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更新CNN参数，使用CNN判断tracking是否跟丢。

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This commit is contained in:
xinyang
2019-04-30 20:20:35 +08:00
parent e3098fe3fa
commit ee83a373d3
16 changed files with 12416 additions and 9793 deletions
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42
armor/src/armor_finder/armor_finder.cpp
View File

@@ -8,20 +8,12 @@
#include <armor_finder/armor_finder.h> #include <armor_finder/armor_finder.h>
ArmorFinder::ArmorFinder(EnemyColor color, Uart &u, string paras_folder) : ArmorFinder::ArmorFinder(EnemyColor color, Uart &u, string paras_folder) :
uart(u), uart(u),
enemy_color(color), enemy_color(color),
state(STANDBY_STATE), state(STANDBY_STATE),
classifier(std::move(paras_folder)), classifier(std::move(paras_folder)),
contour_area(0) contour_area(0)
{ {
// auto para = TrackerToUse::Params();
// para.desc_npca = 1;
// para.desc_pca = 0;
// tracker = TrackerToUse::create(para);
// if(!tracker){
// LOGW("Tracker Not init");
// }
} }
void ArmorFinder::run(cv::Mat &src) { void ArmorFinder::run(cv::Mat &src) {
@@ -39,22 +31,12 @@ void ArmorFinder::run(cv::Mat &src) {
case SEARCHING_STATE: case SEARCHING_STATE:
if(stateSearchingTarget(src_use)){ if(stateSearchingTarget(src_use)){
if((armor_box & cv::Rect2d(0, 0, 640, 480)) == armor_box) { if((armor_box & cv::Rect2d(0, 0, 640, 480)) == armor_box) {
// cv::Mat roi = src_gray.clone()(armor_box); if(!classifier){
// cv::threshold(roi, roi, 200, 255, cv::THRESH_BINARY); cv::Mat roi = src_use.clone()(armor_box), roi_gray;
// contour_area = cv::countNonZero(roi); cv::cvtColor(roi, roi_gray, CV_RGB2GRAY);
// auto para = TrackerToUse::Params(); cv::threshold(roi_gray, roi_gray, 180, 255, cv::THRESH_BINARY);
// para.desc_npca = 1; contour_area = cv::countNonZero(roi_gray);
// para.desc_pca = 0; }
// tracker = TrackerToUse::create(para);
// tracker->init(src_gray, armor_box);
// tracker->update(src_gray, armor_box);
cv::Mat roi = src_use.clone()(armor_box), roi_gray;
cv::cvtColor(roi, roi_gray, CV_RGB2GRAY);
// cv::imshow("boxroi", roi);
// cv::waitKey(0);
cv::threshold(roi_gray, roi_gray, 180, 255, cv::THRESH_BINARY);
contour_area = cv::countNonZero(roi_gray);
LOGW("%d", contour_area);
tracker = TrackerToUse::create(); tracker = TrackerToUse::create();
tracker->init(src_use, armor_box); tracker->init(src_use, armor_box);
state = TRACKING_STATE; state = TRACKING_STATE;

2
armor/src/armor_finder/state_machine/searching_state/searching_state.cpp
View File

@@ -155,7 +155,7 @@ bool ArmorFinder::stateSearchingTarget(cv::Mat &src) {
imageColorSplit(src, split, enemy_color); imageColorSplit(src, split, enemy_color);
cv::threshold(split, src_bin, 130, 255, CV_THRESH_BINARY); cv::threshold(split, src_bin, 130, 255, CV_THRESH_BINARY);
imagePreProcess(src_bin); imagePreProcess(src_bin);
cv::imshow("bin", src_bin); // cv::imshow("bin", src_bin);
// cv::resize(split, split, cv::Size(640, 480)); // cv::resize(split, split, cv::Size(640, 480));
// pipelineLightBlobPreprocess(pmsrc); // pipelineLightBlobPreprocess(pmsrc);
// if(!findLightBlobs(pmsrc, pm_light_blobs)){ // if(!findLightBlobs(pmsrc, pm_light_blobs)){

