mirror of
https://github.com/pjreddie/darknet.git
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Added include/darknet.h
This commit is contained in:
AlexeyAB
793
include/darknet.h
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793
include/darknet.h
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#ifndef DARKNET_API
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#define DARKNET_API
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#if defined(_MSC_VER) && _MSC_VER < 1900
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#define inline __inline
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#endif
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#include <stdlib.h>
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#include <stdio.h>
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#include <string.h>
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#include <pthread.h>
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#include <stdint.h>
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#ifdef LIB_EXPORTS
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#if defined(_MSC_VER)
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#define LIB_API __declspec(dllexport)
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#else
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#define LIB_API __attribute__((visibility("default")))
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#endif
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#else
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#if defined(_MSC_VER)
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#define LIB_API
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#else
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#define LIB_API
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#endif
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#endif
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#ifdef GPU
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#define BLOCK 512
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#include "cuda_runtime.h"
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#include "curand.h"
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#include "cublas_v2.h"
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#ifdef CUDNN
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#include "cudnn.h"
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#endif
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#endif
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#ifdef __cplusplus
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extern "C" {
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#endif
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struct network;
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typedef struct network network;
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struct network_state;
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typedef struct network_state;
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struct layer;
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typedef struct layer layer;
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struct image;
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typedef struct image image;
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struct detection;
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typedef struct detection detection;
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struct load_args;
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typedef struct load_args load_args;
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struct data;
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typedef struct data data;
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struct metadata;
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typedef struct metadata metadata;
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struct tree;
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typedef struct tree tree;
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#define SECRET_NUM -1234
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extern int gpu_index;
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// option_list.h
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typedef struct metadata {
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int classes;
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char **names;
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} metadata;
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// tree.h
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typedef struct tree {
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int *leaf;
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int n;
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int *parent;
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int *child;
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int *group;
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char **name;
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int groups;
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int *group_size;
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int *group_offset;
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} tree;
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// activations.h
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typedef enum {
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LOGISTIC, RELU, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN, SELU
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}ACTIVATION;
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// image.h
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typedef enum{
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PNG, BMP, TGA, JPG
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} IMTYPE;
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// activations.h
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typedef enum{
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MULT, ADD, SUB, DIV
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} BINARY_ACTIVATION;
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// layer.h
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typedef enum {
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CONVOLUTIONAL,
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DECONVOLUTIONAL,
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CONNECTED,
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MAXPOOL,
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SOFTMAX,
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DETECTION,
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DROPOUT,
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CROP,
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ROUTE,
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COST,
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NORMALIZATION,
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AVGPOOL,
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LOCAL,
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SHORTCUT,
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ACTIVE,
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RNN,
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GRU,
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LSTM,
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CRNN,
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BATCHNORM,
