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This tutorial demonstrates how to use a pretrained video classification model to classify an activity (such as dancing, swimming, biking etc) in the given video.
The model architecture used in this tutorial is called MoViNet (Mobile Video Networks). MoVieNets are a family of efficient video classification models trained on huge dataset (Kinetics 600).
In contrast to the i3d models available on TF Hub, MoViNets also support frame-by-frame inference on streaming video.
The pretrained models are available from TF Hub. The TF Hub collection also includes quantized models optimized for TFLite.
The source for these models is available in the TensorFlow Model Garden. This includes a longer version of this tutorial that also covers building and fine-tuning a MoViNet model.
This MoViNet tutorial is part of a series of TensorFlow video tutorials. Here are the other three tutorials:
- Load video data: This tutorial explains how to load and preprocess video data into a TensorFlow dataset pipeline from scratch.
- Build a 3D CNN model for video classification. Note that this tutorial uses a (2+1)D CNN that decomposes the spatial and temporal aspects of 3D data; if you are using volumetric data such as an MRI scan, consider using a 3D CNN instead of a (2+1)D CNN.
- Transfer learning for video classification with MoViNet: This tutorial explains how to use a pre-trained video classification model trained on a different dataset with the UCF-101 dataset.
Setup
For inference on smaller models (A0-A2), CPU is sufficient for this Colab.
sudo apt install -y ffmpeg
pip install -q mediapy
pip uninstall -q -y opencv-python-headless
pip install -q "opencv-python-headless<4.3"
# Import libraries
import pathlib
import matplotlib as mpl
import matplotlib.pyplot as plt
import mediapy as media
import numpy as np
import PIL
import tensorflow as tf
import tensorflow_hub as hub
import tqdm
mpl.rcParams.update({
'font.size': 10,
})
Get the kinetics 600 label list, and print the first few labels:
labels_path = tf.keras.utils.get_file(
fname='labels.txt',
origin='https://raw.githubusercontent.com/tensorflow/models/f8af2291cced43fc9f1d9b41ddbf772ae7b0d7d2/official/projects/movinet/files/kinetics_600_labels.txt'
)
labels_path = pathlib.Path(labels_path)
lines = labels_path.read_text().splitlines()
KINETICS_600_LABELS = np.array([line.strip() for line in lines])
KINETICS_600_LABELS[:20]
Downloading data from https://raw.githubusercontent.com/tensorflow/models/f8af2291cced43fc9f1d9b41ddbf772ae7b0d7d2/official/projects/movinet/files/kinetics_600_labels.txt 9209/9209 ━━━━━━━━━━━━━━━━━━━━ 0s 0us/step array(['abseiling', 'acting in play', 'adjusting glasses', 'air drumming', 'alligator wrestling', 'answering questions', 'applauding', 'applying cream', 'archaeological excavation', 'archery', 'arguing', 'arm wrestling', 'arranging flowers', 'assembling bicycle', 'assembling computer', 'attending conference', 'auctioning', 'backflip (human)', 'baking cookies', 'bandaging'], dtype='<U49')
To provide a simple example video for classification, we can load a short gif of jumping jacks being performed.
Attribution: Footage shared by Coach Bobby Bluford on YouTube under the CC-BY license.
Download the gif.
jumpingjack_url = 'https://github.com/tensorflow/models/raw/f8af2291cced43fc9f1d9b41ddbf772ae7b0d7d2/official/projects/movinet/files/jumpingjack.gif'
jumpingjack_path = tf.keras.utils.get_file(
fname='jumpingjack.gif',
origin=jumpingjack_url,
cache_dir='.', cache_subdir='.',
)
Downloading data from https://github.com/tensorflow/models/raw/f8af2291cced43fc9f1d9b41ddbf772ae7b0d7d2/official/projects/movinet/files/jumpingjack.gif 783318/783318 ━━━━━━━━━━━━━━━━━━━━ 0s 0us/step
Define a function to read a gif file into a tf.Tensor
:
The video's shape is (frames, height, width, colors)
jumpingjack=load_gif(jumpingjack_path)
jumpingjack.shape
2024-03-09 13:25:11.486732: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:282] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected TensorShape([13, 224, 224, 3])
How to use the model
This section contains a walkthrough showing how to use the models from TensorFlow Hub. If you just want to see the models in action, skip to the next section.
There are two versions of each model: base
and streaming
.
