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import numpy as np
import cv2 as cv
import argparse
parser = argparse.ArgumentParser(description='This sample demonstrates Lucas-Kanade Optical Flow calculation. \
The example file can be downloaded from: \
https://www.bogotobogo.com/python/OpenCV_Python/images/mean_shift_tracking/slow_traffic_small.mp4')
parser.add_argument('image', type=str, help='path to image file')
args = parser.parse_args()
cap = cv.VideoCapture(args.image)
# params for ShiTomasi corner detection
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 = (cv.TERM_CRITERIA_EPS | cv.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 = cv.cvtColor(old_frame, cv.COLOR_BGR2GRAY)
p0 = cv.goodFeaturesToTrack(old_gray, mask = None, **feature_params)
# Create a mask image for drawing purposes
mask = np.zeros_like(old_frame)
while(1):
ret, frame = cap.read()
if not ret:
print('No frames grabbed!')
break
frame_gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
# calculate optical flow
p1, st, err = cv.calcOpticalFlowPyrLK(old_gray, frame_gray, p0, None, **lk_params)
# Select good points
if p1 is not None:
good_new = p1[st==1]
good_old = p0[st==1]
# draw the tracks
for i, (new, old) in enumerate(zip(good_new, good_old)):
a, b = new.ravel()
c, d = old.ravel()
mask = cv.line(mask, (int(a), int(b)), (int(c), int(d)), color[i].tolist(), 2)
frame = cv.circle(frame, (int(a), int(b)), 5, color[i].tolist(), -1)
img = cv.add(frame, mask)
cv.imshow('frame', img)
k = cv.waitKey(30) & 0xff
if k == 27:
break
# Now update the previous frame and previous points
old_gray = frame_gray.copy()
p0 = good_new.reshape(-1, 1, 2)
cv.destroyAllWindows()
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