1、训练yolov4
./darknet detector train /home/pc/Documents/yolov4/data/0827/code/obj.data /home/pc/Documents/yolov4/data/0827/code/yolov4-obj.cfg /home/pc/Documents/yolov4/data/0827/code/yolov4.conv.137 -map
2、测试打印map
./darknet detector test /home/pc/Documents/yolov4/data/0810/code/obj.data /home/pc/Documents/yolov4/data/0810/code/yolov4-obj.cfg /home/pc/Documents/yolov4/data/0810/code/backup/yolov4-obj_best.weights
可打印每个类别的ap，以及roi_loss等值
3、前向推理
特别说明：yolov4的darknet新版好像python前向推理接口有更改，作者训练用的是新版本编译生成的libdarknet.so，推理用的是老版本编译生成的libdarknet.so。
需要的自取：链接：https://pan.baidu.com/s/1nMHl41t5FaWePXOyojDO6g
提取码：01wx
3.1、python版本：
#coding=utf-8
import os
import cv2
import numpy as np
import random
import sys
#sys.path.append('/media/em/data_1/yolov4/darknet-master')
#import darknet
#from darknet import *
import darknet
import glob
print(cv2.__version__)
print("flag!")
netMain = None
metaMain = None
altNames = None
out_txt_path = '/home/pc/Documents/yolov4/data/0810/VOCdevkit/test/out_txt/'
configPath = '/home/pc/Documents/yolov4/data/0810/code/yolov4-obj.cfg'
metaPath = '/home/pc/Documents/yolov4/data/0810/voc.data'
weightPath = '/home/pc/Documents/yolov4/data/0810/code/backup/yolov4-obj_best.weights'
def convertBack(x, y, w, h):
xmin = int(round(x - (w / 2)))
xmax = int(round(x + (w / 2)))
ymin = int(round(y - (h / 2)))
ymax = int(round(y + (h / 2)))
return xmin, ymin, xmax, ymax
def draw_box(box,img,color=None,label=None,line_thinkness=None):
line_thinkness = 2
color = line_thinkness or [random.randint(0,255) for _ in range(3)]
c1, c2 = (int(box[0], box[1])), (int(box[2], box[3]))
cv2.rectangle(img, c1, c2, thinness=line_thinkness)
if label:
t_size = cv2.getTextSize(label, 0, fontScale=1/3, thinkness=1)[0]
c2 = c1[0] + t_size[0], c1[1] - t_size - 3
cv2.rectangle(img, c1, c2, color, -1)
cv2.putText(img, label, (c1[0], c1[1] - 2), 0, 1/3, [255, 255, 255],1,lineType=cv2.LINE_AA)
def cvDrawBoxes(detections, img , jpgname, scalar_height, scalar_width):
txtname = os.path.splitext(jpgname)[0] + '.txt'
f = open(out_txt_path + txtname,'w')
for detection in detections:
x, y, w, h = detection[2][0],\
detection[2][1],\
detection[2][2],\
detection[2][3]
xmin, ymin, xmax, ymax = convertBack(
float(x), float(y), float(w), float(h))
ID = detection[0].decode()
rec = str(round(xmin * scalar_width)) + ' ' + str(round(ymin * scalar_height)) + ' ' +str(round(xmax * scalar_width)) + ' ' + str(round(ymax * scalar_height)) + ' ' +ID+'\n'
f.write(str(rec))
pt1 = (xmin, ymin)
pt2 = (xmax, ymax)
cv2.rectangle(img, pt1, pt2, (0, 0, 255), 1)
cv2.putText(img,
detection[0].decode() +
" [" + str(round(detection[1] * 100, 2)) + "]",
(pt1[0], pt1[1] - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
[0, 255, 0], 1)
f.close()
return img
def YOLO():
global netMain, metaMain, altNames
if not os.path.exists(configPath):
raise ValueError("Invalid config path `" + os.path.abspath(configPath)+"`")
if not os.path.exists(weightPath):
raise ValueError("Invalid weight path `" + os.path.abspath(weightPath)+"`")
if not os.path.exists(metaPath):
raise ValueError("Invalid data file path `" + os.path.abspath(metaPath)+"`")
if netMain is None:
netMain = darknet.load_net_custom(configPath.encode("ascii"), weightPath.encode("ascii"), 0, 1) # batch size = 1
if metaMain is None:
metaMain = darknet.load_meta(metaPath.encode("ascii"))
if altNames is None:
try:
with open(metaPath) as metaFH:
metaContents = metaFH.read()
import re
#match=re.search("names *= *(.*)$", metaContents, re.IGNORECASE | re.MULTILINE)
match = re.search("names *= *(.*)$", metaContents,
re.IGNORECASE | re.MULTILINE)
if match:
result = match.group(1)
else:
result = None
try:
if os.path.exists(result):
with open(result) as namesFH:
nameLists = namesFH.read().strip().split("\n")
altNames = [x.strip() for x in nameLists]
except TypeError:
pass
except Exception:
pass
#jpg_path ='/media/em/data_1/cebiaoTest/rightBottom'
jpg_path = '/home/pc/Documents/yolov4/data/6类+背景类/data/VOCdevkit/VOC2007/JPEGImages'
jpg_list = glob.glob(jpg_path+'/*.jpg')
