VSAG/vision_sjtu_19
3
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Merge remote-tracking branch 'origin/master'

Browse Source 
# Conflicts:
#	main.cpp
This commit is contained in:
JiatongSun
2019-08-06 12:10:22 +08:00
parent db2ab1ae22 6cdb6b4832
commit dbe6296a91
20 changed files with 41324 additions and 17921 deletions
Download Patch File Download Diff File Expand all files Collapse all files

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;

146
main.cpp
View File

@@ -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/sjturm/Videos/warming-up-7-big4.avi");
video_chassis = new VideoWrapper("/home/sjturm/Videos/warming-up-7-big4.avi");
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");
}
if (video_gimbal->init()) {
LOGM("video_gimbal source initialization successfully.");
@@ -94,90 +94,90 @@ int main(int argc, char *argv[]) {
cout << "start running" << endl;
do {
CNT_TIME("Total", {
if (mcu_data.state == BIG_ENERGY_STATE) {//大能量机关模式
if (last_state != BIG_ENERGY_STATE) {//若上一帧不是大能量机关模式，即刚往完成切换，则需要初始化
LOGM(STR_CTR(WORD_BLUE, "Start Big Energy!"));
destroyAllWindows();
if (from_camera) {
delete video_gimbal;
video_gimbal = new CameraWrapper(ENERGY_CAMERA_GAIN, 2/*, "armor"*/);
if (video_gimbal->init()) {
LOGM("video_gimbal source initialization successfully.");
} else {
LOGW("video_gimbal source unavailable!");
}
if (mcu_data.state == BIG_ENERGY_STATE) {//大能量机关模式
if (last_state != BIG_ENERGY_STATE) {//若上一帧不是大能量机关模式，即刚往完成切换，则需要初始化
LOGM(STR_CTR(WORD_BLUE, "Start Big Energy!"));
destroyAllWindows();
if (from_camera) {
delete video_gimbal;
video_gimbal = new CameraWrapper(ENERGY_CAMERA_EXPOSURE, ENERGY_CAMERA_GAIN, 2/*, "armor"*/);
if (video_gimbal->init()) {
LOGM("video_gimbal source initialization successfully.");
} else {
LOGW("video_gimbal source unavailable!");
}
checkReconnect(video_chassis->read(chassis_src));
energy.setBigEnergyInit();
}
last_state = mcu_data.state;//更新上一帧状态
ok = checkReconnect(video_gimbal->read(gimbal_src));
video_chassis->read(chassis_src);
checkReconnect(video_chassis->read(chassis_src));
energy.setBigEnergyInit();
}
last_state = mcu_data.state;//更新上一帧状态
ok = checkReconnect(video_gimbal->read(gimbal_src));
video_chassis->read(chassis_src);
#ifdef GIMBAL_FLIP_MODE
flip(gimbal_src, gimbal_src, GIMBAL_FLIP_MODE);
flip(gimbal_src, gimbal_src, GIMBAL_FLIP_MODE);
#endif
#ifdef CHASSIS_FLIP_MODE
flip(chassis_src, chassis_src, CHASSIS_FLIP_MODE);
flip(chassis_src, chassis_src, CHASSIS_FLIP_MODE);
#endif
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);
} 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"*/);
if (video_gimbal->init()) {
