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Get/fetch Data From MongoDB and Display Them in Reactjs Application
Grab data from your MongoDB database and display them into your Reactjs application. In this video, I am going to exactly show you how to get/fetch data from your MongoDB database and display them in Reactjs application
Cat app with React Hooks. In this video, I am going to build a cat app with React hooks. We’ll utilize React useState, and useEffect throughout the video, and clearly explained the use of them.
Github Repo: https://github.com/accimeesterlin/mernapp_youtube
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Everything You Need to Know About Instagram Bot with Python
How to build an Instagram bot using Python
Instagram is the fastest-growing social network, with 1 billion monthly users. It also has the highest engagement rate. To gain followers on Instagram, you’d have to upload engaging content, follow users, like posts, comment on user posts and a whole lot. This can be time-consuming and daunting. But there is hope, you can automate all of these tasks. In this course, we’re going to build an Instagram bot using Python to automate tasks on Instagram.
What you’ll learn:
- Instagram Automation
- Build a Bot with Python
Increase your Instagram followers with a simple Python bot
I got around 500 real followers in 4 days!
Growing an audience is an expensive and painful task. And if you’d like to build an audience that’s relevant to you, and shares common interests, that’s even more difficult. I always saw Instagram has a great way to promote my photos, but I never had more than 380 followers… Every once in a while, I decide to start posting my photos on Instagram again, and I manage to keep posting regularly for a while, but it never lasts more than a couple of months, and I don’t have many followers to keep me motivated and engaged.
The objective of this project is to build a bigger audience and as a plus, maybe drive some traffic to my website where I sell my photos!
A year ago, on my last Instagram run, I got one of those apps that lets you track who unfollowed you. I was curious because in a few occasions my number of followers dropped for no apparent reason. After some research, I realized how some users basically crawl for followers. They comment, like and follow people — looking for a follow back. Only to unfollow them again in the next days.
I can’t say this was a surprise to me, that there were bots in Instagram… It just made me want to build one myself!
And that is why we’re here, so let’s get to it! I came up with a simple bot in Python, while I was messing around with Selenium and trying to figure out some project to use it. Simply put, Selenium is like a browser you can interact with very easily in Python.
Ideally, increasing my Instagram audience will keep me motivated to post regularly. As an extra, I included my website in my profile bio, where people can buy some photos. I think it is a bit of a stretch, but who knows?! My sales are basically zero so far, so it should be easy to track that conversion!
Just what the world needed! Another Instagram bot…
After giving this project some thought, my objective was to increase my audience with relevant people. I want to get followers that actually want to follow me and see more of my work. It’s very easy to come across weird content in the most used hashtags, so I’ve planed this bot to lookup specific hashtags and interact with the photos there. This way, I can be very specific about what kind of interests I want my audience to have. For instance, I really like long exposures, so I can target people who use that hashtag and build an audience around this kind of content. Simple and efficient!
My gallery is a mix of different subjects and styles, from street photography to aerial photography, and some travel photos too. Since it’s my hometown, I also have lots of Lisbon images there. These will be the main topics I’ll use in the hashtags I want to target.
This is not a “get 1000 followers in 24 hours” kind of bot!
So what kind of numbers are we talking about?
I ran the bot a few times in a few different hashtags like “travelblogger”, “travelgram”, “lisbon”, “dronephotography”. In the course of three days I went from 380 to 800 followers. Lots of likes, comments and even some organic growth (people that followed me but were not followed by the bot).
To be clear, I’m not using this bot intensively, as Instagram will stop responding if you run it too fast. It needs to have some sleep commands in between the actions, because after some comments and follows in a short period of time, Instagram stops responding and the bot crashes.
You will be logged into your account, so I’m almost sure that Instagram can know you’re doing something weird if you speed up the process. And most importantly, after doing this for a dozen hashtags, it just gets harder to find new users in the same hashtags. You will need to give it a few days to refresh the user base there.
But I don’t want to follow so many people in the process…
The most efficient way to get followers in Instagram (apart from posting great photos!) is to follow people. And this bot worked really well for me because I don’t care if I follow 2000 people to get 400 followers.
The bot saves a list with all the users that were followed while it was running, so someday I may actually do something with this list. For instance, I can visit each user profile, evaluate how many followers or posts they have, and decide if I want to keep following them. Or I can get the first picture in their gallery and check its date to see if they are active users.
If we remove the follow action from the bot, I can assure you the growth rate will suffer, as people are less inclined to follow based on a single like or comment.
Why will you share your code?!
That’s the debate I had with myself. Even though I truly believe in giving back to the community (I still learn a lot from it too!), there are several paid platforms that do more or less the same as this project. Some are shady, some are used by celebrities. The possibility of starting a similar platform myself, is not off the table yet, so why make the code available?
With that in mind, I decided to add an extra level of difficulty to the process, so I was going to post the code below as an image. I wrote “was”, because meanwhile, I’ve realized the image I’m getting is low quality. Which in turn made me reconsider and post the gist. I’m that nice! The idea behind the image was that if you really wanted to use it, you would have to type the code yourself. And that was my way of limiting the use of this tool to people that actually go through the whole process to create it and maybe even improve it.
I learn a lot more when I type the code myself, instead of copy/pasting scripts. I hope you feel the same way!
The script isn’t as sophisticated as it could be, and I know there’s lots of room to improve it. But hey… it works! I have other projects I want to add to my portfolio, so my time to develop it further is rather limited. Nevertheless, I will try to update this article if I dig deeper.
This is the last subtitle!
You’ll need Python (I’m using Python 3.7), Selenium, a browser (in my case I’ll be using Chrome) and… obviously, an Instagram account! Quick overview regarding what the bot will do:
- Open a browser and login with your credentials
- For every hashtag in the hashtag list, it will open the page and click the first picture to open it
- It will then like, follow, comment and move to the next picture, in a 200 iterations loop (number can be adjusted)
- Saves a list with all the users you followed using the bot
If you reached this paragraph, thank you! You totally deserve to collect your reward! If you find this useful for your profile/brand in any way, do share your experience below :)
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
from time import sleep, strftime
from random import randint
import pandas as pd
chromedriver_path = 'C:/Users/User/Downloads/chromedriver_win32/chromedriver.exe' # Change this to your own chromedriver path!
webdriver = webdriver.Chrome(executable_path=chromedriver_path)
sleep(2)
webdriver.get('https://www.instagram.com/accounts/login/?source=auth_switcher')
sleep(3)
username = webdriver.find_element_by_name('username')
username.send_keys('your_username')
password = webdriver.find_element_by_name('password')
password.send_keys('your_password')
button_login = webdriver.find_element_by_css_selector('#react-root > section > main > div > article > div > div:nth-child(1) > div > form > div:nth-child(3) > button')
button_login.click()
sleep(3)
notnow = webdriver.find_element_by_css_selector('body > div:nth-child(13) > div > div > div > div.mt3GC > button.aOOlW.HoLwm')
notnow.click() #comment these last 2 lines out, if you don't get a pop up asking about notifications
In order to use chrome with Selenium, you need to install chromedriver. It’s a fairly simple process and I had no issues with it. Simply install and replace the path above. Once you do that, our variable webdriver will be our Chrome tab.
In cell number 3 you should replace the strings with your own username and the respective password. This is for the bot to type it in the fields displayed. You might have already noticed that when running cell number 2, Chrome opened a new tab. After the password, I’ll define the login button as an object, and in the following line, I click it.
Once you get in inspect mode find the bit of html code that corresponds to what you want to map. Right click it and hover over Copy. You will see that you have some options regarding how you want it to be copied. I used a mix of XPath and css selectors throughout the code (it’s visible in the find_element_ method). It took me a while to get all the references to run smoothly. At points, the css or the xpath directions would fail, but as I adjusted the sleep times, everything started running smoothly.
In this case, I selected “copy selector” and pasted it inside a find_element_ method (cell number 3). It will get you the first result it finds. If it was find_elements_, all elements would be retrieved and you could specify which to get.
Once you get that done, time for the loop. You can add more hashtags in the hashtag_list. If you run it for the first time, you still don’t have a file with the users you followed, so you can simply create prev_user_list as an empty list.
Once you run it once, it will save a csv file with a timestamp with the users it followed. That file will serve as the prev_user_list on your second run. Simple and easy to keep track of what the bot does.
Update with the latest timestamp on the following runs and you get yourself a series of csv backlogs for every run of the bot.
The code is really simple. If you have some basic notions of Python you can probably pick it up quickly. I’m no Python ninja and I was able to build it, so I guess that if you read this far, you are good to go!
hashtag_list = ['travelblog', 'travelblogger', 'traveler']
# prev_user_list = [] - if it's the first time you run it, use this line and comment the two below
prev_user_list = pd.read_csv('20181203-224633_users_followed_list.csv', delimiter=',').iloc[:,1:2] # useful to build a user log
prev_user_list = list(prev_user_list['0'])
new_followed = []
tag = -1
followed = 0
likes = 0
comments = 0
for hashtag in hashtag_list:
tag += 1
webdriver.get('https://www.instagram.com/explore/tags/'+ hashtag_list[tag] + '/')
sleep(5)
first_thumbnail = webdriver.find_element_by_xpath('//*[@id="react-root"]/section/main/article/div[1]/div/div/div[1]/div[1]/a/div')
first_thumbnail.click()
sleep(randint(1,2))
try:
for x in range(1,200):
username = webdriver.find_element_by_xpath('/html/body/div[3]/div/div[2]/div/article/header/div[2]/div[1]/div[1]/h2/a').text
if username not in prev_user_list:
# If we already follow, do not unfollow
if webdriver.find_element_by_xpath('/html/body/div[3]/div/div[2]/div/article/header/div[2]/div[1]/div[2]/button').text == 'Follow':
webdriver.find_element_by_xpath('/html/body/div[3]/div/div[2]/div/article/header/div[2]/div[1]/div[2]/button').click()
new_followed.append(username)
followed += 1
# Liking the picture
button_like = webdriver.find_element_by_xpath('/html/body/div[3]/div/div[2]/div/article/div[2]/section[1]/span[1]/button/span')
button_like.click()
likes += 1
sleep(randint(18,25))
# Comments and tracker
comm_prob = randint(1,10)
print('{}_{}: {}'.format(hashtag, x,comm_prob))
if comm_prob > 7:
comments += 1
webdriver.find_element_by_xpath('/html/body/div[3]/div/div[2]/div/article/div[2]/section[1]/span[2]/button/span').click()
comment_box = webdriver.find_element_by_xpath('/html/body/div[3]/div/div[2]/div/article/div[2]/section[3]/div/form/textarea')
if (comm_prob < 7):
comment_box.send_keys('Really cool!')
sleep(1)
elif (comm_prob > 6) and (comm_prob < 9):
comment_box.send_keys('Nice work :)')
sleep(1)
elif comm_prob == 9:
comment_box.send_keys('Nice gallery!!')
sleep(1)
elif comm_prob == 10:
comment_box.send_keys('So cool! :)')
sleep(1)
# Enter to post comment
comment_box.send_keys(Keys.ENTER)
sleep(randint(22,28))
# Next picture
webdriver.find_element_by_link_text('Next').click()
sleep(randint(25,29))
else:
webdriver.find_element_by_link_text('Next').click()
sleep(randint(20,26))
# some hashtag stops refreshing photos (it may happen sometimes), it continues to the next
except:
continue
for n in range(0,len(new_followed)):
prev_user_list.append(new_followed[n])
updated_user_df = pd.DataFrame(prev_user_list)
updated_user_df.to_csv('{}_users_followed_list.csv'.format(strftime("%Y%m%d-%H%M%S")))
print('Liked {} photos.'.format(likes))
print('Commented {} photos.'.format(comments))
print('Followed {} new people.'.format(followed))
The print statement inside the loop is the way I found to be able to have a tracker that lets me know at what iteration the bot is all the time. It will print the hashtag it’s in, the number of the iteration, and the random number generated for the comment action. I decided not to post comments in every page, so I added three different comments and a random number between 1 and 10 that would define if there was any comment at all, or one of the three. The loop ends, we append the new_followed users to the previous users “database” and saves the new file with the timestamp. You should also get a small report.
