VSAG/amadeus_26_fb
3
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

大版本更新。

Browse Source
This commit is contained in:
xinyang
2019-07-06 20:20:48 +08:00
parent 9f9050e04a
commit e9a0e9ad7b
23 changed files with 15297 additions and 20022 deletions
Download Patch File Download Diff File Expand all files Collapse all files

1
.gitignore vendored
View File

@@ -2,5 +2,6 @@ cmake-build-debug
build
.idea
Mark
armor_box_photo
tools/TrainCNN/.idea
tools/TrainCNN/__pycache__

1
armor/include/armor_finder/armor_finder.h
View File

@@ -26,6 +26,7 @@ private:
const uint8_t &enemy_color;
State state;
cv::Rect2d armor_box;
int boxid;
cv::Ptr<cv::Tracker> tracker;
cv::Mat src_gray;

6
armor/include/show_images/show_images.h
View File

@@ -9,11 +9,13 @@
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>
#include <armor_finder/armor_finder.h>
#include <map>
extern std::map<int, string> id2name;
void showArmorBoxVector(std::string windows_name, const cv::Mat &src, const std::vector<cv::Rect2d> &armor_box);
void showArmorBox(std::string windows_name, const cv::Mat &src, cv::Rect2d armor_box);
void showArmorBox(std::string windows_name, const cv::Mat &src, cv::Rect2d armor_box, int boxid);
void showContours(std::string windows_name, const cv::Mat &src, const std::vector<LightBlob> &light_blobs);
void showArmorBoxClass(std::string window_names, const cv::Mat &src, vector<cv::Rect2d> boxes[10]);
void showCuoWeiDu(const cv::Mat &src, const std::vector<LightBlob> &light_blobs);
#endif /* _SHOW_IMAGES_H_ */

9
armor/src/armor_finder/armor_finder.cpp
View File

@@ -14,7 +14,8 @@ ArmorFinder::ArmorFinder(uint8_t &color, Serial &u, string paras_folder, const u
state(STANDBY_STATE),
classifier(std::move(paras_folder)),
contour_area(0),
use_classifier(use)
use_classifier(use),
boxid(-1)
{
}
@@ -25,11 +26,11 @@ void ArmorFinder::run(cv::Mat &src) {
cv::cvtColor(src_use, src_gray, CV_RGB2GRAY);
if(show_armor_box){
showArmorBox("box", src, armor_box);
showArmorBox("box", src, armor_box, boxid);
cv::waitKey(1);
}
// stateSearchingTarget(src_use);
// return;
stateSearchingTarget(src_use);
return;
switch (state){
case SEARCHING_STATE:
if(stateSearchingTarget(src_use)){

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

@@ -300,6 +300,7 @@ int Classifier::operator()(const cv::Mat &image) {
vector<MatrixXd> sub = {b, g, r};
vector<vector<MatrixXd>> in = {sub};
MatrixXd result = calculate(in);
// cout << result << "==============" <<endl;
MatrixXd::Index minRow, minCol;
result.maxCoeff(&minRow, &minCol);
return minRow;

60
armor/src/armor_finder/state_machine/searching_state/searching_state.cpp
View File

