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