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添加单目估距。

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This commit is contained in:
xinyang
2019-07-10 13:09:42 +08:00
parent 5c6d96425b
commit 747fb58eb8
12 changed files with 12554 additions and 12542 deletions
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40
armor/src/armor_finder/armor_finder.cpp
View File

@@ -15,7 +15,8 @@
/* */
/*===========================================================================*/
#define LOG_LEVEL LOG_NONE
//#define LOG_LEVEL LOG_NONE
#include <log.h>
#include <options/options.h>
#include <show_images/show_images.h>
@@ -42,24 +43,25 @@ ArmorFinder::ArmorFinder(uint8_t &color, Serial &u, const string &paras_folder,
contour_area(0),
use_classifier(use),
boxid(-1),
tracking_cnt(0)
{
tracking_cnt(0) {
}
extern int box_distance;
void ArmorFinder::run(cv::Mat &src) {
cv::Mat src_use = src.clone(); // 实际参与计算的图像对象
if(show_armor_box){ // 根据条件显示当前目标装甲板
if (show_armor_box) { // 根据条件显示当前目标装甲板
showArmorBox("box", src, armor_box, boxid);
cv::waitKey(1);
}
// stateSearchingTarget(src_use); // for debug
// return;
switch (state){
switch (state) {
case SEARCHING_STATE:
if(stateSearchingTarget(src_use)){
if((armor_box & cv::Rect2d(0, 0, 640, 480)) == armor_box) { // 判断装甲板区域是否脱离图像区域
if(!classifier || !use_classifier){ /* 如果分类器不可用或者不使用分类器 */
if (stateSearchingTarget(src_use)) {
if ((armor_box & cv::Rect2d(0, 0, 640, 480)) == armor_box) { // 判断装甲板区域是否脱离图像区域
if (!classifier || !use_classifier) { /* 如果分类器不可用或者不使用分类器 */
cv::Mat roi = src_use.clone()(armor_box), roi_gray; /* 就使用装甲区域亮点数判断是否跟丢 */
cv::cvtColor(roi, roi_gray, CV_RGB2GRAY);
cv::threshold(roi_gray, roi_gray, 180, 255, cv::THRESH_BINARY);
@@ -69,14 +71,14 @@ void ArmorFinder::run(cv::Mat &src) {
tracker->init(src_use, armor_box);
state = TRACKING_STATE;
tracking_cnt = 0;
LOGM(STR_CTR(WORD_LIGHT_CYAN, "into track"));
// LOGM(STR_CTR(WORD_LIGHT_CYAN, "into track"));
}
}
break;
case TRACKING_STATE:
if(!stateTrackingTarget(src_use) || ++tracking_cnt>100){ // 最多追踪100帧图像
if (!stateTrackingTarget(src_use) || ++tracking_cnt > 100) { // 最多追踪100帧图像
state = SEARCHING_STATE;
LOGM(STR_CTR(WORD_LIGHT_YELLOW ,"into search!"));
// LOGM(STR_CTR(WORD_LIGHT_YELLOW, "into search!"));
}
break;
case STANDBY_STATE:
@@ -85,7 +87,7 @@ void ArmorFinder::run(cv::Mat &src) {
}
}
bool sendTarget(Serial& serial, double x, double y, double z) {
bool sendTarget(Serial &serial, double x, double y, double z) {
static short x_tmp, y_tmp, z_tmp;
static time_t last_time = time(nullptr);
static int fps;
@@ -115,12 +117,16 @@ bool sendTarget(Serial& serial, double x, double y, double z) {
return serial.WriteData(buff, sizeof(buff));
}
#define DISTANCE_HEIGHT_5MM (113.0) // 单位: m*pixel
#define DISTANCE_HEIGHT DISTANCE_HEIGHT_5MM
bool ArmorFinder::sendBoxPosition() {
auto rect = armor_box;
double dx = rect.x + rect.width/2 - 320 - 10;
double dy = rect.y + rect.height/2 - 240 - 20;
double yaw = atan(dx / FOCUS_PIXAL) * 180 / 3.14159265459;
double pitch = atan(dy / FOCUS_PIXAL) * 180 / 3.14159265459;
double dx = rect.x + rect.width / 2 - 320;
double dy = rect.y + rect.height / 2 - 240 - 20;
double yaw = atan(dx / FOCUS_PIXAL) * 180 / PI;
double pitch = atan(dy / FOCUS_PIXAL) * 180 / PI;
double dist = DISTANCE_HEIGHT / armor_box.height;
// cout << yaw << endl;
return sendTarget(serial, yaw, -pitch, 0);
return sendTarget(serial, yaw, -pitch, dist);
}