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NETWORK,
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XNOR,
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REGION,
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YOLO,
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ISEG,
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REORG,
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REORG_OLD,
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UPSAMPLE,
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LOGXENT,
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L2NORM,
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BLANK
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} LAYER_TYPE;
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// layer.h
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typedef enum{
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SSE, MASKED, L1, SEG, SMOOTH,WGAN
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} COST_TYPE;
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// layer.h
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typedef struct update_args {
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int batch;
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float learning_rate;
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float momentum;
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float decay;
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int adam;
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float B1;
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float B2;
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float eps;
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int t;
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} update_args;
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// layer.h
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struct layer {
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LAYER_TYPE type;
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ACTIVATION activation;
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COST_TYPE cost_type;
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void(*forward) (struct layer, struct network_state);
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void(*backward) (struct layer, struct network_state);
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void(*update) (struct layer, int, float, float, float);
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void(*forward_gpu) (struct layer, struct network_state);
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void(*backward_gpu) (struct layer, struct network_state);
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void(*update_gpu) (struct layer, int, float, float, float);
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int batch_normalize;
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int shortcut;
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int batch;
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int forced;
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int flipped;
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int inputs;
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int outputs;
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int nweights;
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int nbiases;
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int extra;
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int truths;
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int h, w, c;
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int out_h, out_w, out_c;
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int n;
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int max_boxes;
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int groups;
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int size;
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int side;
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int stride;
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int reverse;
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int flatten;
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int spatial;
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int pad;
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int sqrt;
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int flip;
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int index;
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int binary;
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int xnor;
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int use_bin_output;
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int steps;
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int hidden;
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int truth;
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float smooth;
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float dot;
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float angle;
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float jitter;
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float saturation;
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float exposure;
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float shift;
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float ratio;
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float learning_rate_scale;
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float clip;
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int focal_loss;
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int noloss;
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int softmax;
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int classes;
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int coords;
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int background;
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int rescore;
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int objectness;
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int does_cost;
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int joint;
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int noadjust;
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int reorg;
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int log;
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int tanh;
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int *mask;
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int total;
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float bflops;
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int adam;
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float B1;
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float B2;
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float eps;
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int t;
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float alpha;
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float beta;
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float kappa;
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float coord_scale;
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float object_scale;
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float noobject_scale;
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float mask_scale;
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float class_scale;
