IMAGE-PYTHON.AW1
Image Processing Masterclass with Python
Get the ultimate combination of skills with Python + Graphics & transform images like a pro with our Python Masterclass.
- 8 Interactive Lessons and 69 topics mapped to the official exam objectives
Intermediate Self-paced · 1 year access
8Interactive Lessons
69Topics
01 / Skills you'll get
What you will be able to do
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
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
Course Highlights
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8 Structured Lessons Comprehensive coverage of core course objectives
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1 Year Full Access Self-paced learning accessible anytime on all devices
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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