Pilbox is an image processing application server built on Python's Tornado web framework using the Python Imaging Library (Pillow). It is not intended to be the primary source of images, but instead acts as a proxy which requests images and resizes them as desired.
- >= Python 2.7
- Pillow 2.8.1
- Tornado 4.0.2
- OpenCV 2.x (optional)
- PycURL 7.x (optional; required for proxy requests)
- Image Libraries: libjpeg-dev, libfreetype6-dev, libwebp-dev, zlib1g-dev, liblcms2-dev
Pilbox can be installed with pip
$ pip install pilbox
Or easy_install
$ easy_install pilbox
Or from source
$ git clone https://github.com/agschwender/pilbox.git
Packaged with Pilbox is a Vagrant configuration file which installs all necessary dependencies on a virtual box using Ansible. See the Vagrant documentation and the Ansible documentation for installation instructions. Once installed, the following will start and provision a virtual machine.
$ vagrant up $ vagrant provision
To access the virtual machine itself, simply...
$ vagrant ssh
When running via Vagrant, the application is automatically started on port 8888 on 192.168.100.100, i.e.
http://192.168.100.100:8888/
Another alternative is to deploy pilbox to hosted services like Heroku. With the included Procfile you can just follow Heroku's Python documentation and get a working Pilbox server. Add any startup options to the command in the Procfile.
To run the application, issue the following command
$ python -m pilbox.app
By default, this will run the application on port 8888 and can be accessed by visiting:
http://localhost:8888/
To see a list of all available options, run
$ python -m pilbox.app --help Usage: pilbox/app.py [OPTIONS] Options: --allowed_hosts list of allowed hosts (default []) --allowed_operations list of allowed operations (default []) --background default hexadecimal bg color (RGB or ARGB) --client_key client key --client_name client name --config path to configuration file --content_type_from_image override content type using image mime type --debug run in debug mode --expand default to expand when rotating --filter default filter to use when resizing --help show this help information --implicit_base_url prepend protocol/host to url paths --max_requests max concurrent requests (default 40) --operation default operation to perform --optimize default to optimize when saving --port run on the given port (default 8888) --position default cropping position --preserve_exif default behavior for Exif information --progressive default to progressive when saving --proxy_host proxy hostname --proxy_port proxy port --quality default jpeg quality, 1-99 or keep --retain default adaptive retain percent, 1-99 --timeout timeout of requests in seconds (default 10) --validate_cert validate certificates (default True)
To use the image processing service, include the application url as you would any other image. E.g. this image url
<img src="http://i.imgur.com/zZ8XmBA.jpg" />
Would be replaced with this image url
<img src="http://localhost:8888/?url=http%3A%2F%2Fi.imgur.com%2FzZ8XmBA.jpg&w=300&h=300&mode=crop" />
This will request the image served at the supplied url and resize it to
300x300 using the crop mode. The below is the list of parameters
that can be supplied to the service.
- url: The url of the image to be resized
- op: The operation to perform: noop, region, resize (default), rotate
- noop: No operation is performed, image is returned as it is received
- region: Select a sub-region from the image
- resize: Resize the image
- rotate: Rotate the image
- fmt: The output format to save as, defaults to the source format
- gif: Save as GIF
- jpeg: Save as JPEG
- png: Save as PNG
- webp: Save as WebP
- opt: The output should be optimized, only relevant to JPEGs and PNGs
- exif: Keep original Exif data in the processed image, only relevant for JPEG
- prog: Enable progressive output, only relevant to JPEGs
- q: The quality, (1-99) or keep, used to save the image, only relevant to JPEGs
- w: The desired width of the image
- h: The desired height of the image
- mode: The resizing method: adapt, clip, crop (default), fill and scale
- adapt: Resize using crop if the resized image retains a supplied percentage of the original image; otherwise fill
- clip: Resize to fit within the desired region, keeping aspect ratio
- crop: Resize so one dimension fits within region, center, cut remaining
- fill: Fills the clipped space with a background color
- scale: Resize to fit within the desired region, ignoring aspect ratio
- bg: Background color used with fill mode (RGB or ARGB)
- RGB: 3- or 6-digit hexadecimal number
- ARGB: 4- or 8-digit hexadecimal number, only relevant for PNG images
- filter: The filtering algorithm used for resizing
- nearest: Fastest, but often images appear pixelated
- bilinear: Faster, can produce acceptable results
- bicubic: Fast, can produce acceptable results
- antialias: Slower, produces the best results
- pos: The crop position
- top-left: Crop from the top left
- top: Crop from the top center
- top-right: Crop from the top right
- left: Crop from the center left
- center: Crop from the center
- right: Crop from the center right
- bottom-left: Crop from the bottom left
- bottom: Crop from the bottom center
- bottom-right: Crop from the bottom right
- face: Identify faces and crop from the midpoint of their position(s)
- x,y: Custom center point position ratio, e.g. 0.0,0.75
- retain: The minimum percentage (1-99) of the original image that must still be visible in the resized image in order to use crop mode
- rect: The region as x,y,w,h; x,y: top-left position, w,h: width/height of region
- deg: The desired rotation angle degrees
- 0-359: The number of degrees to rotate (clockwise)
- auto: Auto rotation based on Exif orientation, only relevant to JPEGs
- expand: Expand the size to include the full rotated image
- client: The client name
- sig: The signature
The url parameter is always required as it dictates the image that
will be manipulated. op is optional and defaults to resize. It
also supports a comma separated list of operations, where each operation
is applied in the order that it appears in the list. Depending on the
operation, additional parameters are required. All image manipulation
requests accept exif, fmt, opt, prog and q. exif
is optional and default to 0 (not preserved). fmt is optional
and defaults to the source image format. opt is optional and
defaults to 0 (disabled). prog is optional and default to 0
(disabled). q is optional and defaults to 90. To ensure
security, all requests also support, client and sig. client
is required only if the client_name is defined within the
configuration file. Likewise, sig is required only if the
client_key is defined within the configuration file. See the
Signing section for details on how to generate the signature.
