湘潭网站建设多少钱 报价表湘潭磐石网络,东莞知名网站推广,网络设计采用的方法和原则,做企业网站用服务器yolov8直接调用zed相机实现三维测距#xff08;python#xff09; 1. 相关配置2. 相关代码3. 实验结果 相关链接 此项目直接调用zed相机实现三维测距#xff0c;无需标定#xff0c;相关内容如下#xff1a; 1.yolov5直接调用zed相机实现三维测距#xff08;python#… yolov8直接调用zed相机实现三维测距python 1. 相关配置2. 相关代码3. 实验结果 相关链接 此项目直接调用zed相机实现三维测距无需标定相关内容如下 1.yolov5直接调用zed相机实现三维测距python 2. yolov4直接调用zed相机实现三维测距 3. WindowsYOLOV8环境配置 4.具体实现效果已在哔哩哔哩发布点击此链接跳转
本篇博文工程源码下载麻烦github给个星星 下载链接https://github.com/up-up-up-up/zed-yolov8
附Zed调用YOLOv7测距也已经实现但是3060笔记本6G显存带不动在大现存服务器上可以运行可能是由于YOLOv7网络结构导致的由于不具备普适性就不再写相关文章了有需要的可以仿照这个代码去改写
1. 相关配置
python3.7 Windows-pycharm zed api 具体配置见 (zed api 配置步骤)
由于我电脑之前python版本为3.7yolov8要求python最低为3.8所以本次实验直接在虚拟环境里进行未配置gpu可能看着卡卡的有需要的可以配置一下原理是一样的
2. 相关代码
主代码 zed-yolo.py具体放置在yolov8主目录下
#!/usr/bin/env python3import sys
import numpy as npimport argparse
import torch
import cv2
import pyzed.sl as sl
from ultralytics import YOLOfrom threading import Lock, Thread
from time import sleepimport ogl_viewer.viewer as gl
import cv_viewer.tracking_viewer as cv_viewerlock Lock()
run_signal False
exit_signal Falsedef xywh2abcd(xywh, im_shape):output np.zeros((4, 2))# Center / Width / Height - BBox corners coordinatesx_min (xywh[0] - 0.5*xywh[2]) #* im_shape[1]x_max (xywh[0] 0.5*xywh[2]) #* im_shape[1]y_min (xywh[1] - 0.5*xywh[3]) #* im_shape[0]y_max (xywh[1] 0.5*xywh[3]) #* im_shape[0]# A ------ B# | Object |# D ------ Coutput[0][0] x_minoutput[0][1] y_minoutput[1][0] x_maxoutput[1][1] y_minoutput[2][0] x_maxoutput[2][1] y_maxoutput[3][0] x_minoutput[3][1] y_maxreturn outputdef detections_to_custom_box(detections, im0):output []for i, det in enumerate(detections):xywh det.xywh[0]# Creating ingestable objects for the ZED SDKobj sl.CustomBoxObjectData()obj.bounding_box_2d xywh2abcd(xywh, im0.shape)obj.label det.clsobj.probability det.confobj.is_grounded Falseoutput.append(obj)return outputdef torch_thread(weights, img_size, conf_thres0.2, iou_thres0.45):global image_net, exit_signal, run_signal, detectionsprint(Intializing Network...)model YOLO(weights)while not exit_signal:if run_signal:lock.acquire()img cv2.cvtColor(image_net, cv2.COLOR_BGRA2RGB)# https://docs.ultralytics.com/modes/predict/#video-suffixesdet model.predict(img, saveFalse, imgszimg_size, confconf_thres, iouiou_thres)[0].cpu().numpy().boxes# ZED CustomBox format (with inverse letterboxing tf applied)detections detections_to_custom_box(det, image_net)lock.release()run_signal Falsesleep(0.01)def main():global image_net, exit_signal, run_signal, detectionscapture_thread Thread(targettorch_thread, kwargs{weights: opt.weights, img_size: opt.img_size, conf_thres: opt.conf_thres})capture_thread.start()print(Initializing Camera...)zed sl.Camera()input_type sl.InputType()if opt.svo is not None:input_type.set_from_svo_file(opt.svo)# Create a InitParameters object and set configuration parametersinit_params sl.InitParameters(input_tinput_type, svo_real_time_modeTrue)init_params.coordinate_units sl.UNIT.METERinit_params.depth_mode sl.DEPTH_MODE.ULTRA # QUALITYinit_params.coordinate_system sl.COORDINATE_SYSTEM.RIGHT_HANDED_Y_UPinit_params.depth_maximum_distance 50runtime_params sl.RuntimeParameters()status zed.open(init_params)if status ! sl.ERROR_CODE.SUCCESS:print(repr(status))exit()image_left_tmp sl.Mat()print(Initialized Camera)positional_tracking_parameters sl.PositionalTrackingParameters()# If the camera is static, uncomment the following line to have better performances and boxes sticked to the ground.# positional_tracking_parameters.set_as_static Truezed.enable_positional_tracking(positional_tracking_parameters)obj_param sl.ObjectDetectionParameters()
