Build a Web App With Python and OpenCv : Image Editing App

Build a Web App With Python and OpenCv : Image Editing App, available at $39.99, has an average rating of 4.65, with 21 lectures, based on 27 reviews, and has 3038 subscribers.

You will learn about Create a web application using an efficient python based framework : Streamlit Create and set different widgets on your app: selectboxes, buttons, radio Buttons, sliders, image uploaders, markdowns, message boxes, …etc Apply image editing techniques (gray-scaling, contrast, brightness, blurriness, sharpness) to an uploaded image Detect faces and eyes in an image using OpenCv Use the different methods and functions provided by streamlit to display your images in the app Cartoonize images and detect edges by applying OpenCV functions This course is ideal for individuals who are Python developers who want to extand their knowledge on deploying efficient modern web apps using streamlit and OpenCv or Programmers who want to apply image processing techniques into a tangible application that can be used by everyone It is particularly useful for Python developers who want to extand their knowledge on deploying efficient modern web apps using streamlit and OpenCv or Programmers who want to apply image processing techniques into a tangible application that can be used by everyone.

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Summary

Title: Build a Web App With Python and OpenCv : Image Editing App

Price: $39.99

Average Rating: 4.65

Number of Lectures: 21

Number of Published Lectures: 21

Number of Curriculum Items: 21

Number of Published Curriculum Objects: 21

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Create a web application using an efficient python based framework : Streamlit
Create and set different widgets on your app: selectboxes, buttons, radio Buttons, sliders, image uploaders, markdowns, message boxes, …etc
Apply image editing techniques (gray-scaling, contrast, brightness, blurriness, sharpness) to an uploaded image
Detect faces and eyes in an image using OpenCv
Use the different methods and functions provided by streamlit to display your images in the app
Cartoonize images and detect edges by applying OpenCV functions

Who Should Attend

Python developers who want to extand their knowledge on deploying efficient modern web apps using streamlit and OpenCv
Programmers who want to apply image processing techniques into a tangible application that can be used by everyone

Target Audiences

Python developers who want to extand their knowledge on deploying efficient modern web apps using streamlit and OpenCv
Programmers who want to apply image processing techniques into a tangible application that can be used by everyone

In this course you are going to build a modern prototype of a web application : image editing app using streamlit which is a python-based framework that provides you with all the tools to build your app from scratch in a simple and fast way. Through this course you are going to learn how to implement different image processing techniques like : gray-scaling, contrast, brightness, sharpness and blurriness and connect them to your application giving the hand to users to choose and control the degree of each one. You will also, learn how to create functions that allow you to detect faces and eyes in images, functions that create cartoon version of your images and other to detect edges of different objects and regions in images.

The content of this course:

Section 1: First steps :

– Anaconda download and installation

– Importing the libraries / packages

Section 2 : Set up the main part of the app

– Setting a title and a subtitle for the app

– Create the ” Detection ” part

– Create the ” About ” part

Section 3 : Connect the image processing techniques to the app

– Option 1 : Gray-scaling

– Option 2 : Contrast

– Option 3 : Brightness

– Option 4 : Blurriness

– Option 5 : Sharpness

– Option 6 : Original

Section 4 : Set up the main part of the app

– Set the features selectbox

– Detect faces (part 1)

– Set the haar cascade files

– Detect faces (part 2)

– Detect eyes

– Cartoonize an image (part 1)

– Cartoonize an image (part 2)

– Cannize an image

Course Curriculum

Chapter 1: First steps

Lecture 1: Anaconda download and installation

Lecture 2: Importing the libraries / packages

Chapter 2: Set up the main part of the app

Lecture 1: Setting a title and a subtitle for the app

Lecture 2: Create the " Detection "part

Lecture 3: Create the " About " part

Chapter 3: Connect the image processing techniques to the app

Lecture 1: Images to use

Lecture 2: Option 1 : Gray-scaling

Lecture 3: Option 2 : Contrast

Lecture 4: Option 3 : Brightness

Lecture 5: Option 4 : Blurriness

Lecture 6: Option 5 : Sharpness

Lecture 7: Option 6 : Original

Chapter 4: Face detection / Eye detection / Cartoonizing / Cannizing

Lecture 1: Set the selectbox features

Lecture 2: Detect faces (part 1)

Lecture 3: Set the haar cascade files

Lecture 4: Haar cascade files

Lecture 5: Detect faces (part 2)

Lecture 6: Detect eyes

Lecture 7: Cartoonize an image (part 1)

Lecture 8: Cartoonize an image (part 2)

Lecture 9: Cannize an image

Instructors

Gasmi Haithem
Data Scientist | Machine Learning Practitioner

Rating Distribution

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2 stars: 0 votes
3 stars: 2 votes
4 stars: 6 votes
5 stars: 19 votes

Frequently Asked Questions

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