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How to use Qt5

2016-12-06

Jaehyek

This page will describe a Qt5 programming procedure, and show how to use the Qt5 API during making the little project “Temperature Test for Android”

0. Assume that Python 3.5 is already installed at your system.

1. download and install the “Qt 5.7.0 for Windows 64-bit (VS 2015, 918 MB)”

please refer to here here

2. install the pyserial

Anaconda python 3.5 will have it already.

3. install the PyQt5 like below

C:\>pip install PyQt5
Collecting PyQt5
  Downloading PyQt5-5.7-cp35-none-win_amd64.whl (68.1MB)
    100% |################################| 68.1MB 13kB/s
Collecting sip (from PyQt5)
  Downloading sip-4.18.1-cp35-none-win_amd64.whl (46kB)
    100% |################################| 51kB 1.7MB/s
Installing collected packages: sip, PyQt5
Successfully installed PyQt5-5.7 sip-4.18.1

please refer to PyQt5 Reference Guide

4. create the UI file of Qt5 using Qt Creator like below image.

QtUI

5. Qt programming for Android Phone Temperature Test

import and main

from PyQt5.QtWidgets import QDialog, QMessageBox
from PyQt5.QtWidgets import QApplication, QWidget
from PyQt5.QtCore import *

if __name__ == "__main__":
    app = QApplication(sys.argv)
    myapp = GUIForm()
    myapp.show()
    app.exec()
    #sys.exit(app.exec_())
    myapp.Update_data()

GUIForm class, QThread, QTimer, click.connect

class GUIForm(QDialog):
    def __init__(self, parent=None):
        QWidget.__init__(self,parent)
        self.ui = Ui_Dialog()
        self.ui.setupUi(self)
        self.first_draw_flag = False
        
        # directory name to read temperature
        self.path_battery_temp = "/sys/class/power_supply/battery/temp"
        self.path_cpu_temp     = "/sys/class/thermal/thermal_zone12/temp"
        self.path_battery_capacity = "/sys/class/power_supply/battery/capacity"
        
        self.art_script_thread = QThread()
        self.get_temp_thread = QThread()     
           
        self.myTimer1 = QTimer()
        self.myTimer2 = QTimer()
        self.myTimer3 = QTimer()
        self.myTimer4 = QTimer()

        # bind widgets to relative call-back functions
        self.ui.pushButton_run_1.clicked.connect(self.Run1)
        self.ui.pushButton_run_2.clicked.connect(self.Run2)
        self.ui.pushButton_run_3.clicked.connect(self.Run3)
        self.ui.pushButton_run_4.clicked.connect(self.Run4)
        self.ui.pushButton_copy_script_1.clicked.connect(self.Copy_script_to_Phone1)
        self.ui.pushButton_copy_script_2.clicked.connect(self.Copy_script_to_Phone2)
        self.ui.pushButton_copy_script_3.clicked.connect(self.Copy_script_to_Phone3)
        self.ui.pushButton_copy_script_4.clicked.connect(self.Copy_script_to_Phone4)
        self.ui.pushButton_init_1.clicked.connect(self.Init_Totcnt1)
        self.ui.pushButton_init_2.clicked.connect(self.Init_Totcnt2)
        self.ui.pushButton_init_3.clicked.connect(self.Init_Totcnt3)
        self.ui.pushButton_init_4.clicked.connect(self.Init_Totcnt4)
        self.ui.Ball_Crack.toggled.connect(self.btnstate1)		                   
        
        # load icon to label
        myPixmap = QtGui.QPixmap(os.getcwd() + "/phone.png")
        myScaledPixmap = myPixmap.scaled(self.ui.label_9.size(), Qt.KeepAspectRatio)
        self.ui.label_9.setPixmap(myScaledPixmap)

font change

    def btnstate1(self): 
            font = QtGui.QFont()
            font.setBold(True)

clicking the run button and timer call-back functions

    def Run1(self):
        if self.equipped[0] == False:
            return        
        myInitParams = 0
        timerCallback = functools.partial(self.RunTempTest, myInitParams)

        if self.myTimer1.isActive():
            print("Timer1 Already Active")
            #self.myTimer1.stop()
        else:
            self.myTimer1.timeout.connect(timerCallback)
            self.myTimer1.start(self.periodic_time*1000) 
            