- The
base
version takes a video as input, and returns the probabilities averaged over the frames. - The
streaming
version takes a video frame and an RNN state as input, and returns the predictions for that frame, and the new RNN state.
The base model
Download the pretrained model from TensorFlow Hub.
%%time
id = 'a2'
mode = 'base'
version = '3'
hub_url = f'https://tfhub.dev/tensorflow/movinet/{id}/{mode}/kinetics-600/classification/{version}'
model = hub.load(hub_url)
CPU times: user 16.9 s, sys: 672 ms, total: 17.6 s Wall time: 18.1 s
This version of the model has one signature
. It takes an image
argument which is a tf.float32
with shape (batch, frames, height, width, colors)
. It returns a dictionary containing one output: A tf.float32
tensor of logits with shape (batch, classes)
.
sig = model.signatures['serving_default']
print(sig.pretty_printed_signature())
Input Parameters: image (KEYWORD_ONLY): TensorSpec(shape=(None, None, None, None, 3), dtype=tf.float32, name='image') Output Type: Dict[['classifier_head', TensorSpec(shape=(None, 600), dtype=tf.float32, name='classifier_head')]] Captures: 139759956646544: TensorSpec(shape=(), dtype=tf.resource, name=None) 139764748771568: TensorSpec(shape=(), dtype=tf.resource, name=None) 139764748779360: TensorSpec(shape=(), dtype=tf.resource, name=None) 139764748778656: TensorSpec(shape=(), dtype=tf.resource, name=None) 139764748779008: TensorSpec(shape=(), dtype=tf.resource, name=None) 139759956645840: TensorSpec(shape=(), dtype=tf.resource, name=None) 139764748778304: TensorSpec(shape=(), dtype=tf.resource, name=None) 139764748777248: TensorSpec(shape=(), dtype=tf.resource, name=None) 139764748777600: TensorSpec(shape=(), dtype=tf.resource, name=None) 139764748777952: TensorSpec(shape=(), dtype=tf.resource, name=None) 139759956646192: TensorSpec(shape=(), dtype=tf.resource, name=None) 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dtype=tf.resource, name=None)
To run this signature on the video you need to add the outer batch
dimension to the video first.
#warmup
sig(image = jumpingjack[tf.newaxis, :1]);
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR I0000 00:00:1709990730.779735 50954 service.cc:145] XLA service 0x7f1ca4006300 initialized for platform Host (this does not guarantee that XLA will be used). Devices: I0000 00:00:1709990730.779797 50954 service.cc:153] StreamExecutor device (0): Host, Default Version I0000 00:00:1709990730.795362 50954 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.
%%time
logits = sig(image = jumpingjack[tf.newaxis, ...])
logits = logits['classifier_head'][0]
print(logits.shape)
print()
(600,) CPU times: user 24.1 s, sys: 771 ms, total: 24.8 s Wall time: 14.4 s
Define a get_top_k
function that packages the above output processing for later.
Convert the logits
to probabilities, and look up the top 5 classes for the video. The model confirms that the video is probably of jumping jacks
.
probs = tf.nn.softmax(logits, axis=-1)
for label, p in get_top_k(probs):
print(f'{label:20s}: {p:.3f}')
jumping jacks : 0.834 zumba : 0.008 lunge : 0.003 doing aerobics : 0.003 polishing metal : 0.002
The streaming model
The previous section used a model that runs over a whole video. Often when processing a video you don't want a single prediction at the end, you want to update predictions frame by frame. The stream
versions of the model allow you to do this.
Load the stream
version of the model.
%%time
id = 'a2'
mode = 'stream'
version = '3'
hub_url = f'https://tfhub.dev/tensorflow/movinet/{id}/{mode}/kinetics-600/classification/{version}'
model = hub.load(hub_url)
WARNING:absl:`state/b1/l4/pool_frame_count` is not a valid tf.function parameter name. Sanitizing to `state_b1_l4_pool_frame_count`. WARNING:absl:`state/b3/l1/pool_buffer` is not a valid tf.function parameter name. Sanitizing to `state_b3_l1_pool_buffer`. WARNING:absl:`state/head/pool_buffer` is not a valid tf.function parameter name. Sanitizing to `state_head_pool_buffer`. WARNING:absl:`state/b1/l1/pool_buffer` is not a valid tf.function parameter name. Sanitizing to `state_b1_l1_pool_buffer`. WARNING:absl:`state/b4/l4/pool_buffer` is not a valid tf.function parameter name. Sanitizing to `state_b4_l4_pool_buffer`. CPU times: user 49.1 s, sys: 1.96 s, total: 51.1 s Wall time: 51.5 s
Using this model is slightly more complex than the base
model. You have to keep track of the internal state of the model's RNNs.
