(axes[row,0], xyz_s[:,hi], xyz_s[:,vi], lbl_colors, dot, f"{vname} - labels", xl, yl)
        scatter2d(axes[row,1], xyz_s[:,hi], xyz_s[:,vi], rgb_s,      dot, f"{vname} - RGB",    xl, yl)

    axes[2,1].text(0.98, 0.02, "\n".join(stats_lines),
        transform=axes[2,1].transAxes, fontsize=6.5,
        va="bottom", ha="right", family="monospace",
        bbox=dict(boxstyle="round,pad=0.4", facecolor="white", alpha=0.75, edgecolor="gray"))

    patches = [mpatches.Patch(color=CLASS_COLORS[k], label=LABEL_NAMES[k]) for k in [0,1,2,255]]
    fig.legend(handles=patches, loc="lower center", ncol=4, fontsize=8, bbox_to_anchor=(0.5,0.01))

    plt.tight_layout(rect=[0,0.03,1,0.97])
    fig.savefig(str(out_png), dpi=100, bbox_inches="tight")
    plt.close(fig)
    print("saved")

def main():
    parser = argparse.ArgumentParser(description="NEST3D QC plots")
    parser.add_argument(
        "--data-dir", type=Path, default=Path("./reconstructions"),
        help="Path to the reconstructions/ folder containing sampleXXX subfolders (default: ./reconstructions)"
    )
    parser.add_argument("--version", choices=["original","corrected"], default="corrected")
    parser.add_argument("--samples", nargs="+", default=None)
    args = parser.parse_args()
    recon_dir = args.data_dir

    sample_dirs = sorted(recon_dir.glob("sample*"))
    if args.samples:
        sample_dirs = [d for d in sample_dirs if d.name in args.samples]

    print(f"Plotting {len(sample_dirs)} samples [{args.version}]\n")

    for sample_dir in sample_dirs:
        sample_id = sample_dir.name
        suffix = "_corrected" if args.version=="corrected" else ""
        ply_path = sample_dir / f"{sample_id}{suffix}.ply"
        if not ply_path.exists():
            print(f"[SKIP] {sample_id}: {ply_path.name} not found")
            continue
        make_plot(sample_id, ply_path, args.version)

    print("\nAll done!")

if __name__ == "__main__":
    main()