IMAGE-PYTHON.AW1 ISBN: 978-1-64459-649-4
Image Processing Masterclass with Python
Get the ultimate combination of skills with Python + Graphics & transform images like a pro with our Python Masterclass.
What you will be able to do
Learn Basic Image and Video Processing
Explore Image Manipulations With Different Python Libraries
Remove objects with seam carving
Use scikit-image and scipy.ndimage with warping/inverse warping
Detect & Change Colors with OpenCV-Python
Use Hashing to find similar images
Image Super-Resolution with Deep Learning Model (SRGAN)
Face morphing, swapping & parsing with scipy.spatial & OpenCV-python
Face detection and recognition with Microsoft Cognitive Vision APIs
Realistic Image Dehazing Using Deep Neural Net
Intermediate Self-paced · 1 year access
01 / About
About This Course
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No credit card required
Image Processing Masterclass with Python ~ Restore, Enlarge & Improve your image with smart functions & skills.
Solve image processing issues, get Python at your fingertips & create a super-resolution image using SRGAN. Become the expert you look up to!
Prepare with interactive training modules & courses that get you the best results
02 / Lessons & labs
See exactly what you will learn and practice
Lessons
8 Interactive Lessons · 69 topics01 Preface +
02 Basic Image and Video Processing 8 topics +
- Display RGB image color channels in 3D
- Video I/O
- Implement Instagram-like Gotham filter
- Explore Image Manipulations With Different Python Libraries
- Object removal with seam carving
- Summary
- Questions
- References
03 More Image Transformation and Manipulation 10 topics +
- Introduction
- Applying Euclidean and Affine transformation on an image
- Implement image transformation with warping/inverse warping using scikit-image and scipy.ndimage
- Image projection with homography using scikit-image
- Detecting Colors and Changing Colors of Objects with OpenCV-Python
- Detecting Covid-19 Virus Objects with Colors in the HSV Colorspace
- Finding duplicate and similar images with hashing
- Summary
- Questions
- References
04 Sampling, Convolution, Discrete Fourier, Cosine and Wavelet Transform 10 topics +
- Introduction
- Fourier Transform Basics
- Sampling to increase/decrease the resolution of an image
- Denoising an image with LPF/Notch filter in the Frequency domain
- Blurring an Image with an LPF in the Frequency Domain
- Edge detection with high pass filters (HPF) in the frequency domain
- Implementation of homomorphic filters
- Summary
- Questions
- References
05 Discrete Cosine/Wavelet Transform and Deconvolution 10 topics +
- Introduction
- Template matching with phase-correlation in the frequency domain
- Image compression with the Discrete Cosine Transform (DCT)
- Image denoising with Discrete Cosine Transform (DCT)
- Deconvolution for image deblurring
- Image Denoising With Wavelets
- Image fusion with wavelets
- Secure spread spectrum digital watermarking with the DCT
- Questions
- References
06 Image Enhancement 11 topics +
- Introduction
- Image Enhancement Filters with PIL for noise removal and smoothing
- Unsharp masking to sharpen an image
- Averaging of images to remove random noise
- Image denoising with curvature-driven algorithms
- Contrast stretching/histogram equalization with opencv-python
- Fingerprint cleaning and minutiaes extraction
- Edge detection with LOG/zero-crossing, canny versus holistically-nested
- Summary
- Questions
- References
07 More Image Enhancement 10 topics +
- Object detection with Hough transform and colors
- Object Saliency Map, Depth Map, And Tone Map (HDR) With OpenCV-python
- Pyramid blending
- Image Super Resolution with Deep Learning Model (SRGAN)
- Low-Light Image Enhancement Using CNNs
- Realistic Image Dehazing Using Deep Neural Net
- Distributed image processing with Dask
- Summary
- Questions
- References
08 Face Image Processing 10 topics +
- Introduction
- Face morphing with dlib, scipy.spatial, and opencv-python
- Facial Landmark Detection with Deep Learning Models
- Implementation of face swapping
- Implementation of face parsing
- Face recognition with FisherFaces
- Face detection and recognition with Microsoft Cognitive Vision APIs
- Summary
- Questions
- References
03 / FAQs
Questions before you start
Can Python be used for image processing?+
Yes, Python is a popular programming language. It can be utilized to process images. It is widely popular because -
- Tools are easy to use.
- Contains powerful libraries such as Pillow & OpenCV.
Is Image Processing in demand?+
AI’s advent has increased the use of Image processing in various fields. This field is in demand & growing as one of the most preferred occupations by individuals.
Which Python version is best for image processing?+
- OpenCV
- SimpleCV
- SimplelTK
- Mahotas
- Scikit-Image
- Pillow
Will I get a certificate after completing this course?+
Yes, upon successful completion, you’ll receive a certificate to showcase your skills in Image Processing using Python.
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