@@ -145,9 +145,9 @@ static bool findLightBlobs(const cv::Mat &src, LightBlobs &light_blobs) {
}
bool angelJudge(const LightBlob &light_blob_i, const LightBlob &light_blob_j) {
float angle_i = light_blob_i.rect.size.width > light_blob_i.rect.size.height ? light_blob_i.rect.angle :
float angle_i = light_blob_i.rect.size.width > light_blob_i.rect.size.height ? light_blob_i.rect.angle:
light_blob_i.rect.angle - 90;
float angle_j = light_blob_j.rect.size.width > light_blob_j.rect.size.height ? light_blob_j.rect.angle :
float angle_j = light_blob_j.rect.size.width > light_blob_j.rect.size.height ? light_blob_j.rect.angle:
light_blob_j.rect.angle - 90;
return abs(angle_i-angle_j)<10;
}
@@ -168,13 +168,42 @@ bool lengthRatioJudge(const LightBlob &light_blob_i, const LightBlob &light_blob
&& light_blob_i.length / light_blob_j.length > 0.5);
}
/* 判断两个灯条的错位度，不知道英文是什么！！！ */
bool CuoWeiDuJudge(const LightBlob &light_blob_i, const LightBlob &light_blob_j){
float angle_i = light_blob_i.rect.size.width > light_blob_i.rect.size.height ? light_blob_i.rect.angle:
light_blob_i.rect.angle - 90;
float angle_j = light_blob_j.rect.size.width > light_blob_j.rect.size.height ? light_blob_j.rect.angle:
light_blob_j.rect.angle - 90;
float angle = (angle_i+angle_j)/2.0/180.0*3.14159265459;
if(abs(angle_i-angle_j)>90){
angle += 3.14159265459/2;
}
Vector2f orientation(cos(angle), sin(angle));
Vector2f p2p(light_blob_j.rect.center.x-light_blob_i.rect.center.x, light_blob_j.rect.center.y-light_blob_i.rect.center.y);
return abs(orientation.dot(p2p)) < 20;
}
bool boxAngleJudge(const LightBlob &light_blob_i, const LightBlob &light_blob_j){
float angle_i = light_blob_i.rect.size.width > light_blob_i.rect.size.height ? light_blob_i.rect.angle:
light_blob_i.rect.angle - 90;
float angle_j = light_blob_j.rect.size.width > light_blob_j.rect.size.height ? light_blob_j.rect.angle:
light_blob_j.rect.angle - 90;
float angle = (angle_i+angle_j)/2.0;
if(abs(angle_i-angle_j)>90){
angle += 90.0;
}
return (-120.0<angle && angle<-60.0) || (60.0<angle && angle<120.0);
}
bool isCoupleLight(const LightBlob &light_blob_i, const LightBlob &light_blob_j, uint8_t enemy_color) {
return light_blob_i.BlobColor == enemy_color &&
light_blob_j.BlobColor == enemy_color &&
lengthRatioJudge(light_blob_i, light_blob_j) &&
lengthJudge(light_blob_i, light_blob_j) &&
heightJudge(light_blob_i, light_blob_j) &&
angelJudge(light_blob_i, light_blob_j);
// heightJudge(light_blob_i, light_blob_j) &&
angelJudge(light_blob_i, light_blob_j) &&
boxAngleJudge(light_blob_i, light_blob_j) &&
CuoWeiDuJudge(light_blob_i, light_blob_j);
}
@@ -195,8 +224,8 @@ static bool findArmorBoxes(LightBlobs &light_blobs, std::vector<cv::Rect2d> &arm
double min_x, min_y, max_x, max_y;
min_x = fmin(rect_left.x, rect_right.x) - 4;
max_x = fmax(rect_left.x + rect_left.width, rect_right.x + rect_right.width) + 4;
min_y = fmin(rect_left.y, rect_right.y) - 0.3*(rect_left.height+rect_right.height)/2.0;
max_y = fmax(rect_left.y + rect_left.height, rect_right.y + rect_right.height) + 0.3*(rect_left.height+rect_right.height)/2.0;
min_y = fmin(rect_left.y, rect_right.y) - 0.5*(rect_left.height+rect_right.height)/2.0;
max_y = fmax(rect_left.y + rect_left.height, rect_right.y + rect_right.height) + 0.5*(rect_left.height+rect_right.height)/2.0;
if (min_x < 0 || max_x > 640 || min_y < 0 || max_y > 480) {
continue;
}
@@ -251,13 +280,13 @@ void get_blob_color(const cv::Mat &src, std::vector<LightBlob> &blobs) {
}
}
int prior_red[] = {0, 2, 3, 4, 1, 5, 7, 8, 9, 6};
int prior_blue[]= {5, 7, 8, 9, 6, 0, 2, 3, 4, 1};
int prior_blue[] = {6, 0, 2, 3, 4, 5, 1, 13, 7, 9, 10, 11, 12, 8};
int prior_red[]= {13, 7, 9, 10, 11, 12, 8, 6, 0, 2, 3, 4, 5, 1};
bool ArmorFinder::stateSearchingTarget(cv::Mat &src) {
cv::Mat split, src_bin/*, edge*/;
LightBlobs light_blobs, light_blobs_, light_blobs_real;
std::vector<cv::Rect2d> armor_boxes, boxes_number[10];
std::vector<cv::Rect2d> armor_boxes, boxes_number[14];
armor_box = cv::Rect2d(0,0,0,0);
cv::cvtColor(src, src_gray, CV_BGR2GRAY);
@@ -295,7 +324,8 @@ bool ArmorFinder::stateSearchingTarget(cv::Mat &src) {
light_blobs_real = light_blobs;
get_blob_color(src, light_blobs_real);
if(show_light_blobs){
showContours("blobs_real", src, light_blobs_real);
showContours("light_blobs", src, light_blobs_real);
// showCuoWeiDu(src, light_blobs_real);
cv::waitKey(1);
}
@@ -319,6 +349,7 @@ bool ArmorFinder::stateSearchingTarget(cv::Mat &src) {
for(auto id : prior_blue){
if(!boxes_number[id].empty()){
armor_box = boxes_number[id][0];
boxid = id;
break;
}
}
@@ -326,6 +357,7 @@ bool ArmorFinder::stateSearchingTarget(cv::Mat &src) {
for(auto id : prior_red){
if(!boxes_number[id].empty()){
armor_box = boxes_number[id][0];
boxid = id;
break;
}
}
@@ -337,9 +369,17 @@ bool ArmorFinder::stateSearchingTarget(cv::Mat &src) {
}
if(show_armor_boxes){
showArmorBoxClass("class", src, boxes_number);
for(int i=0; i<sizeof(boxes_number)/ sizeof(boxes_number[0]); i++){
for(auto &box : boxes_number[i]){
char filename[100];
sprintf(filename, PROJECT_DIR"/armor_box_photo/%s_%d.jpg", id2name[i].data(), time(nullptr)+clock());
cv::imwrite(filename, src(box));
}
}
}
}else{
armor_box = armor_boxes[0];
boxid = -1;
}
if(split.size() == cv::Size(320, 240)){
armor_box.x *= 2;

