Image Augmentation (Pencil & Cartoon) with OpenCV #SYM42

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OpenCV has been with us for over two decades and provides us with a rich open-source library for performing image processing.

In this post I’m going to illustrate how you can use it to convert images (of people) into pencil sketches and cartoon images. As with most examples you find on such technologies there are things it is good at and some things this isn’t good at. Using the typical IT phrase, “It Depends” comes into play with image processing. What might work with one set of images, might not work as well with others.

The example images below consist of the Board of a group called SYM42, or Symposium42. Yes, they said I could use their images and show the output from using OpenCV 🙂 This group was formed by a community to support the community, was born out of an Oracle Community but is now supporting other technologies. They are completely independent of any Vendor which means they can be 100% honest about which aspects of any product do or do not work and are not influenced by the current sales or marketing direction of any company. Check out their About page.

Let’s get started. After downloading the images to process, let’s view them.

import cv2
import matplotlib.pyplot as plt
import numpy as np

dir = '/Users/brendan.tierney/Dropbox/6-Screen-Background/'
file = 'SYM42-Board-Martin.jpg'

image = cv2.imread(dir+file)
img_name = 'Original Image'
#Show the image with matplotlib
#plt.imshow(image)

#OpenCV uses BGR color scheme whereas matplotlib uses RGB colors scheme
#convert BGR image to RGB by using the following
plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
plt.axis(False)
plt.show()

I’m using Jupyter Notebooks for this work. In the above code, you’ll see I’ve commented out the line [#plt.imshow(image)]. This comment doesn’t really work in Jupyter Notebooks and instead you need to swap to using Matplotlib to display the images

To convert to a pencil sketch, we need to convert to pencil sketch, apply a Gaussian Blur, invert the image and perform bit-wise division to get the final pencil sketch.

#convert to grey scale
#cvtColor function
grey_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
#invert the image
invert_image = cv2.bitwise_not(grey_image)

#apply Gaussian Blue : adjust values until you get pencilling effect required
blur_image=cv2.GaussianBlur(invert_image, (21,21),0)  #111,111

#Invert Blurred Image
#Repeat previous step
invblur_image=cv2.bitwise_not(blur_image)

#The sketch can be obtained by performing bit-wise division between 
# the grayscale image and the inverted-blurred image.
sketch_image=cv2.divide(grey_image, invblur_image, scale=256.0)

#display the pencil sketch
plt.imshow(cv2.cvtColor(sketch_image, cv2.COLOR_BGR2RGB))
plt.axis(False)
plt.show()

The following code listing contains the same as above and also includes the code to convert to a cartoon style.

import os
import glob
import cv2
import matplotlib.pyplot as plt
import numpy as np

def edge_mask(img, line_size, blur_value):
  gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
  gray_blur = cv2.medianBlur(gray, blur_value)
  edges = cv2.adaptiveThreshold(gray_blur, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, line_size, blur_value)
  return edges

def cartoon_image(img_cartoon):
    img=img_cartoon
    
    line_size = 7
    blur_value = 7
    edges = edge_mask(img, line_size, blur_value)

    #Clustering - (K-MEANS)
    imgf=np.float32(img).reshape(-1,3)
    criteria=(cv2.TERM_CRITERIA_EPS+cv2.TERM_CRITERIA_MAX_ITER,20,1.0)
    compactness,label,center=cv2.kmeans(imgf,5,None,criteria,10,cv2.KMEANS_RANDOM_CENTERS)
    center=np.uint8(center)
    final_img=center[label.flatten()]
    final_img=final_img.reshape(img.shape)
    
    cartoon=cv2.bitwise_and(final_img,final_img,mask=edges)

    return cartoon

def sketch_image(image_file, blur):
    import_image = cv2.imread(image_file)
    
    #cvtColor function
    grey_image = cv2.cvtColor(import_image, cv2.COLOR_BGR2GRAY)
    #invert the image
    invert_image = cv2.bitwise_not(grey_image)
    
    blur_image=cv2.GaussianBlur(invert_image, (blur,blur),0)   #111,111
    
    #Invert Blurred Image
    #Repeat previous step
    invblur_image=cv2.bitwise_not(blur_image)
    
    sketch_image=cv2.divide(grey_image, invblur_image, scale=256.0)
    cartoon_img=cartoon_image(import_image)
    
    #plot images
#    plt.figure(figsize=(9,6))
    plt.rcParams["figure.figsize"] = (12,8)
    
    #plot cartoon
    plt.subplot(1,3,3)
    plt.imshow(cv2.cvtColor(cartoon_img, cv2.COLOR_BGR2RGB))
    plt.title('Cartoon Image', size=12, color='red')
    plt.axis(False)
    
    #plot sketch
    plt.subplot(1,3,2)
    plt.imshow(cv2.cvtColor(sketch_image, cv2.COLOR_BGR2RGB))
    plt.title('Sketch Image', size=12, color='red')
    plt.axis(False)
    #plot original image
    plt.subplot(1,3,1)
    plt.imshow(cv2.cvtColor(import_image, cv2.COLOR_BGR2RGB))
    plt.title('Original Image', size=12, color='blue')
    plt.axis(False)

    #plot show
    plt.show()

for filepath in glob.iglob(dir+'SYM42-Board*.*'):
    #print(filepath)
    #import_image = cv2.imread(dir+file)
    sketch_image(filepath, 23)

For the SYM42 Board members, we get the following output.

