| import cv2
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| import numpy as np
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| back_sub = cv2.createBackgroundSubtractorKNN(history=500, dist2Threshold=400, detectShadows=True)
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| def get_centroid(x, y, w, h):
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| return (int(x + w / 2), int(y + h / 2))
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| kalman = cv2.KalmanFilter(4, 2)
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| kalman.transitionMatrix = np.array([[1, 0, 1, 0],
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| [0, 1, 0, 1],
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| [0, 0, 1, 0],
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| [0, 0, 0, 1]], np.float32)
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| kalman.measurementMatrix = np.array([[1, 0, 0, 0],
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| [0, 1, 0, 0]], np.float32)
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| kalman.processNoiseCov = np.array([[1e-2, 0, 0, 0],
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| [0, 1e-2, 0, 0],
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| [0, 0, 1, 0],
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| [0, 0, 0, 1]], np.float32)
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| kalman.errorCovPost = np.eye(4, dtype=np.float32)
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| kalman.statePost = np.zeros((4, 1), np.float32)
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| cap = cv2.VideoCapture(0)
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|
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| while True:
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| ret, frame = cap.read()
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| if not ret:
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| break
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| gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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| fg_mask = back_sub.apply(frame)
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| kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5))
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| fg_mask = cv2.morphologyEx(fg_mask, cv2.MORPH_OPEN, kernel, iterations=1)
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| fg_mask = cv2.dilate(fg_mask, kernel, iterations=1)
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| contours, _ = cv2.findContours(fg_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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| for cnt in contours:
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| area = cv2.contourArea(cnt)
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| if area > 100:
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| x, y, w, h = cv2.boundingRect(cnt)
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| centroid = get_centroid(x, y, w, h)
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| cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
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| cv2.circle(frame, centroid, 4, (0, 0, 255), -1)
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| cv2.putText(frame, "Moving Object", (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
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| kalman.correct(np.array([x + w / 2, y + h / 2], np.float32))
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| prediction = kalman.predict()
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| predicted_x, predicted_y = int(prediction[0, 0]), int(prediction[1, 0])
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| cv2.circle(frame, (predicted_x, predicted_y), 4, (255, 0, 0), -1)
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| cv2.imshow('Real-Time Object Tracking with Kalman Filter', frame)
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| if cv2.waitKey(1) & 0xFF == 27:
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| break
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| cap.release()
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| cv2.destroyAllWindows()
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|
