ocropus-gpageseg

#!/usr/bin/env python

# TODO:
# ! add option for padding
# - fix occasionally missing page numbers
# - treat large h-whitespace as separator
# - handle overlapping candidates
# - use cc distance statistics instead of character scale
# - page frame detection
# - read and use text image segmentation mask
# - pick up stragglers
# ? laplacian as well

from __future__ import print_function

import argparse
import glob
import os
import os.path
import sys
import traceback
from multiprocessing import Pool

import numpy as np
from scipy.ndimage import measurements
from scipy.misc import imsave
from scipy.ndimage.filters import gaussian_filter,uniform_filter,maximum_filter

import ocrolib
from ocrolib import psegutils,morph,sl
from ocrolib.exceptions import OcropusException
from ocrolib.toplevel import *

parser = argparse.ArgumentParser(add_help=False)

# error checking
group_error_checking = parser.add_argument_group('error checking')
group_error_checking.add_argument('-n','--nocheck',action="store_true",
                    help="disable error checking on inputs")
# limits
group_error_checking.add_argument('--minscale',type=float,default=12.0,
                    help='minimum scale permitted, default: %(default)s')
group_error_checking.add_argument('--maxlines',type=float,default=300,
                    help='maximum # lines permitted, default: %(default)s')

# scale parameters
group_scale = parser.add_argument_group('scale parameters')
group_scale.add_argument('--scale',type=float,default=0.0,
                    help='the basic scale of the document (roughly, xheight) 0=automatic, default: %(default)s')
group_scale.add_argument('--hscale',type=float,default=1.0,
                    help='non-standard scaling of horizontal parameters, default: %(default)s')
group_scale.add_argument('--vscale',type=float,default=1.0,
                    help='non-standard scaling of vertical parameters, default: %(default)s')

# line parameters
group_line = parser.add_argument_group('line parameters')
group_line.add_argument('--threshold',type=float,default=0.2,
                    help='baseline threshold, default: %(default)s')
group_line.add_argument('--noise',type=int,default=8,
                    help="noise threshold for removing small components from lines, default: %(default)s")
group_line.add_argument('--usegauss',action='store_true',
                    help='use gaussian instead of uniform, default: %(default)s')

# column parameters
group_column = parser.add_argument_group('column parameters')
group_column.add_argument('--maxseps',type=int,default=0,
                    help='maximum black column separators, default: %(default)s')
group_column.add_argument('--sepwiden',type=int,default=10,
                    help='widen black separators (to account for warping), default: %(default)s')
# Obsolete parameter for 'also check for black column separators'
# which can now be triggered simply by a positive maxseps value.
group_column.add_argument('-b','--blackseps',action="store_true",
                    help=argparse.SUPPRESS)

# whitespace column separators
group_column.add_argument('--maxcolseps',type=int,default=3,
                    help='maximum # whitespace column separators, default: %(default)s')
group_column.add_argument('--csminheight',type=float,default=10,
                    help='minimum column height (units=scale), default: %(default)s')
# Obsolete parameter for the 'minimum aspect ratio for column separators'
# used in the obsolete function compute_colseps_morph
group_column.add_argument('--csminaspect',type=float,default=1.1,
                    help=argparse.SUPPRESS)

# output parameters
group_output = parser.add_argument_group('output parameters')
group_output.add_argument('--gray',action='store_true',
                    help='output grayscale lines as well, default: %(default)s')
group_output.add_argument('-p','--pad',type=int,default=3,
                    help='padding for extracted lines, default: %(default)s')
group_output.add_argument('-e','--expand',type=int,default=3,
                    help='expand mask for grayscale extraction, default: %(default)s')

# other parameters
group_others = parser.add_argument_group('others')
group_others.add_argument('-q','--quiet',action='store_true',
                    help='be less verbose, default: %(default)s')
group_others.add_argument('-Q','--parallel',type=int,default=0,
                    help="number of CPUs to use")
group_others.add_argument('-d','--debug',action="store_true")
group_others.add_argument("-h", "--help", action="help", help="show this help message and exit")

