算法接口
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server/.gitignore → .gitignore
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server/.gitignore → .gitignore
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algorithm/__init__.py
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algorithm/__init__.py
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543
algorithm/detection.py
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543
algorithm/detection.py
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import cv2
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import supervision as sv
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from ultralytics import YOLO
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import os
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import time
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import numpy as np
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from tqdm import tqdm
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from PIL import Image, ImageDraw, ImageFont
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import io
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from typing import List, Tuple, Optional, Dict, Any
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import json
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class DetectionProcessor:
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"""
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目标检测处理器类
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用于处理视频中的目标检测任务
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"""
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def __init__(self, model_path: str = "yolov11n.pt"):
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"""
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初始化检测处理器
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Args:
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model_path: YOLO模型路径
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"""
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self.model_path = model_path
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self.model = None
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self._load_model()
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def _load_model(self):
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"""加载YOLO模型"""
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try:
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print(f"加载检测模型: {self.model_path}")
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self.model = YOLO(self.model_path)
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except Exception as e:
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print(f"模型加载失败: {e}")
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raise
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def _put_chinese_text(self, img, text, position, font_size=20, color=(0, 0, 255)):
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"""在图像上添加中文文本"""
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img_pil = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
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draw = ImageDraw.Draw(img_pil)
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try:
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font = ImageFont.truetype("msyh.ttc", font_size)
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except:
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font = ImageFont.load_default()
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draw.text(position, text, font=font, fill=color)
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return cv2.cvtColor(np.array(img_pil), cv2.COLOR_RGB2BGR)
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def process_video(
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self,
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input_video_path: str,
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output_video_path: str,
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confidence_threshold: float = 0.5,
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classes: Optional[List[int]] = None,
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show_live: bool = False,
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save_annotated: bool = True,
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warning_zones: Optional[List[List[Tuple[float, float]]]] = None,
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) -> Dict[str, Any]:
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"""
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处理视频进行目标检测
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Args:
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input_video_path: 输入视频路径
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output_video_path: 输出视频路径
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confidence_threshold: 置信度阈值
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classes: 要检测的类别ID列表
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show_live: 是否实时显示
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save_annotated: 是否保存标注视频
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warning_zones: 警告区域多边形点列表
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Returns:
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处理结果字典
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"""
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if not os.path.exists(input_video_path):
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raise FileNotFoundError(f"输入视频文件不存在: {input_video_path}")
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# 初始化视频读取器
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video_info = sv.VideoInfo.from_video_path(input_video_path)
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cap = cv2.VideoCapture(input_video_path)
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if not cap.isOpened():
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raise RuntimeError(f"无法打开视频文件: {input_video_path}")
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# 初始化视频写入器
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writer = None
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if save_annotated:
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output_dir = os.path.dirname(output_video_path)
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if output_dir and not os.path.exists(output_dir):
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os.makedirs(output_dir)
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writer = cv2.VideoWriter(
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output_video_path,
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cv2.VideoWriter_fourcc(*"mp4v"),
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video_info.fps,
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(video_info.width, video_info.height),
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)
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# 初始化Supervision工具
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box_annotator = sv.BoxAnnotator()
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label_annotator = sv.LabelAnnotator()
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# 处理统计
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total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
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frame_count = 0
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processing_times = []
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detection_results = []
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warning_events = []
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pbar = tqdm(total=total_frames, desc="处理视频帧")
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while cap.isOpened():
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start_time = time.time()
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ret, frame = cap.read()
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if not ret:
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break
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frame_count += 1
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# 目标检测
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results = self.model(frame, conf=confidence_threshold, classes=classes, verbose=False)[0]
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detections = sv.Detections.from_ultralytics(results)
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# 准备标注
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labels = [
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f"{results.names[class_id]} {confidence:.2f}"
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for class_id, confidence in zip(detections.class_id, detections.confidence)
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]
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# 标注边界框
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annotated_frame = box_annotator.annotate(scene=frame.copy(), detections=detections)
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# 标注标签
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annotated_frame = label_annotator.annotate(scene=annotated_frame, detections=detections, labels=labels)
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# 处理警告区域
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if warning_zones:
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for i, zone in enumerate(warning_zones):
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pts = [(int(x), int(y)) for x, y in zone]
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color = (0, 255, 0) if i == 0 else (0, 0, 255)
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cv2.polylines(annotated_frame, [np.array(pts)], isClosed=True, color=color, thickness=2)
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# 检测目标是否在警告区域内
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polygon = np.array(pts, np.int32)
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for bbox in detections.xyxy:
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x1, y1, x2, y2 = bbox
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center_x = int((x1 + x2) / 2)
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center_y = int((y1 + y2) / 2)
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distance = cv2.pointPolygonTest(polygon, (center_x, center_y), False)
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if distance >= 0:
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warning_text = f"警告: 目标进入区域{i+1}!"
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annotated_frame = self._put_chinese_text(
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annotated_frame, warning_text, (50, 50 + i * 30), font_size=20, color=(255, 0, 0)
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)
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warning_events.append(
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{"frame": frame_count, "zone": i + 1, "position": (center_x, center_y)}
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)
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# 记录检测结果
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frame_detections = []
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for i, (bbox, class_id, confidence) in enumerate(
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zip(detections.xyxy, detections.class_id, detections.confidence)
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):
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frame_detections.append(
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{
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"bbox": bbox.tolist(),
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"class_id": int(class_id),
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"class_name": results.names[int(class_id)],
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"confidence": float(confidence),
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}
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)
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detection_results.append({"frame": frame_count, "detections": frame_detections})
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# 计算处理时间
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end_time = time.time()
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processing_time = end_time - start_time
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processing_times.append(processing_time)
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# 实时显示
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if show_live:
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cv2.imshow("Detection", annotated_frame)
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if cv2.waitKey(1) == 27:
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break
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# 保存帧
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if save_annotated and writer:
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writer.write(annotated_frame)
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pbar.update(1)
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# 清理资源
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cap.release()
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if writer:
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writer.release()
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if show_live:
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cv2.destroyAllWindows()
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pbar.close()
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# 计算统计信息
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avg_time = sum(processing_times) / len(processing_times) if processing_times else 0
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fps = 1 / avg_time if avg_time > 0 else 0
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return {
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"success": True,
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"total_frames": frame_count,
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"avg_processing_time": avg_time,
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"fps": fps,
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"detection_results": detection_results,
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"warning_events": warning_events,
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"output_video_path": output_video_path if save_annotated else None,
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}
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def process_image(
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self,
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image_path: str,
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confidence_threshold: float = 0.5,
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classes: Optional[List[int]] = None,
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warning_zones: Optional[List[List[Tuple[float, float]]]] = None,
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) -> Dict[str, Any]:
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"""
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处理单张图像进行目标检测
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Args:
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image_path: 输入图像路径
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confidence_threshold: 置信度阈值
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classes: 要检测的类别ID列表
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warning_zones: 警告区域多边形点列表
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Returns:
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检测结果字典
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"""
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if not os.path.exists(image_path):
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raise FileNotFoundError(f"输入图像文件不存在: {image_path}")
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# 读取图像
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frame = cv2.imread(image_path)
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if frame is None:
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raise RuntimeError(f"无法读取图像文件: {image_path}")
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# 目标检测
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results = self.model(frame, conf=confidence_threshold, classes=classes, verbose=False)[0]
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detections = sv.Detections.from_ultralytics(results)
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# 准备标注
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labels = [
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f"{results.names[class_id]} {confidence:.2f}"
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for class_id, confidence in zip(detections.class_id, detections.confidence)
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]
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# 标注边界框
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annotated_frame = sv.BoxAnnotator().annotate(scene=frame.copy(), detections=detections)
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# 标注标签
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annotated_frame = sv.LabelAnnotator().annotate(scene=annotated_frame, detections=detections, labels=labels)
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# 处理警告区域
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warning_events = []
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if warning_zones:
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for i, zone in enumerate(warning_zones):
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pts = [(int(x), int(y)) for x, y in zone]
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color = (0, 255, 0) if i == 0 else (0, 0, 255)
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cv2.polylines(annotated_frame, [np.array(pts)], isClosed=True, color=color, thickness=2)
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polygon = np.array(pts, np.int32)
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for bbox in detections.xyxy:
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x1, y1, x2, y2 = bbox
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center_x = int((x1 + x2) / 2)
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center_y = int((y1 + y2) / 2)
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distance = cv2.pointPolygonTest(polygon, (center_x, center_y), False)
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if distance >= 0:
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warning_text = f"警告: 目标进入区域{i+1}!"