86
armor/src/show_images/show_images.cpp
View File

@@ -1,7 +1,26 @@
#include <show_images/show_images.h>
#include <log.h>
using namespace cv;
std::map<int, string> id2name = {
{-1, "NO"},
{ 0, "B1"},
{ 1, "B2"},
{ 2, "B3"},
{ 3, "B4"},
{ 4, "B5"},
{ 5, "B7"},
{ 6, "B8"},
{ 7, "R1"},
{ 8, "R2"},
{ 9, "R3"},
{10, "R4"},
{11, "R5"},
{12, "R7"},
{13, "R8"},
};
void showArmorBoxVector(std::string windows_name, const cv::Mat &src, const std::vector<cv::Rect2d> &armor_box) {
static Mat image2show;
if (src.type() == CV_8UC1) // 黑白图像
@@ -27,20 +46,25 @@ void showArmorBoxClass(std::string window_names, const cv::Mat &src, vector<cv::
{
image2show = src.clone();
}
for(int i=0; i<10; i++){
for(int i=0; i<14; i++){
if(!boxes[i].empty()){
for(auto box : boxes[i]){
char buff[2] = {0};
buff[0] = i + '0';
rectangle(image2show, box, Scalar(0, 255, 0), 1);
putText(image2show, buff, Point(box.x+2, box.y+2), cv::FONT_HERSHEY_TRIPLEX, 1, Scalar(255,0,0));
if(i == -1)
putText(image2show, id2name[i], Point(box.x+2, box.y+2), cv::FONT_HERSHEY_TRIPLEX, 1, Scalar(0,255,0));
else if(0<=i && i<7)
putText(image2show, id2name[i], Point(box.x+2, box.y+2), cv::FONT_HERSHEY_TRIPLEX, 1, Scalar(255,0,0));
else if(7<=i && i<14)
putText(image2show, id2name[i], Point(box.x+2, box.y+2), cv::FONT_HERSHEY_TRIPLEX, 1, Scalar(0,0,255));
else
LOGE_INFO("Invalid box id:%d!", i);
}
}
}
imshow(window_names, image2show);
}
void showArmorBox(std::string windows_name, const cv::Mat &src, cv::Rect2d armor_box) {
void showArmorBox(std::string windows_name, const cv::Mat &src, cv::Rect2d armor_box, int boxid) {
static Mat image2show;
if (src.type() == CV_8UC1) // 黑白图像
{
@@ -50,6 +74,14 @@ void showArmorBox(std::string windows_name, const cv::Mat &src, cv::Rect2d armor
image2show = src.clone();
}
rectangle(image2show, armor_box, Scalar(0, 255, 0), 1);
if(boxid == -1)
putText(image2show, id2name[boxid], Point(armor_box.x+2, armor_box.y+2), cv::FONT_HERSHEY_TRIPLEX, 1, Scalar(0,255,0));
else if(0<=boxid && boxid<7)
putText(image2show, id2name[boxid], Point(armor_box.x+2, armor_box.y+2), cv::FONT_HERSHEY_TRIPLEX, 1, Scalar(255,0,0));
else if(7<=boxid && boxid<14)
putText(image2show, id2name[boxid], Point(armor_box.x+2, armor_box.y+2), cv::FONT_HERSHEY_TRIPLEX, 1, Scalar(0,0,255));
else
LOGE_INFO("Invalid box id:%d!", boxid);
imshow(windows_name, image2show);
}
@@ -67,13 +99,49 @@ void showContours(std::string windows_name, const cv::Mat &src, const std::vecto
for(const auto &light_blob:light_blobs)
{
Scalar color;
if(light_blob.BlobColor == BLOB_RED)
rectangle(image2show, light_blob.rect.boundingRect(), Scalar(0,0,255), 3);
if(light_blob.BlobColor == BLOB_BLUE)
rectangle(image2show, light_blob.rect.boundingRect(), Scalar(255,0,0), 3);
color = Scalar(0,0,255);
else if(light_blob.BlobColor == BLOB_BLUE)
color = Scalar(255,0,0);
else
rectangle(image2show, light_blob.rect.boundingRect(), Scalar(0,255,0), 3);
color = Scalar(0,255,0);
cv::Point2f vertices[4];
light_blob.rect.points(vertices);
for (int j = 0; j < 4; j++){
cv::line(image2show, vertices[j], vertices[(j + 1) % 4], color, 2);
}
}
imshow(windows_name, image2show);
}
void drawCuoWeiDu(cv::Mat &src, const LightBlob &light_blob_i, const LightBlob &light_blob_j){
float angle_i = light_blob_i.rect.size.width > light_blob_i.rect.size.height ? light_blob_i.rect.angle:
light_blob_i.rect.angle - 90;
float angle_j = light_blob_j.rect.size.width > light_blob_j.rect.size.height ? light_blob_j.rect.angle:
light_blob_j.rect.angle - 90;
float angle = (angle_i+angle_j)/2.0/180.0*3.14159265459;
if(abs(angle_i-angle_j)>90){
angle += 3.14159265459/2;
}
Point2f orientation(cos(angle), sin(angle));
Vector2f p2p(light_blob_j.rect.center.x-light_blob_i.rect.center.x, light_blob_i.rect.center.y-light_blob_j.rect.center.y);
cv::line(
src,
(light_blob_j.rect.center+light_blob_i.rect.center)/2.0,
(light_blob_j.rect.center+light_blob_i.rect.center)/2.0 + 100*orientation,
Scalar(0,255,0),
2
);
}
void showCuoWeiDu(const cv::Mat &src, const std::vector<LightBlob> &light_blobs){
Mat image2show = src.clone();
for (int i = 0; i < light_blobs.size() - 1; ++i) {
for (int j = i + 1; j < light_blobs.size(); ++j) {
drawCuoWeiDu(image2show, light_blobs[i], light_blobs[j]);
}
}
imshow("CuoWeiDu", image2show);
}

9
main.cpp
View File

@@ -32,7 +32,7 @@ mcu_data mcuData = {
ARMOR_STATE,
0,
1,
ENEMY_RED,
ENEMY_BLUE,
};
int main(int argc, char *argv[]) {
@@ -57,14 +57,9 @@ int main(int argc, char *argv[]) {
WrapperHead *video_armor=nullptr;
WrapperHead *video_energy=nullptr;
if (from_camera) {
video_armor = new CameraWrapper(0, "armor");
video_armor = new CameraWrapper(0/*, "armor"*/);
video_energy = new CameraWrapper(1, "energy");
} else {
// string armor_video, energy_video;
// lastVideo(armor_video, PROJECT_DIR"/armor_video/");
// video_armor = new VideoWrapper(armor_video);
// lastVideo(energy_video, PROJECT_DIR"/energy_video/");
// video_energy = new VideoWrapper(energy_video);
video_armor = new VideoWrapper("/home/sjturm/Desktop/valid_video/armor/65.avi");
video_energy = new VideoWrapper("/home/sjturm/Desktop/valid_video/energy/121.avi");
}

4
others/src/camera/camera_wrapper.cpp
View File

@@ -76,9 +76,9 @@ bool CameraWrapper::init() {
#elif defined(Linux)
CameraSetAeState(h_camera, false);
CameraSetExposureTime(h_camera, 10*1000);
CameraSetAnalogGain(h_camera, 20);
CameraSetAnalogGain(h_camera, 40);
if(mode == 0){
CameraSetGain(h_camera, 100, 130, 112);
CameraSetGain(h_camera, 100, 100, 100);
CameraSetLutMode(h_camera, LUTMODE_PRESET);
}
#endif