# input files
parser.add_argument('files',nargs='+')

args = parser.parse_args()
args.files = ocrolib.glob_all(args.files)

def find(condition):
    "Return the indices where ravel(condition) is true"
    res, = np.nonzero(np.ravel(condition))
    return res
    
def norm_max(v):
    return v/np.amax(v)


def check_page(image):
    if len(image.shape)==3: return "input image is color image %s"%(image.shape,)
    if np.mean(image)<np.median(image): return "image may be inverted"
    h,w = image.shape
    if h<600: return "image not tall enough for a page image %s"%(image.shape,)
    if h>10000: return "image too tall for a page image %s"%(image.shape,)
    if w<600: return "image too narrow for a page image %s"%(image.shape,)
    if w>10000: return "line too wide for a page image %s"%(image.shape,)
    slots = int(w*h*1.0/(30*30))
    _,ncomps = measurements.label(image>np.mean(image))
    if ncomps<10: return "too few connected components for a page image (got %d)"%(ncomps,)
    if ncomps>slots: return "too many connnected components for a page image (%d > %d)"%(ncomps,slots)
    return None


def print_info(*objs):
    print("INFO: ", *objs, file=sys.stdout)


def print_error(*objs):
    print("ERROR: ", *objs, file=sys.stderr)


if len(args.files)<1:
    parser.print_help()
    sys.exit(0)

print_info("")
print_info("#"*10,(" ".join(sys.argv))[:60])
print_info("")

if args.parallel>1:
    args.quiet = 1


def B(a):
    if a.dtype==np.dtype('B'): return a
    return np.array(a,'B')


def DSAVE(title,image):
    if not args.debug: return
    if type(image)==list:
        assert len(image)==3
        image = np.transpose(np.array(image),[1,2,0])
    fname = "_"+title+".png"
    print_info("debug " + fname)
    imsave(fname,image)


################################################################
### Column finding.
###
### This attempts to find column separators, either as extended
### vertical black lines or extended vertical whitespace.
### It will work fairly well in simple cases, but for unusual
### documents, you need to tune the parameters.
################################################################

def compute_separators_morph(binary,scale):
    """Finds vertical black lines corresponding to column separators."""
    d0 = int(max(5,scale/4))
    d1 = int(max(5,scale))+args.sepwiden
    thick = morph.r_dilation(binary,(d0,d1))
    vert = morph.rb_opening(thick,(10*scale,1))
    vert = morph.r_erosion(vert,(d0//2,args.sepwiden))
    vert = morph.select_regions(vert,sl.dim1,min=3,nbest=2*args.maxseps)
    vert = morph.select_regions(vert,sl.dim0,min=20*scale,nbest=args.maxseps)
    return vert


def compute_colseps_morph(binary,scale,maxseps=3,minheight=20,maxwidth=5):
    """Finds extended vertical whitespace corresponding to column separators
    using morphological operations."""
    boxmap = psegutils.compute_boxmap(binary,scale,dtype='B')
    bounds = morph.rb_closing(B(boxmap),(int(5*scale),int(5*scale)))
    bounds = np.maximum(B(1-bounds),B(boxmap))
    cols = 1-morph.rb_closing(boxmap,(int(20*scale),int(scale)))
    cols = morph.select_regions(cols,sl.aspect,min=args.csminaspect)
    cols = morph.select_regions(cols,sl.dim0,min=args.csminheight*scale,nbest=args.maxcolseps)
    cols = morph.r_erosion(cols,(int(0.5+scale),0))
    cols = morph.r_dilation(cols,(int(0.5+scale),0),origin=(int(scale/2)-1,0))
    return cols


def compute_colseps_mconv(binary,scale=1.0):
    """Find column separators using a combination of morphological
    operations and convolution."""
    h,w = binary.shape
    smoothed = gaussian_filter(1.0*binary,(scale,scale*0.5))
    smoothed = uniform_filter(smoothed,(5.0*scale,1))
    thresh = (smoothed<np.amax(smoothed)*0.1)
    DSAVE("1thresh",thresh)
    blocks = morph.rb_closing(binary,(int(4*scale),int(4*scale)))
    DSAVE("2blocks",blocks)
    seps = np.minimum(blocks,thresh)
    seps = morph.select_regions(seps,sl.dim0,min=args.csminheight*scale,nbest=args.maxcolseps)
    DSAVE("3seps",seps)
    blocks = morph.r_dilation(blocks,(5,5))
    DSAVE("4blocks",blocks)
    seps = np.maximum(seps,1-blocks)
    DSAVE("5combo",seps)
    return seps


def compute_colseps_conv(binary,scale=1.0):
    """Find column separators by convoluation and
    thresholding."""
    h,w = binary.shape
    # find vertical whitespace by thresholding
    smoothed = gaussian_filter(1.0*binary,(scale,scale*0.5))
    smoothed = uniform_filter(smoothed,(5.0*scale,1))
    thresh = (smoothed<np.amax(smoothed)*0.1)
    DSAVE("1thresh",thresh)
    # find column edges by filtering
    grad = gaussian_filter(1.0*binary,(scale,scale*0.5),order=(0,1))
    grad = uniform_filter(grad,(10.0*scale,1))
    # grad = abs(grad) # use this for finding both edges
    grad = (grad>0.5*np.amax(grad))
    DSAVE("2grad",grad)
    # combine edges and whitespace
    seps = np.minimum(thresh,maximum_filter(grad,(int(scale),int(5*scale))))
    seps = maximum_filter(seps,(int(2*scale),1))
    DSAVE("3seps",seps)
    # select only the biggest column separators
    seps = morph.select_regions(seps,sl.dim0,min=args.csminheight*scale,nbest=args.maxcolseps)
    DSAVE("4seps",seps)
    return seps