And that’s it!
After a few hours without checking the phone, these were the numbers I was getting. I definitely did not expect it to do so well! In about 4 days since I’ve started testing it, I had around 500 new followers, which means I have doubled my audience in a matter of days. I’m curious to see how many of these new followers I will lose in the next days, to see if the growth can be sustainable. I also had a lot more “likes” in my latest photos, but I guess that’s even more expected than the follow backs.
It would be nice to get this bot running in a server, but I have other projects I want to explore, and configuring a server is not one of them! Feel free to leave a comment below, and I’ll do my best to answer your questions.
I’m actually curious to see how long will I keep posting regularly! If you feel like this article was helpful for you, consider thanking me by buying one of my photos.
How to Make an Instagram Bot With Python and InstaPy
What do SocialCaptain, Kicksta, Instavast, and many other companies have in common? They all help you reach a greater audience, gain more followers, and get more likes on Instagram while you hardly lift a finger. They do it all through automation, and people pay them a good deal of money for it. But you can do the same thing—for free—using InstaPy!
In this tutorial, you’ll learn how to build a bot with Python and InstaPy, which automates your Instagram activities so that you gain more followers and likes with minimal manual input. Along the way, you’ll learn about browser automation with Selenium and the Page Object Pattern, which together serve as the basis for InstaPy.
In this tutorial, you’ll learn:
- How Instagram bots work
- How to automate a browser with Selenium
- How to use the Page Object Pattern for better readability and testability
- How to build an Instagram bot with InstaPy
You’ll begin by learning how Instagram bots work before you build one.
Table of Contents
- How Instagram Bots Work
- How to Automate a Browser
- How to Use the Page Object Pattern
- How to Build an Instagram Bot With InstaPy
- Essential Features
- Additional Features in InstaPy
- Conclusion
Important: Make sure you check Instagram’s Terms of Use before implementing any kind of automation or scraping techniques.
How Instagram Bots Work
How can an automation script gain you more followers and likes? Before answering this question, think about how an actual person gains more followers and likes.
They do it by being consistently active on the platform. They post often, follow other people, and like and leave comments on other people’s posts. Bots work exactly the same way: They follow, like, and comment on a consistent basis according to the criteria you set.
The better the criteria you set, the better your results will be. You want to make sure you’re targeting the right groups because the people your bot interacts with on Instagram will be more likely to interact with your content.
For example, if you’re selling women’s clothing on Instagram, then you can instruct your bot to like, comment on, and follow mostly women or profiles whose posts include hashtags such as #beauty, #fashion, or #clothes. This makes it more likely that your target audience will notice your profile, follow you back, and start interacting with your posts.
How does it work on the technical side, though? You can’t use the Instagram Developer API since it is fairly limited for this purpose. Enter browser automation. It works in the following way:
- You serve it your credentials.
- You set the criteria for who to follow, what comments to leave, and which type of posts to like.
- Your bot opens a browser, types in
https://instagram.comon the address bar, logs in with your credentials, and starts doing the things you instructed it to do.
Next, you’ll build the initial version of your Instagram bot, which will automatically log in to your profile. Note that you won’t use InstaPy just yet.
How to Automate a Browser
For this version of your Instagram bot, you’ll be using Selenium, which is the tool that InstaPy uses under the hood.
First, install Selenium. During installation, make sure you also install the Firefox WebDriver since the latest version of InstaPy dropped support for Chrome. This also means that you need the Firefox browser installed on your computer.
Now, create a Python file and write the following code in it:
from time import sleep
from selenium import webdriver
browser = webdriver.Firefox()
browser.get('https://www.instagram.com/')
sleep(5)
browser.close()
Run the code and you’ll see that a Firefox browser opens and directs you to the Instagram login page. Here’s a line-by-line breakdown of the code:
- Lines 1 and 2 import
sleepandwebdriver. - Line 4 initializes the Firefox driver and sets it to
browser. - Line 6 types
https://www.instagram.com/on the address bar and hits Enter. - Line 8 waits for five seconds so you can see the result. Otherwise, it would close the browser instantly.
- Line 10 closes the browser.
This is the Selenium version of Hello, World. Now you’re ready to add the code that logs in to your Instagram profile. But first, think about how you would log in to your profile manually. You would do the following:
- Go to
https://www.instagram.com/. - Click the login link.
- Enter your credentials.
- Hit the login button.
The first step is already done by the code above. Now change it so that it clicks on the login link on the Instagram home page:
from time import sleep
from selenium import webdriver
browser = webdriver.Firefox()
browser.implicitly_wait(5)
browser.get('https://www.instagram.com/')
login_link = browser.find_element_by_xpath("//a[text()='Log in']")
login_link.click()
sleep(5)
browser.close()
Note the highlighted lines:
- Line 5 sets five seconds of waiting time. If Selenium can’t find an element, then it waits for five seconds to allow everything to load and tries again.
- Line 9 finds the element
<a>whose text is equal toLog in. It does this using XPath, but there are a few other methods you could use. - Line 10 clicks on the found element
<a>for the login link.
Run the script and you’ll see your script in action. It will open the browser, go to Instagram, and click on the login link to go to the login page.
On the login page, there are three important elements:
- The username input
- The password input
- The login button
Next, change the script so that it finds those elements, enters your credentials, and clicks on the login button:
from time import sleep
from selenium import webdriver
browser = webdriver.Firefox()
browser.implicitly_wait(5)
browser.get('https://www.instagram.com/')
login_link = browser.find_element_by_xpath("//a[text()='Log in']")
login_link.click()
sleep(2)
username_input = browser.find_element_by_css_selector("input[name='username']")
password_input = browser.find_element_by_css_selector("input[name='password']")
username_input.send_keys("<your username>")
password_input.send_keys("<your password>")
login_button = browser.find_element_by_xpath("//button[@type='submit']")
login_button.click()
sleep(5)
browser.close()
Here’s a breakdown of the changes:
- Line 12 sleeps for two seconds to allow the page to load.
- Lines 14 and 15 find username and password inputs by CSS. You could use any other method that you prefer.
- Lines 17 and 18 type your username and password in their respective inputs. Don’t forget to fill in
<your username>and<your password>! - Line 20 finds the login button by XPath.
- Line 21 clicks on the login button.
Run the script and you’ll be automatically logged in to to your Instagram profile.
You’re off to a good start with your Instagram bot. If you were to continue writing this script, then the rest would look very similar. You would find the posts that you like by scrolling down your feed, find the like button by CSS, click on it, find the comments section, leave a comment, and continue.
The good news is that all of those steps can be handled by InstaPy. But before you jump into using Instapy, there is one other thing that you should know about to better understand how InstaPy works: the Page Object Pattern.
How to Use the Page Object Pattern
Now that you’ve written the login code, how would you write a test for it? It would look something like the following:
def test_login_page(browser):
browser.get('https://www.instagram.com/accounts/login/')
username_input = browser.find_element_by_css_selector("input[name='username']")
password_input = browser.find_element_by_css_selector("input[name='password']")
username_input.send_keys("<your username>")
password_input.send_keys("<your password>")
login_button = browser.find_element_by_xpath("//button[@type='submit']")
login_button.click()
errors = browser.find_elements_by_css_selector('#error_message')
assert len(errors) == 0
Can you see what’s wrong with this code? It doesn’t follow the DRY principle. That is, the code is duplicated in both the application and the test code.
Duplicating code is especially bad in this context because Selenium code is dependent on UI elements, and UI elements tend to change. When they do change, you want to update your code in one place. That’s where the Page Object Pattern comes in.
With this pattern, you create page object classes for the most important pages or fragments that provide interfaces that are straightforward to program to and that hide the underlying widgetry in the window. With this in mind, you can rewrite the code above and create a HomePage class and a LoginPage class:
from time import sleep
class LoginPage:
def __init__(self, browser):
self.browser = browser
def login(self, username, password):
username_input = self.browser.find_element_by_css_selector("input[name='username']")
password_input = self.browser.find_element_by_css_selector("input[name='password']")
username_input.send_keys(username)
password_input.send_keys(password)
login_button = browser.find_element_by_xpath("//button[@type='submit']")
login_button.click()
sleep(5)
class HomePage:
def __init__(self, browser):
self.browser = browser
self.browser.get('https://www.instagram.com/')
def go_to_login_page(self):
self.browser.find_element_by_xpath("//a[text()='Log in']").click()
sleep(2)
return LoginPage(self.browser)
The code is the same except that the home page and the login page are represented as classes. The classes encapsulate the mechanics required to find and manipulate the data in the UI. That is, there are methods and accessors that allow the software to do anything a human can.
One other thing to note is that when you navigate to another page using a page object, it returns a page object for the new page. Note the returned value of go_to_log_in_page(). If you had another class called FeedPage, then login() of the LoginPage class would return an instance of that: return FeedPage().
Here’s how you can put the Page Object Pattern to use:
from selenium import webdriver
browser = webdriver.Firefox()
browser.implicitly_wait(5)
home_page = HomePage(browser)
login_page = home_page.go_to_login_page()
login_page.login("<your username>", "<your password>")
browser.close()
It looks much better, and the test above can now be rewritten to look like this:
def test_login_page(browser):
home_page = HomePage(browser)
login_page = home_page.go_to_login_page()
login_page.login("<your username>", "<your password>")
errors = browser.find_elements_by_css_selector('#error_message')
assert len(errors) == 0
With these changes, you won’t have to touch your tests if something changes in the UI.
For more information on the Page Object Pattern, refer to the official documentation and to Martin Fowler’s article.
