Multiple Programming Languages

Calling a C Function From Lua

Posted on August 16, 2019 by admin

Prepare So

```
#include <math.h>
```
```
#include <lua.h>
```
```
#include <lauxlib.h>
```
```
#include <lualib.h>
```
```
 
```
```
static int my_sum(lua_State *L){
```
```
    int d1 = luaL_checknumber(L, 1);
```
```
    int d2 = luaL_checknumber(L, 2);
```
```
    lua_pushnumber(L, d1+d2);
```
```
    return 1;
```
```
}
```
```
 
```
```
static int my_info(lua_State *L){
```
```
    lua_pushstring(L, "qinuu");
```
```
    return 1;
```
```
}
```
```
 
```

static const struct luaL_Reg test_lib[] = {

```
    {"my_sum" , my_sum},
```
```
    {"my_info" , my_info},
```
```
    {NULL, NULL}
```
```
};
```
```
 
```
```
int luaopen_test_lib(lua_State *L){
```
```
    //luaL_newlib(L, test_lib); // 5.2
```

    luaL_register(L, "test_lib",test_lib); // lua 5.1

```
    return 1;
```
```
}
```

Test

```
local my_lib = require "test_lib"
```
```
 
```
```
print(type(test_lib))
```
```
 
```
```
print(test_lib.my_sum(23,17))
```
```
 
```
```
print(test_lib.my_info())
```

Profile : Linux -> PNG

Posted on July 11, 2019 by admin

gprof -b emv_clc2 > report.txt gprof2dot.py report.txt > call_graph.dot dot -Tpng call_graph.dot -o call_graph.png

1>LINK : fatal error LNK1104: cannot open file ‘libboost_date_time-vc142-mt-s-x32-1_70.lib’

Posted on June 28, 2019 by admin

C:\boost_1_70_0>.\b2 --with-date_time link=static runtime-link=static

Mastering Qt 5 Second Edition Notes

Posted on June 20, 2019 by admin

Speed up by ignoring std::cout

Posted on May 21, 2019 by admin

Test Code

```
#include <iostream>
```
```
 
```
```
int main(int argc, char *argv[])
```
```
{
```

	//std::cout.setstate(std::ios_base::badbit);

```
	for(int i = 0; i < 100; i ++) {
```
```
		for(int i = 0; i < 100; i ++) {
```
```
			;//std::cout << "" << std::endl;
```
```
		}
```
```
	}
```
```
	return 0;
```
```
}
```

Test it without std::cout

```
root@imx6ul7d:~/tmp# time ./t1_1 
```
```
 
```
```
real    0m0.041s
```
```
user    0m0.020s
```
```
sys     0m0.000s
```

Test it with std::cout, but redirect to /dev/null

root@imx6ul7d:~/tmp# time ./t1_0 > /dev/null

```
 
```
```
real    0m0.096s
```
```
user    0m0.030s
```
```
sys     0m0.030s
```

Test it with std::cout, but set io state

```
root@imx6ul7d:~/tmp# time ./t1_2
```
```
 
```
```
real    0m0.061s
```
```
user    0m0.040s
```
```
sys     0m0.000s
```

Profile : Linux

Posted on May 17, 2019 by admin

Example 1

Add -pg

arm-linux-gnueabihf-g++ -Wall -g -pg hello.cpp -o hello -std=c++17

Test

```
root@imx6ul7d:~# gprof -b hello 
```
```
Flat profile:
```
```
 
```
```
Each sample counts as 0.01 seconds.
```

  %   cumulative   self              self     total

 time   seconds   seconds    calls   s/call   s/call  name

100.00      3.45     3.45        1     3.45     3.45  hehe()

  0.00      3.45     0.00        4     0.00     0.00  std::_Optional_base<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >::_M_is_engaged() const

```
......
```

First Line of Code (Version 2) Notes

Posted on May 6, 2019 by admin

Routes stream to specified buffer

Posted on April 10, 2019 by admin

Developer Command Prompt for VS 2019

```
cl basic_ios_rdbuf.cpp /EHsc
```

MSDN Source Code

```
#include <ios>
```
```
#include <iostream>
```
```
#include <fstream>
```
```
 