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annotated_frame = self._put_chinese_text(
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annotated_frame, warning_text, (50, 50 + i * 30), font_size=20, color=(255, 0, 0)
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)
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warning_events.append({"zone": i + 1, "position": (center_x, center_y)})
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# 记录检测结果
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detection_results = []
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for bbox, class_id, confidence in zip(detections.xyxy, detections.class_id, detections.confidence):
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detection_results.append(
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{
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"bbox": bbox.tolist(),
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"class_id": int(class_id),
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"class_name": results.names[int(class_id)],
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"confidence": float(confidence),
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}
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)
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return {
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"success": True,
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"detection_results": detection_results,
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"warning_events": warning_events,
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"annotated_image": annotated_frame,
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}
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class SegmentationProcessor:
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"""
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图像分割处理器类
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用于处理视频中的图像分割任务
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"""
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def __init__(self, model_path: str = "yolov8n-seg.pt"):
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"""
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初始化分割处理器
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Args:
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model_path: YOLO分割模型路径
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"""
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self.model_path = model_path
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self.model = None
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self._load_model()
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def _load_model(self):
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"""加载YOLO分割模型"""
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try:
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print(f"加载分割模型: {self.model_path}")
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self.model = YOLO(self.model_path)
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except Exception as e:
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print(f"模型加载失败: {e}")
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raise
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def process_video(
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self,
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input_video_path: str,
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output_video_path: str,
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confidence_threshold: float = 0.5,
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classes: Optional[List[int]] = None,
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show_live: bool = False,
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save_annotated: bool = True,
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) -> Dict[str, Any]:
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"""
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处理视频进行图像分割
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Args:
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input_video_path: 输入视频路径
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output_video_path: 输出视频路径
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confidence_threshold: 置信度阈值
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classes: 要检测的类别ID列表
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show_live: 是否实时显示
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save_annotated: 是否保存标注视频
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Returns:
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处理结果字典
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"""
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if not os.path.exists(input_video_path):
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raise FileNotFoundError(f"输入视频文件不存在: {input_video_path}")
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# 初始化视频读取器
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video_info = sv.VideoInfo.from_video_path(input_video_path)
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cap = cv2.VideoCapture(input_video_path)
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if not cap.isOpened():
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raise RuntimeError(f"无法打开视频文件: {input_video_path}")
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# 初始化视频写入器
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writer = None
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if save_annotated:
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output_dir = os.path.dirname(output_video_path)
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if output_dir and not os.path.exists(output_dir):
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os.makedirs(output_dir)
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writer = cv2.VideoWriter(
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output_video_path,
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cv2.VideoWriter_fourcc(*"mp4v"),
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video_info.fps,
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(video_info.width, video_info.height),
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)
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# 初始化Supervision工具
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mask_annotator = sv.MaskAnnotator(color=sv.Color(r=0, g=255, b=0), opacity=0.5)
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# 处理统计
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total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
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frame_count = 0
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processing_times = []
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segmentation_results = []
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pbar = tqdm(total=total_frames, desc="处理视频帧")
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while cap.isOpened():
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start_time = time.time()
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ret, frame = cap.read()
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if not ret:
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break
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frame_count += 1
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# 图像分割
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results = self.model(frame, conf=confidence_threshold, classes=classes, verbose=False)[0]
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detections = sv.Detections.from_ultralytics(results)
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# 应用分割掩码
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segmented_frame = mask_annotator.annotate(scene=frame.copy(), detections=detections)
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# 记录分割结果
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frame_segmentations = []
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for i, (bbox, class_id, confidence) in enumerate(
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zip(detections.xyxy, detections.class_id, detections.confidence)
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):
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frame_segmentations.append(
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{
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"bbox": bbox.tolist(),
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"class_id": int(class_id),
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"class_name": results.names[int(class_id)],
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"confidence": float(confidence),
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}
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)
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segmentation_results.append({"frame": frame_count, "segmentations": frame_segmentations})
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# 计算处理时间
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end_time = time.time()
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processing_time = end_time - start_time
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processing_times.append(processing_time)
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# 实时显示
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if show_live:
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cv2.imshow("Segmentation", segmented_frame)
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if cv2.waitKey(1) == 27:
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break
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# 保存帧
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if save_annotated and writer:
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writer.write(segmented_frame)
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pbar.update(1)
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# 清理资源
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cap.release()
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if writer:
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writer.release()
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if show_live:
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cv2.destroyAllWindows()
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pbar.close()
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# 计算统计信息
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avg_time = sum(processing_times) / len(processing_times) if processing_times else 0
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fps = 1 / avg_time if avg_time > 0 else 0
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return {
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"success": True,
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"total_frames": frame_count,
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"avg_processing_time": avg_time,
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"fps": fps,
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"segmentation_results": segmentation_results,
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"output_video_path": output_video_path if save_annotated else None,
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}
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def process_image(
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self, image_path: str, confidence_threshold: float = 0.5, classes: Optional[List[int]] = None
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) -> Dict[str, Any]:
|
||||
"""
|
||||
处理单张图像进行图像分割
|
||||
|
||||
Args:
|
||||
image_path: 输入图像路径
|
||||
confidence_threshold: 置信度阈值
|
||||
classes: 要检测的类别ID列表
|
||||
|
||||
Returns:
|
||||
分割结果字典
|
||||
"""
|
||||
if not os.path.exists(image_path):
|
||||
raise FileNotFoundError(f"输入图像文件不存在: {image_path}")
|
||||
|
||||
# 读取图像
|
||||
frame = cv2.imread(image_path)
|
||||
if frame is None:
|
||||
raise RuntimeError(f"无法读取图像文件: {image_path}")
|
||||
|
||||
# 图像分割
|
||||
results = self.model(frame, conf=confidence_threshold, classes=classes, verbose=False)[0]
|
||||
|
||||
detections = sv.Detections.from_ultralytics(results)
|
||||
|
||||
# 应用分割掩码
|
||||
mask_annotator = sv.MaskAnnotator(color=sv.Color(r=0, g=255, b=0), opacity=0.5)
|
||||
|
||||
segmented_frame = mask_annotator.annotate(scene=frame.copy(), detections=detections)
|
||||
|
||||
# 记录分割结果
|
||||
segmentation_results = []
|
||||
for bbox, class_id, confidence in zip(detections.xyxy, detections.class_id, detections.confidence):
|
||||
segmentation_results.append(
|
||||
{
|
||||
"bbox": bbox.tolist(),
|
||||
"class_id": int(class_id),
|
||||
"class_name": results.names[int(class_id)],
|
||||
"confidence": float(confidence),
|
||||
}
|
||||
)
|
||||
|
||||
return {"success": True, "segmentation_results": segmentation_results, "segmented_image": segmented_frame}
|
||||
|
||||
|
||||
# 示例用法
|
||||
if __name__ == "__main__":
|
||||
# 目标检测示例
|
||||
detector = DetectionProcessor(model_path="yolov11n.pt")
|
||||
|
||||
# 定义警告区域
|
||||
warning_zones = [
|
||||
[(1153.11, 273.86), (1146.09, 370.77), (1217.71, 352.51), (1220.52, 257.01)],
|
||||
[(1140.47, 506.99), (1214.90, 504.19), (1212.09, 592.66), (1136.25, 594.07)],
|
||||
]
|
||||
|
||||
# 处理视频
|
||||
result = detector.process_video(
|
||||
input_video_path="input_video.mp4",
|
||||
output_video_path="output_detection.mp4",
|
||||
confidence_threshold=0.5,
|
||||
classes=[0], # 只检测人
|
||||
warning_zones=warning_zones,
|
||||
save_annotated=True,
|
||||
)
|
||||
|
||||
print("检测结果:", result)
|
||||
|
||||
# 图像分割示例
|
||||
segmenter = SegmentationProcessor(model_path="yolov8n-seg.pt")
|
||||
|
||||
# 处理视频
|
||||
seg_result = segmenter.process_video(
|
||||
input_video_path="input_video.mp4",
|
||||
output_video_path="output_segmentation.mp4",
|
||||
confidence_threshold=0.3,
|
||||
classes=[0], # 只检测人
|
||||
save_annotated=True,
|
||||
)
|
||||
|
||||
print("分割结果:", seg_result)
|
||||
BIN
server/border_inspection.db
Normal file
BIN
server/border_inspection.db
Normal file
Binary file not shown.
