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Copy pathlucas_canade_method.py
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lucas_canade_method.py
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import numpy as np
import cv2 as cv
# Open the video file
video_path = "slow_traffic_small.mp4" # Name of the video file
cap = cv.VideoCapture(video_path)
# Check if the video file was successfully opened
if not cap.isOpened():
print("Could not open video file!")
exit()
# Parameters for Shi-Tomasi 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))
# Read the first frame and detect corners
ret, old_frame = cap.read()
if not ret:
print("Could not read the first frame of the video!")
cap.release()
exit()
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 True:
ret, frame = cap.read()
if not ret:
print("Video ended or an error occurred!")
break
# Convert the frame to grayscale
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)
# Combine the frame with the mask
img = cv.add(frame, mask)
cv.imshow('Optical Flow', img)
key = cv.waitKey(30) & 0xFF
if key == 27: # Exit on pressing the ESC key
break
# Update the previous frame and points
old_gray = frame_gray.copy()
p0 = good_new.reshape(-1, 1, 2)
cap.release()
cv.destroyAllWindows()