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int bias_match;
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int random;
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float ignore_thresh;
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float truth_thresh;
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float thresh;
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float focus;
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int classfix;
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int absolute;
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int onlyforward;
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int stopbackward;
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int dontload;
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int dontsave;
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int dontloadscales;
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int numload;
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float temperature;
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float probability;
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float scale;
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char * cweights;
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int * indexes;
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int * input_layers;
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int * input_sizes;
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int * map;
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int * counts;
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float ** sums;
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float * rand;
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float * cost;
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float * state;
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float * prev_state;
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float * forgot_state;
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float * forgot_delta;
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float * state_delta;
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float * combine_cpu;
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float * combine_delta_cpu;
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float *concat;
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float *concat_delta;
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float *binary_weights;
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float *biases;
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float *bias_updates;
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float *scales;
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float *scale_updates;
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float *weights;
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float *weight_updates;
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char *align_bit_weights_gpu;
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float *mean_arr_gpu;
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float *align_workspace_gpu;
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float *transposed_align_workspace_gpu;
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int align_workspace_size;
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char *align_bit_weights;
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float *mean_arr;
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int align_bit_weights_size;
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int lda_align;
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int new_lda;
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int bit_align;
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float *col_image;
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float * delta;
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float * output;
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float * loss;
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float * squared;
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float * norms;
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float * spatial_mean;
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float * mean;
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float * variance;
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float * mean_delta;
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float * variance_delta;
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float * rolling_mean;
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float * rolling_variance;
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float * x;
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float * x_norm;
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float * m;
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float * v;
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float * bias_m;
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float * bias_v;
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float * scale_m;
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float * scale_v;
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float *z_cpu;
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float *r_cpu;
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float *h_cpu;
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float * prev_state_cpu;
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float *temp_cpu;
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float *temp2_cpu;
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float *temp3_cpu;
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float *dh_cpu;
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float *hh_cpu;
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float *prev_cell_cpu;
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float *cell_cpu;
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float *f_cpu;
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float *i_cpu;
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float *g_cpu;
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float *o_cpu;
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float *c_cpu;
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float *dc_cpu;
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float * binary_input;
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struct layer *input_layer;
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struct layer *self_layer;
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struct layer *output_layer;
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struct layer *reset_layer;
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struct layer *update_layer;
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struct layer *state_layer;
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struct layer *input_gate_layer;
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struct layer *state_gate_layer;
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struct layer *input_save_layer;
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struct layer *state_save_layer;
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struct layer *input_state_layer;
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struct layer *state_state_layer;
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struct layer *input_z_layer;
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struct layer *state_z_layer;