```
```
int main( ) 
```
```
{
```
```
   using namespace std;
```
```
   ofstream file( "rdbuf.txt" );
```

   streambuf *x = cout.rdbuf( file.rdbuf( ) );

   cout << "test" << endl; // Goes to file

```
   cout.rdbuf(x);
```
```
   cout << "test2" << endl; //normal
```
```
}
```

TensorFlow on Android (Refer to codelabs : tensorflow-for-poets)

Posted on January 23, 2019January 23, 2019 by admin

The google demo is good, but there are some error when you follow it, so I copied and update it.

Install TensorFlow As Normal

```
pip install tensorflow==1.7.1
```

Download Google Demo

git clone https://github.com/googlecodelabs/tensorflow-for-poets-2

```
cd tensorflow-for-poets-2
```

Download TF_Files

wget http://download.tensorflow.org/example_images/flower_photos.tgz

```
tar -xvf flower_photos.tgz -C tf_files
```

Set ARCHITECTURE

```
IMAGE_SIZE=224
```

ARCHITECTURE="mobilenet_0.50_${IMAGE_SIZE}"

Start TensorBoard

tensorboard --logdir tf_files/training_summaries &

Help : retraining script

```
python -m scripts.retrain -h
```

Train

```
python -m scripts.retrain \
```

  --bottleneck_dir=tf_files/bottlenecks \

```
  --how_many_training_steps=500 \
```
```
  --model_dir=tf_files/models/ \
```

  --summaries_dir=tf_files/training_summaries/"${ARCHITECTURE}" \

  --output_graph=tf_files/retrained_graph.pb \

  --output_labels=tf_files/retrained_labels.txt \

```
  --architecture="${ARCHITECTURE}" \
```
```
  --image_dir=tf_files/flower_photos
```

Train with default 4,000 iterations

```
python -m scripts.retrain \
```

  --bottleneck_dir=tf_files/bottlenecks \

  --model_dir=tf_files/models/"${ARCHITECTURE}" \

  --summaries_dir=tf_files/training_summaries/"${ARCHITECTURE}" \

  --output_graph=tf_files/retrained_graph.pb \

  --output_labels=tf_files/retrained_labels.txt \

```
  --architecture="${ARCHITECTURE}" \
```
```
  --image_dir=tf_files/flower_photos
```

Train with Your Own Data

--image_dir= The root folder of the subdirectories which is used as label names by the classification script
This image is copied from google’s codelabs !

The Retrained Model

```
tf_files/retrained_graph.pb
```
```
tf_files/retrained_labels.txt
```

Test

(venv) water@water-G11CD:~/tensorflow-for-poets-2$ python -m scripts.label_image     --graph=tf_files/retrained_graph.pb      --image=tf_files/flower_photos/daisy/21652746_cc379e0eea_m.jpg

```
 
```

2019-01-23 14:13:50.234939: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA

```
 
```
```
Evaluation time (1-image): 0.094s
```
```
 
```
```
daisy (score=0.99938)
```
```
sunflowers (score=0.00033)
```
```
dandelion (score=0.00028)
```
```
roses (score=0.00000)
```
```
tulips (score=0.00000)
```

Now, Prepare for Android

Install PILLOW

```
pip install PILLOW
```

Convert the model to TFLite format

```
IMAGE_SIZE=224
```

toco   --input_file=tf_files/retrained_graph.pb   --output_file=tf_files/optimized_graph.lite   --input_format=TENSORFLOW_GRAPHDEF   --output_format=TFLITE   --input_shape=1,${IMAGE_SIZE},${IMAGE_SIZE},3   --input_array=input   --output_array=final_result   --inference_type=FLOAT   --input_data_type=FLOAT

The TFLite Model

```
tf_files/optimized_graph.lite
```

Install AndroidStudio

unzip android-studio-ide-182.5199772-linux.zip

```
cd android-studio/
```
```
cd bin/
```
```
./studio.sh
```

Maybe You Need to Set Proxy

If you use lantern, you can do it like this!

