| | import cv2
|
| | import torch
|
| | import torch.nn as nn
|
| | import torch.optim as optim
|
| | import numpy as np
|
| |
|
| |
|
| | 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()
|
| |
|
| |
|
| | 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]
|
| |
|
| |
|
| | cap = cv2.VideoCapture(0)
|
| |
|
| | 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:
|
| | break
|
| |
|
| | cap.release()
|
| | cv2.destroyAllWindows()
|
| |
|