152
tools/TrainCNN/backward.py Normal file → Executable file
View File

@@ -5,8 +5,8 @@ from tqdm import tqdm
import generate
import forward
import cv2
import sys
import numpy as np
import mvsdk
print("Finish!")
def save_kernal(fp, val):
@@ -54,7 +54,7 @@ def save_para(folder, paras):
save_bias(fp, paras[7])
STEPS = 100000
STEPS = 5000
BATCH = 30
LEARNING_RATE_BASE = 0.01
LEARNING_RATE_DECAY = 0.99
@@ -62,12 +62,16 @@ MOVING_AVERAGE_DECAY = 0.99
def train(dataset, show_bar=False):
test_images, test_labels = dataset.all_test_sets()
x = tf.placeholder(tf.float32, [None, generate.SRC_ROWS, generate.SRC_COLS, generate.SRC_CHANNELS])
y_= tf.placeholder(tf.float32, [None, forward.OUTPUT_NODES])
nodes, vars = forward.forward(x, 0.001)
keep_rate = tf.placeholder(tf.float32)
nodes, vars = forward.forward(x, 0.01)
y = nodes[-1]
ce = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 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"))
@@ -87,72 +91,118 @@ def train(dataset, show_bar=False):
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
config = tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True))
with tf.Session(config=config) as sess:
init_op = tf.global_variables_initializer()
sess.run(init_op)
bar = tqdm(range(STEPS), dynamic_ncols=True)
bar = tqdm(range(STEPS), ascii=True, dynamic_ncols=True)
for i in bar:
images_samples, labels_samples = dataset.sample_train_sets(BATCH)
_, loss_value, step = sess.run(
[train_op, loss, global_step],
feed_dict={x: images_samples, y_: labels_samples}
feed_dict={x: images_samples, y_: labels_samples, keep_rate:0.7}
)
if i % 100 == 0:
if i % 1000 == 0:
test_samples, test_labels = dataset.sample_test_sets(1000)
acc = sess.run(accuracy, feed_dict={x: test_samples, y_: test_labels})
acc = sess.run(accuracy, feed_dict={x: test_images, y_: test_labels, keep_rate:1.0})
bar.set_postfix({"loss": loss_value, "acc": acc})
# video = cv2.VideoCapture("/home/xinyang/Desktop/Video.mp4")
# _ = True
# while _:
# _, frame = video.read()
# cv2.imshow("Video", frame)
# k = cv2.waitKey(10)
# if k == ord(" "):
# bbox = cv2.selectROI("frame", frame, False)
# print(bbox)
# roi = frame[bbox[1]:bbox[1]+bbox[3], bbox[0]:bbox[0]+bbox[2]]
# roi = cv2.resize(roi, (48, 36))
# cv2.imshow("roi", roi)
# cv2.waitKey(0)
# roi = roi.astype(np.float32)
# roi /= 255.0
# roi = roi.reshape([1, 36, 48, 3])
# res = sess.run(y, feed_dict={x: roi})
# res = res.reshape([forward.OUTPUT_NODES])
# print(np.argmax(res))
# elif k==ord("q"):
# break
# keep = True
# while keep:
# n = input()
# im = cv2.imread(n)
# im = cv2.resize(im, (48, 36))
# cv2.imshow("im", im)
# if cv2.waitKey(0) == ord("q"):
# keep = False
# im = im.astype(np.float32)
# im /= 255.0
# im = im.reshape([1, 36, 48, 3])
# res = sess.run(y, feed_dict={x: im})
# res = res.reshape([forward.OUTPUT_NODES])
# print(np.argmax(res))
test_samples, test_labels = dataset.sample_test_sets(100)
vars_val = sess.run(vars)
save_para("/home/xinyang/Desktop/AutoAim/tools/para", vars_val)
nodes_val = sess.run(nodes, feed_dict={x:test_samples})
return vars_val, nodes_val, test_samples
save_para("/home/xinyang/Desktop/RM_auto-aim/tools/para", vars_val)
print("save done!")
# nodes_val = sess.run(nodes, feed_dict={x:test_images})
# return vars_val, nodes_val
DevList = mvsdk.CameraEnumerateDevice()
nDev = len(DevList)
if nDev < 1:
print("No camera was found!")
return
for i, DevInfo in enumerate(DevList):
print("{}: {} {}".format(i, DevInfo.GetFriendlyName(), DevInfo.GetPortType()))
i = 0 if nDev == 1 else int(input("Select camera: "))
DevInfo = DevList[i]
print(DevInfo)
# 打开相机
hCamera = 0
try:
hCamera = mvsdk.CameraInit(DevInfo, -1, -1)
except mvsdk.CameraException as e:
print("CameraInit Failed({}): {}".format(e.error_code, e.message) )
return
# 获取相机特性描述
cap = mvsdk.CameraGetCapability(hCamera)
# 判断是黑白相机还是彩色相机
monoCamera = (cap.sIspCapacity.bMonoSensor != 0)
# 黑白相机让ISP直接输出MONO数据，而不是扩展成R=G=B的24位灰度
if monoCamera:
mvsdk.CameraSetIspOutFormat(hCamera, mvsdk.CAMERA_MEDIA_TYPE_MONO8)
else:
mvsdk.CameraSetIspOutFormat(hCamera, mvsdk.CAMERA_MEDIA_TYPE_BGR8)
# 相机模式切换成连续采集
mvsdk.CameraSetTriggerMode(hCamera, 0)
# 手动曝光，曝光时间30ms
mvsdk.CameraSetAeState(hCamera, 0)
mvsdk.CameraSetExposureTime(hCamera, 30 * 1000)
# 让SDK内部取图线程开始工作
mvsdk.CameraPlay(hCamera)
# 计算RGB buffer所需的大小，这里直接按照相机的最大分辨率来分配
FrameBufferSize = cap.sResolutionRange.iWidthMax * cap.sResolutionRange.iHeightMax * (1 if monoCamera else 3)
# 分配RGB buffer，用来存放ISP输出的图像
# 备注：从相机传输到PC端的是RAW数据，在PC端通过软件ISP转为RGB数据（如果是黑白相机就不需要转换格式，但是ISP还有其它处理，所以也需要分配这个buffer）
pFrameBuffer = mvsdk.CameraAlignMalloc(FrameBufferSize, 16)
while (cv2.waitKey(1) & 0xFF) != ord('q'):
# 从相机取一帧图片
try:
pRawData, FrameHead = mvsdk.CameraGetImageBuffer(hCamera, 200)
mvsdk.CameraImageProcess(hCamera, pRawData, pFrameBuffer, FrameHead)
mvsdk.CameraReleaseImageBuffer(hCamera, pRawData)
# 此时图片已经存储在pFrameBuffer中，对于彩色相机pFrameBuffer=RGB数据，黑白相机pFrameBuffer=8位灰度数据
# 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 = cv2.resize(frame, (640,480), interpolation = cv2.INTER_LINEAR)
cv2.imshow("Press q to end", frame)
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]]
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]})
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) )
# 关闭相机
mvsdk.CameraUnInit(hCamera)
# 释放帧缓存
mvsdk.CameraAlignFree(pFrameBuffer)
if __name__ == "__main__":
print("Loading data sets...")
dataset = generate.DataSet("/home/xinyang/Desktop/dataset/box")
print("Finish!")
dataset = generate.DataSet("/home/xinyang/Desktop/box_cut")
train(dataset, show_bar=True)
input("Press any key to end...")

