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【“星瑞” O6 评测】 — 车辆速度估计

java 2025/5/8 12:56:57

在这里插入图片描述

前言

星瑞 O6 的 AI 能力依托先进的技术架构得以展现。其采用 Armv9 架构，集成了 Arm®v9 CPU 核心、Arm Immortalis™ GPU 以及安谋科技 “周易” NPU 。这样的异构架构设计是其 AI 性能的基石，在处理 AI 相关任务时，CPU、GPU、NPU 能够协同工作，大大加速 AI 模型的运行效率。​
其中，“周易” NPU 发挥着关键作用，它拥有高达 30TOPS 的算力 。如此强大的算力使得星瑞 O6 能够成功适配多款主流 AI 模型，从而满足生成式 AI 在多元端侧场景的应用需求。

这里我们用NPU做目标检测，再配合supervison库做跟踪，进行车辆行驶速度的估计。

1. 整体步骤

1.1 下载supervision代码

https://github.com/roboflow/supervision/releases

下载测试视频：https://media.roboflow.com/supervision/video-examples/vehicles.mp4

1.2 代码集成

supervision中已经做了行车速度的demo，但是它是用其他检测框架做的检测。这里我们需要做的是将检测部分的替换成我们星瑞O6的NPU后端。具体就不展开了。详细代码如下

