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| from shapely.geometry import box
import cv2
from ultralytics import YOLO
from typing import Tuple
import numpy as np
class DangerDetector:
"""路面危险评估API核心类(精确面积计算版)"""
def __init__(self, model_path: str, conf_threshold: float = 0.5):
self.model = YOLO(model_path)
self.conf_threshold = conf_threshold
self.class_map = {0: "snow", 1: "ice"}
self.priority = {"ice": 3, "snow": 2, "water": 1}
self.messages = {
"ice": "⚠️ 路面存在结冰区域，请保持车距并使用防滑链！",
"snow": "❄️ 路面存在积雪，请保持车距！",
"water": "💧 路面湿滑，建议减速慢行！",
"safe": "✅ 路面状况安全，可正常行驶"
}
def _calculate_danger_level(self, results, image_width: int, image_height: int) -> Tuple[str, str]:
"""优化后的危险评估算法"""
detections = []
if results.boxes:
boxes = results.boxes.cpu().numpy()
for i in range(len(boxes.xyxy)):
x1, y1, x2, y2 = boxes.xyxy[i]
conf = boxes.conf[i]
cls_id = int(boxes.cls[i])
area = (x2 - x1) * (y2 - y1)
detections.append({
"class": self.class_map[cls_id],
"coords": (x1, y1, x2, y2),
"confidence": conf,
"area": area
})
sorted_detections = sorted(
detections,
key=lambda x: (
-self.priority.get(x["class"], 0),
-x["area"],
-x["confidence"]
)
)
covered_areas = []
ice_area = snow_area = water_area = 0
for detection in sorted_detections:
cls = detection["class"]
x1, y1, x2, y2 = detection["coords"]
current_box = box(x1, y1, x2, y2)
current_polygon = current_box
valid_area = current_polygon
for existing in covered_areas:
if valid_area.intersects(existing["geometry"]):
valid_area = valid_area.difference(existing["geometry"])
effective_area = valid_area.area
if effective_area > 0:
for existing in covered_areas:
if current_polygon.intersects(existing["geometry"]):
overlap = existing["geometry"].intersection(current_polygon)
if overlap.area > 0:
if existing["class"] == "ice":
ice_area -= overlap.area
elif existing["class"] == "snow":
snow_area -= overlap.area
elif existing["class"] == "water":
water_area -= overlap.area
if cls == "ice":
ice_area += effective_area
elif cls == "snow":
snow_area += effective_area
elif cls == "water":
water_area += effective_area
new_geometry = current_polygon
for existing in covered_areas:
if new_geometry.intersects(existing["geometry"]):
new_geometry = new_geometry.union(existing["geometry"])
covered_areas.append({
"class": cls,
"geometry": new_geometry,
"confidence": detection["confidence"]
})
total_pixels = image_width * image_height
weighted_ice = ice_area
weighted_snow = snow_area
weighted_water = water_area
danger_value = (weighted_ice + weighted_snow + weighted_water) / total_pixels
thresholds = {
"high": 0.7 ,
"medium": 0.4 ,
"low": 0.2
}
if danger_value >= thresholds["high"]:
danger_level = "高度危险"
elif danger_value >= thresholds["medium"]:
danger_level = "中度危险"
elif danger_value >= thresholds["low"]:
danger_level = "轻度危险"
else:
danger_level = "安全"
dominant_class = max(
["ice", "snow", "water"],
key=lambda x: (ice_area, snow_area, water_area)[["ice", "snow", "water"].index(x)]
)
message = self.messages[dominant_class] if danger_level != "安全" else self.messages["safe"]
return danger_level, message
def predict_from_image(self, image_path: str) -> dict:
"""
从图片文件路径进行预测
:param image_path: 图片文件路径
:return: 包含危险等级和提示信息的字典
"""
results = self.model.predict(
source=image_path,
save=True,
conf=self.conf_threshold
)
img = results[0].orig_img
h, w = img.shape[:2]
danger_level, message = self._calculate_danger_level(results[0], w, h)
return {
"danger_level": danger_level,
"message": message,
"image_size": (w, h),
"results": results
}
def predict_from_array(self, image_array : np.ndarray) -> dict:
"""
从numpy数组进行预测
:param image_array: 输入图像数组 (HWC格式)
:return: 包含危险等级和提示信息的字典
"""
results = self.model.predict(
source=image_array,
save=False,
conf=self.conf_threshold
)
h, w = image_array.shape[:2]
danger_level, message = self._calculate_danger_level(results[0], w, h)
return {
"danger_level": danger_level,
"message": message,
"image_size": (w, h),
"results": results
}
|