Now that you’re familiar with both Selenium and the Page Object Pattern, you’ll feel right at home with InstaPy. You’ll build a basic bot with it next.
Note: Both Selenium and the Page Object Pattern are widely used for other websites, not just for Instagram.
How to Build an Instagram Bot With InstaPy
In this section, you’ll use InstaPy to build an Instagram bot that will automatically like, follow, and comment on different posts. First, you’ll need to install InstaPy:
$ python3 -m pip install instapy
This will install instapy in your system.
Essential Features
Now you can rewrite the code above with InstaPy so that you can compare the two options. First, create another Python file and put the following code in it:
from instapy import InstaPy
InstaPy(username="<your_username>", password="<your_password>").login()
Replace the username and password with yours, run the script, and voilà! With just one line of code, you achieved the same result.
Even though your results are the same, you can see that the behavior isn’t exactly the same. In addition to simply logging in to your profile, InstaPy does some other things, such as checking your internet connection and the status of the Instagram servers. This can be observed directly on the browser or in the logs:
INFO [2019-12-17 22:03:19] [username] -- Connection Checklist [1/3] (Internet Connection Status)
INFO [2019-12-17 22:03:20] [username] - Internet Connection Status: ok
INFO [2019-12-17 22:03:20] [username] - Current IP is "17.283.46.379" and it's from "Germany/DE"
INFO [2019-12-17 22:03:20] [username] -- Connection Checklist [2/3] (Instagram Server Status)
INFO [2019-12-17 22:03:26] [username] - Instagram WebSite Status: Currently Up
Pretty good for one line of code, isn’t it? Now it’s time to make the script do more interesting things than just logging in.
For the purpose of this example, assume that your profile is all about cars, and that your bot is intended to interact with the profiles of people who are also interested in cars.
First, you can like some posts that are tagged #bmw or #mercedes using like_by_tags():
from instapy import InstaPy
session = InstaPy(username="<your_username>", password="<your_password>")
session.login()
session.like_by_tags(["bmw", "mercedes"], amount=5)
Here, you gave the method a list of tags to like and the number of posts to like for each given tag. In this case, you instructed it to like ten posts, five for each of the two tags. But take a look at what happens after you run the script:
INFO [2019-12-17 22:15:58] [username] Tag [1/2]
INFO [2019-12-17 22:15:58] [username] --> b'bmw'
INFO [2019-12-17 22:16:07] [username] desired amount: 14 | top posts [disabled]: 9 | possible posts: 43726739
INFO [2019-12-17 22:16:13] [username] Like# [1/14]
INFO [2019-12-17 22:16:13] [username] https://www.instagram.com/p/B6MCcGcC3tU/
INFO [2019-12-17 22:16:15] [username] Image from: b'mattyproduction'
INFO [2019-12-17 22:16:15] [username] Link: b'https://www.instagram.com/p/B6MCcGcC3tU/'
INFO [2019-12-17 22:16:15] [username] Description: b'Mal etwas anderes \xf0\x9f\x91\x80\xe2\x98\xba\xef\xb8\x8f Bald ist das komplette Video auf YouTube zu finden (n\xc3\xa4here Infos werden folgen). Vielen Dank an @patrick_jwki @thehuthlife und @christic_ f\xc3\xbcr das bereitstellen der Autos \xf0\x9f\x94\xa5\xf0\x9f\x98\x8d#carporn#cars#tuning#bagged#bmw#m2#m2competition#focusrs#ford#mk3#e92#m3#panasonic#cinematic#gh5s#dji#roninm#adobe#videography#music#bimmer#fordperformance#night#shooting#'
INFO [2019-12-17 22:16:15] [username] Location: b'K\xc3\xb6ln, Germany'
INFO [2019-12-17 22:16:51] [username] --> Image Liked!
INFO [2019-12-17 22:16:56] [username] --> Not commented
INFO [2019-12-17 22:16:57] [username] --> Not following
INFO [2019-12-17 22:16:58] [username] Like# [2/14]
INFO [2019-12-17 22:16:58] [username] https://www.instagram.com/p/B6MDK1wJ-Kb/
INFO [2019-12-17 22:17:01] [username] Image from: b'davs0'
INFO [2019-12-17 22:17:01] [username] Link: b'https://www.instagram.com/p/B6MDK1wJ-Kb/'
INFO [2019-12-17 22:17:01] [username] Description: b'Someone said cloud? \xf0\x9f\xa4\x94\xf0\x9f\xa4\xad\xf0\x9f\x98\x88 \xe2\x80\xa2\n\xe2\x80\xa2\n\xe2\x80\xa2\n\xe2\x80\xa2\n#bmw #bmwrepost #bmwm4 #bmwm4gts #f82 #bmwmrepost #bmwmsport #bmwmperformance #bmwmpower #bmwm4cs #austinyellow #davs0 #mpower_official #bmw_world_ua #bimmerworld #bmwfans #bmwfamily #bimmers #bmwpost #ultimatedrivingmachine #bmwgang #m3f80 #m5f90 #m4f82 #bmwmafia #bmwcrew #bmwlifestyle'
INFO [2019-12-17 22:17:34] [username] --> Image Liked!
INFO [2019-12-17 22:17:37] [username] --> Not commented
INFO [2019-12-17 22:17:38] [username] --> Not following
By default, InstaPy will like the first nine top posts in addition to your amount value. In this case, that brings the total number of likes per tag to fourteen (nine top posts plus the five you specified in amount).
Also note that InstaPy logs every action it takes. As you can see above, it mentions which post it liked as well as its link, description, location, and whether the bot commented on the post or followed the author.
You may have noticed that there are delays after almost every action. That’s by design. It prevents your profile from getting banned on Instagram.
Now, you probably don’t want your bot liking inappropriate posts. To prevent that from happening, you can use set_dont_like():
from instapy import InstaPy
session = InstaPy(username="<your_username>", password="<your_password>")
session.login()
session.like_by_tags(["bmw", "mercedes"], amount=5)
session.set_dont_like(["naked", "nsfw"])
With this change, posts that have the words naked or nsfw in their descriptions won’t be liked. You can flag any other words that you want your bot to avoid.
Next, you can tell the bot to not only like the posts but also to follow some of the authors of those posts. You can do that with set_do_follow():
from instapy import InstaPy
session = InstaPy(username="<your_username>", password="<your_password>")
session.login()
session.like_by_tags(["bmw", "mercedes"], amount=5)
session.set_dont_like(["naked", "nsfw"])
session.set_do_follow(True, percentage=50)
If you run the script now, then the bot will follow fifty percent of the users whose posts it liked. As usual, every action will be logged.
You can also leave some comments on the posts. There are two things that you need to do. First, enable commenting with set_do_comment():
from instapy import InstaPy
session = InstaPy(username="<your_username>", password="<your_password>")
session.login()
session.like_by_tags(["bmw", "mercedes"], amount=5)
session.set_dont_like(["naked", "nsfw"])
session.set_do_follow(True, percentage=50)
session.set_do_comment(True, percentage=50)
Next, tell the bot what comments to leave with set_comments():
from instapy import InstaPy
session = InstaPy(username="<your_username>", password="<your_password>")
session.login()
session.like_by_tags(["bmw", "mercedes"], amount=5)
session.set_dont_like(["naked", "nsfw"])
session.set_do_follow(True, percentage=50)
session.set_do_comment(True, percentage=50)
session.set_comments(["Nice!", "Sweet!", "Beautiful :heart_eyes:"])
Run the script and the bot will leave one of those three comments on half the posts that it interacts with.
Now that you’re done with the basic settings, it’s a good idea to end the session with end():
from instapy import InstaPy
session = InstaPy(username="<your_username>", password="<your_password>")
session.login()
session.like_by_tags(["bmw", "mercedes"], amount=5)
session.set_dont_like(["naked", "nsfw"])
session.set_do_follow(True, percentage=50)
session.set_do_comment(True, percentage=50)
session.set_comments(["Nice!", "Sweet!", "Beautiful :heart_eyes:"])
session.end()
This will close the browser, save the logs, and prepare a report that you can see in the console output.
Additional Features in InstaPy
InstaPy is a sizable project that has a lot of thoroughly documented features. The good news is that if you’re feeling comfortable with the features you used above, then the rest should feel pretty similar. This section will outline some of the more useful features of InstaPy.
Quota Supervisor
You can’t scrape Instagram all day, every day. The service will quickly notice that you’re running a bot and will ban some of its actions. That’s why it’s a good idea to set quotas on some of your bot’s actions. Take the following for example:
session.set_quota_supervisor(enabled=True, peak_comments_daily=240, peak_comments_hourly=21)
The bot will keep commenting until it reaches its hourly and daily limits. It will resume commenting after the quota period has passed.
Headless Browser
This feature allows you to run your bot without the GUI of the browser. This is super useful if you want to deploy your bot to a server where you may not have or need the graphical interface. It’s also less CPU intensive, so it improves performance. You can use it like so:
session = InstaPy(username='test', password='test', headless_browser=True)
Note that you set this flag when you initialize the InstaPy object.
Using AI to Analyze Posts
Earlier you saw how to ignore posts that contain inappropriate words in their descriptions. What if the description is good but the image itself is inappropriate? You can integrate your InstaPy bot with ClarifAI, which offers image and video recognition services:
session.set_use_clarifai(enabled=True, api_key='<your_api_key>')
session.clarifai_check_img_for(['nsfw'])
Now your bot won’t like or comment on any image that ClarifAI considers NSFW. You get 5,000 free API-calls per month.
Relationship Bounds
It’s often a waste of time to interact with posts by people who have a lot of followers. In such cases, it’s a good idea to set some relationship bounds so that your bot doesn’t waste your precious computing resources:
session.set_relationship_bounds(enabled=True, max_followers=8500)
With this, your bot won’t interact with posts by users who have more than 8,500 followers.
For many more features and configurations in InstaPy, check out the documentation.
Conclusion
InstaPy allows you to automate your Instagram activities with minimal fuss and effort. It’s a very flexible tool with a lot of useful features.
In this tutorial, you learned:
- How Instagram bots work
- How to automate a browser with Selenium
- How to use the Page Object Pattern to make your code more maintainable and testable
- How to use InstaPy to build a basic Instagram bot
Read the InstaPy documentation and experiment with your bot a little bit. Soon you’ll start getting new followers and likes with a minimal amount of effort. I gained a few new followers myself while writing this tutorial.
Automating Instagram API with Python
Gain active followers - Algorithm
Maybe some of you do not agree it is a good way to grow your IG page by using follow for follow method but after a lot of researching I found the proper way to use this method.
I have done and used this strategy for a while and my page visits also followers started growing.
The majority of people failing because they randomly targeting the followers and as a result, they are not coming back to your page. So, the key is to find people those have same interests with you.
If you have a programming page go and search for IG pages which have big programming community and once you find one, don’t send follow requests to followers of this page. Because some of them are not active even maybe fake accounts. So, in order to gain active followers, go the last post of this page and find people who liked the post.