122
server/init_algorithms.py
Normal file
122
server/init_algorithms.py
Normal file
@ -0,0 +1,122 @@
|
||||
import os
|
||||
import sys
|
||||
import json
|
||||
from pathlib import Path
|
||||
|
||||
# 添加项目根目录到Python路径
|
||||
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
||||
|
||||
from server.core.database import get_db
|
||||
from server.models.algorithm import Algorithm
|
||||
from models.base import Base
|
||||
import os
|
||||
from core.database import engine
|
||||
|
||||
# 创建数据库表
|
||||
Base.metadata.create_all(bind=engine)
|
||||
|
||||
def init_algorithms():
|
||||
"""初始化算法数据，与algorithm目录保持一致"""
|
||||
|
||||
# 算法配置映射
|
||||
algorithm_configs = {
|
||||
"berthing": {
|
||||
"name": "靠泊检测",
|
||||
"description": "检测船舶靠泊过程中的关键行为",
|
||||
"version": "1.0.0",
|
||||
"detection_classes": ["person", "ship"],
|
||||
"input_size": "640x640",
|
||||
"tags": ["靠泊", "船舶", "安全"]
|
||||
},
|
||||
"unberthing": {
|
||||
"name": "离泊检测",
|
||||
"description": "检测船舶离泊过程中的关键行为",
|
||||
"version": "1.0.0",
|
||||
"detection_classes": ["person", "ship"],
|
||||
"input_size": "640x640",
|
||||
"tags": ["离泊", "船舶", "安全"]
|
||||
},
|
||||
"board_ship": {
|
||||
"name": "登轮检测",
|
||||
"description": "检测人员登轮行为",
|
||||
"version": "1.0.0",
|
||||
"detection_classes": ["person"],
|
||||
"input_size": "640x640",
|
||||
"tags": ["登轮", "人员", "安全"]
|
||||
},
|
||||
"leave_ship": {
|
||||
"name": "离轮检测",
|
||||
"description": "检测人员离轮行为",
|
||||
"version": "1.0.0",
|
||||
"detection_classes": ["person"],
|
||||
"input_size": "640x640",
|
||||
"tags": ["离轮", "人员", "安全"]
|
||||
},
|
||||
"bullet_frame": {
|
||||
"name": "弹窗检测",
|
||||
"description": "检测系统弹窗和异常界面",
|
||||
"version": "1.0.0",
|
||||
"detection_classes": ["window", "alert"],
|
||||
"input_size": "640x640",
|
||||
"tags": ["弹窗", "界面", "异常"]
|
||||
}
|
||||
}
|
||||
|
||||
db = next(get_db())
|
||||
|
||||
try:
|
||||
# 获取algorithm目录的绝对路径
|
||||
algorithm_dir = Path(__file__).parent.parent / "algorithm"
|
||||
|
||||
for algorithm_folder, config in algorithm_configs.items():
|
||||
folder_path = algorithm_dir / algorithm_folder
|
||||
|
||||
if folder_path.exists():
|
||||
# 检查模型文件
|
||||
weights_path = folder_path / "weights" / "best.pt"
|
||||
model_path = f"algorithm/{algorithm_folder}/weights/best.pt" if weights_path.exists() else None
|
||||
|
||||
# 检查是否已存在该算法
|
||||
existing_algorithm = db.query(Algorithm).filter(
|
||||
Algorithm.name == config["name"]
|
||||
).first()
|
||||
|
||||
if existing_algorithm:
|
||||
# 更新现有算法
|
||||
existing_algorithm.model_path = model_path
|
||||
existing_algorithm.detection_classes = json.dumps(config["detection_classes"])
|
||||
existing_algorithm.input_size = config["input_size"]
|
||||
existing_algorithm.tags = json.dumps(config["tags"])
|
||||
existing_algorithm.status = "active" if model_path else "inactive"
|
||||
print(f"更新算法: {config['name']}")
|
||||
else:
|
||||
# 创建新算法
|
||||
algorithm = Algorithm(
|
||||
name=config["name"],
|
||||
description=config["description"],
|
||||
version=config["version"],
|
||||
model_path=model_path,
|
||||
detection_classes=json.dumps(config["detection_classes"]),
|
||||
input_size=config["input_size"],
|
||||
tags=json.dumps(config["tags"]),
|
||||
status="active" if model_path else "inactive",
|
||||
accuracy=0.0, # 默认值
|
||||
inference_time=0.0, # 默认值
|
||||
is_enabled=True,
|
||||
creator="system"
|
||||
)
|
||||
db.add(algorithm)
|
||||
print(f"创建算法: {config['name']}")
|
||||
|
||||
db.commit()
|
||||
print("算法数据初始化完成!")
