mwt_preprocessing.py

##
##  Near-shore Wave Tracking
##  mwt_preprocessing.py
##
##  Created by Justin Fung on 9/1/17.
##  Copyright 2017 justin fung. All rights reserved.
##
## ========================================================

"""Routine for preprocessing video frames.

 Method of preprocessing is:
 -1. resize image
 -2. extract foreground
 -3. denoise image
"""

from __future__ import division

import cv2

# Resize factor (downsize) for analysis:
RESIZE_FACTOR = 0.25

# Number of frames that constitute the background history:
BACKGROUND_HISTORY = 900

# Number of gaussians in BG mixture model:
NUM_GAUSSIANS = 5

# Minimum percent of frame considered background:
BACKGROUND_RATIO = 0.7

# Morphological kernel size (square):
MORPH_KERN_SIZE = 3

# Init the background modeling and foreground extraction mask.
mask = cv2.bgsegm.createBackgroundSubtractorMOG(
                                  history=BACKGROUND_HISTORY,
                                  nmixtures=NUM_GAUSSIANS,
                                  backgroundRatio=BACKGROUND_RATIO,
                                  noiseSigma=0)

# Init the morphological transformations for denoising kernel.
kernel = cv2.getStructuringElement(cv2.MORPH_RECT,
                                   (MORPH_KERN_SIZE, MORPH_KERN_SIZE))


def _resize(frame):
    """Resizing function utilizing OpenCV.

    Args:
      frame: A frame from a cv2.video_reader object to process

    Returns:
      resized_frame: the frame, resized
    """
    resized_frame = cv2.resize(frame,
                               None,
                               fx=RESIZE_FACTOR,
                               fy=RESIZE_FACTOR,
                               interpolation=cv2.INTER_AREA)

    return resized_frame


def preprocess(frame):
    """Preprocesses video frames through resizing, background
    modeling, and denoising.

    Args:
      input: A frame from a cv2.video_reader object to process

    Returns:
      output: the preprocessed frame
    """

    # 1. Resize the input.
    output = _resize(frame)

    # 2. Model the background and extract the foreground with a mask.
    output = mask.apply(output)

    # 3. Apply the morphological operators to suppress noise.
    output = cv2.morphologyEx(output, cv2.MORPH_OPEN, kernel)

    return output