94
tools/TrainCNN/cv_grab.py Normal file
View File

@@ -0,0 +1,94 @@
#coding=utf-8
import cv2
import numpy as np
import mvsdk
def main_loop():
# 枚举相机
DevList = mvsdk.CameraEnumerateDevice()
nDev = len(DevList)
if nDev < 1:
print("No camera was found!")
return
for i, DevInfo in enumerate(DevList):
print("{}: {} {}".format(i, DevInfo.GetFriendlyName(), DevInfo.GetPortType()))
i = 0 if nDev == 1 else int(input("Select camera: "))
DevInfo = DevList[i]
print(DevInfo)
# 打开相机
hCamera = 0
try:
hCamera = mvsdk.CameraInit(DevInfo, -1, -1)
except mvsdk.CameraException as e:
print("CameraInit Failed({}): {}".format(e.error_code, e.message) )
return
# 获取相机特性描述
cap = mvsdk.CameraGetCapability(hCamera)
# 判断是黑白相机还是彩色相机
monoCamera = (cap.sIspCapacity.bMonoSensor != 0)
# 黑白相机让ISP直接输出MONO数据，而不是扩展成R=G=B的24位灰度
if monoCamera:
mvsdk.CameraSetIspOutFormat(hCamera, mvsdk.CAMERA_MEDIA_TYPE_MONO8)
else:
mvsdk.CameraSetIspOutFormat(hCamera, mvsdk.CAMERA_MEDIA_TYPE_BGR8)
# 相机模式切换成连续采集
mvsdk.CameraSetTriggerMode(hCamera, 0)
# 手动曝光，曝光时间30ms
mvsdk.CameraSetAeState(hCamera, 0)
mvsdk.CameraSetExposureTime(hCamera, 30 * 1000)
# 让SDK内部取图线程开始工作
mvsdk.CameraPlay(hCamera)
# 计算RGB buffer所需的大小，这里直接按照相机的最大分辨率来分配
FrameBufferSize = cap.sResolutionRange.iWidthMax * cap.sResolutionRange.iHeightMax * (1 if monoCamera else 3)
# 分配RGB buffer，用来存放ISP输出的图像
# 备注：从相机传输到PC端的是RAW数据，在PC端通过软件ISP转为RGB数据（如果是黑白相机就不需要转换格式，但是ISP还有其它处理，所以也需要分配这个buffer）
pFrameBuffer = mvsdk.CameraAlignMalloc(FrameBufferSize, 16)
while (cv2.waitKey(1) & 0xFF) != ord('q'):
# 从相机取一帧图片
try:
pRawData, FrameHead = mvsdk.CameraGetImageBuffer(hCamera, 200)
mvsdk.CameraImageProcess(hCamera, pRawData, pFrameBuffer, FrameHead)
mvsdk.CameraReleaseImageBuffer(hCamera, pRawData)
# 此时图片已经存储在pFrameBuffer中，对于彩色相机pFrameBuffer=RGB数据，黑白相机pFrameBuffer=8位灰度数据
# 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 = cv2.resize(frame, (640,480), interpolation = cv2.INTER_LINEAR)
cv2.imshow("Press q to end", frame)
roi = cv2.selectROI("roi", frame)
roi = frame[roi[1]:roi[1]+roi[3], roi[0]:roi[0]+roi[2]]
print(roi)
cv2.imshow("box", roi)
except mvsdk.CameraException as e:
if e.error_code != mvsdk.CAMERA_STATUS_TIME_OUT:
print("CameraGetImageBuffer failed({}): {}".format(e.error_code, e.message) )
# 关闭相机
mvsdk.CameraUnInit(hCamera)
# 释放帧缓存
mvsdk.CameraAlignFree(pFrameBuffer)
def main():
try:
main_loop()
finally:
cv2.destroyAllWindows()
main()

16
tools/TrainCNN/forward.py
View File

@@ -29,24 +29,25 @@ def max_pool_2x2(x):
CONV1_KERNAL_SIZE = 5
# 第一层卷积输出通道数
CONV1_OUTPUT_CHANNELS = 8
CONV1_OUTPUT_CHANNELS = 6
# 第二层卷积核大小
CONV2_KERNAL_SIZE = 3
# 第二层卷积输出通道数
CONV2_OUTPUT_CHANNELS = 16
CONV2_OUTPUT_CHANNELS = 10
# 第一层全连接宽度
FC1_OUTPUT_NODES = 16
# 第二层全连接宽度（输出标签类型数）
FC2_OUTPUT_NODES = 15
# 输出标签类型数
OUTPUT_NODES = FC2_OUTPUT_NODES
def forward(x, regularizer=None):
def forward(x, regularizer=None, keep_rate=tf.constant(1.0)):
vars = []
nodes = []
@@ -71,16 +72,19 @@ def forward(x, regularizer=None):
pool_shape = pool2.get_shape().as_list()
node = pool_shape[1] * pool_shape[2] * pool_shape[3]
reshaped = tf.reshape(pool2, [-1, node])
reshaped = tf.nn.dropout(reshaped, keep_rate)
fc1_w = tf.nn.dropout(get_weight([node, FC1_OUTPUT_NODES], regularizer), 0.1)
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.dropout(fc1, keep_rate)
vars.extend([fc1_w, fc1_b])
nodes.extend([fc1])
fc2_w = tf.nn.dropout(get_weight([FC1_OUTPUT_NODES, FC2_OUTPUT_NODES], regularizer), 0.1)
fc2_w = get_weight([FC1_OUTPUT_NODES, FC2_OUTPUT_NODES], regularizer)
fc2_b = get_bias([FC2_OUTPUT_NODES])
fc2 = tf.nn.softmax(tf.matmul(fc1, fc2_w) + fc2_b)
# fc2 = tf.nn.softmax(tf.matmul(fc1, fc2_w) + fc2_b)
fc2 = tf.matmul(fc1, fc2_w) + fc2_b
vars.extend([fc2_w, fc2_b])
nodes.extend([fc2])