list(model.signatures.keys())
['call', 'init_states']
The init_states
signature takes the video's shape (batch, frames, height, width, colors)
as input, and returns a large dictionary of tensors containing the initial RNN states:
lines = model.signatures['init_states'].pretty_printed_signature().splitlines()
lines = lines[:10]
lines.append(' ...')
print('.\n'.join(lines))
Input Parameters:. input_shape (KEYWORD_ONLY): TensorSpec(shape=(5,), dtype=tf.int32, name='input_shape'). Output Type:. Dict[['state/b3/l4/pool_frame_count', TensorSpec(shape=(1,), dtype=tf.int32, name='state/b3/l4/pool_frame_count')], ['state/b4/l1/pool_buffer', TensorSpec(shape=(None, 1, 1, 1, 384), dtype=tf.float32, name='state/b4/l1/pool_buffer')], ['state/b4/l2/pool_buffer', TensorSpec(shape=(None, 1, 1, 1, 384), dtype=tf.float32, name='state/b4/l2/pool_buffer')], ['state/b4/l1/pool_frame_count', TensorSpec(shape=(1,), dtype=tf.int32, name='state/b4/l1/pool_frame_count')], ['state/b2/l0/stream_buffer', TensorSpec(shape=(None, 4, None, None, 240), dtype=tf.float32, name='state/b2/l0/stream_buffer')], ['state/b0/l0/pool_buffer', TensorSpec(shape=(None, 1, 1, 1, 40), dtype=tf.float32, name='state/b0/l0/pool_buffer')], ['state/b2/l3/pool_buffer', TensorSpec(shape=(None, 1, 1, 1, 192), dtype=tf.float32, name='state/b2/l3/pool_buffer')], ['state/b3/l1/pool_frame_count', TensorSpec(shape=(1,), dtype=tf.int32, name='state/b3/l1/pool_frame_count')], ['state/b1/l3/pool_frame_count', TensorSpec(shape=(1,), dtype=tf.int32, name='state/b1/l3/pool_frame_count')], ['state/b0/l1/pool_buffer', TensorSpec(shape=(None, 1, 1, 1, 40), dtype=tf.float32, name='state/b0/l1/pool_buffer')], ['state/b3/l5/pool_frame_count', TensorSpec(shape=(1,), dtype=tf.int32, name='state/b3/l5/pool_frame_count')], ['state/b2/l2/stream_buffer', TensorSpec(shape=(None, 2, None, None, 240), dtype=tf.float32, name='state/b2/l2/stream_buffer')], ['state/b4/l3/pool_buffer', TensorSpec(shape=(None, 1, 1, 1, 480), dtype=tf.float32, name='state/b4/l3/pool_buffer')], ['state/b4/l0/pool_buffer', TensorSpec(shape=(None, 1, 1, 1, 480), dtype=tf.float32, name='state/b4/l0/pool_buffer')], ['state/b0/l2/pool_buffer', TensorSpec(shape=(None, 1, 1, 1, 64), dtype=tf.float32, name='state/b0/l2/pool_buffer')], ['state/b1/l1/stream_buffer', TensorSpec(shape=(None, 2, None, None, 120), dtype=tf.float32, name='state/b1/l1/stream_buffer')], ['state/b3/l5/pool_buffer', TensorSpec(shape=(None, 1, 1, 1, 240), dtype=tf.float32, name='state/b3/l5/pool_buffer')], ['state/b4/l6/pool_buffer', TensorSpec(shape=(None, 1, 1, 1, 576), dtype=tf.float32, name='state/b4/l6/pool_buffer')], ['state/b4/l4/pool_frame_count', TensorSpec(shape=(1,), dtype=tf.int32, name='state/b4/l4/pool_frame_count')], ['state/b3/l2/pool_frame_count', TensorSpec(shape=(1,), dtype=tf.int32, name='state/b3/l2/pool_frame_count')], ['state/b3/l0/pool_buffer', TensorSpec(shape=(None, 1, 1, 1, 240), dtype=tf.float32, name='state/b3/l0/pool_buffer')], ['state/b1/l2/stream_buffer', TensorSpec(shape=(None, 2, None, None, 96), dtype=tf.float32, name='state/b1/l2/stream_buffer')], ['state/b2/l4/stream_buffer', TensorSpec(shape=(None, 2, None, None, 