Learn how do image processing with OpenCV and scikit-mage. You can use these libraries to work on edge detection and facial recognition.
- [Instructor] There are several libraries…for working with images in Python.…A few of the most common libraries are…matplotlib, who can display images.…There are some filters in scipy.ndimage package.…We have scikit-image, who is imported as skimage,…as many algorithms built in.…And there's also Pillow, which is imported as PIL,…is used to reshape the images and paint on them.…But by far the biggest…and most comprehensive library is OpenCV.…OpenCV is written in C++ and has great bindings to Python.…
It can perform most of task we'd like to do on images,…including edge detection, color conversion, and more.…And as a nice bonus, it comes with…some pre-trained models we can work with.…Let's take a look at OpenCV and see how it works.…OpenCV can be installed from the conda-forge channel.…We have an image of a coffee mug in the exercise files.…I have placed it in the notebook root directory.…Let's start a new notebook,…and let's call this one Images.…
OpenCV is imported as CV2, import cv2.…And then, let's read our image,…
- Working with Jupyter notebooks
- Using code cells
- Extensions to the Python language
- Markdown cells
- Editing notebooks
- NumPy basics
- Broadcasting, array operations, and ufuncs
- Folium and Geo
- Machine learning with scikit-learn
- Plotting with matplotlib and bokeh
- Branching into Numba, Cython, deep learning, and NLP
Skill Level Intermediate
1. Scientific Python Overview
2. The Jupyter Notebook
3. NumPy Basics
Manage environments5m 11s
6. Folium and Geo
7. NY Taxi Data
10. Other Packages
11. Development Process
Next steps1m 33s
- Mark as unwatched
- Mark all as unwatched
Are you sure you want to mark all the videos in this course as unwatched?
This will not affect your course history, your reports, or your certificates of completion for this course.Cancel
Take notes with your new membership!
Type in the entry box, then click Enter to save your note.
1:30Press on any video thumbnail to jump immediately to the timecode shown.
Notes are saved with you account but can also be exported as plain text, MS Word, PDF, Google Doc, or Evernote.