apollo::perception::trafficlight::EfficientNetRecognition类 参考

#include <efficient_net.h>

类 apollo::perception::trafficlight::EfficientNetRecognition 继承关系图:

apollo::perception::trafficlight::EfficientNetRecognition 的协作图:

Public 成员函数
	EfficientNetRecognition ()=default
	Construct a new Traffic Light Recognition object
	~EfficientNetRecognition ()=default
	Destroy the Traffic Light Recognition object
bool	Init (const TrafficLightRecognitorInitOptions &options) override
	Initialize traffic light recognitor parameters.
bool	Detect (camera::TrafficLightFrame *frame) override
	recogn traffic_light from image.
Public 成员函数 继承自 apollo::perception::trafficlight::BaseTrafficLightRecognitor
	BaseTrafficLightRecognitor ()=default
	Construct a new base traffic light recognitor object.
virtual	~BaseTrafficLightRecognitor ()=default
	Destroy the base traffic light recognitor object.
	DISALLOW_COPY_AND_ASSIGN (BaseTrafficLightRecognitor)

详细描述

在文件 efficient_net.h 第 33 行定义.

构造及析构函数说明

EfficientNetRecognition()

apollo::perception::trafficlight::EfficientNetRecognition::EfficientNetRecognition ( )

default

Construct a new Traffic Light Recognition object

~EfficientNetRecognition()

apollo::perception::trafficlight::EfficientNetRecognition::~EfficientNetRecognition ( )

default

Destroy the Traffic Light Recognition object

成员函数说明

Detect()

bool apollo::perception::trafficlight::EfficientNetRecognition::Detect ( camera::TrafficLightFrame *  frame )

overridevirtual

recogn traffic_light from image.

参数

frame

返回

true

false

实现了 apollo::perception::trafficlight::BaseTrafficLightRecognitor.

在文件 efficient_net.cc 第 112 行定义.

                                                                   {
    if (frame->traffic_lights.size() <= 0) {
        AINFO << "No recognizable traffic lights";
        return true;
    }
    std::vector<base::TrafficLightPtr> candidate;
    candidate.reserve(frame->traffic_lights.size());
    // get traffic light region
    for (base::TrafficLightPtr light : frame->traffic_lights) {
        if (light->region.is_detected) {
            candidate.push_back(light);
        } else {
            light->status.color = base::TLColor::TL_UNKNOWN_COLOR;
            light->status.confidence = 0;
        }
    }
    // classify color
    Perform(frame, &candidate);
 
    return true;
}

Init()

bool apollo::perception::trafficlight::EfficientNetRecognition::Init ( const TrafficLightRecognitorInitOptions &  options )

overridevirtual

Initialize traffic light recognitor parameters.

参数

options

返回

true

false

实现了 apollo::perception::trafficlight::BaseTrafficLightRecognitor.

在文件 efficient_net.cc 第 32 行定义.

                                                                                   {
    gpu_id_ = options.gpu_id;
    BASE_GPU_CHECK(cudaSetDevice(gpu_id_));
    BASE_GPU_CHECK(cudaStreamCreate(&stream_));
 
    std::string config_file = GetConfigFile(options.config_path, options.config_file);
    ACHECK(apollo::cyber::common::GetProtoFromFile(config_file, &model_param_));
    resize_height_ = model_param_.classify_resize_height();
    resize_width_ = model_param_.classify_resize_width();
    unknown_threshold_ = model_param_.classify_threshold();
    scale_ = model_param_.scale();
    mean_.reset(new base::Blob<float>(1, 3, 1, 1));
    float* p = mean_->mutable_cpu_data();
    if (model_param_.is_bgr()) {
        data_provider_image_option_.target_color = base::Color::BGR;
        p[0] = model_param_.mean_b();
        p[1] = model_param_.mean_g();
        p[2] = model_param_.mean_r();
    } else {
        data_provider_image_option_.target_color = base::Color::RGB;
        p[0] = model_param_.mean_r();
        p[1] = model_param_.mean_g();
        p[2] = model_param_.mean_b();
    }
    std::vector<int> shape = {1, 3, resize_height_, resize_width_};
    mean_buffer_.reset(new base::Blob<float>(shape));
 
    // Init model
    if (!InitModel()) {
        AERROR << "Init model error.";
        return false;
    }
    // image data
    image_.reset(new base::Image8U(resize_height_, resize_width_, base::Color::BGR));
 
    return true;
}

---

该类的文档由以下文件生成:

• modules/perception/traffic_light_recognition/recognition/efficient_net/efficient_net.h
• modules/perception/traffic_light_recognition/recognition/efficient_net/efficient_net.cc