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https://github.com/open-mmlab/mmdeploy.git
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a494a6f6ff
* sync SDK changes according to performance benchmarks * fix end-of-file lint * fix clang-format issue * fix clang-format by adding 'clang-format off' * remove useless casts * remove 'data' argument of 'operator()' * change 'Tensor2Img' to 'TensorToImg' according to spec * correct tensor's name according spec Co-authored-by: lvhan028 <lvhan_028@163.com>
69 lines
2.6 KiB
C
69 lines
2.6 KiB
C
// Copyright (c) OpenMMLab. All rights reserved.
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#ifndef MMDEPLOY_DETECTOR_H
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#define MMDEPLOY_DETECTOR_H
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#include "common.h"
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typedef struct mm_instance_mask_t {
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char* data;
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int height;
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int width;
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} mm_instance_mask_t;
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typedef struct mm_detect_t {
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int label_id;
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float score;
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mm_rect_t bbox;
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mm_instance_mask_t* mask;
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} mm_detect_t;
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MM_SDK_API int mmdeploy_detector_create(mm_model_t model, const char* device_name, int device_id,
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mm_handle_t* handle);
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/**
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* @brief Create detector's handle
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* @param model_path detector's config which is supposed to be json. Refer to
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* `@PROJECT_ROOT_DIR/config/detector/retinanet_config.json`
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* @param device_name name of device, such as "cpu", "cuda" and etc.
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* @param device_id id of device.
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* @param handle instance of a detector
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* @return status of creating detector's handle
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*/
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MM_SDK_API int mmdeploy_detector_create_by_path(const char* model_path, const char* device_name,
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int device_id, mm_handle_t* handle);
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/**
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* @brief Apply detector to batch images and get their inference results
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* @param handle detector's handle made by `mmdeploy_detector_create_by_path`
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* @param mats batch images
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* @param mat_count number of images in a batch
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* @param results a consecutive buffer to save detection results of each image.
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* User has to access `result_count` to get the beginning and ending position of
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* an image's detection results. It is created inside and should be destroyed by
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* calling `mmdeploy_detector_release_result`
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* @param result_count a consecutive buffer to save the number of detection
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* results of each image. Its length is equal to `mat_count`. It is created
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* inside and should be destroyed by calling `mmdeploy_detector_release_result`
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* @return status of inference
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*/
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MM_SDK_API int mmdeploy_detector_apply(mm_handle_t handle, const mm_mat_t* mats, int mat_count,
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mm_detect_t** results, int** result_count);
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/** @brief release the inference result buffer
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* @param results a consecutive buffer to save images' detection results
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* @param result_count a consecutive buffer to save the size of detection
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* results of each image in a batch
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* @param count length of `result_count`
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*/
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MM_SDK_API void mmdeploy_detector_release_result(mm_detect_t* results, const int* result_count,
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int count);
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/**
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* @brief destroy detector's handle
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* @param handle detector's handle made by `mmdeploy_detector_create_by_path`
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*/
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MM_SDK_API void mmdeploy_detector_destroy(mm_handle_t handle);
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#endif // MMDEPLOY_DETECTOR_H
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