def compute_colseps(binary,scale):
    """Computes column separators either from vertical black lines or whitespace."""
    print_info("considering at most %g whitespace column separators" % args.maxcolseps)
    colseps = compute_colseps_conv(binary,scale)
    DSAVE("colwsseps",0.7*colseps+0.3*binary)
    if args.blackseps and args.maxseps == 0:
        # simulate old behaviour of blackseps when the default value
        # for maxseps was 2, but only when the maxseps-value is still zero
        # and not set manually to a non-zero value
        args.maxseps = 2
    if args.maxseps > 0:
        print_info("considering at most %g black column separators" % args.maxseps)
        seps = compute_separators_morph(binary,scale)
        DSAVE("colseps",0.7*seps+0.3*binary)
        #colseps = compute_colseps_morph(binary,scale)
        colseps = np.maximum(colseps,seps)
        binary = np.minimum(binary,1-seps)
    return colseps,binary


################################################################
### Text Line Finding.
###
### This identifies the tops and bottoms of text lines by
### computing gradients and performing some adaptive thresholding.
### Those components are then used as seeds for the text lines.
################################################################

def compute_gradmaps(binary,scale):
    # use gradient filtering to find baselines
    boxmap = psegutils.compute_boxmap(binary,scale)
    cleaned = boxmap*binary
    DSAVE("cleaned",cleaned)
    if args.usegauss:
        # this uses Gaussians
        grad = gaussian_filter(1.0*cleaned,(args.vscale*0.3*scale,
                                            args.hscale*6*scale),order=(1,0))
    else:
        # this uses non-Gaussian oriented filters
        grad = gaussian_filter(1.0*cleaned,(max(4,args.vscale*0.3*scale),
                                            args.hscale*scale),order=(1,0))
        grad = uniform_filter(grad,(args.vscale,args.hscale*6*scale))
    bottom = ocrolib.norm_max((grad<0)*(-grad))
    top = ocrolib.norm_max((grad>0)*grad)
    return bottom,top,boxmap


def compute_line_seeds(binary,bottom,top,colseps,scale):
    """Base on gradient maps, computes candidates for baselines
    and xheights.  Then, it marks the regions between the two
    as a line seed."""
    t = args.threshold
    vrange = int(args.vscale*scale)
    bmarked = maximum_filter(bottom==maximum_filter(bottom,(vrange,0)),(2,2))
    bmarked = bmarked*(bottom>t*np.amax(bottom)*t)*(1-colseps)
    tmarked = maximum_filter(top==maximum_filter(top,(vrange,0)),(2,2))
    tmarked = tmarked*(top>t*np.amax(top)*t/2)*(1-colseps)
    tmarked = maximum_filter(tmarked,(1,20))
    seeds = np.zeros(binary.shape,'i')
    delta = max(3,int(scale/2))
    for x in range(bmarked.shape[1]):
        transitions = sorted([(y,1) for y in find(bmarked[:,x])]+[(y,0) for y in find(tmarked[:,x])])[::-1]
        transitions += [(0,0)]
        for l in range(len(transitions)-1):
            y0,s0 = transitions[l]
            if s0==0: continue
            seeds[y0-delta:y0,x] = 1
            y1,s1 = transitions[l+1]
            if s1==0 and (y0-y1)<5*scale: seeds[y1:y0,x] = 1
    seeds = maximum_filter(seeds,(1,int(1+scale)))
    seeds = seeds*(1-colseps)
    DSAVE("lineseeds",[seeds,0.3*tmarked+0.7*bmarked,binary])
    seeds,_ = morph.label(seeds)
    return seeds


################################################################
### The complete line segmentation process.
################################################################

def remove_hlines(binary,scale,maxsize=10):
    labels,_ = morph.label(binary)
    objects = morph.find_objects(labels)
    for i,b in enumerate(objects):
        if sl.width(b)>maxsize*scale:
            labels[b][labels[b]==i+1] = 0
    return np.array(labels!=0,'B')


def compute_segmentation(binary,scale):
    """Given a binary image, compute a complete segmentation into
    lines, computing both columns and text lines."""
    binary = np.array(binary,'B')

    # start by removing horizontal black lines, which only
    # interfere with the rest of the page segmentation
    binary = remove_hlines(binary,scale)

