6. Seam Carving
by Michael Beyeler, Prateek Joshi, Joseph Howse
OpenCV: Computer Vision Projects with Python
- OpenCV: Computer Vision Projects with Python
- Table of Contents
- OpenCV: Computer Vision Projects with Python
- OpenCV: Computer Vision Projects with Python
- Credits
- Preface
- 1. Module 1
- 1. Setting up OpenCV
- Choosing and using the right setup tools
- Running samples
- Finding documentation, help, and updates
- Summary
- 2. Handling Files, Cameras, and GUIs
- 3. Filtering Images
- 4. Tracking Faces with Haar Cascades
- 5. Detecting Foreground/Background Regions and Depth
- A. Integrating with Pygame
- B. Generating Haar Cascades for Custom Targets
- 1. Setting up OpenCV
- 2. Module 2
- 1. Detecting Edges and Applying Image Filters
- 2. Cartoonizing an Image
- 3. Detecting and Tracking Different Body Parts
- 4. Extracting Features from an Image
- Why do we care about keypoints?
- What are keypoints?
- Detecting the corners
- Good Features To Track
- Scale Invariant Feature Transform (SIFT)
- Speeded Up Robust Features (SURF)
- Features from Accelerated Segment Test (FAST)
- Binary Robust Independent Elementary Features (BRIEF)
- Oriented FAST and Rotated BRIEF (ORB)
- Summary
- 5. Creating a Panoramic Image
- 6. Seam Carving
- 7. Detecting Shapes and Segmenting an Image
- 8. Object Tracking
- 9. Object Recognition
- 10. Stereo Vision and 3D Reconstruction
- 11. Augmented Reality
- 3. Module 3
- 1. Fun with Filters
- 2. Hand Gesture Recognition Using a Kinect Depth Sensor
- 3. Finding Objects via Feature Matching and Perspective Transforms
- 4. 3D Scene Reconstruction Using Structure from Motion
- 5. Tracking Visually Salient Objects
- 6. Learning to Recognize Traffic Signs
- 7. Learning to Recognize Emotions on Faces
- A. Bibliography
- Index
In this chapter, we are going to learn about content-aware image resizing, which is also known as seam carving. We will discuss how to detect "interesting" parts in an image and how to use that information to resize a given image without deteriorating those interesting parts.
By the end of this chapter, you will know:
- What is content awareness
- How to quantify "interesting" parts in an image
- How to use dynamic programming for image content analysis
- How to increase and decrease the width of an image without deteriorating the interesting regions while keeping the height constant
- How to make an object disappear from an image
Before we start our discussion about seam carving, we need to understand why it is needed in the first place. Why should we care about the image content? Why can't we just resize the given image and move on with our lives? Well, to answer that question, let's consider the following image:
Now, let's say we want to reduce the width of this image while keeping the height constant. If you do that, it will look something like this:
As you can see, the ducks in the image look skewed, and there's degradation in the overall quality of the image. Intuitively speaking, we can say that the ducks are the "interesting" parts in the image. So when we resize it, we want the ducks to be intact. This is where seam carving comes into the picture. Using seam carving, we can detect these interesting regions and make sure they don't get degraded.
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