LOGM("video_gimbal source initialization successfully.");
} else {
LOGW("video_gimbal source unavailable!");
}
}
energy.setSmallEnergyInit();
}
last_state = mcu_data.state;//更新上一帧状态
ok = checkReconnect(video_gimbal->read(gimbal_src));
#ifdef GIMBAL_FLIP_MODE
flip(gimbal_src, gimbal_src, GIMBAL_FLIP_MODE);
#endif
if (!from_camera) extract(gimbal_src);
if (save_video) saveVideos(gimbal_src);//保存视频
if (show_origin) showOrigin(gimbal_src);//显示原始图像
energy.runSmall(gimbal_src);
} else { // 自瞄模式
if (last_state != ARMOR_STATE) {
LOGM(STR_CTR(WORD_RED, "Start Armor!"));
destroyAllWindows();
if (from_camera) {
delete video_gimbal;
video_gimbal = new CameraWrapper(ARMOR_CAMERA_GAIN, 2/*, "armor"*/);
if (video_gimbal->init()) {
LOGM("video_gimbal source initialization successfully.");
} else {
LOGW("video_gimbal source unavailable!");
}
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);
} 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_EXPOSURE, ENERGY_CAMERA_GAIN, 2/*, "armor"*/);
if (video_gimbal->init()) {
LOGM("video_gimbal source initialization successfully.");
} else {
LOGW("video_gimbal source unavailable!");
}
}
last_state = mcu_data.state;
CNT_TIME(STR_CTR(WORD_GREEN, "read img"), {
if (!checkReconnect(video_gimbal->read(gimbal_src))) continue;
});
energy.setSmallEnergyInit();
}
last_state = mcu_data.state;//更新上一帧状态
ok = checkReconnect(video_gimbal->read(gimbal_src));
#ifdef GIMBAL_FLIP_MODE
flip(gimbal_src, gimbal_src, GIMBAL_FLIP_MODE);
flip(gimbal_src, gimbal_src, GIMBAL_FLIP_MODE);
#endif
if (!from_camera) extract(gimbal_src);
if (save_video) saveVideos(gimbal_src);//保存视频
if (show_origin) showOrigin(gimbal_src);//显示原始图像
energy.runSmall(gimbal_src);
} else { // 自瞄模式
if (last_state != ARMOR_STATE) {
LOGM(STR_CTR(WORD_RED, "Start Armor!"));
destroyAllWindows();
if (from_camera) {
delete video_gimbal;
video_gimbal = new CameraWrapper(ARMOR_CAMERA_EXPOSURE, ARMOR_CAMERA_GAIN, 2/*, "armor"*/);
if (video_gimbal->init()) {
LOGM("video_gimbal source initialization successfully.");
} else {
LOGW("video_gimbal source unavailable!");
}
}
}
last_state = mcu_data.state;
CNT_TIME(STR_CTR(WORD_GREEN, "read img"), {
if(!checkReconnect(video_gimbal->read(gimbal_src))) continue;
});
#ifdef GIMBAL_FLIP_MODE
flip(gimbal_src, gimbal_src, GIMBAL_FLIP_MODE);
#endif
// CNT_TIME("something whatever", {
if (!from_camera) extract(gimbal_src);
if (save_video) saveVideos(gimbal_src);
if (show_origin) showOrigin(gimbal_src);
// });
CNT_TIME(STR_CTR(WORD_CYAN, "Armor Time"), {
armor_finder.run(gimbal_src);
});
}
CNT_TIME(STR_CTR(WORD_CYAN, "Armor Time"), {
armor_finder.run(gimbal_src);
});
}
// cv::waitKey(0);
});
} while (ok);

