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Merge remote-tracking branch 'origin/master'

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This commit is contained in:
xinyang
2019-08-06 14:14:13 +08:00
parent 741ffdc176 6cdb6b4832
commit 9f94287441
20 changed files with 41326 additions and 17923 deletions
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5
armor/src/armor_finder/armor_finder.cpp
View File

@@ -92,8 +92,9 @@ void ArmorFinder::run(cv::Mat &src) {
stateStandBy();
}
end:
antiTop();
// antiTop();
if(target_box.rect != cv::Rect2d())
sendBoxPosition(0);
if (show_armor_box) { // 根据条件显示当前目标装甲板
showArmorBox("box", src, target_box);
cv::waitKey(1);

8
armor/src/armor_finder/find/find_light_blobs.cpp
View File

@@ -27,10 +27,10 @@ static bool isValidLightBlob(const std::vector<cv::Point> &contour, const cv::Ro
// 此函数可以有性能优化.
static uint8_t get_blob_color(const cv::Mat &src, const cv::RotatedRect &blobPos) {
auto region = blobPos.boundingRect();
region.x -= fmax(2, region.width * 0.1);
region.y -= fmax(2, region.height * 0.05);
region.width += 2 * fmax(2, region.width * 0.1);
region.height += 2 * fmax(2, region.height * 0.05);
region.x -= fmax(3, region.width * 0.1);
region.y -= fmax(3, region.height * 0.05);
region.width += 2 * fmax(3, region.width * 0.1);
region.height += 2 * fmax(3, region.height * 0.05);
region &= cv::Rect(0, 0, src.cols, src.rows);
cv::Mat roi = src(region);
int red_cnt = 0, blue_cnt = 0;

22
main.cpp
View File

@@ -30,10 +30,10 @@ using namespace std;
McuData mcu_data = { // 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
ENEMY_BLUE, // 敌方颜色
0, // 能量机关x轴补偿量
0, // 能量机关y轴补偿量
};
@@ -63,11 +63,11 @@ int main(int argc, char *argv[]) {
while (true) {
// 打开视频源
if (from_camera) {
video_gimbal = new CameraWrapper(ARMOR_CAMERA_GAIN, 2/*, "armor"*/);
video_chassis = new CameraWrapper(ENERGY_CAMERA_GAIN, 2/*, "energy"*/);
video_gimbal = new CameraWrapper(ARMOR_CAMERA_EXPOSURE, ARMOR_CAMERA_GAIN, 2/*, "armor"*/);
video_chassis = new CameraWrapper(ENERGY_CAMERA_EXPOSURE, ENERGY_CAMERA_GAIN, 2/*, "energy"*/);
} else {
video_gimbal = new VideoWrapper("/home/sun/项目/RM_auto-aim/gimbal_video/57.avi");
video_chassis = new VideoWrapper("/home/sun/项目/RM_auto-aim/gimbal_video/57.avi");
video_gimbal = new VideoWrapper(PROJECT_DIR"/26.avi");
video_chassis = new VideoWrapper(PROJECT_DIR"/26.avi");
}
if (video_gimbal->init()) {
LOGM("video_gimbal source initialization successfully.");
@@ -100,7 +100,7 @@ int main(int argc, char *argv[]) {
destroyAllWindows();
if (from_camera) {
delete video_gimbal;
video_gimbal = new CameraWrapper(ENERGY_CAMERA_GAIN, 2/*, "armor"*/);
video_gimbal = new CameraWrapper(ENERGY_CAMERA_EXPOSURE, ENERGY_CAMERA_GAIN, 2/*, "armor"*/);
if (video_gimbal->init()) {
LOGM("video_gimbal source initialization successfully.");
} else {
@@ -122,15 +122,15 @@ int main(int argc, char *argv[]) {
if (!from_camera) extract(gimbal_src, chassis_src);
if (save_video) saveVideos(gimbal_src, chassis_src);//保存视频
if (show_origin) showOrigin(gimbal_src, chassis_src);//显示原始图像
// energy.runBig(gimbal_src, chassis_src);
energy.runBig(gimbal_src);
energy.runBig(gimbal_src, chassis_src);
// energy.runBig(gimbal_src);
} else if (mcu_data.state == SMALL_ENERGY_STATE) {
if (last_state != SMALL_ENERGY_STATE) {
LOGM(STR_CTR(WORD_GREEN, "Start Small Energy!"));
destroyAllWindows();
if (from_camera) {
delete video_gimbal;
video_gimbal = new CameraWrapper(ENERGY_CAMERA_GAIN, 2/*, "armor"*/);
video_gimbal = new CameraWrapper(ENERGY_CAMERA_EXPOSURE, ENERGY_CAMERA_GAIN, 2/*, "armor"*/);
if (video_gimbal->init()) {
LOGM("video_gimbal source initialization successfully.");
} else {
@@ -154,7 +154,7 @@ int main(int argc, char *argv[]) {
destroyAllWindows();
if (from_camera) {
delete video_gimbal;
video_gimbal = new CameraWrapper(ARMOR_CAMERA_GAIN, 2/*, "armor"*/);
video_gimbal = new CameraWrapper(ARMOR_CAMERA_EXPOSURE, ARMOR_CAMERA_GAIN, 2/*, "armor"*/);
if (video_gimbal->init()) {
LOGM("video_gimbal source initialization successfully.");
} else {

