sparse-checkpoint2.py

import cv2 as cv
import numpy as np

# Parameters for Shi-Tomasi corner detection
# feature_params = dict(maxCorners = 300, qualityLevel = 0.2, minDistance = 2, blockSize = 7)
# Parameters for Lucas-Kanade optical flow
# lk_params = dict(winSize = (15,15), maxLevel = 2, criteria = (cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 0.03))
# The video feed is read in as a VideoCapture object
# cap = cv.VideoCapture("shibuya.mp4")
# Variable for color to draw optical flow track
# color = (0, 255, 0)
# 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()
    # Converts each frame to grayscale - we previously only converted the first frame to grayscale
    gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
    # Visualizes checkpoint 2
    cv.imshow("grayscale", gray)
    # Updates previous frame
    prev_gray = gray.copy()
    # Frames are read by intervals of 10 milliseconds. The programs breaks out of the while loop when the user presses the 'q' key
    # if cv.waitKey(10) & 0xFF == ord('q'):
        # break
# The following frees up resources and closes all windows
# cap.release()
# cv.destroyAllWindows()