As you can see from these images, some are converted in a way you would expect. While others seem to give little effect.

Thanks to the Board of SYM42 for allowing me to use their images.

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EU Digital Services Act

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The Digital Services Act (DSA) applies to a wide variety of online services, ranging from websites to social networks and online platforms, with a view to “creating a safer digital space in which the fundamental rights of all users of digital services are protected”.

In November 2020, the European Union introduced a new legislation called the Digital Services Act (DSA) to regulate the activities of tech companies operating within the EU. The aim of the DSA is to create a safer and more transparent online environment for EU citizens by imposing new rules and responsibilities on digital service providers. This includes online platforms such as social media, search engines, e-commerce sites, and cloud services. The provisions in the DSA Act will apply from 17th February 2024, thus giving affected parties time to ensure compliance.

The DSA aims to address a number of issues related to digital services, including:

  • Ensuring that digital service providers take responsibility for the content on their platforms and that they have effective measures in place to combat illegal content, such as hate speech, terrorist content, and counterfeit goods.
  • Requiring digital service providers to be more transparent about their advertising practices, and to disclose more information about the algorithms they use to recommend content.
  • Introducing new rules for online marketplaces to prevent the sale of unsafe products and to ensure that consumers are protected when buying online.
  • Strengthening the powers of national authorities to enforce these rules and to hold digital service providers accountable for any violations.

The DSA takes a layered approach to regulation. The basic obligations under the DSA apply to all online intermediary service providers, additional obligations apply to providers in other categories, with the heaviest regulation applying to very large online platforms (VLOPs) and very large online service engines (VLOSEs).

The four categories are:

  1. Intermediary service providers are online services which consist of either a “mere conduit” service, a “caching” service; or a “hosting” service. Examples include online search engines, wireless local area networks, cloud infrastructure services, or content delivery networks.
  2. Hosting services are intermediary service providers who store information at the request of the service user. Examples include cloud services and services enabling sharing information and content online, including file storage and sharing.
  3. Online Platforms are hosting services which also disseminate the information they store to the public at the user’s request. Examples include social media platforms, message boards, app stores, online forums, metaverse platforms, online marketplaces and travel and accommodation platforms.
  4. (a) VLOPs are online platforms having more than 45 million active monthly users in the EU (representing 10% of the population of the EU). (b) VLOSEs are online search engines having more than 45 million active monthly users in the EU (representing 10% of the population of the EU).

Arther Cox provide a useful table of obligations for each of these categories.

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Morse Code with Python

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Morse code is a method used in telecommunication to encode text characters as standardized sequences of two different signal duration, called dots and dashes. Morse code is named after Samuel Morse, one of the inventors of the telegraph (wikipedia).

The example code below illustrates taking input from the terminal, converting it into Morse code, playing the Morse code sound signal, then converts the Morse code back into plain text and prints this to the screen. This is a base set of code you can use and can be easily extended to make it more interactive.

When working with sound and audio in Python there are lots of different libraries available for this. But some of the challenges is trying to pick a suitable one, and one that is still supported in more recent times. One of the most commonly referenced library is called Winsound, but that is for Windows based computers. Not everyone uses Windows machines, just like myself using a Mac. So Winsound wasn’t an option. I selected to use the playsound library, mainly based on how commonly referenced it is.

To play the dots and dashs, I needed some sound files and these were originally sources from Wikimedia. The sound files are available on wikimedia, but these come in ogg file formats. I’ve converted the dot and dash files to mp3 files and theses can be downloaded from here, dot download and dash download. I also included a Error sound file in my code for when an error occurs! Error download.

When you download the code and sound files, you might need to adjust the timing for playing the Morse code sound files, as this might be dependent on your computer

The Morse code mapping was setup as a dictionary and a reverse mapping of this dictionary was used to translate morse code into plain text. 

