File size: 8,297 Bytes
c1a4584 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 | """Predict gravity for a static-camera video given a directory of frames.
Runs GeoCalib per-frame, then aggregates using confidence-weighted spherical
mean with MAD-based outlier rejection.
Usage:
python predict_video_gravity.py <frames_dir> [--camera_model pinhole|simple_radial|simple_divisional]
"""
import argparse
import json
import math
from pathlib import Path
import torch
import numpy as np
from geocalib import GeoCalib
from geocalib.gravity import Gravity
from geocalib.utils import print_calibration
IMAGE_EXTS = {".jpg", ".jpeg", ".png", ".bmp", ".webp"}
def angle_between(v1: torch.Tensor, v2: torch.Tensor) -> torch.Tensor:
"""Angular distance (radians) between two unit vectors."""
dot = (v1 * v2).sum(dim=-1).clamp(-1.0, 1.0)
return torch.acos(dot)
def spherical_mean(vecs: torch.Tensor, weights: torch.Tensor | None = None) -> torch.Tensor:
"""Compute the weighted spherical mean of unit vectors."""
if weights is not None:
weights = weights / weights.sum()
mean = (vecs * weights.unsqueeze(-1)).sum(dim=0)
else:
mean = vecs.mean(dim=0)
return torch.nn.functional.normalize(mean, dim=-1)
def predict_video_gravity(
frames_dir: str,
camera_model: str = "pinhole",
mad_threshold: float = 3.0,
max_frames: int | None = None,
device: str | None = None,
) -> dict:
"""Predict gravity for a static-camera video.
Args:
frames_dir: Directory containing image frames.
camera_model: GeoCalib camera model ('pinhole', 'simple_radial', 'simple_divisional').
mad_threshold: Outlier rejection threshold in units of MAD (higher = less aggressive).
max_frames: If set, subsample at most this many frames.
device: Torch device string. Defaults to CUDA if available.
Returns:
dict with keys:
gravity: Gravity object (final estimate)
roll_deg, pitch_deg: angles in degrees
n_frames: total frames processed
n_inliers: frames kept after outlier rejection
per_frame: list of per-frame dicts
"""
if device is None:
device = "cuda" if torch.cuda.is_available() else "cpu"
frames_dir = Path(frames_dir)
image_paths = sorted(p for p in frames_dir.iterdir() if p.suffix.lower() in IMAGE_EXTS)
if not image_paths:
raise ValueError(f"No images found in {frames_dir}")
if max_frames is not None and len(image_paths) > max_frames:
# Uniformly subsample
indices = np.round(np.linspace(0, len(image_paths) - 1, max_frames)).astype(int)
image_paths = [image_paths[i] for i in indices]
weights = "pinhole" if camera_model == "pinhole" else "distorted"
model = GeoCalib(weights=weights).to(device)
model.eval()
print(f"Running GeoCalib on {len(image_paths)} frames...")
gravity_vecs = [] # [N, 3] unit vectors
confidences = [] # [N] scalar confidence per frame
per_frame = []
with torch.no_grad():
for i, path in enumerate(image_paths):
try:
img = model.load_image(str(path)).to(device)
results = model.calibrate(img, camera_model=camera_model)
grav = results["gravity"] # Gravity object, shape [1]
vec = grav.vec3d.squeeze(0).cpu() # [3]
# Use mean of up- and latitude-confidence as frame weight
up_conf = results["up_confidence"].mean().item()
lat_conf = results["latitude_confidence"].mean().item()
conf = (up_conf + lat_conf) / 2.0
gravity_vecs.append(vec)
confidences.append(conf)
per_frame.append({
"path": str(path),
"vec": vec,
"roll_deg": math.degrees(grav.roll.item()),
"pitch_deg": math.degrees(grav.pitch.item()),
"confidence": conf,
"outlier": False,
})
if (i + 1) % 10 == 0 or i == len(image_paths) - 1:
print(f" [{i+1}/{len(image_paths)}] roll={per_frame[-1]['roll_deg']:.1f}° "
f"pitch={per_frame[-1]['pitch_deg']:.1f}° conf={conf:.3f}")
except Exception as e:
print(f" Warning: failed on {path.name}: {e}")
if not gravity_vecs:
raise RuntimeError("No frames successfully processed.")