19
tools/TrainCNN/generate.py
View File

@@ -2,10 +2,8 @@ import numpy as np
import os
import cv2
import random
from forward import OUTPUT_NODES
import sys
import os
from tqdm import tqdm
from forward import OUTPUT_NODES
# 原图像行数
SRC_ROWS = 36
@@ -24,7 +22,7 @@ class DataSet:
self.test_labels = []
self.generate_data_sets(folder)
def file2nparray(self, name, random=False):
def file2nparray(self, name):
image = cv2.imread(name)
image = cv2.resize(image, (SRC_COLS, SRC_ROWS))
image = image.astype(np.float32)
@@ -42,16 +40,12 @@ class DataSet:
files = os.listdir(dir)
for file in tqdm(files, postfix={"loading id": i}, dynamic_ncols=True):
if file[-3:] == "jpg":
try:
if random.random() > 0.2:
self.train_samples.append(self.file2nparray("%s/%s" % (dir, file)))
self.train_labels.append(self.id2label(i))
else:
self.test_samples.append(self.file2nparray("%s/%s" % (dir, file)))
self.test_labels.append(self.id2label(i))
except:
print("%s/%s" % (dir, file))
continue
self.train_samples = np.array(self.train_samples)
self.train_labels = np.array(self.train_labels)
self.test_samples = np.array(self.test_samples)
@@ -67,15 +61,6 @@ class DataSet:
labels.append(self.train_labels[id])
return np.array(samples), np.array(labels)
def sample_test_sets(self, length):
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)
def all_train_sets(self):
return self.train_samples[:], self.train_labels[:]

111
tools/TrainCNN/grab.py Normal file
View File

@@ -0,0 +1,111 @@
#coding=utf-8
import mvsdk
def main():
# 枚举相机
DevList = mvsdk.CameraEnumerateDevice()
nDev = len(DevList)
if nDev < 1:
print("No camera was found!")
return
for i, DevInfo in enumerate(DevList):
print("{}: {} {}".format(i, DevInfo.GetFriendlyName(), DevInfo.GetPortType()))
i = 0 if nDev == 1 else int(input("Select camera: "))
DevInfo = DevList[i]
print(DevInfo)
# 打开相机
hCamera = 0
try:
hCamera = mvsdk.CameraInit(DevInfo, -1, -1)
except mvsdk.CameraException as e:
print("CameraInit Failed({}): {}".format(e.error_code, e.message) )
return
# 获取相机特性描述
cap = mvsdk.CameraGetCapability(hCamera)
PrintCapbility(cap)
# 判断是黑白相机还是彩色相机
monoCamera = (cap.sIspCapacity.bMonoSensor != 0)
# 黑白相机让ISP直接输出MONO数据，而不是扩展成R=G=B的24位灰度
if monoCamera:
mvsdk.CameraSetIspOutFormat(hCamera, mvsdk.CAMERA_MEDIA_TYPE_MONO8)
# 相机模式切换成连续采集
mvsdk.CameraSetTriggerMode(hCamera, 0)
# 手动曝光，曝光时间30ms
mvsdk.CameraSetAeState(hCamera, 0)
mvsdk.CameraSetExposureTime(hCamera, 30 * 1000)
# 让SDK内部取图线程开始工作
mvsdk.CameraPlay(hCamera)
# 计算RGB buffer所需的大小，这里直接按照相机的最大分辨率来分配
FrameBufferSize = cap.sResolutionRange.iWidthMax * cap.sResolutionRange.iHeightMax * (1 if monoCamera else 3)
# 分配RGB buffer，用来存放ISP输出的图像
# 备注：从相机传输到PC端的是RAW数据，在PC端通过软件ISP转为RGB数据（如果是黑白相机就不需要转换格式，但是ISP还有其它处理，所以也需要分配这个buffer）
pFrameBuffer = mvsdk.CameraAlignMalloc(FrameBufferSize, 16)
# 从相机取一帧图片
try:
pRawData, FrameHead = mvsdk.CameraGetImageBuffer(hCamera, 2000)
mvsdk.CameraImageProcess(hCamera, pRawData, pFrameBuffer, FrameHead)
mvsdk.CameraReleaseImageBuffer(hCamera, pRawData)
# 此时图片已经存储在pFrameBuffer中，对于彩色相机pFrameBuffer=RGB数据，黑白相机pFrameBuffer=8位灰度数据
# 该示例中我们只是把图片保存到硬盘文件中
status = mvsdk.CameraSaveImage(hCamera, "./grab.bmp", pFrameBuffer, FrameHead, mvsdk.FILE_BMP, 100)
if status == mvsdk.CAMERA_STATUS_SUCCESS:
print("Save image successfully. image_size = {}X{}".format(FrameHead.iWidth, FrameHead.iHeight) )
else:
print("Save image failed. err={}".format(status) )
except mvsdk.CameraException as e:
print("CameraGetImageBuffer failed({}): {}".format(e.error_code, e.message) )
# 关闭相机
mvsdk.CameraUnInit(hCamera)
# 释放帧缓存
mvsdk.CameraAlignFree(pFrameBuffer)
def PrintCapbility(cap):
for i in range(cap.iTriggerDesc):
desc = cap.pTriggerDesc[i]
print("{}: {}".format(desc.iIndex, desc.GetDescription()) )
for i in range(cap.iImageSizeDesc):
desc = cap.pImageSizeDesc[i]
print("{}: {}".format(desc.iIndex, desc.GetDescription()) )
for i in range(cap.iClrTempDesc):
desc = cap.pClrTempDesc[i]
print("{}: {}".format(desc.iIndex, desc.GetDescription()) )
for i in range(cap.iMediaTypeDesc):
desc = cap.pMediaTypeDesc[i]
print("{}: {}".format(desc.iIndex, desc.GetDescription()) )
for i in range(cap.iFrameSpeedDesc):
desc = cap.pFrameSpeedDesc[i]
print("{}: {}".format(desc.iIndex, desc.GetDescription()) )
for i in range(cap.iPackLenDesc):
desc = cap.pPackLenDesc[i]
print("{}: {}".format(desc.iIndex, desc.GetDescription()) )
for i in range(cap.iPresetLut):
desc = cap.pPresetLutDesc[i]
print("{}: {}".format(desc.iIndex, desc.GetDescription()) )
for i in range(cap.iAeAlmSwDesc):
desc = cap.pAeAlmSwDesc[i]
print("{}: {}".format(desc.iIndex, desc.GetDescription()) )
for i in range(cap.iAeAlmHdDesc):
desc = cap.pAeAlmHdDesc[i]
print("{}: {}".format(desc.iIndex, desc.GetDescription()) )
for i in range(cap.iBayerDecAlmSwDesc):
desc = cap.pBayerDecAlmSwDesc[i]
print("{}: {}".format(desc.iIndex, desc.GetDescription()) )
for i in range(cap.iBayerDecAlmHdDesc):
desc = cap.pBayerDecAlmHdDesc[i]
print("{}: {}".format(desc.iIndex, desc.GetDescription()) )
main()