3
others/include/camera/camera_wrapper.h
View File

@@ -41,8 +41,9 @@ private:
RoundQueue<cv::Mat, 2> src_queue;
public:
int gain;
int exposure;
CameraWrapper(int gain, int camera_mode=1, const std::string &n="NULL");
CameraWrapper(int exposure, int gain, int camera_mode=1, const std::string &n="NULL");
~CameraWrapper() final;
bool init() final;

8
others/include/config/setconfig.h
View File

@@ -8,8 +8,12 @@
#warning "Without config.h"
#endif
#ifndef CAMERA_EXPOSURE
#define CAMERA_EXPOSURE (10)
#ifndef ARMOR_CAMERA_EXPOSURE
#define ARMOR_CAMERA_EXPOSURE (10)
#endif
#ifndef ENERGY_CAMERA_EXPOSURE
#define ENERGY_CAMERA_EXPOSURE (10)
#endif
#ifndef CAMERA_RED_GAIN

9
others/src/additions.cpp
View File

@@ -77,14 +77,16 @@ cv::VideoWriter initVideoWriter(const std::string &filename_prefix) {
bool checkReconnect(bool is_camera_0_connect, bool is_camera_1_connect) {
if (!is_camera_0_connect) {
int curr_gain = ((CameraWrapper* )video_gimbal)->gain;
int curr_exposure = ((CameraWrapper* )video_gimbal)->exposure;
delete video_gimbal;
video_gimbal = new CameraWrapper(curr_gain, 0/*, "armor"*/);
video_gimbal = new CameraWrapper(curr_exposure, curr_gain, 0/*, "armor"*/);
is_camera_0_connect = video_gimbal->init();
}
if (!is_camera_1_connect) {
int curr_gain = ((CameraWrapper* )video_gimbal)->gain;
int curr_exposure = ((CameraWrapper* )video_gimbal)->exposure;
delete video_chassis;
video_chassis = new CameraWrapper(curr_gain, 0/*, "energy"*/);
video_chassis = new CameraWrapper(curr_exposure, curr_gain, 0/*, "energy"*/);
is_camera_1_connect = video_chassis->init();
}
return is_camera_0_connect && is_camera_1_connect;
@@ -93,8 +95,9 @@ bool checkReconnect(bool is_camera_0_connect, bool is_camera_1_connect) {
bool checkReconnect(bool is_camera_connect) {
if (!is_camera_connect) {
int curr_gain = ((CameraWrapper* )video_gimbal)->gain;
int curr_exposure = ((CameraWrapper* )video_gimbal)->exposure;
delete video_gimbal;
video_gimbal = new CameraWrapper(curr_gain, 0/*, "armor"*/);
video_gimbal = new CameraWrapper(curr_exposure, curr_gain, 0/*, "armor"*/);
is_camera_connect = video_gimbal->init();
}
return is_camera_connect;