vecs = torch.stack(gravity_vecs) # [N, 3]
confs = torch.tensor(confidences) # [N]
# --- Step 1: Compute initial unweighted spherical mean ---
mean_vec = spherical_mean(vecs)
# --- Step 2: Compute per-frame angle to the mean ---
angles = angle_between(vecs, mean_vec.unsqueeze(0).expand_as(vecs)) # [N]
# --- Step 3: MAD-based outlier rejection ---
median_angle = angles.median()
mad = (angles - median_angle).abs().median()
mad = mad.clamp(min=1e-6) # avoid division by zero
threshold = median_angle + mad_threshold * mad
inlier_mask = angles <= threshold
n_inliers = inlier_mask.sum().item()
print(f"\nOutlier rejection: {n_inliers}/{len(vecs)} frames kept "
f"(threshold={math.degrees(threshold.item()):.2f}°, "
f"median angle={math.degrees(median_angle.item()):.2f}°, "
f"MAD={math.degrees(mad.item()):.2f}°)")
for i, is_outlier in enumerate((~inlier_mask).tolist()):
per_frame[i]["outlier"] = is_outlier
# --- Step 4: Confidence-weighted spherical mean over inliers ---
inlier_vecs = vecs[inlier_mask]
inlier_confs = confs[inlier_mask]
final_vec = spherical_mean(inlier_vecs, weights=inlier_confs)
final_gravity = Gravity(final_vec.unsqueeze(0).to(device))
roll_deg = math.degrees(final_gravity.roll.item())
pitch_deg = math.degrees(final_gravity.pitch.item())
return {
"gravity": final_gravity,
"vec": final_vec,
"roll_deg": roll_deg,
"pitch_deg": pitch_deg,
"n_frames": len(vecs),
"n_inliers": n_inliers,
"per_frame": per_frame,
}
def main():
parser = argparse.ArgumentParser(description="Predict gravity for a static-camera video.")
parser.add_argument("frames_dir", help="Directory of image frames.")
parser.add_argument(
"--camera_model",
default="pinhole",
choices=["pinhole", "simple_radial", "simple_divisional"],
)
parser.add_argument(
"--mad_threshold",
type=float,
default=3.0,
help="MAD multiplier for outlier rejection (default: 3.0).",
)
parser.add_argument(
"--max_frames",
type=int,
default=None,
help="Uniformly subsample to at most this many frames.",
)
parser.add_argument("--device", default=None, help="Torch device (default: auto).")
parser.add_argument("--output_path", default=None, help="Save results as JSON to this path.")
args = parser.parse_args()
result = predict_video_gravity(
args.frames_dir,
camera_model=args.camera_model,
mad_threshold=args.mad_threshold,
max_frames=args.max_frames,
device=args.device,
)
print("\n=== Final Gravity Estimate ===")
print(f" Roll: {result['roll_deg']:+.2f}°")
print(f" Pitch: {result['pitch_deg']:+.2f}°")
print(f" Vec3D: {result['vec'].tolist()}")
print(f" Frames: {result['n_inliers']} inliers / {result['n_frames']} total")
if args.output_path is not None:
output = {
"roll_deg": result["roll_deg"],
"pitch_deg": result["pitch_deg"],
"vec3d": result["vec"].tolist(),
"n_frames": result["n_frames"],
"n_inliers": result["n_inliers"],
"per_frame": [
{
"path": f["path"],
"roll_deg": f["roll_deg"],
"pitch_deg": f["pitch_deg"],
"confidence": f["confidence"],
"outlier": f["outlier"],
}
for f in result["per_frame"]
],
}
Path(args.output_path).write_text(json.dumps(output, indent=2))
print(f"\nSaved to {args.output_path}")
if __name__ == "__main__":
main()
|