2344
tools/TrainCNN/mvsdk.py Normal file
View File

File diff suppressed because it is too large Load Diff

16
tools/para/conv1_b
View File

@@ -1,9 +1,7 @@
8
0.026843265
0.13687223
0.355584
-2.171335
2.0351274
1.8288306
-4.113487
-4.7374034
6
2.196893
0.07216131
0.30069783
-0.4587247
0.25476167
-0.07236218

1052
tools/para/conv1_w
View File

File diff suppressed because it is too large Load Diff

28
tools/para/conv2_b
View File

@@ -1,17 +1,11 @@
16
1.252942
8.216776
-0.25801975
0.23331891
-1.0068187
-1.1067235
-0.40771145
-0.43731463
-1.4359887
-0.2637226
-0.41042513
5.653234
11.668375
-1.4110142
-0.1370871
-0.19704156
10
-0.20609309
-0.22031759
0.1367356
0.3687642
0.41563538
0.56676525
-0.18027179
0.23183917
-0.42312288
-0.071102634

1696
tools/para/conv2_w
View File

File diff suppressed because it is too large Load Diff

32
tools/para/fc1_b
View File

@@ -1,17 +1,17 @@
16
-0.20538531
7.367273
-0.18452525
6.532006
25.536476
-0.18481636
-7.2863836
-0.106642306
-13.070918
-0.20218277
12.15478
-0.28686985
-0.0753381
-0.18774705
-0.45540679
-0.81279093
0.58729273
-0.46309644
-0.16430101
0.43460655
-0.12165885
-0.23968913
2.1033192
-0.19900312
-0.0075173783
0.05968375
-0.13455966
-0.203078
3.4187536
-0.17911159
0.2670588
-0.58640796

29122
tools/para/fc1_w
View File

File diff suppressed because it is too large Load Diff

28
tools/para/fc2_b
View File

@@ -1,12 +1,16 @@
11
3.028916
1.399315
12.311913
1.9181013
-6.701019
4.332221
-1.2238123
3.367433
-12.5565
-5.421737
0.19371712
15
3.3147552
0.06590654
-0.37396204
-0.22522521
-0.9601034
-0.9866448
-0.091494516
0.08088531
-0.87962383
-0.5273214
-0.18194006
-0.035499398
-1.7873636
0.48932117
0.20472674

