def filter_and_bbox(imageIn): from gamera.toolkits.ocr.classes import Page my_filter(imageIn) #p = StepTwo(imageIn) p = Page(imageIn) p.segment() return show_lines(p)
def __init__(self, img): self.img = img #cknn = knn.kNNInteractive([], ["aspect_ratio", "volume64regions", "moments", "nholes_extended"], 0) #cknn.from_xml_filename("x01/classifier-all-2/classifier_glyphs.xml") #if(opt.ccsfilter): # the_ccs = ccs #else: the_ccs = img.cc_analysis() self.median_cc = int(median([cc.nrows for cc in the_ccs])) #autogroup = ClassifyCCs(cknn) #autogroup.parts_to_group = 3 #autogroup.grouping_distance = max([2,median_cc / 8]) Page.__init__(self, img)#, classify_ccs=autogroup)
def __init__(self, img): self.img = img # cknn = knn.kNNInteractive([], ["aspect_ratio", "volume64regions", "moments", "nholes_extended"], 0) # cknn.from_xml_filename("x01/classifier-all-2/classifier_glyphs.xml") # if(opt.ccsfilter): # the_ccs = ccs # else: the_ccs = img.cc_analysis() self.median_cc = int(median([cc.nrows for cc in the_ccs])) # autogroup = ClassifyCCs(cknn) # autogroup.parts_to_group = 3 # autogroup.grouping_distance = max([2,median_cc / 8]) Page.__init__(self, img) # , classify_ccs=autogroup)
img = img.rotate(rotation,0) if opt.verbosity > 0: print "rotated with",rotation,"angle" if(opt.auto_group): cknn = knn.kNNInteractive([], ["aspect_ratio", "volume64regions", "moments", "nholes_extended"], 0) cknn.from_xml_filename(opt.trainfile) if(opt.ccsfilter): the_ccs = ccs else: the_ccs = img.cc_analysis() median_cc = int(median([cc.nrows for cc in the_ccs])) autogroup = ClassifyCCs(cknn) autogroup.parts_to_group = 3 autogroup.grouping_distance = max([2,median_cc / 8]) p = Page(img, classify_ccs=autogroup) if opt.verbosity > 0: print "autogrouping glyphs activated." print "maximal autogroup distance:", autogroup.grouping_distance else: p = Page(img) if opt.verbosity > 0: print "start page segmentation..." t = time.time() p.segment() if opt.verbosity > 0: t = time.time() - t print "\t segmentation done [",t,"sec]"
print "rotated with", rotation, "angle" if (opt.auto_group): cknn = knn.kNNInteractive( [], ["aspect_ratio", "volume64regions", "moments", "nholes_extended"], 0) cknn.from_xml_filename(opt.trainfile) if (opt.ccsfilter): the_ccs = ccs else: the_ccs = img.cc_analysis() median_cc = int(median([cc.nrows for cc in the_ccs])) autogroup = ClassifyCCs(cknn) autogroup.parts_to_group = 3 autogroup.grouping_distance = max([2, median_cc / 8]) p = Page(img, classify_ccs=autogroup) if opt.verbosity > 0: print "autogrouping glyphs activated." print "maximal autogroup distance:", autogroup.grouping_distance else: p = Page(img) if opt.verbosity > 0: print "start page segmentation..." t = time.time() p.segment() if opt.verbosity > 0: t = time.time() - t print "\t segmentation done [", t, "sec]"