tattoo-test/probe_lena2.gd

# Measurements for the full-body Lena bake: bone heights, leg UV overlap,
# torso half-width, and fabric (bra/underwear) color stats.
extends SceneTree

const GLB_PATH := "C:/Users/CAN/tinqs-ltd/ariki-game/assets/quaternius/derived-bodies/Ariki_Female_QuatSkin.glb"
const GRID := 256

func _init() -> void:
	var doc := GLTFDocument.new()
	var state := GLTFState.new()
	if doc.append_from_file(GLB_PATH, state) != OK:
		quit(1)
		return
	var scene := doc.generate_scene(state)
	var skel: Skeleton3D = null
	var body: Node = null
	var stack: Array = [scene]
	while not stack.is_empty():
		var n: Node = stack.pop_back()
		if n is Skeleton3D and skel == null:
			skel = n
		if (n is MeshInstance3D or n.get_class() == "ImporterMeshInstance3D") \
				and String(n.name).to_lower().contains("lena"):
			body = n
		for c in n.get_children():
			stack.push_back(c)

	for bn in ["neck_01", "spine_03", "spine_02", "spine_01", "pelvis",
			"thigh_r", "calf_r", "foot_r", "clavicle_r"]:
		print("bone %s: %s" % [bn, skel.get_bone_global_rest(skel.find_bone(bn)).origin])

	var mesh = body.mesh
	if mesh is ImporterMesh:
		mesh = mesh.get_mesh()
	var arrays: Array = mesh.surface_get_arrays(0)
	var verts: PackedVector3Array = arrays[Mesh.ARRAY_VERTEX]
	var uvs: PackedVector2Array = arrays[Mesh.ARRAY_TEX_UV]
	var normals: PackedVector3Array = arrays[Mesh.ARRAY_NORMAL]
	var vbones: PackedInt32Array = arrays[Mesh.ARRAY_BONES]
	var vweights = arrays[Mesh.ARRAY_WEIGHTS]
	var nverts := verts.size()
	var influences := int(float(vbones.size()) / float(nverts))

	var skin: Skin = body.skin
	var bind_bone := PackedInt32Array()
	for i in skin.get_bind_count():
		var b := skin.get_bind_bone(i)
		if b < 0:
			b = skel.find_bone(skin.get_bind_name(i))
		bind_bone.append(b)

	# leg UV overlap (same method that showed 100% for arms)
	var sets := {}
	for side in [["r", ["thigh_r", "calf_r", "foot_r"]], ["l", ["thigh_l", "calf_l", "foot_l"]]]:
		var ids := {}
		for bn in side[1]:
			ids[skel.find_bone(bn)] = true
		var binds := {}
		for i in bind_bone.size():
			if ids.has(bind_bone[i]):
				binds[i] = true
		var mask := PackedByteArray()
		mask.resize(GRID * GRID)
		for v in nverts:
			var w := 0.0
			for k in influences:
				if binds.has(vbones[v * influences + k]):
					w += vweights[v * influences + k]
			if w > 0.3:
				var gx := clampi(int(uvs[v].x * GRID), 0, GRID - 1)
				var gy := clampi(int(uvs[v].y * GRID), 0, GRID - 1)
				mask[gy * GRID + gx] = 1
		sets[side[0]] = mask
	var r: PackedByteArray = sets["r"]
	var l: PackedByteArray = sets["l"]
	var cr := 0
	var ov := 0
	for i in GRID * GRID:
		if r[i] == 1:
			cr += 1
			if l[i] == 1:
				ov += 1
	print("LEG UV overlap: %d/%d cells (%.0f%%)" % [ov, cr, 100.0 * ov / maxf(cr, 1)])

	# arm weights (to exclude arms when measuring torso width)
	var arm_ids := {}
	for bn in ["upperarm_l", "lowerarm_l", "hand_l", "upperarm_r", "lowerarm_r", "hand_r"]:
		arm_ids[skel.find_bone(bn)] = true
	var arm_binds := {}
	for i in bind_bone.size():
		if arm_ids.has(bind_bone[i]):
			arm_binds[i] = true

	# torso extents + fabric stats: front-facing verts in the chest band
	var mat: Material = mesh.surface_get_material(0)
	var img: Image = (mat as BaseMaterial3D).albedo_texture.get_image()
	img.clear_mipmaps()
	img.convert(Image.FORMAT_RGBA8)
	var tw := img.get_width()
	var th := img.get_height()
	var neck_y: float = skel.get_bone_global_rest(skel.find_bone("neck_01")).origin.y
	var pelvis_y: float = skel.get_bone_global_rest(skel.find_bone("pelvis")).origin.y
	var maxx_front := 0.0
	var maxx_back := 0.0
	var n_fab := 0
	var n_skin := 0
	var sat_fab := 0.0
	var sat_skin := 0.0
	var lum_fab := 0.0
	var lum_skin := 0.0
	for v in nverts:
		var aw := 0.0
		for k in influences:
			if arm_binds.has(vbones[v * influences + k]):
				aw += vweights[v * influences + k]
		if aw > 0.3:
			continue
		var p := verts[v]
		if p.y < pelvis_y or p.y > neck_y:
			continue
		if normals[v].z > 0.25:
			maxx_front = maxf(maxx_front, absf(p.x))
		elif normals[v].z < -0.25:
			maxx_back = maxf(maxx_back, absf(p.x))
		var px := clampi(int(uvs[v].x * tw), 0, tw - 1)
		var py := clampi(int(uvs[v].y * th), 0, th - 1)
		var c := img.get_pixel(px, py)
		var mx := maxf(c.r, maxf(c.g, c.b))
		var mn := minf(c.r, minf(c.g, c.b))
		var sat := (mx - mn) / maxf(mx, 0.001)
		var lum := (c.r + c.g + c.b) / 3.0
		# crude split: bra/underwear are pale + desaturated
		if sat < 0.15 and lum > 0.55:
			n_fab += 1
			sat_fab += sat
			lum_fab += lum
		else:
			n_skin += 1
			sat_skin += sat
			lum_skin += lum
	print("torso band y %.2f..%.2f  half-width front=%.3f back=%.3f" % [pelvis_y, neck_y, maxx_front, maxx_back])
	print("fabric-ish verts: %d (avg sat %.3f lum %.3f)" % [n_fab, sat_fab / maxf(n_fab, 1), lum_fab / maxf(n_fab, 1)])
	print("skin-ish verts:   %d (avg sat %.3f lum %.3f)" % [n_skin, sat_skin / maxf(n_skin, 1), lum_skin / maxf(n_skin, 1)])
	quit()