418
tools/para/fc2_w
View File

@@ -1,178 +1,242 @@
16
11
-6.6445126e-34
-3.249375e-34
1.1159086e-33
3.8638357e-34
1.1999902e-33
-4.773489e-34
6.573301e-35
-2.7309886e-34
-3.5826868e-34
-2.058676e-35
2.9253375e-34
-0.028950384
0.6513481
-0.090478554
-0.20445836
-0.033577178
0.07797232
0.54864347
-0.29817155
-0.0972982
-0.06013593
-0.12082546
3.2078713e-35
9.031606e-34
2.9989992e-34
-1.8143521e-34
-4.3935644e-34
-7.0448736e-34
7.1789805e-35
-1.0773237e-33
4.1924878e-35
2.6375152e-35
7.6904e-34
0.7534392
0.041451767
-0.16353445
-0.060047485
0.00937684
-0.046534266
0.12362613
-0.15848428
-0.24788214
-0.10429883
0.07533859
-0.059416637
-0.41185078
-0.17300163
-0.5048911
-0.27550554
-0.3164118
0.41110015
0.5305193
0.54406047
0.41247433
0.37498224
-1.8530121e-34
9.619048e-34
-9.084177e-35
-6.3287863e-34
-5.0406337e-34
6.430404e-34
3.175955e-34
4.679148e-34
-9.705965e-35
-1.4937167e-34
-3.7373778e-34
-1.8647024e-34
2.157429e-34
9.178287e-35
2.0542673e-34
4.1186567e-34
-1.4028581e-34
-1.9601842e-35
1.2199764e-34
6.605314e-36
4.5839516e-35
4.1222883e-34
-5.649719e-34
6.8534397e-34
1.5284239e-34
4.6217582e-35
-2.6860813e-34
6.4033865e-34
-1.9073337e-34
-4.5628154e-34
-2.5596114e-34
3.5286568e-34
-4.590898e-34
-0.016765846
0.011994723
-0.26132298
0.52835166
-0.21429977
0.047839653
0.0091085555
-0.27048072
0.35106397
-0.05962828
-0.06534093
-5.9855516e-34
-3.8872762e-34
1.4836724e-34
-3.7528057e-34
9.244409e-35
3.8288393e-34
1.7450431e-34
-2.1571653e-34
-8.635735e-34
-1.1816434e-33
2.75913e-34
-0.11307323
-0.05993526
-0.13786606
0.0066387164
0.0024843283
0.59352225
-0.13324556
-0.275834
-0.13921
-0.023196468
0.5097328
-3.878958e-34
-3.7806562e-34
-7.8518477e-35
-3.8417675e-35
5.504886e-34
-4.2347166e-34
3.77638e-34
-6.449212e-34
3.723454e-34
-3.4782797e-34
-7.3213066e-35
-4.4892873e-34
3.4874208e-34
1.1700748e-33
-1.1355761e-34
1.1225075e-33
8.598829e-34
-4.3217242e-35
-2.7770687e-34
-4.541627e-34
2.895937e-34
5.4065008e-34
8.211584e-34
2.4092055e-34
-1.1384675e-33
6.7052264e-34
-8.305206e-34
-1.8370869e-34
-5.012333e-34
9.2541105e-34
5.402706e-34
-3.0262877e-34
7.088514e-34
-1.7485143e-34
-2.366834e-34
6.501108e-34
-5.722031e-34
1.1429626e-33
4.9021696e-35
1.1040688e-34
-1.0464325e-33
1.6525106e-34
-3.9707304e-34
2.1401144e-34
-0.051736742
-0.042417962
0.051013805
0.16345194
0.5187456
-0.14417858
-0.0539816
-0.15638705
-0.30926377
0.42976364
-0.029886993
15
0.032564595
0.2617493
-0.32648245
-0.09931708
-0.24592394
-0.046472594
0.33926198
-0.12294629
0.35003394
-0.21551898
-0.017696692
-0.14498983
-0.1035376
0.38845813
-0.025425002
0.009146354
-0.10837719
-0.26169685
0.24757256
0.12278806
0.173229
-0.13405079
-0.12579814
-0.055770937
-0.18405183
0.28358203
0.07445254
0.23714246
-0.13335316
0.010074598
8.8978056e-36
-3.2673058e-35
-5.345727e-35
-1.2187582e-35
2.1464323e-35
6.242724e-35
-1.5923025e-35
8.5710344e-35
-1.0859142e-34
3.2036078e-35
9.889982e-35
9.5151974e-35
-2.9864197e-35
4.3109238e-35
-3.5075268e-35
0.056317
-0.041549474
-0.07867665
-0.18685594
0.0036230602
0.26440525
-0.040618088
-0.011269322
-0.037696317
0.01897098
-0.12073718
0.017303245
0.33418366
0.0023682562
0.02849121
5.652079e-35
-2.3405746e-35
7.754459e-35
-7.9132345e-35
-3.652418e-35
2.7482412e-35
-5.490258e-35
-4.0657551e-35
-8.77158e-35
-1.6310674e-35
6.9110407e-35
2.8374646e-35
7.249574e-35
-8.326536e-36
-4.2208914e-35
3.380163e-07
0.00018250165
-4.091571e-13
-5.742375e-20
1.9097627e-29
-1.0989738e-34
4.988384e-06
-6.4157313e-25
0.0046886215
-4.15727e-06
8.565781e-06
1.3159001e-08
-6.0661813e-27
0.003999361
4.6603424e-12
0.05875436
0.1978433
0.2357523
0.26039347
0.29742035
0.23000301
0.22130986
0.32969925
-0.25387922
-0.21843708
-0.35505614
-0.18760061
-0.26636004
-0.3437664
-0.31676972
-2.9646424e-21
1.6212045e-32
-4.972171e-35
3.321333e-35
-3.0660306e-36
-1.2462985e-35
-6.15827e-35
-7.708171e-35
-8.6527984e-35
-9.63909e-35
-4.329017e-36
-1.6798441e-35
6.4576066e-36
1.0103299e-34
5.888647e-35
-7.013437e-07
-4.0569785e-06
-1.6326982e-07
-5.024649e-09
1.0218174e-08
-5.870887e-17
2.4735778e-05
-1.8678125e-28
-5.81377e-18
4.2194547e-08
-8.974654e-09
-8.819107e-18
-4.0505355e-36
1.4275389e-15
2.5213077e-35
0.0455311
-0.16154826
-0.12516226
-0.15351692
-0.15327676
-0.101601385
-0.09675424
-0.009882243
0.14380045
0.17609678
0.15136641
0.18814708
0.14553012
0.08837449
-0.033248488
-5.5479194e-13
-1.301003e-35
6.407329e-35
1.9132001e-35
9.564731e-15
-4.683806e-19
1.8975264e-24
2.1182613e-16
-3.6244807e-35
8.7545505e-28
6.0832183e-21
-8.545767e-31
5.2584422e-14
2.1925994e-22
-3.8261837e-20
-1.0880043e-34
-6.6652585e-35
-6.389439e-35
3.2505208e-35
-4.973718e-35
-3.3143227e-35
-8.6873607e-35
-7.993331e-35
-7.852833e-36
-6.3270696e-35
5.258114e-35
2.1151958e-35
7.3324824e-35
7.1793427e-35
-9.0051764e-35
0.3847243
-0.008498104
0.030268772
-0.13264672
0.030948505
-0.07938414
-0.04668712
-0.16404602
0.07313376
0.1522345
0.00048681977
0.01413009
-0.09327267
-0.055540953
-0.100918815
-0.06997617
-0.100417346
-0.11185439
-0.108010836
-0.1542093
-0.026418801
-0.0976361
0.2631115
0.110037416
-0.038920112
0.03310242
-0.07849237
0.087744445
-0.016706102
0.42764086
-0.063509755
-0.20127158
0.3405362
0.10242782
-0.25828084
-0.18461828
0.18166192
-0.13771532
-0.14198124
0.4270196
0.16850737
-0.13088605
-0.18872121
0.22758731
-0.07991603
-0.06114433
0.22688313
0.013428835
-0.12416983
0.32349384
-0.081210054
-0.33148897
-0.1045747
0.20436902
0.018065251
-0.15008682
0.3795789
-0.022265602
-0.2928385
0.012199368