240), dtype=tf.float32, name='state/b2/l4/stream_buffer')], ['state/b2/l4/pool_buffer', TensorSpec(shape=(None, 1, 1, 1, 240), dtype=tf.float32, name='state/b2/l4/pool_buffer')], ['state/b4/l5/pool_frame_count', TensorSpec(shape=(1,), dtype=tf.int32, name='state/b4/l5/pool_frame_count')], ['state/head/pool_frame_count', TensorSpec(shape=(1,), dtype=tf.int32, name='state/head/pool_frame_count')], ['state/b0/l2/pool_frame_count', TensorSpec(shape=(1,), dtype=tf.int32, name='state/b0/l2/pool_frame_count')], ['state/b4/l6/pool_frame_count', TensorSpec(shape=(1,), dtype=tf.int32, name='state/b4/l6/pool_frame_count')], ['state/b4/l5/stream_buffer', TensorSpec(shape=(None, 2, None, None, 480), dtype=tf.float32, name='state/b4/l5/stream_buffer')], ['state/b1/l3/pool_buffer', TensorSpec(shape=(None, 1, 1, 1, 96), dtype=tf.float32, name='state/b1/l3/pool_buffer')], ['state/b3/l0/pool_frame_count', TensorSpec(shape=(1,), dtype=tf.int32, name='state/b3/l0/pool_frame_count')], ['state/b3/l3/pool_frame_count', TensorSpec(shape=(1,), dtype=tf.int32, name='state/b3/l3/pool_frame_count')], ['state/b1/l4/pool_frame_count', TensorSpec(shape=(1,), dtype=tf.int32, name='state/b1/l4/pool_frame_count')], ['state/b1/l2/pool_buffer', TensorSpec(shape=(None, 1, 1, 1, 96), dtype=tf.float32, name='state/b1/l2/pool_buffer')], ['state/b3/l1/pool_buffer', TensorSpec(shape=(None, 1, 1, 1, 240), dtype=tf.float32, name='state/b3/l1/pool_buffer')], ['state/b2/l1/pool_buffer', TensorSpec(shape=(None, 1, 1, 1, 160), dtype=tf.float32, name='state/b2/l1/pool_buffer')], ['state/b2/l3/stream_buffer', TensorSpec(shape=(None, 2, None, None, 192), dtype=tf.float32, name='state/b2/l3/stream_buffer')], ['state/b3/l1/stream_buffer', TensorSpec(shape=(None, 2, None, None, 240), dtype=tf.float32, name='state/b3/l1/stream_buffer')], ['state/b1/l1/pool_frame_count', TensorSpec(shape=(1,), dtype=tf.int32, name='state/b1/l1/pool_frame_count')], ['state/b0/l1/stream_buffer', TensorSpec(shape=(None, 2, None, None, 40), dtype=tf.float32, name='state/b0/l1/stream_buffer')], ['state/b3/l3/pool_buffer', TensorSpec(shape=(None, 1, 1, 1, 240), dtype=tf.float32, name='state/b3/l3/pool_buffer')], ['state/b1/l4/pool_buffer', TensorSpec(shape=(None, 1, 1, 1, 120), dtype=tf.float32, name='state/b1/l4/pool_buffer')], ['state/b4/l4/pool_buffer', TensorSpec(shape=(None, 1, 1, 1, 480), dtype=tf.float32, name='state/b4/l4/pool_buffer')], ['state/b4/l2/pool_frame_count', TensorSpec(shape=(1,), dtype=tf.int32, name='state/b4/l2/pool_frame_count')], ['state/b3/l5/stream_buffer', TensorSpec(shape=(None, 2, None, None, 240), dtype=tf.float32, name='state/b3/l5/stream_buffer')], ['state/b1/l0/pool_buffer', TensorSpec(shape=(None, 1, 1, 1, 96), dtype=tf.float32, name='state/b1/l0/pool_buffer')], ['state/b4/l0/pool_frame_count', TensorSpec(shape=(1,), dtype=tf.int32, name='state/b4/l0/pool_frame_count')], ['state/b3/l2/pool_buffer', TensorSpec(shape=(None, 1, 1, 1, 240), dtype=tf.float32, name='state/b3/l2/pool_buffer')], ['state/b3/l0/stream_buffer', TensorSpec(shape=(None, 4, None, None, 240), dtype=tf.float32, name='state/b3/l0/stream_buffer')], ['state/b2/l2/pool_frame_count', TensorSpec(shape=(1,), dtype=tf.int32, name='state/b2/l2/pool_frame_count')], ['state/b3/l2/stream_buffer', TensorSpec(shape=(None, 