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struct layer *input_r_layer;
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struct layer *state_r_layer;
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struct layer *input_h_layer;
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struct layer *state_h_layer;
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struct layer *wz;
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struct layer *uz;
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struct layer *wr;
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struct layer *ur;
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struct layer *wh;
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struct layer *uh;
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struct layer *uo;
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struct layer *wo;
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struct layer *uf;
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struct layer *wf;
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struct layer *ui;
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struct layer *wi;
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struct layer *ug;
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struct layer *wg;
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tree *softmax_tree;
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size_t workspace_size;
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#ifdef GPU
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int *indexes_gpu;
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float *z_gpu;
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float *r_gpu;
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float *h_gpu;
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float *temp_gpu;
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float *temp2_gpu;
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float *temp3_gpu;
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float *dh_gpu;
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float *hh_gpu;
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float *prev_cell_gpu;
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float *cell_gpu;
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float *f_gpu;
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float *i_gpu;
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float *g_gpu;
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float *o_gpu;
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float *c_gpu;
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float *dc_gpu;
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// adam
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float *m_gpu;
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float *v_gpu;
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float *bias_m_gpu;
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float *scale_m_gpu;
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float *bias_v_gpu;
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float *scale_v_gpu;
|
||||
|
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float * combine_gpu;
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float * combine_delta_gpu;
|
||||
|
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float * prev_state_gpu;
|
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float * forgot_state_gpu;
|
||||
float * forgot_delta_gpu;
|
||||
float * state_gpu;
|
||||
float * state_delta_gpu;
|
||||
float * gate_gpu;
|
||||
float * gate_delta_gpu;
|
||||
float * save_gpu;
|
||||
float * save_delta_gpu;
|
||||
float * concat_gpu;
|
||||
float * concat_delta_gpu;
|
||||
|
||||
float *binary_input_gpu;
|
||||
float *binary_weights_gpu;
|
||||
|
||||
float * mean_gpu;
|
||||
float * variance_gpu;
|
||||
|
||||
float * rolling_mean_gpu;
|
||||
float * rolling_variance_gpu;
|
||||
|
||||
float * variance_delta_gpu;
|
||||
float * mean_delta_gpu;
|
||||
|
||||
float * col_image_gpu;
|
||||
|
||||
float * x_gpu;
|
||||
float * x_norm_gpu;
|
||||
float * weights_gpu;
|
||||
float * weight_updates_gpu;
|
||||
float * weight_change_gpu;
|
||||
|
||||
float * weights_gpu16;
|
||||
float * weight_updates_gpu16;
|
||||
|
||||
float * biases_gpu;
|
||||
float * bias_updates_gpu;
|
||||
float * bias_change_gpu;
|
||||
|
||||
float * scales_gpu;
|
||||
float * scale_updates_gpu;
|
||||
float * scale_change_gpu;
|
||||
|
||||
float * output_gpu;
|
||||
float * loss_gpu;
|
||||
float * delta_gpu;
|
||||
float * rand_gpu;
|
||||
float * squared_gpu;
|
||||
float * norms_gpu;
|
||||
#ifdef CUDNN
|
||||
cudnnTensorDescriptor_t srcTensorDesc, dstTensorDesc;
|
||||
cudnnTensorDescriptor_t srcTensorDesc16, dstTensorDesc16;
|
||||
cudnnTensorDescriptor_t dsrcTensorDesc, ddstTensorDesc;
|
||||
cudnnTensorDescriptor_t dsrcTensorDesc16, ddstTensorDesc16;
|
||||
cudnnTensorDescriptor_t normTensorDesc, normDstTensorDesc, normDstTensorDescF16;
|
||||
cudnnFilterDescriptor_t weightDesc, weightDesc16;
|
||||
cudnnFilterDescriptor_t dweightDesc, dweightDesc16;
|
||||
cudnnConvolutionDescriptor_t convDesc;
|
||||
cudnnConvolutionFwdAlgo_t fw_algo, fw_algo16;
|
||||
cudnnConvolutionBwdDataAlgo_t bd_algo, bd_algo16;
|
||||
cudnnConvolutionBwdFilterAlgo_t bf_algo, bf_algo16;
|
||||
cudnnPoolingDescriptor_t poolingDesc;
|
||||
#endif // CUDNN
|
||||
#endif // GPU
|
||||
};
|
||||
|
||||
|
||||
// network.h
|
||||
typedef enum {
|
||||
CONSTANT, STEP, EXP, POLY, STEPS, SIG, RANDOM
|
||||
} learning_rate_policy;
|
||||
|
||||
// network.h
|
||||
typedef struct network {
|
||||
int n;
|
||||
int batch;
|
||||
uint64_t *seen;
|
||||
int *t;
|
||||
float epoch;
|
||||
int subdivisions;
|
||||
layer *layers;
|
||||
float *output;
|
||||
learning_rate_policy policy;
|
||||
|
||||
float learning_rate;
|
||||
float momentum;
|
||||
float decay;
|
||||
float gamma;
|
||||
float scale;
|
||||
float power;
|
||||
int time_steps;
|
||||
int step;
|
||||
int max_batches;
|
||||
float *scales;
|
||||
int *steps;
|
||||
int num_steps;
|
||||
int burn_in;
|
||||
int cudnn_half;
|
||||
|
||||
int adam;
|
||||
float B1;
|
||||
float B2;
|
||||
float eps;
|
||||
|
||||
int inputs;
|
||||
int outputs;
|
||||
int truths;
|
||||
int notruth;
|
||||
int h, w, c;
|
||||
int max_crop;
|
||||
int min_crop;
|
||||
float max_ratio;
|
||||
float min_ratio;
|
||||
int center;
|
||||
int flip; // horizontal flip 50% probability augmentaiont for classifier training (default = 1)
|
||||
float angle;
|
||||
float aspect;
|
||||
float exposure;
|
||||
float saturation;
|
||||
float hue;
|
||||
int random;
|
||||
int small_object;
|
||||
|
||||
int gpu_index;
|
||||
tree *hierarchy;
|
||||
|
||||
float *input;
|
||||
float *truth;
|
||||
float *delta;
|
||||
float *workspace;
|
||||
int train;
|
||||
int index;
|
||||
float *cost;
|
||||
float clip;
|
||||
|
||||
#ifdef GPU
|
||||
//float *input_gpu;
|
||||
//float *truth_gpu;
|
||||
float *delta_gpu;
|
||||
float *output_gpu;
|
||||
|
||||
float *input_state_gpu;
|
||||
|
||||
float **input_gpu;
|
||||
float **truth_gpu;
|
||||
float **input16_gpu;
|
||||
float **output16_gpu;
|
||||
size_t *max_input16_size;
|
||||
size_t *max_output16_size;
|
||||
int wait_stream;
|
||||
#endif
|
||||
} network;
|
||||
|
||||
// network.h
|
||||
typedef struct network_state {
|
||||
float *truth;
|
||||
float *input;
|
||||
float *delta;
|
||||
float *workspace;
|
||||
int train;
|
||||
int index;
|
||||
network net;
|
||||
} network_state;
|
||||
|
||||
