| import cv2
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| import numpy as np
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| import time
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| from sklearn.neighbors import KNeighborsClassifier
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| from collections import defaultdict, deque
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|
|
| back_sub = cv2.createBackgroundSubtractorKNN(history=500, dist2Threshold=400, detectShadows=True)
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| cap = cv2.VideoCapture(0)
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|
|
|
|
| object_traces = defaultdict(lambda: deque(maxlen=30))
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| object_last_seen = {}
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| object_id_counter = 0
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|
|
|
|
| knn = KNeighborsClassifier(n_neighbors=3)
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| features_set = []
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| labels_set = []
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| frame_count = 0
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| learning_interval = 30
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|
|
| def apply_noise_reduction(mask):
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| kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
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| mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel, iterations=2)
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| mask = cv2.dilate(mask, kernel, iterations=1)
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| return mask
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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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|
|
| def calculate_direction(trace):
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| if len(trace) < 2:
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| return "-"
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| dx = trace[-1][0] - trace[0][0]
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| dy = trace[-1][1] - trace[0][1]
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| if abs(dx) > abs(dy):
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| return "چپ" if dx < 0 else "راست"
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| else:
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| return "بالا" if dy < 0 else "پایین"
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|
|
| def calculate_speed(trace, duration):
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| if len(trace) < 2 or duration == 0:
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| return 0
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| dist = np.linalg.norm(np.array(trace[-1]) - np.array(trace[0]))
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| return dist / duration
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|
|
| def count_direction_changes(trace):
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| changes = 0
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| for i in range(2, len(trace)):
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| dx1 = trace[i-1][0] - trace[i-2][0]
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| dx2 = trace[i][0] - trace[i-1][0]
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| if dx1 * dx2 < 0:
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| changes += 1
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| return changes
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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
|
|
|
| gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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| fg_mask = back_sub.apply(frame)
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| fg_mask = apply_noise_reduction(fg_mask)
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|
|
| contours, _ = cv2.findContours(fg_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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|
|
| current_ids = []
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| for cnt in contours:
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| area = cv2.contourArea(cnt)
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| if area < 150:
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| continue
|
|
|
| x, y, w, h = cv2.boundingRect(cnt)
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| centroid = get_centroid(x, y, w, h)
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|
|
|
|
| matched_id = None
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| for oid, trace in object_traces.items():
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| if np.linalg.norm(np.array(trace[-1]) - np.array(centroid)) < 50:
|
| matched_id = oid
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| break
|
|
|
| if matched_id is None:
|
| matched_id = object_id_counter
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| object_id_counter += 1
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|
|
| object_traces[matched_id].append(centroid)
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| object_last_seen[matched_id] = time.time()
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| current_ids.append(matched_id)
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|
|
| trace = object_traces[matched_id]
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| duration = time.time() - object_last_seen[matched_id] + 0.001
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| speed = calculate_speed(trace, duration)
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| direction = calculate_direction(trace)
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| direction_changes = count_direction_changes(trace)
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| total_move = sum(np.linalg.norm(np.array(trace[i]) - np.array(trace[i-1])) for i in range(1, len(trace)))
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|
|
|
|
| feature = [w, h, centroid[0], centroid[1], area, speed, direction_changes]
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| label = 1
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|
|
|
|
| if speed > 100 or direction_changes > 4:
|
| label = 2
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|
|
| features_set.append(feature)
|
| labels_set.append(label)
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|
|
| if len(features_set) > 10 and frame_count % learning_interval == 0:
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| knn.fit(features_set, labels_set)
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| print("مدل بهروزرسانی شد.")
|
|
|
| predicted = "-"
|
| if len(features_set) > 10:
|
| predicted = knn.predict([feature])[0]
|
|
|
|
|
| cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0) if label == 1 else (0, 0, 255), 2)
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| cv2.circle(frame, centroid, 4, (255, 255, 255), -1)
|
| cv2.putText(frame, f"ID: {matched_id} | جهت: {direction} | سرعت: {int(speed)}", (x, y - 25),
|
| cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 0), 1)
|
| cv2.putText(frame, f"رفتار: {'عادی' if predicted == 1 else 'مشکوک'}", (x, y - 5),
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| cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 255), 1)
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|
|
| frame_count += 1
|
|
|
|
|
| for oid in list(object_last_seen):
|
| if time.time() - object_last_seen[oid] > 2:
|
| object_traces.pop(oid, None)
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| object_last_seen.pop(oid, None)
|
|
|
| cv2.imshow("هوش رفتاری", frame)
|
| if cv2.waitKey(1) & 0xFF == 27:
|
| break
|
|
|
| cap.release()
|
| cv2.destroyAllWindows()
|
|
|
