ImageLayerMethod

Apply the GIF disposal methods set in the current image sequence to form a fully defined animation sequence without, as it should be displayed. Effectively converting a GIF animation into a 'film strip' like animation.

Crop the second and later frames to the smallest rectangle that contains all the differences between the two images. No GIF disposal methods are taken into account.

This does not preserve a animation's normal working, especially when a animation used GIF disposal methods such as 'Previous' or 'Background'.

As CompareAnyLayer but crop to the bounds of any opaque pixels which become transparent in the second frame. That is the smallest image needed to mask or erase pixels for the next frame.

As CompareAnyLayer but crop to pixels that add extra color to the next image, as a result of overlaying color pixels. That is the smallest single overlaid image to add or change colors.

This can, be used with the -compose alpha composition method 'change-mask', to reduce the image to just the pixels that need to be overlaid.

This is like CoalesceLayer but shows the look of the animation after the GIF disposal method has been applied, before the next sub-frame image is overlaid. That is the 'dispose' image that results from the application of the GIF disposal method. This allows you to check what is going wrong with a particular animation you may be developing.

Optimize a coalesced animation into GIF animation using a number of general techniques. This is currently a short cut to apply both the OptimizeImageLayer and OptimizeTransLayer methods but will expand to include other methods.

Optimize a coalesced animation into GIF animation by reducing the number of pixels per frame as much as possible by attempting to pick the best GIF disposal method to use, while ensuring the result will continue to animate properly.

There is no guarantee that the best optimization will be found. But then no reasonably fast GIF optimization algorithm can do this. However this does seem to do better than most other GIF frame optimizers seen.

As OptimizeImageLayer but attempt to improve the overall optimization by adding extra frames to the animation, without changing the final look or timing of the animation. The frames are added to attempt to separate the clearing of pixels from the overlaying of new additional pixels from one animation frame to the next. If this does not improve the optimization (for the next frame only), it will fall back to the results of the previous normal OptimizeImageLayer technique.

There is the possibility that the change in the disposal style will result in a worsening in the optimization of later frames, though this is unlikely. In other words there no guarantee that it is better than the normal 'optimize-frame' technique.

Given a GIF animation, replace any pixel in the sub-frame overlay images with transparency, if it does not change the resulting animation by more than the current fuzz factor.

This should allow a existing frame optimized GIF animation to compress into a smaller file size due to larger areas of one (transparent) color rather than a pattern of multiple colors repeating the current disposed image of the last frame.

Remove (and merge time delays) of duplicate consecutive images, so as to simplify layer overlays of coalesced animations. Usually this is a result of using a constant time delay across the whole animation, or after a larger animation was split into smaller sub-animations. The duplicate frames could also have been used as part of some frame optimization methods.

Remove any image with a zero time delay, unless ALL the images have a zero time delay (and is not a proper timed animation, a warning is then issued). In a GIF animation, such images are usually frames which provide partial intermediary updates between the frames that are actually displayed to users. These frames are usually added for improved frame optimization in GIF animations.

Alpha Composition of two image lists, separated by a "null:" image, with the destination image list first, and the source images last. An image from each list are composited together until one list is finished. The separator image and source image lists are removed.

The geometry offset is adjusted according to gravity in accordance of the virtual canvas size of the first image in each list. Unlike a normal composite operation, the canvas offset is also added to the final composite positioning of each image.

If one of the image lists only contains one image, that image is applied to all the images in the other image list, regardless of which list it is. In this case it is the image meta-data of the list which preserved.

As FlattenLayer but merging all the given image layers into a new layer image just large enough to hold all the image without clipping or extra space. The new image's virtual offset will prevere the position of the new layer, even if this offset is negative. the virtual canvas size of the first image is preserved.

Caution is advised when handling image layers with negative offsets as few image file formats handle them correctly.

Create a canvas the size of the first images virtual canvas using the current background color, and compose each image in turn onto that canvas. Images falling outside that canvas will be clipped. Final image will have a zero virtual canvas offset.

This is usually used as one of the final 'image layering' operations overlaying all the prepared image layers into a final image.

For a single image this method can also be used to fillout a virtual canvas with real pixels, or to underlay a opaque color to remove transparency from an image.

As FlattenLayer but expanding the initial canvas size of the first image so as to hold all the image layers. However as a virtual canvas is 'locked' to the origin, by definition, image layers with a negative offsets will still be clipped by the top and left edges.

This method is commonly used to layout individual image using various offset but without knowing the final canvas size. The resulting image will, like FlattenLayer not have any virtual offset, so can be saved to any image file format. This method corresponds to mosaic, above.

Find the minimal bounds of all the images in the current image sequence, then adjust the offsets so all images are contained on a minimal positive canvas. None of the image data is modified, only the virtual canvas size and offset. Then all the images will have the same canvas size, and all will have a positive offset, at least one image will touch every edge of that canvas with actual pixel data, though that data may be transparent.