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Program to perform simple image manipulation like smoothing, rotating, downsampling, detecting edges (x derivative, y derivative and magnitude of gradients), and plotting the gradient vectors.

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Image Processing for Computer Vision

It is a program to perform simple image manipulation with following requirements:

  • Load an image by reading it from a file or capturing it directly.
  • Image should be read as a 3 channel color image.
  • Program should work on any size of image.
  • On pressing a support key, apply corresponding function on the original image.
  • When implementing convolution, use Cython to speed up execution.

Program was implemented using Python, Cython, and OpenCV. Refer the report for further implementation details and instructions to run the code: View Report

List of Support Keys:

  • i - Reload the original image (cancel any previous processing).
  • w - Save the current image into the file 'out.jpg'.
  • g - Convert the image to grayscale using the openCV conversion function.
  • G - Convert the image to grayscale using implemented conversion function.
  • c - Cycle through the color channels of the image showing a different channel every time the key is pressed.
  • s - Convert the image to grayscale and smooth it using the openCV function. Use track bar to control the amount of smoothing.
  • S - Convert the image to grayscale and smooth it using implemented function which should perform convolution with a suitable filter. Use track bar to control the amount of smoothing. Convolution function is implemented from scratch using Cython.
  • d - Downsample the image by a factor of 2 without smoothing.
  • D - Downsample the image by a factor of 2 with smoothing.
  • x - Convert the image to grayscale and perform convolution with an x derivative filter. Normalize the obtained values to the range [0,255].
  • y - Convert the image to grayscale and perform convolution with a y derivative filter. Normalize the obtained values to the range [0,255].
  • m - Show the magnitude of the gradient normalized to the range [0,255]. The gradient is computed based on the x and y derivative of the image.
  • p - Convert the image to grayscale and plot the gradient vectors of the image every N pixels. Length of vector is a fixed value K. Use track bar to control N.
  • r - Convert the image to grayscale and rotate it using an angle of theta degrees. Use track bar to control theta.
  • h - Display support keys

Results

  1. Grayscale image with OpenCV and conversion function:
OpenCV Conversion Filter

  1. Cycle through color channels of the image:
Red Channel Green Channel Blue Channel

  1. Smoothing image with OpenCV and convolution function:
OpenCV Cython Function

  1. Downsampling with and without smoothing:
Downsampling without smoothing Downsampling with smoothing

  1. X & Y derivative and Magnitude of gradient:
X derivative Y derivative Magnitude of gradient

  1. Plot gradient vectors of image every N pixels:
Plotted gradient vectors at every 50 pixels

  1. Roatation of image:
Rotated image at 45 degrees

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Program to perform simple image manipulation like smoothing, rotating, downsampling, detecting edges (x derivative, y derivative and magnitude of gradients), and plotting the gradient vectors.

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