import time
from playsound import playsound

toMorse = {'a': ".-",    'b': "-...", 
           'c': "-.-.",  'd': "-..", 
           'e': ".",     'f': "..-.", 
           'g': "--.",   'h': "....", 
           'i': "..",    'j': ".---", 
           'k': "-.-",   'l': ".-..", 
           'm': "--",    'n': "-.", 
           'o': "---",   'p': ".--.", 
           'q': "--.-",  'r': ".-.", 
           's': "...",   't': "-", 
           'u': "..-",   'v': "...-", 
           'w': ".--",   'x': "-..-", 
           'y': "-.--",  'z': "--..",
           '1': ".----", '2': "..---", 
           '3': "...--", '4': "....-", 
           '5': ".....", '6': "-....", 
           '7': "--...", '8': "---..", 
           '9': "----.", '0': "-----",
           ' ': " ",   '.': ".-.-.-", 
           ',': "--..--", '?': "..--..", 
           "'": ".----.", '@': ".--.-.", 
           '-': "-....-", '"': ".-..-.", 
           ':': "---...", ';': "---...",
           '=': "-...-",  '!': "-.-.--", 
           '/': "-..-.",  '(': "-.--.", 
           ')': "-.--.-", 'á': ".--.-", 
           'Ă©': "..-.."}

#sounds from https://commons.wikimedia.org/wiki/Morse_code
soundPath = "/Users/brendan.tierney/Dropbox/4-Datasets/morse_code_audio/"
#adjust this value to change time between dots/dashes
tBetween = 0.1

def play_morse_beep():
    playsound(soundPath + 'Dot_morse_code.mp3')
    time.sleep(1 * tBetween)
    
def play_morse_dash():
    playsound(soundPath + 'Dash_morse_code.mp3')
    time.sleep(2 * tBetween)
    
def play_morse_space():
    time.sleep(2 * tBetween)

def play_morse_error():
    playsound(soundPath + 'Error_invalid_char.mp3')
    time.sleep(2 * tBetween)

def text_to_morse(inStr):
    mStr = ""
    for c in [x for x in inStr]:
        m = toMorse[c]
        mStr += m + ' '

    print("morse=",mStr) 
    return mStr

def play_morse(inMorse):
    for m in inMorse:
        if m == ".":
            play_morse_beep()
        elif m == "-":
            play_morse_dash()
        elif m == " ":
            play_morse_space()
        else:
            play_morse_error()
            
#Get Input Text
from colorama import Fore, Back, Style

print(Fore.RED + '')

inputStr = input("Enter text -> morse :").lower() #.strip()
print(Fore.BLACK + '' + inputStr)

mStr = text_to_morse(inputStr)
play_morse(mStr)

play_morse(mStr)

Then to reverse the Morse code.

#reverse the k,v 
mToE = {}
for key, value in toMorse.items():
        mToE[value] = key

def morse_to_english(inStr):
    inStr = inStr.split(" ")
    engStr = [] 
    for c in inStr:
        if c in mToE:
            engStr.append(mToE[c])

    return "".join(engStr)

x=morse_to_english(mStr)
print(x)

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Oracle 23c Free – Developer Release

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Oracle 23c if finally available, in the form of Oracle 23c FREE – Developer Release. There was lots of excitement in some parts of the IT community about this release, some of which is to do with people having to wait a while for this release, given 22c was never released due to Covid!

But some caution is needed and reining back on the excitement is needed.

Why? This release isn’t the full bells and whistles full release of 23c Database. There has been several people from Oracle emphasizing the name of this release is Oracle 23c Free – Developer Release. There are a few things to consider with this release. It isn’t a GA (General Available) Release which is due later this year (maybe). Oracle 23c Free – Developer Release is an early release to allow developers to start playing with various developer focused new features. Some people have referred to this as the 23c Beta version 2 release, and this can be seen in the DB header information. It could be viewed in a similar way as the XE releases we had previously. XE was always Free, so we now we have a rename and emphasis of this. These have been many, many organizations using the XE release to build applications. Also the the XE releases were a no cost option, or what most people would like to say, the FREE version.

For the full 23c Database release we will get even more features, but most of these will probably be larger enterprise scale scenarios.

Now it’s time you to go play with 23c Free – Developer Release. Here are some useful links

I’ll be writing posts on some of the more interesting new features and I’ll add the links to those below. I’ll also add some links to post by other people:

This entry was posted in 23c, database, Oracle and tagged , , .

Automated Data Visualizations in Python

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Creating data visualizations in Python can be a challenge. For some it an be easy, but for most (and particularly new people to the language) they always have to search for the commands in the documentation or using some search engine. Over the past few years we have seem more and more libraries coming available to assist with many of the routine and tedious steps in most data science and machine learning projects. I’ve written previously about some data profiling libraries in Python. These are good up to a point, but additional work/code is needed to explore the data to suit your needs. One of these Python libraries, designed to make your initial work on a new data set easier is called AutoViz. It’s good to see there is continued development work on this library, which can be really help for creating initial sets of charts for all the variables in your data set, plus it has some additional features which help to make it very useful and cuts down on some of the additional code you might need to write.

The outputs from AutoViz are very extensive, and are just too long to show in this post. The images below will give you an indication of what if typically generated. I’d encourage you to install the library and run it on one of your data sets to see the full extent of what it can do. For this post, I’ll concentrate on some of the commands/parameters/settings to get the most out of AutoViz.

