File size: 3,693 Bytes
208faa0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 | """Batch optical flow from 25-frame rgb_align/ (24 flow vis frames)."""
import json
import sys
import time
from pathlib import Path
import cv2
import imageio
import numpy as np
import torch
sys.path.insert(0, "/project/llmsvgen/sunkai/robomaster_3d/CoAF")
from tools.flow_dataset.io import save_vis_png
from tools.flow_dataset.visualize import flow_hwc_to_colorwheel_bgr
from tools.unimatch_flow.model import build_unimatch_estimator
DATASET_ROOT = Path("/project/llmsvgen/sunkai/robomaster_3d/Casual_CoAF/coaf_dataset_24_25")
RAW_ROOT = DATASET_ROOT / "raw"
OUTPUT_ROOT = DATASET_ROOT / "modalities" / "flow"
RGB_ALIGN_FRAMES = 25
GLOBAL_MAX_FLOW = 20.0
WIDTH = 512
HEIGHT = 512
FPS = 8
def read_rgb_align_frames(rgb_dir: Path, target_size: int):
frames = []
for i in range(1, RGB_ALIGN_FRAMES + 1):
path = rgb_dir / f"frame_{i:04d}.png"
if not path.exists():
break
img = cv2.imread(str(path))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
if img.shape[0] != target_size or img.shape[1] != target_size:
img = cv2.resize(img, (target_size, target_size), interpolation=cv2.INTER_LANCZOS4)
frames.append(img)
return frames
def main():
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
estimator = build_unimatch_estimator(
device,
preset_name="gmflow_s2_reg6_mixdata",
height=HEIGHT,
width=WIDTH,
)
episodes = sorted(RAW_ROOT.glob("episode_*"))
start_time = time.time()
processed = 0
failed = []
for ep_dir in episodes:
ep_name = ep_dir.name
out_dir = OUTPUT_ROOT / ep_name
if (out_dir / "preview.mp4").exists():
processed += 1
continue
rgb_dir = ep_dir / "rgb_align"
try:
frames = read_rgb_align_frames(rgb_dir, WIDTH)
if len(frames) != RGB_ALIGN_FRAMES:
raise ValueError(f"Expected {RGB_ALIGN_FRAMES} frames, got {len(frames)}")
raw_dir = out_dir / "raw"
vis_dir = out_dir / "vis"
raw_dir.mkdir(parents=True, exist_ok=True)
vis_dir.mkdir(parents=True, exist_ok=True)
# 25 flow frames aligned with reason index k (k=0: zero flow at episode start)
flow_frames_vis = []
for i in range(RGB_ALIGN_FRAMES):
if i == 0:
flow = np.zeros((HEIGHT, WIDTH, 2), dtype=np.float32)
else:
flow = estimator.predict_hwc(frames[i - 1], frames[i])
np.save(str(raw_dir / f"flow_{i+1:04d}.npy"), flow)
vis = flow_hwc_to_colorwheel_bgr(flow, GLOBAL_MAX_FLOW)
cv2.imwrite(str(vis_dir / f"frame_{i+1:04d}.png"), vis)
flow_frames_vis.append(cv2.cvtColor(vis, cv2.COLOR_BGR2RGB))
imageio.mimsave(
str(out_dir / "preview.mp4"),
flow_frames_vis,
fps=FPS,
codec="libx264",
macro_block_size=1,
)
meta = {
"num_input_frames": RGB_ALIGN_FRAMES,
"num_flow_frames": len(flow_frames_vis),
"aligned_with": "reason_indices",
}
(out_dir / "meta.json").write_text(json.dumps(meta, indent=2) + "\n")
processed += 1
except Exception as e:
failed.append({"episode": ep_name, "error": str(e)})
print(f"\nDone! {processed}/{len(episodes)}, {len(failed)} failed")
if failed:
(OUTPUT_ROOT / "flow_failures.json").write_text(json.dumps(failed, indent=2) + "\n")
if __name__ == "__main__":
main()
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