# obj_param.detection_model sl.OBJECT_DETECTION_MODEL.CUSTOM_BOX_OBJECTSobj_param.enable_tracking Truezed.enable_object_detection(obj_param)objects sl.Objects()obj_runtime_param sl.ObjectDetectionRuntimeParameters()# Displaycamera_infos zed.get_camera_information()camera_res camera_infos.camera_resolution# Create OpenGL viewerviewer gl.GLViewer()point_cloud_res sl.Resolution(min(camera_res.width, 720), min(camera_res.height, 404))point_cloud_render sl.Mat()viewer.init(camera_infos.camera_model, point_cloud_res, obj_param.enable_tracking)point_cloud sl.Mat(point_cloud_res.width, point_cloud_res.height, sl.MAT_TYPE.F32_C4, sl.MEM.CPU)image_left sl.Mat()# Utilities for 2D displaydisplay_resolution sl.Resolution(min(camera_res.width, 1280), min(camera_res.height, 720))image_scale [display_resolution.width / camera_res.width, display_resolution.height / camera_res.height]image_left_ocv np.full((display_resolution.height, display_resolution.width, 4), [245, 239, 239, 255], np.uint8)# # Utilities for tracks view# camera_config camera_infos.camera_configuration# tracks_resolution sl.Resolution(400, display_resolution.height)# track_view_generator cv_viewer.TrackingViewer(tracks_resolution, camera_config.fps, init_params.depth_maximum_distance)# track_view_generator.set_camera_calibration(camera_config.calibration_parameters)# image_track_ocv np.zeros((tracks_resolution.height, tracks_resolution.width, 4), np.uint8)# Camera posecam_w_pose sl.Pose()while viewer.is_available() and not exit_signal:if zed.grab(runtime_params) sl.ERROR_CODE.SUCCESS:# -- Get the imagelock.acquire()zed.retrieve_image(image_left_tmp, sl.VIEW.LEFT)image_net image_left_tmp.get_data()lock.release()run_signal True# -- Detection running on the other threadwhile run_signal:sleep(0.001)# Wait for detectionslock.acquire()# -- Ingest detectionszed.ingest_custom_box_objects(detections)lock.release()zed.retrieve_objects(objects, obj_runtime_param)# -- Display# Retrieve display datazed.retrieve_measure(point_cloud, sl.MEASURE.XYZRGBA, sl.MEM.CPU, point_cloud_res)point_cloud.copy_to(point_cloud_render)zed.retrieve_image(image_left, sl.VIEW.LEFT, sl.MEM.CPU, display_resolution)zed.get_position(cam_w_pose, sl.REFERENCE_FRAME.WORLD)# 3D renderingviewer.updateData(point_cloud_render, objects)# 2D renderingnp.copyto(image_left_ocv, image_left.get_data())cv_viewer.render_2D(image_left_ocv, image_scale, objects, obj_param.enable_tracking)global_image image_left_ocv# global_image cv2.hconcat([image_left_ocv, image_track_ocv])# # Tracking view# track_view_generator.generate_view(objects, cam_w_pose, image_track_ocv, objects.is_tracked)cv2.imshow(ZED | 2D View and Birds View, global_image)key cv2.waitKey(10)if key 27:exit_signal Trueelse:exit_signal Trueviewer.exit()exit_signal Truezed.close()if __name__ __main__:parser argparse.ArgumentParser()parser.add_argument(--weights, typestr, defaultyolov8n.pt, helpmodel.pt path(s))parser.add_argument(--svo, typestr, defaultNone, helpoptional svo file)parser.add_argument(--img_size, typeint, default416, helpinference size (pixels))parser.add_argument(--conf_thres, typefloat, default0.4, helpobject confidence threshold)opt parser.parse_args()with torch.no_grad():main()
3. 实验结果
测距图感觉挺精准的 视频展示: Zed相机YOLOv8目标检测跟踪