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;

63
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)
@@ -49,24 +51,24 @@ 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
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])
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))
# ce = tf.nn.weighted_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1), pos_weight=1)
ce = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1))
# ce = tf.nn.weighted_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1), pos_weight=1)
cem = tf.reduce_mean(ce)
loss= cem + tf.add_n(tf.get_collection("losses"))
loss = cem + tf.add_n(tf.get_collection("losses"))
global_step = tf.Variable(0, trainable=False)
learning_rate = tf.train.exponential_decay(
@@ -92,31 +94,33 @@ 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],
feed_dict={x: images_samples, y_: labels_samples, keep_rate:0.3}
feed_dict={x: images_samples, y_: labels_samples, keep_rate: 0.3}
)
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===============")
for n in range(forward.OUTPUT_NODES):
print("label: %d, precise: %f, recall: %f" %
(n, np.mean(real[output==n]==n), np.mean(output[real==n]==n)))
(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===============")
for n in range(forward.OUTPUT_NODES):
print("label: %d, precise: %f, recall: %f" %
(n, np.mean(real[output==n]==n), np.mean(output[real==n]==n)))
(n, np.mean(real[output == n] == n), np.mean(output[real == n] == n)))
print("\n")
if train_acc >= 0.99 and test_acc >= 0.99:
vars_val = sess.run(vars)
@@ -131,8 +135,8 @@ def train(dataset, show_bar=False):
# pred = sess.run(y, feed_dict={x: test_images, keep_rate:1.0})
# nodes_val = sess.run(nodes, feed_dict={x:test_images})
# return vars_val, nodes_val
# nodes_val = sess.run(nodes, feed_dict={x:test_images})
# return vars_val, nodes_val
DevList = mvsdk.CameraEnumerateDevice()
nDev = len(DevList)
if nDev < 1:
@@ -150,7 +154,7 @@ def train(dataset, show_bar=False):
try:
hCamera = mvsdk.CameraInit(DevInfo, -1, -1)
except mvsdk.CameraException as e:
print("CameraInit Failed({}): {}".format(e.error_code, e.message) )
print("CameraInit Failed({}): {}".format(e.error_code, e.message))
return
# 获取相机特性描述
@@ -192,24 +196,25 @@ 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)
frame = cv2.resize(frame, (640, 480), interpolation=cv2.INTER_LINEAR)
cv2.imshow("Press q to end", frame)
if (cv2.waitKey(1)&0xFF) == ord(' '):
if (cv2.waitKey(1) & 0xFF) == ord(' '):
roi = cv2.selectROI("roi", frame)
roi = frame[roi[1]:roi[1]+roi[3], roi[0]:roi[0]+roi[2]]
roi = frame[roi[1]:roi[1] + roi[3], roi[0]:roi[0] + roi[2]]
print(roi)
cv2.imshow("box", roi)
image = cv2.resize(roi, (48, 36))
image = image.astype(np.float32) / 255.0
out = sess.run(y, feed_dict={x:[image]})
out = sess.run(y, feed_dict={x: [image]})
print(out)
print(np.argmax(out))
except mvsdk.CameraException as e:
if e.error_code != mvsdk.CAMERA_STATUS_TIME_OUT:
print("CameraGetImageBuffer failed({}): {}".format(e.error_code, e.message) )
print("CameraGetImageBuffer failed({}): {}".format(e.error_code, e.message))
# 关闭相机
mvsdk.CameraUnInit(hCamera)
@@ -219,9 +224,9 @@ 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_resize")
train(dataset, show_bar=True)
input("press enter to continue...")

13
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
@@ -62,8 +62,8 @@ def forward(x, regularizer=None, keep_rate=tf.constant(1.0)):
[CONV1_KERNAL_SIZE, CONV1_KERNAL_SIZE, int(x.shape[3]), CONV1_OUTPUT_CHANNELS]
)
conv1_b = get_bias([CONV1_OUTPUT_CHANNELS])
conv1 = tf.nn.relu(tf.nn.bias_add(conv2d(x, conv1_w), conv1_b))
pool1 = avg_pool_2x2(conv1)
conv1 = tf.nn.relu(tf.nn.bias_add(conv2d(x, conv1_w), conv1_b))
pool1 = avg_pool_2x2(conv1)
print("conv1: ", conv1.shape)
print("pool1: ", pool1.shape)
vars.extend([conv1_w, conv1_b])
@@ -100,7 +100,7 @@ def forward(x, regularizer=None, keep_rate=tf.constant(1.0)):
fc1_w = get_weight([node, FC1_OUTPUT_NODES], regularizer)
fc1_b = get_bias([FC1_OUTPUT_NODES])
fc1 = tf.nn.relu(tf.matmul(reshaped, fc1_w) + fc1_b)
fc1 = tf.nn.relu(tf.matmul(reshaped, fc1_w) + fc1_b)
vars.extend([fc1_w, fc1_b])
vars_name.extend(["fc1_w", "fc1_b"])
nodes.extend([fc1])
@@ -113,4 +113,3 @@ def forward(x, regularizer=None, keep_rate=tf.constant(1.0)):
nodes.extend([fc2])
return nodes, vars, vars_name

51
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:
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(self.file2nparray("%s/%s" % (dir, file)))
self.test_labels.append(self.id2label(i))
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)
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)
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
View File