20
armor/src/armor_finder/state_machine/tracking_state/tracking_state.cpp
View File

@@ -12,12 +12,20 @@ bool ArmorFinder::stateTrackingTarget(cv::Mat &src) {
return false; return false;
} }
cv::Mat roi = src.clone()(armor_box), roi_gray; cv::Mat roi = src.clone()(armor_box);
cv::cvtColor(roi, roi_gray, CV_RGB2GRAY); if(classifier){
cv::threshold(roi_gray, roi_gray, 180, 255, cv::THRESH_BINARY); cv::resize(roi, roi, cv::Size(48, 36));
contour_area = cv::countNonZero(roi_gray); if(classifier(roi) == 0){
if(abs(cv::countNonZero(roi_gray) - contour_area) > contour_area * 0.3){ return false;
return false; }
}else{
cv::Mat roi_gray;
cv::cvtColor(roi, roi_gray, CV_RGB2GRAY);
cv::threshold(roi_gray, roi_gray, 180, 255, cv::THRESH_BINARY);
contour_area = cv::countNonZero(roi_gray);
if(abs(cv::countNonZero(roi_gray) - contour_area) > contour_area * 0.3){
return false;
}
} }
return sendBoxPosition(); return sendBoxPosition();
} }

52
main.cpp
View File

@@ -15,7 +15,7 @@
#include <options/options.h> #include <options/options.h>
#include <thread> #include <thread>
#define DO_NOT_CNT_TIME //#define DO_NOT_CNT_TIME
#include <log.h> #include <log.h>
#define PATH PROJECT_DIR #define PATH PROJECT_DIR
@@ -52,13 +52,13 @@ int main(int argc, char *argv[]){
WrapperHead *video_armor; WrapperHead *video_armor;
WrapperHead *video_energy; WrapperHead *video_energy;
if(from_camera) { if(from_camera) {
// video_armor = new CameraWrapper(); video_armor = new CameraWrapper(0);
video_energy = new CameraWrapper(); // video_energy = new CameraWrapper(1);
}else { }else {
video_armor = new VideoWrapper("/home/xinyang/Desktop/Video.mp4"); video_armor = new VideoWrapper("/home/xinyang/Desktop/Video.mp4");
video_energy = new VideoWrapper("/home/xinyang/Desktop/Video.mp4"); video_energy = new VideoWrapper("/home/xinyang/Desktop/Video.mp4");
} }
if (video_energy->init()) { if (video_armor->init()) {
cout << "Video source initialization successfully." << endl; cout << "Video source initialization successfully." << endl;
} }
@@ -70,28 +70,32 @@ int main(int argc, char *argv[]){
energy.setAllyColor(ally_color); energy.setAllyColor(ally_color);
energy.setRotation(energy_part_rotation); energy.setRotation(energy_part_rotation);
while (video_energy->read(energy_src) && video_energy->read(armor_src)) bool ok=true;
{
if(show_origin) { while (ok){
imshow("enery src", energy_src); CNT_TIME(WORD_LIGHT_CYAN, "Total", {
imshow("armor src", armor_src); ok = video_armor->read(energy_src) && video_armor->read(armor_src);
} if (show_origin) {
if(state == ENERGY_STATE){ imshow("enery src", energy_src);
if(from_camera==0){ imshow("armor src", armor_src);
energy.extract(energy_src);
} }
energy.run(energy_src); if (state == ENERGY_STATE) {
}else{ if (from_camera == 0) {
CNT_TIME(WORD_LIGHT_BLUE, "Armor Time", { energy.extract(energy_src);
armorFinder.run(armor_src); }
}); energy.run(energy_src);
} } else {
if (waitKey(1) == 'q') { CNT_TIME(WORD_LIGHT_BLUE, "Armor Time", {
flag = false; armorFinder.run(armor_src);
break; });
} }
if (waitKey(1) == 'q') {
flag = false;
break;
}
});
} }
delete video_energy; delete video_armor;
cout << "Program fails. Restarting" << endl; cout << "Program fails. Restarting" << endl;
} }

