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import cv2
import numpy as np
import time
from sklearn.neighbors import KNeighborsClassifier
from collections import defaultdict, deque
back_sub = cv2.createBackgroundSubtractorKNN(history=500, dist2Threshold=400, detectShadows=True)
cap = cv2.VideoCapture(0)
# ذخیره مسیر اشیاء
object_traces = defaultdict(lambda: deque(maxlen=30)) # آخرین ۳۰ نقطه هر شیء
object_last_seen = {}
object_id_counter = 0
# برای یادگیری real-time
knn = KNeighborsClassifier(n_neighbors=3)
features_set = []
labels_set = []
frame_count = 0
learning_interval = 30
def apply_noise_reduction(mask):
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel, iterations=2)
mask = cv2.dilate(mask, kernel, iterations=1)
return mask
def get_centroid(x, y, w, h):
return (int(x + w / 2), int(y + h / 2))
def calculate_direction(trace):
if len(trace) < 2:
return "-"
dx = trace[-1][0] - trace[0][0]
dy = trace[-1][1] - trace[0][1]
if abs(dx) > abs(dy):
return "چپ" if dx < 0 else "راست"
else:
return "بالا" if dy < 0 else "پایین"
def calculate_speed(trace, duration):
if len(trace) < 2 or duration == 0:
return 0
dist = np.linalg.norm(np.array(trace[-1]) - np.array(trace[0]))
return dist / duration
def count_direction_changes(trace):
changes = 0
for i in range(2, len(trace)):
dx1 = trace[i-1][0] - trace[i-2][0]
dx2 = trace[i][0] - trace[i-1][0]
if dx1 * dx2 < 0: # تغییر جهت افقی
changes += 1
return changes
while True:
ret, frame = cap.read()
if not ret:
break
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
fg_mask = back_sub.apply(frame)
fg_mask = apply_noise_reduction(fg_mask)
contours, _ = cv2.findContours(fg_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
current_ids = []
predicted = 1 # مقدار پیش‌فرض برای پیش‌بینی (در صورتی که پیش‌بینی انجام نشود)
for cnt in contours:
area = cv2.contourArea(cnt)
if area < 150:
continue
x, y, w, h = cv2.boundingRect(cnt)
centroid = get_centroid(x, y, w, h)
# شناسایی یا ایجاد شناسه جدید
matched_id = None
for oid, trace in object_traces.items():
if np.linalg.norm(np.array(trace[-1]) - np.array(centroid)) < 50:
matched_id = oid
break
if matched_id is None:
matched_id = object_id_counter
object_id_counter += 1
object_traces[matched_id].append(centroid)
object_last_seen[matched_id] = time.time()
current_ids.append(matched_id)
trace = object_traces[matched_id]
duration = time.time() - object_last_seen[matched_id] + 0.001
speed = calculate_speed(trace, duration)
direction = calculate_direction(trace)
direction_changes = count_direction_changes(trace)
total_move = sum(np.linalg.norm(np.array(trace[i]) - np.array(trace[i-1])) for i in range(1, len(trace)))
# ویژگی برای مدل
feature = [w, h, centroid[0], centroid[1], area, speed, direction_changes]
label = 1 # کلاس پیش‌فرض: عادی
# برچسب‌گذاری خودکار ساده:
if speed > 100 or direction_changes > 4:
label = 2 # مشکوک
features_set.append(feature)
labels_set.append(label)
# آموزش مدل فقط زمانی که داده‌های کافی وجود داشته باشد
if len(features_set) > 10 and frame_count % learning_interval == 0:
knn.fit(features_set, labels_set)
print("مدل به‌روزرسانی شد.")
# فقط پیش‌بینی بعد از آموزش
if len(features_set) > 10:
predicted = knn.predict([feature])[0]
# رسم اطلاعات روی فریم
cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0) if label == 1 else (0, 0, 255), 2)
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),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 255), 1)
frame_count += 1
# حذف آی‌دی‌های قدیمی
for oid in list(object_last_seen):
if time.time() - object_last_seen[oid] > 2:
object_traces.pop(oid, None)
object_last_seen.pop(oid, None)
cv2.imshow("هوش رفتاری", frame)
if cv2.waitKey(1) & 0xFF == 27:
break
cap.release()
cv2.destroyAllWindows()
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