Python for Computer Vision OpenCV Deep Learning Free Video Course 2021
Learn the latest computer vision techniques with Python, Open CV, and deep learning!
Created by Best Seller Jose Portal Last updated 12/2018 English which you will learn.
- Understand the basics of NumPy.
- Pair and open images with NumPy.
- Use OpenCV to work with image files.
- Use Python and OpenCV to draw shapes on photos and videos.
- Demonstrate image manipulation with OpenV, including smoothing, blurring, thresholds, and shapes.
- Create colorful histograms from Open CVs.
- Python and OpenStream Video with Python and OpenCV.
- Find objects with Open CV and Python, including corner, edge, and grid detection techniques.
- Create facial detection software.
- Segment images with watershed algorithm
- Track the object in the video.
- Use Python and Deep Learning to rate the image.
- Train your own custom pictures with TensorFlow, Caras and Python.
Course Contents Extend to all 92 lectures 14:06:59 requirements.
- Must have a clear understanding of the basics.
- Windows 10 or Mac OS or Ubuntu.
- Permission to install on the computer is required.
- Webcam if you want to learn the content of video streaming.
Welcome to the last online course on Computer Vision for Python!
This course is a great way for you to learn how to use Python programming language for computer vision.
We are exploring how to use Python and OpenCV (Open Computer Vision) libraries to analyze images and video data.
The most popular platform in the world produces volume of never before seen image and video data. Every 60 seconds users upload more than 300 hours of video to YouTube, Netflix users play over 80,000 hours of video, and Instagram users love more than 2 million photos! Developing developers is now more than ever needed to work with image and video data using computer vision.
Computer vision allows us to take advantage of imagery and video data, including applications in various industries, including self-driving cars, social network apps, medical diagnostics, and more.
As one of the fastest-growing languages in popularity, it’s vital to harness the power of computer vision libraries to learn from all this image and video data.
In this course we will teach you everything you need to know to become a computer vision specialist! The $ 20 billion industry will be one of the most important employment markets in the coming years.
We will begin the course with numeric processing with the NumPy library and learn how to open and manipulate images with NumPy. This will then proceed to open the image basics and work with it using the OpenCV library. Then we will begin to understand how to implement and apply multiple effects, including color mapping, mixing, thresholding, gradient, and more.
We will then move on to understanding the video basics with OpenCV, including working on playing video from a webcam. We will then learn about live video topics, such as optical flow and object detection. Including face detection and object tracking.
We will then move on to an entire section of the course devoted to the latest deep learning topics, including image recognition and custom image classification. We will also cover the latest advanced deep learning networks, including the UOLO (you only see once) deep learning network.
This course covers all this and more, including the following topics:
- Photos with NumPy
- Image and video basics with NumPy.
- Color maps.
- Blending and Sticky Images
- Image Thresholding.
- Blurring and smoothness.
- Morphological operators.
- Video streaming with Open CV
- Object detection.
- Template matching.
- Corner, Edge, and Grid Detection
- Contour Detection
- Feature Matching.
- Water shadow algorithm
- Face recognition
- Object tracking.
- Optical flow
- Deep learning with Caras
- Carousel and Conventional Networks.
- Customized deep learning networks.
- State of the Art UOLO Networks.
- And much more!
If you have any questions about the course, feel free to message me at the Academy!
Thank you for checking out the course page, and I hope you find it inside!
For whom this course is juice:
- Python developers interested in computer vision and deep learning. This course is not for complete python beginners.