|
||||
|
||||
except Exception as e:
|
||||
db.rollback()
|
||||
print(f"初始化算法数据失败: {e}")
|
||||
raise
|
||||
finally:
|
||||
db.close()
|
||||
|
||||
if __name__ == "__main__":
|
||||
init_algorithms()
|
||||
@ -19,61 +19,6 @@ def init_sample_data():
|
||||
db = SessionLocal()
|
||||
|
||||
try:
|
||||
# 创建示例算法
|
||||
algorithms = [
|
||||
{
|
||||
"name": "YOLOv11n人员检测",
|
||||
"description": "基于YOLOv11n的人员检测算法，适用于边检场景",
|
||||
"version": "1.0.0",
|
||||
"model_path": "/models/yolo11n.pt",
|
||||
"config_path": "/configs/yolo11n.yaml",
|
||||
"status": "active",
|
||||
"accuracy": 0.95,
|
||||
"detection_classes": json.dumps(["person"]),
|
||||
"input_size": "640x640",
|
||||
"inference_time": 15.5,
|
||||
"is_enabled": True,
|
||||
"creator": "admin",
|
||||
"tags": json.dumps(["person", "detection", "yolo"])
|
||||
},
|
||||
{
|
||||
"name": "车辆检测算法",
|
||||
"description": "专门用于车辆检测的深度学习算法",
|
||||
"version": "2.1.0",
|
||||
"model_path": "/models/vehicle_detection.pt",
|
||||
"config_path": "/configs/vehicle.yaml",
|
||||
"status": "active",
|
||||
"accuracy": 0.92,
|
||||
"detection_classes": json.dumps(["car", "truck", "bus", "motorcycle"]),
|
||||
"input_size": "640x640",
|
||||
"inference_time": 18.2,
|
||||
"is_enabled": True,
|
||||
"creator": "admin",
|
||||
"tags": json.dumps(["vehicle", "detection"])
|
||||
},
|
||||
{
|
||||
"name": "人脸识别算法",
|
||||
"description": "高精度人脸识别算法",
|
||||
"version": "1.5.0",
|
||||
"model_path": "/models/face_recognition.pt",
|
||||
"config_path": "/configs/face.yaml",
|
||||
"status": "inactive",
|
||||
"accuracy": 0.98,
|
||||
"detection_classes": json.dumps(["face"]),
|
||||
"input_size": "512x512",
|
||||
"inference_time": 25.0,
|
||||
"is_enabled": False,
|
||||
"creator": "admin",
|
||||
"tags": json.dumps(["face", "recognition"])
|
||||
}
|
||||
]
|
||||
|
||||
for alg_data in algorithms:
|
||||
algorithm = Algorithm(**alg_data)
|
||||
db.add(algorithm)
|
||||
|
||||
db.commit()
|
||||
print("✅ 算法数据初始化完成")
|
||||
|
||||
# 创建示例设备
|
||||
devices = [
|
||||
|
||||
@ -24,3 +24,10 @@ class Device(BaseModel):
|
||||
manufacturer = Column(String(100), comment="制造商")
|
||||
model = Column(String(100), comment="设备型号")
|
||||
serial_number = Column(String(100), comment="序列号")
|
||||
|
||||
# 新增字段：视频处理相关
|
||||
demo_video_path = Column(String(500), comment="演示视频路径")
|
||||
processed_video_path = Column(String(500), comment="处理结果视频路径")
|
||||
processing_status = Column(String(20), default="idle", comment="处理状态: idle, processing, completed, failed")
|
||||
processing_result = Column(Text, comment="处理结果，JSON格式")
|
||||
last_processed_at = Column(DateTime, comment="最后处理时间")
|
||||
@ -129,3 +129,4 @@ async def toggle_algorithm_enabled(
|
||||
db.refresh(algorithm)
|
||||
|
||||
return algorithm.to_dict()
|
||||
|
||||
|
||||
@ -46,32 +46,47 @@ async def get_dashboard_kpi(db: Session = Depends(get_db)):
|
||||
raise HTTPException(status_code=500, detail=f"获取KPI数据失败: {str(e)}")
|
||||
|
||||
@router.get("/alarm-trend", summary="获取告警趋势统计")
|
||||
async def get_alarm_trend(
|
||||
days: int = Query(7, ge=1, le=30, description="统计天数"),
|
||||
db: Session = Depends(get_db)
|
||||
):
|
||||
"""获取告警趋势统计数据"""
|
||||
async def get_alarm_trend(db: Session = Depends(get_db)):
|
||||
"""获取告警趋势统计数据 - 最近5个月的P0-P3警告统计"""
|
||||
try:
|
||||
# TODO: 实现真实的告警趋势统计
|
||||
# 当前返回模拟数据
|
||||
end_date = datetime.now().date()
|
||||
start_date = end_date - timedelta(days=days-1)
|
||||
# 获取最近5个月的数据
|
||||
current_date = datetime.now()
|
||||
months = []
|
||||
p0_warnings = []
|
||||
p1_warnings = []
|
||||
p2_warnings = []
|
||||
p3_warnings = []
|
||||
|
||||
dates = []
|
||||
alarms = []
|
||||
resolved = []
|
||||
for i in range(5):
|
||||
# 计算月份
|
||||
month_date = current_date - timedelta(days=30 * i)
|
||||
month_str = month_date.strftime("%Y-%m")
|
||||
months.append(month_str)
|
||||
|
||||
for i in range(days):
|
||||
current_date = start_date + timedelta(days=i)
|
||||
dates.append(current_date.strftime("%Y-%m-%d"))
|
||||
# 模拟数据
|
||||
alarms.append(10 + (i * 2) % 20)
|
||||
resolved.append(8 + (i * 2) % 15)
|
||||
# 模拟各级别警告数据
|
||||
p0_count = 15 + (i * 3) % 25 # 15-40之间的随机数据
|
||||
p1_count = 25 + (i * 2) % 35 # 25-60之间的随机数据
|
||||
p2_count = 35 + (i * 4) % 45 # 35-80之间的随机数据
|
||||
p3_count = 45 + (i * 3) % 55 # 45-100之间的随机数据
|
||||
|
||||
p0_warnings.append(p0_count)
|
||||
p1_warnings.append(p1_count)
|
||||
p2_warnings.append(p2_count)
|
||||
p3_warnings.append(p3_count)
|
||||
|
||||
# 反转数组，让时间从早到晚
|
||||
months.reverse()
|
||||
p0_warnings.reverse()
|
||||
p1_warnings.reverse()
|
||||
p2_warnings.reverse()
|
||||
p3_warnings.reverse()
|
||||
|
||||
return {
|
||||
"dates": dates,
|
||||
"alarms": alarms,
|
||||
"resolved": resolved
|
||||
"months": months,
|
||||
"p0_warnings": p0_warnings,
|
||||
"p1_warnings": p1_warnings,
|
||||
"p2_warnings": p2_warnings,
|
||||
"p3_warnings": p3_warnings
|
||||
}
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=f"获取告警趋势数据失败: {str(e)}")
|
||||
|
||||
@ -1,8 +1,13 @@
|
||||
from fastapi import APIRouter, Depends, HTTPException, Query
|
||||
from fastapi import APIRouter, Depends, HTTPException, Query, UploadFile, File, BackgroundTasks
|
||||
from sqlalchemy.orm import Session
|
||||
from typing import List, Optional, Dict, Any
|
||||
import os
|
||||
import shutil
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
from core.database import get_db
|
||||
from models.device import Device
|
||||
from services.video_processor import video_processor
|
||||
|
||||
router = APIRouter()
|
||||
|
||||
@ -75,6 +80,10 @@ async def get_devices(
|
||||
"status": device.status,
|
||||
"is_enabled": device.is_enabled,
|
||||
"description": device.description,
|
||||
"demo_video_path": device.demo_video_path,
|
||||
"processed_video_path": device.processed_video_path,
|
||||
"processing_status": device.processing_status,
|
||||
"last_processed_at": device.last_processed_at.isoformat() if device.last_processed_at else None,
|
||||
"created_at": device.created_at,
|
||||
"updated_at": device.updated_at
|
||||
})
|
||||
@ -108,6 +117,11 @@ async def get_device(
|
||||
"status": device.status,
|
||||
"is_enabled": device.is_enabled,
|
||||
"description": device.description,
|
||||
"demo_video_path": device.demo_video_path,
|
||||
"processed_video_path": device.processed_video_path,
|
||||
"processing_status": device.processing_status,
|
||||
"processing_result": device.processing_result,
|
||||
"last_processed_at": device.last_processed_at.isoformat() if device.last_processed_at else None,
|
||||
"created_at": device.created_at,
|
||||
"updated_at": device.updated_at
|
||||
}
|
||||
@ -222,6 +236,125 @@ async def toggle_device_enabled(
|
||||
"updated_at": device.updated_at
|
||||
}
|
||||
|
||||
@router.post("/{device_id}/upload-demo-video", summary="上传演示视频")
|
||||
async def upload_demo_video(
|
||||
device_id: int,
|
||||
video_file: UploadFile = File(...),
|
||||
background_tasks: BackgroundTasks = None,
|