        # thread to run the art scripts
        self.art_script_thread = ART_Scrtipt(self.device[0], "ART_Temp_Test_Script.py")
        self.art_script_thread.start()

call the plot function triggered by timer

    def RunTempTest(self,  idx):
        global g_read_temp_flag
            
        if self.test_result[idx] == False:      #  Test Error
            return
        
        if g_read_temp_flag == True:
            self.PlotFunc(idx)
            
        if self.CpuTemp[idx] == 0:
            return

QThread running for transfer a ART script

class ART_Scrtipt(QThread):    # Power On/Off Thread
    def __init__(self, device_id,  test_script):
        QThread.__init__(self)
        self.device_id = device_id
        self.test_script = test_script
  
    def run(self):
        timestring = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
        str1 = '[' + timestring + ']  ' 
        print("%s Enter Test Script %s  !!!" % (str1,  self.device_id))	
        adbcmd.adbcmd(self.device_id, 'am broadcast -a "com.lge.art.ACTION_START_SCRIPT" --es "files" %s ' %(self.test_script))
        self.quit()

QThread running for reading temperature from phone

class Get_Temp_from_Phone(QThread):    # Power On/Off Thread
    def __init__(self, path_battery_temp,  path_cpu_temp):
        QThread.__init__(self)
        self.path_battery_temp = path_battery_temp
        self.path_cpu_temp = path_cpu_temp    # XO_THERM
        
    def run(self):
        global g_device
        global g_Cpu_Temp		
        global g_Bat_Temp
        global g_read_temp_flag
        
        # read temperature of a phone
        g_Bat_Temp[count] <= value
        g_Cpu_Temp[count] <= value 
        
    def stop(self):
         self.exit_thread =  1
         self.quit()

plot the graph with temperature

    def PlotFunc(self,  idx):
        global g_Temp

        self.init_data()
        if idx == 0:
            pointer  = self.ui.widget_1.canvas
            X_data  = self.lstX1
            Y_data  = self.lstY1
            X1_data = self.lstX1_1
            Y1_data = self.lstY1_1 
        
        pointer.axes.clear()
        pointer.axes.set_xlabel('Time(sec)')
        pointer.axes.set_ylabel('Temperature($^{\circ}$C)')
        pointer.axes.xaxis.set_ticks_position('bottom')
        pointer.axes.yaxis.set_ticks_position('left')
        #pointer.axes.spines['top'].set_color('red')     
        pointer.axes.grid(color='b',  linewidth=1) 
        
        if self.ball_crack_flag == False:		
            pointer.axes.plot(self.X_upper_limit,   self.Y_upper_limit,   'r-',  linestyle = '--')   
            pointer.axes.plot(self.X_center_limit,  self.Y_center_limit,  'c',   linestyle = '-',  label='baseline')    
            pointer.axes.plot(self.X_bottom_limit, self.Y_bottom_limit, 'r-',  linestyle = '--')    
        else:
            pointer.axes.set_ylim(int(self.save_low_temp[idx]-2), int(self.save_high_temp[idx]+2))
            
        pointer.axes.plot(X1_data,  Y1_data,  'b-',  label='Temp', linewidth=1)   # , 'b-', , 'g-' ,  marker='o'
        
        pointer.draw()

Final. run the Python scripts. Below is the working screen .