2, None, None, 240), dtype=tf.float32, name='state/b3/l2/stream_buffer')], ['state/b4/l0/stream_buffer', TensorSpec(shape=(None, 4, None, None, 480), dtype=tf.float32, name='state/b4/l0/stream_buffer')], ['state/b0/l1/pool_frame_count', TensorSpec(shape=(1,), dtype=tf.int32, name='state/b0/l1/pool_frame_count')], ['state/b1/l3/stream_buffer', TensorSpec(shape=(None, 2, None, None, 96), dtype=tf.float32, name='state/b1/l3/stream_buffer')], ['state/b2/l1/pool_frame_count', TensorSpec(shape=(1,), dtype=tf.int32, name='state/b2/l1/pool_frame_count')], ['state/b0/l2/stream_buffer', TensorSpec(shape=(None, 2, None, None, 64), dtype=tf.float32, name='state/b0/l2/stream_buffer')], ['state/b2/l0/pool_buffer', TensorSpec(shape=(None, 1, 1, 1, 240), dtype=tf.float32, name='state/b2/l0/pool_buffer')], ['state/b3/l3/stream_buffer', TensorSpec(shape=(None, 2, None, None, 240), dtype=tf.float32, name='state/b3/l3/stream_buffer')], ['state/b1/l4/stream_buffer', TensorSpec(shape=(None, 2, None, None, 120), dtype=tf.float32, name='state/b1/l4/stream_buffer')], ['state/b3/l4/pool_buffer', TensorSpec(shape=(None, 1, 1, 1, 144), dtype=tf.float32, name='state/b3/l4/pool_buffer')], ['state/b2/l3/pool_frame_count', TensorSpec(shape=(1,), dtype=tf.int32, name='state/b2/l3/pool_frame_count')], ['state/b4/l5/pool_buffer', TensorSpec(shape=(None, 1, 1, 1, 480), dtype=tf.float32, name='state/b4/l5/pool_buffer')], ['state/b1/l0/pool_frame_count', TensorSpec(shape=(1,), dtype=tf.int32, name='state/b1/l0/pool_frame_count')], ['state/b0/l0/pool_frame_count', TensorSpec(shape=(1,), dtype=tf.int32, name='state/b0/l0/pool_frame_count')], ['state/b2/l2/pool_buffer', TensorSpec(shape=(None, 1, 1, 1, 240), dtype=tf.float32, name='state/b2/l2/pool_buffer')], ['state/b1/l2/pool_frame_count', TensorSpec(shape=(1,), dtype=tf.int32, name='state/b1/l2/pool_frame_count')], ['state/b4/l3/pool_frame_count', TensorSpec(shape=(1,), dtype=tf.int32, name='state/b4/l3/pool_frame_count')], ['state/b1/l0/stream_buffer', TensorSpec(shape=(None, 2, None, None, 96), dtype=tf.float32, name='state/b1/l0/stream_buffer')], ['state/head/pool_buffer', TensorSpec(shape=(None, 1, 1, 1, 640), dtype=tf.float32, name='state/head/pool_buffer')], ['state/b2/l0/pool_frame_count', TensorSpec(shape=(1,), dtype=tf.int32, name='state/b2/l0/pool_frame_count')], ['state/b1/l1/pool_buffer', TensorSpec(shape=(None, 1, 1, 1, 120), dtype=tf.float32, name='state/b1/l1/pool_buffer')], ['state/b2/l4/pool_frame_count', TensorSpec(shape=(1,), dtype=tf.int32, name='state/b2/l4/pool_frame_count')], ['state/b2/l1/stream_buffer', TensorSpec(shape=(None, 2, None, None, 160), dtype=tf.float32, name='state/b2/l1/stream_buffer')]]. Captures:. None. ...
initial_state = model.init_states(jumpingjack[tf.newaxis, ...].shape)
type(initial_state)
dict
list(sorted(initial_state.keys()))[:5]
['state/b0/l0/pool_buffer', 'state/b0/l0/pool_frame_count', 'state/b0/l1/pool_buffer', 'state/b0/l1/pool_frame_count', 'state/b0/l1/stream_buffer']
Once you have the initial state for the RNNs, you can pass the state and a video frame as input (keeping the (batch, frames, height, width, colors)
shape for the video frame). The model returns a (logits, state)
pair.