Unofficial Instagram API
In order to query data from Instagram I am going to use the very cool, yet unofficial, Instagram API written by Pasha Lev.
**Note:**Before you test it make sure you verified your phone number in your IG account.
The program works pretty well so far but in case of any problems I have to put disclaimer statement here:
Disclaimer: This post published educational purposes only as well as to give general information about Instagram API. I am not responsible for any actions and you are taking your own risk.
Let’s start by installing and then logging in with API.
pip install InstagramApi
from InstagramAPI import InstagramAPI
api = InstagramAPI("username", "password")
api.login()
Once you run the program you will see “Login success!” in your console.
Get users from liked list
We are going to search for some username (your target page) then get most recent post from this user. Then, get users who liked this post. Unfortunately, I can’t find solution how to paginate users so right now it gets about last 500 user.
users_list = []
def get_likes_list(username):
api.login()
api.searchUsername(username)
result = api.LastJson
username_id = result['user']['pk'] # Get user ID
user_posts = api.getUserFeed(username_id) # Get user feed
result = api.LastJson
media_id = result['items'][0]['id'] # Get most recent post
api.getMediaLikers(media_id) # Get users who liked
users = api.LastJson['users']
for user in users: # Push users to list
users_list.append({'pk':user['pk'], 'username':user['username']})
Follow Users
Once we get the users list, it is time to follow these users.
IMPORTANT NOTE: set time limit as much as you can to avoid automation detection.
from time import sleep
following_users = []
def follow_users(users_list):
api.login()
api.getSelfUsersFollowing() # Get users which you are following
result = api.LastJson
for user in result['users']:
following_users.append(user['pk'])
for user in users_list:
if not user['pk'] in following_users: # if new user is not in your following users
print('Following @' + user['username'])
api.follow(user['pk'])
# after first test set this really long to avoid from suspension
sleep(20)
else:
print('Already following @' + user['username'])
sleep(10)
Unfollow Users
This function will look users which you are following then it will check if this user follows you as well. If user not following you then you are unfollowing as well.
follower_users = []
def unfollow_users():
api.login()
api.getSelfUserFollowers() # Get your followers
result = api.LastJson
for user in result['users']:
follower_users.append({'pk':user['pk'], 'username':user['username']})
api.getSelfUsersFollowing() # Get users which you are following
result = api.LastJson
for user in result['users']:
following_users.append({'pk':user['pk'],'username':user['username']})
for user in following_users:
if not user['pk'] in follower_users: # if the user not follows you
print('Unfollowing @' + user['username'])
api.unfollow(user['pk'])
# set this really long to avoid from suspension
sleep(20)
Full Code with extra functions
Here is the full code of this automation
import pprint
from time import sleep
from InstagramAPI import InstagramAPI
import pandas as pd
users_list = []
following_users = []
follower_users = []
class InstaBot:
def __init__(self):
self.api = InstagramAPI("your_username", "your_password")
def get_likes_list(self,username):
api = self.api
api.login()
api.searchUsername(username) #Gets most recent post from user
result = api.LastJson
username_id = result['user']['pk']
user_posts = api.getUserFeed(username_id)
result = api.LastJson
media_id = result['items'][0]['id']
api.getMediaLikers(media_id)
users = api.LastJson['users']
for user in users:
users_list.append({'pk':user['pk'], 'username':user['username']})
bot.follow_users(users_list)
def follow_users(self,users_list):
api = self.api
api.login()
api.getSelfUsersFollowing()
result = api.LastJson
for user in result['users']:
following_users.append(user['pk'])
for user in users_list:
if not user['pk'] in following_users:
print('Following @' + user['username'])
api.follow(user['pk'])
# set this really long to avoid from suspension
sleep(20)
else:
print('Already following @' + user['username'])
sleep(10)
def unfollow_users(self):
api = self.api
api.login()
api.getSelfUserFollowers()
result = api.LastJson
for user in result['users']:
follower_users.append({'pk':user['pk'], 'username':user['username']})
api.getSelfUsersFollowing()
result = api.LastJson
for user in result['users']:
following_users.append({'pk':user['pk'],'username':user['username']})
for user in following_users:
if not user['pk'] in [user['pk'] for user in follower_users]:
print('Unfollowing @' + user['username'])
api.unfollow(user['pk'])
# set this really long to avoid from suspension
sleep(20)
bot = InstaBot()
# To follow users run the function below
# change the username ('instagram') to your target username
bot.get_likes_list('instagram')
# To unfollow users uncomment and run the function below
# bot.unfollow_users()
it will look like this:
some extra functions to play with API:
def get_my_profile_details():
api.login()
api.getSelfUsernameInfo()
result = api.LastJson
username = result['user']['username']
full_name = result['user']['full_name']
profile_pic_url = result['user']['profile_pic_url']
followers = result['user']['follower_count']
following = result['user']['following_count']
media_count = result['user']['media_count']
df_profile = pd.DataFrame(
{'username':username,
'full name': full_name,
'profile picture URL':profile_pic_url,
'followers':followers,
'following':following,
'media count': media_count,
}, index=[0])
df_profile.to_csv('profile.csv', sep='\t', encoding='utf-8')
def get_my_feed():
image_urls = []
api.login()
api.getSelfUserFeed()
result = api.LastJson
# formatted_json_str = pprint.pformat(result)
# print(formatted_json_str)
if 'items' in result.keys():
for item in result['items'][0:5]:
if 'image_versions2' in item.keys():
image_url = item['image_versions2']['candidates'][1]['url']
image_urls.append(image_url)
df_feed = pd.DataFrame({
'image URL':image_urls
})
df_feed.to_csv('feed.csv', sep='\t', encoding='utf-8')
Building an Instagram Bot with Python and Selenium to Gain More Followers
Let’s build an Instagram bot to gain more followers! — I know, I know. That doesn’t sound very ethical, does it? But it’s all justified for educational purposes.
Coding is a super power — we can all agree. That’s why I’ll leave it up to you to not abuse this power. And I trust you’re here to learn how it works. Otherwise, you’d be on GitHub cloning one of the countless Instagram bots there, right?
You’re convinced? — Alright, now let’s go back to unethical practices.
The Plan
So here’s the deal, we want to build a bot in Python and Selenium that goes on the hashtags we specify, likes random posts, then follows the posters. It does that enough — we get follow backs. Simple as that.
Here’s a pretty twisted detail though: we want to keep track of the users we follow so the bot can unfollow them after the number of days we specify.
Setup
So first things first, I want to use a database to keep track of the username and the date added. You might as well save/load from/to a file, but we want this to be ready for more features in case we felt inspired in the future.
So make sure you create a database (I named mine instabot — but you can name it anything you like) and create a table called followed_users within the database with two fields (username, date_added)
Remember the installation path. You’ll need it.
You’ll also need the following python packages:
- selenium
- mysql-connector
Getting down to it
Alright, so first thing we’ll be doing is creating settings.json. Simply a .json file that will hold all of our settings so we don’t have to dive into the code every time we want to change something.
Settings
settings.json:
{
"db": {
"host": "localhost",
"user": "root",
"pass": "",
"database": "instabot"
},
"instagram": {
"user": "",
"pass": ""
},
"config": {
"days_to_unfollow": 1,
"likes_over": 150,
"check_followers_every": 3600,
"hashtags": []
}
}
As you can see, under “db”, we specify the database information. As I mentioned, I used “instabot”, but feel free to use whatever name you want.
You’ll also need to fill Instagram info under “instagram” so the bot can login into your account.
“config” is for our bot’s settings. Here’s what the fields mean:
days_to_unfollow: number of days before unfollowing users
likes_over: ignore posts if the number of likes is above this number
check_followers_every: number of seconds before checking if it’s time to unfollow any of the users
hashtags: a list of strings with the hashtag names the bot should be active on
Constants
Now, we want to take these settings and have them inside our code as constants.
Create Constants.py:
import json
INST_USER= INST_PASS= USER= PASS= HOST= DATABASE= POST_COMMENTS= ''
LIKES_LIMIT= DAYS_TO_UNFOLLOW= CHECK_FOLLOWERS_EVERY= 0
HASHTAGS= []
def init():
global INST_USER, INST_PASS, USER, PASS, HOST, DATABASE, LIKES_LIMIT, DAYS_TO_UNFOLLOW, CHECK_FOLLOWERS_EVERY, HASHTAGS
# read file
data = None
with open('settings.json', 'r') as myfile:
data = myfile.read()
obj = json.loads(data)
INST_USER = obj['instagram']['user']
INST_PASS = obj['instagram']['pass']
USER = obj['db']['user']
HOST = obj['db']['host']
PASS = obj['db']['pass']
DATABASE = obj['db']['database']
LIKES_LIMIT = obj['config']['likes_over']
CHECK_FOLLOWERS_EVERY = obj['config']['check_followers_every']
HASHTAGS = obj['config']['hashtags']
DAYS_TO_UNFOLLOW = obj['config']['days_to_unfollow']
the init() function we created reads the data from settings.json and feeds them into the constants we declared.
Engine
Alright, time for some architecture. Our bot will mainly operate from a python script with an init and update methods. Create BotEngine.py:
import Constants
def init(webdriver):
return
def update(webdriver):
return
We’ll be back later to put the logic here, but for now, we need an entry point.
Entry Point
Create our entry point, InstaBot.py:
from selenium import webdriver
import BotEngine
chromedriver_path = 'YOUR CHROMEDRIVER PATH'
webdriver = webdriver.Chrome(executable_path=chromedriver_path)
BotEngine.init(webdriver)
BotEngine.update(webdriver)
webdriver.close()
chromedriver_path = ‘YOUR CHROMEDRIVER PATH’ webdriver = webdriver.Chrome(executable_path=chromedriver_path)
BotEngine.init(webdriver)
BotEngine.update(webdriver)
webdriver.close()
Of course, you’ll need to swap “YOUR CHROMEDRIVER PATH” with your actual ChromeDriver path.
Time Helper
We need to create a helper script that will help us calculate elapsed days since a certain date (so we know if we should unfollow user)
Create TimeHelper.py:
import datetime
def days_since_date(n):
diff = datetime.datetime.now().date() - n
return diff.days
Database
Create DBHandler.py. It’ll contain a class that handles connecting to the Database for us.
import mysql.connector
import Constants
class DBHandler:
def __init__(self):
DBHandler.HOST = Constants.HOST
DBHandler.USER = Constants.USER
DBHandler.DBNAME = Constants.DATABASE
DBHandler.PASSWORD = Constants.PASS
HOST = Constants.HOST
USER = Constants.USER
DBNAME = Constants.DATABASE
PASSWORD = Constants.PASS
@staticmethod
def get_mydb():
if DBHandler.DBNAME == '':
Constants.init()
db = DBHandler()
mydb = db.connect()
return mydb
def connect(self):
mydb = mysql.connector.connect(
host=DBHandler.HOST,
user=DBHandler.USER,
passwd=DBHandler.PASSWORD,
database = DBHandler.DBNAME
)
return mydb
As you can see, we’re using the constants we defined.