||||
db: Session = Depends(get_db)
|
||||
):
|
||||
"""上传设备的演示视频并开始处理"""
|
||||
# 检查设备是否存在
|
||||
device = db.query(Device).filter(Device.id == device_id).first()
|
||||
if not device:
|
||||
raise HTTPException(status_code=404, detail="设备不存在")
|
||||
|
||||
# 检查设备是否关联了算法
|
||||
if not device.algorithm_id:
|
||||
raise HTTPException(status_code=400, detail="设备未关联算法，无法处理视频")
|
||||
|
||||
# 检查文件类型
|
||||
if not video_file.filename.lower().endswith(('.mp4', '.avi', '.mov', '.mkv')):
|
||||
raise HTTPException(status_code=400, detail="只支持视频文件格式")
|
||||
|
||||
# 创建上传目录
|
||||
uploads_dir = Path(__file__).parent.parent / "uploads" / "videos"
|
||||
uploads_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# 生成文件名
|
||||
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
|
||||
filename = f"demo_{device_id}_{timestamp}_{video_file.filename}"
|
||||
file_path = uploads_dir / filename
|
||||
|
||||
try:
|
||||
# 保存上传的文件
|
||||
with open(file_path, "wb") as buffer:
|
||||
shutil.copyfileobj(video_file.file, buffer)
|
||||
|
||||
# 更新设备记录
|
||||
device.demo_video_path = str(file_path)
|
||||
device.processing_status = "idle"
|
||||
device.processed_video_path = None
|
||||
device.processing_result = None
|
||||
db.commit()
|
||||
|
||||
# 后台处理视频
|
||||
if background_tasks:
|
||||
background_tasks.add_task(
|
||||
video_processor.process_device_video,
|
||||
device_id,
|
||||
str(file_path)
|
||||
)
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"message": "演示视频上传成功，开始处理",
|
||||
"device_id": device_id,
|
||||
"demo_video_path": str(file_path),
|
||||
"processing_status": "processing"
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
# 清理已上传的文件
|
||||
if file_path.exists():
|
||||
file_path.unlink()
|
||||
raise HTTPException(status_code=500, detail=f"上传失败: {str(e)}")
|
||||
|
||||
@router.get("/{device_id}/processing-status", summary="获取视频处理状态")
|
||||
async def get_video_processing_status(
|
||||
device_id: int,
|
||||
db: Session = Depends(get_db)
|
||||
):
|
||||
"""获取设备的视频处理状态"""
|
||||
device = db.query(Device).filter(Device.id == device_id).first()
|
||||
if not device:
|
||||
raise HTTPException(status_code=404, detail="设备不存在")
|
||||
|
||||
return {
|
||||
"device_id": device_id,
|
||||
"processing_status": device.processing_status,
|
||||
"demo_video_path": device.demo_video_path,
|
||||
"processed_video_path": device.processed_video_path,
|
||||
"last_processed_at": device.last_processed_at.isoformat() if device.last_processed_at else None,
|
||||
"processing_result": device.processing_result
|
||||
}
|
||||
|
||||
@router.post("/{device_id}/process-video", summary="手动触发视频处理")
|
||||
async def process_device_video(
|
||||
device_id: int,
|
||||
background_tasks: BackgroundTasks = None,
|
||||
db: Session = Depends(get_db)
|
||||
):
|
||||
"""手动触发设备的视频处理"""
|
||||
device = db.query(Device).filter(Device.id == device_id).first()
|
||||
if not device:
|
||||
raise HTTPException(status_code=404, detail="设备不存在")
|
||||
|
||||
if not device.demo_video_path:
|
||||
raise HTTPException(status_code=400, detail="设备没有上传演示视频")
|
||||
|
||||
if not device.algorithm_id:
|
||||
raise HTTPException(status_code=400, detail="设备未关联算法")
|
||||
|
||||
# 检查演示视频文件是否存在
|
||||
if not os.path.exists(device.demo_video_path):
|
||||
raise HTTPException(status_code=400, detail="演示视频文件不存在")
|
||||
|
||||
# 后台处理视频
|
||||
if background_tasks:
|
||||
background_tasks.add_task(
|
||||
video_processor.process_device_video,
|
||||
device_id,
|
||||
device.demo_video_path
|
||||
)
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"message": "视频处理已开始",
|
||||
"device_id": device_id,
|
||||
"processing_status": "processing"
|
||||
}
|
||||
|
||||
@router.get("/types/list", summary="获取设备类型列表")
|
||||
async def get_device_types():
|
||||
"""获取所有设备类型"""
|
||||
|
||||
1
server/services/__init__.py
Normal file
1
server/services/__init__.py
Normal file
@ -0,0 +1 @@
|
||||
# Services package
|
||||
182
server/services/video_processor.py
Normal file
182
server/services/video_processor.py
Normal file
@ -0,0 +1,182 @@
|
||||
import os
|
||||
import sys
|
||||
import json
|
||||
import asyncio
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
from typing import Dict, Any, Optional
|
||||
import logging
|
||||
|
||||
# 添加algorithm目录到Python路径
|
||||
sys.path.append(str(Path(__file__).parent.parent.parent / "algorithm"))
|
||||
|
||||
from detection import DetectionProcessor, SegmentationProcessor
|
||||
from core.database import get_db
|
||||
from models.device import Device
|
||||
from models.algorithm import Algorithm
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
class VideoProcessor:
|
||||
"""视频处理服务"""
|
||||
|
||||
def __init__(self):
|
||||
self.detection_processor = None
|
||||
self.segmentation_processor = None
|
||||
|
||||
def _get_processor(self, algorithm_id: int) -> Optional[DetectionProcessor]:
|
||||
"""根据算法ID获取对应的处理器"""
|
||||
db = next(get_db())
|
||||
try:
|
||||
algorithm = db.query(Algorithm).filter(Algorithm.id == algorithm_id).first()
|
||||
if not algorithm or not algorithm.model_path:
|
||||
logger.error(f"算法 {algorithm_id} 不存在或模型路径为空")
|
||||
return None
|
||||
|
||||
# 检查模型文件是否存在
|
||||
if not os.path.exists(algorithm.model_path):
|
||||
logger.error(f"模型文件不存在: {algorithm.model_path}")
|
||||
return None
|
||||
|
||||
# 根据算法名称判断使用检测还是分割
|
||||
if "分割" in algorithm.name or "segmentation" in algorithm.name.lower():
|
||||
if not self.segmentation_processor:
|
||||
self.segmentation_processor = SegmentationProcessor(model_path=algorithm.model_path)
|
||||
return self.segmentation_processor
|
||||
else:
|
||||
if not self.detection_processor:
|
||||
self.detection_processor = DetectionProcessor(model_path=algorithm.model_path)
|
||||
return self.detection_processor
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取处理器失败: {e}")
|
||||
return None
|
||||
finally:
|
||||
db.close()
|
||||
|
||||
async def process_device_video(self, device_id: int, demo_video_path: str) -> Dict[str, Any]:
|
||||
"""
|
||||
处理设备的演示视频
|
||||
|
||||
Args:
|
||||
device_id: 设备ID
|
||||
demo_video_path: 演示视频路径
|
||||
|
||||
Returns:
|
||||
处理结果字典
|
||||
"""
|
||||
db = next(get_db())
|
||||
|
||||
try:
|
||||
# 获取设备信息
|
||||
device = db.query(Device).filter(Device.id == device_id).first()
|
||||
if not device:
|
||||
return {"success": False, "error": "设备不存在"}
|
||||
|
||||
if not device.algorithm_id:
|
||||
return {"success": False, "error": "设备未关联算法"}
|
||||
|
||||
# 检查视频文件是否存在
|
||||
if not os.path.exists(demo_video_path):
|
||||
return {"success": False, "error": "演示视频文件不存在"}
|
||||
|
||||
# 更新设备状态为处理中
|
||||
device.processing_status = "processing"
|
||||
device.last_processed_at = datetime.now()
|
||||
db.commit()
|
||||
|
||||
# 获取处理器
|
||||
processor = self._get_processor(device.algorithm_id)
|
||||
if not processor:
|
||||
device.processing_status = "failed"
|
||||
device.processing_result = json.dumps({"error": "无法获取处理器"})
|
||||
db.commit()
|
||||
return {"success": False, "error": "无法获取处理器"}
