| import cv2
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| import numpy as np
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| from collections import deque, defaultdict
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| import time
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|
|
|
|
| trace_len = 20
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| min_area = 500
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|
|
|
|
| object_traces = defaultdict(lambda: deque(maxlen=trace_len))
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| long_term_memory = defaultdict(list)
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| next_object_id = 1
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| object_centroids = {}
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|
|
| def count_direction_changes(trace):
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| count = 0
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| for i in range(2, len(trace)):
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| v1 = np.array(trace[i - 1]) - np.array(trace[i - 2])
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| v2 = np.array(trace[i]) - np.array(trace[i - 1])
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| if np.dot(v1, v2) < 0:
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| count += 1
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| return count
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|
|
| def extract_features(trace):
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| if len(trace) < 2:
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| return [0, 0, 0, 0]
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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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| total_distance = sum(np.linalg.norm(np.array(trace[i]) - np.array(trace[i-1])) for i in range(1, len(trace)))
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| avg_speed = total_distance / (len(trace) + 1e-6)
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| direction_changes = count_direction_changes(trace)
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| return [dx, dy, avg_speed, direction_changes]
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|
|
| def ai_brain(trace, memory):
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| if len(trace) < 3:
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| return "Unknown"
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| dx, dy, speed, changes = extract_features(trace)
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|
|
| if len(memory) >= 5 and memory.count("Erratic") > 3:
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| return "Suspicious"
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| if speed > 150 and changes > 4:
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| return "Erratic"
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| if speed < 5 and changes == 0:
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| return "Idle"
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| return "Normal"
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|
|
| def get_color(i):
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| np.random.seed(i)
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| return tuple(int(x) for x in np.random.randint(100, 255, 3))
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|
|
|
|
| cap = cv2.VideoCapture(0)
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| ret, prev = cap.read()
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| prev_gray = cv2.cvtColor(prev, cv2.COLOR_BGR2GRAY)
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| prev_gray = cv2.GaussianBlur(prev_gray, (21, 21), 0)
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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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| gray_blur = cv2.GaussianBlur(gray, (21, 21), 0)
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|
|
|
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| delta = cv2.absdiff(prev_gray, gray_blur)
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| thresh = cv2.threshold(delta, 25, 255, cv2.THRESH_BINARY)[1]
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| thresh = cv2.dilate(thresh, None, iterations=2)
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|
|
| contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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| current_centroids = []
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|
|
| for cnt in contours:
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| if cv2.contourArea(cnt) < min_area:
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| continue
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| (x, y, w, h) = cv2.boundingRect(cnt)
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| cx, cy = x + w // 2, y + h // 2
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| current_centroids.append((cx, cy))
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| matched_id = None
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|
|
|
|
| for object_id, last_centroid in object_centroids.items():
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| if np.linalg.norm(np.array([cx, cy]) - np.array(last_centroid)) < 50:
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| matched_id = object_id
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| break
|
|
|
| if matched_id is None:
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| matched_id = next_object_id
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| next_object_id += 1
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|
|
| object_centroids[matched_id] = (cx, cy)
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| object_traces[matched_id].append((cx, cy))
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| trace = object_traces[matched_id]
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|
|
| behavior = ai_brain(trace, [m['status'] for m in long_term_memory[matched_id]])
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| long_term_memory[matched_id].append({'status': behavior, 'timestamp': time.time()})
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|
|
| color = get_color(matched_id)
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| cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
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| cv2.putText(frame, f"ID {matched_id}", (x, y - 20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)
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| cv2.putText(frame, f"Behavior: {behavior}", (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 255), 1)
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|
|
|
|
| inactive_ids = [obj_id for obj_id in object_centroids if obj_id not in [id for id, _ in object_centroids.items()]]
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| for iid in inactive_ids:
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| object_centroids.pop(iid, None)
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|
|
| prev_gray = gray_blur.copy()
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| cv2.imshow("Motion AI", frame)
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| if cv2.waitKey(1) & 0xFF == ord("q"):
|
| break
|
|
|
| cap.release()
|
| cv2.destroyAllWindows()
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|
|