//typedef struct {
|
||||
// int w;
|
||||
// int h;
|
||||
// float scale;
|
||||
// float rad;
|
||||
// float dx;
|
||||
// float dy;
|
||||
// float aspect;
|
||||
//} augment_args;
|
||||
|
||||
// image.h
|
||||
typedef struct image {
|
||||
int w;
|
||||
int h;
|
||||
int c;
|
||||
float *data;
|
||||
} image;
|
||||
|
||||
//typedef struct {
|
||||
// int w;
|
||||
// int h;
|
||||
// int c;
|
||||
// float *data;
|
||||
//} image;
|
||||
|
||||
// box.h
|
||||
typedef struct box {
|
||||
float x, y, w, h;
|
||||
} box;
|
||||
|
||||
// box.h
|
||||
typedef struct detection{
|
||||
box bbox;
|
||||
int classes;
|
||||
float *prob;
|
||||
float *mask;
|
||||
float objectness;
|
||||
int sort_class;
|
||||
} detection;
|
||||
|
||||
// matrix.h
|
||||
typedef struct matrix {
|
||||
int rows, cols;
|
||||
float **vals;
|
||||
} matrix;
|
||||
|
||||
// data.h
|
||||
typedef struct data {
|
||||
int w, h;
|
||||
matrix X;
|
||||
matrix y;
|
||||
int shallow;
|
||||
int *num_boxes;
|
||||
box **boxes;
|
||||
} data;
|
||||
|
||||
// data.h
|
||||
typedef enum {
|
||||
CLASSIFICATION_DATA, DETECTION_DATA, CAPTCHA_DATA, REGION_DATA, IMAGE_DATA, COMPARE_DATA, WRITING_DATA, SWAG_DATA, TAG_DATA, OLD_CLASSIFICATION_DATA, STUDY_DATA, DET_DATA, SUPER_DATA, LETTERBOX_DATA, REGRESSION_DATA, SEGMENTATION_DATA, INSTANCE_DATA, ISEG_DATA
|
||||
} data_type;
|
||||
|
||||
// data.h
|
||||
typedef struct load_args {
|
||||
int threads;
|
||||
char **paths;
|
||||
char *path;
|
||||
int n;
|
||||
int m;
|
||||
char **labels;
|
||||
int h;
|
||||
int w;
|
||||
int c; // color depth
|
||||
int out_w;
|
||||
int out_h;
|
||||
int nh;
|
||||
int nw;
|
||||
int num_boxes;
|
||||
int min, max, size;
|
||||
int classes;
|
||||
int background;
|
||||
int scale;
|
||||
int center;
|
||||
int coords;
|
||||
int small_object;
|
||||
float jitter;
|
||||
int flip;
|
||||
float angle;
|
||||
float aspect;
|
||||
float saturation;
|
||||
float exposure;
|
||||
float hue;
|
||||
data *d;
|
||||
image *im;
|
||||
image *resized;
|
||||
data_type type;
|
||||
tree *hierarchy;
|
||||
} load_args;
|
||||
|
||||
// data.h
|
||||
typedef struct box_label {
|
||||
int id;
|
||||
float x, y, w, h;
|
||||
float left, right, top, bottom;
|
||||
} box_label;
|
||||
|
||||
// list.h
|
||||
//typedef struct node {
|
||||
// void *val;
|
||||
// struct node *next;
|
||||
// struct node *prev;
|
||||
//} node;
|
||||
|
||||
// list.h
|
||||
//typedef struct list {
|
||||
// int size;
|
||||
// node *front;
|
||||
// node *back;
|
||||
//} list;
|
||||
|
||||
// -----------------------------------------------------
|
||||
|
||||
|
||||
// parser.c
|
||||
LIB_API network *load_network(char *cfg, char *weights, int clear);
|
||||
LIB_API network *load_network_custom(char *cfg, char *weights, int clear, int batch);
|
||||
LIB_API network *load_network(char *cfg, char *weights, int clear);
|
||||
|
||||
// network.c
|
||||
LIB_API load_args get_base_args(network *net);
|
||||
|
||||
// box.h
|
||||
LIB_API void do_nms_sort(detection *dets, int total, int classes, float thresh);
|
||||
LIB_API void do_nms_obj(detection *dets, int total, int classes, float thresh);
|
||||
|
||||
// network.h
|
||||
LIB_API float *network_predict(network net, float *input);
|
||||
LIB_API detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num, int letter);
|
||||
LIB_API void free_detections(detection *dets, int n);
|
||||
LIB_API void fuse_conv_batchnorm(network net);
|
||||
LIB_API void calculate_binary_weights(network net);
|
||||
|
||||
LIB_API layer* get_network_layer(network* net, int i);
|
||||
LIB_API detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num, int letter);
|
||||
LIB_API detection *make_network_boxes(network *net, float thresh, int *num);
|
||||
LIB_API void reset_rnn(network *net);
|
||||
LIB_API float *network_predict_image(network *net, image im);
|
||||
LIB_API float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, network *existing_net);
|
||||
LIB_API void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map);
|
||||
LIB_API int network_width(network *net);
|
||||
LIB_API int network_height(network *net);
|
||||
LIB_API void optimize_picture(network *net, image orig, int max_layer, float scale, float rate, float thresh, int norm);
|
||||
|
||||
// image.h
|
||||
LIB_API image resize_image(image im, int w, int h);
|
||||
LIB_API image letterbox_image(image im, int w, int h);
|
||||
LIB_API void rgbgr_image(image im);
|
||||
LIB_API image make_image(int w, int h, int c);
|
||||
LIB_API image load_image_color(char *filename, int w, int h);
|
||||
LIB_API void free_image(image m);
|
||||
|
||||
// layer.h
|
||||
LIB_API void free_layer(layer);
|
||||
|
||||
// data.c
|
||||
LIB_API void free_data(data d);
|
||||
LIB_API pthread_t load_data(load_args args);
|
||||
LIB_API pthread_t load_data_in_thread(load_args args);
|
||||
|
||||
// cuda.h
|
||||
LIB_API void cuda_pull_array(float *x_gpu, float *x, size_t n);
|
||||
LIB_API void cuda_set_device(int n);
|
||||
|
||||
// utils.h
|
||||
LIB_API void free_ptrs(void **ptrs, int n);
|
||||
LIB_API void top_k(float *a, int n, int k, int *index);
|
||||
|
||||
// tree.h
|
||||
LIB_API tree *read_tree(char *filename);
|
||||
|
||||
// option_list.h
|
||||
LIB_API metadata get_metadata(char *file);
|
||||
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
#endif // __cplusplus
|
||||
#endif // DARKNET_API
|
||||
660
include/yolo_v2_class.hpp
Normal file
660
include/yolo_v2_class.hpp
Normal file
@ -0,0 +1,660 @@
|
||||
#pragma once
|
||||
#ifdef LIB_EXPORTS
|
||||
#if defined(_MSC_VER)
|
||||
#define LIB_EXPORTS __declspec(dllexport)
|
||||
#else
|
||||
#define LIB_EXPORTS __attribute__((visibility("default")))
|
||||
#endif
|
||||
#else
|
||||
#if defined(_MSC_VER)
|
||||
#define LIB_EXPORTS __declspec(dllimport)
|
||||
#else
|
||||
#define LIB_EXPORTS
|
||||
#endif
|
||||
#endif
|
||||
|
||||
struct bbox_t {
|
||||
unsigned int x, y, w, h; // (x,y) - top-left corner, (w, h) - width & height of bounded box
|
||||
float prob; // confidence - probability that the object was found correctly
|
||||
unsigned int obj_id; // class of object - from range [0, classes-1]
|
||||
unsigned int track_id; // tracking id for video (0 - untracked, 1 - inf - tracked object)
|
||||
unsigned int frames_counter;// counter of frames on which the object was detected
|
||||
};
|
||||
|
||||
struct image_t {
|
||||
int h; // height
|
||||
int w; // width
|
||||
int c; // number of chanels (3 - for RGB)
|
||||
float *data; // pointer to the image data
|
||||
};
|
||||
|
||||
#define C_SHARP_MAX_OBJECTS 1000
|
||||
struct bbox_t_container {
|
||||
bbox_t candidates[C_SHARP_MAX_OBJECTS];
|
||||
};
|
||||
|
||||
#ifdef __cplusplus
|
||||
#include <memory>
|
||||
#include <vector>
|
||||
#include <deque>
|
||||
#include <algorithm>
|
||||
|
||||
#ifdef OPENCV
|
||||
#include <opencv2/opencv.hpp> // C++
|
||||
#include "opencv2/highgui/highgui_c.h" // C
|
||||
#include "opencv2/imgproc/imgproc_c.h" // C
|
||||
#endif // OPENCV
|
||||
|
||||
extern "C" LIB_EXPORTS int init(const char *configurationFilename, const char *weightsFilename, int gpu);
|
||||
extern "C" LIB_EXPORTS int detect_image(const char *filename, bbox_t_container &container);
|
||||
extern "C" LIB_EXPORTS int detect_mat(const uint8_t* data, const size_t data_length, bbox_t_container &container);
|
||||
extern "C" LIB_EXPORTS int dispose();
|
||||
extern "C" LIB_EXPORTS int get_device_count();
|
||||
extern "C" LIB_EXPORTS int get_device_name(int gpu, char* deviceName);
|
||||
|
||||
class Detector {
|
||||
std::shared_ptr<void> detector_gpu_ptr;
|
