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DeeJanuzandClaude Opus 5.5
1a76d1560b
The history of frame-hands (~/Desktop/Projects/frame-hands on the Frame), filtered to what moves: the camera broker (camd/), the tracker and its offline tools (trackd/), the shared file layouts (include/), the ncnn models, the analysis tools, and the calibration and model helpers they import from the Python prototype. The reverse-engineering notes, probes, camprobe, and the rest of the prototype stay in frame-hands. Unchanged here: the renames to ft- names and Frametop paths follow. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
66 lines
2.9 KiB
Python
66 lines
2.9 KiB
Python
"""Which color camera is which, and how their calibration maps onto fh-camd's images.
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usage: python tools/check_color.py REC_DIR [--sets N]
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A recording made with fh-camd --with-color holds color_video<N> frames with each set.
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This matches features between the two color images and scores every reading of the
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calibration: which video node is passthrough_left, and whether the calibration's
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cropRegion is subtracted from x ('subtract') or not ('none'). Only the right reading
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makes true matches' rays meet in front of both cameras. Then it checks the winner against
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the side tracking cameras, which tests the CAD-to-head chain shared with them.
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"""
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import argparse
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import itertools
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import os
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import sys
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import numpy as np
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sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), '..'))
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from tools.check_sides import load_cams, matches, score # noqa: E402
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from tools.show_set import index, read_set # noqa: E402
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from tracker import calib # noqa: E402
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def load_color(crop):
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root = os.environ.get('FRAME_JOB_DEVICE_ROOT', '') # frame-job's copy of the device files
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return calib.load_color(root + calib.ARCTURUS_EEPROM, root + calib.DEVICE_JSON, crop=crop)
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument('rec')
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ap.add_argument('--sets', type=int, default=8)
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a = ap.parse_args()
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path = os.path.join(a.rec, 'sets.bin')
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offs = index(path)
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sets = [read_set(path, offs[n]) for n in np.linspace(0, len(offs) - 1, a.sets).astype(int)]
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nodes = sorted(k for k in sets[0] if k.startswith('color_video'))
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if len(nodes) != 2:
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sys.exit('need two color_video<N> cameras in the recording (fh-camd --with-color); found %s' % nodes)
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pairs = [matches(s[nodes[0]][0], s[nodes[1]][0]) for s in sets]
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print('%d sets, %d matches between %s and %s' % (len(sets), sum(len(p[0]) for p in pairs), *nodes))
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best = None
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for crop, left in itertools.product(['subtract', 'none'], nodes):
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cams = load_color(crop)
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right = nodes[1] if left == nodes[0] else nodes[0]
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cam = {left: cams['passthrough_left'], right: cams['passthrough_right']}
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s = np.mean([score(cam[nodes[0]], cam[nodes[1]], ua, ub) for ua, ub in pairs])
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print(' %s = passthrough_left, crop %-8s: %3.0f%% of matches meet' % (left, crop, 100 * s))
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if best is None or s > best[0]:
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best = (s, crop, left, cam)
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s, crop, left, cam = best
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print('best: %s = passthrough_left, crop %s (%.0f%%)' % (left, crop, 100 * s))
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mono = load_cams()
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for node in nodes:
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for side in ['slam_left', 'slam_right']:
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ms = [matches(st[node][0], st[side][0]) for st in sets if side in st]
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sc = np.mean([score(cam[node], mono[side], ua, ub) for ua, ub in ms]) if ms else 0
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print(' %s vs %-10s: %4d matches, %3.0f%% meet' % (node, side, sum(len(m[0]) for m in ms), 100 * sc))
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if __name__ == '__main__':
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main()
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