Two ways to compress your images!
This Python package specializes in reducing the size of images. It contains three main functions :crop(), compress() and image_size(). The crop() function reduces the size of an image by reducing the height and width of the image to the size specified by the user. The compress() function reduces the file size of an image by reducing the number of bits used in each colour channel of the image. The image_size function simply calculates and returns the size in bytes of the image passed into it. The size returned is then used to check whether the compress() function did its job correctly or not. The package could be used by people to reduce the size of images (dimensions or file size), which could then be uploaded to social media platforms or other websites and applications.
crop(image, height, width)- Description: This function will reduce the image to the specified size removing rows and columns of pixels from the borders.
- Input:
- image (3d array)
- desired_height (integer)
- desired_width (integer)
- Output:
- cropped image (3d array, size
desired_height x desired_width x 3)
- cropped image (3d array, size
compress(image, b = 4)- Description: This function compresses the image by reducing the number of bits for each channel based on user input.
- Input:
- image (3d array)
- b (integer, range [1, 7] (number of bits used for each channel in the compressed image))
- Output:
- image (3d array, compressed to
bbits)
- image (3d array, compressed to
image_size(image)- Description: Calculates and returns the size of an image in bytes.
- Input:
- image (3d array)
- Output:
- size (integer, size in bytes)
There already are packages for image processing in R and Python:
The existing packages are very comprehensive and provide many functions such as transformations, filters, file conversions and other advanced functions. Our package focuses specifically on image compression and reducing image size using K-means Clustering. This package is not a wrapper or an improvement of an existing package. It simply uses an unsupervised learning algorithm (K-means clustering) to reduce the number of bits used to represent an image.