Billy Chandler

Billy Chandler


Introduction to Face Processing with Computer Vision

Ever wonder how Facebook’s facial recognition or Snapchat’s filters work? Faces are a fundamental piece of photography, and building applications around them has never been easier with open-source libraries and pre-trained models. In this talk, we’ll help you understand some of the computer vision and machine learning techniques behind these applications. Then, we’ll use this knowledge to develop our own prototypes to tackle tasks such as face detection (e.g. digital cameras), recognition (e.g. Facebook Photos), classification (e.g. identifying emotions), manipulation (e.g. Snapchat filters), and more.

#MachineLearning #ComputerVision #OpenCV #Python #DataScience

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Introduction to Face Processing with Computer Vision

Top 6 Alternatives To Hugging Face

  • With Hugging Face raising $40 million funding, NLPs has the potential to provide us with a smarter world ahead.

In recent news, US-based NLP startup, Hugging Face  has raised a whopping $40 million in funding. The company is building a large open-source community to help the NLP ecosystem grow. Its transformers library is a python-based library that exposes an API for using a variety of well-known transformer architectures such as BERT, RoBERTa, GPT-2, and DistilBERT. Here is a list of the top alternatives to Hugging Face .

Watson Assistant




#opinions #alternatives to hugging face #chatbot #hugging face #hugging face ai #hugging face chatbot #hugging face gpt-2 #hugging face nlp #hugging face transformer #ibm watson #nlp ai #nlp models #transformers

Computer Vision using Mediapipe

Computer vision can be defined as a field of artificial intelligence that trains computers to interpret and understand the visual world. Using digital images from cameras and videos and deep learning models, machines can accurately identify and classify objects and then react to what they “see.”

Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos.

In today’s world computer vision is very useful in many fields such as — :

*_ Inventory management — : _**In the case of inventory management, the applications can be in the field of security camera image analysis where a computer vision algorithm can generate a very accurate estimate of the items available in the store. Another field can be Analyzing the use of shelf space to identify suboptimal configurations.

* **Manufacturing — : **In the Field of manufacturing Computer vision can help in **predictive maintenance **of the machines.

*** Healthcare — : In the field of healthcare computer Vision can be used in medical image analysis.** Images from CT scans and X-rays are analyzed to find anomalies such as tumors or search for signs of neurological illnesses.

* **Autonomous vehicles — : **The field of computer vision plays a central role in the domain of autonomous vehicles since it allows them to perceive and understand the environment around them in order to operate correctly. One of the most exciting challenges in computer vision is object detection in images and videos. This involves locating a varying number of objects and the ability to classify them, in order to distinguish if an object is a traffic light, a car, or a person, as in the video below.

#computer-vision #opencv #mediapipe #anaconda-navigator #python #computer vision using mediapipe

Macey  Kling

Macey Kling


Solving Some Image Processing,Computer Vision Problems With Python Libraries

In this article, a few image processing/computer vision problems and their solutions with python libraries (scikit-image, PIL, opencv-python) will be discussed. Some of the problems are from the exercises from this book (available on Amazon). Here is the GitHub repository with the codes from the book and my blog on WordPress and a playlist on youtube. Also, here is the github repository of the codes for my new book (available on Amazon).

Wave Transform

  1. Use scikit-image’s warp() function to implement the _wave _transform.
  2. Note that wave transform can be expressed with the following equations:

Image for post

We shall use the mandrill image to implement the wave transform. The next python code fragment shows how to do it:

**from** **import** imread

**from** skimage.transform **import** warp

**import** matplotlib.pylab as plt

**def** wave(xy):

xy[:, 1] **+=** 20*****np.sin(2*****np.pi*****xy[:, 0]**/**64)

**return** xy

im **=** imread('images/mandrill.jpg')

im **=** warp(im, wave)


The next figure shows the original mandrill input image and the output image obtained after applying the wave transform.

Image for post

Image for post

2. Swirl Transform

  1. Use scikit-image’s warp() function to implement the swirl transform.
  2. Note that swirl transform can be expressed with the following equations

Image for post

We shall use the mandrill image to implement the wave transform. The next python code fragment shows how to do it:

#computer-vision #machine-learning #image-processing

Macey  Kling

Macey Kling


How To Deter Adversarial Attacks In Computer Vision Models

While computer vision has become one of the most used technologies across the globe, computer vision models are not immune to threats. One of the reasons for this threat is the underlying lack of robustness of the models. Indrajit Kar, who is the Principal Solution Architect at Accenture, took through a talk at CVDC 2020 on how to make AI more resilient to attack.

As Kar shared, AI has become the new target for attackers, and the instances of manipulation and adversaries have increased dramatically over the last few years. From companies such as Google and Tesla to startups are affected by adversarial attacks.

“While we celebrate advancements in AI, deep neural networks (DNNs)—the algorithms intrinsic to much of AI—have recently been proven to be at risk from attack through seemingly benign inputs. It is possible to fool DNNs by making subtle alterations to input data that often either remain undetected or are overlooked if presented to a human,” he said.

Type Of Adversarial Attacks

Alterations to images that are so small as to remain unnoticed by humans can cause DNNs to misinterpret the image content. As many AI systems take their input from external sources—voice recognition devices or social media upload, for example—this ability to be tricked by adversarial input opens a new, often intriguing, security threat. This has called for an increase in cybersecurity which is coming together to address the crevices in computer vision and machine learning.

#developers corner #adversarial attacks #computer vision #computer vision adversarial attack

Osiki  Douglas

Osiki Douglas


The Best Project to Start in Computer Vision with Python

GrabCut — A Google Colab NoteBook implementation for Image Matting (background removal)

Follow the article along with the complete code implementation on GitHub. Open the notebook in Google Colab, import your image(s), and run the cells!Originally published on, read it 2 days before on my blog!

Image matting is an extremely interesting task where the goal is to find any object of interest, or human, in a picture and remove its background. This task is hard to achieve due to its complexity, finding the person, people, or objects with the perfect contour. This post reviews an exciting technique using basic computer vision algorithms to achieve this task. The GrabCut algorithm. It is swift but not very precise for complex objects like humans or animals. Nonetheless, it can be handy in specific contexts and is a perfect applied first project to start in computer vision and python! As mentioned above, the implementation uses Google Colab, thus having no requirements or setup needed, making it an exciting project to duplicate for learning.

#computer-vision #python #ai #machine-learning #artificial-intelligence #the best project to start in computer vision with python