sparse-checkpoint5.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)
# Finds the strongest corners in the first frame by Shi-Tomasi method - we will track the optical flow for these corners
# https://docs.opencv.org/3.0-beta/modules/imgproc/doc/feature_detection.html#goodfeaturestotrack
# prev = cv.goodFeaturesToTrack(prev_gray, mask = None, **feature_params)
# Creates an image filled with zero intensities with the same dimensions as the frame - for later drawing purposes
mask = np.zeros_like(first_frame)

# 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)
    # Calculates sparse optical flow by Lucas-Kanade method
    # https://docs.opencv.org/3.0-beta/modules/video/doc/motion_analysis_and_object_tracking.html#calcopticalflowpyrlk
    # next, status, error = cv.calcOpticalFlowPyrLK(prev_gray, gray, prev, None, **lk_params)
    # Selects good feature points for previous position
    # good_old = prev[status == 1]
    # Selects good feature points for next position
    # good_new = next[status == 1]
    # Draws the optical flow tracks
    for i, (new, old) in enumerate(zip(good_new, good_old)):
        # Returns a contiguous flattened array as (x, y) coordinates for new point
        a, b = new.ravel()
        # Returns a contiguous flattened array as (x, y) coordinates for old point
        c, d = old.ravel()
        # Draws line between new and old position with green color and 2 thickness
        mask = cv.line(mask, (a, b), (c, d), color, 2)
        # Draws filled circle (thickness of -1) at new position with green color and radius of 3
        frame = cv.circle(frame, (a, b), 3, color, -1)
    # Overlays the optical flow tracks on the original frame
    output = cv.add(frame, mask)
    # Updates previous frame
    # prev_gray = gray.copy()
    # Updates previous good feature points
    # prev = good_new.reshape(-1, 1, 2)
    # Opens a new window and displays the output frame
    cv.imshow("sparse optical flow", output)
    # 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()