@@ -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 @@
4
5
5
0.2558705
0.50322264
0.5455292
0.3266633
0.45495397
0.33181077
0.4608274
0.44561535
0.30626073
0.5907795
0.45085132
0.32935262
0.33333394
0.42217836
0.47149
0.26239944
0.106058985
0.4186091
0.33606878
0.47154978
0.29413304
0.2689295
0.043931372
0.41913906
0.3930859
-0.42042395
-0.19382745
-0.37871224
-0.36034295
-0.35385096
-0.13182718
-0.15764016
-0.18180493
-0.35480255
-0.38093758
-0.26962438
-0.13433722
-0.11590976
-0.12100729
-0.41767576
-0.088450246
-0.15360384
-0.21754275
-0.33203712
-0.36672434
-0.33835274
-0.09806653
-0.25720394
-0.48649773
-0.28400043
0.015362165
0.17902431
0.34508342
0.30716935
0.40274766
0.14824653
0.3363854
0.61393374
0.61485726
0.185212
0.27937973
0.4085319
0.56713074
0.50924385
0.14822921
-0.11108435
0.1610854
0.2802524
0.04015192
0.08895228
-0.19824773
-0.06844609
-0.069947205
-0.21471545
-0.21651222
-0.28975108
-0.36048082
-0.121744774
-0.32950944
-0.2776884
-0.38737568
-0.09360009
-0.116772
-0.2323865
-0.3063033
-0.09292303
-0.120976545
-0.098381124
-0.13898924
-0.24542217
-0.018138783
0.1710496
0.116988935
-0.30227587
-0.25962535
-0.20448786
-0.0741184
-0.021265341
-0.21863441
-0.24914841
0.3989018
0.38929248
0.24901842
0.28578317
0.35204405
0.21083018
0.30775398
0.17769343
0.24103095
0.43094385
0.24827914
0.12439884
0.11712248
0.3559909
0.16364971
0.038071744
0.13404451
0.12091028
-0.023739764
0.08232509
-0.0009516679
0.15716548
-0.10213259
0.0639761
-0.10133095
-0.009949305
0.09284781
0.40186858
0.25586432
0.14342904
0.18849348
0.32841587
0.3101156
0.4123818
0.081568226
0.27615616
0.34650272
0.5694975
0.23792551
0.29921243
0.33980817
0.49298054
0.60243934
0.42826438
0.27227885
0.1948353
0.3062418
0.46926793
0.38606012
0.13854483
1.1108605
1.6619372
2.0544205
1.9469278
1.4682909
1.3129491
2.1375504
2.6406689
2.372072
1.4637381
1.306143
2.2453067
2.5116432
2.3163383
1.5179764
1.1999305
1.7840445
2.1370907
1.8682098
1.1948502
0.7644319
1.183056
1.2842283
1.0330013
0.5353385
0.357544
0.4590763
0.57521755
0.40281096
0.15203553
0.66836417
0.83691925
1.0917046
0.85563093
0.4185427
0.8069128
1.1880662
1.2418958
0.98703665
0.45376477
0.8052862
1.1458472
1.0837129
0.8163929
0.5508064
0.84074473
1.0375632
1.0029732
0.73068154
0.4793337
-0.08648754
0.05313617
0.055096295
-0.03580858
-0.14844808
-0.21981032
0.0398141
-0.20938458
-0.21056286
-0.19792278
-0.06431722
-0.11274485
-0.13580588
-0.14901595
-0.06539131
-0.1703085
-0.044613708
-0.057758514
-0.2657268
-0.29268163
-0.2251643
-0.25461197
-0.28155822
-0.32777157
-0.3724289
0.16466168
0.44934547
0.43256435
0.47452745
0.3779059
0.20698643
0.52107
0.30781397
0.4135147
0.29526445
0.25912467
0.54579914
0.4920371
0.43895373
0.38483474
0.33639383
0.47148314
0.5128957
0.5233807
0.34624586
0.25116408
0.44631884
0.48539376
0.2042739
0.40311766
0.10143137
0.8512386
1.06337
0.82065773
0.22343788
0.51960915
1.0729233
1.5161965
1.220796
0.4743648
0.6052472
1.3462025
1.4488021
1.060846
0.3372449
0.678654
0.9629113
0.96115077
0.70128
0.17687131
0.20191273
0.5066497
0.54581857
0.121072926
-0.21051502
0.15686111
0.14152882
0.56596214
0.36766517
0.07481751
0.45787486
0.58694565
0.58544177
0.4505106
0.32433945
0.6037763
0.8595476
1.0671577
0.9353241
0.4558103
0.6039449
1.1056548
1.0452515
0.5983469
0.4096005
0.46692336
0.82347125
0.8806483
