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StreamLit OpenCV Computer Vision Web App
Rating: 4.5 out of 5(17 ratings)
93 students
Created byAugmented AI
Last updated 9/2021
English

What you'll learn

  • Build a StreamLit Analytics Dashboard
  • Integrate Computer Vision into StreamLit
  • Learn how to use MediaPipes Face Landmark Detection on Images and Video
  • Implement Widgets, Sliders and checkboxes
  • OpenCV Python and StreamLit WebApp Development

Course content

1 section • 9 lectures • 1h 21m total length

  • Introduction0:55

    Today we are going to learn how to build from scratch a Computer Vision using an interface using StreamLit in Python and OpenCV. We'll start off by codding the StreamLit interface with Python only and then combine it with Googles' Media Pipe Library to perform face landmark detection in real-time. From there we'll create three pages:

    • The first page will tell us a little about the Web App and the Author,

    • The second one helps us to input Face Mesh on a single image, and

    • The third will allow us to implement real-time face landmark detection on a video.

    What's really great about this is that unlike native OpenCV apps is that you can actually interact with the app and make adjustments and create neat and --- dashboards with this.

    If you don't already know, StreamLit done data scripts into shareable web apps in minutes. All in Python. All for free. No front-end experience required.

  • Getting the Project Files2:02
  • StreamLit Framework8:10
  • Streamlit Image Resize Function and Sidebar6:51
  • About Me Page Design4:15
  • Image Detection Page21:06
  • Video Detection Part-113:12
  • Video Detection Part-215:20
  • Video Detection Part-39:44

Requirements

  • Python Programming Experience
  • OpenCV Background

Description

*Price goes up to $39 on the 1st of April 2022. Price increase is due to newer content added to the course every month.

In this course, we are going to learn how to build from scratch a Computer Vision Web Application using StreamLit in Python and OpenCV. We'll start off by coding the StreamLit User Interface with Python only and then combine it with Googles' Media Pipe Library to perform face landmark detection in real-time. From there we'll create three pages:

  1. The first webapp page will tell us a little about the Web App and the Author,

  2. The second page of the UI one helps us to infer Face-Mesh on a single image, and

  3. The third will allow us to implement Real-Time face landmark detection on a video at 30FPS.

What's really great about this is that unlike native OpenCV apps is that you can actually interact with the app and make adjustments and create neat and professional dashboards with this.

If you don't already know, StreamLit can turn data scripts into shareable web apps in minutes. All in Python. All for free. NO front-end, HTML, JAVA experience required.

This course is a full practical course, no fluff, just straight on practical coding.

Requirements

Please ensure that you have the following:

  • Basic understanding of Computer Vision

  • Python Programming Skills

  • Mid to high range PC/ Laptop

  • Windows 10/Ubuntu

30 Day Udemy Refund Guarantee

If you are not happy with this course for any reason, you are covered by Udemy's 30 day no questions asked refund guarantee.

Who this course is for:

  • Students who want to create presentable computer vision web apps
  • Students who want to learn StreamLit and how to integrate it with Computer Vision

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