    # do the column finding
    if not args.quiet: print_info("computing column separators")
    colseps,binary = compute_colseps(binary,scale)

    # now compute the text line seeds
    if not args.quiet: print_info("computing lines")
    bottom,top,boxmap = compute_gradmaps(binary,scale)
    seeds = compute_line_seeds(binary,bottom,top,colseps,scale)
    DSAVE("seeds",[bottom,top,boxmap])

    # spread the text line seeds to all the remaining
    # components
    if not args.quiet: print_info("propagating labels")
    llabels = morph.propagate_labels(boxmap,seeds,conflict=0)
    if not args.quiet: print_info("spreading labels")
    spread = morph.spread_labels(seeds,maxdist=scale)
    llabels = np.where(llabels>0,llabels,spread*binary)
    segmentation = llabels*binary
    return segmentation


################################################################
### Processing each file.
################################################################

def process1(job):
    fname,i = job
    global base
    base,_ = ocrolib.allsplitext(fname)
    outputdir = base

    try:
        binary = ocrolib.read_image_binary(base+".bin.png")
    except IOError:
        try:
            binary = ocrolib.read_image_binary(fname)
        except IOError:
            if ocrolib.trace: traceback.print_exc()
            print_error("cannot open either %s.bin.png or %s" % (base, fname))
            return

    checktype(binary,ABINARY2)

    if not args.nocheck:
        check = check_page(np.amax(binary)-binary)
        if check is not None:
            print_error("%s SKIPPED %s (use -n to disable this check)" % (fname, check))
            return

    if args.gray:
        if os.path.exists(base+".nrm.png"):
            gray = ocrolib.read_image_gray(base+".nrm.png")
        checktype(gray,GRAYSCALE)

    binary = 1-binary # invert

    if args.scale==0:
        scale = psegutils.estimate_scale(binary)
    else:
        scale = args.scale
    print_info("scale %f" % (scale))
    if np.isnan(scale) or scale>1000.0:
        print_error("%s: bad scale (%g); skipping\n" % (fname, scale))
        return
    if scale<args.minscale:
        print_error("%s: scale (%g) less than --minscale; skipping\n" % (fname, scale))
        return

    # find columns and text lines

    if not args.quiet: print_info("computing segmentation")
    segmentation = compute_segmentation(binary,scale)
    if np.amax(segmentation)>args.maxlines:
        print_error("%s: too many lines %g" % (fname, np.amax(segmentation)))
        return
    if not args.quiet: print_info("number of lines %g" % np.amax(segmentation))

    # compute the reading order

    if not args.quiet: print_info("finding reading order")
    lines = psegutils.compute_lines(segmentation,scale)
    order = psegutils.reading_order([l.bounds for l in lines])
    lsort = psegutils.topsort(order)

    # renumber the labels so that they conform to the specs

    nlabels = np.amax(segmentation)+1
    renumber = np.zeros(nlabels,'i')
    for i,v in enumerate(lsort): renumber[lines[v].label] = 0x010000+(i+1)
    segmentation = renumber[segmentation]

    # finally, output everything

    if not args.quiet: print_info("writing lines")
    if not os.path.exists(outputdir):
        os.mkdir(outputdir)
    lines = [lines[i] for i in lsort]
    ocrolib.write_page_segmentation("%s.pseg.png"%outputdir,segmentation)
    cleaned = ocrolib.remove_noise(binary,args.noise)
    for i,l in enumerate(lines):
        binline = psegutils.extract_masked(1-cleaned,l,pad=args.pad,expand=args.expand)
        ocrolib.write_image_binary("%s/01%04x.bin.png"%(outputdir,i+1),binline)
        if args.gray:
            grayline = psegutils.extract_masked(gray,l,pad=args.pad,expand=args.expand)
            ocrolib.write_image_gray("%s/01%04x.nrm.png"%(outputdir,i+1),grayline)
    print_info("%6d  %s %4.1f %d" % (i, fname,  scale,  len(lines)))

if len(args.files)==1 and os.path.isdir(args.files[0]):
    files = glob.glob(args.files[0]+"/????.png")
else:
    files = args.files


def safe_process1(job):
    fname,i = job
    try:
        process1(job)
    except OcropusException as e:
        if e.trace:
            traceback.print_exc()
        else:
            print_info(fname+":"+e)
    except Exception as e:
        traceback.print_exc()

if args.parallel<2:
    count = 0
    for i,f in enumerate(files):
        if args.parallel==0: print_info(f)
        count += 1
        safe_process1((f,i+1))
else:
    pool = Pool(processes=args.parallel)
    jobs = []
    for i,f in enumerate(files): jobs += [(f,i+1)]
    result = pool.map(process1,jobs)