0.70173585
0.1955107
-0.13702177
0.21050254
0.36467823
0.5402022
0.40832496
0.1387463
0.19035818
0.5167714
0.4201423
0.5083453
-0.051313818
0.3746486
0.63895
0.63608116
0.5083911
0.030827409
0.27007273
0.47528267
0.48452747
0.34628776
-0.012611773
0.14452507
0.24958582
0.24960107
0.09575767
0.4102461
0.16927694
0.25459746
0.14307313
0.22898436
0.27788675
0.18775325
0.17099449
0.29020998
0.12541296
0.24403335
0.20092988
0.2530348
0.33398342
0.21587689
0.27797526
0.18172115
0.29734236
0.3033162
0.16939764
0.34889936
0.48381668
0.48425403
0.3000259
0.25757793
0.11239407
0.386269
0.37761775
0.3539963
0.14623448
0.38261685
0.27638507
0.2794403
0.40329304
0.33892003
0.24993245
0.37628546
0.33176056
0.50890356
0.2852119
0.22748284
0.39088395
0.4474231
0.39215663
0.28798044
0.17954169
0.42095885
0.3309427
0.39483225
0.35767367
-0.34148782
0.02509864
0.25426164
0.15086658
-0.057787858
-0.2226174
0.24070105
0.27239412
0.37470704
0.1674131
0.09444013
0.27578765
0.35690555
0.28290647
0.12817492
0.03777765
0.09609396
0.3188331
0.23712997
0.13440847
-0.25731155
0.04675058
0.09360328
-0.02120951
-0.06166972
0.060209647
0.5212034
0.6552245
1.1144388
0.87935585
0.24459931
0.82035065
1.1500704
1.2405574
1.1069012
0.18140063
0.8443062
1.2205642
1.1933382
0.90612733
0.32195792
0.6922394
1.0292921
1.0286875
0.94453853
0.24980143
0.5049856
0.74141055
0.7709616
0.5752724
0.17445572
0.07466022
-0.015276491
-0.04395921
-0.008553515
0.029532298
0.06765997
0.03481855
0.04099438
-0.044199117
0.16554591
0.06800105
-0.03995652
-0.006857331
0.075305216
0.3299388
0.11230784
0.20430015
0.039782282
0.111880876
0.13587818
0.1557568
0.16976745
0.14694355
0.32170033
-0.18491521
-0.15177341
0.035023104
0.048158385
0.29693007
-0.006829154
-0.046136864
0.1754336
0.24777764
0.2627956
0.13168007
-0.019214094
0.24687423
0.2204638
0.19275916
0.18035437
0.06386187
0.37380666
0.5433795
0.376532
0.20183952
0.3305568
0.33506978
0.43323225
0.3904595
0.33164555
0.74478495
1.172562
1.0576042
0.67135817
0.69610703
1.0034271
1.1407355
1.1464756
0.8193199
0.8561313
1.1586075
1.4729795
1.2343186
0.91004986
0.5244973
1.1102248
1.1765453
0.95950526
0.68363196
0.4048567
0.711899
0.9118936
0.52612954
0.39844343
-0.24287723
-0.052871097
0.07779769
0.11068013
0.1380818
-0.22056513
0.09362486
0.24791956
0.4390582
0.39413136
0.05775401
0.21399756
0.37228948
0.46872202
0.2922827
-0.10300668
0.03232777
0.34301633
0.29448983
0.044470448
-0.19424956
0.073454
-0.07377538
-0.01566619
-0.2193558
-0.48625758
-0.38693368
-0.51900584
-0.33943936
-0.28173432
-0.4239597
-0.43534985
-0.3587125
-0.33114514
-0.41164148
-0.44184446
-0.37156865
-0.26118872
-0.27542785
-0.2835131
-0.27856454
-0.30060688
-0.30000862
-0.26170778
-0.050582133
-0.17061539
-0.06258438
-0.31230104
-0.05868557
-0.16282676
-0.19877611
-0.41529313
-0.2597882
-0.29055542
-0.24465893
-0.37368765
-0.2763039
-0.16017292
-0.15159445
-0.09162989
-0.08486519
-0.214406
0.036507495
-0.18162069
-0.12156283
0.13781281
0.14959772
0.13058542
-0.04205288
-0.025140587
-0.031447563
-0.023655139
0.16892198
0.24700916
-0.05444677
0.4252524
1.0224841
1.0721366
1.0220312
0.62805945
0.60496604
1.2682245
1.3119446
1.2881455
0.93665457
0.7735541
1.3761095
1.3804561
1.3370488
0.932327
0.8111979
0.8535967
1.2361982
0.90493304
0.70505273
0.48489514
0.7006285
0.74297637
0.76787454
0.35748747

