This is the code repository for Advanced Computer Vision Projects [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
Python’s wealth of powerful packages along with its clear syntax make state-of-the art computer vision and machine learning accessible to developers with a variety of backgrounds. This video course will equip you with the tools and skills to utilize the latest and greatest algorithms in computer vision, making applications that weren’t possible until recent years.
In this course, you’ll continue to use TensorFlow and extend it to generate full captions from images. Later, you’ll see how to read text from license plates from real-world images using Google’s Tesseract Software. Finally, you’ll see how to track human body poses using “DeeperCut” within TensorFlow.
At the end of this course, you’ll develop an application that can estimate human poses within images and will be able to take on the world with best practices in computer vision with machine learning.
- Apply LSTMs to automated image captioning
- Know how to read text from real-world images
- See how to extract human pose data from images
- Understand the TensorFlow workflow model
To fully benefit from the coverage included in this course, you will need:
This video course is for Python developers who wish to learn the latest cutting-edge algorithms to solve computer vision problems that were impossible until recently.
This course has the following software requirements:
This course has the following software requirements:
This course has been tested on the following system configuration: ● OS: Windows 10 ● Processor: Intel i7 4th generation mobile ● Memory: 32 GB ● Hard Disk Space: 1 TB ● Video Card: GeForce GTX 970m