Project 8

import cv2
import numpy as np

# Read in video file
cap = cv2.VideoCapture("traffic.mp4")


# Create Shi-Tomasi corner detector
feature_params = dict( maxCorners = 100,
                       qualityLevel = 0.3,
                       minDistance = 7,
                       blockSize = 7 )



# Parameters for lucas kanade optical flow
lk_params = dict( winSize  = (15,15),
                  maxLevel = 2,
                  criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))


# Create some random colors
color = np.random.randint(0,255,(100,3))

# Take first frame and find corners in it
ret, old_frame = cap.read()
old_gray = cv2.cvtColor(old_frame, cv2.COLOR_BGR2GRAY)
prev_points = cv2.goodFeaturesToTrack(old_gray, mask = None, **feature_params)

fourcc = cv2.VideoWriter_fourcc(*"mp4v")
out = cv2.VideoWriter("output.mp4", fourcc, 20.0, (old_frame.shape[1], old_frame.shape[0]), True)


# Create a mask image for drawing purposes
mask = np.zeros_like(old_frame)

while True:
    ret,frame = cap.read()
    if not ret:
        break
    frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # calculate optical flow
    new_points, status, err = cv2.calcOpticalFlowPyrLK(old_gray, frame_gray, prev_points, None, **lk_params)

    # Select good points
    good_new = new_points[status==1]
    good_old = prev_points[status==1]

    # draw the tracks
    for i,(new,old) in enumerate(zip(good_new,good_old)):
        a,b = new.ravel()
        c,d = old.ravel()
        mask = cv2.line(mask, (int(a),int(b)),(int(c),int(d)), color[i].tolist(), 2)
        frame = cv2.circle(frame,(int(a),int(b)),5,color[i].tolist(),-1)
    img = cv2.add(frame,mask)
    
    out.write(img)
    
    

    

    # Now update the previous frame and previous points
    old_gray = frame_gray.copy()
    prev_points = good_new.reshape(-1,1,2)


cap.release()
out.release()