||||
std::deque<std::vector<bbox_t>> prev_bbox_vec_deque;
|
||||
const int cur_gpu_id;
|
||||
public:
|
||||
float nms = .4;
|
||||
bool wait_stream;
|
||||
|
||||
LIB_EXPORTS Detector(std::string cfg_filename, std::string weight_filename, int gpu_id = 0);
|
||||
LIB_EXPORTS ~Detector();
|
||||
|
||||
LIB_EXPORTS std::vector<bbox_t> detect(std::string image_filename, float thresh = 0.2, bool use_mean = false);
|
||||
LIB_EXPORTS std::vector<bbox_t> detect(image_t img, float thresh = 0.2, bool use_mean = false);
|
||||
static LIB_EXPORTS image_t load_image(std::string image_filename);
|
||||
static LIB_EXPORTS void free_image(image_t m);
|
||||
LIB_EXPORTS int get_net_width() const;
|
||||
LIB_EXPORTS int get_net_height() const;
|
||||
LIB_EXPORTS int get_net_color_depth() const;
|
||||
|
||||
LIB_EXPORTS std::vector<bbox_t> tracking_id(std::vector<bbox_t> cur_bbox_vec, bool const change_history = true,
|
||||
int const frames_story = 10, int const max_dist = 150);
|
||||
|
||||
std::vector<bbox_t> detect_resized(image_t img, int init_w, int init_h, float thresh = 0.2, bool use_mean = false)
|
||||
{
|
||||
if (img.data == NULL)
|
||||
throw std::runtime_error("Image is empty");
|
||||
auto detection_boxes = detect(img, thresh, use_mean);
|
||||
float wk = (float)init_w / img.w, hk = (float)init_h / img.h;
|
||||
for (auto &i : detection_boxes) i.x *= wk, i.w *= wk, i.y *= hk, i.h *= hk;
|
||||
return detection_boxes;
|
||||
}
|
||||
|
||||
#ifdef OPENCV
|
||||
std::vector<bbox_t> detect(cv::Mat mat, float thresh = 0.2, bool use_mean = false)
|
||||
{
|
||||
if(mat.data == NULL)
|
||||
throw std::runtime_error("Image is empty");
|
||||
auto image_ptr = mat_to_image_resize(mat);
|
||||
return detect_resized(*image_ptr, mat.cols, mat.rows, thresh, use_mean);
|
||||
}
|
||||
|
||||
std::shared_ptr<image_t> mat_to_image_resize(cv::Mat mat) const
|
||||
{
|
||||
if (mat.data == NULL) return std::shared_ptr<image_t>(NULL);
|
||||
|
||||
cv::Size network_size = cv::Size(get_net_width(), get_net_height());
|
||||
cv::Mat det_mat;
|
||||
if (mat.size() != network_size)
|
||||
cv::resize(mat, det_mat, network_size);
|
||||
else
|
||||
det_mat = mat; // only reference is copied
|
||||
|
||||
return mat_to_image(det_mat);
|
||||
}
|
||||
|
||||
static std::shared_ptr<image_t> mat_to_image(cv::Mat img_src)
|
||||
{
|
||||
cv::Mat img;
|
||||
cv::cvtColor(img_src, img, cv::COLOR_RGB2BGR);
|
||||
std::shared_ptr<image_t> image_ptr(new image_t, [](image_t *img) { free_image(*img); delete img; });
|
||||
std::shared_ptr<IplImage> ipl_small = std::make_shared<IplImage>(img);
|
||||
*image_ptr = ipl_to_image(ipl_small.get());
|
||||
return image_ptr;
|
||||
}
|
||||
|
||||
private:
|
||||
|
||||
static image_t ipl_to_image(IplImage* src)
|
||||
{
|
||||
unsigned char *data = (unsigned char *)src->imageData;
|
||||
int h = src->height;
|
||||
int w = src->width;
|
||||
int c = src->nChannels;
|
||||
int step = src->widthStep;
|
||||
image_t out = make_image_custom(w, h, c);
|
||||
int count = 0;
|
||||
|
||||
for (int k = 0; k < c; ++k) {
|
||||
for (int i = 0; i < h; ++i) {
|
||||
int i_step = i*step;
|
||||
for (int j = 0; j < w; ++j) {
|
||||
out.data[count++] = data[i_step + j*c + k] / 255.;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return out;
|
||||
}
|
||||
|
||||
static image_t make_empty_image(int w, int h, int c)
|
||||
{
|
||||
image_t out;
|
||||
out.data = 0;
|
||||
out.h = h;
|
||||
out.w = w;
|
||||
out.c = c;
|
||||
return out;
|
||||
}
|
||||
|
||||
static image_t make_image_custom(int w, int h, int c)
|
||||
{
|
||||
image_t out = make_empty_image(w, h, c);
|
||||
out.data = (float *)calloc(h*w*c, sizeof(float));
|
||||
return out;
|
||||
}
|
||||
|
||||
#endif // OPENCV
|
||||
|
||||
};
|
||||
|
||||
|
||||
|
||||
#if defined(TRACK_OPTFLOW) && defined(OPENCV) && defined(GPU)
|
||||
|
||||
#include <opencv2/cudaoptflow.hpp>
|
||||
#include <opencv2/cudaimgproc.hpp>
|
||||
#include <opencv2/cudaarithm.hpp>
|
||||
#include <opencv2/core/cuda.hpp>
|
||||
|
||||
class Tracker_optflow {
|
||||
public:
|
||||
const int gpu_count;
|
||||
const int gpu_id;
|
||||
const int flow_error;
|
||||
|
||||
|
||||
Tracker_optflow(int _gpu_id = 0, int win_size = 9, int max_level = 3, int iterations = 8000, int _flow_error = -1) :
|
||||
gpu_count(cv::cuda::getCudaEnabledDeviceCount()), gpu_id(std::min(_gpu_id, gpu_count-1)),
|
||||
flow_error((_flow_error > 0)? _flow_error:(win_size*4))
|
||||
{
|
||||
int const old_gpu_id = cv::cuda::getDevice();
|
||||
cv::cuda::setDevice(gpu_id);
|
||||
|
||||
stream = cv::cuda::Stream();
|
||||
|
||||
sync_PyrLKOpticalFlow_gpu = cv::cuda::SparsePyrLKOpticalFlow::create();
|
||||
sync_PyrLKOpticalFlow_gpu->setWinSize(cv::Size(win_size, win_size)); // 9, 15, 21, 31
|
||||
sync_PyrLKOpticalFlow_gpu->setMaxLevel(max_level); // +- 3 pt
|
||||
sync_PyrLKOpticalFlow_gpu->setNumIters(iterations); // 2000, def: 30
|
||||
|
||||
cv::cuda::setDevice(old_gpu_id);
|
||||
}
|
||||
|
||||
// just to avoid extra allocations
|
||||
cv::cuda::GpuMat src_mat_gpu;
|
||||
cv::cuda::GpuMat dst_mat_gpu, dst_grey_gpu;
|
||||
cv::cuda::GpuMat prev_pts_flow_gpu, cur_pts_flow_gpu;
|
||||
cv::cuda::GpuMat status_gpu, err_gpu;
|
||||
|
||||
cv::cuda::GpuMat src_grey_gpu; // used in both functions
|
||||
cv::Ptr<cv::cuda::SparsePyrLKOpticalFlow> sync_PyrLKOpticalFlow_gpu;
|
||||
cv::cuda::Stream stream;
|
||||
|
||||
std::vector<bbox_t> cur_bbox_vec;
|
||||
std::vector<bool> good_bbox_vec_flags;
|
||||
cv::Mat prev_pts_flow_cpu;
|
||||
|
||||
void update_cur_bbox_vec(std::vector<bbox_t> _cur_bbox_vec)
|
||||
{
|
||||
cur_bbox_vec = _cur_bbox_vec;
|
||||
good_bbox_vec_flags = std::vector<bool>(cur_bbox_vec.size(), true);
|
||||
cv::Mat prev_pts, cur_pts_flow_cpu;
|
||||
|
||||
for (auto &i : cur_bbox_vec) {
|
||||
float x_center = (i.x + i.w / 2.0F);
|
||||
float y_center = (i.y + i.h / 2.0F);
|
||||
prev_pts.push_back(cv::Point2f(x_center, y_center));
|
||||
}
|
||||
|
||||
if (prev_pts.rows == 0)
|
||||
prev_pts_flow_cpu = cv::Mat();
|
||||
else
|
||||
cv::transpose(prev_pts, prev_pts_flow_cpu);
|
||||
|
||||
if (prev_pts_flow_gpu.cols < prev_pts_flow_cpu.cols) {
|
||||
prev_pts_flow_gpu = cv::cuda::GpuMat(prev_pts_flow_cpu.size(), prev_pts_flow_cpu.type());
|
||||
cur_pts_flow_gpu = cv::cuda::GpuMat(prev_pts_flow_cpu.size(), prev_pts_flow_cpu.type());
|
||||
|
||||
status_gpu = cv::cuda::GpuMat(prev_pts_flow_cpu.size(), CV_8UC1);
|
||||
err_gpu = cv::cuda::GpuMat(prev_pts_flow_cpu.size(), CV_32FC1);
|
||||
}
|
||||
|
||||
prev_pts_flow_gpu.upload(cv::Mat(prev_pts_flow_cpu), stream);
|
||||
}
|
||||
|
||||
|
||||
void update_tracking_flow(cv::Mat src_mat, std::vector<bbox_t> _cur_bbox_vec)
|
||||
{
|
||||
int const old_gpu_id = cv::cuda::getDevice();
|
||||
if (old_gpu_id != gpu_id)
|
||||
cv::cuda::setDevice(gpu_id);
|
||||
|
||||
if (src_mat.channels() == 3) {
|
||||
if (src_mat_gpu.cols == 0) {
|
||||
src_mat_gpu = cv::cuda::GpuMat(src_mat.size(), src_mat.type());
|
||||
src_grey_gpu = cv::cuda::GpuMat(src_mat.size(), CV_8UC1);
|
||||
}
|
||||
|
||||
update_cur_bbox_vec(_cur_bbox_vec);
|
||||
|
||||
//src_grey_gpu.upload(src_mat, stream); // use BGR
|
||||
src_mat_gpu.upload(src_mat, stream);
|
||||
cv::cuda::cvtColor(src_mat_gpu, src_grey_gpu, CV_BGR2GRAY, 1, stream);
|
||||
}
|
||||
if (old_gpu_id != gpu_id)
|
||||
cv::cuda::setDevice(old_gpu_id);
|
||||
}
|
||||
|
||||
|
||||
std::vector<bbox_t> tracking_flow(cv::Mat dst_mat, bool check_error = true)
|
||||
{
|
||||
if (sync_PyrLKOpticalFlow_gpu.empty()) {
|
||||
std::cout << "sync_PyrLKOpticalFlow_gpu isn't initialized \n";
|
||||
