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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
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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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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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predicted = 1
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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
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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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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:
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matched_id = oid
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break
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if matched_id is None:
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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:
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label = 2
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features_set.append(feature)
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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.")
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if len(features_set) > 10:
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predicted = knn.predict([feature])[0]
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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)
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cv2.putText(frame, f"ID: {matched_id} | جهت: {direction} | سرعت: {int(speed)}", (x, y - 25),
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cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 0), 1)
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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
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for oid in list(object_last_seen):
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if time.time() - object_last_seen[oid] > 2:
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object_traces.pop(oid, None)
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object_last_seen.pop(oid, None)
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cv2.imshow("هوش رفتاری", 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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