美团-深度学习及AR在移动端打车场景下的应用

图一

纬=伪胃

纬纬

  func renderer(_ renderer: SCNSceneRenderer, updateAtTime time: TimeInterval) {           guard let renderLocations = self.netGroupInfo?.locations?.filter({ (userLocation) -> Bool in               return userLocation.userId != GroupMenberManager.sharedInstance.getCurrentUserID()           }) else {               return           }           DispatchQueue.main.async {               guard let camera = self.sceneView.pointOfView else { return }               //当前用户定位               let currentLocation = UserLocation()                currentLocation.latitude = GroupMenberManager.sharedInstance.userLatitude               currentLocation.longitude = GroupMenberManager.sharedInstance.userLongitute               // 循环处理当前组内其他成员               for renderLocation in renderLocations {                 // 两点间距离公式求得距离用来控制3D模型字体大小，直观的反应距离的远近                   let distance = currentLocation.distanceFrom(renderLocation)                 // 求得两个用户间的坐标关系                   let angle = currentLocation.angleFrom(renderLocation)                 // 根据上述公式求得3D模型要渲染的最终位置  compassAngle为实时获取的陀螺仪指南针方向                   var position = SCNVector3(x: 0, y: 0, z: -3).roateInHorizontalPlaneBy(angle: self.compassAngle - angle)                   position = camera.convertPosition(position, to: nil)                 //稳定在水平上                   position.y = 0;                 //更新位置                   self.virtualObjectManager.findVirtualObject(renderLocation.userId ?? "")?.scnNode.position = position                 //根据距离更新模型文字和大小                   self.virtualObjectManager.findVirtualObject(renderLocation.userId ?? "")?.changeNodeTextAnSize(text: renderLocation.userTitle, distance: distance)               }           }       }

import coremltools  # 调用caffe转换器的convert方法执行转换 coreml_model = coremltools.converters.caffe.convert(('web_car.caffemodel', 'deploy.prototxt'), image_input_names = 'data', class_labels = 'class_labels.txt')  # 保存转换生成的分类器模型文件 coreml_model.save('CarRecognition.mlmodel')

import coremltools  # 调用caffe转换器的convert方法执行转换 coreml_model = coremltools.converters.caffe.convert(('googlenet_finetune_web_car.caffemodel', 'deploy.prototxt'), image_input_names = 'data', class_labels = 'cars.txt')  # 设置元数据 coreml_model.author = 'Audebert, Nicolas and Le Saux, Bertrand and Lefevre Sebastien' coreml_model.license = 'MIT' coreml_model.short_description = 'Predict the brand & model of a car.' coreml_model.input_description['data'] = 'An image of a car.' coreml_model.output_description['prob'] = 'The probabilities that the input image is a car.' coreml_model.output_description['classLabel'] = 'The most likely type of car, for the given input.'  # 保存转换生成的分类器模型文件 coreml_model.save('CarRecognition.mlmodel')

let carModel = CarRecognition() let output = try carModel.prediction(image: ref)

图十

图十一

文章来源: 美团-深度学习及AR在移动端打车场景下的应用

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