return cur_bbox_vec;
|
||||
}
|
||||
|
||||
int const old_gpu_id = cv::cuda::getDevice();
|
||||
if(old_gpu_id != gpu_id)
|
||||
cv::cuda::setDevice(gpu_id);
|
||||
|
||||
if (dst_mat_gpu.cols == 0) {
|
||||
dst_mat_gpu = cv::cuda::GpuMat(dst_mat.size(), dst_mat.type());
|
||||
dst_grey_gpu = cv::cuda::GpuMat(dst_mat.size(), CV_8UC1);
|
||||
}
|
||||
|
||||
//dst_grey_gpu.upload(dst_mat, stream); // use BGR
|
||||
dst_mat_gpu.upload(dst_mat, stream);
|
||||
cv::cuda::cvtColor(dst_mat_gpu, dst_grey_gpu, CV_BGR2GRAY, 1, stream);
|
||||
|
||||
if (src_grey_gpu.rows != dst_grey_gpu.rows || src_grey_gpu.cols != dst_grey_gpu.cols) {
|
||||
stream.waitForCompletion();
|
||||
src_grey_gpu = dst_grey_gpu.clone();
|
||||
cv::cuda::setDevice(old_gpu_id);
|
||||
return cur_bbox_vec;
|
||||
}
|
||||
|
||||
////sync_PyrLKOpticalFlow_gpu.sparse(src_grey_gpu, dst_grey_gpu, prev_pts_flow_gpu, cur_pts_flow_gpu, status_gpu, &err_gpu); // OpenCV 2.4.x
|
||||
sync_PyrLKOpticalFlow_gpu->calc(src_grey_gpu, dst_grey_gpu, prev_pts_flow_gpu, cur_pts_flow_gpu, status_gpu, err_gpu, stream); // OpenCV 3.x
|
||||
|
||||
cv::Mat cur_pts_flow_cpu;
|
||||
cur_pts_flow_gpu.download(cur_pts_flow_cpu, stream);
|
||||
|
||||
dst_grey_gpu.copyTo(src_grey_gpu, stream);
|
||||
|
||||
cv::Mat err_cpu, status_cpu;
|
||||
err_gpu.download(err_cpu, stream);
|
||||
status_gpu.download(status_cpu, stream);
|
||||
|
||||
stream.waitForCompletion();
|
||||
|
||||
std::vector<bbox_t> result_bbox_vec;
|
||||
|
||||
if (err_cpu.cols == cur_bbox_vec.size() && status_cpu.cols == cur_bbox_vec.size())
|
||||
{
|
||||
for (size_t i = 0; i < cur_bbox_vec.size(); ++i)
|
||||
{
|
||||
cv::Point2f cur_key_pt = cur_pts_flow_cpu.at<cv::Point2f>(0, i);
|
||||
cv::Point2f prev_key_pt = prev_pts_flow_cpu.at<cv::Point2f>(0, i);
|
||||
|
||||
float moved_x = cur_key_pt.x - prev_key_pt.x;
|
||||
float moved_y = cur_key_pt.y - prev_key_pt.y;
|
||||
|
||||
if (abs(moved_x) < 100 && abs(moved_y) < 100 && good_bbox_vec_flags[i])
|
||||
if (err_cpu.at<float>(0, i) < flow_error && status_cpu.at<unsigned char>(0, i) != 0 &&
|
||||
((float)cur_bbox_vec[i].x + moved_x) > 0 && ((float)cur_bbox_vec[i].y + moved_y) > 0)
|
||||
{
|
||||
cur_bbox_vec[i].x += moved_x + 0.5;
|
||||
cur_bbox_vec[i].y += moved_y + 0.5;
|
||||
result_bbox_vec.push_back(cur_bbox_vec[i]);
|
||||
}
|
||||
else good_bbox_vec_flags[i] = false;
|
||||
else good_bbox_vec_flags[i] = false;
|
||||
|
||||
//if(!check_error && !good_bbox_vec_flags[i]) result_bbox_vec.push_back(cur_bbox_vec[i]);
|
||||
}
|
||||
}
|
||||
|
||||
cur_pts_flow_gpu.swap(prev_pts_flow_gpu);
|
||||
cur_pts_flow_cpu.copyTo(prev_pts_flow_cpu);
|
||||
|
||||
if (old_gpu_id != gpu_id)
|
||||
cv::cuda::setDevice(old_gpu_id);
|
||||
|
||||
return result_bbox_vec;
|
||||
}
|
||||
|
||||
};
|
||||
|
||||
#elif defined(TRACK_OPTFLOW) && defined(OPENCV)
|
||||
|
||||
//#include <opencv2/optflow.hpp>
|
||||
#include <opencv2/video/tracking.hpp>
|
||||
|
||||
class Tracker_optflow {
|
||||
public:
|
||||
const int flow_error;
|
||||
|
||||
|
||||
Tracker_optflow(int win_size = 9, int max_level = 3, int iterations = 8000, int _flow_error = -1) :
|
||||
flow_error((_flow_error > 0)? _flow_error:(win_size*4))
|
||||
{
|
||||
sync_PyrLKOpticalFlow = cv::SparsePyrLKOpticalFlow::create();
|
||||
sync_PyrLKOpticalFlow->setWinSize(cv::Size(win_size, win_size)); // 9, 15, 21, 31
|
||||
sync_PyrLKOpticalFlow->setMaxLevel(max_level); // +- 3 pt
|
||||
|
||||
}
|
||||
|
||||
// just to avoid extra allocations
|
||||
cv::Mat dst_grey;
|
||||
cv::Mat prev_pts_flow, cur_pts_flow;
|
||||
cv::Mat status, err;
|
||||
|
||||
cv::Mat src_grey; // used in both functions
|
||||
cv::Ptr<cv::SparsePyrLKOpticalFlow> sync_PyrLKOpticalFlow;
|
||||
|
||||
std::vector<bbox_t> cur_bbox_vec;
|
||||
std::vector<bool> good_bbox_vec_flags;
|
||||
|
||||
void update_cur_bbox_vec(std::vector<bbox_t> _cur_bbox_vec)
|
||||
{
|
||||
cur_bbox_vec = _cur_bbox_vec;
|
||||
good_bbox_vec_flags = std::vector<bool>(cur_bbox_vec.size(), true);
|
||||
cv::Mat prev_pts, cur_pts_flow;
|
||||
|
||||
for (auto &i : cur_bbox_vec) {
|
||||
float x_center = (i.x + i.w / 2.0F);
|
||||
float y_center = (i.y + i.h / 2.0F);
|
||||
prev_pts.push_back(cv::Point2f(x_center, y_center));
|
||||
}
|
||||
|
||||
if (prev_pts.rows == 0)
|
||||
prev_pts_flow = cv::Mat();
|
||||
else
|
||||
cv::transpose(prev_pts, prev_pts_flow);
|
||||
}
|
||||
|
||||
|
||||
void update_tracking_flow(cv::Mat new_src_mat, std::vector<bbox_t> _cur_bbox_vec)
|
||||
{
|
||||
if (new_src_mat.channels() == 3) {
|
||||
|
||||
update_cur_bbox_vec(_cur_bbox_vec);
|
||||
|
||||
cv::cvtColor(new_src_mat, src_grey, CV_BGR2GRAY, 1);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
std::vector<bbox_t> tracking_flow(cv::Mat new_dst_mat, bool check_error = true)
|
||||
{
|
||||
if (sync_PyrLKOpticalFlow.empty()) {
|
||||
std::cout << "sync_PyrLKOpticalFlow isn't initialized \n";
|
||||
return cur_bbox_vec;
|
||||
}
|
||||
|
||||
cv::cvtColor(new_dst_mat, dst_grey, CV_BGR2GRAY, 1);
|
||||
|
||||
if (src_grey.rows != dst_grey.rows || src_grey.cols != dst_grey.cols) {
|
||||
src_grey = dst_grey.clone();
|
||||
return cur_bbox_vec;
|
||||
}
|
||||
|
||||
if (prev_pts_flow.cols < 1) {
|
||||
return cur_bbox_vec;
|
||||
}
|
||||
|
||||
////sync_PyrLKOpticalFlow_gpu.sparse(src_grey_gpu, dst_grey_gpu, prev_pts_flow_gpu, cur_pts_flow_gpu, status_gpu, &err_gpu); // OpenCV 2.4.x
|
||||
sync_PyrLKOpticalFlow->calc(src_grey, dst_grey, prev_pts_flow, cur_pts_flow, status, err); // OpenCV 3.x
|
||||
|
||||
dst_grey.copyTo(src_grey);
|
||||
|
||||
std::vector<bbox_t> result_bbox_vec;
|
||||
|
||||
if (err.rows == cur_bbox_vec.size() && status.rows == cur_bbox_vec.size())
|
||||
{
|
||||
for (size_t i = 0; i < cur_bbox_vec.size(); ++i)
|
||||
{
|
||||
cv::Point2f cur_key_pt = cur_pts_flow.at<cv::Point2f>(0, i);
|
||||
cv::Point2f prev_key_pt = prev_pts_flow.at<cv::Point2f>(0, i);
|
||||
|
||||
float moved_x = cur_key_pt.x - prev_key_pt.x;
|
||||
float moved_y = cur_key_pt.y - prev_key_pt.y;
|
||||
|
||||
if (abs(moved_x) < 100 && abs(moved_y) < 100 && good_bbox_vec_flags[i])
|
||||
if (err.at<float>(0, i) < flow_error && status.at<unsigned char>(0, i) != 0 &&
|
||||
((float)cur_bbox_vec[i].x + moved_x) > 0 && ((float)cur_bbox_vec[i].y + moved_y) > 0)
|
||||
{
|
||||
cur_bbox_vec[i].x += moved_x + 0.5;
|
||||
cur_bbox_vec[i].y += moved_y + 0.5;
|
||||
result_bbox_vec.push_back(cur_bbox_vec[i]);
|
||||
}
|
||||
else good_bbox_vec_flags[i] = false;
|
||||
else good_bbox_vec_flags[i] = false;
|
||||
|
||||
//if(!check_error && !good_bbox_vec_flags[i]) result_bbox_vec.push_back(cur_bbox_vec[i]);
|
||||
}
|
||||
}
|
||||
|
||||
prev_pts_flow = cur_pts_flow.clone();
|
||||
|
||||
return result_bbox_vec;
|
||||
}
|
||||
|
||||
};
|
||||
#else
|
||||
|
||||
class Tracker_optflow {};
|
||||
|
||||
#endif // defined(TRACK_OPTFLOW) && defined(OPENCV)
|
||||
|
||||
|
||||
#ifdef OPENCV
|
||||
|
||||
static cv::Scalar obj_id_to_color(int obj_id) {
|
||||
int const colors[6][3] = { { 1,0,1 },{ 0,0,1 },{ 0,1,1 },{ 0,1,0 },{ 1,1,0 },{ 1,0,0 } };
|
||||
int const offset = obj_id * 123457 % 6;
|
||||
int const color_scale = 150 + (obj_id * 123457) % 100;
|
||||
cv::Scalar color(colors[offset][0], colors[offset][1], colors[offset][2]);
|
||||
color *= color_scale;
|
||||
return color;
|
||||
}
|
||||
|
||||
class preview_boxes_t {
|
||||
enum { frames_history = 30 }; // how long to keep the history saved
|
||||
|
||||
struct preview_box_track_t {
|
||||
unsigned int track_id, obj_id, last_showed_frames_ago;
|
||||
bool current_detection;
|
||||
bbox_t bbox;
|
||||
cv::Mat mat_obj, mat_resized_obj;