For resizing, either the w or h parameter is required. If only
one dimension is specified, the application will determine the other
dimension using the aspect ratio. mode is optional and defaults to
crop. filter is optional and defaults to antialias. bg
is optional and defaults to fff. pos is optional and defaults to
center. retain is optional and defaults to 75.
For region sub-selection, rect is required. For rotating, deg is
required. expand is optional and defaults to 0 (disabled). It is
recommended that this feature not be used as it typically does not
produce high quality images.
Note, all built-in defaults can be overridden by setting them in the configuration file. See the Configuration section for more details.
The following images show the various resizing modes in action for an
original image size of 640x428 that is being resized to 500x400.
The adaptive resize mode combines both crop and fill resize modes
to ensure that the image always matches the requested size and a minimum
percentage of the image is always visible. Adaptive resizing will first
calculate how much of the image will be retained if crop is used. Then,
if that percentage is equal to or above the requested minimum retained
percentage, crop mode will be used. If it is not, fill will be used. The
first figure uses a retain value of 80 to illustrate the
adaptive crop behavior.
Whereas the second figure requires a minimum of 99 to illustrate the
adaptive fill behavior
The image is resized to fit within a 500x400 box, maintaining aspect
ratio and producing an image that is 500x334. Clipping is useful
when no portion of the image can be lost and it is acceptable that the
image not be exactly the supplied dimensions, but merely fit within the
dimensions.
The image is resized so that one dimension fits within the 500x400
box. It is then centered and the excess is cut from the image. Cropping
is useful when the position of the subject is known and the image must
be exactly the supplied size.
Similar to clip, fill resizes the image to fit within a 500x400 box.
Once clipped, the image is centered within the box and all left over
space is filled with the supplied background color. Filling is useful
when no portion of the image can be lost and it must be exactly the
supplied size.
The image is clipped to fit within the 500x400 box and then
stretched to fill the excess space. Scaling is often not useful in
production environments as it generally produces poor quality images.
This mode is largely included for completeness.
To run all tests, issue the following command
$ python -m pilbox.test.runtests
To run individual tests, simply indicate the test to be run, e.g.
$ python -m pilbox.test.runtests pilbox.test.signature_test
In order to secure requests so that unknown third parties cannot easily
use the resize service, the application can require that requests
provide a signature. To enable this feature, set the client_key
option. The signature is a hexadecimal digest generated from the client
key and the query string using the HMAC-SHA1 message authentication code
(MAC) algorithm. The below python code provides an example
implementation.
import hashlib
import hmac
def derive_signature(key, qs):
m = hmac.new(key, None, hashlib.sha1)
m.update(qs)
return m.hexdigest()
The signature is passed to the application by appending the sig
parameter to the query string; e.g.
x=1&y=2&z=3&sig=c9516346abf62876b6345817dba2f9a0c797ef26. Note, the
application does not include the leading question mark when verifying
the supplied signature. To verify your signature implementation, see the
pilbox.signature command described in the Tools section.
All options that can be supplied to the application via the command line, can also be specified in the configuration file. Configuration files are simply python files that define the options as variables. The below is an example configuration.
