This fixes rendering by account for the 1px texture padding around icons that were stretched with icon-text-fit. We've added the 1px padding before, but didn't scale it accordingly when we are resizing the icon when it is stretched to fit the text. Adjusts the code to match the logic in GL JS.
port https://github.com/mapbox/mapbox-gl-js/pull/4781
This improves legibility of labels that follow lines in pitched views.
The previous approach used the limited information in the shader to
calculate put the glyph in approximatelyright place. The new approach
does this more accurately by doing it on the cpu where we have access to
the entire line geometry.
* Always return image metrics exclusive of padding
* Work with integer coordinates whenever possible
* Eliminate redundant SpriteAtlasElement members
* Fix asymmetric re-padding in getIconQuad when pixelRatio != 1
* Add explanatory comments
* Update gl-js and generate style code
* Factor out packUint8Pair() helper function
* Draft implementation of DDS for {text,icon}-size
Ports https://github.com/mapbox/mapbox-gl-js/pull/4455
* Fix text-size/composite-function-line-placement test
* Refactor to PaintPropertyBinders-like strategy
* Dedupe gl::Program construction
* Use exponential function base for interpolation
* Dedupe coveringZoomStops method
* Fixup tests
* Fix CI errors (hidden within #if block)
This converts the style property classes (CirclePaintProperties and so on) to the same tuple-based approach as gl::Attribute and gl::Uniform. The approach is outlined in https://github.com/mapbox/cpp/blob/master/C%2B%2B%20Structural%20Metaprogramming.md.
The main advantage of this approach is it allows writing algorithms that work on sets of style properties, without resorting to code generation or manually repetitive code. This lets us iterate on approaches to data-driven properties more easily.
Another advantage is that the cascading, unevaluated, and evaluated states of a set of properties exist as independent structures, instead of individual properties holding their own state. This is a more functional approach that makes data flow clearer and reduces state.
