A Cheatsheet to Find the Best-Fit Python Library

Over 137,000 libraries exist in Python’s repository. So, how do you choose the right one for your machine learning project? A cheat sheet on proven uses can help.

Nothing beats Python in finding solutions to complex mathematical and computational problems. It is a versatile language that can be easily used across domains and is easier to debug too.

45% of tech organizations use Python for their machine learning and AI projects. – Builtwith.com

Python libraries are a work in progress and their use cases and toolkits are continuously advancing. Therefore, AI engineers need to keep a constant tab on the latest developments, more so if they intend to use Python for their machine learning projects.

**Python can be used by beginners and experienced AI Engineers, which makes it a popular choice across levels.
**
Before we begin with the cheat sheet, please note that Python libraries can be multi-purpose and can be placed in multiple categories. Also, the use of libraries is not constrained to the highlighted tasks.

This cheat sheet aims to help you dig out the best fit.

Top Python Libraries for Deep Learning
We’ve weighed the pros and cons and arrived at these four top picks.

TensorFlow
A plug-and-play library with an extensive resource of commonly-used machine learning models and algorithms, TensorFlow comes with facial recognition capabilities. It is one of the biggest open-source Python libraries and a must-have for beginners.

Level: Good for beginners

Keras
It runs on top of libraries like CNTK, Theano, and TensorFlow, and offers specific support to deep learning applications. Easier prototyping, modularity, and a user-friendly interface make it an excellent choice for beginners.

Level: Excellent choice for beginners

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What is GEEK

Buddha Community

A Cheatsheet to Find the Best-Fit Python Library

bindu singh

1647351133

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Shardul Bhatt

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Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas.

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities.

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly.

5 Reasons to Utilize Python for Programming Web Apps

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions.

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events.

Simple to read and compose

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building.

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties.

Utilized by the best

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player.

Massive community support

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions.

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking.

Progressive applications

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.

Summary

Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential.

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

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Anissa Barrows

1669099573

What Is Face Recognition? Facial Recognition with Python and OpenCV

In this article, we will know what is face recognition and how is different from face detection. We will go briefly over the theory of face recognition and then jump on to the coding section. At the end of this article, you will be able to make a face recognition program for recognizing faces in images as well as on a live webcam feed.

What is Face Detection?

In computer vision, one essential problem we are trying to figure out is to automatically detect objects in an image without human intervention. Face detection can be thought of as such a problem where we detect human faces in an image. There may be slight differences in the faces of humans but overall, it is safe to say that there are certain features that are associated with all the human faces. There are various face detection algorithms but Viola-Jones Algorithm is one of the oldest methods that is also used today and we will use the same later in the article. You can go through the Viola-Jones Algorithm after completing this article as I’ll link it at the end of this article.

Face detection is usually the first step towards many face-related technologies, such as face recognition or verification. However, face detection can have very useful applications. The most successful application of face detection would probably be photo taking. When you take a photo of your friends, the face detection algorithm built into your digital camera detects where the faces are and adjusts the focus accordingly.

For a tutorial on Real-Time Face detection

What is Face Recognition?

face recognition

Now that we are successful in making such algorithms that can detect faces, can we also recognise whose faces are they?

Face recognition is a method of identifying or verifying the identity of an individual using their face. There are various algorithms that can do face recognition but their accuracy might vary. Here I am going to describe how we do face recognition using deep learning.

So now let us understand how we recognise faces using deep learning. We make use of face embedding in which each face is converted into a vector and this technique is called deep metric learning. Let me further divide this process into three simple steps for easy understanding:

Face Detection: The very first task we perform is detecting faces in the image or video stream. Now that we know the exact location/coordinates of face, we extract this face for further processing ahead.

Feature Extraction: Now that we have cropped the face out of the image, we extract features from it. Here we are going to use face embeddings to extract the features out of the face. A neural network takes an image of the person’s face as input and outputs a vector which represents the most important features of a face. In machine learning, this vector is called embedding and thus we call this vector as face embedding. Now how does this help in recognizing faces of different persons?

While training the neural network, the network learns to output similar vectors for faces that look similar. For example, if I have multiple images of faces within different timespan, of course, some of the features of my face might change but not up to much extent. So in this case the vectors associated with the faces are similar or in short, they are very close in the vector space. Take a look at the below diagram for a rough idea:

Now after training the network, the network learns to output vectors that are closer to each other(similar) for faces of the same person(looking similar). The above vectors now transform into:

We are not going to train such a network here as it takes a significant amount of data and computation power to train such networks. We will use a pre-trained network trained by Davis King on a dataset of ~3 million images. The network outputs a vector of 128 numbers which represent the most important features of a face.

Now that we know how this network works, let us see how we use this network on our own data. We pass all the images in our data to this pre-trained network to get the respective embeddings and save these embeddings in a file for the next step.

Comparing faces: Now that we have face embeddings for every face in our data saved in a file, the next step is to recognise a new t image that is not in our data. So the first step is to compute the face embedding for the image using the same network we used above and then compare this embedding with the rest of the embeddings we have. We recognise the face if the generated embedding is closer or similar to any other embedding as shown below:

So we passed two images, one of the images is of Vladimir Putin and other of George W. Bush. In our example above, we did not save the embeddings for Putin but we saved the embeddings of Bush. Thus when we compared the two new embeddings with the existing ones, the vector for Bush is closer to the other face embeddings of Bush whereas the face embeddings of Putin are not closer to any other embedding and thus the program cannot recognise him.

What is OpenCV

In the field of Artificial Intelligence, Computer Vision is one of the most interesting and Challenging tasks. Computer Vision acts like a bridge between Computer Software and visualizations around us. It allows computer software to understand and learn about the visualizations in the surroundings. For Example: Based on the color, shape and size determining the fruit. This task can be very easy for the human brain however in the Computer Vision pipeline, first we gather the data, then we perform the data processing activities and then we train and teach the model to understand how to distinguish between the fruits based on size, shape and color of fruit.

Currently, various packages are present to perform machine learning, deep learning and computer vision tasks. By far, computer vision is the best module for such complex activities. OpenCV is an open-source library. It is supported by various programming languages such as R, Python. It runs on most of the platforms such as Windows, Linux and MacOS.

To know more about how face recognition works on opencv, check out the free course on face recognition in opencv.

Advantages of OpenCV:

OpenCV is an open-source library and is free of cost.
As compared to other libraries, it is fast since it is written in C/C++.
It works better on System with lesser RAM
To supports most of the Operating Systems such as Windows, Linux and MacOS.

Installation:

Here we will be focusing on installing OpenCV for python only. We can install OpenCV using pip or conda(for anaconda environment).

Using pip:

Using pip, the installation process of openCV can be done by using the following command in the command prompt.

pip install opencv-python

Anaconda:

If you are using anaconda environment, either you can execute the above code in anaconda prompt or you can execute the following code in anaconda prompt.

conda install -c conda-forge opencv

Face Recognition using Python

In this section, we shall implement face recognition using OpenCV and Python. First, let us see the libraries we will need and how to install them:

OpenCV
dlib
Face_recognition

OpenCV is an image and video processing library and is used for image and video analysis, like facial detection, license plate reading, photo editing, advanced robotic vision, optical character recognition, and a whole lot more.

The dlib library, maintained by Davis King, contains our implementation of “deep metric learning” which is used to construct our face embeddings used for the actual recognition process.

The face_recognition library, created by Adam Geitgey, wraps around dlib’s facial recognition functionality, and this library is super easy to work with and we will be using this in our code. Remember to install dlib library first before you install face_recognition.

To install OpenCV, type in command prompt

pip install opencv-python

I have tried various ways to install dlib on Windows but the easiest of all of them is via Anaconda. First, install Anaconda (here is a guide to install it) and then use this command in your command prompt:

conda install -c conda-forge dlib

Next to install face_recognition, type in command prompt

pip install face_recognition

Now that we have all the dependencies installed, let us start coding. We will have to create three files, one will take our dataset and extract face embedding for each face using dlib. Next, we will save these embedding in a file.

