# brainbox.video¶

Functions for analyzing video frame data

Functions

 `frame_diff` Outputs pythagorean distance between two frames :param frame1: A numpy array of pixels with a shape of either (m, n, 3) or (m, n) :param frame2: A numpy array of pixels with a shape of either (m, n, 3) or (m, n) :return: An array with a shape equal to the input frames `frame_diffs` Return the difference between frames. `motion_energy` Returns a min-max normalized vector of motion energy between frames.
`frame_diff`(frame1, frame2)[source]

Outputs pythagorean distance between two frames :param frame1: A numpy array of pixels with a shape of either (m, n, 3) or (m, n) :param frame2: A numpy array of pixels with a shape of either (m, n, 3) or (m, n) :return: An array with a shape equal to the input frames

`frame_diffs`(frames, diff=1)[source]

Return the difference between frames. May also take difference between more than 1 frames. Values are normalized between 0-255. :param frames: Array or list of frames, where each frame is either (y, x) or (y, x, 3). :param diff: Take difference between frames N and frames N + diff. :return: uint8 array with shape (n-diff, y, x).

`motion_energy`(frames, diff=2, kernel=None, normalize=True)[source]

Returns a min-max normalized vector of motion energy between frames. :param frames: A list of ndarray of frames. :param diff: Take difference between frames N and frames N + diff. :param kernel: An optional Gaussian smoothing to apply with a given kernel size. :param normalize: If True, motion energy is min-max normalized :return df_: A vector of length n frames - diff, normalized between 0 and 1. :return stDev: The standard deviation between the frames (not normalized).

Example 1 - Calculate normalized difference between consecutive frames

df, std = motion_energy(frames, diff=1)

Example 2 - Calculate smoothed difference between every 2nd frame

df, _ = motion_energy(frames, kernel=(9, 9))