# Stage 35 (read-only): does the approved concept turnaround describe OUR body?
#
#   blender --background --python 35_silhouette.py -- <concept_front.png> <our_front.png>
#
# This decides how the turnaround can be used. If the silhouettes match, the concept renders could
# be projected onto the mesh as a texture source. If they do not, they are a style and tone
# reference only — and quietly projecting them would smear one body's paint onto another's shape.
# Widths are normalised by figure height, so framing and resolution cancel.
import bpy, sys, os
import numpy as np

argv = sys.argv[sys.argv.index("--") + 1:]


def load(p):
    im = bpy.data.images.load(p)
    w, h = im.size
    b = np.empty(w * h * 4, dtype=np.float32)
    im.pixels.foreach_get(b)
    a = b.reshape(h, w, 4)[:, :, :3].copy()
    bpy.data.images.remove(im)
    return a[::-1]


def profile(p):
    A = load(p)
    sat = A.max(axis=2) - A.min(axis=2)
    m = (sat > 0.06) & (A.max(axis=2) > 0.15)
    ys, xs = np.nonzero(m)
    y0, y1 = ys.min(), ys.max()
    Hf = float(y1 - y0)
    cx = xs.mean()
    out = []
    for t in np.arange(0.02, 1.0, 0.02):
        r = int(round(y0 + t * Hf))
        row = np.nonzero(m[r])[0]
        if len(row) < 2:
            out.append((t, np.nan, np.nan))
            continue
        # full extent (includes arms where they are out), and the CENTRAL run (the torso/leg)
        full = (row.max() - row.min()) / Hf
        # central run: walk out from the pixel nearest the body's x centre
        k = row[np.argmin(np.abs(row - cx))]
        lo = k
        while lo - 1 in set(row.tolist()) if False else (lo - 1 >= 0 and m[r, lo - 1]):
            lo -= 1
        hi = k
        while hi + 1 < m.shape[1] and m[r, hi + 1]:
            hi += 1
        out.append((t, full, (hi - lo) / Hf))
    return np.array(out), Hf


A, ha = profile(argv[0])
B, hb = profile(argv[1])
print(f"concept figure height {ha:.0f} px   ours {hb:.0f} px")
print("\n  t     concept_full  ours_full   concept_core  ours_core   core_diff")
diffs = []
for (t, fa, ca), (_, fb, cb) in zip(A, B):
    if np.isnan(ca) or np.isnan(cb):
        continue
    d = cb - ca
    # the central run is only meaningful where it is a single body part:
    # below the arms (t>0.30) and above the ankles
    flag = ""
    if 0.30 < t < 0.95:
        diffs.append(d)
        if abs(d) > 0.02:
            flag = "  <-- differs"
    print(f" {t:.2f}   {fa:9.3f}   {fb:9.3f}   {ca:10.3f}  {cb:9.3f}   {d:+8.3f}{flag}")
if diffs:
    diffs = np.array(diffs)
    print(f"\nCORE WIDTH (torso/legs, t 0.30-0.95), ours minus concept, as fraction of height:")
    print(f"  mean {diffs.mean():+.4f}   mean|d| {np.abs(diffs).mean():.4f}   "
          f"max|d| {np.abs(diffs).max():.4f}")
    print(f"  bands differing by >2% of height: {int((np.abs(diffs)>0.02).sum())} of {len(diffs)}")
print("SILHOUETTE_DONE")