Making thumbnails fast(engineering.khanacademy.org)
engineering.khanacademy.org
Making thumbnails fast
http://engineering.khanacademy.org/posts/making-thumbnails-fast.htm
6 comments
I haven't used Numba -- looks fast and easy!
But am I missing something? Numpy has everything you need already, natively, no? Some slicing or a dot product should get you there... no need for ufuncs or einsum, I think...
But am I missing something? Numpy has everything you need already, natively, no? Some slicing or a dot product should get you there... no need for ufuncs or einsum, I think...
avg = (rgb[...,0]+rgb[...,1]+rgb[...,2]) * (1.0 / 3.0)
gray = np.dot(rgb, [0.299, 0.587, 0.114])More generally, for an image im with shape (width, height, channels) and a square transformation matrix M of shape (channels, channels), you can do :
res = np.dot(im, M.T)
Agreed, for some reason when I was looking at this last night I thought I couldn't use broadcasting, I've added the example to the gist: https://gist.github.com/ahmadia/c1f8be119f3cb2d2b8e5
Would you believe that Numba is 4 times faster for the sort of simple transformations described in the blog post?
(See aktiur's response below, some performance gains come from avoiding a copy)
Would you believe that Numba is 4 times faster for the sort of simple transformations described in the blog post?
(See aktiur's response below, some performance gains come from avoiding a copy)
Numba is indeed pretty impressive, but you're not comparing exactly the same thing with this code.
In the Numba case, you're basically modifying the image in place: it means no allocating a new array, no full copying. However, your pure-numpy code basically creates a new array (the result of np.dot) before copying it back entirely in image.
If you write the two functions so that they both return a new numpy array and do not touch the original one, the time difference drops from 4 times faster to 2.5 times faster. That's still an impressive difference, but at the loss of a bit of flexibility.
https://gist.github.com/aktiur/e1cddee8f699ded49824
N.B.: numpy.dot does not use broadcasting, i.e. it does not allocate a temporary array to extend the smaller one. The function handles n-dimensional arrays by summing on the last index of the first array, and on the second last of the second array.
In the Numba case, you're basically modifying the image in place: it means no allocating a new array, no full copying. However, your pure-numpy code basically creates a new array (the result of np.dot) before copying it back entirely in image.
If you write the two functions so that they both return a new numpy array and do not touch the original one, the time difference drops from 4 times faster to 2.5 times faster. That's still an impressive difference, but at the loss of a bit of flexibility.
https://gist.github.com/aktiur/e1cddee8f699ded49824
N.B.: numpy.dot does not use broadcasting, i.e. it does not allocate a temporary array to extend the smaller one. The function handles n-dimensional arrays by summing on the last index of the first array, and on the second last of the second array.
Thanks, I clearly wasn't being careful. I'll update my Gist...
edit: On reviewing, I think the intent of the original blog post was to modify images in place (or at least to do it as quickly as possible with in-place filtering ok). In that case, I think my comparison is fair, since NumPy doesn't offer a faster way to do the requested operation. I didn't try out einsum, but I think Numba would outperform that as well.
edit: On reviewing, I think the intent of the original blog post was to modify images in place (or at least to do it as quickly as possible with in-place filtering ok). In that case, I think my comparison is fair, since NumPy doesn't offer a faster way to do the requested operation. I didn't try out einsum, but I think Numba would outperform that as well.
"Performing these kinds of image manipulations efficiently is essentially a solved problem. There are many freely available software toolkits that would be more than sufficient for our needs. However, our server infrastructure doesn't allow us to use those libraries, so we ended up implementing these operations ourselves."
Can you explain what prevented using the existing libraries in your server infrastructure?
Can you explain what prevented using the existing libraries in your server infrastructure?
What i can gather from the post is that it's because they are using Google App Engine, and GAE does not allow the use of arbitrary native libraries. Some of the most popular Python libraries that use native code are supported[1] (in the post it's mentioned that they are already using numpy and PIL), but if you need something else besides that, you're out of luck.
[1]: https://cloud.google.com/appengine/docs/python/tools/librari...
