138 lines
4.9 KiB
C
138 lines
4.9 KiB
C
#ifndef __YOLOV5__
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#define __YOLOV5__
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#include <stdio.h>
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#include <string.h>
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#include "xmedia_svp_std.h"
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#include "xmedia_svp.h"
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#include "svp_nms.h"
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#include "xmedia_svp_quantize.h"
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#include "svp_core.h"
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#include "svp_movement.h"
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#include "svp_bytetracker.h"
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#ifdef __cplusplus
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extern "C" {
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#endif
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// sigmoid 查表法
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#define TABLE_SIZE 100000
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#define TABLE_MIN (-100.0f)
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#define TABLE_MAX (100.0f)
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#define TABLE_200 (200.0f)
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#define UNSIGNED_8_BIT_NUM 256
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#define TABLE_500 (500.0f)
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// 每个特征中的x、y、w、h、socre长度
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#define XYWHF_LEN 5
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#define XYWH_LEN 4
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#define LAYER_MAX_NUM 10
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#define LAYER_MIN_NUM 3
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#define LAYER_NUM_FOUR 4
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// 每层feature_map对应的anchor
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#define ANCHOR_NUM 3
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/** 模型输入默认分辨率 */
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#define INPUT_DEFAULT_WIDE 640
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#define INPUT_DEFAULT_HIGH 360
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/** 默认检测阈值 */
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#define DEFAULT_DETECT_THRESHOLD 0.45f
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/** 默认IOU阈值 */
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#define DEFAULT_IOU_THRESHOLD 0.5f
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/** 分类器默认阈值*/
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#define DEFAULT_CLASSIFIER_THRESHOLD 0.01f
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#define DEFAULT_BBOX_NUM 512
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/** 模型检测类型数量 */
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#define DETECTION_MODEL_SINGLE 1
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#define DETECTION_MODEL_DOUBLE 2
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#define DETECTION_MODEL_TRIPLE 3
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#define DETECTION_MODEL_MULTI 4
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#define DETECTION_MODEL_SIX 6
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#define DETECTION_MODEL_PCNMV_FIRESMOKE 11
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#define DETECTION_MODEL_MAX 80
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// 稳定框算法系数
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#define SVP_IOU_STABLE_THRESH_UPPER 0.85f
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#define SVP_IOU_STABLE_THRESH_MIDDLE 0.7f
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#define SVP_STABLE_HORIZONTAL_FACTOR 6
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#define SVP_STABLE_VERTICAL_FACTOR 5
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#define SVP_UNIFORM_FACTOR_A 2
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#define SVP_UNIFORM_FACTOR_B 3
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#define SVP_STABLE_HISTORY_RATIO 0.8f
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#define SVP_STABLE_CURRENT_RATIO 0.2f
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// 目标框面积大小限制
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#define MAX_AREA_ATTR 4
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#define MINI_AREA 100
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typedef struct {
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xmedia_svp_rect rect;
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xmedia_svp_class_type class_type;
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} svp_stable_rect;
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typedef struct {
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svp_stable_rect stable_targets[XMEDIA_SVP_MAX_TARGET_NUM];
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xmedia_u32 num;
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} svp_stable_box;
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typedef struct {
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xmedia_svp_point anchors[LAYER_MAX_NUM][ANCHOR_NUM]; // 锚点值
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xmedia_u8 layer_num; // 锚点层数
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} xmedia_svp_detect_anchors;
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typedef struct {
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xmedia_u32 w; // 模型支持的图像宽度
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xmedia_u32 h; // 模型支持的图像高度
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xmedia_u32 num; // 模型的检测类别数量
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xmedia_u32 feature[LAYER_MAX_NUM]; // 每个feature_map的大小
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xmedia_u32 output_num; // npu输出所有的特征数量
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xmedia_float thres_desig; // 检测阈值的反sigmoid值
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quanlize_param quanlize[LAYER_MAX_NUM]; // 反量化需要的参数
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xmedia_npu_model model; // npu参数
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xmedia_svp_alg_type type; // 算法类型
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xmedia_float detect_threshold; // 置信度阈值，建议值0.55f
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xmedia_float classifier_threshold; // 分类器阈值，建议值0.01f
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xmedia_float iou_threshold; // iou相交比阈值，建议值0.5f
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xmedia_u32 max_target_num; // 最大目标数，最大值10
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xmedia_bool smart_venc_enable;
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xmedia_bool smart_ae_enable;
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xmedia_bool smart_venc_array[XMEDIA_SVP_MAX_VENC_CHN_NUM];
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xmedia_bool smart_ae_array[XMEDIA_SVP_MAX_VI_PIPE_NUM];
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svp_stable_box record_result; // 记录历史框,用于稳定算法
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xmedia_svp_detect_anchors anchors; // 锚点值
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xmedia_u64 cost_time; // process接口耗时
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xmedia_bool aov_flag; // 是否为aov模型
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xmedia_bool aov_only_target; // 是否仅判断有无目标
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svp_movement movement; // 静止过滤相关参数
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svp_tracklet tracklet;
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xmedia_u8 track_id_arry[SVP_ALG_MAX_TARGET_NUM * SVP_MAX_LOST_COUNT]; // 追踪id数组
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xmedia_s32 track_id_grow; // 追踪id增加
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xmedia_void *private_data; // 实例私有数据
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xmedia_float *sigmoid_table; // sigmoid查表
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xmedia_float *sigmoid_dequantize_table;
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xmedia_float *sigmoid_dequantize_table_x2;
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xmedia_float *sigmoid_dequantize_table_x6;
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xmedia_float *sigmoid_dequantize_table_x2_sq;
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svp_base_result *bbox;
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} yolov5_detect_param;
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xmedia_void set_default_yolov5_detect_param(yolov5_detect_param *param);
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xmedia_s32 detect_process(yolov5_detect_param *param, const xmedia_video_frame_info *input_image,
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xmedia_svp_yolov5_output *result, const xmedia_svp_detect_anchors anchors_yaml);
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xmedia_s32 detect_init(yolov5_detect_param *param, const xmedia_svp_alg_type type,
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const xmedia_svp_modules *model, xmedia_s32 layer_num);
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xmedia_s32 detect_deinit(yolov5_detect_param *param);
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xmedia_s32 svp_get_detect_result_yolov5_bz(yolov5_detect_param *param, xmedia_svp_yolov5_output *result,
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const xmedia_svp_detect_anchors anchors_yaml);
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#ifdef __cplusplus
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}
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#endif
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#endif
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