6
others/include/camera/camera_wrapper.h
View File

@@ -36,11 +36,7 @@ private:
public: public:
CameraWrapper(); CameraWrapper(int camera_mode=1, const std::string &n="NULL");
CameraWrapper(const std::string &n);
CameraWrapper(int camera_mode);
CameraWrapper(const std::string &n,int camera_mode);
~CameraWrapper() final; ~CameraWrapper() final;
bool init() final; bool init() final;

50
others/src/camera/camera_wrapper.cpp
View File

@@ -9,39 +9,13 @@ using std::cout;
using std::endl; using std::endl;
using namespace cv; using namespace cv;
CameraWrapper::CameraWrapper(): CameraWrapper::CameraWrapper(int camera_mode, const std::string &n):
name("NULL"),
mode(1),
camera_cnts(2),
camera_status(-1),
iplImage(nullptr),
channel(3){
}
CameraWrapper::CameraWrapper(const std::string &n):
name(n),
mode(1),
camera_cnts(2),
camera_status(-1),
iplImage(nullptr),
channel(3){
}
CameraWrapper::CameraWrapper(int camera_mode):
name("NULL"),
mode(camera_mode),
camera_cnts(2),
camera_status(-1),
iplImage(nullptr),
channel(3){
}
CameraWrapper::CameraWrapper(const std::string &n,int camera_mode):
name(n), name(n),
mode(camera_mode), mode(camera_mode),
camera_cnts(2), camera_cnts(2),
camera_status(-1), camera_status(-1),
iplImage(nullptr), iplImage(nullptr),
rgb_buffer(nullptr),
channel(3){ channel(3){
} }
@@ -51,9 +25,9 @@ bool CameraWrapper::init() {
//枚举设备，并建立设备列表 //枚举设备，并建立设备列表
int camera_enumerate_device_status = CameraEnumerateDevice(camera_enum_list, &camera_cnts); int camera_enumerate_device_status = CameraEnumerateDevice(camera_enum_list, &camera_cnts);
//cout<<"camera enumerate device status: "<<camera_enumerate_device_status<<endl; if(camera_enumerate_device_status != CAMERA_STATUS_SUCCESS){
//cout<<"camera number: "<<camera_cnts<<endl; LOGE("CameraEnumerateDevice fail with %d!", camera_enumerate_device_status);
}
//没有连接设备 //没有连接设备
if (camera_cnts == 0) { if (camera_cnts == 0) {
LOGE("No camera device detected!"); LOGE("No camera device detected!");
@@ -67,16 +41,17 @@ bool CameraWrapper::init() {
camera_status = CameraInit(&camera_enum_list[i], -1, -1, &h_camera); camera_status = CameraInit(&camera_enum_list[i], -1, -1, &h_camera);
if (camera_status != CAMERA_STATUS_SUCCESS) { if (camera_status != CAMERA_STATUS_SUCCESS) {
LOGE("Camera 0 initialization failed with code %d. See camera_status.h to find the code meaning.", camera_status); LOGE("Camera 0 initialization failed with code %d. See camera_status.h to find the code meaning.", camera_status);
return false; goto stop;
} }
CameraGetFriendlyName(h_camera, camera_name); CameraGetFriendlyName(h_camera, camera_name);
if(name=="NULL" || strcmp(name.data(), camera_name)==0){ if(name=="NULL" || strcmp(name.data(), camera_name)==0){
break; break;
} }
stop:
CameraUnInit(h_camera); CameraUnInit(h_camera);
} }
if(i >= camera_cnts){ if(i >= camera_cnts){
LOGE("No device name %s!", name.data()); LOGE("No device name %s or device open error!!", name.data());
return false; return false;
} }
@@ -90,11 +65,11 @@ bool CameraWrapper::init() {
tCapability.sResolutionRange.iWidthMax * 3); tCapability.sResolutionRange.iWidthMax * 3);
if(mode == 0){ if(mode == 0){
// 不使用自动曝光 // 不使用自动曝光
CameraSetAeState(h_camera, false); CameraSetAeState(h_camera, true);
// 曝光时间10ms // 曝光时间10ms
CameraSetExposureTime(h_camera, 10000); // CameraSetExposureTime(h_camera, 10000);
// 模拟增益4 // 模拟增益4
CameraSetAnalogGain(h_camera, 63); CameraSetAnalogGain(h_camera, 64);
// 使用预设LUT表 // 使用预设LUT表
CameraSetLutMode(h_camera, LUTMODE_PRESET); CameraSetLutMode(h_camera, LUTMODE_PRESET);
// 抗频闪 // 抗频闪
@@ -188,6 +163,7 @@ CameraWrapper::~CameraWrapper()
{ {
CameraUnInit(h_camera); CameraUnInit(h_camera);
//注意，先反初始化后再free //注意，先反初始化后再free
free(rgb_buffer); if(rgb_buffer != nullptr)
free(rgb_buffer);
} }