After just seeing the first frame, the model is not convinced that the video is of "jumping jacks":
inputs = initial_state.copy()
# Add the batch axis, take the first frme, but keep the frame-axis.
inputs['image'] = jumpingjack[tf.newaxis, 0:1, ...]
# warmup
model(inputs);
logits, new_state = model(inputs)
logits = logits[0]
probs = tf.nn.softmax(logits, axis=-1)
for label, p in get_top_k(probs):
print(f'{label:20s}: {p:.3f}')
print()
golf chipping : 0.427 tackling : 0.134 lunge : 0.056 stretching arm : 0.053 passing american football (not in game): 0.039
If you run the model in a loop, passing the updated state with each frame, the model quickly converges to the correct result:
%%time
state = initial_state.copy()
all_logits = []
for n in range(len(jumpingjack)):
inputs = state
inputs['image'] = jumpingjack[tf.newaxis, n:n+1, ...]
result, state = model(inputs)
all_logits.append(logits)
probabilities = tf.nn.softmax(all_logits, axis=-1)
CPU times: user 1.5 s, sys: 374 ms, total: 1.87 s Wall time: 696 ms
for label, p in get_top_k(probabilities[-1]):
print(f'{label:20s}: {p:.3f}')
golf chipping : 0.427 tackling : 0.134 lunge : 0.056 stretching arm : 0.053 passing american football (not in game): 0.039
id = tf.argmax(probabilities[-1])
plt.plot(probabilities[:, id])
plt.xlabel('Frame #')
plt.ylabel(f"p('{KINETICS_600_LABELS[id]}')");
You may notice that the final probability is much more certain than in the previous section where you ran the base
model. The base
model returns an average of the predictions over the frames.
for label, p in get_top_k(tf.reduce_mean(probabilities, axis=0)):
print(f'{label:20s}: {p:.3f}')
golf chipping : 0.427 tackling : 0.134 lunge : 0.056 stretching arm : 0.053 passing american football (not in game): 0.039
Animate the predictions over time
The previous section went into some details about how to use these models. This section builds on top of that to produce some nice inference animations.
The hidden cell below to defines helper functions used in this section.
Start by running the streaming model across the frames of the video, and collecting the logits:
init_states = model.init_states(jumpingjack[tf.newaxis].shape)
# Insert your video clip here
video = jumpingjack
images = tf.split(video[tf.newaxis], video.shape[0], axis=1)
all_logits = []
# To run on a video, pass in one frame at a time
states = init_states
for image in tqdm.tqdm(images):
# predictions for each frame
logits, states = model({**states, 'image': image})
all_logits.append(logits)
# concatenating all the logits
logits = tf.concat(all_logits, 0)
# estimating probabilities
probs = tf.nn.softmax(logits, axis=-1)
100%|██████████| 13/13 [00:00<00:00, 18.92it/s]
final_probs = probs[-1]
print('Top_k predictions and their probablities\n')
for label, p in get_top_k(final_probs):
print(f'{label:20s}: {p:.3f}')
Top_k predictions and their probablities jumping jacks : 0.999 zumba : 0.000 doing aerobics : 0.000 dancing charleston : 0.000 slacklining : 0.000
Convert the sequence of probabilities into a video:
# Generate a plot and output to a video tensor
plot_video = plot_streaming_top_preds(probs, video, video_fps=8.)
0%| | 0/13 [00:00<?, ?it/s]/tmpfs/tmp/ipykernel_50732/567636217.py:112: MatplotlibDeprecationWarning: The tostring_rgb function was deprecated in Matplotlib 3.8 and will be removed two minor releases later. Use buffer_rgba instead. data = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8) 100%|██████████| 13/13 [00:06<00:00, 1.88it/s]
# For gif format, set codec='gif'
media.show_video(plot_video, fps=3)
Resources
The pretrained models are available from TF Hub. The TF Hub collection also includes quantized models optimized for TFLite.
The source for these models is available in the TensorFlow Model Garden. This includes a longer version of this tutorial that also covers building and fine-tuning a MoViNet model.
Next Steps
To learn more about working with video data in TensorFlow, check out the following tutorials: