a148b4a543
- autorig: landmark detection from vertex cloud (markers JSON override), mixamorig 22-bone skeleton fit, bone-heat weights + nearest-bone rescue - retarget: world-space quaternion delta transfer, hip-height scale compensation, fuzzy/explicit bone mapping (CMU map bundled) - smoke: procedural test humanoid + synthetic BVH -> rig -> retarget -> verify - deploy: push to gitea, pull + smoke on m3ultra and m1ultra Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
75 lines
2.3 KiB
Python
75 lines
2.3 KiB
Python
"""Verify a MIRPAMO output GLB: armature present, meshes skinned,
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animations present with real motion. Prints a JSON report; exit 1 on failure.
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Usage: blender -b --python bpy/verify_glb.py -- --in out/final.glb \
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[--expect-anim] [--min-frames 10]
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"""
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import argparse
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import json
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import os
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import sys
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import bpy
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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from common import (clean_scene, count_fcurves, die, find_armature,
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find_meshes, import_any, stage_args)
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument("--in", dest="inp", required=True)
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ap.add_argument("--expect-anim", action="store_true")
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ap.add_argument("--min-frames", type=int, default=10)
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args = ap.parse_args(stage_args())
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clean_scene()
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objs = import_any(args.inp)
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arm = find_armature(objs)
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meshes = find_meshes(objs)
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report = {
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"file": args.inp,
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"armature": arm.name if arm else None,
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"bones": len(arm.data.bones) if arm else 0,
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"meshes": [m.name for m in meshes],
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"skinned": [],
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"actions": {},
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}
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if not arm:
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print(json.dumps(report, indent=2))
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die("no armature in output")
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if not meshes:
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print(json.dumps(report, indent=2))
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die("no meshes in output")
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for m in meshes:
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has_arm_mod = any(mod.type == "ARMATURE" for mod in m.modifiers)
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has_groups = len(m.vertex_groups) > 0
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report["skinned"].append(
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{"mesh": m.name, "armature_mod": has_arm_mod,
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"vertex_groups": len(m.vertex_groups)})
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if not (has_arm_mod and has_groups):
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print(json.dumps(report, indent=2))
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die(f"mesh {m.name} is not skinned")
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for act in bpy.data.actions:
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fr = act.frame_range
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n_frames = int(fr[1] - fr[0]) + 1
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report["actions"][act.name] = {
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"frames": n_frames, "fcurves": count_fcurves(act)}
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if args.expect_anim:
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good = [a for a, v in report["actions"].items()
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if v["frames"] >= args.min_frames and v["fcurves"] > 0]
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if not good:
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print(json.dumps(report, indent=2))
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die(f"no action with >= {args.min_frames} frames found")
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print(json.dumps(report, indent=2))
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print("[mirpamo] verify OK")
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main()
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