76
tools/TrainCNN/backward.py
View File

@@ -1,10 +1,12 @@
#!/usr/bin/python3 #!/usr/bin/python3
print("Preparing...")
import tensorflow as tf import tensorflow as tf
from tqdm import tqdm from tqdm import tqdm
import generate import generate
import forward import forward
import cv2 import cv2
import numpy as np import numpy as np
print("Finish!")
def save_kernal(fp, val): def save_kernal(fp, val):
print(val.shape[2], file=fp) print(val.shape[2], file=fp)
@@ -91,7 +93,7 @@ def train(dataset, show_bar=False):
init_op = tf.global_variables_initializer() init_op = tf.global_variables_initializer()
sess.run(init_op) sess.run(init_op)
bar = tqdm(range(STEPS), ascii=True, dynamic_ncols=True) bar = tqdm(range(STEPS), dynamic_ncols=True)
for i in bar: for i in bar:
images_samples, labels_samples = dataset.sample_train_sets(BATCH) images_samples, labels_samples = dataset.sample_train_sets(BATCH)
@@ -111,41 +113,41 @@ def train(dataset, show_bar=False):
# bar.cursor.restore() # bar.cursor.restore()
# bar.draw(value=i+1) # bar.draw(value=i+1)
video = cv2.VideoCapture("/home/xinyang/Desktop/Video.mp4") # video = cv2.VideoCapture("/home/xinyang/Desktop/Video.mp4")
_ = True # _ = True
while _: # while _:
_, frame = video.read() # _, frame = video.read()
cv2.imshow("Video", frame) # cv2.imshow("Video", frame)
k = cv2.waitKey(10) # k = cv2.waitKey(10)
if k == ord(" "): # if k == ord(" "):
bbox = cv2.selectROI("frame", frame, False) # bbox = cv2.selectROI("frame", frame, False)
print(bbox) # print(bbox)
roi = frame[bbox[1]:bbox[1]+bbox[3], bbox[0]:bbox[0]+bbox[2]] # roi = frame[bbox[1]:bbox[1]+bbox[3], bbox[0]:bbox[0]+bbox[2]]
roi = cv2.resize(roi, (48, 36)) # roi = cv2.resize(roi, (48, 36))
cv2.imshow("roi", roi) # cv2.imshow("roi", roi)
cv2.waitKey(0) # cv2.waitKey(0)
roi = roi.astype(np.float32) # roi = roi.astype(np.float32)
roi /= 255.0 # roi /= 255.0
roi = roi.reshape([1, 36, 48, 3]) # roi = roi.reshape([1, 36, 48, 3])
res = sess.run(y, feed_dict={x: roi}) # res = sess.run(y, feed_dict={x: roi})
res = res.reshape([forward.OUTPUT_NODES]) # res = res.reshape([forward.OUTPUT_NODES])
print(np.argmax(res)) # print(np.argmax(res))
elif k==ord("q"): # elif k==ord("q"):
break # break
keep = True # keep = True
while keep: # while keep:
n = input() # n = input()
im = cv2.imread(n) # im = cv2.imread(n)
im = cv2.resize(im, (48, 36)) # im = cv2.resize(im, (48, 36))
cv2.imshow("im", im) # cv2.imshow("im", im)
if cv2.waitKey(0) == ord("q"): # if cv2.waitKey(0) == ord("q"):
keep = False # keep = False
im = im.astype(np.float32) # im = im.astype(np.float32)
im /= 255.0 # im /= 255.0
im = im.reshape([1, 36, 48, 3]) # im = im.reshape([1, 36, 48, 3])
res = sess.run(y, feed_dict={x: im}) # res = sess.run(y, feed_dict={x: im})
res = res.reshape([forward.OUTPUT_NODES]) # res = res.reshape([forward.OUTPUT_NODES])
print(np.argmax(res)) # print(np.argmax(res))
vars_val = sess.run(vars) vars_val = sess.run(vars)
save_para("/home/xinyang/Desktop/AutoAim/tools/para", vars_val) save_para("/home/xinyang/Desktop/AutoAim/tools/para", vars_val)
@@ -154,5 +156,7 @@ def train(dataset, show_bar=False):
if __name__ == "__main__": if __name__ == "__main__":
print("Loading data sets...")
dataset = generate.DataSet("/home/xinyang/Desktop/DataSets/box") dataset = generate.DataSet("/home/xinyang/Desktop/DataSets/box")
print("Finish!")
train(dataset, show_bar=True) train(dataset, show_bar=True)