out_image_path = '/home/pc/Documents/yolov4/data/6类+背景类/data/VOCdevkit/VOC2007/'
if not os.path.exists(out_image_path):
os.mkdir(out_image_path)
print(out_image_path + '----创建成功')
list_jpg = os.listdir(jpg_path)
random.seed(1)
colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(metaMain.classes)]
#darknet_image = darknet.make_image(darknet.network_width(netMain), darknet.network_height(netMain), 3)
for image_name in list_jpg:
#image_name = 'img.jpg'
print(image_name)
cv_img = cv2.imread(jpg_path + '/' + image_name)
Scalar_height = cv_img.shape[0] / darknet.network_width(netMain)
Scalar_width = cv_img.shape[1] / darknet.network_height(netMain)
#cv_img = cv2.imread(image_name)
#bgr_img = cv_img[:, :, ::-1]#BGR 转 RGB
bgr_img = cv2.cvtColor(cv_img, cv2.COLOR_BGR2RGB)
#height, width = bgr_img.shape[:2]
resize_img = cv2.resize(bgr_img, (darknet.network_width(netMain), darknet.network_height(netMain)), interpolation=cv2.INTER_LINEAR)
darknet_image = darknet.make_image(darknet.network_width(netMain), darknet.network_height(netMain), 3)
#resize_img = cv2.resize(bgr_img, (darknet.network_width(netMain), darknet.network_height(netMain)), interpolation=cv2.INTER_LINEAR)
darknet.copy_image_from_bytes(darknet_image, resize_img.tobytes())
#darknet_img, _ = darknet.array_to_image(resize_img)#转换为darknet识别的图片类型格式
result_vec = darknet.detect_image(netMain, altNames, darknet_image, thresh=0.25)
#for result in result_vec:
#x, y, w, h = result[2][0], result[2][1], [2][2], result[2][3]
#conf = result[1]
#x *= width / darknet.network_width(netMain)
#w *= width / darknet.network_width(netMain)
#y *= height / darknet.network_height(netMain)
#h *= height / darknet.network_height(netMain)
#xyxy = np.array([x-w/2, y-h/2, x+w/2, y+h/2])
#label = result[0].decode()
#index = altNames.index(label)
#label = f'{label}{conf:.2f}'
#label = f'{label} {conf:.2f}'
#out_image = cv2.resize(resize_img, (1280, 960), interpolation=cv2.INTER_LINEAR)
w_image = cvDrawBoxes(result_vec, resize_img , image_name, Scalar_height, Scalar_width)
show_image = cv2.resize(w_image, (1280, 960), interpolation=cv2.INTER_LINEAR)
show_image = cv2.cvtColor(show_image, cv2.COLOR_BGR2RGB)
#print("1111")
cv2.imshow("result", w_image)
new_name = 'out_' + image_name
cv2.imwrite(out_image_path + new_name, w_image)
cv2.waitKey(0)
print("finish!")
#cv2.waitKey(0)
#cv2.waitKey(0)
if __name__ == "__main__":
YOLO()
3.2、c++版本
（1）配置文件：CMakeLists.txt
cmake_minimum_required(VERSION 3.5)
project(yolov4)
find_package( OpenCV 3 REQUIRED )
link_directories("/home/pc/Documents/0811/darknet-master/")
set(CMAKE_CXX_STANDARD 14)
include_directories(${OpenCV_INCLUDE_DIRS})
add_executable(yolov4 main.cpp yolo_v2_class.hpp)
target_link_libraries(yolov4 ${OpenCV_LIBS} libdarknet.so libpthread.so.0)
(2)头文件：yolo_v2_class.hpp
#ifndef YOLO_V2_CLASS_HPP
#define YOLO_V2_CLASS_HPP
#ifndef LIB_API
#ifdef LIB_EXPORTS
#if defined(_MSC_VER)
#define LIB_API __declspec(dllexport)
#else
#define LIB_API __attribute__((visibility("default")))
#endif
#else
#if defined(_MSC_VER)
#define LIB_API
#else
#define LIB_API
#endif
#endif
#endif
#define C_SHARP_MAX_OBJECTS 1000
#define OPENCV
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
float x_3d, y_3d, z_3d; // center of object (in Meters) if ZED 3D Camera is used
};
struct image_t {
int h; // height
int w; // width
int c; // number of chanels (3 - for RGB)
float *data; // pointer to the image data
};
struct bbox_t_container {
bbox_t candidates[C_SHARP_MAX_OBJECTS];
};
#ifdef __cplusplus
#include <memory>
#include <vector>
#include <deque>
#include <algorithm>
#include <chrono>
#include <string>
#include <sstream>
#include <iostream>
#include <cmath>
#ifdef OPENCV
#include <opencv2/opencv.hpp> // C++
#include <opencv2/highgui/highgui_c.h> // C
#include <opencv2/imgproc/imgproc_c.h> // C
#endif
extern "C" LIB_API int init(const char *configurationFilename, const char *weightsFilename, int gpu);
extern "C" LIB_API int detect_image(const char *filename, bbox_t_container &container);