7
others/src/camera/camera_wrapper.cpp
View File

@@ -11,7 +11,7 @@
using namespace std;
using namespace cv;
CameraWrapper::CameraWrapper(int gain, int camera_mode, const std::string &n) :
CameraWrapper::CameraWrapper(int exposure, int gain, int camera_mode, const std::string &n) :
name(n),
init_done(false),
mode(camera_mode),
@@ -20,7 +20,8 @@ CameraWrapper::CameraWrapper(int gain, int camera_mode, const std::string &n) :
iplImage(nullptr),
rgb_buffer(nullptr),
channel(3),
gain(gain){
gain(gain),
exposure(exposure){
}
void cameraCallback(CameraHandle hCamera, BYTE *pFrameBuffer, tSdkFrameHead* pFrameHead,PVOID pContext){
@@ -85,7 +86,7 @@ bool CameraWrapper::init() {
CameraReadParameterFromFile(h_camera, PROJECT_DIR"/others/MV-UB31-Group0.config");
CameraLoadParameter(h_camera, PARAMETER_TEAM_A);
CameraSetAeState(h_camera, false);
CameraSetExposureTime(h_camera, CAMERA_EXPOSURE * 1000);
CameraSetExposureTime(h_camera, exposure * 1000);
CameraSetAnalogGain(h_camera, gain);
#endif
double t;

25
tools/TrainCNN/backward.py
View File

@@ -8,8 +8,10 @@ import forward
import cv2
import numpy as np
import mvsdk
print("Finish!")
def save_kernal(fp, val):
print(val.shape[2], file=fp)
print(val.shape[3], file=fp)
@@ -49,9 +51,9 @@ def save_para(folder, paras, names, info):
fp.close()
STEPS = 50000
BATCH = 30
LEARNING_RATE_BASE = 0.0005
STEPS = 100000
BATCH = 40
LEARNING_RATE_BASE = 0.0002
LEARNING_RATE_DECAY = 0.99
MOVING_AVERAGE_DECAY = 0.99
@@ -60,7 +62,7 @@ def train(dataset, show_bar=False):
x = tf.placeholder(tf.float32, [None, generate.SRC_ROWS, generate.SRC_COLS, generate.SRC_CHANNELS])
y_ = tf.placeholder(tf.float32, [None, forward.OUTPUT_NODES])
keep_rate = tf.placeholder(tf.float32)
nodes, vars, vars_name = forward.forward(x, 0.01)
nodes, vars, vars_name = forward.forward(x, 0.01, keep_rate)
y = nodes[-1]
ce = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1))
@@ -92,7 +94,7 @@ def train(dataset, show_bar=False):
bar = tqdm(range(STEPS), ascii=True, dynamic_ncols=True)
for i in bar:
images_samples, labels_samples = dataset.sample_train_sets(BATCH)
images_samples, labels_samples = dataset.sample_train_sets(BATCH, 0.03)
_, loss_value, step = sess.run(
[train_op, loss, global_step],
@@ -100,8 +102,9 @@ def train(dataset, show_bar=False):
)
if step % 500 == 0:
test_images, test_labels = dataset.sample_test_sets(6000)
test_acc, output = sess.run([accuracy, y], feed_dict={x: test_images, y_: test_labels, keep_rate:1.0})
test_images, test_labels = dataset.sample_test_sets(10000)
test_acc, output = sess.run([accuracy, y],
feed_dict={x: test_images, y_: test_labels, keep_rate: 1.0})
output = np.argmax(output, axis=1)
real = np.argmax(test_labels, axis=1)
print("=============test-set===============")
@@ -109,8 +112,9 @@ def train(dataset, show_bar=False):
print("label: %d, precise: %f, recall: %f" %
(n, np.mean(real[output == n] == n), np.mean(output[real == n] == n)))
train_images, train_labels = dataset.sample_train_sets(6000)
train_acc, output = sess.run([accuracy, y], feed_dict={x: train_images, y_: train_labels, keep_rate:1.0})
train_images, train_labels = dataset.sample_train_sets(10000)
train_acc, output = sess.run([accuracy, y],
feed_dict={x: train_images, y_: train_labels, keep_rate: 1.0})
output = np.argmax(output, axis=1)
real = np.argmax(train_labels, axis=1)
print("=============train-set===============")
@@ -192,7 +196,8 @@ def train(dataset, show_bar=False):
# 把pFrameBuffer转换成opencv的图像格式以进行后续算法处理
frame_data = (mvsdk.c_ubyte * FrameHead.uBytes).from_address(pFrameBuffer)
frame = np.frombuffer(frame_data, dtype=np.uint8)
frame = frame.reshape((FrameHead.iHeight, FrameHead.iWidth, 1 if FrameHead.uiMediaType == mvsdk.CAMERA_MEDIA_TYPE_MONO8 else 3) )
frame = frame.reshape((FrameHead.iHeight, FrameHead.iWidth,
1 if FrameHead.uiMediaType == mvsdk.CAMERA_MEDIA_TYPE_MONO8 else 3))
frame = cv2.resize(frame, (640, 480), interpolation=cv2.INTER_LINEAR)
cv2.imshow("Press q to end", frame)