Open Project

Open an existing Android Studio project

Choose android/tflite

Gradle Sync, YES

Build, Make Project

android/tflite/app/build/outputs/apk/debug/app-debug.apk

Test on Your Phone

Add your model files to the project, and then Make Project, Test Again

cp tf_files/optimized_graph.lite android/tflite/app/src/main/assets/graph.lite

cp tf_files/retrained_labels.txt android/tflite/app/src/main/assets/labels.txt

How to train YOLOv3 to detect Peppa Pig

Posted on January 22, 2019January 23, 2019 by admin

Prepare

Collecting Data from the Web with Python If you like

PASCAL VOC

.
├── VOCdevkit
│ └── VOC2018
│ ├── Annotations
├── ImageSets
│ │ └── Main // name of *.jpg without “.jpg”
├── JPEGImages // *.jpg

Annotation

LabelImg is a graphical image annotation tool

Example : XML

```
<annotation>
```
```
	<folder>000</folder>
```
```
	<filename>000000.jpg</filename>
```

	<path>/home/water/Machine_Learning/Peppa_Pig/train/000/000000.jpg</path>

```
	<source>
```
```
		<database>Unknown</database>
```
```
	</source>
```
```
	<size>
```
```
		<width>410</width>
```
```
		<height>256</height>
```
```
		<depth>3</depth>
```
```
	</size>
```
```
	<segmented>0</segmented>
```
```
	<object>
```
```
		<name>Peppa</name>
```
```
		<pose>Unspecified</pose>
```
```
		<truncated>0</truncated>
```
```
		<difficult>0</difficult>
```
```
		<bndbox>
```
```
			<xmin>64</xmin>
```
```
			<ymin>87</ymin>
```
```
			<xmax>166</xmax>
```
```
			<ymax>226</ymax>
```
```
		</bndbox>
```
```
	</object>
```
```
	<object>
```
```
		<name>Peppa</name>
```
```
		<pose>Unspecified</pose>
```
```
		<truncated>0</truncated>
```
```
		<difficult>0</difficult>
```
```
		<bndbox>
```
```
			<xmin>290</xmin>
```
```
			<ymin>77</ymin>
```
```
			<xmax>333</xmax>
```
```
			<ymax>131</ymax>
```
```
		</bndbox>
```
```
	</object>
```
```
</annotation>
```

Generate Labels for VOC

Darknet wants a .txt file for each image with a line for each ground truth object in the image that looks like:

```
<object-class> <x> <y> <width> <height>
```

voc_label.py

```
import xml.etree.ElementTree as ET
```
```
import pickle
```
```
import os
```
```
from os import listdir, getcwd
```
```
from os.path import join
```
```
 
```

sets=[('2018', 'train'), ('2018', 'val')]

```
 
```
```
classes = ["Peppa"]
```
```
 
```
```
def convert(size, box):
```
```
    dw = 1./size[0]
```
```
    dh = 1./size[1]
```
```
    x = (box[0] + box[1])/2.0
```
```
    y = (box[2] + box[3])/2.0
```
```
    w = box[1] - box[0]
```
```
    h = box[3] - box[2]
```
```
    x = x*dw
```
```
    w = w*dw
```
```
    y = y*dh
```
```
    h = h*dh
```
```
    return (x,y,w,h)
```
```
 
```
```
def convert_annotation(year, image_id):
```

    in_file = open('VOCdevkit/VOC%s/Annotations/%s.xml'%(year, image_id))

    out_file = open('VOCdevkit/VOC%s/labels/%s.txt'%(year, image_id), 'w')

```
    tree=ET.parse(in_file)
```
```
    root = tree.getroot()
```
```
    size = root.find('size')
```
```
    w = int(size.find('width').text)
```
```
    h = int(size.find('height').text)
```
```
 
