Tag Archives: interpolation

Changing Video Resolution using OpenCV-Python

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In this tutorial, I will show how to change the resolution of the video using OpenCV-Python. This blog is based on interpolation methods (Chapter-5) which we have discussed earlier.

Here, I will convert a 640×480 video to 1280×720. Let’s see how to do this

Steps:

  1. Load a video using cv2.VideoCapture()
  2. Create a VideoWriter object using cv2.VideoWriter()
  3. Extract frame by frame
  4. Resize the frames using cv2.resize()
  5. Save the frames to a video file using cv2.VideoWriter()
  6. Release the VideoWriter and destroy all windows

Code:

Here, I have used Bicubic as the interpolation method, you can use any. Hope you enjoy reading.

If you have any doubt/suggestion please feel free to ask and I will do my best to help or improve myself. Good-bye until next time.

Image Interpolation using OpenCV-Python

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In the previous blogs, we discussed the algorithm behind the

  1. nearest neighbor 
  2. bilinear and
  3. bicubic interpolation methods using a 2×2 image.

Now, let’s do the same using OpenCV on a real image. First, let’s take an image, either you can load one or can make own image. Loading an image from the device looks like this

This is a 20×22 apple image that looks like this.

Now, let’s zoom it 10 times using each interpolation method. The OpenCV command for doing this is

where fx and fy are scale factors along x and y, dsize refers to the output image size and the interpolation flag refers to which method we are going to use. Either you specify (fx, fy) or dsize, OpenCV calculates the other automatically. Let’s see how to use this function

Nearest Neighbor Interpolation

In this we use cv2.INTER_NEAREST as the interpolation flag in the cv2.resize() function as shown below

Output: 

Clearly, this produces a pixelated or blocky image. Also, it doesn’t introduce any new data.

Bilinear Interpolation

In this we use cv2.INTER_LINEAR flag as shown below

Output: 

This produces a smooth image than the nearest neighbor but the results for sharp transitions like edges are not ideal because the results are a weighted average of 2 surrounding pixels.

Bicubic Interpolation

In this we use cv2.INTER_CUBIC flag as shown below

Output: 

Clearly, this produces a sharper image than the above 2 methods. See the white patch on the left side of the apple. This method balances processing time and output quality fairly well.

Next time, when you are resizing an image using any software, wisely use the interpolation method as this can affect your result to a great extent. Hope you enjoy reading.

If you have any doubts/suggestions please feel free to ask and I will do my best to help or improve myself. Good-bye until next time.

Image Demosaicing or Interpolation methods

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In the previous blog, we discussed the Bayer filter and how we can form a color image from a Bayer image. But we didn’t discuss much about interpolation or demosaicing algorithms so in this blog let’s discuss these algorithms in detail.

According to Wikipedia, Interpolation is a method of constructing new data points within the range of a discrete set of known data points. Image interpolation refers to the “guess” of intensity values at missing locations.

The big question is why we need interpolation if we are able to capture intensity values at all the pixels using Image sensor?

  1.  Bayer filter, where we need to find missing color information at each pixel.
  2.  Projecting low-resolution image to a high-resolution screen or vice versa. For example, we prefer watching videos in the full-screen mode.
  3.  Image Inpainting, Image Warping etc.
  4.  Geometric Transformations.

There are plenty of Interpolation methods available but we will discuss only the frequently used. Interpolation algorithms can be classified as

Non-adaptive perform interpolation in a fixed pattern for every pixel, while adaptive algorithms detect local spatial features, like edges, of the pixel neighborhood and make effective choices depending on the algorithm.

Let’s discuss the maths behind each interpolation method in the subsequent blogs.

In the next blog, we will see how the nearest neighbor method works. Hope you enjoy reading.

If you have any doubt/suggestion please feel free to ask and I will do my best to help or improve myself. Good-bye until next time.