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import cv2
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
# ======== AI MODEL (PyTorch) ========
device = torch.device("cpu")
label_map = {"Idle": 0, "Normal": 1, "Erratic": 2}
reverse_label = {v: k for k, v in label_map.items()}
class BehaviorAI(nn.Module):
def __init__(self):
super().__init__()
self.model = nn.Sequential(
nn.Linear(4, 16),
nn.ReLU(),
nn.Linear(16, 8),
nn.ReLU(),
nn.Linear(8, 3)
)
self.loss_fn = nn.CrossEntropyLoss()
self.optimizer = optim.Adam(self.model.parameters(), lr=0.001)
def forward(self, x):
return self.model(x)
def predict_behavior(self, features):
self.model.eval()
with torch.no_grad():
x = torch.tensor([features], dtype=torch.float32).to(device)
logits = self.model(x)
pred = torch.argmax(logits, dim=-1).item()
return reverse_label[pred]
def learn_from(self, features, label):
self.model.train()
x = torch.tensor([features], dtype=torch.float32).to(device)
y = torch.tensor([label_map[label]], dtype=torch.long).to(device)
logits = self.model(x)
loss = self.loss_fn(logits, y)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# ======== FEATURE EXTRACTION ========
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]
speeds = []
directions = []
for i in range(1, len(trace)):
x1, y1 = trace[i-1]
x2, y2 = trace[i]
dist = np.linalg.norm([x2 - x1, y2 - y1])
speeds.append(dist)
directions.append(np.arctan2(y2 - y1, x2 - x1))
avg_speed = np.mean(speeds)
direction_changes = np.sum(np.abs(np.diff(directions)))
return [dx, dy, avg_speed, direction_changes]
# ======== MAIN REAL-TIME TRACKING ========
cap = cv2.VideoCapture(0) # یا 'video.mp4' برای فایل
bg_subtractor = cv2.createBackgroundSubtractorMOG2()
traces = {}
next_id = 0
ai = BehaviorAI()
while True:
ret, frame = cap.read()
if not ret:
break
fgmask = bg_subtractor.apply(frame)
contours, _ = cv2.findContours(fgmask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
current_positions = []
for cnt in contours:
if cv2.contourArea(cnt) < 500:
continue
x, y, w, h = cv2.boundingRect(cnt)
cx, cy = x + w // 2, y + h // 2
current_positions.append((cx, cy))
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
new_traces = {}
matched_ids = set()
for cx, cy in current_positions:
min_dist = float('inf')
matched_id = None
for id, trace in traces.items():
if len(trace) == 0:
continue
prev_x, prev_y = trace[-1]
dist = np.linalg.norm([cx - prev_x, cy - prev_y])
if dist < 50 and id not in matched_ids:
min_dist = dist
matched_id = id
if matched_id is None:
matched_id = next_id
next_id += 1
new_traces[matched_id] = []
else:
new_traces[matched_id] = traces[matched_id]
new_traces[matched_id].append((cx, cy))
matched_ids.add(matched_id)
traces = new_traces
for id, trace in traces.items():
if len(trace) >= 2:
for i in range(1, len(trace)):
cv2.line(frame, trace[i-1], trace[i], (255, 0, 0), 2)
features = extract_features(trace)
behavior = ai.predict_behavior(features)
if len(trace) >= 10:
if features[2] < 2:
label = "Idle"
elif features[3] > 4:
label = "Erratic"
else:
label = "Normal"
ai.learn_from(features, label)
cv2.putText(frame, f"ID:{id} AI:{behavior}", trace[-1], cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 255), 1)
cv2.imshow("Real-Time Tracker with AI", frame)
if cv2.waitKey(1) == 27: # ESC
break
cap.release()
cv2.destroyAllWindows()
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