File size: 5,912 Bytes
f348660 | 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 | """Depth Anything 3 metric-depth inference for Smoke-Eval sequences.
This inference-only adapter follows the official ByteDance-Seed
Depth-Anything-3 Python API. The upstream package supplies the model
architecture; this file supplies the artifact's local weights, camera
calibration, sequence sharding, and output contract.
"""
import argparse
from pathlib import Path
import cv2
import numpy as np
import torch
from accelerate import Accelerator
from safetensors.torch import load_file
from tqdm.auto import tqdm
INTRINSICS = np.array(
[[365.13, 0.0, 445.43], [0.0, 365.13, 261.18], [0.0, 0.0, 1.0]],
dtype=np.float32,
)
SCALE_FACTOR = 1.15 * 365.13 / 300.0
TARGET_SIZE = (896, 504)
class Calibrator:
"""Defish DJI frames and map them to the ZED-aligned view."""
def __init__(self):
k_dji = np.array(
[
[718.48555551, 0.0, 963.36465011],
[0.0, 720.25844189, 537.87569913],
[0.0, 0.0, 1.0],
],
dtype=np.float64,
)
d_dji = np.array(
[0.19022699, 0.03466753, 0.05858962, -0.07070669],
dtype=np.float64,
)
new_k = cv2.fisheye.estimateNewCameraMatrixForUndistortRectify(
k_dji,
d_dji,
(1920, 1080),
np.eye(3),
balance=0.2,
fov_scale=1.0,
)
self.map1, self.map2 = cv2.fisheye.initUndistortRectifyMap(
k_dji,
d_dji,
np.eye(3),
new_k,
(1920, 1080),
cv2.CV_16SC2,
)
self.homography = np.array(
[
[
0.8274446551892256,
-0.0742944198979625,
80.23797348979947,
],
[
-0.014725864916652691,
0.8471179917075127,
28.27366063997317,
],
[
-5.083573451500717e-05,
-6.846079418201229e-05,
1.0,
],
],
dtype=np.float64,
)
def __call__(self, rgb: np.ndarray) -> np.ndarray:
bgr = cv2.cvtColor(rgb, cv2.COLOR_RGB2BGR)
if bgr.shape[:2] != (1080, 1920):
bgr = cv2.resize(bgr, (1920, 1080), interpolation=cv2.INTER_LINEAR)
bgr = cv2.remap(bgr, self.map1, self.map2, cv2.INTER_LINEAR)
bgr = cv2.warpPerspective(bgr, self.homography, (1918, 1105))
bgr = bgr[115:760, 255:1400]
bgr = cv2.resize(bgr, TARGET_SIZE, interpolation=cv2.INTER_AREA)
return cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--data_root", required=True)
parser.add_argument("--checkpoint", required=True)
parser.add_argument("--output_dir", required=True)
parser.add_argument("--model_name", default="da3metric-large")
parser.add_argument("--batch_size", type=int, default=16)
parser.add_argument("--sequences", nargs="*", default=None)
return parser.parse_args()
@torch.no_grad()
def main() -> None:
args = parse_args()
from depth_anything_3.api import DepthAnything3
class AccelerateFP16DepthAnything3(DepthAnything3):
"""Use the official API while leaving autocast to Accelerate."""
@torch.inference_mode()
def forward(
self,
image,
extrinsics=None,
intrinsics=None,
export_feat_layers=None,
infer_gs=False,
use_ray_pose=False,
ref_view_strategy="saddle_balanced",
):
return self.model(
image,
extrinsics,
intrinsics,
export_feat_layers,
infer_gs,
use_ray_pose,
ref_view_strategy,
)
accelerator = Accelerator(mixed_precision="fp16")
data_root = Path(args.data_root)
output_dir = Path(args.output_dir)
sequences = sorted(path for path in data_root.iterdir() if path.is_dir())
if args.sequences:
requested = set(args.sequences)
sequences = [path for path in sequences if path.name in requested]
local_sequences = sequences[
accelerator.process_index :: accelerator.num_processes
]
model = AccelerateFP16DepthAnything3(model_name=args.model_name)
model.load_state_dict(load_file(args.checkpoint, device="cpu"), strict=True)
model = model.to(accelerator.device).eval()
calibrate = Calibrator()
if accelerator.is_main_process:
output_dir.mkdir(parents=True, exist_ok=True)
accelerator.wait_for_everyone()
for sequence in local_sequences:
rgb = np.load(sequence / "dji_rgb.npy", mmap_mode="r")
depth_chunks = []
for start in tqdm(
range(0, len(rgb), args.batch_size),
desc=sequence.name,
disable=not accelerator.is_local_main_process,
):
end = min(start + args.batch_size, len(rgb))
images = [
calibrate(np.asarray(rgb[index])) for index in range(start, end)
]
intrinsics = np.repeat(INTRINSICS[None], len(images), axis=0)
with accelerator.autocast():
prediction = model.inference(
images,
intrinsics=intrinsics,
process_res=896,
process_res_method="upper_bound_resize",
)
depth_chunks.append(prediction.depth * SCALE_FACTOR)
depth = np.concatenate(depth_chunks).astype(np.float32, copy=False)
np.save(output_dir / f"{sequence.name.lower()}_pred.npy", depth)
accelerator.wait_for_everyone()
if __name__ == "__main__":
main()
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