Resizing and cropping images are the two basic geometry transformations. Like other Uploadcare image transformations, they're non-destructive and applied at the CDN delivery stage.
The order of operations is important. For example, when you resize an image first, the following crop operation will use the coordinates of the resized image.
Downscales an image proportionally to fit the given width and height in pixels.
If the dimensions are not specified, it'll be
Resizes an image to one or two dimensions. When you set both width and height explicitly, it may result in a distorted image. If you specify either side, this operation will preserve the original aspect ratio and resize the image accordingly.
scale_crop behavior when a source image is smaller than
the requested dimensions. The order is important: a
stretch operation applies to its following
You can use multiple resizing operations with individual
stretch behavior for each one.
on— an image can be scaled up (default).
off— an image won't be scaled up.
fill— no image scaling up. The background color fills the remaining parts.
Content-aware resize helps retaining original proportions of faces and other visually sensible objects while resizing the rest of the image using intelligent algorithms.
Smart resize works best for solid, gradient, and homogenous backgrounds that'll be expanded. If background stretching can't be done without artifacts, a solid fill with a matching color will be applied instead.
Note: We are working on improving Smart resize and behaviour may change. Contact us if you found out regression.
Crops an image by using specified dimensions and offsets. You can set offset in
pixels and percent.
left. If no values are set, the top-left image corner is used.
Note: In URL,
% is an escape character. Therefore, use a
sequence or a
p shortcut for percent.
If given dimensions are greater than the ones of the original image, the image
is rendered inside a color-filled box. The color of that box can be changed via
Scales an image until it fully covers the specified dimensions; the rest gets cropped. Mostly used to place images with various dimensions into placeholders (e.g., square shaped).
Here's an example that shows the difference between three operations:
Nare the optional offsets along one or both of the axes in percentages; other possible values include:
center— a keyword responsible for centering the crop focus.
:type is specified.
Switching the crop type to one of the
smart modes enables the content-aware
mechanics. Uploadcare applies AI-based algorithms to detect faces and other
visually sensible objects to crop the background and not the main object.
Content-aware mechanics of the smart crop are based on the following methods of image analysis:
- Face Detection,
:typeis set to
smart_faces. Face detection identifies and locates human faces in digital images.
- Object Detection,
:typeis set to
smart_object. Object detection identifies and locates most visually important regions in the image.
- Corner Points Detection,
:typeis set to
smart_points. This method analyses image pixels to find the high contrast corners in the image, useful for abstract, landscape, and art images. The corner points have much less semantic information than other methods and designed to be used as a fallback.
The methods you include separating by underscore are applied sequentially. The
algorithm switches to the next method only if no regions were found by the
previous one. For example
smart_faces_objects_points applies face detection
initially as the first step. Only when no faces are found on an image, the
object detection will be used. Finally, when no objects are found, the corner
points will be detected.
If specified methods were unable to find regions or points of interest, the
:offset setting will be used to crop an image. When no
specified, images get center-cropped.
Smart crop always tries to save the maximum amount of pixels from the original image. It first applies scale operation and then cuts horizontal or vertical excesses.
Centering on faces or objects
when no faces found.
Centering on objects or faces
when no objects found.
Centering on corner points.