16
tools/para/conv2_b
View File

@@ -1,7 +1,9 @@
6
0.7724807
-0.87169737
-0.42373934
1.2762932
0.41549298
-0.471356
8
-0.552405
-0.26979932
0.94369894
-0.000667131
0.34170008
0.34912753
1.0474143
-0.39599916

506
tools/para/conv2_w
View File

@@ -1,220 +1,292 @@
4
6
8
3
3
-0.48382398
-0.36632606
-0.5114979
-0.14837296
-0.44867024
-0.4505499
-0.35961452
-0.28533116
-0.25498483
0.21828032
0.40261173
0.4014606
0.4331948
0.57971007
0.48511884
0.4658404
0.23746017
0.25643173
-0.42512512
-0.32099026
-0.4071419
-0.07042966
-0.18999289
-0.41244265
-0.05782267
-0.26706457
-0.39842066
-0.42635453
-0.54127145
-0.50406104
-0.2970212
-0.2382909
-0.373955
-0.26274928
0.09654497
0.0068873395
0.4851344
0.71676046
0.83653075
0.36806846
0.59301347
0.43608147
0.26763174
0.24044617
0.3138616
0.32284674
0.48771864
0.47890294
0.59924245
0.58470404
0.66103566
0.4588577
0.66345775
0.5417458
0.33620733
0.6311169
0.5712364
0.27047473
0.5663411
0.62967324
0.23675068
0.18819211
0.26856497
-0.24069026
0.06490354
-0.20222004
0.12556747
-0.081858225
-0.14404371
0.15644343
0.0056566484
-0.27573076
0.6260534
0.6114578
0.4143709
0.78654826
0.6670694
0.3971862
0.88891
0.7200758
0.4331128
0.5375012
0.69176656
0.5500048
0.76812154
0.8269794
1.0226717
0.521159
0.94419754
0.90338296
-0.037811022
0.11793418
-0.09947335
0.021074628
-0.15378062
-0.18899523
-0.21934026
-0.32867467
-0.4491826
0.17542738
0.22719054
-0.14938504
0.34901783
0.23809595
-0.29363006
0.14916919
0.1632361
-0.4462076
0.054292772
0.19464755
0.17956488
0.07731212
0.19792339
0.30400968
-0.039353892
0.14735414
0.18343678
0.23884599
0.30334124
0.21813673
0.12249429
0.38291413
0.22569945
0.25905365
0.33474848
0.0037524912
0.30396467
0.1846092
-0.121808626
0.7777901
0.397566
0.16061927
0.7874323
0.28439802
0.02147841
-0.14355433
0.30293295
0.046388533
0.19284728
0.61074996
0.4691993
0.35759884
0.6659188
0.45006895
0.22897986
0.15777227
0.3959882
0.16480199
0.09506794
0.072772376
-0.15479131
-0.025542457
0.07265022
0.26197845
0.46316504
0.08801226
0.45165384
0.68206656
0.201606
0.49978983
0.38383794
0.08902087
0.11398645
0.34106824
0.19092785
0.096165456
0.31517446
0.39403793
-0.12153064
-0.044383097
0.21368785
0.057617243
0.2975022
0.2852436
0.37886962
0.24867283
0.037312325
0.26485252
0.05155589
-0.04473265
0.55817014
0.46386358
0.1817731
0.7077902
0.6132932
0.38195252
0.57377195
0.47097227
0.12158713
0.21322925
0.31647044
0.5527038
0.47462723
0.6149222
0.65007806
0.11145787
0.44169205
0.51498264
0.072121754
0.02085787
-0.02330407
0.0030019032
0.13602144
-0.16190982
-0.10533785
-0.1734815
-0.2510002
0.4735372
0.58126503
0.19582687
0.45889878
0.54310197
0.21080907
0.20235369
0.2099934
-0.04946122
-0.32726097
-0.0048638796
-0.32321292
0.13319272
0.012297149
-0.10513384
0.19061892
0.4101147
0.08123453
0.2558452
0.38487348
0.27844468
0.5007668
0.550769
0.114901684
0.4507436
0.3535035
0.3076304
0.1479239
0.09292402
-0.4041034
0.38143447
-0.069753066
-0.27865863
0.16946647
-0.045003142
-0.111094154
0.0009370859