extern "C" LIB_API int detect_mat(const uint8_t* data, const size_t data_length, bbox_t_container &container);
extern "C" LIB_API int dispose();
extern "C" LIB_API int get_device_count();
extern "C" LIB_API int get_device_name(int gpu, char* deviceName);
extern "C" LIB_API bool built_with_cuda();
extern "C" LIB_API bool built_with_cudnn();
extern "C" LIB_API bool built_with_opencv();
extern "C" LIB_API void send_json_custom(char const* send_buf, int port, int timeout);
class Detector {
std::shared_ptr<void> detector_gpu_ptr;
std::deque<std::vector<bbox_t>> prev_bbox_vec_deque;
std::string _cfg_filename, _weight_filename;
public:
const int cur_gpu_id;
float nms = .4;
bool wait_stream;
LIB_API Detector(std::string cfg_filename, std::string weight_filename, int gpu_id = 0);
LIB_API ~Detector();
LIB_API std::vector<bbox_t> detect(std::string image_filename, float thresh = 0.2, bool use_mean = false);
LIB_API std::vector<bbox_t> detect(image_t img, float thresh = 0.2, bool use_mean = false);
static LIB_API image_t load_image(std::string image_filename);
static LIB_API void free_image(image_t m);
LIB_API int get_net_width() const;
LIB_API int get_net_height() const;
LIB_API int get_net_color_depth() const;
LIB_API std::vector<bbox_t> tracking_id(std::vector<bbox_t> cur_bbox_vec, bool const change_history = true,
int const frames_story = 5, int const max_dist = 40);
LIB_API void *get_cuda_context();
//LIB_API bool send_json_http(std::vector<bbox_t> cur_bbox_vec, std::vector<std::string> obj_names, int frame_id,
// std::string filename = std::string(), int timeout = 400000, int port = 8070);
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;
if (img_src.channels() == 4) cv::cvtColor(img_src, img, cv::COLOR_RGBA2BGR);
else if (img_src.channels() == 3) cv::cvtColor(img_src, img, cv::COLOR_RGB2BGR);
else if (img_src.channels() == 1) cv::cvtColor(img_src, img, cv::COLOR_GRAY2BGR);
else std::cerr << " Warning: img_src.channels() is not 1, 3 or 4. It is = " << img_src.channels() << std::endl;
std::shared_ptr<image_t> image_ptr(new image_t, [](image_t *img) { free_image(*img); delete img; });
*image_ptr = mat_to_image_custom(img);
return image_ptr;
}
private:
static image_t mat_to_image_custom(cv::Mat mat)
{
int w = mat.cols;
int h = mat.rows;
int c = mat.channels();
image_t im = make_image_custom(w, h, c);
unsigned char *data = (unsigned char *)mat.data;
int step = mat.step;
for (int y = 0; y < h; ++y) {
for (int k = 0; k < c; ++k) {
for (int x = 0; x < w; ++x) {
im.data[k*w*h + y*w + x] = data[y*step + x*c + k] / 255.0f;
}
}
}
return im;
}
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
public:
bool send_json_http(std::vector<bbox_t> cur_bbox_vec, std::vector<std::string> obj_names, int frame_id,
std::string filename = std::string(), int timeout = 400000, int port = 8070)
{
std::string send_str;
char *tmp_buf = (char *)calloc(1024, sizeof(char));
if (!filename.empty()) {
sprintf(tmp_buf, "{\n \"frame_id\":%d, \n \"filename\":\"%s\", \n \"objects\": [ \n", frame_id, filename.c_str());
}
else {
sprintf(tmp_buf, "{\n \"frame_id\":%d, \n \"objects\": [ \n", frame_id);
}
send_str = tmp_buf;
free(tmp_buf);
for (auto & i : cur_bbox_vec) {
char *buf = (char *)calloc(2048, sizeof(char));
sprintf(buf, " {\"class_id\":%d, \"name\":\"%s\", \"absolute_coordinates\":{\"center_x\":%d, \"center_y\":%d, \"width\":%d, \"height\":%d}, \"confidence\":%f",
i.obj_id, obj_names[i.obj_id].c_str(), i.x, i.y, i.w, i.h, i.prob);
//sprintf(buf, " {\"class_id\":%d, \"name\":\"%s\", \"relative_coordinates\":{\"center_x\":%f, \"center_y\":%f, \"width\":%f, \"height\":%f}, \"confidence\":%f",
// i.obj_id, obj_names[i.obj_id], i.x, i.y, i.w, i.h, i.prob);
send_str += buf;
if (!std::isnan(i.z_3d)) {
sprintf(buf, "\n , \"coordinates_in_meters\":{\"x_3d\":%.2f, \"y_3d\":%.2f, \"z_3d\":%.2f}",
i.x_3d, i.y_3d, i.z_3d);
send_str += buf;
}
send_str += "}\n";
free(buf);
}
//send_str += "\n ] \n}, \n";
send_str += "\n ] \n}";
send_json_custom(send_str.c_str(), port, timeout);
