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Running
on
Zero
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# Copyright (c) 2025 ByteDance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import cv2
import imageio
import numpy as np
from tqdm.auto import tqdm
from depth_anything_3.utils.parallel_utils import async_call
from depth_anything_3.utils.pca_utils import PCARGBVisualizer
@async_call
def export_to_feat_vis(
prediction,
export_dir,
fps=15,
):
"""Export feature visualization with PCA.
Args:
prediction: Model prediction containing feature maps
export_dir: Directory to export results
fps: Frame rate for output video (default: 15)
"""
out_dir = os.path.join(export_dir, "feat_vis")
os.makedirs(out_dir, exist_ok=True)
images = prediction.processed_images
for k, v in prediction.aux.items():
if not k.startswith("feat_layer_"):
continue
os.makedirs(os.path.join(out_dir, k), exist_ok=True)
viz = PCARGBVisualizer(basis_mode="fixed", percentile_mode="global", clip_percent=10.0)
viz.fit_reference(v)
feats_vis = viz.transform_video(v)
for idx in tqdm(range(len(feats_vis))):
img = images[idx]
feat_vis = (feats_vis[idx] * 255).astype(np.uint8)
feat_vis = cv2.resize(
feat_vis, (img.shape[1], img.shape[0]), interpolation=cv2.INTER_NEAREST
)
save_path = os.path.join(out_dir, f"{k}/{idx:06d}.jpg")
save = np.concatenate([img, feat_vis], axis=1)
imageio.imwrite(save_path, save, quality=95)
cmd = (
"ffmpeg -loglevel error -hide_banner -y "
f"-framerate {fps} -start_number 0 "
f"-i {out_dir}/{k}/%06d.jpg "
f"-c:v libx264 -pix_fmt yuv420p "
f"{out_dir}/{k}.mp4"
)
os.system(cmd)
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