In the next file we will compare the faces with the existing the recognise faces in images and next we will do the same but recognise faces in live webcam feed

Extracting features from Face

First, you need to get a dataset or even create one of you own. Just make sure to arrange all images in folders with each folder containing images of just one person.

Next, save the dataset in a folder the same as you are going to make the file. Now here is the code:

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from imutils import paths

import face_recognition

import pickle

import cv2

import os

#get paths of each file in folder named Images

#Images here contains my data(folders of various persons)

imagePaths = list(paths.list_images('Images'))

knownEncodings = []

knownNames = []

# loop over the image paths

for (i, imagePath) in enumerate(imagePaths):

# extract the person name from the image path

name = imagePath.split(os.path.sep)[-2]

# load the input image and convert it from BGR (OpenCV ordering)

# to dlib ordering (RGB)

image = cv2.imread(imagePath)

rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

#Use Face_recognition to locate faces

boxes = face_recognition.face_locations(rgb,model='hog')

# compute the facial embedding for the face

encodings = face_recognition.face_encodings(rgb, boxes)

# loop over the encodings

for encoding in encodings:

knownEncodings.append(encoding)

knownNames.append(name)

#save emcodings along with their names in dictionary data

data = {"encodings": knownEncodings, "names": knownNames}

#use pickle to save data into a file for later use

f = open("face_enc", "wb")

f.write(pickle.dumps(data))

f.close()

Now that we have stored the embedding in a file named “face_enc”, we can use them to recognise faces in images or live video stream.

Face Recognition in Live webcam Feed

Here is the script to recognise faces on a live webcam feed:

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import face_recognition

import imutils

import pickle

import time

import cv2

import os

#find path of xml file containing haarcascade file

cascPathface = os.path.dirname(

cv2.__file__) + "/data/haarcascade_frontalface_alt2.xml"

# load the harcaascade in the cascade classifier

faceCascade = cv2.CascadeClassifier(cascPathface)

# load the known faces and embeddings saved in last file

data = pickle.loads(open('face_enc', "rb").read())

print("Streaming started")

video_capture = cv2.VideoCapture(0)

# loop over frames from the video file stream

while True:

# grab the frame from the threaded video stream

ret, frame = video_capture.read()

gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

faces = faceCascade.detectMultiScale(gray,

scaleFactor=1.1,

minNeighbors=5,

minSize=(60, 60),

flags=cv2.CASCADE_SCALE_IMAGE)

# convert the input frame from BGR to RGB

rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

# the facial embeddings for face in input

encodings = face_recognition.face_encodings(rgb)

names = []

# loop over the facial embeddings incase

# we have multiple embeddings for multiple fcaes

for encoding in encodings:

#Compare encodings with encodings in data["encodings"]

#Matches contain array with boolean values and True for the embeddings it matches closely

#and False for rest

matches = face_recognition.compare_faces(data["encodings"],

encoding)

#set name =inknown if no encoding matches

name = "Unknown"

# check to see if we have found a match

if True in matches:

#Find positions at which we get True and store them

matchedIdxs = [i for (i, b) in enumerate(matches) if b]

counts = {}

# loop over the matched indexes and maintain a count for

# each recognized face face

for i in matchedIdxs:

#Check the names at respective indexes we stored in matchedIdxs

name = data["names"][i]

#increase count for the name we got

counts[name] = counts.get(name, 0) + 1

#set name which has highest count

name = max(counts, key=counts.get)

# update the list of names

names.append(name)

# loop over the recognized faces

for ((x, y, w, h), name) in zip(faces, names):

# rescale the face coordinates

# draw the predicted face name on the image

cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

cv2.putText(frame, name, (x, y), cv2.FONT_HERSHEY_SIMPLEX,

0.75, (0, 255, 0), 2)

cv2.imshow("Frame", frame)

if cv2.waitKey(1) & 0xFF == ord('q'):

break

video_capture.release()

cv2.destroyAllWindows()

https://www.youtube.com/watch?v=fLnGdkZxRkg

Although in the example above we have used haar cascade to detect faces, you can also use face_recognition.face_locations to detect a face as we did in the previous script