return true;
}
};
// --------------------------------------------------------------------------------
#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 = 15, 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() == 1 || src_mat.channels() == 3 || src_mat.channels() == 4) {
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);
}
if (src_mat.channels() == 1) {
src_mat_gpu.upload(src_mat, stream);
src_mat_gpu.copyTo(src_grey_gpu);
}
else if (src_mat.channels() == 3) {
src_mat_gpu.upload(src_mat, stream);
cv::cuda::cvtColor(src_mat_gpu, src_grey_gpu, CV_BGR2GRAY, 1, stream);
}
else if (src_mat.channels() == 4) {
src_mat_gpu.upload(src_mat, stream);
cv::cuda::cvtColor(src_mat_gpu, src_grey_gpu, CV_BGRA2GRAY, 1, stream);
}
else {
std::cerr << " Warning: src_mat.channels() is not: 1, 3 or 4. It is = " << src_mat.channels() << " \n";
return;
}
}
update_cur_bbox_vec(_cur_bbox_vec);
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 = 15, 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() == 1) {
src_grey = new_src_mat.clone();
}
else if (new_src_mat.channels() == 3) {
cv::cvtColor(new_src_mat, src_grey, CV_BGR2GRAY, 1);
}
else if (new_src_mat.channels() == 4) {
cv::cvtColor(new_src_mat, src_grey, CV_BGRA2GRAY, 1);
}
else {
std::cerr << " Warning: new_src_mat.channels() is not: 1, 3 or 4. It is = " << new_src_mat.channels() << " \n";
return;
}
update_cur_bbox_vec(_cur_bbox_vec);
}
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();
//std::cerr << " Warning: src_grey.rows != dst_grey.rows || src_grey.cols != dst_grey.cols \n";
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);
}
}
}
}
};
class track_kalman_t
{
int track_id_counter;
std::chrono::steady_clock::time_point global_last_time;
float dT;
public:
int max_objects; // max objects for tracking
int min_frames; // min frames to consider an object as detected
const float max_dist; // max distance (in px) to track with the same ID
cv::Size img_size; // max value of x,y,w,h
struct tst_t {
int track_id;
int state_id;
std::chrono::steady_clock::time_point last_time;
int detection_count;
tst_t() : track_id(-1), state_id(-1) {}
};
std::vector<tst_t> track_id_state_id_time;
std::vector<bbox_t> result_vec_pred;
struct one_kalman_t;
std::vector<one_kalman_t> kalman_vec;
struct one_kalman_t
{
cv::KalmanFilter kf;
cv::Mat state;
cv::Mat meas;
int measSize, stateSize, contrSize;
void set_delta_time(float dT) {
kf.transitionMatrix.at<float>(2) = dT;
kf.transitionMatrix.at<float>(9) = dT;
}
void set(bbox_t box)
{
initialize_kalman();
kf.errorCovPre.at<float>(0) = 1; // px
kf.errorCovPre.at<float>(7) = 1; // px
kf.errorCovPre.at<float>(14) = 1;
kf.errorCovPre.at<float>(21) = 1;
kf.errorCovPre.at<float>(28) = 1; // px
kf.errorCovPre.at<float>(35) = 1; // px
state.at<float>(0) = box.x;
state.at<float>(1) = box.y;
state.at<float>(2) = 0;
state.at<float>(3) = 0;
state.at<float>(4) = box.w;
state.at<float>(5) = box.h;
// <<<< Initialization
kf.statePost = state;
}
// Kalman.correct() calculates: statePost = statePre + gain * (z(k)-measurementMatrix*statePre);
// corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k))
void correct(bbox_t box) {
meas.at<float>(0) = box.x;
meas.at<float>(1) = box.y;
meas.at<float>(2) = box.w;
meas.at<float>(3) = box.h;
kf.correct(meas);
bbox_t new_box = predict();
if (new_box.w == 0 || new_box.h == 0) {
set(box);
//std::cerr << " force set(): track_id = " << box.track_id <<
// ", x = " << box.x << ", y = " << box.y << ", w = " << box.w << ", h = " << box.h << std::endl;
}
}
// Kalman.predict() calculates: statePre = TransitionMatrix * statePost;
// predicted state (x'(k)): x(k)=A*x(k-1)+B*u(k)
bbox_t predict() {
bbox_t box;
state = kf.predict();
box.x = state.at<float>(0);
box.y = state.at<float>(1);
box.w = state.at<float>(4);
box.h = state.at<float>(5);
return box;
}
void initialize_kalman()
{
kf = cv::KalmanFilter(stateSize, measSize, contrSize, CV_32F);
// Transition State Matrix A
// Note: set dT at each processing step!
