To Install OpenCV, you can use the following command : Installing The OpenCV Package and Importing It
#Edge blending freeware code
The below code is in reference to the official implementation, whose video tutorial you can find here. We have used two images with two different backgrounds on purpose to see how the blending technique comes into play and compare the difference between a merged image and a blended image. Now that we know how the Image Pyramid works, we will try to implement a basic image pyramid for image blending using both Gaussian and laplacian techniques.
It can be termed as a filter that detects features from the image and stores them in memory for further processing. It also uses a linear transformation to decompose an image into various components by multiplying it with a set of transformation functions. It does so by gray scaling the image to be processed, focusing on the image outlines at different sizes, and comparing its differences. The Laplacian Pyramid technique, on the other hand, keeps track of differences between the multiple sizes of images and stores the information for further use. Image cross-correlation, convolution and matrix multiplication are the core aspects of this operation. It has several other applications such as Progressive image transmission, Scale-invariant template matching and Efficient feature search. Its goal is to define the representation of what information in the image is explicitly available at different scales. The Gaussian Pyramid technique focuses on resizing the image, reducing it to one fourth in every cycle.
Each copy is then convolved with the original image, creating a new and unique image. At first from the image, it creates copies of the target pattern and reconstructs it over several expanded scales. It searches for a target pattern over many repetitive scales. A pyramid or pyramid representation is a type of multi-scale representation where an image is repeatedly smoothened and subsampled. Image Blending techniques mainly focus on two major aspects for creating a blended image by using Image Pyramids, namely Gaussian and Laplacian Pyramids. To start working with Image Blending, we would need to know the bare basics of image processing techniques. Using OpenCV, we can combine the photos, we combine layer masks and gradients and explore other numerous possibilities. The OpenCV library contains most of the functions that we would require for working with images. With the help of Python and the OpenCV library, it becomes relatively very easy to work with visual inputs such as images or videos. To achieve this, we can simply make a copy of an image and transfer each source pixel’s values into a pixel in the target image. The concept of blending images is comparatively very easy. Image Blending is mixing two images of the corresponding pixel values to create a new target image.
#Edge blending freeware software
Our idea would be to explore the domain further, to discover what more can be done to perform operations that are not typically built in existing software and find new ways to teach and educate oneself. Image processing programs such as Photoshop and other freeware alternatives such as GIMP and offer a wide range of possibilities to edit and manipulate images and photos. Due to this reason, study and research revolving around images, such as image processing, image recognition, and image blending, have created a buzz among masses of people developing new technologies. Studies have shown that around 65% of the population constitute visual learners. Visuals help improve learning enormously and on multiple levels. In today’s era, images play a vital role in cognitive development as visuals hold more appeal than plain text to curious and intuitive minds.
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