Django Field that implement the following features:
- Django-Storages compatible (S3)
- Resize images to different sizes
- Access thumbnails on model level, no template tags required
- Preserves original image
- Asynchronous rendering (Celery & Co)
- Multi threading and processing for optimum performance
- Restrict accepted image dimensions
- Rename files to a standardized name (using a callable upload_to)
Simply install the latest stable package using the command
pip install django-stdimage
and add 'stdimage' to INSTALLED_APPs in your settings.py, that's it!
StdImageField works just like Django's own
ImageField
except that you can specify different sized variations.
Variations are specified withing a dictionary. The key will will be the attribute referencing the resized image. A variation can be defined both as a tuple or a dictionary.
Example:
from stdimage.models import StdImageField
class MyModel(models.Model):
# works just like django's ImageField
image = StdImageField(upload_to='path/to/img')
# creates a thumbnail resized to maximum size to fit a 100x75 area
image = StdImageField(upload_to='path/to/img',
variations={'thumbnail': {'width': 100, 'height': 75}})
# is the same as dictionary-style call
image = StdImageField(upload_to='path/to/img', variations={'thumbnail': (100, 75)})
# creates a thumbnail resized to 100x100 croping if necessary
image = StdImageField(upload_to='path/to/img', variations={
'thumbnail': {"width": 100, "height": 100, "crop": True}
})
## Full ammo here. Please note all the definitions below are equal
image = StdImageField(upload_to=upload_to, blank=True, variations={
'large': (600, 400),
'thumbnail': (100, 100, True),
'medium': (300, 200),
})For using generated variations in templates use myimagefield.variation_name.
Example:
<a href="{{ object.myimage.url }}"><img alt="" src="{{ object.myimage.thumbnail.url }}"/></a>By default StdImageField stores images without modifying the file name. If you want to use more consistent file names you can use the build in upload callables.
Example:
from stdimage.utils import UploadToUUID, UploadToClassNameDir, UploadToAutoSlug, \
UploadToAutoSlugClassNameDir
class MyClass(models.Model):
title = models.CharField(max_length=50)
# Gets saved to MEDIA_ROOT/myclass/#FILENAME#.#EXT#
image1 = StdImageField(upload_to=UploadToClassNameDir())
# Gets saved to MEDIA_ROOT/myclass/pic.#EXT#
image2 = StdImageField(upload_to=UploadToClassNameDir(name='pic'))
# Gets saved to MEDIA_ROOT/images/#UUID#.#EXT#
image3 = StdImageField(upload_to=UploadToUUID(path='images'))
# Gets saved to MEDIA_ROOT/myclass/#UUID#.#EXT#
image4 = StdImageField(upload_to=UploadToClassNameDirUUID())
# Gets save to MEDIA_ROOT/images/#SLUG#.#EXT#
image5 = StdImageField(upload_to=UploadToAutoSlug(populate_from='title'))
# Gets save to MEDIA_ROOT/myclass/#SLUG#.#EXT#
image6 = StdImageField(upload_to=UploadToAutoSlugClassNameDir(populate_from='title'))The StdImageField doesn't implement any size validation. Validation can be specified using the validator attribute
and using a set of validators shipped with this package.
Validators can be used for both Forms and Models.
Example
from stdimage.validators import MinSizeValidator, MaxSizeValidator
class MyClass(models.Model)
image1 = StdImageField(validators=[MinSizeValidator(800, 600)])
image2 = StdImageField(validators=[MaxSizeValidator(1028, 768)])CAUTION: The MaxSizeValidator should be used with caution. As storage isn't expensive, you shouldn't restrict upload dimensions. If you seek prevent users form overflowing your memory you should restrict the HTTP upload body size.
Django dropped support
for automated deletions in version 1.3.
Implementing file deletion should be done
inside your own applications using the post_delete or pre_delete signal.
Clearing the field if blank is true, does not delete the file. This can also be achieved using pre_save and post_save signals.
This packages contains two signal callback methods that handle file deletion for all SdtImageFields of a model.
from stdimage.utils import pre_delete_delete_callback, pre_save_delete_callback
post_delete.connect(pre_delete_delete_callback, sender=MyModel)
pre_save.connect(pre_save_delete_callback, sender=MyModel)Warning: You should not use the signal callbacks in production. They may result in data loss.
Tools like celery allow to execute time-consuming tasks outside of the request. If you don't want to wait for your variations to be rendered in request, StdImage provides your the option to pass a async keyword and a util. Note that the callback is not transaction save, but the file will be there. This example is based on celery.
tasks.py:
try:
from django.apps import apps
get_model = apps.get_model
except ImportError:
from django.db.models.loading import get_model
from celery import shared_task
from stdimage.utils import render_variations
@shared_task
def process_photo_image(file_name, variations, storage):
render_variations(file_name, variations, replace=True, storage=storage)
obj = get_model('myapp', 'Photo').objects.get(image=file_name)
obj.processed = True
obj.save()models.py:
from django.db import models
from stdimage.models import StdImageField
from stdimage.utils import UploadToClassNameDir
from tasks import process_photo_image
def image_processor(file_name, variations, storage):
process_photo_image.delay(file_name, variations, storage)
return False # prevent default rendering
class AsyncImageModel(models.Model)
image = StdImageField(
# above task definition can only handle one model object per image filename
upload_to=UploadToClassNameDir(),
render_variations=image_processor # pass boolean or callable
)
processed = models.BooleanField(default=False) # flag that could be used for view querysetsYou might want to add new variations to a field. That means you need to render new variations for missing fields. This can be accomplished using a management command.
python manage.py rendervariations 'app_name.model_name.field_name' [--replace]The replace option will replace all existing files.
Since version 2 stdImage supports multiprocessing. Every image is rendered in separate process. It not only increased performance but the garbage collection and therefore the huge memory footprint from previous versions.
Note: PyPy seems to have some problems regarding multiprocessing, for that matter all multiprocessing is disabled in PyPy.