// [ 1 0 dT 0 0 0 ]
// [ 0 1 0 dT 0 0 ]
// [ 0 0 1 0 0 0 ]
// [ 0 0 0 1 0 0 ]
// [ 0 0 0 0 1 0 ]
// [ 0 0 0 0 0 1 ]
cv::setIdentity(kf.transitionMatrix);
// Measure Matrix H
// [ 1 0 0 0 0 0 ]
// [ 0 1 0 0 0 0 ]
// [ 0 0 0 0 1 0 ]
// [ 0 0 0 0 0 1 ]
kf.measurementMatrix = cv::Mat::zeros(measSize, stateSize, CV_32F);
kf.measurementMatrix.at<float>(0) = 1.0f;
kf.measurementMatrix.at<float>(7) = 1.0f;
kf.measurementMatrix.at<float>(16) = 1.0f;
kf.measurementMatrix.at<float>(23) = 1.0f;
// Process Noise Covariance Matrix Q - result smoother with lower values (1e-2)
// [ Ex 0 0 0 0 0 ]
// [ 0 Ey 0 0 0 0 ]
// [ 0 0 Ev_x 0 0 0 ]
// [ 0 0 0 Ev_y 0 0 ]
// [ 0 0 0 0 Ew 0 ]
// [ 0 0 0 0 0 Eh ]
//cv::setIdentity(kf.processNoiseCov, cv::Scalar(1e-3));
kf.processNoiseCov.at<float>(0) = 1e-2;
kf.processNoiseCov.at<float>(7) = 1e-2;
kf.processNoiseCov.at<float>(14) = 1e-2;// 5.0f;
kf.processNoiseCov.at<float>(21) = 1e-2;// 5.0f;
kf.processNoiseCov.at<float>(28) = 5e-3;
kf.processNoiseCov.at<float>(35) = 5e-3;
// Measures Noise Covariance Matrix R - result smoother with higher values (1e-1)
cv::setIdentity(kf.measurementNoiseCov, cv::Scalar(1e-1));
//cv::setIdentity(kf.errorCovPost, cv::Scalar::all(1e-2));
// <<<< Kalman Filter
set_delta_time(0);
}
one_kalman_t(int _stateSize = 6, int _measSize = 4, int _contrSize = 0) :
kf(_stateSize, _measSize, _contrSize, CV_32F), measSize(_measSize), stateSize(_stateSize), contrSize(_contrSize)
{
state = cv::Mat(stateSize, 1, CV_32F); // [x,y,v_x,v_y,w,h]
meas = cv::Mat(measSize, 1, CV_32F); // [z_x,z_y,z_w,z_h]
//cv::Mat procNoise(stateSize, 1, type)
// [E_x,E_y,E_v_x,E_v_y,E_w,E_h]
initialize_kalman();
}
};
// ------------------------------------------
track_kalman_t(int _max_objects = 1000, int _min_frames = 3, float _max_dist = 40, cv::Size _img_size = cv::Size(10000, 10000)) :
max_objects(_max_objects), min_frames(_min_frames), max_dist(_max_dist), img_size(_img_size),
track_id_counter(0)
{
kalman_vec.resize(max_objects);
track_id_state_id_time.resize(max_objects);
result_vec_pred.resize(max_objects);
}
float calc_dt() {
dT = std::chrono::duration<double>(std::chrono::steady_clock::now() - global_last_time).count();
return dT;
}
static float get_distance(float src_x, float src_y, float dst_x, float dst_y) {
return sqrtf((src_x - dst_x)*(src_x - dst_x) + (src_y - dst_y)*(src_y - dst_y));
}
void clear_old_states() {
// clear old bboxes
for (size_t state_id = 0; state_id < track_id_state_id_time.size(); ++state_id)
{
float time_sec = std::chrono::duration<double>(std::chrono::steady_clock::now() - track_id_state_id_time[state_id].last_time).count();
float time_wait = 0.5; // 0.5 second
if (track_id_state_id_time[state_id].track_id > -1)
{
if ((result_vec_pred[state_id].x > img_size.width) ||
(result_vec_pred[state_id].y > img_size.height))
{
track_id_state_id_time[state_id].track_id = -1;
}
if (time_sec >= time_wait || track_id_state_id_time[state_id].detection_count < 0) {
//std::cerr << " remove track_id = " << track_id_state_id_time[state_id].track_id << ", state_id = " << state_id << std::endl;
track_id_state_id_time[state_id].track_id = -1; // remove bbox
}
}
}
}
tst_t get_state_id(bbox_t find_box, std::vector<bool> &busy_vec)
{
tst_t tst;
tst.state_id = -1;
float min_dist = std::numeric_limits<float>::max();
for (size_t i = 0; i < max_objects; ++i)
{
if (track_id_state_id_time[i].track_id > -1 && result_vec_pred[i].obj_id == find_box.obj_id && busy_vec[i] == false)
{
bbox_t pred_box = result_vec_pred[i];
float dist = get_distance(pred_box.x, pred_box.y, find_box.x, find_box.y);
