| import cv2
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| import numpy as np
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| from sklearn.neighbors import KNeighborsClassifier
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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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| cap = cv2.VideoCapture(0)
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| knn = KNeighborsClassifier(n_neighbors=3)
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| object_features = []
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| object_labels = []
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| learning_interval = 30
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| frame_count = 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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| features = [w, h, centroid[0], centroid[1]]
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| object_features.append(features)
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| object_labels.append(1)
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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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| frame_count += 1
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| if frame_count % learning_interval == 0 and len(object_features) > 5:
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| knn.fit(object_features, object_labels)
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| print("Model updated!")
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| if len(object_features) > 5 and frame_count % learning_interval == 0:
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| predicted_label = knn.predict([features])[0]
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| cv2.putText(frame, f"Predicted: {predicted_label}", (x, y - 30), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
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| cv2.imshow('Optimized Object Tracking', 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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|
