Example #1
0
def compute_geomaps(fnames,shapedict,old_model,use_gt=1,size=32,debug=0,old_order=1):
    """Given a shape dictionary and an existing line geometry
    estimator, compute updated geometric maps for each entry
    in the shape dictionary."""
    if debug>0: gray(); ion()
    shape = (shapedict.k,size,size)
    bls = zeros(shape)
    xls = zeros(shape)
    count = 0
    for fno,fname in enumerate(fnames):
        if fno%20==0: print fno,fname,count
        if use_gt:
            # don't use lines with many capital letters for training because
            # they result in bad models
            gt = ocrolib.read_text(ocrolib.fvariant(fname,"txt","gt"))
            if len(re.sub(r'[^A-Z]','',gt))>=0.3*len(re.sub(r'[^a-z]','',gt)): continue
            if len(re.sub(r'[^0-9]','',gt))>=0.3*len(re.sub(r'[^a-z]','',gt)): continue
        image = 1-ocrolib.read_image_gray(fname)
        if debug>0 and fno%debug==0: clf(); subplot(411); imshow(image)
        try:
            blp,xlp = old_model.lineFit(image,order=old_order)
        except:
            traceback.print_exc()
            continue
        blimage = zeros(image.shape)
        h,w = image.shape
        for x in range(w): blimage[clip(int(polyval(blp,x)),0,h-1),x] = 1
        xlimage = zeros(image.shape)
        for x in range(w): xlimage[clip(int(polyval(xlp,x)),0,h-1),x] = 1
        if debug>0 and fno%debug==0: 
            subplot(413); imshow(xlimage+0.3*image)
            subplot(414); imshow(blimage+0.3*image)
        try: 
            seg = lineseg.ccslineseg(image)
        except: 
            continue
        if debug>0 and fno%debug==0: subplot(412); morph.showlabels(seg)
        shape = None
        for sub,transform,itransform_add in extract_chars(seg):
            if shape is None: shape = sub.shape
            assert sub.shape==shape
            count += 1
            best = shapedict.predict1(sub)
            bls[best] += transform(blimage)
            xls[best] += transform(xlimage)
        if debug==1: ginput(1,100)
        elif debug>1: ginput(1,0.01)
    for i in range(len(bls)): bls[i] *= bls[i].shape[1]*1.0/max(1e-6,sum(bls[i]))
    for i in range(len(xls)): xls[i] *= xls[i].shape[1]*1.0/max(1e-6,sum(xls[i]))
    return bls,xls
Example #2
0
 def allchars():
     count = 0
     for fno,fname in enumerate(fnames):
         if fno%20==0: print fno,fname,count
         image = 1-ocrolib.read_image_gray(fname)
         try:
             seg = lineseg.ccslineseg(image)
         except:
             traceback.print_exc()
             continue
         seg = morph.renumber_by_xcenter(seg)
         for e in extract_chars(seg):
             count += 1
             yield e
Example #3
0
 def allchars():
     count = 0
     for fno, fname in enumerate(fnames):
         if fno % 20 == 0: print fno, fname, count
         image = 1 - ocrolib.read_image_gray(fname)
         try:
             seg = lineseg.ccslineseg(image)
         except:
             traceback.print_exc()
             continue
         seg = morph.renumber_by_xcenter(seg)
         for e in extract_chars(seg):
             count += 1
             yield e
Example #4
0
def blxlimages(image,shapedict,bls,xls):
    image = (image>ocrolib.midrange(image))
    if amax(image)==0: raise RecognitionError("empty line")
    seg = lineseg.ccslineseg(image)
    # ion(); subplot(311); imshow(image); subplot(312); morph.showlabels(seg); ginput(1,0.1); raw_input()
    seg = morph.renumber_by_xcenter(seg)
    blimage = zeros(image.shape)
    xlimage = zeros(image.shape)
    for sub,transform,itransform_add in extract_chars(seg):
        best = shapedict.predict1(sub)
        bli = bls[best].reshape(32,32)
        xli = xls[best].reshape(32,32)
        itransform_add(blimage,bli)
        itransform_add(xlimage,xli)
    return blimage,xlimage
Example #5
0
def blxlimages(image, shapedict, bls, xls):
    image = (image > ocrolib.midrange(image))
    if amax(image) == 0: raise RecognitionError("empty line")
    seg = lineseg.ccslineseg(image)
    # ion(); subplot(311); imshow(image); subplot(312); morph.showlabels(seg); ginput(1,0.1); raw_input()
    seg = morph.renumber_by_xcenter(seg)
    blimage = zeros(image.shape)
    xlimage = zeros(image.shape)
    for sub, transform, itransform_add in extract_chars(seg):
        best = shapedict.predict1(sub)
        bli = bls[best].reshape(32, 32)
        xli = xls[best].reshape(32, 32)
        itransform_add(blimage, bli)
        itransform_add(xlimage, xli)
    return blimage, xlimage
Example #6
0
def compute_geomaps(fnames,
                    shapedict,
                    old_model,
                    use_gt=1,
                    size=32,
                    debug=0,
                    old_order=1):
    """Given a shape dictionary and an existing line geometry
    estimator, compute updated geometric maps for each entry
    in the shape dictionary."""
    if debug > 0:
        gray()
        ion()
    shape = (shapedict.k, size, size)
    bls = zeros(shape)
    xls = zeros(shape)
    count = 0
    for fno, fname in enumerate(fnames):
        if fno % 20 == 0: print fno, fname, count
        if use_gt:
            # don't use lines with many capital letters for training because
            # they result in bad models
            gt = ocrolib.read_text(ocrolib.fvariant(fname, "txt", "gt"))
            if len(re.sub(r'[^A-Z]', '',
                          gt)) >= 0.3 * len(re.sub(r'[^a-z]', '', gt)):
                continue
            if len(re.sub(r'[^0-9]', '',
                          gt)) >= 0.3 * len(re.sub(r'[^a-z]', '', gt)):
                continue
        image = 1 - ocrolib.read_image_gray(fname)
        if debug > 0 and fno % debug == 0:
            clf()
            subplot(411)
            imshow(image)
        try:
            blp, xlp = old_model.lineFit(image, order=old_order)
        except:
            traceback.print_exc()
            continue
        blimage = zeros(image.shape)
        h, w = image.shape
        for x in range(w):
            blimage[clip(int(polyval(blp, x)), 0, h - 1), x] = 1
        xlimage = zeros(image.shape)
        for x in range(w):
            xlimage[clip(int(polyval(xlp, x)), 0, h - 1), x] = 1
        if debug > 0 and fno % debug == 0:
            subplot(413)
            imshow(xlimage + 0.3 * image)
            subplot(414)
            imshow(blimage + 0.3 * image)
        try:
            seg = lineseg.ccslineseg(image)
        except:
            continue
        if debug > 0 and fno % debug == 0:
            subplot(412)
            morph.showlabels(seg)
        shape = None
        for sub, transform, itransform_add in extract_chars(seg):
            if shape is None: shape = sub.shape
            assert sub.shape == shape
            count += 1
            best = shapedict.predict1(sub)
            bls[best] += transform(blimage)
            xls[best] += transform(xlimage)
        if debug == 1: ginput(1, 100)
        elif debug > 1: ginput(1, 0.01)
    for i in range(len(bls)):
        bls[i] *= bls[i].shape[1] * 1.0 / max(1e-6, sum(bls[i]))
    for i in range(len(xls)):
        xls[i] *= xls[i].shape[1] * 1.0 / max(1e-6, sum(xls[i]))
    return bls, xls