Firstly there is the install via pip command or install using Anaconda.

pip3 install autoviz

For comparison purposes I’m going to use the same data set as I’ve used in the data profiling post (see above). Here’s a quick snippet from that post to load the data and perform the data profiling (see post for output)

import pandas as pd
import pandas_profiling as pp

#load the data into a pandas dataframe
df = pd.read_csv("/Users/brendan.tierney/Downloads/Video_Games_Sales_as_at_22_Dec_2016.csv")

We can not skip to using AutoViz. It supports the loading and analzying of data sets direct from a file or from a pandas dataframe. In the following example I’m going to use the dataframe (df) created above.

from autoviz import AutoViz_Class

AV = AutoViz_Class()
df2 = AV.AutoViz(filename="", dfte=df)  #for a file, fill in the filename and remove dfte parameter

This will analyze the data and create lots and lots of charts for you. Some are should in the following image. One the helpful features is the ‘data cleaning improvements’ section where it has performed a data quality assessment and makes some suggestions to improve the data, maybe as part of the data preparation/cleaning step.

There is also an option for creating different types of out put with perhaps the Bokeh charts being particularly useful.

  • chart_format='bokeh': interactive Bokeh dashboards are plotted in Jupyter Notebooks.
  • chart_format='server', dashboards will pop up for each kind of chart on your web browser.
  • chart_format='html', interactive Bokeh charts will be silently saved as Dynamic HTML files under AutoViz_Plots directory
df2 = AV.AutoViz(filename="", dfte=df, chart_format='bokeh')

The next example allows for the report and charts to be focused on a particular dependent or target variable, particular in scenarios where classification will be used.

df2 = AV.AutoViz(filename="", dfte=df, depVar="Platform")

A little warning when using this library. It can take a little bit of time for it to run and create all the different charts. On the flip side, it save you from having to write lots and lots of code!

This entry was posted in Analytics, Data Science, Python and tagged , , , .

Annual Look at Database Trends (Jan 2023)

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Monitoring trends in the popularity and usage of different Database vendors can be a interesting exercise. The marketing teams from each vendor do an excellent job of promoting their Database, along with the sales teams, developer advocates, and the user communities. Some of these are more active than others and it varies across the Database market on what their choice is for promoting their products. One of the problems with these various types of marketing, is how can be believe what they are saying about how “awesome” their Database is, and then there are some who actively talk about how “rubbish” (or saying something similar) other Databases area. I do wonder how this really works for these people and vendors when to go negative about their competitors. A few months ago I wrote about “What does Legacy Really Mean?“. That post was prompted by someone from one Database Vendor calling their main competitor Database a legacy product. They are just name calling withing providing any proof or evidence to support what they are saying.

Getting back to the topic of this post, I’ve gathered some data and obtained some league tables from some sites. These will help to have a closer look at what is really happening in the Database market throughout 2022. Two popular sites who constantly monitor the wider internet and judge how popular Databases area globally. These sites are DB-Engines and TOPDB Top Database index. These are well know and are frequently cited. Both of these sites give some details of how they calculate their scores, with one focused mainly on how common the Database appears in searches across different search engines, while the other one, in addition to search engine results/searches, also looks across different websites, discussion forms, social media, job vacancies, etc.

The first image below is a comparison of the league tables from DB-Engines taken in January 2022 and January 2023. I’ve divided this image into three sections/boxes. Overall for the first 10 places, not a lot has changed. The ranking scores have moved slightly in most cases but not enough to change their position in the rank. Even with a change of score by 30+ points is a very small change and doesn’t really indicate any great change in the score as these scores are ranked in a manner where, “when system A has twice as large a value in the DB-Engines Ranking as system B, then it is twice as popular when averaged over the individual evaluation criteria“. Using this explanation, Oracle would be twice as popular when compared to PostgreSQL. This is similar across 2022 and 2023.

Next we’ll look a ranking from TOPDB Top Database index. The image below compares January 2022 and January 2023. TOPDB uses a different search space and calculation for its calculation. The rankings from TOPDB do show some changes in the ranks and these are different to those from DB-Engines. Here we see the top three ranks remain the same with some small percentage changes, and nothing to get excited about. In the second box covering ranks 4-7 we do some changes with PostgreSQL improving by two position and MongoDB. These changes do seem to reflect what I’ve been seeing in the marketplace with MongoDB being replaced by PostgreSQL and MySQL, with this multi-model architecture where you can have relational, document, and other data models in the one Database. It’s important to note Oracle and SQL Server also support this. Over the past couple of years there has been a growing awareness of and benefits of having relation and document (and others) data models in the one database. This approach makes sense both for developer productivity, and for data storage and management.

The next gallery of images is based on some Python code I’ve written to look a little bit closer at the top five Databases. In this case these are Oracle, MySQL, SQL Server, PostgreSQL and MongoDB. This gallery plots a bar chart for each Database for their top 15 Counties, and compares them with the other four Databases. The results are interesting and we can see some geographic aspects to the popularity of the Databases.