-0.09843764
-0.0066839033
0.015192114
0.18330237
-0.091846526
-0.093767345
-0.036863815
-0.023914052
-0.10209015
-0.02862692
-0.11142951
-0.21141496
-0.2307057
-0.06516248
0.020834371
-0.031347763
-0.039252505
-0.27983642
-0.03970919
0.20645477
-0.12705953
0.33821788
0.13405067
0.2445887
0.44188142
-0.0038982031
-0.13979559
-0.20385139
0.117509395
-0.005715718
-0.080601916
0.09685326
-0.066362664
0.008437556
-0.10210685
0.7539002
0.6952307
0.28752255
0.80205274
0.4766971
0.17297089
0.2882549
0.098174155
0.0020970956
0.19470583
0.09950261
-0.09694061
0.29215577
0.12278695
-0.037522893
0.17075685
0.33552113
0.13674323
-0.07577733
0.31630826
0.34378958
-0.15304123
0.12785366
0.086952396
-0.31330797
-0.001654931
0.12654537
-0.86122245
-0.68093973
-0.22129026
-0.8326889
-0.7481235
-0.14792675
-0.8588496
-0.66041034
0.013661266
-0.06239793
-0.1387425
-0.059888713
0.03536964
0.062088303
0.011600256
0.08357481
0.025985636
-0.057800315
-0.8058461
-0.8655415
-0.8878307
-1.0268383
-0.8895686
-0.870193
-0.92634207
-0.7991516
-0.84145725
0.22383228
0.04903484
-0.65011233
0.34974617
-0.22466241
-0.44892547
0.043155216
-0.32732686
-0.85459894
-0.11874903
-0.47783476
-0.5260095
-0.29938856
-0.46704254
-0.4449409
-0.48969412
-0.42000425
-0.5598123
-0.5296795
-0.8521746
-0.67211694
-0.80781704
-0.94732684
-0.57418257
-0.77279216
-0.6926591
-0.45179915
-0.19991966
-0.0625157
-0.5307898
0.15330955
-0.0213713
-0.26861903
0.23867297
0.22922835
0.16720639
0.59522456
0.6892708
0.3867164
0.44517902
0.47505063
0.4654438
0.22130506
0.387733
0.19797981
0.33539343
0.24232638
-0.10490481
0.13891292
-0.076867625
0.12750566
0.05550657
-0.02150966
0.17238574
-0.092911184
-0.16721976
0.009027423
-0.19101651
-0.029071758
-0.069601566
-0.09827422
-0.08287598
-0.022561137
-0.22663862
-0.47552612
-0.17845595
-0.46161267
-0.43686563
-0.22974852
-0.36118308
-0.2722955
-0.14243911
0.1847469
0.34657878
0.42981294
0.18554401
0.33521423
0.27269012
0.45541444
0.41730106
0.16927144
0.22624063
0.30033535
0.116721414
0.12135912
0.284733
0.3730829
0.09463956
0.22056812
0.1317617
0.15912417
0.19767475
-0.22404052
-0.23200901
-0.36393234
-0.4101935
-0.24772424
-0.6499398
-0.6035362
-0.13825883
-0.21811308
-0.31700268
0.10087058
0.0777835
-0.14945805
0.26013204
0.35406354
0.11850203
0.024606973
0.12879103
-0.095771246
0.35835484
0.28432438
0.17027797
0.04460398
0.2106631
-0.09346488
0.24641933
0.034816552
-0.12438573
0.54997367
0.31058714
-0.21279928
0.17215224
0.010456621
0.013317943
-0.008930922
-0.030233396
0.008761597
0.03480156
0.037845217
-0.031645544
-0.08237756
0.0050705066
-0.06625244
0.3866179
0.29360098
0.35600394
0.2591941
0.18063474
0.38357696
0.23095702
0.17364517
0.15391372
-0.5093487
-0.22992536
0.27934852
-0.2374083
0.039487418
0.3051096
-0.056948155
0.24535167
0.19844466
-0.10930642
-0.05106426
0.0033689137
-0.09231472
0.27303606
0.1450588
0.102419965
0.22690499
0.26383835
0.6005564
0.97944826
0.6462157
0.5086828
0.93819946
0.600236
0.19520918
0.19155432
-0.1607842