17
tools/TrainCNN/generate.py
View File

@@ -3,6 +3,9 @@ import os
import cv2 import cv2
import random import random
from forward import OUTPUT_NODES from forward import OUTPUT_NODES
import sys
import os
from tqdm import tqdm
# 原图像行数 # 原图像行数
SRC_ROWS = 36 SRC_ROWS = 36
@@ -22,10 +25,14 @@ class DataSet:
self.generate_data_sets(folder) self.generate_data_sets(folder)
def file2nparray(self, name): def file2nparray(self, name):
image = cv2.imread(name) try:
image = cv2.resize(image, (SRC_COLS, SRC_ROWS)) image = cv2.imread(name)
image = image.astype(np.float32) image = cv2.resize(image, (SRC_COLS, SRC_ROWS))
return image / 255.0 image = image.astype(np.float32)
return image / 255.0
except:
print(name)
sys.exit(-1)
def id2label(self, id): def id2label(self, id):
a = np.zeros([OUTPUT_NODES]) a = np.zeros([OUTPUT_NODES])
@@ -37,7 +44,7 @@ class DataSet:
for i in range(OUTPUT_NODES): for i in range(OUTPUT_NODES):
dir = "%s/%d" % (folder, i) dir = "%s/%d" % (folder, i)
files = os.listdir(dir) files = os.listdir(dir)
for file in files: for file in tqdm(files, postfix={"loading id": i}, dynamic_ncols=True):
if file[-3:] == "jpg": if file[-3:] == "jpg":
if random.random() > 0.2: if random.random() > 0.2:
self.train_samples.append(self.file2nparray("%s/%s" % (dir, file))) self.train_samples.append(self.file2nparray("%s/%s" % (dir, file)))

12
tools/para/conv1_b
View File

@@ -1,5 +1,7 @@
4 6
-0.0581224 0.6241298
1.6767645 1.1128483
3.2315264 0.92715627
-0.062396575 2.6803706
-2.4037633
1.6914424