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Migrating SAS files to CSV and database

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Many organizations have been using SAS Software to analyse their data for many decades. As with most technologies organisations will move to alternative technologies are some point. Recently I’ve experienced this kind of move. In doing so any data stored in one format for the older technology needed to be moved or migrated to the newer technology. SAS Software can process data in a variety of format, one of which is their own internal formats. Thankfully Pandas in Python as a function to read such SAS files into a pandas dataframe, from which it can be saved into some alternative format such as CSV. The following code illustrates this conversion, where it will migrate all the SAS files in a particular directory, converts them to CSV and saves the new file in a destination directory. It also copies any existing CSV files to the new destination, while ignore any other files. The following code is helpful for batch processing of files.

import os
import pandas as pd

#define the Source and Destination directories
source_dir='/Users/brendan.tierney/Dropbox/4-Datasets/SAS_Data_Sets'
dest_dir=source_dir+'/csv'

#What file extensions to read and convert
file_ext='.sas7bdat'

#Create output directory if it does not already exist
if not os.path.exists(dest_dir):
    os.mkdir(dest_dir)
else:  #If directory exists, delete all files
    for filename in os.listdir(source_dir):
        os.remove(filename)

#Process each file
for filename in os.listdir(source_dir):
    #Process the SAS file
    if filename.endswith(file_ext):
        print('.processing file :',filename) 
        print('...converting file to csv')
        df=pd.read_sas(os.path.join(source_dir, filename))
        df.to_csv(os.path.join(dest_dir, filename))
        print('.....finished creating CSV file')
    elif filename.endswith('csv'):
        #Copy any CSV files to the Destination Directory
        print('.copying CSV file')
        cmd_copy='cp '+os.path.join(source_dir, filename)+' '+os.path.join(dest_dir, filename)
        os.system(cmd_copy)
        print('.....finished copying CSV file')
    else:
        #Ignore any other type of files
        print('.ignoring file :',filename)

print('--Finished--')

That’s it. All the files have now been converted and located in the destination directory.

For most, the above might be all you need, but sometimes you’ll need to move the the newer technology. In most cases the newer technology will easily use the CSV files. But in some instance your final destination might be a database. In my scenarios I use the CSV2DB app developed by Gerald Venzi. You can download the code from GitHub. You can use this to load CSV files into Oracle, MySQL, PostgreSQL, SQL Server and Db2 databases. Here’s and example of the command line to load into an Oracle Database.

csv2db load -f /Users/brendan.tierney/Dropbox/4-Datasets/SAS_Data_Sets/csv -t pva97nk -u analytics -p analytics  -d PDB1

This entry was posted in Analytics, CSV, Python, SAS and tagged , , .

Downloading your Twitter Archive

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Over the past few weeks there has been various dramas with Twitter, its new owner, how the staff have been treated, etc. All this drama has also impacted the day-to-day users of Twitter. Will Twitter survive? If it does, how will it be different? Maybe nothing will change for now or for a while, and we might see some gradual changes as a use of the platform. Many people have either moved to Mastodon, although it has many limitations and might not be suitable for everyone.

Maybe Twitter will survive? Only time will tell.

But what about all your Twitter posts, replies and interactions? What if Twitter disappears and all your content over the past few years or even long (10+ years). One thing you can do, and do it now, is to download your Twitter Archive. This will contain a those tweets, replies and interactions over the years. There will be a lot of valuable information in those and it would be a shame to have lost it.

These are the step I’ve followed to Download your Twitter Archive:

1. To to your Twitter home page, and click on the More icon in the menu on the left hand side of the page. Select “Settings and Support” from the menu options. Click “Settings and privacy.” Select “Your account” from the menu options.

2. Click “Download an archive of your data.” You’ll be asked to enter your password, do so and click “Confirm.”

3. Next you’ll have to verify your identity. Click “Send code” to your email and/or your phone number. You’ll be redirected to the “Account information” page where you’ll be able to enter the code that was sent to your email or phone.

4. Once Twitter has verified it is really you, click “Request archive.”

5. When your download is ready to be downloaded, you’ll receive an email from Twitter with a link to the zip file containing your archive.

That’s it. You now have your Twitter data, to use as you want.

There are several reasons why downloading your Twitter archive can be valuable. Here are some additional benefits:

  • It allows you to access and view your entire tweet history in one place, which can be useful for personal or professional purposes. For example, you might want to review your tweets to see what you have shared in the past, or to see how your online presence has evolved over time.
  • It can be a useful backup of your tweet history, in case your account is ever deleted or suspended. By downloading your archive, you will have a copy of your tweets that you can refer to or use to restore your account if necessary.
  • It can be useful for researching and analyzing your Twitter activity. For example, you might want to use the archive to see which tweets have been most popular, or to identify trends or patterns in your tweeting behavior.
  • It can be useful for exporting your tweet data to other platforms or tools for further analysis or processing. For example, you might want to use the data in your archive to create visualizations or to perform sentiment analysis on your tweets.

