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import cv2 as cv
import numpy as np
# The video feed is read in as a VideoCapture object
cap = cv.VideoCapture("shibuya.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, None, 0.5, 3, 15, 3, 5, 1.2, 0)
# 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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