26
tools/para/conv3_b
View File

@@ -1,9 +1,17 @@
8
1.8607159
0.8140447
0.02090901
0.12166861
1.401096
1.6002591
0.9205242
0.52036697
16
0.18449903
0.58430743
0.26267084
0.15215518
0.17875981
0.26728117
0.38014928
0.32304344
0.20345993
0.051697854
0.08828824
0.17220211
0.30045128
0.7321825
0.32318887
0.64071155

1586
tools/para/conv3_w
View File

File diff suppressed because it is too large Load Diff

112
tools/para/fc1_b
View File

@@ -1,51 +1,61 @@
50
0.27732438
-0.42984757
0.77958596
0.50327533
-0.4008876
-0.013967511
0.2507846
-0.22089948
-0.074136175
-0.69092935
-0.016178658
0.3531398
0.017891733
0.46746168
-0.14891404
0.22245038
0.16725647
-0.01826465
0.30017307
-0.13458814
-0.03478663
-0.017660717
-0.40806273
-0.030123862
0.2801973
0.27446425
0.5195524
0.64331424
0.008353927
0.35976186
0.59728014
-0.061037164
-0.5944482
0.569236
-0.36547965
0.8237916
0.32342234
0.32420716
-0.012236862
-0.030580053
0.48695275
-0.5278198
-0.329191
0.72754157
-0.013564502
0.30172536
-0.44324636
0.6108572
-0.10568087
-0.19255634
60
-0.039159678
0.029541148
0.46619368
0.39806518
0.17867364
-0.17273445
0.049488015
0.3954818
0.006907484
-0.65876406
0.20659731
0.33826667
0.102885984
-0.3502921
0.25347513
-0.26725784
0.07709998
-0.04353281
-0.18644187
0.3938342
-0.025656475
0.4239288
-0.19141191
-0.4522367
-0.11141183
-0.4640547
0.5636213
-0.089869745
0.21497346
0.20390627
-0.25163013
-0.46159822
0.08521984
0.04547417
0.22536296
0.26892447
0.24108179
0.3650224
-0.12725092
0.35256448
-0.21991397
-0.0061194855
-0.029125368
0.120918676
-0.18983841
-0.46133506
-0.12631308
0.26642975
0.12274385
0.060510684
-0.037945624
0.40486556
0.09022891
-0.100966334
0.114066094
0.29244682
0.16922234
-0.060573082
0.5995527
-0.091098264

54396
tools/para/fc1_w
View File

File diff suppressed because it is too large Load Diff

30
tools/para/fc2_b
View File

@@ -1,16 +1,16 @@
15
0.6056967
-0.13792518
-0.21883932
-0.20325783
-0.43291906
-0.25540975
0.39662966
-0.1248626
0.011282514
-0.27768156
-0.27011594
-0.06521889
-0.46317244
0.37428737
0.1406347
0.04959415
-0.15338269
-0.30548725
-0.21866404
-0.4217409
-0.032290496
0.31138745
0.1469873
0.07883086
-0.11234011
-0.15009451
0.19498166
-0.13619435
0.37006247
0.41003358

1652
tools/para/fc2_w
View File

File diff suppressed because it is too large Load Diff