The class contains a static method get_mydb() that returns a database connection we can use.
Now, let’s define a DB user script that contains the DB operations we need to perform on the user.
Create DBUsers.py:
import datetime, TimeHelper
from DBHandler import *
import Constants
#delete user by username
def delete_user(username):
mydb = DBHandler.get_mydb()
cursor = mydb.cursor()
sql = "DELETE FROM followed_users WHERE username = '{0}'".format(username)
cursor.execute(sql)
mydb.commit()
#add new username
def add_user(username):
mydb = DBHandler.get_mydb()
cursor = mydb.cursor()
now = datetime.datetime.now().date()
cursor.execute("INSERT INTO followed_users(username, date_added) VALUES(%s,%s)",(username, now))
mydb.commit()
#check if any user qualifies to be unfollowed
def check_unfollow_list():
mydb = DBHandler.get_mydb()
cursor = mydb.cursor()
cursor.execute("SELECT * FROM followed_users")
results = cursor.fetchall()
users_to_unfollow = []
for r in results:
d = TimeHelper.days_since_date(r[1])
if d > Constants.DAYS_TO_UNFOLLOW:
users_to_unfollow.append(r[0])
return users_to_unfollow
#get all followed users
def get_followed_users():
users = []
mydb = DBHandler.get_mydb()
cursor = mydb.cursor()
cursor.execute("SELECT * FROM followed_users")
results = cursor.fetchall()
for r in results:
users.append(r[0])
return users
Account Agent
Alright, we’re about to start our bot. We’re creating a script called AccountAgent.py that will contain the agent behavior.
Import some modules, some of which we need for later and write a login function that will make use of our webdriver.
Notice that we have to keep calling the sleep function between actions. If we send too many requests quickly, the Instagram servers will be alarmed and will deny any requests you send.
from time import sleep
import datetime
import DBUsers, Constants
import traceback
import random
def login(webdriver):
#Open the instagram login page
webdriver.get('https://www.instagram.com/accounts/login/?source=auth_switcher')
#sleep for 3 seconds to prevent issues with the server
sleep(3)
#Find username and password fields and set their input using our constants
username = webdriver.find_element_by_name('username')
username.send_keys(Constants.INST_USER)
password = webdriver.find_element_by_name('password')
password.send_keys(Constants.INST_PASS)
#Get the login button
try:
button_login = webdriver.find_element_by_xpath(
'//*[@id="react-root"]/section/main/div/article/div/div[1]/div/form/div[4]/button')
except:
button_login = webdriver.find_element_by_xpath(
'//*[@id="react-root"]/section/main/div/article/div/div[1]/div/form/div[6]/button/div')
#sleep again
sleep(2)
#click login
button_login.click()
sleep(3)
#In case you get a popup after logging in, press not now.
#If not, then just return
try:
notnow = webdriver.find_element_by_css_selector(
'body > div.RnEpo.Yx5HN > div > div > div.mt3GC > button.aOOlW.HoLwm')
notnow.click()
except:
return
Also note how we’re getting elements with their xpath. To do so, right click on the element, click “Inspect”, then right click on the element again inside the inspector, and choose Copy->Copy XPath.
Another important thing to be aware of is that element hierarchy change with the page’s layout when you resize or stretch the window. That’s why we’re checking for two different xpaths for the login button.
Now go back to BotEngine.py, we’re ready to login.
Add more imports that we’ll need later and fill in the init function
import AccountAgent, DBUsers
import Constants
import datetime
def init(webdriver):
Constants.init()
AccountAgent.login(webdriver)
def update(webdriver):
return
If you run our entry script now (InstaBot.py) you’ll see the bot logging in.
Perfect, now let’s add a method that will allow us to follow people to AccountAgent.py:
def follow_people(webdriver):
#all the followed user
prev_user_list = DBUsers.get_followed_users()
#a list to store newly followed users
new_followed = []
#counters
followed = 0
likes = 0
#Iterate theough all the hashtags from the constants
for hashtag in Constants.HASHTAGS:
#Visit the hashtag
webdriver.get('https://www.instagram.com/explore/tags/' + hashtag+ '/')
sleep(5)
#Get the first post thumbnail and click on it
first_thumbnail = webdriver.find_element_by_xpath(
'//*[@id="react-root"]/section/main/article/div[1]/div/div/div[1]/div[1]/a/div')
first_thumbnail.click()
sleep(random.randint(1,3))
try:
#iterate over the first 200 posts in the hashtag
for x in range(1,200):
t_start = datetime.datetime.now()
#Get the poster's username
username = webdriver.find_element_by_xpath('/html/body/div[3]/div[2]/div/article/header/div[2]/div[1]/div[1]/h2/a').text
likes_over_limit = False
try:
#get number of likes and compare it to the maximum number of likes to ignore post
likes = int(webdriver.find_element_by_xpath(
'/html/body/div[3]/div[2]/div/article/div[2]/section[2]/div/div/button/span').text)
if likes > Constants.LIKES_LIMIT:
print("likes over {0}".format(Constants.LIKES_LIMIT))
likes_over_limit = True
print("Detected: {0}".format(username))
#If username isn't stored in the database and the likes are in the acceptable range
if username not in prev_user_list and not likes_over_limit:
#Don't press the button if the text doesn't say follow
if webdriver.find_element_by_xpath('/html/body/div[3]/div[2]/div/article/header/div[2]/div[1]/div[2]/button').text == 'Follow':
#Use DBUsers to add the new user to the database
DBUsers.add_user(username)
#Click follow
webdriver.find_element_by_xpath('/html/body/div[3]/div[2]/div/article/header/div[2]/div[1]/div[2]/button').click()
followed += 1
print("Followed: {0}, #{1}".format(username, followed))
new_followed.append(username)
# Liking the picture
button_like = webdriver.find_element_by_xpath(
'/html/body/div[3]/div[2]/div/article/div[2]/section[1]/span[1]/button')
button_like.click()
likes += 1
print("Liked {0}'s post, #{1}".format(username, likes))
sleep(random.randint(5, 18))
# Next picture
webdriver.find_element_by_link_text('Next').click()
sleep(random.randint(20, 30))
except:
traceback.print_exc()
continue
t_end = datetime.datetime.now()
#calculate elapsed time
t_elapsed = t_end - t_start
print("This post took {0} seconds".format(t_elapsed.total_seconds()))
except:
traceback.print_exc()
continue
#add new list to old list
for n in range(0, len(new_followed)):
prev_user_list.append(new_followed[n])
print('Liked {} photos.'.format(likes))
print('Followed {} new people.'.format(followed))
It’s pretty long, but generally here’s the steps of the algorithm:
For every hashtag in the hashtag constant list:
- Visit the hashtag link
- Open the first thumbnail
- Now, execute the following code 200 times (first 200 posts in the hashtag)
- Get poster’s username, check if not already following, follow, like the post, then click next
- If already following just click next quickly
Now we might as well implement the unfollow method, hopefully the engine will be feeding us the usernames to unfollow in a list:
def unfollow_people(webdriver, people):
#if only one user, append in a list
if not isinstance(people, (list,)):
p = people
people = []
people.append(p)
for user in people:
try:
webdriver.get('https://www.instagram.com/' + user + '/')
sleep(5)
unfollow_xpath = '//*[@id="react-root"]/section/main/div/header/section/div[1]/div[1]/span/span[1]/button'
unfollow_confirm_xpath = '/html/body/div[3]/div/div/div[3]/button[1]'
if webdriver.find_element_by_xpath(unfollow_xpath).text == "Following":
sleep(random.randint(4, 15))
webdriver.find_element_by_xpath(unfollow_xpath).click()
sleep(2)
webdriver.find_element_by_xpath(unfollow_confirm_xpath).click()
sleep(4)
DBUsers.delete_user(user)
except Exception:
traceback.print_exc()
continue
Now we can finally go back and finish the bot by implementing the rest of BotEngine.py:
import AccountAgent, DBUsers
import Constants
import datetime
def init(webdriver):
Constants.init()
AccountAgent.login(webdriver)
def update(webdriver):
#Get start of time to calculate elapsed time later
start = datetime.datetime.now()
#Before the loop, check if should unfollow anyone
_check_follow_list(webdriver)
while True:
#Start following operation
AccountAgent.follow_people(webdriver)
#Get the time at the end
end = datetime.datetime.now()
#How much time has passed?
elapsed = end - start
#If greater than our constant to check on
#followers, check on followers
if elapsed.total_seconds() >= Constants.CHECK_FOLLOWERS_EVERY:
#reset the start variable to now
start = datetime.datetime.now()
#check on followers
_check_follow_list(webdriver)
def _check_follow_list(webdriver):
print("Checking for users to unfollow")
#get the unfollow list
users = DBUsers.check_unfollow_list()
#if there's anyone in the list, start unfollowing operation
if len(users) > 0:
AccountAgent.unfollow_people(webdriver, users)
Conclusion
And that’s it — now you have yourself a fully functional Instagram bot built with Python and Selenium. There are many possibilities for you to explore now, so make sure you’re using this newly gained skill to solve real life problems!
You can get the source code for the whole project from this GitHub repository.
Building a simple Instagram bot with Python tutorial
Here we build a simple bot using some simple Python which beginner to intermediate coders can follow.