7
tools/TrainCNN/forward.py
View File

@@ -35,16 +35,16 @@ CONV1_OUTPUT_CHANNELS = 4
CONV2_KERNAL_SIZE = 3
# 第二层卷积输出通道数
CONV2_OUTPUT_CHANNELS = 6
CONV2_OUTPUT_CHANNELS = 8
# 第三层卷积核大小
CONV3_KERNAL_SIZE = 3
# 第三层卷积输出通道数
CONV3_OUTPUT_CHANNELS = 8
CONV3_OUTPUT_CHANNELS = 16
# 第一层全连接宽度
FC1_OUTPUT_NODES = 50
FC1_OUTPUT_NODES = 60
# 第二层全连接宽度（输出标签类型数）
FC2_OUTPUT_NODES = 15
@@ -113,4 +113,3 @@ def forward(x, regularizer=None, keep_rate=tf.constant(1.0)):
nodes.extend([fc2])
return nodes, vars, vars_name

47
tools/TrainCNN/generate.py
View File

@@ -4,6 +4,7 @@ import cv2
import random
from tqdm import tqdm
from forward import OUTPUT_NODES
# 原图像行数
SRC_ROWS = 36
@@ -40,38 +41,64 @@ class DataSet:
files = os.listdir(dir)
for file in tqdm(files, postfix={"loading id": i}, dynamic_ncols=True):
if file[-3:] == "jpg":
sample = self.file2nparray("%s/%s" % (dir, file))
label = self.id2label(i)
if random.random() > 0.7:
self.train_samples.append(self.file2nparray("%s/%s" % (dir, file)))
self.train_labels.append(self.id2label(i))
self.train_samples.append(sample)
self.train_labels.append(label)
if i == 0:
tmp = sample.copy()
tmp = tmp[:, :, ::-1]
self.train_samples.append(tmp)
self.train_labels.append(label)
else:
self.test_samples.append(self.file2nparray("%s/%s" % (dir, file)))
self.test_labels.append(self.id2label(i))
tmp = sample.copy()
tmp = 1.2 * tmp
tmp = np.where(tmp > 1, 1, tmp)
tmp = np.where(tmp < 0, 0, tmp)
self.train_samples.append(tmp)
self.train_labels.append(label)
tmp = sample.copy()
tmp = 0.8 * tmp
tmp = np.where(tmp > 1, 1, tmp)
tmp = np.where(tmp < 0, 0, tmp)
self.train_samples.append(tmp)
self.train_labels.append(label)
else:
self.test_samples.append(sample)
self.test_labels.append(label)
self.train_samples = np.array(self.train_samples)
self.train_labels = np.array(self.train_labels)
self.test_samples = np.array(self.test_samples)
self.test_labels = np.array(self.test_labels)
return sets
def sample_train_sets(self, length):
def sample_train_sets(self, length, std=0.0):
samples = []
labels = []
for i in range(length):
id = random.randint(0, len(self.train_samples) - 1)
samples.append(self.train_samples[id])
labels.append(self.train_labels[id])
return np.array(samples), np.array(labels)
samples = np.array(samples).copy()
samples += np.random.normal(0, std, samples.shape)
labels = np.array(labels)
return samples, labels
def sample_test_sets(self, length):
def sample_test_sets(self, length, std=0.0):
samples = []
labels = []
for i in range(length):
id = random.randint(0, len(self.test_samples) - 1)
samples.append(self.test_samples[id])
labels.append(self.test_labels[id])
return np.array(samples), np.array(labels)
samples = np.array(samples).copy()
samples += np.random.normal(0, std, samples.shape)
labels = np.array(labels)
return samples, labels
def all_train_sets(self):
def all_train_sets(self, std=0.0):
return self.train_samples[:], self.train_labels[:]
def all_test_sets(self):
def all_test_sets(self, std=0.0):
return self.test_samples[:], self.test_labels[:]

8
tools/para/conv1_b
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@@ -1,5 +1,5 @@
4
-0.028037319
0.5437552
0.26716635
0.15804483
0.021338634
-0.28292254
0.39307028
0.46493095

600
tools/para/conv1_w
View File

@@ -2,303 +2,303 @@
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5
5
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