Advanced topics

Custom image model

The Image model can be customised, allowing additional fields to be added to images.

To do this, you need to add two models to your project:

  • The image model itself that inherits from wagtail.wagtailimages.models.AbstractImage. This is where you would add your additional fields
  • The renditions model that inherits from wagtail.wagtailimages.models.AbstractRendition. This is used to store renditions for the new model.

Here’s an example:

from django.db import models
from django.db.models.signals import pre_delete
from django.dispatch import receiver

from wagtail.wagtailimages.models import Image, AbstractImage, AbstractRendition

class CustomImage(AbstractImage):
    # Add any extra fields to image here

    # eg. To add a caption field:
    # caption = models.CharField(max_length=255)

    admin_form_fields = Image.admin_form_fields + (
        # Then add the field names here to make them appear in the form:
        # 'caption',

class CustomRendition(AbstractRendition):
    image = models.ForeignKey(CustomImage, related_name='renditions')

    class Meta:
        unique_together = (
            ('image', 'filter', 'focal_point_key'),

# Delete the source image file when an image is deleted
@receiver(pre_delete, sender=CustomImage)
def image_delete(sender, instance, **kwargs):

# Delete the rendition image file when a rendition is deleted
@receiver(pre_delete, sender=CustomRendition)
def rendition_delete(sender, instance, **kwargs):


If you are using image feature detection, follow these instructions to enable it on your custom image model: Feature detection and custom image models

Then set the WAGTAILIMAGES_IMAGE_MODEL setting to point to it:


Migrating from the builtin image model

When changing an existing site to use a custom image model. No images will be copied to the new model automatically. Copying old images to the new model would need to be done manually with a data migration.

Any templates that reference the builtin image model will still continue to work as before but would need to be updated in order to see any new images.

Animated GIF support

Pillow (Wagtail’s default image library) doesn’t support resizing animated GIFs. If you need animated GIFs in your site, install Wand.

When Wand is installed, Wagtail will automatically start using it for resizing GIF files, and will continue to resize other images with Pillow.

Feature Detection

Wagtail has the ability to automatically detect faces and features inside your images and crop the images to those features.

Feature detection uses OpenCV to detect faces/features in an image when the image is uploaded. The detected features stored internally as a focal point in the focal_point_{x, y, width, height} fields on the Image model. These fields are used by the fill image filter when an image is rendered in a template to crop the image.


Feature detection requires OpenCV which can be a bit tricky to install as it’s not currently pip-installable.

Installing OpenCV on Debian/Ubuntu

Debian and ubuntu provide an apt-get package called python-opencv:

sudo apt-get install python-opencv python-numpy

This will install PyOpenCV into your site packages. If you are using a virtual environment, you need to make sure site packages are enabled or Wagtail will not be able to import PyOpenCV.

Enabling site packages in the virtual environment

If you are not using a virtual envionment, you can skip this step.

Enabling site packages is different depending on whether you are using pyvenv (Python 3.3+ only) or virtualenv to manage your virtual environment.


Go into your pyvenv directory and open the pyvenv.cfg file then set include-system-site-packages to true.


Go into your virtualenv directory and delete a file called lib/python-x.x/no-global-site-packages.txt.

Testing the OpenCV installation

You can test that OpenCV can be seen by Wagtail by opening up a python shell (with your virtual environment active) and typing:

import cv

If you don’t see an ImportError, it worked. (If you see the error libdc1394 error: Failed to initialize libdc1394, this is harmless and can be ignored.)

Switching on feature detection in Wagtail

Once OpenCV is installed, you need to set the WAGTAILIMAGES_FEATURE_DETECTION_ENABLED setting to True:



Manually running feature detection

Feature detection runs when new images are uploaded in to Wagtail. If you already have images in your Wagtail site and would like to run feature detection on them, you will have to run it manually.

You can manually run feature detection on all images by running the following code in the python shell:

from wagtail.wagtailimages.models import Image

for image in Image.objects.all():
    if not image.has_focal_point():

Feature detection and custom image models

When using a Custom image model, you need to add a signal handler to the model to trigger feature detection whenever a new image is uploaded:

# Do feature detection when a user saves an image without a focal point
@receiver(pre_save, sender=CustomImage)
def image_feature_detection(sender, instance, **kwargs):
    # Make sure the image doesn't already have a focal point
    if not instance.has_focal_point():
        # Set the focal point


This example will always run feature detection regardless of whether the WAGTAILIMAGES_FEATURE_DETECTION_ENABLED setting is set.

Add a check for this setting if you still want it to have effect.