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更新反陀螺和分类器参数。

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This commit is contained in:
xinyang
2019-07-19 13:05:19 +08:00
parent e1c7e04138
commit ad036071b0
16 changed files with 26847 additions and 18278 deletions
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2
armor/include/armor_finder/armor_finder.h
View File

@@ -108,7 +108,7 @@ private:
Serial &serial; // 串口对象，引用外部变量，用于和能量机关共享同一个变量
const uint8_t &use_classifier; // 标记是否启用CNN分类器，引用外部变量，自动变化
ArmorBox::BoxOrientation last_orient; // 上一帧目标装甲板方向，用于反陀螺
timeval last_switch_time; // 上一次发生装甲板方向切换的时间
timeval last_front_time; // 上一次发生装甲板方向切换的时间
int anti_top_cnt; // 满足条件的装甲板方向切换持续次数，用于反陀螺
AntiTopState anti_top_state; // 当前是否识别到陀螺

32
armor/src/armor_finder/anti_top/anti_top.cpp
View File

@@ -3,12 +3,15 @@
//
#include <armor_finder/armor_finder.h>
#include <log.h>
static double getTimeIntervalms(const timeval& now, const timeval &last){
return (now.tv_sec-last.tv_sec)*1000.0 + (now.tv_usec-last.tv_usec)/1000.0;
}
void ArmorFinder::antiTop() {
double top_periodms = 0;
timeval curr_time;
if(anti_top_state == ANTI_TOP){
cout << "anti top" << endl;
}else if(anti_top_state == NORMAL){
@@ -23,23 +26,26 @@ void ArmorFinder::antiTop() {
return;
}
}
if(orientation!=last_orient){
timeval curr_time;
if(orientation != last_orient){
gettimeofday(&curr_time, nullptr);
auto interval = getTimeIntervalms(curr_time, last_switch_time);
cout << interval << endl;
if(50 < interval && interval < 700){
anti_top_cnt++;
}else{
auto interval = getTimeIntervalms(curr_time, last_front_time);
if(interval > 700){
anti_top_cnt = 0;
}
last_switch_time = curr_time;
}else{
timeval curr_time;
gettimeofday(&curr_time, nullptr);
if(getTimeIntervalms(curr_time, last_switch_time) > 700) {
anti_top_state = NORMAL;
if(orientation == ArmorBox::FRONT){
last_front_time = curr_time;
}
}else if(interval > 150){
if(orientation == ArmorBox::FRONT){
anti_top_cnt++;
if(anti_top_state == ANTI_TOP){
top_periodms = interval;
LOGM(STR_CTR(WORD_LIGHT_GREEN, "top period: %.1lf ms"), top_periodms);
}
last_front_time = curr_time;
}
}
last_orient = orientation;
}
if(anti_top_cnt > 4){
anti_top_state = ANTI_TOP;

6
armor/src/armor_finder/armor_box/armor_box.cpp
View File

@@ -53,9 +53,11 @@ ArmorBox::BoxOrientation ArmorBox::getOrientation() const{
switch (id) {
case R1:
case R7:
case R8:
case B1:
case B7:
if(lengthDistanceRatio() < 0.3){
case B8:
if(lengthDistanceRatio() < 0.30){
return FRONT;
}else{
return SIDE;
@@ -68,7 +70,7 @@ ArmorBox::BoxOrientation ArmorBox::getOrientation() const{
case B3:
case B4:
case B5:
if (lengthDistanceRatio() < 0.43) {
if (lengthDistanceRatio() < 0.45) {
return FRONT;
}else{
return SIDE;

1
armor/src/armor_finder/send_target/send_target.cpp
View File

@@ -35,6 +35,7 @@ static bool sendTarget(Serial &serial, double x, double y, double z) {
}
bool ArmorFinder::sendBoxPosition() {
if(armor_box.rect == cv::Rect2d()) return false;
auto rect = armor_box.rect;
double dx = rect.x + rect.width / 2 - 320;
double dy = rect.y + rect.height / 2 - 240 - 20;

2
main.cpp
View File

@@ -30,7 +30,7 @@ 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
BIG_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

2
others/src/camera/camera_wrapper.cpp
View File

@@ -83,7 +83,7 @@ bool CameraWrapper::init() {
CameraSetGain(h_camera, CAMERA_BLUE_GAIN, CAMERA_GREEN_GAIN, CAMERA_RED_GAIN);
CameraSetLutMode(h_camera, LUTMODE_PRESET);
}
CameraSetOnceWB(h_camera);
// CameraSetOnceWB(h_camera);
#endif
double t;
CameraGetExposureTime(h_camera, &t);

12
tools/TrainCNN/backward.py
View File

@@ -54,9 +54,9 @@ def save_para(folder, paras):
save_bias(fp, paras[7])
STEPS = 20000
STEPS = 100000
BATCH = 50
LEARNING_RATE_BASE = 0.005
LEARNING_RATE_BASE = 0.001
LEARNING_RATE_DECAY = 0.99
MOVING_AVERAGE_DECAY = 0.99
@@ -101,7 +101,7 @@ 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:0.5}
feed_dict={x: images_samples, y_: labels_samples, keep_rate:0.3}
)
if i % 100 == 0:
@@ -206,9 +206,9 @@ def train(dataset, show_bar=False):
if __name__ == "__main__":
# import os
# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
# os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
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...")

2
tools/TrainCNN/forward.py
View File

@@ -38,7 +38,7 @@ CONV2_KERNAL_SIZE = 3
CONV2_OUTPUT_CHANNELS = 12
# 第一层全连接宽度
FC1_OUTPUT_NODES = 20
FC1_OUTPUT_NODES = 30
# 第二层全连接宽度（输出标签类型数）
FC2_OUTPUT_NODES = 15

12
tools/para/conv1_b
View File

@@ -1,7 +1,7 @@
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0.27160552
2.29277
0.3149763
-0.014892017
2.3465085
0.00036684246
-0.15470123
1.5563219

900
tools/para/conv1_w
View File

@@ -2,453 +2,453 @@
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