|
||||
preview_box_track_t() : track_id(0), obj_id(0), last_showed_frames_ago(frames_history), current_detection(false) {}
|
||||
};
|
||||
std::vector<preview_box_track_t> preview_box_track_id;
|
||||
size_t const preview_box_size, bottom_offset;
|
||||
bool const one_off_detections;
|
||||
public:
|
||||
preview_boxes_t(size_t _preview_box_size = 100, size_t _bottom_offset = 100, bool _one_off_detections = false) :
|
||||
preview_box_size(_preview_box_size), bottom_offset(_bottom_offset), one_off_detections(_one_off_detections)
|
||||
{}
|
||||
|
||||
void set(cv::Mat src_mat, std::vector<bbox_t> result_vec)
|
||||
{
|
||||
size_t const count_preview_boxes = src_mat.cols / preview_box_size;
|
||||
if (preview_box_track_id.size() != count_preview_boxes) preview_box_track_id.resize(count_preview_boxes);
|
||||
|
||||
// increment frames history
|
||||
for (auto &i : preview_box_track_id)
|
||||
i.last_showed_frames_ago = std::min((unsigned)frames_history, i.last_showed_frames_ago + 1);
|
||||
|
||||
// occupy empty boxes
|
||||
for (auto &k : result_vec) {
|
||||
bool found = false;
|
||||
// find the same (track_id)
|
||||
for (auto &i : preview_box_track_id) {
|
||||
if (i.track_id == k.track_id) {
|
||||
if (!one_off_detections) i.last_showed_frames_ago = 0; // for tracked objects
|
||||
found = true;
|
||||
break;
|
||||
}
|
||||
}
|
||||
if (!found) {
|
||||
// find empty box
|
||||
for (auto &i : preview_box_track_id) {
|
||||
if (i.last_showed_frames_ago == frames_history) {
|
||||
if (!one_off_detections && k.frames_counter == 0) break; // don't show if obj isn't tracked yet
|
||||
i.track_id = k.track_id;
|
||||
i.obj_id = k.obj_id;
|
||||
i.bbox = k;
|
||||
i.last_showed_frames_ago = 0;
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// draw preview box (from old or current frame)
|
||||
for (size_t i = 0; i < preview_box_track_id.size(); ++i)
|
||||
{
|
||||
// get object image
|
||||
cv::Mat dst = preview_box_track_id[i].mat_resized_obj;
|
||||
preview_box_track_id[i].current_detection = false;
|
||||
|
||||
for (auto &k : result_vec) {
|
||||
if (preview_box_track_id[i].track_id == k.track_id) {
|
||||
if (one_off_detections && preview_box_track_id[i].last_showed_frames_ago > 0) {
|
||||
preview_box_track_id[i].last_showed_frames_ago = frames_history; break;
|
||||
}
|
||||
bbox_t b = k;
|
||||
cv::Rect r(b.x, b.y, b.w, b.h);
|
||||
cv::Rect img_rect(cv::Point2i(0, 0), src_mat.size());
|
||||
cv::Rect rect_roi = r & img_rect;
|
||||
if (rect_roi.width > 1 || rect_roi.height > 1) {
|
||||
cv::Mat roi = src_mat(rect_roi);
|
||||
cv::resize(roi, dst, cv::Size(preview_box_size, preview_box_size), cv::INTER_NEAREST);
|
||||
preview_box_track_id[i].mat_obj = roi.clone();
|
||||
preview_box_track_id[i].mat_resized_obj = dst.clone();
|
||||
preview_box_track_id[i].current_detection = true;
|
||||
preview_box_track_id[i].bbox = k;
|
||||
}
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
void draw(cv::Mat draw_mat, bool show_small_boxes = false)
|
||||
{
|
||||
// draw preview box (from old or current frame)
|
||||
for (size_t i = 0; i < preview_box_track_id.size(); ++i)
|
||||
{
|
||||
auto &prev_box = preview_box_track_id[i];
|
||||
|
||||
// draw object image
|
||||
cv::Mat dst = prev_box.mat_resized_obj;
|
||||
if (prev_box.last_showed_frames_ago < frames_history &&
|
||||
dst.size() == cv::Size(preview_box_size, preview_box_size))
|
||||
{
|
||||
cv::Rect dst_rect_roi(cv::Point2i(i * preview_box_size, draw_mat.rows - bottom_offset), dst.size());
|
||||
cv::Mat dst_roi = draw_mat(dst_rect_roi);
|
||||
dst.copyTo(dst_roi);
|
||||
|
||||
cv::Scalar color = obj_id_to_color(prev_box.obj_id);
|
||||
int thickness = (prev_box.current_detection) ? 5 : 1;
|
||||
cv::rectangle(draw_mat, dst_rect_roi, color, thickness);
|
||||
|
||||
unsigned int const track_id = prev_box.track_id;
|
||||
std::string track_id_str = (track_id > 0) ? std::to_string(track_id) : "";
|
||||
putText(draw_mat, track_id_str, dst_rect_roi.tl() - cv::Point2i(-4, 5), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.9, cv::Scalar(0, 0, 0), 2);
|
||||
|
||||
std::string size_str = std::to_string(prev_box.bbox.w) + "x" + std::to_string(prev_box.bbox.h);
|
||||
putText(draw_mat, size_str, dst_rect_roi.tl() + cv::Point2i(0, 12), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.8, cv::Scalar(0, 0, 0), 1);
|
||||
|
||||
if (!one_off_detections && prev_box.current_detection) {
|
||||
cv::line(draw_mat, dst_rect_roi.tl() + cv::Point2i(preview_box_size, 0),
|
||||
cv::Point2i(prev_box.bbox.x, prev_box.bbox.y + prev_box.bbox.h),
|
||||
color);
|
||||
}
|
||||
|
||||
if (one_off_detections && show_small_boxes) {
|
||||
cv::Rect src_rect_roi(cv::Point2i(prev_box.bbox.x, prev_box.bbox.y),
|
||||
cv::Size(prev_box.bbox.w, prev_box.bbox.h));
|
||||
unsigned int const color_history = (255 * prev_box.last_showed_frames_ago) / frames_history;
|
||||
color = cv::Scalar(255 - 3 * color_history, 255 - 2 * color_history, 255 - 1 * color_history);
|
||||
if (prev_box.mat_obj.size() == src_rect_roi.size()) {
|
||||
prev_box.mat_obj.copyTo(draw_mat(src_rect_roi));
|
||||
}
|
||||
cv::rectangle(draw_mat, src_rect_roi, color, thickness);
|
||||
putText(draw_mat, track_id_str, src_rect_roi.tl() - cv::Point2i(0, 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.8, cv::Scalar(0, 0, 0), 1);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
};
|
||||
#endif // OPENCV
|
||||
|
||||
//extern "C" {
|
||||
#endif // __cplusplus
|
||||
|
||||
/*
|
||||
// C - wrappers
|
||||
LIB_EXPORTS void create_detector(char const* cfg_filename, char const* weight_filename, int gpu_id);
|
||||
LIB_EXPORTS void delete_detector();
|
||||
LIB_EXPORTS bbox_t* detect_custom(image_t img, float thresh, bool use_mean, int *result_size);
|
||||
LIB_EXPORTS bbox_t* detect_resized(image_t img, int init_w, int init_h, float thresh, bool use_mean, int *result_size);
|
||||
LIB_EXPORTS bbox_t* detect(image_t img, int *result_size);
|
||||
LIB_EXPORTS image_t load_img(char *image_filename);
|
||||
LIB_EXPORTS void free_img(image_t m);
|
||||
|
||||
#ifdef __cplusplus
|
||||
} // extern "C"
|
||||
|
||||
static std::shared_ptr<void> c_detector_ptr;
|
||||
static std::vector<bbox_t> c_result_vec;
|
||||
|
||||
void create_detector(char const* cfg_filename, char const* weight_filename, int gpu_id) {
|
||||
c_detector_ptr = std::make_shared<LIB_EXPORTS Detector>(cfg_filename, weight_filename, gpu_id);
|
||||
}
|
||||
|
||||
void delete_detector() { c_detector_ptr.reset(); }
|
||||
|
||||
bbox_t* detect_custom(image_t img, float thresh, bool use_mean, int *result_size) {
|
||||
c_result_vec = static_cast<Detector*>(c_detector_ptr.get())->detect(img, thresh, use_mean);
|
||||
*result_size = c_result_vec.size();
|
||||
return c_result_vec.data();
|
||||
}
|
||||
|
||||
bbox_t* detect_resized(image_t img, int init_w, int init_h, float thresh, bool use_mean, int *result_size) {
|
||||
c_result_vec = static_cast<Detector*>(c_detector_ptr.get())->detect_resized(img, init_w, init_h, thresh, use_mean);
|
||||
*result_size = c_result_vec.size();
|
||||
return c_result_vec.data();
|
||||
}
|
||||
|
||||
bbox_t* detect(image_t img, int *result_size) {
|
||||
return detect_custom(img, 0.24, true, result_size);
|
||||
}
|
||||
|
||||
image_t load_img(char *image_filename) {
|
||||
return static_cast<Detector*>(c_detector_ptr.get())->load_image(image_filename);
|
||||
}
|
||||
void free_img(image_t m) {
|
||||
static_cast<Detector*>(c_detector_ptr.get())->free_image(m);
|
||||
}
|
||||
|
||||
#endif // __cplusplus
|
||||
*/
|
||||
Reference in New Issue
Block a user