Animate 2D

Display a labels layer above of an image layer using the add_labels and add_image APIs

/home/runner/work/napari-animation/napari-animation/examples/animate2D.py:16: FutureWarning: `napari.view_image` is deprecated and will be removed in napari 0.7.0.
Use `viewer = napari.Viewer(); viewer.add_image(...)` instead.
  viewer = napari.view_image(blobs.astype(float), name="blobs")
Rendering frames...

  0%|          | 0/166 [00:00<?, ?it/s]IMAGEIO FFMPEG_WRITER WARNING: input image is not divisible by macro_block_size=16, resizing from (1086, 635) to (1088, 640) to ensure video compatibility with most codecs and players. To prevent resizing, make your input image divisible by the macro_block_size or set the macro_block_size to 1 (risking incompatibility).

  1%|          | 1/166 [00:00<00:24,  6.68it/s]
  3%|▎         | 5/166 [00:00<00:07, 20.54it/s]
  6%|▌         | 10/166 [00:00<00:05, 29.92it/s]
  9%|▉         | 15/166 [00:00<00:04, 34.45it/s]
 12%|█▏        | 20/166 [00:00<00:03, 36.89it/s]
 14%|█▍        | 24/166 [00:00<00:03, 37.58it/s]
 17%|█▋        | 28/166 [00:00<00:03, 37.39it/s]
 19%|█▉        | 32/166 [00:00<00:03, 35.49it/s]
 22%|██▏       | 36/166 [00:01<00:04, 30.70it/s]
 24%|██▍       | 40/166 [00:01<00:04, 28.22it/s]
 26%|██▌       | 43/166 [00:01<00:04, 25.73it/s]
 28%|██▊       | 46/166 [00:01<00:04, 24.61it/s]
 30%|██▉       | 49/166 [00:01<00:04, 23.79it/s]
 31%|███▏      | 52/166 [00:01<00:04, 23.71it/s]
 33%|███▎      | 55/166 [00:01<00:04, 23.50it/s]
 35%|███▍      | 58/166 [00:02<00:04, 23.43it/s]
 37%|███▋      | 61/166 [00:02<00:04, 23.34it/s]
 39%|███▊      | 64/166 [00:02<00:04, 23.23it/s]
 40%|████      | 67/166 [00:02<00:04, 23.12it/s]
 42%|████▏     | 70/166 [00:02<00:04, 23.08it/s]
 44%|████▍     | 73/166 [00:02<00:03, 23.42it/s]
 46%|████▌     | 76/166 [00:02<00:03, 22.62it/s]
 48%|████▊     | 79/166 [00:03<00:03, 22.43it/s]
 49%|████▉     | 82/166 [00:03<00:03, 22.69it/s]
 51%|█████     | 85/166 [00:03<00:03, 22.78it/s]
 53%|█████▎    | 88/166 [00:03<00:03, 22.23it/s]
 55%|█████▍    | 91/166 [00:03<00:03, 23.20it/s]
 57%|█████▋    | 94/166 [00:03<00:02, 24.67it/s]
 58%|█████▊    | 97/166 [00:03<00:02, 25.85it/s]
 60%|██████    | 100/166 [00:03<00:02, 26.59it/s]
 62%|██████▏   | 103/166 [00:03<00:02, 27.13it/s]
 64%|██████▍   | 106/166 [00:04<00:02, 27.57it/s]
 66%|██████▌   | 109/166 [00:04<00:02, 28.09it/s]
 67%|██████▋   | 112/166 [00:04<00:01, 28.56it/s]
 69%|██████▉   | 115/166 [00:04<00:01, 28.25it/s]
 71%|███████   | 118/166 [00:04<00:01, 28.25it/s]
 73%|███████▎  | 121/166 [00:04<00:01, 28.35it/s]
 75%|███████▍  | 124/166 [00:04<00:01, 27.95it/s]
 77%|███████▋  | 127/166 [00:04<00:01, 28.20it/s]
 78%|███████▊  | 130/166 [00:04<00:01, 28.11it/s]
 80%|████████  | 133/166 [00:05<00:01, 28.15it/s]
 82%|████████▏ | 136/166 [00:05<00:01, 28.35it/s]
 84%|████████▎ | 139/166 [00:05<00:00, 28.20it/s]
 86%|████████▌ | 142/166 [00:05<00:00, 28.50it/s]
 87%|████████▋ | 145/166 [00:05<00:00, 28.36it/s]
 90%|████████▉ | 149/166 [00:05<00:00, 28.81it/s]
 92%|█████████▏| 153/166 [00:05<00:00, 29.95it/s]
 95%|█████████▍| 157/166 [00:05<00:00, 31.35it/s]
 97%|█████████▋| 161/166 [00:05<00:00, 32.17it/s]
 99%|█████████▉| 165/166 [00:06<00:00, 32.53it/s]
100%|██████████| 166/166 [00:06<00:00, 27.25it/s]

from skimage import data
from scipy import ndimage as ndi
from napari_animation import Animation
import napari


blobs = data.binary_blobs(length=128, volume_fraction=0.1, n_dim=3)
viewer = napari.view_image(blobs.astype(float), name="blobs")
labeled = ndi.label(blobs)[0]
viewer.add_labels(labeled, name="blob ID")

animation = Animation(viewer)
viewer.update_console({"animation": animation})

animation.capture_keyframe()
viewer.camera.zoom = 0.2
animation.capture_keyframe()
viewer.camera.zoom = 10.0
viewer.camera.center = (0, 40.0, 10.0)
animation.capture_keyframe()
viewer.dims.current_step = (60, 0, 0)
animation.capture_keyframe(steps=60)
viewer.dims.current_step = (0, 0, 0)
animation.capture_keyframe(steps=60)
viewer.reset_view()
animation.capture_keyframe()
animation.animate("animate2D.mp4", canvas_only=False)