深度学习目标检测中yolov5单目相机测速测距pyqt文章目录一、环境准备1. 安装必要的库2. 下载YOLOv5模型二、目标检测与深度估计1. 使用YOLOv5进行目标检测2. 单目相机深度估计三、运动分析与轨迹追踪1. 轨迹追踪四、PyQt GUI开发1. 创建基本的PyQt界面五、整合与测试yolov5单目相机测速测距测速测距pyqt基于YOLOv5的单目相机测速、测距并结合PyQt进行可视化展示是一个复杂的项目。它涉及到多个步骤和技术点包括目标检测、深度估计、运动分析以及GUI开发。大致的框架和学习代码示例帮助你开始这个项目。仅供参考学习使用。一、环境准备1. 安装必要的库确保安装了以下Python库pipinstalltorch torchvision opencv-python PyQt5 numpy scipy2. 下载YOLOv5模型从YOLOv5官方仓库下载预训练模型gitclone https://github.com/ultralytics/yolov5.gitcdyolov5 pipinstall-rrequirements.txt二、目标检测与深度估计1. 使用YOLOv5进行目标检测importtorchfrommodels.experimentalimportattempt_loadfromutils.datasetsimportLoadStreams,LoadImagesfromutils.generalimportcheck_img_size,non_max_suppression,scale_coordsfromutils.torch_utilsimportselect_device,time_synchronizeddefdetect_objects(weightsyolov5s.pt,imgsz640,sourcedata/images):deviceselect_device()halfdevice.type!cpu# half precision only supported on CUDAmodelattempt_load(weights,map_locationdevice)# load FP32 modelstrideint(model.stride.max())# model strideimgszcheck_img_size(imgsz,sstride)# check img_sizeifhalf:model.half()# to FP16datasetLoadImages(source,img_sizeimgsz,stridestride)namesmodel.module.namesifhasattr(model,module)elsemodel.names colors[[random.randint(0,255)for_inrange(3)]for_innames]forpath,img,im0s,vid_capindataset:imgtorch.from_numpy(img).to(device)imgimg.half()ifhalfelseimg.float()# uint8 to fp16/32img/255.0# 0 - 255 to 0.0 - 1.0ifimg.ndimension()3:imgimg.unsqueeze(0)predmodel(img,augmentFalse)[0]prednon_max_suppression(pred,0.25,0.45,classesNone,agnosticFalse)fori,detinenumerate(pred):# detections per imagep,s,im0,framepath,,im0s,getattr(dataset,frame,0)gntorch.tensor(im0.shape)[[1,0,1,0]]# normalization gain whwhiflen(det):det[:,:4]scale_coords(img.shape[2:],det[:,:4],im0.shape).round()for*xyxy,conf,clsinreversed(det):labelf{names[int(cls)]}{conf:.2f}plot_one_box(xyxy,im0,labellabel,colorcolors[int(cls)],line_thickness3)returnim02. 单目相机深度估计使用预训练的深度估计模型如MiDaS进行深度估计importcv2importtorchimportnumpyasnpfrommidas.dpt_depthimportDPTDepthModelfrommidas.midas_netimportMidasNetfrommidas.transformsimportResize,NormalizeImage,PrepareForNetdefestimate_depth(image_path):model_typedpt_large# MiDaS v3 - Large (highest accuracy, slowest inference speed)midasDPTDepthModel(pathmodels/dpt_large-midas-2f21e586.pt,backbonevitl16_384,non_negativeTrue,)devicetorch.device(cuda)iftorch.cuda.is_available()elsetorch.device(cpu)midas.to(device)midas.eval()transformResize(384,384,resize_targetNone,keep_aspect_ratioTrue,ensure_multiple_of32,resize_methodupper_bound,image_interpolation_methodcv2.INTER_CUBIC,)imagecv2.imread(image_path)imagecv2.cvtColor(image,cv2.COLOR_BGR2RGB)/255.0image_transformedtransform({image:image})[image]image_transformedtorch.from_numpy(image_transformed).permute(2,0,1).unsqueeze(0).to(device)withtorch.no_grad():predictionmidas(image_transformed)predictiontorch.nn.functional.interpolate(prediction.unsqueeze(1),sizeimage.shape[:2],modebicubic,align_cornersFalse,).squeeze().cpu().numpy()returnprediction三、运动分析与轨迹追踪1. 轨迹追踪使用卡尔曼滤波器进行目标跟踪importnumpyasnpimportcv2classKalmanFilter:kfcv2.KalmanFilter(4,2)kf.measurementMatrixnp.array([[1,0,0,0],[0,1,0,0]],np.float32)kf.transitionMatrixnp.array([[1,0,1,0],[0,1,0,1],[0,0,1,0],[0,0,0,1]],np.float32)defpredict(self,coordX,coordY):measurednp.array([[np.float32(coordX)],[np.float32(coordY)]])self.kf.correct(measured)predictedself.kf.predict()x,yint(predicted[0]),int(predicted[1])returnx,y四、PyQt GUI开发1. 创建基本的PyQt界面importsysfromPyQt5.QtWidgetsimportQApplication,QMainWindow,QLabel,QVBoxLayout,QWidgetfromPyQt5.QtGuiimportQPixmapclassMainWindow(QMainWindow):def__init__(self):super().__init__()self.setWindowTitle(YOLOv5 Single Camera Detection System)layoutQVBoxLayout()self.labelQLabel(self)layout.addWidget(self.label)containerQWidget()container.setLayout(layout)self.setCentralWidget(container)defupdate_image(self,image_path):pixmapQPixmap(image_path)self.label.setPixmap(pixmap)if__name____main__:appQApplication(sys.argv)windowMainWindow()window.show()sys.exit(app.exec_())五、整合与测试将上述各个部分整合起来形成一个完整的系统。你可以根据实际需求调整代码逻辑和参数设置。构建出一个功能完善的YOLOv5单目测距检测系统。代码示例仅供参考学习使用。