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1. #DJANGO WEBP CONVERTER MAC OS#
2. #DJANGO WEBP CONVERTER INSTALL#
3. #DJANGO WEBP CONVERTER ARCHIVE#
4. #DJANGO WEBP CONVERTER CODE#

Last but not least, if you want to run the above programs without typing their absolute paths, add the directory ~/libwebp-0.6.1-linux-x86-32/bin to your PATH environmental variable in your ~/.bashrc file. You can see all options for any of the tools above by running them without any arguments or using the -longhelp flag, for example. You can view the converted webp image using the vwebp tool. cwebp -q 60 Cute-Baby-Girl.png -o Cute-Baby-Girl.webp $ cwebp -q 60 Cute-Baby-Girl.png -o Cute-Baby-Girl.webp To convert an image to webp, you can use the cwebp tool, where the -q switch defines the output quality and -o specifies the output file. webpinfo – used to view info about a webp image file.img2webp – tools for converting a sequence of images into an animated webp file.gif2webp – tool for converting GIF images to webp.anim_dump – tool to dump the difference between animation images.anim_diff – tool to display the difference between animation images.$ tar -xvf libwebp-0.6.Īs you can see from the above screen shot, the package contains a precompiled library ( libwebp) for adding webp encoding or decoding to your programs and various webp utilities listed below.

#DJANGO WEBP CONVERTER ARCHIVE#

Now extract the archive file and move into the extracted package directory as follows. On other Linux distributions, start by downloading the webp package from Googles repository using the wget command as follows.

#DJANGO WEBP CONVERTER INSTALL#

Thankfully, the webp package is present in the Ubuntu official repositories, you can install it using the APT package manager as shown. With this modern image format, webmasters and web developers can create smaller, richer images that make the web faster.

#DJANGO WEBP CONVERTER MAC OS#

To use it, you need to download pre-compiled utilities for Linux, Windows and Mac OS X. WebP is a relatively new, open source image format that offers exceptional lossless and lossy compression for images on the web, designed by Google. In this article, we will share with you a new image format called webp for creating compressed and quality images for the web. As a hint though, you can convert a PIL image to the raw frame data that imageio expects with something like numpy.array(im).One of the numerous best practices you will hear of, for optimizing your web-site performance is using compressed images.

#DJANGO WEBP CONVERTER CODE#

I tried this myself but I ended up with mixed up colours, so I don't have working code to provide. You just create a video writer using imageio.get_writer(path), and add frames to it sequentially with writer.append_data(frame_data). Imageio, the library that moviepy uses internally, provides a way to do this. It would be better to keep them in memory (and ideally, only one in memory at once). I suspect the biggest slowdown is writing every frame to the disk, then reading them again. (no new images, one paste, one conversion) (one new image, two pastes, one conversion) Your code for handling partial frames does more work than it needs to: new_frame = ('RGBA', im.size) Instead, you could just open in in processImage and pass the opened image to analyseImage. This isn't a huge thing since it only happens once per image, but reading from disk is still a relatively slow operation so it's best not to do it more than necessary. You read the image from the disk in both analyseImage and processImage. (I'd try it myself, but my Python installation isn't set up on this laptop.)Ī few optimisation pointers from looking at the code: Maybe saving each frame as PNG is slow worth trying TIF or JPEG. I don't know much about PIL, so if there's a better way to use the lib instead, I'm not going to see it. PIL objects aren't pickle-able, so you'll have to write temp files, which it seems you're already doing. If processImage is the slow part, you can use multiprocessing.Pool and assign each worker (which can run on a separate CPU core) its own range of frames to process. Video transcoding is usually an "embarrassingly parallel" task, and processImage is doing things in one big sequence.