Fix randint TypeError in video tutorial by casting frame count to int

[joaoascenso02]

Problem

In frames_from_video_file (inside site/en/tutorials/load_data/video.ipynb), the code fails when running:

sample_video = frames_from_video_file(video_path, n_frames=10)
sample_video.shape

with the following error:

TypeError: 'float' object cannot be interpreted as an integer

This happens because:

  video_length = src.get(cv2.CAP_PROP_FRAME_COUNT)

  need_length = 1 + (n_frames - 1) * frame_step

  if need_length > video_length:
    start = 0
  else:
    max_start = video_length - need_length
    start = random.randint(0, max_start + 1)

cv2.VideoCapture().get(cv2.CAP_PROP_FRAME_COUNT) returns a float.
As a result, max_start is a float, and random.randint requires integers.

Fix

Cast max_start to int:

max_start = int(video_length - need_length)

Result

The function now works correctly, and the tutorial executes without raising TypeError.

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[github-actions]

Preview

Preview and run these notebook edits with Google Colab:

• site/en/tutorials/load_data/video.ipynb

Rendered notebook diffs available on ReviewNB.com.

Format and style

Use the TensorFlow docs notebook tools to format for consistent source diffs and lint for style:

$ python3 -m pip install -U --user git+https://github.com/tensorflow/docs

$ python3 -m tensorflow_docs.tools.nbfmt notebook.ipynb

$ python3 -m tensorflow_docs.tools.nblint --arg=repo:tensorflow/docs notebook.ipynb

If commits are added to the pull request, synchronize your local branch: git pull origin fix/video-tutorial-randint