1. A method for automatic diagnostics of images related to pantographs, comprising the steps of: a) capturing, using an image capture apparatus located in one of a plurality of image acquisition sites, an image that shows a pantograph of a locomotive, said image being taken from an aerial view during the travel of said locomotive, said image comprising a gliding area of a plurality of slippers of said pantograph; b) identifying, using a module for classifying the pantograph model, a model of said pantograph within a plurality of pantograph models, based on said image captured in step a); c) determining, using a module for classifying materials, a material of which said slippers are composed among a plurality of materials, based on said pantograph model identified in step b); d) determining, using a module for classifying degree of wear, a value related to state of wear for each one of said plurality of said slippers, based on said type of material determined in step c); and e) determining, using a module for analyzing end portions, whether the end portions of said pantograph are damaged, based on a plurality of portions of said image extracted from said image, each portion containing the image of said gliding area of one of said slippers; wherein the step e) comprises the steps of: mm) applying an inverse of a first inverse perspective mapping (IPM) transformation matrix to an image that comprises said plurality of portions of said image, to generate a first image in which said gliding areas are shown in the original perspective; nn) applying to said first image calculated in step mm) a second IPM transformation matrix, to generate a second image in which the ends of said gliding areas are displayed in a front perspective; oo) isolating a plurality of portions of said second image, each comprising an end seen in a front position; pp) applying an edge detection filter to each one of said plurality of portions isolated in step oo); qq) binarizing with Otsu's method each one of said plurality of portions filtered in step pp); rr) removing, from each one of said plurality of portions binarized in step qq), conglomerates that are not consistent in terms of dimensions and/or form factor; ss) calculating, for each one of said plurality of portions calculated in step rr), a one-dimensional profile based on the columns of the pixels of said portion; tt) measuring, for each one of said one-dimensional profiles calculated in step ss), a degree of match with each one of a plurality of end models stored in the system; uu) applying a Hough transform to each one of said plurality of portions calculated in step rr); vv) calculating a difference between the images generated in step uu) in the space of said Hough transform; ww) generating a characteristics vector, said vector comprising the values generated in steps tt) and vv); and xx) applying a classifying system to said characteristics vector generated in step ww), to establish a degree of damage of said ends.