float movement_dist = std::max(max_dist, static_cast<float>(std::max(pred_box.w, pred_box.h)) );
if ((dist < movement_dist) && (dist < min_dist)) {
min_dist = dist;
tst.state_id = i;
}
}
}
if (tst.state_id > -1) {
track_id_state_id_time[tst.state_id].last_time = std::chrono::steady_clock::now();
track_id_state_id_time[tst.state_id].detection_count = std::max(track_id_state_id_time[tst.state_id].detection_count + 2, 10);
tst = track_id_state_id_time[tst.state_id];
busy_vec[tst.state_id] = true;
}
else {
//std::cerr << " Didn't find: obj_id = " << find_box.obj_id << ", x = " << find_box.x << ", y = " << find_box.y <<
// ", track_id_counter = " << track_id_counter << std::endl;
}
return tst;
}
tst_t new_state_id(std::vector<bool> &busy_vec)
{
tst_t tst;
// find empty cell to add new track_id
auto it = std::find_if(track_id_state_id_time.begin(), track_id_state_id_time.end(), [&](tst_t &v) { return v.track_id == -1; });
if (it != track_id_state_id_time.end()) {
it->state_id = it - track_id_state_id_time.begin();
//it->track_id = track_id_counter++;
it->track_id = 0;
it->last_time = std::chrono::steady_clock::now();
it->detection_count = 1;
tst = *it;
busy_vec[it->state_id] = true;
}
return tst;
}
std::vector<tst_t> find_state_ids(std::vector<bbox_t> result_vec)
{
std::vector<tst_t> tst_vec(result_vec.size());
std::vector<bool> busy_vec(max_objects, false);
for (size_t i = 0; i < result_vec.size(); ++i)
{
tst_t tst = get_state_id(result_vec[i], busy_vec);
int state_id = tst.state_id;
int track_id = tst.track_id;
// if new state_id
if (state_id < 0) {
tst = new_state_id(busy_vec);
state_id = tst.state_id;
track_id = tst.track_id;
if (state_id > -1) {
kalman_vec[state_id].set(result_vec[i]);
//std::cerr << " post: ";
}
}
//std::cerr << " track_id = " << track_id << ", state_id = " << state_id <<
// ", x = " << result_vec[i].x << ", det_count = " << tst.detection_count << std::endl;
if (state_id > -1) {
tst_vec[i] = tst;
result_vec_pred[state_id] = result_vec[i];
result_vec_pred[state_id].track_id = track_id;
}
}
return tst_vec;
}
std::vector<bbox_t> predict()
{
clear_old_states();
std::vector<bbox_t> result_vec;
for (size_t i = 0; i < max_objects; ++i)
{
tst_t tst = track_id_state_id_time[i];
if (tst.track_id > -1) {
bbox_t box = kalman_vec[i].predict();
result_vec_pred[i].x = box.x;
result_vec_pred[i].y = box.y;
result_vec_pred[i].w = box.w;
result_vec_pred[i].h = box.h;
if (tst.detection_count >= min_frames)
{
if (track_id_state_id_time[i].track_id == 0) {
track_id_state_id_time[i].track_id = ++track_id_counter;
result_vec_pred[i].track_id = track_id_counter;
}
result_vec.push_back(result_vec_pred[i]);
}
}
}
//std::cerr << " result_vec.size() = " << result_vec.size() << std::endl;
//global_last_time = std::chrono::steady_clock::now();
return result_vec;
}
std::vector<bbox_t> correct(std::vector<bbox_t> result_vec)
{
calc_dt();
clear_old_states();
for (size_t i = 0; i < max_objects; ++i)
track_id_state_id_time[i].detection_count--;
std::vector<tst_t> tst_vec = find_state_ids(result_vec);
for (size_t i = 0; i < tst_vec.size(); ++i) {
tst_t tst = tst_vec[i];
int state_id = tst.state_id;
if (state_id > -1)
{
kalman_vec[state_id].set_delta_time(dT);
kalman_vec[state_id].correct(result_vec_pred[state_id]);
}
}
result_vec = predict();
global_last_time = std::chrono::steady_clock::now();
return result_vec;
}
};
// ----------------------------------------------
#endif // OPENCV
#endif // __cplusplus
#endif // YOLO_V2_CLASS_HPP
（3）主文件：
#pragma once
#include <iostream>
#include <iomanip>
#include <string>
#include <vector>
#include <queue>
