Motion blur
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
When we apply the motion blurring effect, it will look like you captured the picture while moving in a particular direction. For example, you can make an image look like it was captured from a moving car.
The input and output images will look like the following ones:
Following is the code to achieve this motion blurring effect:
import cv2
import numpy as np
img = cv2.imread('input.jpg')
cv2.imshow('Original', img)
size = 15
# generating the kernel
kernel_motion_blur = np.zeros((size, size))
kernel_motion_blur[int((size-1)/2), :] = np.ones(size)
kernel_motion_blur = kernel_motion_blur / size
# applying the kernel to the input image
output = cv2.filter2D(img, -1, kernel_motion_blur)
cv2.imshow('Motion Blur', output)
cv2.waitKey(0)We are reading the image as usual. We are then constructing a motion blur kernel. A motion blur kernel averages the pixel values in a particular direction. It's like a directional low pass filter. A 3x3 horizontal motion-blurring kernel would look this:
This will blur the image in a horizontal direction. You can pick any direction and it will work accordingly. The amount of blurring will depend on the size of the kernel. So, if you want to make the image blurrier, just pick a bigger size for the kernel. To see the full effect, we have taken a 15x15 kernel in the preceding code. We then use filter2D to apply this kernel to the input image, to obtain the motion-blurred output.
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