| """Summarise a matrix_capture.py run: mix, fight statistics, coverage maps and sample sheets. |
| |
| usage: matrix_report.py DATA_DIR OUT_DIR |
| |
| Writes OUT_DIR/report.json, coverage-<map>.png (top-down: grey = walkable |
| blocks from the plan, colour = player frames per 2 m cell, with eight yaw |
| sectors per 10 m block counted), and sheet-<map>.jpg (graybox | cartoon | war | |
| punk at mid-clip for a spread of team sizes). |
| """ |
| import collections |
| import json |
| import math |
| import random |
| import sys |
| from pathlib import Path |
|
|
| import cv2 |
| import numpy as np |
|
|
| LOOKS = ('original', 'cartoon', 'war', 'punk') |
|
|
|
|
| def frame_at(video, index): |
| cap = cv2.VideoCapture(str(video)) |
| cap.set(cv2.CAP_PROP_POS_FRAMES, index) |
| ok, image = cap.read() |
| cap.release() |
| return image if ok else np.zeros((544, 960, 3), np.uint8) |
|
|
|
|
| def main(): |
| data, out = Path(sys.argv[1]), Path(sys.argv[2]) |
| out.mkdir(parents=True, exist_ok=True) |
| jobs = [json.loads(l) for l in (data / 'jobs.jsonl').read_text().splitlines() if l.strip()] |
| report = {} |
| for map_id in ('paris', 'tsinghua'): |
| metas = [json.loads((c / 'meta.json').read_text()) for c in sorted((data / map_id).glob('clip_*')) if (c / 'meta.json').exists()] |
| fights = [m for m in metas if m['teams'][1]] |
| positions, sectors = [], collections.defaultdict(set) |
| for m in metas: |
| for line in (data / map_id / f"clip_{m['clip']:06d}" / 'frames.jsonl').read_text().splitlines()[::6]: |
| row = json.loads(line) |
| p, yaw = row['state']['player'], row['state']['yaw'] |
| positions.append((p['x'], p['z'])) |
| sectors[(math.floor(p['x'] / 10), math.floor(p['z'] / 10))].add(int(((yaw % (2 * math.pi)) / (math.pi / 4))) % 8) |
| planned = {(math.floor(j['setup']['player']['x'] / 10), math.floor(j['setup']['player']['z'] / 10)) for j in jobs if j['map'] == map_id} |
| report[map_id] = dict( |
| clips=len(metas), planned=sum(j['map'] == map_id for j in jobs), |
| teams=dict(sorted(collections.Counter(f"{m['teams'][0]}v{m['teams'][1]}" for m in metas).items())), |
| distance=dict(collections.Counter(m['distance'] for m in fights)), side=dict(collections.Counter(m['side'] for m in fights)), |
| landmark_clips=sum(m['anchor'] is not None for m in metas), max_drift=max((m['max_drift'] for m in metas), default=None), |
| fight_bullet_frame_share=round(float(np.mean([m['bullet_frame_share'] for m in fights])), 3) if fights else None, |
| fight_mean_alive_bots=round(float(np.mean([m['mean_alive_bots'] for m in fights])), 2) if fights else None, |
| fight_player_shots=round(float(np.mean([m['shots'] for m in fights])), 1) if fights else None, |
| fight_kills=round(float(np.mean([m['kills'] for m in fights])), 2) if fights else None, |
| blocks_visited=len(sectors), blocks_planned=len(planned), |
| yaw_sectors_per_block=round(float(np.mean([len(s) for s in sectors.values()])), 2) if sectors else None, |
| yaw_histogram=[sum(k in s for s in sectors.values()) for k in range(8)], |
| wall_s_mean=round(float(np.mean([m['wall_s'] for m in metas])), 1) if metas else None) |
| |
| xs, zs = [p[0] for p in positions], [p[1] for p in positions] |
| if positions: |
| x0, x1, z0, z1 = min(xs) - 4, max(xs) + 4, min(zs) - 4, max(zs) + 4 |
| cell = 2.0 |
| grid = np.zeros((int((z1 - z0) / cell) + 1, int((x1 - x0) / cell) + 1)) |
| for x, z in positions: |
| grid[int((z - z0) / cell), int((x - x0) / cell)] += 1 |
| norm = np.log1p(grid) / max(1e-6, np.log1p(grid).max()) |
| image = cv2.applyColorMap((norm * 255).astype(np.uint8), cv2.COLORMAP_INFERNO) |
| image[grid == 0] = (40, 40, 40) |
| for bx, bz in planned: |
| x, z = int((bx * 10 - x0) / cell), int((bz * 10 - z0) / cell) |
| cv2.rectangle(image, (x, z), (x + 5, z + 5), (90, 90, 90), 1) |
| image = cv2.resize(image, None, fx=8, fy=8, interpolation=cv2.INTER_NEAREST) |
| cv2.imwrite(str(out / f'coverage-{map_id}.png'), image[::-1]) |
| |
| rng = random.Random(0) |
| by_team = collections.defaultdict(list) |
| for m in metas: |
| by_team[tuple(m['teams'])].append(m) |
| rows = [] |
| for team in sorted(by_team)[:9]: |
| m = rng.choice(by_team[team]) |
| clip = data / map_id / f"clip_{m['clip']:06d}" |
| tiles = [cv2.resize(frame_at(clip / f'{look}.mp4', 120), (384, 218), interpolation=cv2.INTER_AREA) for look in LOOKS] |
| row = np.concatenate(tiles, 1) |
| cv2.putText(row, f"{m['teams'][0]}v{m['teams'][1]} {m['distance']} {m['side']} clip {m['clip']}", (8, 22), 0, .6, (0, 0, 0), 4) |
| cv2.putText(row, f"{m['teams'][0]}v{m['teams'][1]} {m['distance']} {m['side']} clip {m['clip']}", (8, 22), 0, .6, (255, 255, 255), 2) |
| rows.append(row) |
| if rows: |
| cv2.imwrite(str(out / f'sheet-{map_id}.jpg'), np.concatenate(rows, 0), [cv2.IMWRITE_JPEG_QUALITY, 82]) |
| (out / 'report.json').write_text(json.dumps(report, indent=1)) |
| print(json.dumps(report, indent=1)) |
|
|
|
|
| if __name__ == '__main__': |
| main() |
|
|