Overall, downloading your Twitter archive can provide a range of benefits, including the ability to access and view your tweet history, create a backup of your tweets, and use the data for research or analysis.

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What does Legacy really mean?

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In the IT industry we hear the term “legacy” being using, but that does it mean? It can mean a lot of different things and it really depends on the person who is saying it, their context, what they want to portray and their intended meaning. In a lot of cases people seem to use it without knowing the meaning or the impact it can have. This can result in negative impact and not in the way the person intended.

Before looking at some (and there can be lots) possible meanings, lets have a look at what one person said recently.

“Migrating away from legacy databases like Oracle can seem like a daunting undertaking for businesses. But it doesn’t have to be.”

To give context to this quote, the person works for a company selling products, services, support, etc for PostgreSQL and wants everyone to move to ProtgreSQL (Postgres), which is understandable given their role. There’s nothing wrong with trying to convince people/companies to use software that you sell lots of services and additional software to support it. What is interesting is they used the work “legacy”.

Legacy can mean lots of different things to different people. Here are some examples of how legacy is used within the IT industry.

  • The product is old and out of date
  • The product has no relevancy in software industry today
  • Software or hardware that has been superseded
  • Any software that has just been released (yes I’ve come across this use)
  • Outdated computing software and/or hardware that is still in use. The system still meets the needs it was originally designed for, but doesn’t allow for growth
  • Anything in production
  • Software that has come to an end of life with no updates, patching and/or no product roadmap
  • …

Going back to the quote given above, let’s look a little closer at their intended use. As we can see from the list above the use of the word “legacy” can be used in derogatory way and can try to make one software appear better then it’s old, out of date, not current, hard to use, etc competitor.

If you were to do a side-by-side comparison of PostgreSQL and Oracle, there would be a lot of the same or very similar features. But there are differences too and this, in PostgreSQL case, we see various vendors offering add-on software you can pay for. This is kind of similar with Oracle where you need to license various add-ons, or if you are using a Cloud offering it may come as part of the package. On a features comparison level when these are similar, saying one is “legacy” doesn’t seem right. Maybe its about how old the software is, as in legacy being old software. The first release of Oracle was 1979 and we now get yearly update releases (previously it could be every 2-4 years). PostgresSQL, or its previous names date back to 1974 with the first release of Ingres, which later evolved to Postgres in early 1980s, and took on the new name of PostgreSQL in 1996. Are both products today still the same as what they had in the 1970s, 1980s, 1990s, etc. The simple answer is No, they have both evolved and matured since then. Based on this can we say PostgreSQL is legacy or is more of a Legacy product than Oracle Database which was released in 1979 (5 years after Ingres)? Yes we can.

I’m still very confused by the quote (given above) as to what “legacy” might mean, in their scenario. Apart from and (trying) to ignore the derogatory aspect of “they” are old and out of date, and look at us we are new and better, it is difficult to see what they are trying to achieve.

In a similar example on a LinkedIn discussion where one person said MongoDB was legacy, was a little surprising. MongoDB is very good at what it does and has a small number of use cases. The problem with MongoDB is it is used in scenarios when it shouldn’t be used and just causes too many data architecture problems. For me, the main problem driving these issues is how software engineering and programming is taught in Universities (and other third level institutions). They are focused on JavaScript which makes using MongoDB so so easy. And its’ Agile, and the data model can constantly change. This is great, up until you need to use that data. Then it becomes a nightmare.

Getting back to saying MongoDB is legacy, again comes back to the person saying it. They work at a company who is selling cloud based data engineering and analytic services. Is using cloud services the only thing people should be using? For me it is No but a hybrid cloud and on-premises approach will work based for most. Some of the industry analysts are now promoting this, saying vendors offering both will succeed into the future, where does only offering cloud based services will have limited growth, unless the adapt now.

What about other types legacy software applications. Here is an example Stew Ashton posted on Twitter. “I once had a colleague who argued, in writing, that changing the dev stack had the advantage of forcing a rewrite of “legacy applications” – which he had coded the previous year! Either he thought he had greatly improved, or he wanted guaranteed job security”

There are lots and lots of more examples out there and perhaps you will encounter some when you are attending presentations or sales pitches from various vendors. If you hear, then saying one product is “legacy” get them to define their meaning of it and to give specific examples to illustrate it. Does their meaning match with one from the list given above, or something else. Are they just using the word to make another product appear inferior without knowing the meaning or the differences in the product? Their intended meaning within their context is what defines their meaning, which may be different to yours.

This entry was posted in database and tagged , , .

AI Liability Act

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Over the past few weeks we have seem a number of new Artificial Intelligence (AI) Acts or Laws, either being proposed or are at an advanced stage of enactment. One of these is the EU AI Liability Act (also), and is supposed be be enacted and work hand-in-hand with the EU AI Act.