Here’s the code on GitHub
https://github.com/aj-4/ig-followers
Build A (Full-Featured) Instagram Bot With Python
Source Code: https://github.com/jg-fisher/instagram-bot
How to Get Instagram Followers/Likes Using Python
In this video I show you how to program your own Instagram Bot using Python and Selenium.
https://www.youtube.com/watch?v=BGU2X5lrz9M
Code Link:
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
import time
import random
import sys
def print_same_line(text):
sys.stdout.write('\r')
sys.stdout.flush()
sys.stdout.write(text)
sys.stdout.flush()
class InstagramBot:
def __init__(self, username, password):
self.username = username
self.password = password
self.driver = webdriver.Chrome()
def closeBrowser(self):
self.driver.close()
def login(self):
driver = self.driver
driver.get("https://www.instagram.com/")
time.sleep(2)
login_button = driver.find_element_by_xpath("//a[@href='/accounts/login/?source=auth_switcher']")
login_button.click()
time.sleep(2)
user_name_elem = driver.find_element_by_xpath("//input[@name='username']")
user_name_elem.clear()
user_name_elem.send_keys(self.username)
passworword_elem = driver.find_element_by_xpath("//input[@name='password']")
passworword_elem.clear()
passworword_elem.send_keys(self.password)
passworword_elem.send_keys(Keys.RETURN)
time.sleep(2)
def like_photo(self, hashtag):
driver = self.driver
driver.get("https://www.instagram.com/explore/tags/" + hashtag + "/")
time.sleep(2)
# gathering photos
pic_hrefs = []
for i in range(1, 7):
try:
driver.execute_script("window.scrollTo(0, document.body.scrollHeight);")
time.sleep(2)
# get tags
hrefs_in_view = driver.find_elements_by_tag_name('a')
# finding relevant hrefs
hrefs_in_view = [elem.get_attribute('href') for elem in hrefs_in_view
if '.com/p/' in elem.get_attribute('href')]
# building list of unique photos
[pic_hrefs.append(href) for href in hrefs_in_view if href not in pic_hrefs]
# print("Check: pic href length " + str(len(pic_hrefs)))
except Exception:
continue
# Liking photos
unique_photos = len(pic_hrefs)
for pic_href in pic_hrefs:
driver.get(pic_href)
time.sleep(2)
driver.execute_script("window.scrollTo(0, document.body.scrollHeight);")
try:
time.sleep(random.randint(2, 4))
like_button = lambda: driver.find_element_by_xpath('//span[@aria-label="Like"]').click()
like_button().click()
for second in reversed(range(0, random.randint(18, 28))):
print_same_line("#" + hashtag + ': unique photos left: ' + str(unique_photos)
+ " | Sleeping " + str(second))
time.sleep(1)
except Exception as e:
time.sleep(2)
unique_photos -= 1
if __name__ == "__main__":
username = "USERNAME"
password = "PASSWORD"
ig = InstagramBot(username, password)
ig.login()
hashtags = ['amazing', 'beautiful', 'adventure', 'photography', 'nofilter',
'newyork', 'artsy', 'alumni', 'lion', 'best', 'fun', 'happy',
'art', 'funny', 'me', 'followme', 'follow', 'cinematography', 'cinema',
'love', 'instagood', 'instagood', 'followme', 'fashion', 'sun', 'scruffy',
'street', 'canon', 'beauty', 'studio', 'pretty', 'vintage', 'fierce']
while True:
try:
# Choose a random tag from the list of tags
tag = random.choice(hashtags)
ig.like_photo(tag)
except Exception:
ig.closeBrowser()
time.sleep(60)
ig = InstagramBot(username, password)
ig.login()
Build An INSTAGRAM Bot With Python That Gets You Followers
Instagram Automation Using Python
How to Create an Instagram Bot | Get More Followers
Building a simple Instagram Influencer Bot with Python tutorial
#python #chatbot #web-development
1657081614
How to Automate Excel with Python | Python Excel Tutorial (OpenPyXL)
How to Automate Excel with Python
In this article, We will show how we can use python to automate Excel . A useful Python library is Openpyxl which we will learn to do Excel Automation
What is OPENPYXL
Openpyxl is a Python library that is used to read from an Excel file or write to an Excel file. Data scientists use Openpyxl for data analysis, data copying, data mining, drawing charts, styling sheets, adding formulas, and more.
Workbook: A spreadsheet is represented as a workbook in openpyxl. A workbook consists of one or more sheets.
Sheet: A sheet is a single page composed of cells for organizing data.
Cell: The intersection of a row and a column is called a cell. Usually represented by A1, B5, etc.
Row: A row is a horizontal line represented by a number (1,2, etc.).
Column: A column is a vertical line represented by a capital letter (A, B, etc.).
Openpyxl can be installed using the pip command and it is recommended to install it in a virtual environment.
pip install openpyxl
CREATE A NEW WORKBOOK
We start by creating a new spreadsheet, which is called a workbook in Openpyxl. We import the workbook module from Openpyxl and use the function Workbook() which creates a new workbook.
from openpyxl
import Workbook
#creates a new workbook
wb = Workbook()
#Gets the first active worksheet
ws = wb.active
#creating new worksheets by using the create_sheet method
ws1 = wb.create_sheet("sheet1", 0) #inserts at first position
ws2 = wb.create_sheet("sheet2") #inserts at last position
ws3 = wb.create_sheet("sheet3", -1) #inserts at penultimate position
#Renaming the sheet
ws.title = "Example"
#save the workbook
wb.save(filename = "example.xlsx")READING DATA FROM WORKBOOK
We load the file using the function load_Workbook() which takes the filename as an argument. The file must be saved in the same working directory.
#loading a workbook
wb = openpyxl.load_workbook("example.xlsx")GETTING SHEETS FROM THE LOADED WORKBOOK
#getting sheet names
wb.sheetnames
result = ['sheet1', 'Sheet', 'sheet3', 'sheet2']
#getting a particular sheet
sheet1 = wb["sheet2"]
#getting sheet title
sheet1.title
result = 'sheet2'
#Getting the active sheet
sheetactive = wb.active
result = 'sheet1'ACCESSING CELLS AND CELL VALUES
#get a cell from the sheet
sheet1["A1"] <
Cell 'Sheet1'.A1 >
#get the cell value
ws["A1"].value 'Segment'
#accessing cell using row and column and assigning a value
d = ws.cell(row = 4, column = 2, value = 10)
d.value
10ITERATING THROUGH ROWS AND COLUMNS
#looping through each row and column
for x in range(1, 5):
for y in range(1, 5):
print(x, y, ws.cell(row = x, column = y)
.value)
#getting the highest row number
ws.max_row
701
#getting the highest column number
ws.max_column
19There are two functions for iterating through rows and columns.
Iter_rows() => returns the rows
Iter_cols() => returns the columns {
min_row = 4, max_row = 5, min_col = 2, max_col = 5
} => This can be used to set the boundaries
for any iteration.Example:
#iterating rows
for row in ws.iter_rows(min_row = 2, max_col = 3, max_row = 3):
for cell in row:
print(cell) <
Cell 'Sheet1'.A2 >
<
Cell 'Sheet1'.B2 >
<
Cell 'Sheet1'.C2 >
<
Cell 'Sheet1'.A3 >
<
Cell 'Sheet1'.B3 >
<
Cell 'Sheet1'.C3 >
#iterating columns
for col in ws.iter_cols(min_row = 2, max_col = 3, max_row = 3):
for cell in col:
print(cell) <
Cell 'Sheet1'.A2 >
<
Cell 'Sheet1'.A3 >
<
Cell 'Sheet1'.B2 >
<
Cell 'Sheet1'.B3 >
<
Cell 'Sheet1'.C2 >
<
Cell 'Sheet1'.C3 >To get all the rows of the worksheet we use the method worksheet.rows and to get all the columns of the worksheet we use the method worksheet.columns. Similarly, to iterate only through the values we use the method worksheet.values.
Example:
for row in ws.values:
for value in row:
print(value)WRITING DATA TO AN EXCEL FILE
Writing to a workbook can be done in many ways such as adding a formula, adding charts, images, updating cell values, inserting rows and columns, etc… We will discuss each of these with an example.
CREATING AND SAVING A NEW WORKBOOK
#creates a new workbook
wb = openpyxl.Workbook()
#saving the workbook
wb.save("new.xlsx")ADDING AND REMOVING SHEETS
#creating a new sheet
ws1 = wb.create_sheet(title = "sheet 2")
#creating a new sheet at index 0
ws2 = wb.create_sheet(index = 0, title = "sheet 0")
#checking the sheet names
wb.sheetnames['sheet 0', 'Sheet', 'sheet 2']
#deleting a sheet
del wb['sheet 0']
#checking sheetnames
wb.sheetnames['Sheet', 'sheet 2']ADDING CELL VALUES
#checking the sheet value
ws['B2'].value
null
#adding value to cell
ws['B2'] = 367
#checking value
ws['B2'].value
367ADDING FORMULAS
We often require formulas to be included in our Excel datasheet. We can easily add formulas using the Openpyxl module just like you add values to a cell.
For example:
import openpyxl
from openpyxl
import Workbook
wb = openpyxl.load_workbook("new1.xlsx")
ws = wb['Sheet']
ws['A9'] = '=SUM(A2:A8)'
wb.save("new2.xlsx")The above program will add the formula (=SUM(A2:A8)) in cell A9. The result will be as below.
MERGE/UNMERGE CELLS
Two or more cells can be merged to a rectangular area using the method merge_cells(), and similarly, they can be unmerged using the method unmerge_cells().
For example:
Merge cells
#merge cells B2 to C9
ws.merge_cells('B2:C9')
ws['B2'] = "Merged cells"Adding the above code to the previous example will merge cells as below.
UNMERGE CELLS
#unmerge cells B2 to C9
ws.unmerge_cells('B2:C9')The above code will unmerge cells from B2 to C9.
INSERTING AN IMAGE
To insert an image we import the image function from the module openpyxl.drawing.image. We then load our image and add it to the cell as shown in the below example.
Example:
import openpyxl
from openpyxl
import Workbook
from openpyxl.drawing.image
import Image
wb = openpyxl.load_workbook("new1.xlsx")
ws = wb['Sheet']
#loading the image(should be in same folder)
img = Image('logo.png')
ws['A1'] = "Adding image"
#adjusting size
img.height = 130
img.width = 200
#adding img to cell A3
ws.add_image(img, 'A3')
wb.save("new2.xlsx")Result:
CREATING CHARTS
Charts are essential to show a visualization of data. We can create charts from Excel data using the Openpyxl module chart. Different forms of charts such as line charts, bar charts, 3D line charts, etc., can be created. We need to create a reference that contains the data to be used for the chart, which is nothing but a selection of cells (rows and columns). I am using sample data to create a 3D bar chart in the below example:
Example
import openpyxl
from openpyxl
import Workbook
from openpyxl.chart
import BarChart3D, Reference, series
wb = openpyxl.load_workbook("example.xlsx")
ws = wb.active
values = Reference(ws, min_col = 3, min_row = 2, max_col = 3, max_row = 40)
chart = BarChart3D()
chart.add_data(values)
ws.add_chart(chart, "E3")
wb.save("MyChart.xlsx")Result
How to Automate Excel with Python with Video Tutorial
Welcome to another video! In this video, We will cover how we can use python to automate Excel. I'll be going over everything from creating workbooks to accessing individual cells and stylizing cells. There is a ton of things that you can do with Excel but I'll just be covering the core/base things in OpenPyXl.