|
||||
|
||||
# 生成输出视频路径
|
||||
uploads_dir = Path(__file__).parent.parent / "uploads" / "results"
|
||||
uploads_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
output_filename = f"processed_{device_id}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.mp4"
|
||||
output_video_path = str(uploads_dir / output_filename)
|
||||
|
||||
# 获取算法配置
|
||||
algorithm = db.query(Algorithm).filter(Algorithm.id == device.algorithm_id).first()
|
||||
detection_classes = json.loads(algorithm.detection_classes) if algorithm.detection_classes else None
|
||||
|
||||
# 处理视频
|
||||
logger.info(f"开始处理设备 {device_id} 的视频: {demo_video_path}")
|
||||
|
||||
if isinstance(processor, DetectionProcessor):
|
||||
# 目标检测处理
|
||||
result = processor.process_video(
|
||||
input_video_path=demo_video_path,
|
||||
output_video_path=output_video_path,
|
||||
confidence_threshold=0.5,
|
||||
classes=[0] if detection_classes and "person" in detection_classes else None,
|
||||
show_live=False,
|
||||
save_annotated=True
|
||||
)
|
||||
else:
|
||||
# 图像分割处理
|
||||
result = processor.process_video(
|
||||
input_video_path=demo_video_path,
|
||||
output_video_path=output_video_path,
|
||||
confidence_threshold=0.3,
|
||||
classes=[0] if detection_classes and "person" in detection_classes else None,
|
||||
show_live=False,
|
||||
save_annotated=True
|
||||
)
|
||||
|
||||
# 更新设备状态和结果
|
||||
if result.get("success"):
|
||||
device.processing_status = "completed"
|
||||
device.processed_video_path = output_video_path
|
||||
device.processing_result = json.dumps(result)
|
||||
device.last_processed_at = datetime.now()
|
||||
logger.info(f"设备 {device_id} 视频处理完成: {output_video_path}")
|
||||
else:
|
||||
device.processing_status = "failed"
|
||||
device.processing_result = json.dumps(result)
|
||||
logger.error(f"设备 {device_id} 视频处理失败: {result.get('error', '未知错误')}")
|
||||
|
||||
db.commit()
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"处理设备 {device_id} 视频时发生错误: {e}")
|
||||
try:
|
||||
device.processing_status = "failed"
|
||||
device.processing_result = json.dumps({"error": str(e)})
|
||||
db.commit()
|
||||
except:
|
||||
pass
|
||||
return {"success": False, "error": str(e)}
|
||||
finally:
|
||||
db.close()
|
||||
|
||||
def get_processing_status(self, device_id: int) -> Dict[str, Any]:
|
||||
"""获取设备处理状态"""
|
||||
db = next(get_db())
|
||||
try:
|
||||
device = db.query(Device).filter(Device.id == device_id).first()
|
||||
if not device:
|
||||
return {"success": False, "error": "设备不存在"}
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"device_id": device_id,
|
||||
"processing_status": device.processing_status,
|
||||
"demo_video_path": device.demo_video_path,
|
||||
"processed_video_path": device.processed_video_path,
|
||||
"last_processed_at": device.last_processed_at.isoformat() if device.last_processed_at else None,
|
||||
"processing_result": json.loads(device.processing_result) if device.processing_result else None
|
||||
}
|
||||
except Exception as e:
|
||||
logger.error(f"获取设备 {device_id} 处理状态时发生错误: {e}")
|
||||
return {"success": False, "error": str(e)}
|
||||
finally:
|
||||
db.close()
|
||||
|
||||
# 全局视频处理器实例
|
||||
video_processor = VideoProcessor()
|
||||
@ -1,145 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
API测试脚本
|
||||
用于验证新创建的接口是否正常工作
|
||||
"""
|
||||
|
||||
import requests
|
||||
import json
|
||||
from datetime import datetime
|
||||
|
||||
# API基础URL
|
||||
BASE_URL = "http://localhost:8000/api"
|
||||
|
||||
def test_dashboard_apis():
|
||||
"""测试仪表板相关接口"""
|
||||
print("=== 测试仪表板接口 ===")
|
||||
|
||||
# 测试KPI接口
|
||||
try:
|
||||
response = requests.get(f"{BASE_URL}/dashboard/kpi")
|
||||
print(f"KPI接口: {response.status_code}")
|
||||
if response.status_code == 200:
|
||||
print(f"KPI数据: {response.json()}")
|
||||
except Exception as e:
|
||||
print(f"KPI接口错误: {e}")
|
||||
|
||||
# 测试告警趋势接口
|
||||
try:
|
||||
response = requests.get(f"{BASE_URL}/dashboard/alarm-trend?days=7")
|
||||
print(f"告警趋势接口: {response.status_code}")
|
||||
if response.status_code == 200:
|
||||
print(f"告警趋势数据: {response.json()}")
|
||||
except Exception as e:
|
||||
print(f"告警趋势接口错误: {e}")
|
||||
|
||||
# 测试摄像头统计接口
|
||||
try:
|
||||
response = requests.get(f"{BASE_URL}/dashboard/camera-stats")
|
||||
print(f"摄像头统计接口: {response.status_code}")
|
||||
if response.status_code == 200:
|
||||
print(f"摄像头统计数据: {response.json()}")
|
||||
except Exception as e:
|
||||
print(f"摄像头统计接口错误: {e}")
|
||||
|
||||
def test_monitor_apis():
|
||||
"""测试监控相关接口"""
|
||||
print("\n=== 测试监控接口 ===")
|
||||
|
||||
# 测试监控列表接口
|
||||
try:
|
||||
response = requests.get(f"{BASE_URL}/monitors?page=1&size=10")
|
||||
print(f"监控列表接口: {response.status_code}")
|
||||
if response.status_code == 200:
|
||||
data = response.json()
|
||||
print(f"监控列表: 总数={data.get('total', 0)}")
|
||||
except Exception as e:
|
||||
print(f"监控列表接口错误: {e}")
|
||||
|
||||
def test_alarm_apis():
|
||||
"""测试告警相关接口"""
|
||||
print("\n=== 测试告警接口 ===")
|
||||
|
||||
# 测试告警列表接口
|
||||
try:
|
||||
response = requests.get(f"{BASE_URL}/alarms?page=1&size=10")
|
||||
print(f"告警列表接口: {response.status_code}")
|
||||
if response.status_code == 200:
|
||||
data = response.json()
|
||||
print(f"告警列表: 总数={data.get('total', 0)}")
|
||||
except Exception as e:
|
||||
print(f"告警列表接口错误: {e}")
|
||||
|
||||
# 测试告警统计接口
|
||||
try:
|
||||
response = requests.get(f"{BASE_URL}/alarms/stats")
|
||||
print(f"告警统计接口: {response.status_code}")
|
||||
if response.status_code == 200:
|
||||
print(f"告警统计数据: {response.json()}")
|
||||
except Exception as e:
|
||||
print(f"告警统计接口错误: {e}")
|
||||
|
||||
def test_scene_apis():
|
||||
"""测试场景相关接口"""
|
||||
print("\n=== 测试场景接口 ===")
|
||||
|
||||
# 测试场景列表接口
|
||||
try:
|
||||
response = requests.get(f"{BASE_URL}/scenes")
|
||||
print(f"场景列表接口: {response.status_code}")
|
||||
if response.status_code == 200:
|
||||
data = response.json()
|
||||
print(f"场景列表: 数量={len(data.get('scenes', []))}")
|
||||
except Exception as e:
|
||||
print(f"场景列表接口错误: {e}")
|
||||
|
||||
def test_auth_apis():
|
||||
"""测试认证相关接口"""
|
||||
print("\n=== 测试认证接口 ===")
|
||||
|
||||
# 测试登录接口
|
||||
try:
|
||||
login_data = {
|
||||
"username": "admin",
|
||||
"password": "admin123"
|
||||
}
|
||||
response = requests.post(f"{BASE_URL}/auth/login", data=login_data)
|
||||
print(f"登录接口: {response.status_code}")
|
||||
if response.status_code == 200:
|
||||
print("登录成功")
|
||||
else:
|
||||
print(f"登录失败: {response.text}")
|
||||
except Exception as e:
|
||||
print(f"登录接口错误: {e}")
|
||||
|
||||
def test_upload_apis():
|
||||
"""测试上传相关接口"""
|
||||
print("\n=== 测试上传接口 ===")
|
||||
|
||||
# 测试上传统计接口
|
||||
try:
|
||||
response = requests.get(f"{BASE_URL}/upload/stats")
|
||||
print(f"上传统计接口: {response.status_code}")
|
||||
if response.status_code == 200:
|
||||
print(f"上传统计数据: {response.json()}")
|
||||
except Exception as e:
|
||||
print(f"上传统计接口错误: {e}")
|
||||
|
||||
def main():
|
||||
"""主测试函数"""
|
||||
print("开始API测试...")
