Image Processing Masterclass with Python

Get the ultimate combination of skills with Python + Graphics & transform images like a pro with our Python Masterclass.

(IMAGE-PYTHON.AW1) / आईएसबीएन : 978-1-64459-649-4
Lessons
Lab
TestPrep
AI Tutor (ऐड ऑन)
निःशुल्क परीक्षण प्राप्त करें

इस कोर्स के बारे में

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

कौशल जो आपको प्राप्त होंगे

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

1

Preface

2

Basic Image and Video Processing

  • 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
3

More Image Transformation and Manipulation

  • 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
4

Sampling, Convolution, Discrete Fourier, Cosine and Wavelet Transform

  • 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
5

Discrete Cosine/Wavelet Transform and Deconvolution

  • 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
6

Image Enhancement

  • 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
7

More Image Enhancement

  • 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
8

Face Image Processing

  • 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

कोई प्रश्न? FAQ देखें

  Want to Learn More?

हमसे अभी संपर्क करें

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.

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.

  • OpenCV
  • SimpleCV
  • SimplelTK
  • Mahotas
  • Scikit-Image
  • Pillow

Yes, upon successful completion, you’ll receive a certificate to showcase your skills in Image Processing using Python.

Enhance & Resize any Image with Python

Make images look as good as real with Python. Start now!

$239.99

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