Python OpenCV – Dense optical flow

Prerequisites: Python OpenCV, Grayscaling

Optical flow is the motion of objects between the consecutive frames of the sequence, caused by the relative motion between the camera and the object. It can be of two types-Sparse Optical flow and Dense Optical flow.

Dense Optical flow

Dense Optical flow computes the optical flow vector for every pixel of the frame which may be responsible for its slow speed but leading to a better accurate result. It can be used for detecting motion in the videos, video segmentation, learning structure from motion. There can be various kinds of implementations of dense optical flow. The example below will follow the Farneback method along with OpenCV.

Franeback Method

The first step is that the method approximates the windows of image frames by a quadratic polynomial with the help of the polynomial expansion transform. Next, by observing how the polynomial transforms under the state of motion. i.e. to estimate displacement fields. Dense optical flow is computed, after a series of refinements.

For OpenCV’s implementation, the magnitude and direction of optical flow from a 2-D channel array of flow vectors are computed for the optical flow problem. The angle (direction) of flow by hue is visualized and the distance (magnitude) of flow by the value of HSV color representation. The strength of HSV is always set to a maximum of 255 for optimal visibility.
The method defined is caclopticalFlowFarneback() .

Below is the implementation.





import cv2 as cv
import numpy as np
# The video feed is read in as
# a VideoCapture object
cap = cv.VideoCapture("videoplayback.mp4")
# ret = a boolean return value from
# getting the frame, first_frame = the
# first frame in the entire video sequence
ret, first_frame =
# Converts frame to grayscale because we
# only need the luminance channel for
# detecting edges - less computationally 
# expensive
prev_gray = cv.cvtColor(first_frame, cv.COLOR_BGR2GRAY)
# Creates an image filled with zero
# intensities with the same dimensions 
# as the frame
mask = np.zeros_like(first_frame)
# Sets image saturation to maximum
mask[..., 1] = 255
    # ret = a boolean return value from getting
    # the frame, frame = the current frame being
    # projected in the video
    ret, frame =
    # Opens a new window and displays the input
    # frame
    cv.imshow("input", frame)
    # Converts each frame to grayscale - we previously 
    # only converted the first frame to grayscale
    gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
    # Calculates dense optical flow by Farneback method
    flow = cv.calcOpticalFlowFarneback(prev_gray, gray, 
    # Computes the magnitude and angle of the 2D vectors
    magnitude, angle = cv.cartToPolar(flow[..., 0], flow[..., 1])
    # Sets image hue according to the optical flow 
    # direction
    mask[..., 0] = angle * 180 / np.pi / 2
    # Sets image value according to the optical flow
    # magnitude (normalized)
    mask[..., 2] = cv.normalize(magnitude, None, 0, 255, cv.NORM_MINMAX)
    # Converts HSV to RGB (BGR) color representation
    rgb = cv.cvtColor(mask, cv.COLOR_HSV2BGR)
    # Opens a new window and displays the output frame
    cv.imshow("dense optical flow", rgb)
    # Updates previous frame
    prev_gray = gray
    # Frames are read by intervals of 1 millisecond. The
    # programs breaks out of the while loop when the
    # user presses the 'q' key
    if cv.waitKey(1) & 0xFF == ord('q'):
# The following frees up resources and
# closes all windows





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