In much the same way Basemap can take an image in a Plate Carree map projection (e.g. blue marble) and transform it onto another projection in a non-affine way, I would like to be able to apply a non-affine transformation to an image, only using the proper matplotlib Transform framework. To me, this means that I should not have to pre-compute the projected image before adding it to the axes, instead I should be able to pass the source image and the Transformation stack should take care of transforming (warping) it for me (just like I can with a Path).
As far as I can tell, there is no current matplotlib functionality to do this (as I understand it, some backends can cope with affine image transformations, but this has not been plumbed-in in the same style as the Transform of paths and is done in the Image classes themselves). (note: I am aware that there is some code to do affine transforms in certain backends - http://matplotlib.sourceforge.net/examples/api/demo_affine_image.html - which is currently broken [I have a fix for this], but it doesn't fit into the Transform framework at present.) I have code which will do the actual warping for my particular case, and all I need to do is hook it in nicely... I was thinking of adding a method to the Transform class which implements this functionality, psuedo code stubs are included: class Transform: ... def transform_image(self, image): return self.transform_image_affine(self.transform_image_non_affine(image)) def transform_image_non_affine(self, image): if not self.is_affine: raise NotImplementedError('This is the hard part.') return image ... def transform_image_affine(self, image): # could easily handle scale & translations (by changing the extent), but not rotations... raise NotImplementedError("Need to do this. But rule out rotations completely.") This could then be used by the Image artist to do something like: class Image(Artist, ...): ... def draw(self, renderer, *args, **kwargs): transform = self.get_transform() timg = transform.transform_image_non_affine(self) affine = transform.get_affine() ... renderer.draw_image(timg, ..., affine) And the backends could implement: class Renderer*... def draw_image(..., img, ..., transform=None): # transform must be an affine transform if transform.is_affine and i_can_handle_affines: ... # convert the Transform into the backend's transform form else: timage = transform.transform_image(img) The warping mechanism itself would be fairly simple, in that it assigns coordinate values to each pixel in the source cs (coordinate system), transforms those points into the target cs, from which a bounding box can be identified. The bbox is then treated as the bbox of the target (warped) image, which is given an arbitrary resolution. Finally the target image pixel coordinates are computed and their associated pixel values are calculated by interpolating from the source image (using target cs pixel values). As mentioned, I have written the image warping code (for my higher dimensional coordinate system case using scipy.interpolate.NearestNDInterpolator) successfully already, so the main motivations for this mail then, are: * To get a feel for whether anyone else would find this functionality useful? Where else can it be used and in what ways? * To get feedback on the proposed change to the Transform class, whether such a change would be acceptable and what pitfalls lie ahead. * To hear alternative approaches to solving the same problem. * To make sure I haven't missed a concept that already exists in the Image module (there are 6 different "image" classes in there, 4 of which undocumented) * To find out if anyone else wants to collaborate in making the required change. Thanks in advance for your time, ------------------------------------------------------------------------------ Virtualization & Cloud Management Using Capacity Planning Cloud computing makes use of virtualization - but cloud computing also focuses on allowing computing to be delivered as a service. http://www.accelacomm.com/jaw/sfnl/114/51521223/ _______________________________________________ Matplotlib-devel mailing list Matplotlib-devel@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-devel