There are different view or focus perspectives between these two EU AI acts. For the EU AI Act, the focus is from the technical perspective and those who develop AI solutions. On the other side of things is the EU AI Liability Act whose perspective is from the end-user/consumer point.

The aim of the EU AI Liability Act is to create a framework for trust in AI technology, and when a person has been harmed by the use of the AI, provides a structure to claim compensation. Just like other EU laws to protect the consumers from defective or harmful products, the AI Liability Act looks to do similar for when a person is harmed in some way by the use or application of AI.

Most of the examples given for how AI might harm a person includes the use of robotics, drones, and when AI is used in the recruitment process, where is automatically selects a candidate based on the AI algorithms. Some other examples include data loss from tech products or caused by tech products, smart-home systems, cyber security, products where people are selected or excluded based on algorithms.

Harm can be difficult to define, and although some attempt has been done to define this in the Act, additional work is needed to by the good people refining the Act, to provide clarifications on this and how its definition can evolve post enactment to ensure additional scenarios can be included without the need for updates to the Act, which can be a lengthy process. A similar task is being performed on the list of high-risk AI in the EU AI Act, where they are proposing to maintain a webpages listing such.

Vice-president for values and transparency, Věra Jourová, said that for AI tech to thrive in the EU, it is important for people to trust digital innovation. She added that the new proposals would give customers “tools for remedies in case of damage caused by AI so that they have the same level of protection as with traditional technologies”

Didier Reynders, the EU’s justice commissioner says, “The new rules apply when a product that functions thanks to AI technology causes damage and that this damage is the result of an error made by manufacturers, developers or users of this technology.

The EU defines “an error” in this case to include not just mistakes in how the A.I. is crafted, trained, deployed, or functions, but also if the “error” is the company failing to comply with a lot of the process and governance requirements stipulated in the bloc’s new A.I. Act. The new liability rules say that if an organization has not complied with their “duty of care” under the new A.I. Act—such as failing to conduct appropriate risk assessments, testing, and monitoring—and a liability claim later arises, there will be a presumption that the A.I. was at fault. This creates an additional way of forcing compliance with the EU AI Act.

The EU Liability Act says that a court can now order a company using a high-risk A.I. system to turn over evidence of how the software works. A balancing test will be applied to ensure that trade secrets and other confidential information is not needlessly disclosed. The EU warns that if a company or organization fails to comply with a court-ordered disclosure, the courts will be free to presume the entity using the A.I. software is liable.

The EU Liability Act will go through some changes and refinement with the aim for it to be enacted at the same time as the EU AI Act. How long will this process that is a little up in the air, considering the EU AI Act should have been adopted by now and we could be in the 2 year process for enactment. But the EU AI Act is still working its way through the different groups in the EU. There has been some indications these might conclude in 2023, but lets wait and see. If the EU Liability Act is only starting the process now, there could be some additional details if the EU wants both Acts to be effective at the same time.

This entry was posted in AI, AI Regulations, Artificial Intelligence and tagged , , .

Preparing images for #DeepLearning by removing background

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There are a number of methods available for preparing images for input to a variety of purposes. For example, for input to deep learning, other image processing models/applications/systems, etc. But sometimes you just need a quick tool to perform a certain task. An example of this is I regularly have to edit images to extract just a certain part of it, or to filter out all the background colors and/or objects etc. There are a a variety of tools available to help you with this kind of task. For me, I’m a Mac user, so I use the instant alpha feature available in some of the Mac products. But what if you are not a Mac user, what can you use.

I’ve recently come across a very useful Python library that takes all or most of the hard work out of doing such tasks, and has proved to be extremely useful for some demos and projects I’ve been working on. The Python library I’m using is remgb (Remove Background). It isn’t perfect, but it does a pretty good job and only in a small number of modified images, did I need to do some additional processing.

Let’s get started with setting things up to use remgb. I did encounter some minor issues installing it, and I’ve give the workarounds below, just in case you encounter the same.

pip3 install remgb

This will install lots of required libraries and will check for compatibility with what you have installed. The first time I ran the install it generated some errors. It also suggested I update my version of pip, which I did, then uninstalled the remgb library and installed again. No errors this time.

When I ran the code below, I got some errors about accessing a document on google drive or it had reached the maximum number of views/downloads. The file it is trying to access is an onix model. If you click on the link, you can download the file. Create a directory called .u2net (in your home directory) and put the onix file into it. Make sure the directory is readable. After doing that everything runs smoothly for me.

