dense-checkpoint3.py

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 = cap.read()
# 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)

while(cap.isOpened()):
    # ret = a boolean return value from getting the frame, frame = the current frame being projected in the video
    ret, frame = cap.read()
    # 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
    # https://docs.opencv.org/3.0-beta/modules/video/doc/motion_analysis_and_object_tracking.html#calcopticalflowfarneback
    flow = cv.calcOpticalFlowFarneback(prev_gray, gray, None, 0.5, 3, 15, 3, 5, 1.2, 0)
    # 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'):
        break
# The following frees up resources and closes all windows
cap.release()
cv.destroyAllWindows()