5
armor/src/armor_finder/searching_state/searching_state.cpp
View File

@@ -94,7 +94,7 @@ static double nonZeroRateOfRotateRect_opt(const cv::Mat &bin, const cv::RotatedR
}
static bool isValidLightBlob(const cv::Mat &bin, const cv::RotatedRect &rect) {
return (lw_rate(rect) > 1.8) &&
return (lw_rate(rect) > 1.5) &&
// (rect.size.width*rect.size.height < 3000) &&
(rect.size.width * rect.size.height > 1) &&
// (nonZeroRateOfRotateRect_opt(bin, rect) > 0.8);
@@ -262,13 +262,14 @@ bool ArmorFinder::stateSearchingTarget(cv::Mat &src) {
std::vector<cv::Rect2d> boxes_number[15]; // 装甲板候选区放置在对应id位置
armor_box = cv::Rect2d(0, 0, 0, 0); // 重置目标装甲板位置
boxid = -1; // 重置目标装甲板id
cv::split(src, channels); /************************/
if (enemy_color == ENEMY_BLUE) /* */
color = channels[0]; /* 根据目标颜色进行通道提取 */
else if (enemy_color == ENEMY_RED) /* */
color = channels[2]; /************************/
cv::threshold(color, src_bin, 190, 255, CV_THRESH_BINARY); // 二值化对应通道
cv::threshold(color, src_bin, 170, 255, CV_THRESH_BINARY); // 二值化对应通道
imagePreProcess(src_bin); // 开闭运算
if (!findLightBlobs(src_bin, light_blobs)) {
return false;

15
main.cpp
View File

@@ -1,11 +1,11 @@
/*******************************************************************/
/**********************************************************************/
/* ____ _ _____ _ _ ____ __ __ ______ __ */
/*/ ___| | |_ _| | | | | _ \| \/ | / ___\ \ / / */
/*\___ \ _ | | | | | | | |_____| |_) | |\/| |_____| | \ \ / / */
/* / ___| | |_ _| | | | | _ \| \/ | / ___\ \ / / */
/* \___ \ _ | | | | | | | |_____| |_) | |\/| |_____| | \ \ / / */
/* ___) | |_| | | | | |_| |_____| _ <| | | |_____| |___ \ V / */
/*|____/ \___/ |_| \___/ |_| \_\_| |_| \____| \_/ */
/* |____/ \___/ |_| \___/ |_| \_\_| |_| \____| \_/ */
/* */
/*******************************************************************/
/**********************************************************************/
#include <iostream>
#include <thread>
@@ -27,7 +27,7 @@ using namespace std;
mcu_data mcuData = { // 单片机端回传结构体
0, // 当前云台yaw角
0, // 当前云台pitch角
SMALL_ENERGY_STATE, // 当前状态，自瞄-大符-小符
ARMOR_STATE, // 当前状态，自瞄-大符-小符
0, // 云台角度标记位
1, // 是否启用数字识别
ENEMY_RED, // 敌方颜色
@@ -43,6 +43,8 @@ ArmorFinder armorFinder(mcuData.enemy_color, serial, PROJECT_DIR"/tools/para/",
// 能量机关主程序对象
Energy energy(serial, mcuData.enemy_color);
int box_distance = 0;
int main(int argc, char *argv[]) {
process_options(argc, argv); // 处理命令行参数
thread receive(uartReceive, &serial); // 开启串口接收线程
@@ -120,7 +122,6 @@ int main(int argc, char *argv[]) {
energy.runBig(gimble_src);
}
}
// cv::waitKey(3);
});
} while (ok);
delete video_gimble;

12
tools/TrainCNN/backward.py
View File

@@ -54,9 +54,9 @@ def save_para(folder, paras):
save_bias(fp, paras[7])
STEPS = 5000
STEPS = 10000
BATCH = 30
LEARNING_RATE_BASE = 0.01
LEARNING_RATE_BASE = 0.005
LEARNING_RATE_DECAY = 0.99
MOVING_AVERAGE_DECAY = 0.99
@@ -103,7 +103,7 @@ def train(dataset, show_bar=False):
_, loss_value, step = sess.run(
[train_op, loss, global_step],
feed_dict={x: images_samples, y_: labels_samples, keep_rate:0.7}
feed_dict={x: images_samples, y_: labels_samples, keep_rate:0.5}
)
if i % 100 == 0:
@@ -204,5 +204,9 @@ def train(dataset, show_bar=False):
if __name__ == "__main__":
dataset = generate.DataSet("/home/xinyang/Workspace/dataset/box_cut")
import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
dataset = generate.DataSet("/home/xinyang/Workspace/box_cut")
train(dataset, show_bar=True)
input("press enter to continue...")

12
tools/para/conv1_b
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@@ -1,7 +1,7 @@
6
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0.7093666
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0.83760846

900
tools/para/conv1_w
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@@ -2,453 +2,453 @@
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