⭐️ Timestamps ⭐️
00:00 | Introduction
02:14 | Installing openpyxl
03:19 | Testing Installation
04:25 | Loading an Existing Workbook
06:46 | Accessing Worksheets
07:37 | Accessing Cell Values
08:58 | Saving Workbooks
09:52 | Creating, Listing and Changing Sheets
11:50 | Creating a New Workbook
12:39 | Adding/Appending Rows
14:26 | Accessing Multiple Cells
20:46 | Merging Cells
22:27 | Inserting and Deleting Rows
23:35 | Inserting and Deleting Columns
24:48 | Copying and Moving Cells
26:06 | Practical Example, Formulas & Cell Styling
📄 Resources 📄
OpenPyXL Docs: https://openpyxl.readthedocs.io/en/stable/
Code Written in This Tutorial: https://github.com/techwithtim/ExcelPythonTutorial
Subscribe: https://www.youtube.com/c/TechWithTim/featured
1646698200
Face Recognition with OpenCV and Python
Introduction
What is face recognition? Or what is recognition? When you look at an apple fruit, your mind immediately tells you that this is an apple fruit. This process, your mind telling you that this is an apple fruit is recognition in simple words. So what is face recognition then? I am sure you have guessed it right. When you look at your friend walking down the street or a picture of him, you recognize that he is your friend Paulo. Interestingly when you look at your friend or a picture of him you look at his face first before looking at anything else. Ever wondered why you do that? This is so that you can recognize him by looking at his face. Well, this is you doing face recognition.
But the real question is how does face recognition works? It is quite simple and intuitive. Take a real life example, when you meet someone first time in your life you don't recognize him, right? While he talks or shakes hands with you, you look at his face, eyes, nose, mouth, color and overall look. This is your mind learning or training for the face recognition of that person by gathering face data. Then he tells you that his name is Paulo. At this point your mind knows that the face data it just learned belongs to Paulo. Now your mind is trained and ready to do face recognition on Paulo's face. Next time when you will see Paulo or his face in a picture you will immediately recognize him. This is how face recognition work. The more you will meet Paulo, the more data your mind will collect about Paulo and especially his face and the better you will become at recognizing him.
Now the next question is how to code face recognition with OpenCV, after all this is the only reason why you are reading this article, right? OK then. You might say that our mind can do these things easily but to actually code them into a computer is difficult? Don't worry, it is not. Thanks to OpenCV, coding face recognition is as easier as it feels. The coding steps for face recognition are same as we discussed it in real life example above.
- Training Data Gathering: Gather face data (face images in this case) of the persons you want to recognize
- Training of Recognizer: Feed that face data (and respective names of each face) to the face recognizer so that it can learn.
- Recognition: Feed new faces of the persons and see if the face recognizer you just trained recognizes them.
OpenCV comes equipped with built in face recognizer, all you have to do is feed it the face data. It's that simple and this how it will look once we are done coding it.
OpenCV Face Recognizers
OpenCV has three built in face recognizers and thanks to OpenCV's clean coding, you can use any of them by just changing a single line of code. Below are the names of those face recognizers and their OpenCV calls.
- EigenFaces Face Recognizer Recognizer -
cv2.face.createEigenFaceRecognizer() - FisherFaces Face Recognizer Recognizer -
cv2.face.createFisherFaceRecognizer() - Local Binary Patterns Histograms (LBPH) Face Recognizer -
cv2.face.createLBPHFaceRecognizer()
We have got three face recognizers but do you know which one to use and when? Or which one is better? I guess not. So why not go through a brief summary of each, what you say? I am assuming you said yes :) So let's dive into the theory of each.
EigenFaces Face Recognizer
This algorithm considers the fact that not all parts of a face are equally important and equally useful. When you look at some one you recognize him/her by his distinct features like eyes, nose, cheeks, forehead and how they vary with respect to each other. So you are actually focusing on the areas of maximum change (mathematically speaking, this change is variance) of the face. For example, from eyes to nose there is a significant change and same is the case from nose to mouth. When you look at multiple faces you compare them by looking at these parts of the faces because these parts are the most useful and important components of a face. Important because they catch the maximum change among faces, change the helps you differentiate one face from the other. This is exactly how EigenFaces face recognizer works.
EigenFaces face recognizer looks at all the training images of all the persons as a whole and try to extract the components which are important and useful (the components that catch the maximum variance/change) and discards the rest of the components. This way it not only extracts the important components from the training data but also saves memory by discarding the less important components. These important components it extracts are called principal components. Below is an image showing the principal components extracted from a list of faces.
Principal Components 
source
You can see that principal components actually represent faces and these faces are called eigen faces and hence the name of the algorithm.
So this is how EigenFaces face recognizer trains itself (by extracting principal components). Remember, it also keeps a record of which principal component belongs to which person. One thing to note in above image is that Eigenfaces algorithm also considers illumination as an important component.
Later during recognition, when you feed a new image to the algorithm, it repeats the same process on that image as well. It extracts the principal component from that new image and compares that component with the list of components it stored during training and finds the component with the best match and returns the person label associated with that best match component.
Easy peasy, right? Next one is easier than this one.
FisherFaces Face Recognizer
This algorithm is an improved version of EigenFaces face recognizer. Eigenfaces face recognizer looks at all the training faces of all the persons at once and finds principal components from all of them combined. By capturing principal components from all the of them combined you are not focusing on the features that discriminate one person from the other but the features that represent all the persons in the training data as a whole.
This approach has drawbacks, for example, images with sharp changes (like light changes which is not a useful feature at all) may dominate the rest of the images and you may end up with features that are from external source like light and are not useful for discrimination at all. In the end, your principal components will represent light changes and not the actual face features.
Fisherfaces algorithm, instead of extracting useful features that represent all the faces of all the persons, it extracts useful features that discriminate one person from the others. This way features of one person do not dominate over the others and you have the features that discriminate one person from the others.
Below is an image of features extracted using Fisherfaces algorithm.
Fisher Faces 
source
You can see that features extracted actually represent faces and these faces are called fisher faces and hence the name of the algorithm.
One thing to note here is that even in Fisherfaces algorithm if multiple persons have images with sharp changes due to external sources like light they will dominate over other features and affect recognition accuracy.
Getting bored with this theory? Don't worry, only one face recognizer is left and then we will dive deep into the coding part.
Local Binary Patterns Histograms (LBPH) Face Recognizer
I wrote a detailed explaination on Local Binary Patterns Histograms in my previous article on face detection using local binary patterns histograms. So here I will just give a brief overview of how it works.
We know that Eigenfaces and Fisherfaces are both affected by light and in real life we can't guarantee perfect light conditions. LBPH face recognizer is an improvement to overcome this drawback.
Idea is to not look at the image as a whole instead find the local features of an image. LBPH alogrithm try to find the local structure of an image and it does that by comparing each pixel with its neighboring pixels.
Take a 3x3 window and move it one image, at each move (each local part of an image), compare the pixel at the center with its neighbor pixels. The neighbors with intensity value less than or equal to center pixel are denoted by 1 and others by 0. Then you read these 0/1 values under 3x3 window in a clockwise order and you will have a binary pattern like 11100011 and this pattern is local to some area of the image. You do this on whole image and you will have a list of local binary patterns.
LBP Labeling 
Now you get why this algorithm has Local Binary Patterns in its name? Because you get a list of local binary patterns. Now you may be wondering, what about the histogram part of the LBPH? Well after you get a list of local binary patterns, you convert each binary pattern into a decimal number (as shown in above image) and then you make a histogram of all of those values. A sample histogram looks like this.
Sample Histogram 
I guess this answers the question about histogram part. So in the end you will have one histogram for each face image in the training data set. That means if there were 100 images in training data set then LBPH will extract 100 histograms after training and store them for later recognition. Remember, algorithm also keeps track of which histogram belongs to which person.
Later during recognition, when you will feed a new image to the recognizer for recognition it will generate a histogram for that new image, compare that histogram with the histograms it already has, find the best match histogram and return the person label associated with that best match histogram.
Below is a list of faces and their respective local binary patterns images. You can see that the LBP images are not affected by changes in light conditions.
LBP Faces 
source
The theory part is over and now comes the coding part! Ready to dive into coding? Let's get into it then.
Coding Face Recognition with OpenCV
The Face Recognition process in this tutorial is divided into three steps.
- Prepare training data: In this step we will read training images for each person/subject along with their labels, detect faces from each image and assign each detected face an integer label of the person it belongs to.
- Train Face Recognizer: In this step we will train OpenCV's LBPH face recognizer by feeding it the data we prepared in step 1.
- Testing: In this step we will pass some test images to face recognizer and see if it predicts them correctly.
[There should be a visualization diagram for above steps here]
To detect faces, I will use the code from my previous article on face detection. So if you have not read it, I encourage you to do so to understand how face detection works and its Python coding.
Import Required Modules
Before starting the actual coding we need to import the required modules for coding. So let's import them first.
- cv2: is OpenCV module for Python which we will use for face detection and face recognition.
- os: We will use this Python module to read our training directories and file names.
- numpy: We will use this module to convert Python lists to numpy arrays as OpenCV face recognizers accept numpy arrays.
#import OpenCV module
import cv2
#import os module for reading training data directories and paths
import os
#import numpy to convert python lists to numpy arrays as
#it is needed by OpenCV face recognizers
import numpy as np
#matplotlib for display our images
import matplotlib.pyplot as plt
%matplotlib inline
Training Data
The more images used in training the better. Normally a lot of images are used for training a face recognizer so that it can learn different looks of the same person, for example with glasses, without glasses, laughing, sad, happy, crying, with beard, without beard etc. To keep our tutorial simple we are going to use only 12 images for each person.
So our training data consists of total 2 persons with 12 images of each person. All training data is inside training-data folder. training-data folder contains one folder for each person and each folder is named with format sLabel (e.g. s1, s2) where label is actually the integer label assigned to that person. For example folder named s1 means that this folder contains images for person 1. The directory structure tree for training data is as follows:
training-data
|-------------- s1
| |-- 1.jpg
| |-- ...
| |-- 12.jpg
|-------------- s2
| |-- 1.jpg
| |-- ...
| |-- 12.jpg
The test-data folder contains images that we will use to test our face recognizer after it has been successfully trained.
As OpenCV face recognizer accepts labels as integers so we need to define a mapping between integer labels and persons actual names so below I am defining a mapping of persons integer labels and their respective names.
Note: As we have not assigned label 0 to any person so the mapping for label 0 is empty.
#there is no label 0 in our training data so subject name for index/label 0 is empty
subjects = ["", "Tom Cruise", "Shahrukh Khan"]
Prepare training data
You may be wondering why data preparation, right? Well, OpenCV face recognizer accepts data in a specific format. It accepts two vectors, one vector is of faces of all the persons and the second vector is of integer labels for each face so that when processing a face the face recognizer knows which person that particular face belongs too.
For example, if we had 2 persons and 2 images for each person.
PERSON-1 PERSON-2
img1 img1
img2 img2
Then the prepare data step will produce following face and label vectors.
FACES LABELS
person1_img1_face 1
person1_img2_face 1
person2_img1_face 2
person2_img2_face 2
Preparing data step can be further divided into following sub-steps.
- Read all the folder names of subjects/persons provided in training data folder. So for example, in this tutorial we have folder names:
s1, s2. - For each subject, extract label number. Do you remember that our folders have a special naming convention? Folder names follow the format
sLabelwhereLabelis an integer representing the label we have assigned to that subject. So for example, folder names1means that the subject has label 1, s2 means subject label is 2 and so on. The label extracted in this step is assigned to each face detected in the next step. - Read all the images of the subject, detect face from each image.