import argparse
from collections import defaultdict, dequeimport os
import cv2
import numpy as np
import torch
import timeimport sys
sys.path.insert(0, "/home/radxa/1_AI_models/supervision-0.25.0")
_abs_path = os.path.join(os.path.dirname("/home/radxa/1_AI_models/ai_model_hub/models/ComputeVision/Object_Detection/onnx_yolov8_l/inference_npu.py"), "../../../../")
sys.path.append(_abs_path)from utils.image_process import preprocess_object_detect_method1
from utils.object_detect_postprocess import postprocess_yolo, xywh2xyxy
from utils.NOE_Engine import EngineInfer
from PIL import Imageimport supervision as svSOURCE = np.array([[1252, 787], [2298, 803], [5039, 2159], [-550, 2159]])TARGET_WIDTH = 25
TARGET_HEIGHT = 250TARGET = np.array([[0, 0],[TARGET_WIDTH - 1, 0],[TARGET_WIDTH - 1, TARGET_HEIGHT - 1],[0, TARGET_HEIGHT - 1],]
)class CIX_MODEL(object):def __init__(self, model_path):self.model = EngineInfer(model_path)def __call__(self, pilimage):pilimage = Image.fromarray(pilimage)src_shape, new_shape, show_image, data = preprocess_object_detect_method1(pilimage, target_size=(640, 640), mode="BGR")pred = self.model.forward(data.astype(np.float32))[0]pred = np.reshape(pred, (84, 8400))pred = np.transpose(pred, (1, 0))results = postprocess_yolo(pred, 0.3, 0.45)bbox_xywh = results[:, :4]bbox_xyxy = xywh2xyxy(bbox_xywh)x_scale = src_shape[1] / new_shape[1]y_scale = src_shape[0] / new_shape[0]bbox_xyxy *= (x_scale, y_scale, x_scale, y_scale)results[:, :4] = bbox_xyxyreturn resultsdef add_fps(image, fps, thickness):height, width, _ = image.shapefont = cv2.FONT_HERSHEY_SIMPLEXfont_scale = 2color = (0, 0, 255)  text = f"FPS: {fps:.2f}"text_size = cv2.getTextSize(text, font, font_scale, thickness)[0]x = width - text_size[0] - 10  y = text_size[1] + 10  cv2.putText(image, text, (x, y), font, font_scale, color, thickness)class ViewTransformer:def __init__(self, source: np.ndarray, target: np.ndarray) -> None:source = source.astype(np.float32)target = target.astype(np.float32)self.m = cv2.getPerspectiveTransform(source, target)def transform_points(self, points: np.ndarray) -> np.ndarray:if points.size == 0:return pointsreshaped_points = points.reshape(-1, 1, 2).astype(np.float32)transformed_points = cv2.perspectiveTransform(reshaped_points, self.m)return transformed_points.reshape(-1, 2)def parse_arguments() -> argparse.Namespace:parser = argparse.ArgumentParser(description="Vehicle Speed Estimation using Ultralytics and Supervision")parser.add_argument("--source_video_path",required=True,help="Path to the source video file",type=str,)parser.add_argument("--target_video_path",required=True,help="Path to the target video file (output)",type=str,)parser.add_argument("--confidence_threshold",default=0.3,help="Confidence threshold for the model",type=float,)parser.add_argument("--iou_threshold", default=0.7, help="IOU threshold for the model", type=float)return parser.parse_args()if __name__ == "__main__":args = parse_arguments()video_info = sv.VideoInfo.from_video_path(video_path=args.source_video_path)model = CIX_MODEL("/home/radxa/1_AI_models/ai_model_hub/asserts/models/yolov8_l/yolov8_l.cix")byte_track = sv.ByteTrack(frame_rate=video_info.fps, track_activation_threshold=args.confidence_threshold)thickness = sv.calculate_optimal_line_thickness(resolution_wh=video_info.resolution_wh)text_scale = sv.calculate_optimal_text_scale(resolution_wh=video_info.resolution_wh)box_annotator = sv.BoxAnnotator(thickness=thickness)label_annotator = sv.LabelAnnotator(text_scale=text_scale,text_thickness=thickness,text_position=sv.Position.BOTTOM_CENTER,)trace_annotator = sv.TraceAnnotator(thickness=thickness,trace_length=video_info.fps * 2,position=sv.Position.BOTTOM_CENTER,)frame_generator = sv.get_video_frames_generator(source_path=args.source_video_path)polygon_zone = sv.PolygonZone(polygon=SOURCE)view_transformer = ViewTransformer(source=SOURCE, target=TARGET)coordinates = defaultdict(lambda: deque(maxlen=video_info.fps))with sv.VideoSink(args.target_video_path, video_info) as sink:for frame in frame_generator:start_time = time.perf_counter()result = model(frame)detections = sv.Detections(xyxy=result[:, :4],confidence=result[:, 4],class_id=result[:, 5].astype(int))detections = detections[detections.confidence > args.confidence_threshold]detections = detections[polygon_zone.trigger(detections)]detections = detections.with_nms(threshold=args.iou_threshold)detections = byte_track.update_with_detections(detections=detections)points = detections.get_anchors_coordinates(anchor=sv.Position.BOTTOM_CENTER)points = view_transformer.transform_points(points=points).astype(int)for tracker_id, [_, y] in zip(detections.tracker_id, points):coordinates[tracker_id].append(y)labels = []for tracker_id in detections.tracker_id:if len(coordinates[tracker_id]) < video_info.fps / 2:labels.append(f"#{tracker_id}")else:coordinate_start = coordinates[tracker_id][-1]coordinate_end = coordinates[tracker_id][0]distance = abs(coordinate_start - coordinate_end)use_time = len(coordinates[tracker_id]) / video_info.fpsspeed = distance / use_time * 3.6labels.append(f"#{tracker_id} {int(speed)} km/h")end_time = time.perf_counter()annotated_frame = frame.copy()annotated_frame = trace_annotator.annotate(scene=annotated_frame, detections=detections)annotated_frame = box_annotator.annotate(scene=annotated_frame, detections=detections)annotated_frame = label_annotator.annotate(scene=annotated_frame, detections=detections, labels=labels)fps = 1 / (end_time - start_time)add_fps(annotated_frame, fps, thickness)cv2.imwrite("data/test.jpg", annotated_frame)sink.write_frame(annotated_frame)cv2.imshow("frame", annotated_frame)if cv2.waitKey(1) & 0xFF == ord("q"):breakcv2.destroyAllWindows()

1.3 运行demo

source /home/radxa/1_AI_models/ai_model_hub/.venv/bin/activate
python examples/speed_estimation/cix_example.py --source_video_path data/vehicles.mp4  --target_video_path data/vehicles-cixyolov8l.mp4   --confidence_threshold 0.3    --iou_threshold 0.5

会将可视化结果保存成视频。
https://www.bilibili.com/video/BV1ikGmzVEGL/?share_source=copy_web&vd_source=7a2e45904f91fef2ece71d1ce19c1650

2.总结

整个pipeline，检测+跟踪下来只有6.xxfps，检测是可以做到20+fps的。

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