Image Processing

Manually Applied Different Procedures in Image Processing

Table of Contents

• About the Project
  • Tech Stack
  • File Structure

• Results and Demo

• Troubleshooting
• Contributors
• Acknowledgements and Resources

Tech Stack

This section should list the technologies you used for this project. Leave any add-ons/plugins for the prerequisite section. Here are a few examples.

• Numpy
• OpenCV
• Matplotlib

File Structure

.
├── Images                  # Folder containing all the images and results
├── ApplyKernel.ipynb       # Code for Applying different filters like blurring and sharpening on the image
├── EdgeDetection.ipynb     # Code for detecting edge in an image using vertical, horizontal, Sobel and cranny edge detection               
├── Masking.ipynb           # Code for Masking the blue colored ball from the image
├── Morphological.ipynb     # Code for applying Erosion and Dilation operations of a binary image
├── README.md
├── ROI.ipynb               # Code for extracting the Region Of Interest from the image and masking it on a different part of Image
└── RotateImage.ipynb       # Code for Rotating an image by any angle

Results and Demo

Image Rotation

Image rotation is a common image processing routine with applications in matching, alignment, and other image-based algorithms.

Applying Kernels

An image kernel is a small matrix used to apply effects like the ones you might find in Photoshop or Gimp, such as blurring, sharpening, outlining or embossing. They're also used in machine learning for 'feature extraction', a technique for determining the most important portions of an image. We will be applying 2 kernel operations

1. Blurring2) Sharpening

Input images

Output

Box Filter	Gaussian Filter	Sharpen Filter
Box Filter	Gaussian Filter	Sharpen Filter

Edge-Detection

Edge Detection is a technique used for finding the boundaries of objects within images. IT works by detecting discontinuities in an image.Here we will be using 4 methods for edge Detection

1. Vertical Edge Detection
2. Horizontal Edge Detection
3. Sobel Edge Detection
4. Cranny Edge Detection

Input images

Output

Vertical Edge Detection
Horizontal Edge Detection
Sobel Edge Detection
Canny Edge Detection

Morphological Transformation

Morphological Transformations are some simple operations based on the image shape. It is normally performed on binary images.The basic operations we will be doing will be

1. Erosion - It basically erodes away the boundaries of the foreground object. Hence used for image noise reduction.
2. Dilation - It is the opposite of erosion. It increase the area of the object. Used for restoring eroded images.

Output

Original	Erosion	Dilation

Masking

A mask is a binary image consisting of zero- and non-zero values. If a mask is applied to another binary or to a grayscale image of the same size, all pixels which are zero in the mask are set to zero in the output image. All others remain unchanged.

Original	Masking

Region Of Interest

A region of interest (ROI) is a portion of an image that you want to filter or perform some other operation on. You define an ROI by creating a binary mask, which is a binary image that is the same size as the image you want to process with pixels that define the ROI set to 1 and all other pixels set to 0.

Original	ROI

Contributors

• Saad Hashmi

Acknowledgements and Resources

• SRA VJTI
• Refered OpenCV Tutorial for achieving the basics of the above operations
• Refered this for achieving Canny Edge Detection

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