Face Recognition in Images

The script for detecting and recognising faces in images is almost similar to what you saw above. Try it yourself and if you can’t take a look at the code below:

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import face_recognition

import imutils

import pickle

import time

import cv2

import os

#find path of xml file containing haarcascade file

cascPathface = os.path.dirname(

cv2.__file__) + "/data/haarcascade_frontalface_alt2.xml"

# load the harcaascade in the cascade classifier

faceCascade = cv2.CascadeClassifier(cascPathface)

# load the known faces and embeddings saved in last file

data = pickle.loads(open('face_enc', "rb").read())

#Find path to the image you want to detect face and pass it here

image = cv2.imread(Path-to-img)

rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

#convert image to Greyscale for haarcascade

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

faces = faceCascade.detectMultiScale(gray,

scaleFactor=1.1,

minNeighbors=5,

minSize=(60, 60),

flags=cv2.CASCADE_SCALE_IMAGE)

# the facial embeddings for face in input

encodings = face_recognition.face_encodings(rgb)

names = []

# loop over the facial embeddings incase

# we have multiple embeddings for multiple fcaes

for encoding in encodings:

#Compare encodings with encodings in data["encodings"]

#Matches contain array with boolean values and True for the embeddings it matches closely

#and False for rest

matches = face_recognition.compare_faces(data["encodings"],

encoding)

#set name =inknown if no encoding matches

name = "Unknown"

# check to see if we have found a match

if True in matches:

#Find positions at which we get True and store them

matchedIdxs = [i for (i, b) in enumerate(matches) if b]

counts = {}

# loop over the matched indexes and maintain a count for

# each recognized face face

for i in matchedIdxs:

#Check the names at respective indexes we stored in matchedIdxs

name = data["names"][i]

#increase count for the name we got

counts[name] = counts.get(name, 0) + 1

#set name which has highest count

name = max(counts, key=counts.get)

# update the list of names

names.append(name)

# loop over the recognized faces

for ((x, y, w, h), name) in zip(faces, names):

# rescale the face coordinates

# draw the predicted face name on the image

cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)

cv2.putText(image, name, (x, y), cv2.FONT_HERSHEY_SIMPLEX,

0.75, (0, 255, 0), 2)

cv2.imshow("Frame", image)

cv2.waitKey(0)

Output:

InputOutput

This brings us to the end of this article where we learned about face recognition.

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Original article source at: https://www.mygreatlearning.com

#python #opencv

Ray Patel

1619571780

Top 20 Most Useful Python Modules or Packages

March 25, 2021 Deepak@321 0 Comments

Welcome to my blog, In this article, we will learn the top 20 most useful python modules or packages and these modules every Python developer should know.

Hello everybody and welcome back so in this article I’m going to be sharing with you 20 Python modules you need to know. Now I’ve split these python modules into four different categories to make little bit easier for us and the categories are:

Web Development
Data Science
Machine Learning
AI and graphical user interfaces.

Near the end of the article, I also share my personal favorite Python module so make sure you stay tuned to see what that is also make sure to share with me in the comments down below your favorite Python module.

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August Larson

1624246080

How to Find Best Locations for your Restaurants with Python

Solve a Set Covering Problem with CVXPY

Motivation

After simulating real-life events in your restaurant, your restaurant starts to attract more customers so you decided to open chain restaurants at other locations.

Since many customers prefer to eat close by, you want your restaurants to be at most 15 miles away from areas 1, 2, 3, 4, and 5. The optimal solution is to build a minimal number of restaurants that are within 15 miles of all other areas.

Provided that your restaurants can only be placed at areas 1, 2, 3, 4, or 5, which locations should you build your restaurants?

Image by Author

This is called the set covering problem. In this article, you will learn how to solve this problem using CVXPY.

What is CVXPY?

CVXPY is a Python-embedded modeling language for convex optimization problems like the one above. It is similar to PuLP, but its syntax is simpler and more intuitive.

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