This video introduces the fundamentals of accessing information contained within an image. Sometimes it can be difficult to visualize data structures, but it is particularly important for computer vision applications to stay aware of what data you are accessing and keep track of data types. Patrick also exposes some of the initial uses of Numpy for efficient matrix processing.
- [Narrator] Sometimes it can be difficult…to visualize data structures.…But it is particularly important…for computer vision applications…to stay aware of what data you are accessing…and keeping track of the data types.…To get started,…note that I've already changed my directory…into the chapter two, model two begin folder.…Additionally, we still have our opencv-logo.png.…Opening my python3 evironment,…I'm going to import our numpy library,…as well as our cv2 library.…
Next, I'm going to read back in our logo…using cv2.imread then opencv-logo.png.…And this time, I'm going to pass in a value of one,…which is the same as saying…we want to use the default colors and channels…provided with the image.…If we were to pass in a value of zero,…we would actually read in the image as black and white.…Now that we have loaded in our image,…let's actually take a look at the data it contains.…Simply printing it out will appear to you…as a long array of values,…these are actually all the pixels…found in the image itself.…
If we use type on our image variable,…
AuthorPatrick W. Crawford
- Installing and configuring OpenCV
- Data types and structures
- Image types
- Manipulating pixels
- Scaling and rotating images
- Using video inputs
- Creating custom interfaces
- Object detection
- Face and feature detection
- Template matching
Skill Level Intermediate
Python: Programming Efficientlywith Michele Vallisneri2h 15m Intermediate
NumPy Data Science Essential Trainingwith Charles Kelly3h 54m Intermediate
1. Install and Configure OpenCV
2. Basic Image Operations
3. Object Detection
4. Face and Feature Detection
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