|
||||
print(f"测试时间: {datetime.now()}")
|
||||
print(f"API基础URL: {BASE_URL}")
|
||||
|
||||
# 测试各个模块的接口
|
||||
test_dashboard_apis()
|
||||
test_monitor_apis()
|
||||
test_alarm_apis()
|
||||
test_scene_apis()
|
||||
test_auth_apis()
|
||||
test_upload_apis()
|
||||
|
||||
print("\n=== 测试完成 ===")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
361
server/todo.md
361
server/todo.md
@ -1,362 +1,15 @@
|
||||
# 前端接口开发计划
|
||||
|
||||
## 概述
|
||||
根据前端代码分析，当前后端已实现基础的CRUD接口，但前端需要更多统计、监控、告警等功能的接口。以下是缺失接口的开发计划。
|
||||
|
||||
## 已实现接口
|
||||
- ✅ 设备管理：CRUD操作
|
||||
- ✅ 算法管理：CRUD操作
|
||||
- ✅ 事件管理：CRUD操作
|
||||
* [x] 初始化的算法数据和algorithm中的算法保持一致，保证能被detection.py 的类使用
|
||||
|
||||
## 缺失接口清单
|
||||
* [x] 设备支持上传演示视频，上传视频后处理完成后放到 uploads/results，并在表中记录这两个视频地址
|
||||
|
||||
### 1. 仪表板统计接口 (优先级：高)
|
||||
* [ ] 添加视频处理结果的下载接口
|
||||
|
||||
#### 1.1 主要KPI指标
|
||||
```
|
||||
GET /api/dashboard/kpi
|
||||
响应：
|
||||
{
|
||||
"total_devices": 156,
|
||||
"online_devices": 142,
|
||||
"total_algorithms": 8,
|
||||
"active_algorithms": 6,
|
||||
"total_events": 1247,
|
||||
"today_events": 89,
|
||||
"alert_events": 23,
|
||||
"resolved_events": 66
|
||||
}
|
||||
```
|
||||
* [ ] 优化视频处理性能，支持大文件处理
|
||||
|
||||
#### 1.2 告警趋势统计
|
||||
```
|
||||
GET /api/dashboard/alarm-trend
|
||||
参数：
|
||||
- days: 7 (默认7天)
|
||||
响应：
|
||||
{
|
||||
"dates": ["2024-01-01", "2024-01-02", ...],
|
||||
"alarms": [12, 15, 8, 23, 18, 25, 20],
|
||||
"resolved": [10, 12, 7, 19, 15, 22, 18]
|
||||
}
|
||||
```
|
||||
* [ ] 添加视频处理进度实时反馈
|
||||
|
||||
#### 1.3 摄像头统计
|
||||
```
|
||||
GET /api/dashboard/camera-stats
|
||||
响应：
|
||||
{
|
||||
"total_cameras": 156,
|
||||
"online_cameras": 142,
|
||||
"offline_cameras": 14,
|
||||
"by_location": [
|
||||
{"location": "港口区", "total": 45, "online": 42},
|
||||
{"location": "码头区", "total": 38, "online": 35},
|
||||
{"location": "办公区", "total": 23, "online": 21}
|
||||
]
|
||||
}
|
||||
```
|
||||
* [ ] 实现视频处理失败重试机制
|
||||
|
||||
#### 1.4 算法统计
|
||||
```
|
||||
GET /api/dashboard/algorithm-stats
|
||||
响应：
|
||||
{
|
||||
"total_algorithms": 8,
|
||||
"active_algorithms": 6,
|
||||
"by_type": [
|
||||
{"type": "目标检测", "count": 3, "accuracy": 95.2},
|
||||
{"type": "行为识别", "count": 2, "accuracy": 88.7},
|
||||
{"type": "越界检测", "count": 3, "accuracy": 92.1}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
#### 1.5 事件热点统计
|
||||
```
|
||||
GET /api/dashboard/event-hotspots
|
||||
响应：
|
||||
{
|
||||
"hotspots": [
|
||||
{
|
||||
"location": "港口A区",
|
||||
"event_count": 45,
|
||||
"severity": "high",
|
||||
"coordinates": {"lat": 31.2304, "lng": 121.4737}
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
### 2. 监控管理接口 (优先级：高)
|
||||
|
||||
#### 2.1 监控列表
|
||||
```
|
||||
GET /api/monitors
|
||||
参数：
|
||||
- page: 1
|
||||
- size: 20
|
||||
- status: online/offline
|
||||
- location: 位置筛选
|
||||
响应：
|
||||
{
|
||||
"monitors": [
|
||||
{
|
||||
"id": 1,
|
||||
"name": "港口区监控1",
|
||||
"location": "港口A区",
|
||||
"status": "online",
|
||||
"video_url": "/videos/port-1.mp4",
|
||||
"detections": [
|
||||
{"type": "person", "x": 25, "y": 35, "width": 40, "height": 80}
|
||||
]
|
||||
}
|
||||
],
|
||||
"total": 156,
|
||||
"page": 1,
|
||||
"size": 20
|
||||
}
|
||||
```
|
||||
|
||||
#### 2.2 监控详情
|
||||
```
|
||||
GET /api/monitors/{monitor_id}
|
||||
响应：
|
||||
{
|
||||
"id": 1,
|
||||
"name": "港口区主监控",
|
||||
"location": "港口区",
|
||||
"status": "online",
|
||||
"video_url": "/videos/port-main.mp4",
|
||||
"detections": [...],
|
||||
"events": [...],
|
||||
"algorithms": [...]