#include <fstream>
#include <thread>
#include <future>
#include <atomic>
#include <mutex> // std::mutex, std::unique_lock
#include <cmath>
int deply;
#include "yolo_v2_class.hpp" // imported functions from DLL
#include <opencv2/opencv.hpp> // C++
#include <opencv2/core/version.hpp>
#include <opencv2/videoio/videoio.hpp>
/*
#pragma comment(lib, "opencv_cudaoptflow" OPENCV_VERSION ".lib")
#pragma comment(lib, "opencv_cudaimgproc" OPENCV_VERSION ".lib")
#pragma comment(lib, "opencv_core" OPENCV_VERSION ".lib")
#pragma comment(lib, "opencv_imgproc" OPENCV_VERSION ".lib")
#pragma comment(lib, "opencv_highgui" OPENCV_VERSION ".lib")
*/
using namespace std;
void draw_boxes(cv::Mat mat_img, std::vector<bbox_t> result_vec, std::vector<std::string> obj_names,
int current_det_fps = -1, int current_cap_fps = -1)
{
int const colors[6][3] = { { 1,0,1 },{ 0,0,1 },{ 0,1,1 },{ 0,1,0 },{ 1,1,0 },{ 1,0,0 } };
for (auto &i : result_vec) {
cv::Scalar color = obj_id_to_color(i.obj_id);
cv::rectangle(mat_img, cv::Rect(i.x, i.y, i.w, i.h), color, 2);
if (obj_names.size() > i.obj_id) {
std::string obj_name = obj_names[i.obj_id];
if (i.track_id > 0) obj_name += " - " + std::to_string(i.track_id);
cv::Size const text_size = getTextSize(obj_name, cv::FONT_HERSHEY_COMPLEX_SMALL, 1.2, 2, 0);
int max_width = (text_size.width > i.w + 2) ? text_size.width : (i.w + 2);
max_width = std::max(max_width, (int)i.w + 2);
//max_width = std::max(max_width, 283);
std::string coords_3d;
if (!std::isnan(i.z_3d)) {
std::stringstream ss;
ss << std::fixed << std::setprecision(2) << "x:" << i.x_3d << "m y:" << i.y_3d << "m z:" << i.z_3d << "m ";
coords_3d = ss.str();
cv::Size const text_size_3d = getTextSize(ss.str(), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.8, 1, 0);
int const max_width_3d = (text_size_3d.width > i.w + 2) ? text_size_3d.width : (i.w + 2);
if (max_width_3d > max_width) max_width = max_width_3d;
}
cv::rectangle(mat_img, cv::Point2f(std::max((int)i.x - 1, 0), std::max((int)i.y - 35, 0)),
cv::Point2f(std::min((int)i.x + max_width, mat_img.cols - 1), std::min((int)i.y, mat_img.rows - 1)),
color, CV_FILLED, 8, 0);
putText(mat_img, obj_name, cv::Point2f(i.x, i.y - 16), cv::FONT_HERSHEY_COMPLEX_SMALL, 1.2, cv::Scalar(0, 0, 0), 2);
if(!coords_3d.empty()) putText(mat_img, coords_3d, cv::Point2f(i.x, i.y-1), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.8, cv::Scalar(0, 0, 0), 1);
}
}
if (current_det_fps >= 0 && current_cap_fps >= 0) {
std::string fps_str = "FPS detection: " + std::to_string(current_det_fps) + " FPS capture: " + std::to_string(current_cap_fps);
putText(mat_img, fps_str, cv::Point2f(10, 20), cv::FONT_HERSHEY_COMPLEX_SMALL, 1.2, cv::Scalar(50, 255, 0), 2);
}
}
std::vector<std::string> objects_names_from_file(std::string const filename) {
std::ifstream file(filename);
std::vector<std::string> file_lines;
if (!file.is_open()) return file_lines;
for(std::string line; getline(file, line);) file_lines.push_back(line);
std::cout << "object names loaded \n";
return file_lines;
}
//void vec_index(std::string names_file, std::string cfg_file, std::string weights_file, cv::Mat inputimage);
std::vector<bbox_t> vec_index(std::string names_file, std::string cfg_file, std::string weights_file, cv::Mat inputimage)
{
std::cout << "Flag12!"<<std::endl;
Detector detector(cfg_file, weights_file);
std::cout << "Flag11!"<<std::endl;
auto obj_names = objects_names_from_file(names_file);
//cv::Mat mat_img;
preview_boxes_t large_preview(100, 150, false), small_preview(50, 50, true);
bool show_small_boxes = false;
cv::Mat mat_img = inputimage.clone();
