Source code for brainbox.video

"""Functions for analyzing video frame data"""
import numpy as np
import cv2


[docs] def frame_diff(frame1, frame2): """ 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 """ if frame1.shape != frame2.shape: raise ValueError('Frames must have the same shape') diff32 = np.float32(frame1) - np.float32(frame2) if frame1.ndim == 3: norm32 = (np.sqrt(diff32[:, :, 0] ** 2 + diff32[:, :, 1] ** 2 + diff32[:, :, 2] ** 2) / np.sqrt(255 ** 2 * 3)) else: norm32 = np.sqrt(diff32 ** 2 * 3) / np.sqrt(255 ** 2 * 3) return np.uint8(norm32 * 255)
[docs] def frame_diffs(frames, diff=1): """ 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). """ frames = np.array(frames, dtype=np.float32) if frames.shape[0] < diff: raise ValueError('Difference must be less than number of frames') diff32 = frames[diff:] - frames[:-diff] # Normalize if frames.ndim == 4: norm32 = np.sqrt((diff32 ** 2).sum(axis=3)) / np.sqrt(255 ** 2 * 3) else: norm32 = np.sqrt(diff32 ** 2 * 3) / np.sqrt(255 ** 2 * 3) return np.uint8(norm32 * 255)
[docs] def motion_energy(frames, diff=2, kernel=None, normalize=True): """ 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)) """ df = frame_diffs(frames, diff) # Smooth with a Gaussian blur TODO Use median blur instead if kernel is not None: df = cv2.GaussianBlur(df, (9, 9), 0) stDev = np.array([cv2.meanStdDev(x)[1] for x in df]).squeeze() # Feature scaling df_ = df.sum(axis=(1, 2)) if normalize: df_ = (df_ - df_.min()) / (df_.max() - df_.min()) return df_, stDev