QtTempTest

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Deep Learning Useful Sites

2016-12-05

Jaehyek

Deep-Learning

Deep-Learning Machine-Learning
Conference
- 제1회 딥러닝 컨퍼런스, 2017년 2월 16-18일
Image Recognition
Natural Language
- Implementation of Character Recussive Nueral Network 한글 Character RNN 구현
Audio
- SampleRNN: An Unconditional End-to-End Neural Audio Generation Model Github, Site
Others
Read All

Deep Learning Datasets

2016-12-05

Jaehyek

Deep-Learning

Deep-Learning Datasets

list

Site_Name_title	Purpose
20 Newsgroups	The text from 20000 messages taken from 20 Usenet newsgroups for text analysis, classification, etc.
Amazon Reviews	Over 142 million product reviews for sentiment analysis, recommender systems, and more.
Football Strategy	Thousands of scenarios to make the best coaching decisions.
Horses for Courses	Horse-racing data for predicting race results.
Human Activity Recognition with Smartphones	Sensor data for recognizing the human activity - walking, sitting, etc.
Labeled Faces in the Wild	13,000 named faces for facial recognition. Multiple training and test sets
National Survey on Drug Use and Health
NORB 3D Object Recognition	Binocular images of 50 toy figurines for 3D object recognition from image.
One Million Songs	Audio features and metadata for a subset (10,000) of the one million popular songs dataset for recognition/classification.
SMS Spam Collection	A collection of 5,574 SMS (text) messages, some spam, some normal, for spam filtering.
Hate Speech Identification	A sampling of Twitter posts that have been judged based on whether they are offensive or contain hate speech, as a training set for text analysis.
Hidden Beauty of Flickr Pictures	15,000 Flickr photo IDs that have received ratings based on aesthetics, for image analysis.
Yahoo Instant Messenger Friends Connectivity Graph	Connections between Yahoo users who communicate with each other using Yahoo messenger, can be used to identify key social contacts/influencers. Add dataset to cart to access.
Record of Heart Sound	Recordings of normal and abnormal heartbeats, used to recognize heart murmur, etc.
Prostate Cancer	Tumor and nontumor samples, used to recognize prostate cancer.
Wine Quality	Chemical properties of red and white wines (separately) and quality, for classification.
Mushroom Identification	For hypothetically classifying mushrooms as edible or poisonous based on its characteristics.
UFO Reports	80,000 historic reports for classification or regression. This dataset has been standardized from the source data at nuforc.org.
Militarized Interstate Disputes	Nearly 200 years of international threats, conflicts, etc. for modelling or prediction. Includes action taken, level of hostility, fatalities, and outcomes.
NBA & MLB Stats	Current and past season stats for teams and players for fantasy sports predictions.
Sign Language
MusicNet	MusicNet is a collection of 330 freely-licensed classical music recordings, together with over 1 million annotated labels indicating the precise time of each note every recording, the instrument that plays each note, and the note’s position in the metrical structure of the composition. The labels are acquired from musical scores aligned to recordings by dynamic time warping. The labels are verified by trained musicians; we estimate a labeling error rate of 4%. We offer the MusicNet labels to the machine learning and music communities as a resource for training models and a common benchmark for comparing results.
ProductHunt
Reddit	1.7 billion Reddit comments
VQA2	visual question answering dataset, now 2X larger
UCI ML Repo	351 datasets
Hacker News	Full comment dump of HN
FIRE	Fundus Image Registration Dataset
LASIESTA	Labeled and Annotated Sequences for Integral Evaluation of SegmenTation Algorithms
LAKH MIDI Dataset	Its goal is to facilitate large-scale music information retrieval, both symbolic (using the MIDI files alone) and audio content-based (using information extracted from the MIDI files as annotations for the matched audio files).
Lamem	Large-scale Image Memorability
Pratheepan dataset	Human Skin Detection dataset
COCO-Stuff dataset	COCO-Stuff semantic segmentation dataset
NewsQA	Maluuba’s News QA is a new machine reading comprehension dataset for developing algorithms capable of answering questions requiring human-level comprehension and reasoning skills. This dataset of CNN news articles has over 110K Q&A pairs. Questions are written by humans in natural language. Questions may not have answers and answers may be multiword passages.
Awesome Public Datasets	A massive Github repo of accessible, public datasets. The datasets are not, by nature, completely clean and purpose-built for ML.
ImageNet	The ImageNet project is a large visual database designed for use in visual object recognition software research
Element List Scientific Data Directory	An online repository of links to free, publicaly available scientific datasets, mostly from university, industry, and government research programs.
IMDB dataset
MSCOCO	Image segmentation and object recognition
Google Books Ngrams
OpenML repository	Almost 20k datasets
Enron Email Corpus	The Enron Corpus is a large database of over 600,000 emails generated by 158 employees of the Enron Corporation and acquired by the Federal Energy Regulatory Commission during its investigation after the company’s collapse.
German Traffic Signs	German Traffic Sign Detection Benchmark (GTSDB). The first was used in a competition at IJCNN 2011.
SYNTHIA	500.000 frames of annotated vídeo from a virtualcity. labels for stereo, optical flow, semántica segmentación, odometry…
Elektra	over 20 different autonomous driving datasets: pedestrians, semantic segmentation, stereo…
Cornell Movie–Dialogs Corpus	This corpus contains a large metadata-rich collection of fictional conversations extracted from raw movie scripts:

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Welcome to StackEdit!

2016-12-04

Jaehyek

html

Markdown syntax
Web Markdown editor은 이 site 을 참조하자: here

혹은 한글설명은 여기에서 참조하자

해시 태그 샵 기호를 사용해 제목 서식을 지정할 수 있습니다.

해시 태그 샵 기호를 사용해 제목 서식을 지정할 수 있습니다.

해시 태그 샵 기호를 사용해 제목 서식을 지정할 수 있습니다.

별표 두 개를 사용해 글자를 굵게 할 수 있습니다.

언더바를 사용해 글자를 기울일 수 있습니다.

혹은 별표 하나만 사용해도 글자를 기울일 수 있습니다.
- 사과
- 복숭아
- 포도
1. 사과
2. 복숭아
3. 포도
- 사과
참고: 숫자 목록으로 내용을 시작한 뒤 어떤 이유로 인해 글 머리 기호 목록을 바로 다음에 넣고 싶다면, 두 목록 사이를 제목이나 부제목으로 서로 분리시켜줘야 합니다. 이렇게 하지 않으면 마크다운이 다음 목록 항목을 알맞게 처리하지 않을 겁니다.
- 바나나
- 수박
이제 “마크다운(Markdown)”자 바로 앞에 “>” 오른쪽 꺾쇠 괄호 기호를 입력하면 아래 문단을 인용문으로 지정할 수 있습니다.

쿠키 한 입 베어 물고 녹차. 녹차 한 입 마시고 쿠키. 바사삭 바사삭 느낌도 좋지만 촉촉한 느낌도 좋아. 컵 케이크 마카롱 젤리 먹고 후식으로는 티라미수. 연하고 부드러운 수플레 치즈 케이크에 초콜릿 푸딩.

마크다운(Markdown)은 일반 텍스트 문서의 양식을 편집하는 문법이다. README 파일이나 온라인 문서, 혹은 일반 텍스트 편집기로 문서 양식을 편집할 때 쓰인다. 마크다운을 이용해 작성된 문서는 쉽게 HTML 등 다른 문서형태로 변환이 가능하다.

아래 이메일 주소를 입력하거나 여러분만의 주소를 넣은 뒤, 꺾쇠 괄호 기호 (<>)로 감싸보세요. 아래 쪽의 주소 링크 스크린샷에서 결과를 볼 수 있습니다

jaehyek@gmail.com

주소가 보이는 링크입니다. https://jaehyek.github.io

주소 대신 단어가 보이는 링크입니다. 링크

이미지 넣기

Youtube 넣기

만일 Youtube 주소가 https://www.youtube.com/watch?v=GDsmyigv9tw 이면

구글 로고입니다:
참고: 참조 문자는 이미지를 구분할 수 있도록 돕는 이름이면 어떤 것이든 쓸 수 있습니다.

##가로 선 넣기

가로 선은 문서에서 내용을 구분 지어 나눌 때 유용합니다. 마크다운에서 가로 선을 추가하려면, 선을 표시해야 할 곳에 세 개나 혹은 그 이상의 중간 선(대시) 기호를 입력하기만 하면 됩니다.