```
```
    for obj in root.iter('object'):
```

        difficult = obj.find('difficult').text

```
        cls = obj.find('name').text
```

        if cls not in classes or int(difficult) == 1:

```
            continue
```
```
        cls_id = classes.index(cls)
```
```
        xmlbox = obj.find('bndbox')
```

        b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text))

```
        bb = convert((w,h), b)
```

        out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')

```
 
```
```
wd = getcwd()
```
```
 
```
```
for year, image_set in sets:
```

    if not os.path.exists('VOCdevkit/VOC%s/labels/'%(year)):

        os.makedirs('VOCdevkit/VOC%s/labels/'%(year))

    image_ids = open('VOCdevkit/VOC%s/ImageSets/Main/%s.txt'%(year, image_set)).read().strip().split()

    list_file = open('%s_%s.txt'%(year, image_set), 'w')

```
    for image_id in image_ids:
```

        list_file.write('%s/VOCdevkit/VOC%s/JPEGImages/%s.jpg\n'%(wd, year, image_id))

        convert_annotation(year, image_id)

```
    list_file.close()
```

peppa.data

```
classes= 1
```

train  = /home/d/Downloads/Peppa_VOC/2018_train.txt

valid  = /home/d/Downloads/Peppa_VOC/2018_val.txt

```
names = data/peppa.names
```
```
backup = backup
```

yolov3-voc-peppa.cfg

```
[net]
```
```
# Testing
```
```
batch=1
```
```
subdivisions=1
```
```
# Training
```
```
 batch=64
```
```
 subdivisions=2
```
```
width=416
```
```
height=416
```
```
channels=3
```
```
momentum=0.9
```
```
decay=0.0005
```
```
angle=0
```
```
saturation = 1.5
```
```
exposure = 1.5
```
```
hue=.1
```
```
 
```
```
learning_rate=0.001
```
```
burn_in=1000
```
```
max_batches = 500200
```
```
policy=steps
```
```
steps=400000,450000
```
```
scales=.1,.1
```
```
 
```
```
[convolutional]
```
```
batch_normalize=1
```
```
filters=16
```
```
size=3
```
```
stride=1
```
```
pad=1
```
```
activation=leaky
```
```
 
```
```
[maxpool]
```
```
size=2
```
```
stride=2
```
```
 
```
```
[convolutional]
```
```
batch_normalize=1
```
```
filters=32
```
```
size=3
```
```
stride=1
```
```
pad=1
```
```
activation=leaky
```
```
 
```
```
[maxpool]
```
```
size=2
```
```
stride=2
```
```
 
```
```
[convolutional]
```
```
batch_normalize=1
```
```
filters=64
```
```
size=3
```
```
stride=1
```
```
pad=1
```
```
activation=leaky
```
```
 
```
```
[maxpool]
```
```
size=2
```
```
stride=2
```
```
 
```
```
[convolutional]
```
```
batch_normalize=1
```
```
filters=128
```
```
size=3
```
```
stride=1
```
```
pad=1
```
```
activation=leaky
```
```
 
```
```
[maxpool]
```
```
size=2
```
```
stride=2
```
```
 
```
```
[convolutional]
```
```
batch_normalize=1
```
```
filters=256
```
```
size=3
```
```
stride=1
```
```
pad=1
```
```
activation=leaky
```
```
 
```
```
[maxpool]
```
```
size=2
```
```
stride=2
```
```
 
```
```
[convolutional]
```
```
batch_normalize=1
```
```
filters=512
```
```
size=3
```
```
stride=1
```
```
pad=1
```
```
activation=leaky
```
```
 
```
```
[maxpool]
```
```
size=2
```
```
stride=1
```
```
 
```
```
[convolutional]
```
```
batch_normalize=1
```
```
filters=1024
```
```
size=3
```
```
stride=1
```
```
pad=1
```
```
activation=leaky
```
```
 
```
```
###########
```
```
 
```
```
[convolutional]
```
```
batch_normalize=1
```
```
filters=256
```
```
size=1
```
```
stride=1
```
```
pad=1
```
```
activation=leaky
```
```
 