- Add each face to faces vector with corresponding subject label (extracted in above step) added to labels vector.
[There should be a visualization for above steps here]
Did you read my last article on face detection? No? Then you better do so right now because to detect faces, I am going to use the code from my previous article on face detection. So if you have not read it, I encourage you to do so to understand how face detection works and its coding. Below is the same code.
#function to detect face using OpenCV
def detect_face(img):
#convert the test image to gray image as opencv face detector expects gray images
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#load OpenCV face detector, I am using LBP which is fast
#there is also a more accurate but slow Haar classifier
face_cascade = cv2.CascadeClassifier('opencv-files/lbpcascade_frontalface.xml')
#let's detect multiscale (some images may be closer to camera than others) images
#result is a list of faces
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=5);
#if no faces are detected then return original img
if (len(faces) == 0):
return None, None
#under the assumption that there will be only one face,
#extract the face area
(x, y, w, h) = faces[0]
#return only the face part of the image
return gray[y:y+w, x:x+h], faces[0]
I am using OpenCV's LBP face detector. On line 4, I convert the image to grayscale because most operations in OpenCV are performed in gray scale, then on line 8 I load LBP face detector using cv2.CascadeClassifier class. After that on line 12 I use cv2.CascadeClassifier class' detectMultiScale method to detect all the faces in the image. on line 20, from detected faces I only pick the first face because in one image there will be only one face (under the assumption that there will be only one prominent face). As faces returned by detectMultiScale method are actually rectangles (x, y, width, height) and not actual faces images so we have to extract face image area from the main image. So on line 23 I extract face area from gray image and return both the face image area and face rectangle.
Now you have got a face detector and you know the 4 steps to prepare the data, so are you ready to code the prepare data step? Yes? So let's do it.
#this function will read all persons' training images, detect face from each image
#and will return two lists of exactly same size, one list
# of faces and another list of labels for each face
def prepare_training_data(data_folder_path):
#------STEP-1--------
#get the directories (one directory for each subject) in data folder
dirs = os.listdir(data_folder_path)
#list to hold all subject faces
faces = []
#list to hold labels for all subjects
labels = []
#let's go through each directory and read images within it
for dir_name in dirs:
#our subject directories start with letter 's' so
#ignore any non-relevant directories if any
if not dir_name.startswith("s"):
continue;
#------STEP-2--------
#extract label number of subject from dir_name
#format of dir name = slabel
#, so removing letter 's' from dir_name will give us label
label = int(dir_name.replace("s", ""))
#build path of directory containin images for current subject subject
#sample subject_dir_path = "training-data/s1"
subject_dir_path = data_folder_path + "/" + dir_name
#get the images names that are inside the given subject directory
subject_images_names = os.listdir(subject_dir_path)
#------STEP-3--------
#go through each image name, read image,
#detect face and add face to list of faces
for image_name in subject_images_names:
#ignore system files like .DS_Store
if image_name.startswith("."):
continue;
#build image path
#sample image path = training-data/s1/1.pgm
image_path = subject_dir_path + "/" + image_name
#read image
image = cv2.imread(image_path)
#display an image window to show the image
cv2.imshow("Training on image...", image)
cv2.waitKey(100)
#detect face
face, rect = detect_face(image)
#------STEP-4--------
#for the purpose of this tutorial
#we will ignore faces that are not detected
if face is not None:
#add face to list of faces
faces.append(face)
#add label for this face
labels.append(label)
cv2.destroyAllWindows()
cv2.waitKey(1)
cv2.destroyAllWindows()
return faces, labels
I have defined a function that takes the path, where training subjects' folders are stored, as parameter. This function follows the same 4 prepare data substeps mentioned above.
(step-1) On line 8 I am using os.listdir method to read names of all folders stored on path passed to function as parameter. On line 10-13 I am defining labels and faces vectors.
(step-2) After that I traverse through all subjects' folder names and from each subject's folder name on line 27 I am extracting the label information. As folder names follow the sLabel naming convention so removing the letter s from folder name will give us the label assigned to that subject.
(step-3) On line 34, I read all the images names of of the current subject being traversed and on line 39-66 I traverse those images one by one. On line 53-54 I am using OpenCV's imshow(window_title, image) along with OpenCV's waitKey(interval) method to display the current image being traveresed. The waitKey(interval) method pauses the code flow for the given interval (milliseconds), I am using it with 100ms interval so that we can view the image window for 100ms. On line 57, I detect face from the current image being traversed.
(step-4) On line 62-66, I add the detected face and label to their respective vectors.
But a function can't do anything unless we call it on some data that it has to prepare, right? Don't worry, I have got data of two beautiful and famous celebrities. I am sure you will recognize them!
Let's call this function on images of these beautiful celebrities to prepare data for training of our Face Recognizer. Below is a simple code to do that.
#let's first prepare our training data
#data will be in two lists of same size
#one list will contain all the faces
#and other list will contain respective labels for each face
print("Preparing data...")
faces, labels = prepare_training_data("training-data")
print("Data prepared")
#print total faces and labels
print("Total faces: ", len(faces))
print("Total labels: ", len(labels))
Preparing data...
Data prepared
Total faces: 23
Total labels: 23
This was probably the boring part, right? Don't worry, the fun stuff is coming up next. It's time to train our own face recognizer so that once trained it can recognize new faces of the persons it was trained on. Read? Ok then let's train our face recognizer.
Train Face Recognizer
As we know, OpenCV comes equipped with three face recognizers.
- EigenFace Recognizer: This can be created with
cv2.face.createEigenFaceRecognizer() - FisherFace Recognizer: This can be created with
cv2.face.createFisherFaceRecognizer() - Local Binary Patterns Histogram (LBPH): This can be created with
cv2.face.LBPHFisherFaceRecognizer()
I am going to use LBPH face recognizer but you can use any face recognizer of your choice. No matter which of the OpenCV's face recognizer you use the code will remain the same. You just have to change one line, the face recognizer initialization line given below.
#create our LBPH face recognizer
face_recognizer = cv2.face.createLBPHFaceRecognizer()
#or use EigenFaceRecognizer by replacing above line with
#face_recognizer = cv2.face.createEigenFaceRecognizer()
#or use FisherFaceRecognizer by replacing above line with
#face_recognizer = cv2.face.createFisherFaceRecognizer()
Now that we have initialized our face recognizer and we also have prepared our training data, it's time to train the face recognizer. We will do that by calling the train(faces-vector, labels-vector) method of face recognizer.
#train our face recognizer of our training faces
face_recognizer.train(faces, np.array(labels))
Did you notice that instead of passing labels vector directly to face recognizer I am first converting it to numpy array? This is because OpenCV expects labels vector to be a numpy array.
Still not satisfied? Want to see some action? Next step is the real action, I promise!
Prediction
Now comes my favorite part, the prediction part. This is where we actually get to see if our algorithm is actually recognizing our trained subjects's faces or not. We will take two test images of our celeberities, detect faces from each of them and then pass those faces to our trained face recognizer to see if it recognizes them.
Below are some utility functions that we will use for drawing bounding box (rectangle) around face and putting celeberity name near the face bounding box.
#function to draw rectangle on image
#according to given (x, y) coordinates and
#given width and heigh
def draw_rectangle(img, rect):
(x, y, w, h) = rect
cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 2)
#function to draw text on give image starting from
#passed (x, y) coordinates.
def draw_text(img, text, x, y):
cv2.putText(img, text, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.5, (0, 255, 0), 2)
First function draw_rectangle draws a rectangle on image based on passed rectangle coordinates. It uses OpenCV's built in function cv2.rectangle(img, topLeftPoint, bottomRightPoint, rgbColor, lineWidth) to draw rectangle. We will use it to draw a rectangle around the face detected in test image.
Second function draw_text uses OpenCV's built in function cv2.putText(img, text, startPoint, font, fontSize, rgbColor, lineWidth) to draw text on image.
Now that we have the drawing functions, we just need to call the face recognizer's predict(face) method to test our face recognizer on test images. Following function does the prediction for us.
#this function recognizes the person in image passed
#and draws a rectangle around detected face with name of the
#subject
def predict(test_img):
#make a copy of the image as we don't want to chang original image
img = test_img.copy()
#detect face from the image
face, rect = detect_face(img)
#predict the image using our face recognizer
label= face_recognizer.predict(face)
#get name of respective label returned by face recognizer
label_text = subjects[label]
#draw a rectangle around face detected
draw_rectangle(img, rect)
#draw name of predicted person
draw_text(img, label_text, rect[0], rect[1]-5)
return img
- line-6 read the test image
- line-7 detect face from test image
- line-11 recognize the face by calling face recognizer's
predict(face)method. This method will return a lable - line-12 get the name associated with the label
- line-16 draw rectangle around the detected face
- line-18 draw name of predicted subject above face rectangle
Now that we have the prediction function well defined, next step is to actually call this function on our test images and display those test images to see if our face recognizer correctly recognized them. So let's do it. This is what we have been waiting for.
print("Predicting images...")
#load test images
test_img1 = cv2.imread("test-data/test1.jpg")
test_img2 = cv2.imread("test-data/test2.jpg")
#perform a prediction
predicted_img1 = predict(test_img1)
predicted_img2 = predict(test_img2)
print("Prediction complete")
#create a figure of 2 plots (one for each test image)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))
#display test image1 result
ax1.imshow(cv2.cvtColor(predicted_img1, cv2.COLOR_BGR2RGB))
#display test image2 result
ax2.imshow(cv2.cvtColor(predicted_img2, cv2.COLOR_BGR2RGB))
#display both images
cv2.imshow("Tom cruise test", predicted_img1)
cv2.imshow("Shahrukh Khan test", predicted_img2)
cv2.waitKey(0)
cv2.destroyAllWindows()
cv2.waitKey(1)
cv2.destroyAllWindows()
Predicting images...
Prediction complete
wohooo! Is'nt it beautiful? Indeed, it is!
End Notes
Face Recognition is a fascinating idea to work on and OpenCV has made it extremely simple and easy for us to code it. It just takes a few lines of code to have a fully working face recognition application and we can switch between all three face recognizers with a single line of code change. It's that simple.
Although EigenFaces, FisherFaces and LBPH face recognizers are good but there are even better ways to perform face recognition like using Histogram of Oriented Gradients (HOGs) and Neural Networks. So the more advanced face recognition algorithms are now a days implemented using a combination of OpenCV and Machine learning. I have plans to write some articles on those more advanced methods as well, so stay tuned!
Download Details:
Author: informramiz
Source Code: https://github.com/informramiz/opencv-face-recognition-python
License: MIT License
1620466520
Your Data Architecture: Simple Best Practices for Your Data Strategy
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
1620629020
Getting Started With Data Lakes
Frameworks for Efficient Enterprise Analytics
The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
Introduction
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management