(* field-def.sml * * COPYRIGHT (c) 2010 The Diderot Project (http://diderot.cs.uchicago.edu) * All rights reserved. *) structure FieldDef = struct (* the static definition of a field value *) datatype field_def = CONV of ImageInfo.info * Kernel.kernel (* convolution *) | DIFF of int * field_def (* k levels of differentiation *) | NEG of field_def | SUM of field_def * field_def (* scaling too? *) (* normalize a field definition by pushing the DIFF operators to the * leaves *) fun normalize fld = let fun norm fld = (case fld of CONV _ => fld | DIFF(k, fld) => diff (k, fld) | NEG fld => NEG(norm fld) | SUM(fld1, fld2) => SUM(norm fld1, norm fld2) (* end case *)) and diff (k, fld) = (case fld of CONV _ => DIFF(k, fld) | DIFF(k', fld) => diff (k+k', fld) | NEG fld => NEG(diff(k, fld)) | SUM(fld1, fld2) => SUM(diff(k, fld1), diff(k, fld2)) (* end case *)) in norm fld end (* equality test for field definitions *) fun same (CONV(img1, kern1), CONV(img2, kern2)) = ImageInfo.same(img1, img2) andalso Kernel.same(kern1, kern2) | same (DIFF(k1, fld1), DIFF(k2, fld2)) = (k1 = k2) andalso same(fld1, fld2) | same (NEG fld1, NEG fld2) = same(fld1, fld2) | same (SUM(fld11, fld12), SUM(fld21, fld22)) = same(fld11, fld21) andalso same(fld12, fld22) (* hash value *) fun hash (CONV(img, kern)) = 0w3 * ImageInfo.hash img + Kernel.hash kern | hash (DIFF(k, fld)) = Word.fromInt k * 0w17 + hash fld | hash (NEG fld) = 0w3 * hash fld + 0w11 | hash (SUM(fld1, fld2)) = 0w7 * hash fld1 + hash fld2 end
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The output has ended thus: NEG fld) = 0w3 * hash fld + 0w11 | hash (SUM(fld1, fld2)) = 0w7 * hash fld1 + hash fld2 end