4.34. algo_yolox_post

For post-processing interfaces with yolox model, internally implemented using thread pools.

4.34.1. Constructor

Interface:

algo_yolox_post(const std::vector<int>& shape,
                        int network_w=640,
                        int network_h=640,
                        int max_queue_size=20);

Parameters:

• shape: std::vector<int>

Input parameters. The shape of the input data.

• network_w: int

Input parameters. The input width of the model, which defaults to 640.

• network_h: int

Input parameters. The input height of the model, which defaults to 640.

• max_queue_size: int

Input parameters. Maximum length of cached data.

4.34.2. push_data

Input data. The value of batchsize other than 1 is supported.

Interface:

int push_data(
    std::vector<int> channel_idx,
    std::vector<int> image_idx,
    TensorPTRWithName input_data,
    std::vector<float> dete_threshold,
    std::vector<float> nms_threshold,
    std::vector<int> ost_w,
    std::vector<int> ost_h,
    std::vector<std::vector<int>> padding_attr);

Parameters:

• channel_idx: std::vector<int>

Input parameters. The channel number of the input image.

• image_idx: std::vector<int>

Input parameters. The sequence number of the input image.

• input_data: TensorPTRWithName

Input parameters. The input data.

• dete_threshold: std::vector<float>

Input parameters. Detection threshold sequence.

• nms_threshold: std::vector<float>

Input parameters. nms threshold.

• ost_w: std::vector<int>

Input parameters. The width of original image.

• ost_h: std::vector<int>

Input parameters. The height of original image.

• padding_attrs: std::vector<std::vector<int> >

Input parameters. The attribute list of the fill image, starting point coordinate x, starting point coordinate y, width after scaling, height after scaling.

Returns:

Return 0 if successful, otherwise failed.

4.34.3. get_result_npy

Get the final detection result.

Interface:

std::tuple<std::vector<std::tuple<int, int, int, int ,int, float>>,int,int> get_result_npy();

Returns: tuple[tuple[left, top, right, bottom, class_id, score],channel_idx, image_idx]

• left: int

The left x coordinate of the detection result.

• top: int

The top y coordinate of the detection result.

• right: int

The right x coordinate of the detection result.

• bottom: int

The bottom y coordinate of the detection result.

• class_id: int

Category number of detection result.

• score: float

Score of detection result.

• channel_idx: int

The channel index of original image.

• image_idx: int

The image index of original image.