쿠키 한 입 베어 물고 녹차. 녹차 한 입 마시고 쿠키. 바사삭 바사삭 느낌도 좋지만 촉촉한 느낌도 좋아. 컵 케이크 마카롱 젤리 먹고 후식으로는 티라미수. 연하고 부드러운 수플레 치즈 케이크에 초콜릿 푸딩.

마크다운(Markdown)은 일반 텍스트 문서의 양식을 편집하는 문법이다. README 파일이나 온라인 문서, 혹은 일반 텍스트 편집기로 문서 양식을 편집할 때 쓰인다. 마크다운을 이용해 작성된 문서는 쉽게 HTML 등 다른 문서형태로 변환이 가능하다

##강제 개행 마크다운에서 강제로 줄 바꿈 하고 싶을 경우 일부 편집기에서는 줄 끝에 둘 이상의 공백을 넣으면 줄 바꿈으로 적용됩니다. HTML을 사용할 경우, 여백 사이에
</br>을 넣으면 됩니다

#멀티마크다운 사용 방법

##취소선 넣기 취소선이라는 단어의 양쪽 끝에 물결 모양 기호를 두 개씩 추가하세요(~~취소선~~). 이 방법은 HTML 태그의 ~~취소선~~을 사용하는 것 보다 훨씬 더 쉽습니다
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Jekyll Help Sites

2016-12-03

Jaehyek

Jekyll

jekyll

다음 사이트를 참조하자
Run Jekyll on Windows http://jekyll-windows.juthilo.com/
Jekyll Docments http://jekyllrb.com/
Jekyll 한글 문서 http://jekyllrb-ko.github.io/docs/usage/
Liquid tag 설명 https://github.com/Shopify/liquid/wiki/Liquid-for-Designers
Multiple _posts directories https://github.com/jekyll/jekyll/issues/2440
Jekull Themes http://themes.jekyllrc.org/
Jekull Themes http://jekyllthemes.org/

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HTML Tag list

2016-12-03

Jaehyek

html

html tag

다음 사이트를 참조하자
HTML 태그소스 명령어 정리표 http://tip.daum.net/openknow/63622006
알레폰드의 IT 이모저모 http://202psj.tistory.com/575
이글루의 HTML 태그 정리표 http://egloos.zum.com/fundoodle/v/3131696
이글루의 HTML 태그 정리 http://egloos.zum.com/woong/v/2537395

HTML5〃기본 구조/시맨틱 태그(HEAD/BODY) http://hunit.tistory.com/172
HTML5〃텍스트(TEXT) 태그 정리 http://hunit.tistory.com/173
HTML5〃목록 태그(UL / OL / DL ) http://hunit.tistory.com/175

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8/11

Welcome to Jaehyek's Blog!

How to use Qt5

0. Assume that Python 3.5 is already installed at your system.

1. download and install the “Qt 5.7.0 for Windows 64-bit (VS 2015, 918 MB)”

2. install the pyserial

3. install the PyQt5 like below

4. create the UI file of Qt5 using Qt Creator like below image.

5. Qt programming for Android Phone Temperature Test

Final. run the Python scripts. Below is the working screen .

Deep Learning Useful Sites

Conference

Image Recognition

Natural Language

Audio

Others

Deep Learning Datasets

list

Welcome to StackEdit!

해시 태그 샵 기호를 사용해 제목 서식을 지정할 수 있습니다.

해시 태그 샵 기호를 사용해 제목 서식을 지정할 수 있습니다.

해시 태그 샵 기호를 사용해 제목 서식을 지정할 수 있습니다.

이제 “마크다운(Markdown)”자 바로 앞에 “>” 오른쪽 꺾쇠 괄호 기호를 입력하면 아래 문단을 인용문으로 지정할 수 있습니다.

이미지 넣기

Youtube 넣기

Jekyll Help Sites

HTML Tag list