# General settings port = 8888 # Set client name and key if the application requires signed requests. The # client must sign the request using the client_key, see README for # instructions. client_name = "sample" client_key = "3NdajqH8mBLokepU4I2Bh6KK84GUf1lzjnuTdskY" # Set the allowed hosts as an alternative to signed requests. Only those # images which are served from the following hosts will be requested. allowed_hosts = ["localhost"] # Request-related settings max_requests = 50 timeout = 7.5 # Set default resizing options background = "ccc" filter = "bilinear" mode = "crop" position = "top" # Set default rotating options expand = False # Set default saving options format = None optimize = 1 quality = "90"
To verify that your client application is generating correct signatures, use the signature command.
$ python -m pilbox.signature --key=abcdef "x=1&y=2&z=3" Query String: x=1&y=2&z=3 Signature: c9516346abf62876b6345817dba2f9a0c797ef26 Signed Query String: x=1&y=2&z=3&sig=c9516346abf62876b6345817dba2f9a0c797ef26
The application allows the use of the resize functionality via the command line.
$ python -m pilbox.image --width=300 --height=300 http://i.imgur.com/zZ8XmBA.jpg > /tmp/foo.jpg
If a new mode is added or a modification was made to the libraries that would change the current expected output for tests, run the generate test command to regenerate the expected output for the test cases.
$ python -m pilbox.test.genexpected
The application itself does not include any caching. It is recommended that the application run behind a CDN for larger applications or behind varnish for smaller ones.
Defaults for the application have been optimized for quality rather than performance. If you wish to get higher performance out of the application, it is recommended you use a less computationally expensive filtering algorithm and a lower JPEG quality. For example, add the following to the configuration.
# Set default resizing options filter = "bicubic" quality = 75
While it is generally recommended to use Pilbox as a standalone server, it can also be used as a library. To extend from it and build a custom image processing server, use the following example.
#!/usr/bin/env python
import tornado.gen
from pilbox.app import PilboxApplication, ImageHandler, \
start_server, parse_command_line
class CustomApplication(PilboxApplication):
def get_handlers(self):
return [(r"/(\d+)x(\d+)/(.+)", CustomImageHandler)]
class CustomImageHandler(ImageHandler):
def prepare(self):
self.args = self.request.arguments.copy()
@tornado.gen.coroutine
def get(self, w, h, url):
self.args.update(dict(w=w, h=h, url=url))
self.validate_request()
resp = yield self.fetch_image()
self.render_image(resp)
def get_argument(self, name, default=None):
return self.args.get(name, default)
if __name__ == "__main__":
parse_command_line()
start_server(CustomApplication())
- 0.1: Image resizing fit
- 0.1.1: Image cropping
- 0.1.2: Image scaling
- 0.2: Configuration integration
- 0.3: Signature generation
- 0.3.1: Signature command-line tool
- 0.4: Image resize command-line tool
- 0.5: Facial recognition cropping
- 0.6: Fill resizing mode
- 0.7: Resize using crop position
- 0.7.1: Resize using a single dimension, maintaining aspect ratio
- 0.7.2: Added filter and quality options
- 0.7.3: Support python 3
- 0.7.4: Fixed cli for image generation
- 0.7.5: Write output in 16K blocks
- 0.8: Added support for ARGB (alpha-channel)
- 0.8.1: Increased max clients and write block sizes
- 0.8.2: Added configuration for max clients and timeout
- 0.8.3: Only allow http and https protocols
- 0.8.4: Added support for WebP
- 0.8.5: Added format option and configuration overrides for mode and format
- 0.8.6: Added custom position support
- 0.9: Added rotate operation
- 0.9.1: Added sub-region selection operation
- 0.9.4: Added Pilbox as a PyPI package
- 0.9.10: Converted README to reStructuredText
- 0.9.14: Added Sphinx docs
- 0.9.15: Added implicit base url to configuration
- 0.9.16: Added validate cert to configuration
- 0.9.17: Added support for GIF format
- 0.9.18: Fix for travis builds on python 2.6 and 3.3
- 0.9.19: Validate cert fix
- 0.9.20: Added optimize option
- 0.9.21: Added console script entry point
- 1.0.0: Modified for easier library usage
- 1.0.1: Added allowed operations and default operation
- 1.0.2: Modified to allow override of http content type
- 1.0.3: Safely catch image save errors
- 1.0.4: Added progressive option
- 1.1.0: Proxy server support
- 1.1.1: Added JPEG auto rotation based on Exif orientation
- 1.1.2: Added keep JPEG quality option and set JPEG subsampling to keep
- 1.1.3: Fix auto rotation on JPEG with missing Exif data
- 1.1.4: Exception handling around invalid Exif data
- 1.1.5: Fix image requests without content types
- 1.1.6: Support custom applications that need command line arguments
- 1.1.7: Support adapt resize mode
- 1.1.8: Add preserve Exif flag
- 1.1.9: Increase Pillow version to 2.8.1
- How to reconcile unavailable color profiles?
- Add backends (S3, file system, etc...) if necessary