[1]: https://cloud.google.com/appengine/docs/python/tools/librari...
I'll never cease to be amazed at the hoops people will jump through so they can feel cool and say "we use the cloud".
The App Engine sandbox is not a good feature of app engine and is removed in the new Managed VM version (naturally in perpetual beta).
I've seen people spend a lot of time trying to avoid "using the cloud" and end up wasting a lot of money and time when they could have just used some AWS services and been done with it.
I've seen people spend a lot of time trying to avoid "using the cloud" and end up wasting a lot of money and time when they could have just used some AWS services and been done with it.
> they could have just used some AWS services and been done with it.
Like the author did with GAE? I think you missed my point completely.
Like the author did with GAE? I think you missed my point completely.
This is a pretty informative post about optimizing custom python code to do some image manipulations, but if you just want to make images like in the post, a service like Imgix could do a pretty good job and would work with App Engine, no matter which language you choose.
I spent 5 minutes with the imgix sandbox and made this, which is pretty close to the samples (I didn't have the correct font or a white colored logo):
https://sandbox.imgix.com/view?url=https%3A%2F%2Fassets.imgi...
I spent 5 minutes with the imgix sandbox and made this, which is pretty close to the samples (I didn't have the correct font or a white colored logo):
https://sandbox.imgix.com/view?url=https%3A%2F%2Fassets.imgi...
Using PIL's im.convert to do lightening and desaturation is a great trick! I've used PIL and love it for ease of use, but had to look elsewhere for performance. It strikes me as funny how the function name and docs saying that convert() is for color space conversion prevented me from considering other possibilities.
The numpy example is amusing though; sending a python lambda into a numpy op, especially a lambda that does something numpy is designed to do natively... no wonder it went slow. But if they'd used native numpy ops, it would be comparable to this PIL solution, with or without pre-multiplying the matrix ops. I haven't tried it, but it's possible it would even be faster. And you have a lot more options for non-affine operations with numpy, whereas the 4x3 is PIL's limit. Making numpy fast makes you have to organize your programs differently, but it is well worth learning.
The numpy example is amusing though; sending a python lambda into a numpy op, especially a lambda that does something numpy is designed to do natively... no wonder it went slow. But if they'd used native numpy ops, it would be comparable to this PIL solution, with or without pre-multiplying the matrix ops. I haven't tried it, but it's possible it would even be faster. And you have a lot more options for non-affine operations with numpy, whereas the 4x3 is PIL's limit. Making numpy fast makes you have to organize your programs differently, but it is well worth learning.
Hi,
Cairos compositing operations provide a good way of doing this and are available from python via CairoCFFI.
They provide a multiply operator http://cairographics.org/operators/ and others.
For an advertising site we needed arbitrary tinting so we lightened the images with Cairo, then added a color mask over the top in the web page.
S
Cairos compositing operations provide a good way of doing this and are available from python via CairoCFFI.
They provide a multiply operator http://cairographics.org/operators/ and others.
For an advertising site we needed arbitrary tinting so we lightened the images with Cairo, then added a color mask over the top in the web page.
S
For thesu not restricted to Python, the ImageMagick convert tool is probably the quickest way to get something like this working.
As a few others have pointed out, sending a lambda function through NumPy is almost always the last thing you want to do. Unfortunately, you were in a situation where you were either going to have to do something really painful like using einsum: https://stackoverflow.com/questions/29989059/matrix-multipli... or writing your own ufunc:https://docs.scipy.org/doc/numpy-dev/user/c-info.ufunc-tutor...
I suspect that your primary limitation was the Google Compute Engine infrastructure. I'm not familiar with the limitations there, but a quick search on Google turns up a fairly limited set of libraries indeed.
I thought it would be interesting to adapt your code slightly to use Numba acceleration. Here's what it looks like:
I observed 25ms per image https://gist.github.com/ahmadia/c1f8be119f3cb2d2b8e5 processing times on my laptop on 1280x720 pixels.
Re-reading your post, I suspect that einsum might actually be your cup of tea, but I really enjoy the simplicity and performance of using Numba for these sort of tasks.