The image, though smaller in size retains almost its original quality and doesn’t appear smudgy! That’s how you compress an image in python using PIL. The compressed image should be fine because it is supposed to be of “lossless” in terms of image quality. The size of the image, that is the resolution has now decreased to 986KB from 0.99MB, hence the compression has been successful. Let’s look at the dimensions of the new image. But this new image will be smaller in size. The output would include a newly created image called “Compressed.PNG” in the directory where your original image is stored. "The given image has been compressed, download the files to notice the difference in file size." Image.save(filename, optimize=True, quality=85) Image = image.resize((maxwidth, round(newheight))) # Calculating the new height of the compressed image # calculating the aspect ratio of the image # Calculating the width and height of the original photo If you don’t already have them, run the following in your command prompt.įilepath = os.path.join(os.getcwd(), image_file) We will start by importing the required modules which should be installed in your system before proceeding further. In this section of the tutorial, we will get into the code for implementing the pillow library to compress an image in python. Compressing Images with Python and PIL (Code) Suggested: Python Pillow Module – A Brief Introduction. May not sometimes support layering of images.may lead to loss of important pixel data.Compressed images can be easily shared through emails and social media sites faster.In websites that require large amounts of image data from millions of users, compression helps in optimizing speed and memory usage.Lossless image reduction will retain almost the same quality and look of the image while removing the irrelevant pixel data in order to speed up image processing.Reduced image sizes consumes less bandwidth.To increase the speed of file transfer, such as, uploading or downloading of images to and from the internet.There are many advantages and disadvantages of image compression in python. Advantages and Disadvantages of Image Compression It was developed by Jeffrey A Clark and is supported by Tidelift. It is one of the more efficient methods which can be used for faster access to the pixel data of an image. Pillow is a free and open-source python library used for image processing, manipulating and saving and opening files in various formats like PNG, JPG, etc. On websites that require tons of image uploads from lakhs and lakhs of users, image compression becomes a need more than just a want. Using Pillow, we can compress and reduce the size of images easily. Pillow (PIL) is a popular library in Python for image compression. In Python, numerous libraries contain pre-defined functions for a variety of purposes. Using Pillow for Image Compression in Python Call this function with your image file to get a compressed version, allowing for storage optimization and faster transfers while maintaining image quality. Install it with pip install pillow, then create a function to resize and save the image with optimized quality. To compress images in Python, use the Pillow library. Using the library Pillow in Python, we can compress images in any format such as JPG, PNG, etc. The pace at which technological advances are happening around the world today, we need to enhance the speed at which we upload images for official purposes where the resolution doesn’t matter as much as the proof of the document. Efficient file transfer with lower network bandwidth is also the reason why compressing images is the need of the hour. Storage optimization can be achieved by compressing image files. To transmit data in an efficient form and to reduce the number of bits required to render an image. Its popularity can be attributed to reducing the irrelevancy and redundancy of the image data. Compressing images to speed up image processing and analysis is very common nowadays.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |