Add a tool that measures emotes from a store's 3D preview

tools/capture drives a store page's skin viewer in headless Firefox, stepping its clock one tick at a time and taking every tick from ten fixed cameras. It then fits our own rig to the frames by rendering the model (a Python port of LimbBend and PoseApplier) and matching outlines and colours, and writes the result as Emotecraft JSON. It measures pixels only and never reads the page's animation data. The README covers the steps, the checks and the limits. Co-Authored-By: Claude Opus 5.5 <[email protected]>
2026-09-29 16:55:08 +02:00
parent 7f8de39253
commit df9a2587e6
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+"""Puts captured frames next to our render of the fitted pose, with the same skin and cameras.
+
+    .venv/bin/python compare.py <name> [tick] [views]
+
+Writes frames/<name>/compare.png: for each view, theirs on the left and ours on the right.
+"""
+import json
+import sys
+from pathlib import Path
+
+import numpy as np
+from PIL import Image
+
+from model import Model, rig_to_pose
+from render import Camera, render_colors
+
+name = sys.argv[1]
+tick = int(sys.argv[2]) if len(sys.argv) > 2 else 0
+views = sys.argv[3].split(",") if len(sys.argv) > 3 else ["y000", "y090", "y180", "y270", "y045"]
+base = Path("frames") / name
+meta = json.loads((base / "views.json").read_text())
+data = json.loads((base / "rig.json").read_text())
+rig = np.array(next(t["rig"] for t in data["ticks"] if t["tick"] == tick))
+skin = np.asarray(Image.open(Path("skins") / f"{meta.get('skin', 'Steve')}.png").convert("RGBA"))
+model = Model(spacing=0.12, skin=skin)
+scale = 2
+cams = {v: Camera(meta["views"][v], meta["size"], meta["aspect"], scale) for v in views}
+ours = render_colors(np.concatenate(list(model.pose(rig_to_pose(rig)).values())), model.colors, data["calib"], cams)
+bg = np.array([40, 40, 52], np.uint8)
+tiles = []
+for v in views:
+    theirs = np.asarray(Image.open(base / v / f"{tick:04d}.png").convert("RGBA").reduce(scale))
+    a = np.where(theirs[..., 3:] > 127, theirs[..., :3], bg)
+    image, mask = ours[v]
+    b = np.where(mask[..., None], (image * 255).astype(np.uint8), bg)
+    tiles.append(np.concatenate([a, b], axis=1))
+sheet = np.concatenate(tiles, axis=0)
+ys, xs = np.nonzero((sheet != bg).any(axis=2))
+Image.fromarray(sheet[:, max(xs.min() - 8, 0):xs.max() + 8]).save(base / "compare.png")
+print("wrote", base / "compare.png")