//cv::imshow("img", mat_img);
//cv::waitKey(0);
auto det_image = detector.mat_to_image_resize(mat_img);
auto start = std::chrono::steady_clock::now();
std::vector<bbox_t> result_vec = detector.detect_resized(*det_image, mat_img.size().width, mat_img.size().height);
auto end = std::chrono::steady_clock::now();
std::chrono::duration<double> spent = end - start;
std::cout << " Time: " << spent.count() << " sec \n";
draw_boxes(mat_img, result_vec, obj_names);
//std::cout << "obj_name" << obj_names.size()<< std::endl;
for (auto &i : result_vec) {
//id_names.push_back(obj_names[i.obj_id]);
//box.push_back(i.obj_id,i.x,i.y,i.w,i.h,i.prob);
if (obj_names.size() > i.obj_id) std::cout << obj_names[i.obj_id] << " - ";
std::cout << "obj_id = " << i.obj_id << ", x = " << i.x << ", y = " << i.y
<< ", w = " << i.w << ", h = " << i.h
<< std::setprecision(3) << ", prob = " << i.prob << std::endl;}
cv::imwrite("out.jpg", mat_img);
//cv::resize(mat_img,mat_img,cv::Size(1280,960));
cv::imshow("window name", mat_img);
//show_console_result(result_vec, obj_names);
cv::waitKey(0);
return result_vec;
}
int main(int argc, char *argv[])
{
std::string names_file = "/home/pc/Documents/yolov4/code/yolov4_c/model/voc.names";
std::string cfg_file = "/home/pc/Documents/yolov4/code/yolov4_c/model/voc.data";
std::string weights_file = "/home/pc/Documents/yolov4/code/yolov4_c/model/yolo-obj_best.weights";
std::string filename = "/home/pc/Documents/yolov4/code/yolov4_c/model/1.jpg";
#
//std::string names_file = "/media/em/data_1/yolov4/darknet-master/data/voc.names";
//std::string cfg_file = "/media/em/data_1/yolov4/darknet-master/cfg/yolo-obj.cfg";
//std::string weights_file = "/media/em/data_1/yolov4/darknet-master/backup/backup20200704/yolo-obj_8000.weights";
//std::string filename ="/media/em/data_1/0716/VOCdevkit_24/VOC2007/JPEGImages/2.jpg";
//auto out_obj_names = objects_names_from_file(names_file);
if(0)
{
cv::VideoCapture cap(0);
double rate = cap.get(CV_CAP_PROP_FPS);
deply = cvRound(1000.000/rate);
if(!cap.isOpened())
{
std::cout<<"Read carmera fail!"<<std::endl;
return -1;
}
while(true)
{
cv::Mat frame;
cap >> frame;
//cv::imshow("cap",frame);
if(frame.empty())
{
break;
}
else
{
cv::Mat inputimage = frame.clone();
std::vector<bbox_t> result_vec = vec_index(names_file, cfg_file, weights_file, inputimage);
for (auto &i : result_vec) {
//std::cout << obj_names[i.obj_id] << " - ";
std::cout << "obj_id = " << i.obj_id << ", x = " << i.x << ", y = " << i.y
<< ", w = " << i.w << ", h = " << i.h
<< std::setprecision(3) << ", prob = " << i.prob << std::endl;
}
//cv::imwrite("1.jpg",out_image);
//cv::imshow("demo",out_image);
//cv::waitKey(10);
}
//cv::waitKey(deply);
}
}
std::cout << "flag!"<<std::endl;
cv::Mat inputimage = cv::imread(filename);
//cv::imshow("img", inputimage);
//cv::waitKey(0);
if(inputimage.empty())
{
std::cout << "Read image fail!" << std::endl;
return -1;
}
std::vector<bbox_t> result_vec = vec_index(names_file, cfg_file, weights_file, inputimage);
for (auto &i : result_vec) {
//std::cout << out_obj_names[i.obj_id] << " - ";
std::cout << "obj_id = " << i.obj_id << ", x = " << i.x << ", y = " << i.y
<< ", w = " << i.w << ", h = " << i.h
<< std::setprecision(3) << ", prob = " << i.prob << std::endl;}
//cv::Mat out_image = vec_index(names_file, cfg_file, weights_file, inputimage);
//cv::imwrite("1.jpg",out_image);
//cv::imshow("demo",inputimage);
//cv::waitKey(10);
//int a;
//std::cin >> a;
return 0;
}
4、提取网络层参数：冻结层
darknet partial cfg/yolov4-obj.cfg yolov4-obj.weights yolov4.conv.136 136
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