OpenCV entry to advanced: combat three typical projects
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OpenCV entry to advanced: actual combat three typical projects
Vehicle detection/face recognition + image Mosaic + text recognition
Face recognition, autonomous driving, object detection… OpenCV is the cornerstone of the future high-tech industry. This course will take you to systematically master the core knowledge system of computer vision, break through the heavy and difficult points of OpenCV, and actually implement the three typical projects of “vehicle detection, text recognition and image stitching”, and effectively accumulate the practical experience and ability of computer vision.
Suitable for people with visual processing business needs, or interested in graphics algorithms or audio and video developers, or interested in computer vision developers skills required to master at least one development language Python language foundation OpenCV4 Python3 is preferred
Chapter Contents:
Chapter 1 course introduction and study guide this chapter as the course content introduction, mainly introduces the course actual combat project, the course learning method and the course content detailed layout, I hope we can pass this course, learn something, learn something.
A total of 3 periods (31 minutes)
OpenCV development environment construction (14:04) Look at the second chapter of OpenCV development environment to build a good thing will be the first tool, no matter what kind of system. This chapter will take you through a quick hands-on setup of an OpenCV development environment.
There are 6 sessions (80 minutes)
2-2 Build an OpenCV development environment in Windows (10:56) 2-3 Build an OpenCV development environment in Ubuntu (10:15) 2-4 2-6 How to use tools to develop OpenCV efficiently (07:52 This chapter will first take you to understand the “vehicle detection” through the project, take you to control how to load audio and video files, and show the audio and video files, and finally will take you to theory OpenCV control mouse, TrackBar control application.
There are 12 sessions (131 minutes)
3-2 How to create a display window with OpenCV (22:29) 3-3 How to load an image with OpenCV (06:44) 3-4 How to load an image with OpenCV (10:51) 3-5 How to use OpenCV to collect video from camera (14:34) 3-7 How to read video frames from multimedia files (05:05) 3-8 How to record video data into multimedia files (15:01) 3-9 Code optimization (07:40) 3-10 OpenCV control mouse (17:17) 3-11 OpenCV TrackBar control (05:40) 3-12 actual use of TrackBar (11:02) Chapter 4 OpenCV must know will be the foundation This chapter takes you through the basics of controlling OpenCV, including color space transformations, ROI, the most important OpenCV constructor, Mat, and image properties.
A total of 12 sections (106 minutes) close the list
4-1 RGB and BGR【OpenCV color space 】 (09:46) 4-2 HSV and HSL【OpenCV color space 】 (09:14) 4-2 practical OpenCV color space conversion (18:41) 4-4 image operation base Numpy【 root operation 】 (19:13) 4-5 Matrix retrieval and assignment of Numpy fundamental operations (10:36) 4-6 Numpy fundamental operations three -ROI (08:33) 4-7 Homework section 4-8 OpenCV important construction Mat (07:10) 4-9 Deep and shallow copy of Mat In this chapter, you can control the most basic graphics drawing in OpenCV, including lines, rectangles, circles, and so on. Finally, the control of the mouse in the previous course is separated from the basic drawing of the graph, and you can complete a classic drawing homework hand in hand.
There are 8 sessions (74 minutes)
5-1 OpenCV draw straight line (13:17) 5-2 OpenCV draw ellipse (11:57) 5-3 OpenCV draw ellipse (11:57) 5-4 OpenCV draw polygon (08:19) 5-5 OpenCV draw text (05:49) OpenCV basic graphics rendering summary (04:07) 5-8 Homework section 6 OpenCV arithmetic and bit operations this chapter detailed introduction to the image operation and bit operations, Finally, students can know how to use the methods in the course by adding watermarks to the images.
There are 7 sessions (51 minutes)
6-1 image addition operation (09:17) 6-2 image subtraction operation (05:20) 6-3 image fusion (07:26) 6-4 OpenCV bit operation – non operation (05:25) 6-5 OpenCV bit operation – and operation (04:35) 6-6 In this chapter, you will control several basic operations of image transformation, such as image enlargement, reduction, rotation, etc. These operations are often used in our daily life and work.
Chapter 8 Filters in OpenCV This chapter takes you to understand the filters in OpenCV, including low-pass filtering and high-pass filtering. Low-pass filtering is used for noise reduction, and high-pass filtering is used for edge detection. These methods are the foundation for object recognition.
Chapter 9 is the central content in OpenCV, morphology of OpenCV morphology after it can be together as a small area, also can be large area divided into many small pieces, also can get rid of the noise after it, later learning higher-order learning can only at the roots of this chapter content, therefore this chapter content you must firmly in control.
Chapter 10 Object Recognition Object recognition is one of the most important applications of computer vision, such as face recognition, vehicle detection and so on belong to the category of object recognition. This chapter will take the vehicle identification as an example to separate the previous system to control how to stop the detection of vehicles on the road.
Chapter 11 Feature Point Detection and Matching Feature point detection and matching is a very important content in computer vision. Not all image operations stop processing each pixel, some only need to use 4 vertices, such as image Mosaic, two-dimensional code positioning, etc. This chapter first to control what is the corner point, know what is the feature point and the method of detecting the feature point, finally to the image Mosaic as an example, take you to control how to use these technologies to complete the image Mosaic. .
Chapter 12 Image Segmentation and Restoration This chapter will learn image segmentation and restoration. Image segmentation is an important category in computer vision, through which we can do the statistics of objects, background transformation and many other operations, and image repair can be said to be its inverse operation.
Chapter 13 Machine Learning Machine learning is a hot technology today, it is an important way to solve computer vision problems. It consists of two parts: the traditional machine learning approach and the new deep learning-based approach. This chapter will be based on deep learning method of face recognition & vehicle recognition, the traditional method is only a brief understanding.
This course is constantly being updated