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| import numpy as np
from typing import Tuple, Optional
class OccupantPostureMonitor:
"""
乘员姿态监控系统
基于 3D 深度摄像头检测危险坐姿
"""
def __init__(self):
self.danger_distance = 20.0
self.dashboard_plane = np.array([0, 0, 1, -30])
def analyze_posture(self, depth_map: np.ndarray,
body_keypoints: dict) -> dict:
"""
分析乘员姿态
Args:
depth_map: 深度图 (H, W), 单位 cm
body_keypoints: 身体关键点
- head: (x, y, z) 头部位置
- torso: [(x,y,z), ...] 躯干关键点
- feet: [(x,y,z), ...] 脚部位置
Returns:
姿态分析结果
"""
result = {
'is_safe': True,
'warnings': [],
'min_distance': float('inf'),
'feet_on_dash': False,
'lean_forward': False
}
if 'feet' in body_keypoints:
result['feet_on_dash'] = self._check_feet_on_dashboard(
body_keypoints['feet'],
depth_map
)
if result['feet_on_dash']:
result['warnings'].append('FEET_ON_DASHBOARD')
result['is_safe'] = False
if 'head' in body_keypoints and 'torso' in body_keypoints:
result['lean_forward'], min_dist = self._check_lean_forward(
body_keypoints['head'],
body_keypoints['torso'],
depth_map
)
result['min_distance'] = min_dist
if result['lean_forward']:
result['warnings'].append('LEAN_FORWARD')
result['is_safe'] False
return result
def _check_feet_on_dashboard(self, feet_positions: list,
depth_map: np.ndarray) -> bool:
"""
检测脚是否放在仪表板上
仪表板区域定义为：
- z < 40cm (距摄像头较近)
- y > 车辆中线
"""
for foot in feet_positions:
x, y, z = foot
if z < 40:
if 0 <= int(y) < depth_map.shape[0] and 0 <= int(x) < depth_map.shape[1]:
depth_value = depth_map[int(y), int(x)]
if depth_value < 40:
return True
return False
def _check_lean_forward(self, head_pos: tuple,
torso_points: list,
depth_map: np.ndarray) -> Tuple[bool, float]:
"""
检测身体前倾
Returns:
(是否前倾, 最小距离)
"""
hx, hy, hz = head_pos
dashboard_z = 30
head_distance = hz - dashboard_z
if torso_points:
torso_distances = [p[2] - dashboard_z for p in torso_points]
min_torso_distance = min(torso_distances)
else:
min_torso_distance = head_distance
min_distance = min(head_distance, min_torso_distance)
is_dangerous = min_distance < self.danger_distance
return is_dangerous, min_distance
def get_airbag_strategy(self, posture_result: dict,
occupant_size: str) -> dict:
"""
根据姿态和乘员体型确定安全气囊策略
Args:
posture_result: 姿态分析结果
occupant_size: 乘员体型 ('small', 'medium', 'large')
Returns:
安全气囊策略
"""
strategy = {
'airbag_enabled': True,
'deployment_force': 'standard',
'pretensioner_timing': 'normal',
'suppress_warning': False
}
if not posture_result['is_safe']:
if posture_result['feet_on_dash']:
strategy['airbag_enabled'] = False
strategy['suppress_warning'] = True
elif posture_result['lean_forward']:
strategy['deployment_force'] = 'low'
if occupant_size == 'small':
if strategy['deployment_force'] == 'standard':
strategy['deployment_force'] = 'low'
strategy['pretensioner_timing'] = 'early'
elif occupant_size == 'large':
strategy['deployment_force'] = 'high'
return strategy
if __name__ == "__main__":
monitor = OccupantPostureMonitor()
depth_map = np.random.uniform(30, 100, (480, 640))
body_keypoints = {
'head': (320, 240, 35),
'torso': [
(320, 300, 40),
(320, 350, 45),
(320, 400, 50)
],
'feet': []
}
result = monitor.analyze_posture(depth_map, body_keypoints)
print(f"姿态安全: {result['is_safe']}")
print(f"警告: {result['warnings']}")
print(f"最小距离: {result['min_distance']:.1f} cm")
strategy = monitor.get_airbag_strategy(result, 'medium')
print(f"安全气囊策略: {strategy}")
|