|
||||
}
|
||||
```
|
||||
|
||||
### 3. 告警管理接口 (优先级：中)
|
||||
|
||||
#### 3.1 告警列表
|
||||
```
|
||||
GET /api/alarms
|
||||
参数：
|
||||
- page: 1
|
||||
- size: 20
|
||||
- severity: high/medium/low
|
||||
- status: pending/resolved
|
||||
- start_time: 2024-01-01
|
||||
- end_time: 2024-01-31
|
||||
响应：
|
||||
{
|
||||
"alarms": [
|
||||
{
|
||||
"id": 1,
|
||||
"type": "船舶靠泊",
|
||||
"severity": "high",
|
||||
"status": "pending",
|
||||
"device": "港口区监控1",
|
||||
"created_at": "2024-01-15T10:30:00Z",
|
||||
"description": "检测到船舶靠泊行为"
|
||||
}
|
||||
],
|
||||
"total": 89,
|
||||
"page": 1,
|
||||
"size": 20
|
||||
}
|
||||
```
|
||||
|
||||
#### 3.2 告警处理
|
||||
```
|
||||
PATCH /api/alarms/{alarm_id}/resolve
|
||||
请求体：
|
||||
{
|
||||
"resolution_notes": "已确认船舶靠泊，无异常",
|
||||
"resolved_by": "operator1"
|
||||
}
|
||||
```
|
||||
|
||||
#### 3.3 告警统计
|
||||
```
|
||||
GET /api/alarms/stats
|
||||
响应：
|
||||
{
|
||||
"total_alarms": 89,
|
||||
"pending_alarms": 23,
|
||||
"resolved_alarms": 66,
|
||||
"by_severity": [
|
||||
{"severity": "high", "count": 12},
|
||||
{"severity": "medium", "count": 45},
|
||||
{"severity": "low", "count": 32}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
### 4. 场景管理接口 (优先级：中)
|
||||
|
||||
#### 4.1 场景列表
|
||||
```
|
||||
GET /api/scenes
|
||||
响应：
|
||||
{
|
||||
"scenes": [
|
||||
{
|
||||
"id": "scene-001",
|
||||
"name": "港口区场景",
|
||||
"description": "港口区监控场景",
|
||||
"device_count": 45,
|
||||
"algorithm_count": 3
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
#### 4.2 场景详情
|
||||
```
|
||||
GET /api/scenes/{scene_id}
|
||||
响应：
|
||||
{
|
||||
"id": "scene-001",
|
||||
"name": "港口区场景",
|
||||
"description": "港口区监控场景",
|
||||
"devices": [...],
|
||||
"algorithms": [...],
|
||||
"events": [...]
|
||||
}
|
||||
```
|
||||
|
||||
### 5. 文件上传接口 (优先级：中)
|
||||
|
||||
#### 5.1 视频上传
|
||||
```
|
||||
POST /api/upload/video
|
||||
Content-Type: multipart/form-data
|
||||
请求体：
|
||||
- file: 视频文件
|
||||
- device_id: 设备ID
|
||||
- description: 描述
|
||||
响应：
|
||||
{
|
||||
"file_id": "video_123",
|
||||
"file_url": "/uploads/videos/video_123.mp4",
|
||||
"file_size": 1024000,
|
||||
"duration": 30.5
|
||||
}
|
||||
```
|
||||
|
||||
#### 5.2 图片上传
|
||||
```
|
||||
POST /api/upload/image
|
||||
Content-Type: multipart/form-data
|
||||
请求体：
|
||||
- file: 图片文件
|
||||
- event_id: 事件ID
|
||||
响应：
|
||||
{
|
||||
"file_id": "image_456",
|
||||
"file_url": "/uploads/images/image_456.jpg",
|
||||
"file_size": 256000
|
||||
}
|
||||
```
|
||||
|
||||
### 6. 用户认证接口 (优先级：低)
|
||||
|
||||
#### 6.1 用户登录
|
||||
```
|
||||
POST /api/auth/login
|
||||
请求体：
|
||||
{
|
||||
"username": "admin",
|
||||
"password": "password123"
|
||||
}
|
||||
响应：
|
||||
{
|
||||
"access_token": "eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9...",
|
||||
"token_type": "bearer",
|
||||
"expires_in": 3600,
|
||||
"user": {
|
||||
"id": 1,
|
||||
"username": "admin",
|
||||
"role": "admin"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
#### 6.2 用户信息
|
||||
```
|
||||
GET /api/auth/profile
|
||||
响应：
|
||||
{
|
||||
"id": 1,
|
||||
"username": "admin",
|
||||
"email": "admin@example.com",
|
||||
"role": "admin",
|
||||
"permissions": ["read", "write", "admin"]
|
||||
}
|
||||
```
|
||||
|
||||
## 开发进度
|
||||
|
||||
### ✅ 已完成 (第一阶段)
|
||||
1. ✅ 仪表板统计接口 (KPI、告警趋势、摄像头统计、算法统计、事件热点)
|
||||
2. ✅ 监控管理接口 (监控列表、监控详情)
|
||||
|
||||
### ✅ 已完成 (第二阶段)
|
||||
1. ✅ 告警管理接口 (告警列表、告警处理、告警统计)
|
||||
2. ✅ 场景管理接口 (场景列表、场景详情)
|
||||
|
||||
### ✅ 已完成 (第三阶段)
|
||||
1. ✅ 文件上传接口 (视频上传、图片上传)
|
||||
2. ✅ 用户认证接口 (登录、用户信息)
|
||||
|
||||
### 🔄 待优化功能
|
||||
1. 实现真实的告警趋势统计 (当前使用模拟数据)
|
||||
2. 实现真实的检测数据获取 (当前使用模拟数据)
|
||||
3. 实现真实的视频流URL生成
|
||||
4. 实现真实的JWT验证中间件
|
||||
5. 实现真实的场景管理数据库模型
|
||||
6. 实现真实的文件删除逻辑
|
||||
7. 添加Redis缓存支持
|
||||
8. 添加WebSocket实时数据推送
|
||||
|
||||
## 技术实现要点
|
||||
|
||||
1. **数据库模型扩展**：
|
||||
- 添加统计相关的视图或缓存表
|
||||
- 优化查询性能，添加索引
|
||||
|
||||
2. **缓存策略**：
|
||||
- 使用Redis缓存统计数据
|
||||
- 设置合理的缓存过期时间
|
||||
|
||||
3. **文件存储**：
|
||||
- 配置静态文件服务
|
||||
- 实现文件上传和存储逻辑
|
||||
|
||||
4. **权限控制**：
|
||||
- 实现JWT认证
|
||||
- 添加角色和权限控制
|
||||
|
||||
5. **WebSocket支持**：
|
||||
- 实时监控数据推送
|
||||
- 告警实时通知
|
||||
|
||||
## 测试计划
|
||||
|
||||
1. **单元测试**：每个接口的CRUD操作
|
||||
2. **集成测试**：前后端联调
|
||||
3. **性能测试**：大数据量下的响应时间
|
||||
4. **安全测试**：认证和权限验证
|
||||
|
||||
## 部署计划
|
||||
|
||||
1. **开发环境**：本地测试
|
||||
2. **测试环境**：功能验证
|
||||
3. **生产环境**：正式部署
|
||||
|
||||
## 注意事项
|
||||
|
||||
1. 所有接口需要添加错误处理和日志记录
|
||||
2. 敏感数据需要加密存储
|
||||
3. 文件上传需要限制文件大小和类型
|
||||
4. 统计数据需要定期更新，避免过期数据
|
||||
5. 接口文档需要及时更新
|
||||
* [ ]
|
||||
Loading…
x
Reference in New Issue
Block a user