The code I’ve given below is typical of what I’ve been doing on some projects. I have a folder with lots of images where I want to remove the background and only keep the key foreground object. Then save the modified images to another directory. It is these image that can be used in products like Amazon Rekognition, Oracle AI Services, and lots of other similar offerings.

from rembg import remove
from PIL import Image
import os
from colorama import Fore, Back, Style

sourceDir = '/Users/brendan.tierney/Dropbox/4-Datasets/F1-Drivers/'
destDir = '/Users/brendan.tierney/Dropbox/4-Datasets/F1-Drivers-NewImages/'

print('Searching = ', sourceDir)

files = os.listdir(sourceDir)
for file in files:
    try:
        inputFile = sourceDir + file
        outputFile = destDir + file

        with open(inputFile, 'rb') as i:
            print(Fore.BLACK + '..reading file : ', file)
            input = i.read()
            print(Fore.CYAN + '...removing background...')
            output = remove(input)

        try:        
            with open(outputFile, 'wb') as o:
                print(Fore.BLUE + '.....writing file : ', outputFile)  
                o.write(output)
        except:
            print(Fore.RED + 'Error writing file :', outputFile)

except:
    print(Fore.RED + 'Error processing file :', file)

print(Fore.BLACK + '---')
print(Fore.BLACK + 'Finished processing all files')
print(Fore.BLACK + '---')

For this demonstration I’ve used images of the F1 drivers for 2022. I had collected five images of each driver with different backgrounds including, crowds, pit-lane, giving media interviews, indoor and outdoor images.

Generally the results were very good. Here are some results with the before and after.

As you can see from these image there are some where a shadow remains and the library wasn’t able to remove it. The following images gives some additional examples of this. The first is with Bottas and his car, where the car wasn’t removed. The second driver is Vettel where the library captures his long hair and keeps it in the filtered image.

This entry was posted in deep learning, Machine Learning, Python and tagged , , .

Number of rows in each Table – Various Databases

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A possible common task developers perform is to find out how many records exists in every table in a schema. In the examples below I’ll show examples for the current schema of the developer, but these can be expanded easily to include tables in other schemas or for all schemas across a database.

These example include the different ways of determining this information across the main databases including Oracle, MySQL, Postgres, SQL Server and Snowflake.

A little warning before using these queries. They may or may not give the true accurate number of records in the tables. These examples illustrate extracting the number of records from the data dictionaries of the databases. This is dependent on background processes being run to gather this information. These background processes run from time to time, anything from a few minutes to many tens of minutes. So, these results are good indication of the number of records in each table.

Oracle

SELECT table_name, num_rows
FROM user_tables
ORDER BY num_rows DESC;

or 

SELECT table_name,
       to_number(
          extractvalue(xmltype(
             dbms_xmlgen.getxml('select count(*) c from '||table_name))
                ,'/ROWSET/ROW/C')) tab_count
FROM   user_tables
ORDER BY tab_count desc;

Using PL/SQL we can do something like the following.

DECLARE
   val NUMBER;
BEGIN
   FOR i IN (SELECT table_name FROM user_tables ORDER BY table_name desc) LOOP
      EXECUTE IMMEDIATE 'SELECT count(*) FROM '|| i.table_name INTO val;
      DBMS_OUTPUT.PUT_LINE(i.table_name||' -> '|| val );
   END LOOP;
END;

MySQL

SELECT table_name, table_rows
FROM INFORMATION_SCHEMA.TABLES
WHERE table_type = 'BASE TABLE'
AND  TABLE_SCHEMA = current_user();

Using Common Table Expressions (CTE), using WITH clause

WITH table_list AS (
SELECT
    table_name
  FROM information_schema.tables 
  WHERE table_schema = current_user() 
  AND
        table_type = 'BASE TABLE'
) 
SELECT CONCAT(
            GROUP_CONCAT(CONCAT("SELECT '",table_name,"' table_name,COUNT(*) rows FROM ",table_name) SEPARATOR " UNION "),
            ' ORDER BY table_name'
        )
INTO @sql
FROM table_list;

Postgres

select relname as table_name, n_live_tup as num_rows from pg_stat_user_tables;

An alternative is

select n.nspname as table_schema,
       c.relname as table_name,
       c.reltuples as rows
from pg_class c
join pg_namespace n on n.oid = c.relnamespace
where c.relkind = 'r'
      and n.nspname = current_user
order by c.reltuples desc;

SQL Server

SELECT tab.name, 
       sum(par.rows) 
FROM   sys.tables tab INNER JOIN sys.partitions par 
          ON tab.object_id = par.object_id 
WHERE  schema_name(tab.schema_id) = current_user

Snowflake

SELECT t.table_schema, t.table_name, t.row_count 
FROM   information_schema.tables t
WHERE  t.table_type = 'BASE TABLE'
AND    t.table_schema = current_user
order by t.row_count desc;

The examples give above are some of the ways to obtain this information. As with most things, there can be multiple ways of doing it, and most people will have their preferred one which is based on their background and preferences.

As you can see from the code given above they are all very similar, with similar syntax, etc. The only thing different is the name of the data dictionary table/view containing the information needed. Yes, knowing what data dictionary views to query can be a little challenging as you switch between databases.

This entry was posted in MySQL, Oracle, Oracle Database, postgres, Snowflake, SQL, SQL Server and tagged , , , , , .