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| import os |
| import cv2 |
| import imageio |
| import numpy as np |
| from tqdm.auto import tqdm |
|
|
| from ...utils.parallel_utils import async_call |
| from ...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) |
|
|