```
```
[convolutional]
```
```
batch_normalize=1
```
```
filters=512
```
```
size=3
```
```
stride=1
```
```
pad=1
```
```
activation=leaky
```
```
 
```
```
[convolutional]
```
```
size=1
```
```
stride=1
```
```
pad=1
```
```
filters=18
```
```
activation=linear
```
```
 
```
```
 
```
```
 
```
```
[yolo]
```
```
mask = 3,4,5
```

anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319

```
classes=1
```
```
num=6
```
```
jitter=.3
```
```
ignore_thresh = .7
```
```
truth_thresh = 1
```
```
random=1
```
```
 
```
```
[route]
```
```
layers = -4
```
```
 
```
```
[convolutional]
```
```
batch_normalize=1
```
```
filters=128
```
```
size=1
```
```
stride=1
```
```
pad=1
```
```
activation=leaky
```
```
 
```
```
[upsample]
```
```
stride=2
```
```
 
```
```
[route]
```
```
layers = -1, 8
```
```
 
```
```
[convolutional]
```
```
batch_normalize=1
```
```
filters=256
```
```
size=3
```
```
stride=1
```
```
pad=1
```
```
activation=leaky
```
```
 
```
```
[convolutional]
```
```
size=1
```
```
stride=1
```
```
pad=1
```
```
filters=18
```
```
activation=linear
```
```
 
```
```
[yolo]
```
```
mask = 0,1,2
```

anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319

```
classes=1
```
```
num=6
```
```
jitter=.3
```
```
ignore_thresh = .7
```
```
truth_thresh = 1
```
```
random=1
```

Train

./darknet detector train cfg/peppa.data cfg/yolov3-voc-peppa.cfg darknet53.conv.74

Test

Reference

```
@article{yolov3,
```

  title={YOLOv3: An Incremental Improvement},

  author={Redmon, Joseph and Farhadi, Ali},

```
  journal = {arXiv},
```
```
  year={2018}
```
```
}
```

Prepare So

Test

Test Code

Test it without std::cout root@imx6ul7d:~/tmp# time ./t1_1 real 0m0.041suser 0m0.020ssys 0m0.000s

Test it with std::cout, but redirect to /dev/null root@imx6ul7d:~/tmp# time ./t1_0 > /dev/null real 0m0.096suser 0m0.030ssys 0m0.030s

Test it with std::cout, but set io state root@imx6ul7d:~/tmp# time ./t1_2 real 0m0.061suser 0m0.040ssys 0m0.000s

Example 1

Add -pg arm-linux-gnueabihf-g++ -Wall -g -pg hello.cpp -o hello -std=c++17

Developer Command Prompt for VS 2019

MSDN Source Code

Install TensorFlow As Normal

Download Google Demo

Download TF_Files

Set ARCHITECTURE

Start TensorBoard

Help : retraining script

Train

Train with default 4,000 iterations

Train with Your Own Data

The Retrained Model

Test

Now, Prepare for Android

Install PILLOW

Convert the model to TFLite format

The TFLite Model

Install AndroidStudio

Maybe You Need to Set Proxy

Open Project

Open an existing Android Studio project

Choose android/tflite

Gradle Sync, YES

Build, Make Project

Test on Your Phone

Add your model files to the project, and then Make Project, Test Again

Prepare

PASCAL VOC

Annotation

Example : XML

Generate Labels for VOC

voc_label.py

peppa.data

yolov3-voc-peppa.cfg

Train

Test

Reference

Test it without std::cout

root@imx6ul7d:~/tmp# time ./t1_1

real 0m0.041s
user 0m0.020s
sys 0m0.000s

Test it with std::cout, but redirect to /dev/null

root@imx6ul7d:~/tmp# time ./t1_0 > /dev/null

real 0m0.096s
user 0m0.030s
sys 0m0.030s

Test it with std::cout, but set io state

root@imx6ul7d:~/tmp# time ./t1_2

real 0m0.061s
user 0m0.040s
sys 0m0.000s

Add -pg

arm-linux-gnueabihf-g++ -Wall -g -pg hello.cpp -o hello -std=c++17