752
tools/para/conv1_w
View File

@@ -1,304 +1,454 @@
3 3
4 6
5 5
5 5
-0.09578384 -1.8745673
-0.031667184 -1.4418055
-0.03574733 -0.68810487
-0.07420163 -0.42730802
-0.07083298 -0.019043377
-0.10318954 -2.2401865
-0.11602546 -1.5337001
-0.11085167 -0.5666739
-0.103610046 0.1119876
-0.14557937 0.24716255
-0.12054949 -2.2945662
-0.06578051 -1.4734446
-0.08435572 -0.298631
-0.19572483 0.19550888
-0.09539226 0.45057848
0.017180158 -2.4559898
-0.17646453 -1.097164
0.012836289 -0.035884537
0.042035762 0.23402216
-0.07413422 0.3693794
0.022820458 -1.7285618
-0.17639516 -0.8927671
-0.013574861 0.026205042
-0.21071187 0.19274746
-0.16708027 0.029146997
-1.6791077 -1.3690577
-0.8732547 -1.2756748
-0.62556607 -0.32751036
-0.1343183 0.3233031
0.6870372 0.55376107
-1.7945777 -1.4835969
-0.6462273 -1.3036382
-0.025292864 -0.75256515
0.6085611 -0.13608748
1.3821552 0.2254278
-1.9115293 -1.6409502
-0.48282698 -1.4188147
0.34351134 -1.1627024
0.9869285 -0.62049645
1.7713234 -0.20085949
-2.2809803 -2.2059698
-0.99386275 -1.7030506
0.20275873 -1.5584862
0.9147746 -1.2325822
1.6360829 -0.6749467
-2.3458858 -2.8320985
-1.346736 -2.3289032
-0.28391594 -1.811129
0.21863805 -1.6702974
0.7296322 -1.0760157
-0.5302408 -1.3135052
-0.6949225 -0.90016854
-0.6122597 -0.47004116
-0.0903403 -0.17118637
0.14856876 -0.2732028
-0.8822072 -1.3109291
-0.7706789 -0.36519817
-0.49262017 -0.10975292
0.6243497 -0.06533194
0.66593724 -0.559026
-1.5360619 -0.8616304
-1.3898377 -0.06857806
-0.92962474 0.19276045
0.36608103 -0.14059812
0.588196 -0.7283358
-2.7650883 -0.57705516
-2.3923235 0.20748538
-1.717527 0.26281402
-0.2559399 -0.112977654
0.36749873 -0.6980696
-3.3480062 -0.44812128
-3.2545216 0.40235195
-2.2618701 0.26663
-0.9648994 -0.10336824
-0.34887952 -0.5741328
0.03783735 -0.70127535
-0.07453201 -1.1506568
-0.07471388 -1.3301972
-0.0631298 -1.4370873
-0.20015839 -0.9951577
-0.106851965 -0.3111034
0.052634396 -0.5350495
-0.16060589 -0.78488415
-0.11185629 -1.3619742
0.0110560795 -1.2252145
-0.13090858 0.10870424
0.057774767 -0.044690743
-0.09082217 -0.33280057
-0.040152475 -1.180621
-0.15688111 -1.5130968
0.09390928 0.32003295
-0.17913546 0.6245109
-0.19692281 0.3391734
-0.101161905 -0.45662346
-0.07144116 -1.2023503
-0.0845031 0.66186905
-0.1521108 0.6943321
-0.2379836 0.753682
-0.08451268 -0.05385303
0.022526033 -0.69504744
0.06433074 -0.86241025
0.060779992 0.05611875
0.003859223 0.7984623
0.12578689 1.4339544
-0.1593921 2.0078037
-0.24375896 -0.6121641
-0.00515739 0.3216552
-0.13875745 0.7244501
-0.029245121 1.4381241
0.06698596 1.9696213
0.07524989 -0.29659018
-0.15280414 0.13415621
0.06684234 0.61756736
-0.103343554 1.0113128
-0.14438255 1.3834459
-0.17690061 0.12413465
-0.04459241 0.3135587
0.054825865 0.32853445
-0.014794532 0.7981063
-0.050230242 0.83142936
-0.058984764 -0.004516094
-0.021197675 0.32636595
-0.025196737 0.55108666
-0.15630491 0.6248727
0.003225123 0.60938156
-0.36556605 -0.38326544
-1.2341456 -1.2629062
-2.9586911 -0.385575
-3.5086863 0.11398253
-2.3485963 0.40000448
-0.06364161 -1.0602297
0.08460486 -1.1948771
-0.9314071 -0.6945023
-1.3847001 -0.4127804
-1.1141336 0.029964926
0.10117749 -1.4086407
0.958686 -1.3599807
1.0455878 -1.3076004
0.39773872 -1.1330422
0.3941553 -0.38810351
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