diff --git a/OLD/noisegen.ipynb b/OLD/noisegen.ipynb new file mode 100644 index 00000000..d3748662 --- /dev/null +++ b/OLD/noisegen.ipynb @@ -0,0 +1,246 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n" + ] + } + ], + "source": [ + "%pylab inline" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "float32\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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6fI/z0A3Uhuh2gW/TCNXxlY8t+GtBMn271FR69Zw8n3OaS1P0mfjnaSLybyJy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\nXrBo/D+/22v1TtrJywAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "page = mean(imread(\"page.png\"), 2)\n", + "page -= amin(page)\n", + "page /= amax(page)\n", + "print page.dtype\n", + "imshow(page)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+7jh7ZTgAcI6q15KY76qmSkdNaNQBxACpprfngZMgUiP7I/v9lAD/FzJl/SRG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QAAAg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\nPRR3NNMY1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0pD++UtJ36dS2VZXgYc9Ui+GVA10Q+BIbr0WbJCNnC5JElNAQewUJblo65B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HvzBReMmf8ElbZfnqFZQOiPLSkP2qOSJrSUfS71ok00hwNGlkLeko1+EptOS74eD9KlOjVMef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\nqI+mWebjXX/jdf+0VMt8nz3S1e9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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "figsize(10,10)\n", + "from scipy.ndimage import interpolation\n", + "\n", + "def rgeometry(image,eps=0.03,delta=0.3):\n", + " m = array([[1+eps*randn(),0.0],[eps*randn(),1.0+eps*randn()]])\n", + " w,h = image.shape\n", + " c = array([w/2.0,h/2])\n", + " d = c-dot(m,c)+array([randn()*delta,randn()*delta])\n", + " return interpolation.affine_transform(image,m,offset=d,order=1,mode='constant',cval=image[0,0])\n", + "\n", + "for i in range(9):\n", + " subplot(3,3,i+1)\n", + " imshow(rgeometry(page))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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a24NbaaRA9xAdrVtXK4/woMdR4tEF99ztcOJd8Uy+8EFhKM+XVkSqey9S8snG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\nM7WDZsr77JwyRrOOhEI8JXdNUKq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uKpf2rM9aSGPaN8rBeMohEri8zIMcn6hMiOyxIpV1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\n9Mx5JtQMxAATLMGOTHPI4XfGW3ahg7EFQ4j7r4sU+Wzrz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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from scipy.ndimage import filters\n", + "\n", + "def rdistort(image,distort=3.0,dsigma=10.0,cval=0):\n", + " h,w = image.shape\n", + " hs = randn(h,w)\n", + " ws = randn(h,w)\n", + " hs = filters.gaussian_filter(hs,dsigma)\n", + " ws = filters.gaussian_filter(ws,dsigma)\n", + " hs *= distort/amax(hs)\n", + " ws *= distort/amax(ws)\n", + " def f(p):\n", + " return (p[0]+hs[p[0],p[1]],p[1]+ws[p[0],p[1]])\n", + " return interpolation.geometric_transform(image,f,output_shape=(h,w),\n", + " order=1,mode='constant',cval=cval)\n", + "\n", + "for i in range(2):\n", + " subplot(1,2,i+1)\n", + " imshow(rdistort(page, distort=100.0, dsigma=100.0))" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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K+KS7/0Wx3i3A14CHgN9z9783s63Aue7+imKbXwTOdPdX1e17XRPpVj2K0KRy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\nxwM7gMPTRgzAt4DD0//XAd/MPG13mlaWXrSfzcBmgKPWtZ9G0zRaZxZ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def rnoise(image, threshold=0.5, sigma=1.0, strength=0.1, nmax=0.1):\n", + " a = asarray(image,'f')\n", + " a = a*1.0/amax(a)\n", + " if sigma>0.0:\n", + " a = filters.gaussian_filter(a,sigma)\n", + " a += clip(randn(*a.shape)*strength,-nmax, nmax)\n", + " a = array(a>threshold,'f')\n", + " return a\n", + "\n", + "noisy = rnoise(page)\n", + "subplot(221)\n", + "imshow(page)\n", + "subplot(222)\n", + "imshow(page[200:300,200:300])\n", + "subplot(223)\n", + "imshow(noisy)\n", + "subplot(224)\n", + "imshow(noisy[200:300,200:300])" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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f0aOx5QdGi5gAlhET2DTBoQrM7AxJl0s6X9Ijkv5c0otiN+DuByQdkGZ3FM2K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\naauUc9JXCxBLC+WcT2ifIU5R6gGY+/IcYiv2wuw7t9qMVd9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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def rnoisex(image, density=1.0, threshold=0.5, sigma=1.0, strength=0.1, nmax=0.1):\n", + " a = asarray(image,'f')\n", + " a = a*1.0/amax(a)\n", + " orig = sum(a>threshold)\n", + " if sigma>0.0:\n", + " a = filters.gaussian_filter(a,sigma)\n", + " a += clip(randn(*a.shape)*strength,-nmax, nmax)\n", + " thresholds = linspace(0.0, 1.0, 50)\n", + " sums = array([sum(a>t) for t in thresholds])\n", + " best = argmin(abs(density*orig-sums))\n", + " a = array(a>thresholds[best],'f')\n", + " return a\n", + "\n", + "for i,t in enumerate(linspace(0.8, 1.2, 9)):\n", + " subplot(3,3,i+1)\n", + " title(\"%s\" % t)\n", + " imshow(rnoisex(page, density=t)[200:300,200:300])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/ocrolib/common.py b/ocrolib/common.py index 27c0f26e..614add07 100644 --- a/ocrolib/common.py +++ b/ocrolib/common.py @@ -279,7 +279,6 @@ def read_page_segmentation(fname): segmentation = make_seg_black(segmentation) return segmentation -@checks(str,PAGESEG) def write_page_segmentation(fname,image): """Writes a page segmentation, that is an RGB image whose values encode the segmentation of a page.""" diff --git a/ocrolib/lstm.py b/ocrolib/lstm.py index 64184d11..bf10ab56 100644 --- a/ocrolib/lstm.py +++ b/ocrolib/lstm.py @@ -380,7 +380,8 @@ def weights(self): def ffunc(x): "Nonlinearity used for gates." - return 1.0/(1.0+exp(-x)) + # cliping to avoid overflows + return 1.0/(1.0+exp(clip(-x,-20,20))) def fprime(x,y=None): "Derivative of nonlinearity used for gates." if y is None: y = sigmoid(x) diff --git a/ocropus-dewarp b/ocropus-dewarp index fbd9907c..50923e3a 100755 --- a/ocropus-dewarp +++ b/ocropus-dewarp @@ -2,20 +2,17 @@ from __future__ import print_function -import random as pyrandom import re -from pylab import * -import os.path -import ocrolib import argparse -import matplotlib -import numpy +import sys + +import numpy as np +from scipy.misc import imsave + +import ocrolib from ocrolib import lineest -import ocrolib.lstm as lstm -import traceback -import scipy -numpy.seterr(divide='raise',over='raise',invalid='raise',under='ignore') +np.seterr(divide='raise',over='raise',invalid='raise',under='ignore') parser = argparse.ArgumentParser("dewarp lines as used for the recognizer") @@ -36,9 +33,9 @@ if len(inputs)==0: print("# inputs", len(inputs)) if args.lineest=="center": - lnorm = lineest.CenterNormalizer() + lnorm = lineest.CenterNormalizer() else: - raise Exception(args.lineest+": unknown line normalizer") + raise Exception(args.lineest+": unknown line normalizer") lnorm.setHeight(args.height) @@ -47,9 +44,9 @@ for fname in inputs: base,_ = ocrolib.allsplitext(fname) try: line = ocrolib.read_image_gray(fname) - lnorm.measure(amax(line)-line) - line = lnorm.normalize(line,cval=amax(line)) - scipy.misc.imsave(base+".dew.png",line) + lnorm.measure(np.amax(line)-line) + line = lnorm.normalize(line,cval=np.amax(line)) + imsave(base+".dew.png",line) except Exception as e: print("ERROR", e) continue diff --git a/ocropus-econf b/ocropus-econf index 2b3c68d0..58b62909 100755 --- a/ocropus-econf +++ b/ocropus-econf @@ -3,14 +3,21 @@ from __future__ import print_function -import warnings,numpy,argparse,sys,os,os.path,multiprocessing,codecs +import warnings +import argparse +import sys +import os.path +import multiprocessing +import codecs from collections import Counter + +import numpy as np + import ocrolib -from pylab import * from ocrolib import edist # disable rank warnings from polyfit -warnings.simplefilter('ignore',numpy.RankWarning) +warnings.simplefilter('ignore',np.RankWarning) parser = argparse.ArgumentParser(description = """ Compute the edit distances between ground truth and recognizer output. diff --git a/ocropus-errs b/ocropus-errs index abf6af0a..786a4ed8 100755 --- a/ocropus-errs +++ b/ocropus-errs @@ -3,7 +3,12 @@ from __future__ import print_function -import argparse,sys,os,os.path,multiprocessing +import argparse +import sys +import os +import os.path +import multiprocessing + import ocrolib from ocrolib import edist @@ -27,6 +32,7 @@ args.files = ocrolib.glob_all(args.files) if not ".gt." in args.files[0]: sys.stderr.write("warning: compare on .gt.txt files, not .txt files\n") + def process1(fname): # fgt = ocrolib.allsplitext(fname)[0]+args.gtextension gt = ocrolib.project_text(ocrolib.read_text(fname),kind=args.kind) diff --git a/ocropus-gpageseg b/ocropus-gpageseg index c89bef66..9c379283 100755 --- a/ocropus-gpageseg +++ b/ocropus-gpageseg @@ -13,15 +13,20 @@ from __future__ import print_function +import argparse +import glob +import os +import os.path import sys -from pylab import * -import argparse,glob,os,os.path import traceback import codecs +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 -from multiprocessing import Pool + import ocrolib from ocrolib import psegutils,morph,sl from ocrolib.exceptions import OcropusException @@ -106,18 +111,25 @@ 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/amax(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 mean(image)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>mean(image)) + _,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 @@ -126,6 +138,7 @@ def check_page(image): def print_info(*objs): print("INFO: ", " ".join(objs)) + def print_error(*objs): print("ERROR: ", " ".join(objs), file=sys.stderr) @@ -141,21 +154,22 @@ print_info("") if args.parallel>1: args.quiet = 1 + def B(a): - if a.dtype==dtype('B'): return a - return array(a,'B') + 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 = transpose(array(image),[1,2,0]) + image = np.transpose(np.array(image),[1,2,0]) fname = "_"+title+".png" print_info("debug " + fname) imsave(fname,image) - ################################################################ ### Column finding. ### @@ -176,12 +190,13 @@ def compute_separators_morph(binary,scale): 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 = maximum(B(1-bounds),B(boxmap)) + 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) @@ -189,25 +204,27 @@ def compute_colseps_morph(binary,scale,maxseps=3,minheight=20,maxwidth=5): 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 = (smoothed0.5*amax(grad)) + grad = (grad>0.5*np.amax(grad)) DSAVE("2grad",grad) # combine edges and whitespace - seps = minimum(thresh,maximum_filter(grad,(int(scale),int(5*scale)))) + 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 @@ -232,6 +249,7 @@ def compute_colseps_conv(binary,scale=1.0): 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) @@ -247,12 +265,11 @@ def compute_colseps(binary,scale): seps = compute_separators_morph(binary,scale) DSAVE("colseps",0.7*seps+0.3*binary) #colseps = compute_colseps_morph(binary,scale) - colseps = maximum(colseps,seps) - binary = minimum(binary,1-seps) + colseps = np.maximum(colseps,seps) + binary = np.minimum(binary,1-seps) return colseps,binary - ################################################################ ### Text Line Finding. ### @@ -279,6 +296,7 @@ def compute_gradmaps(binary,scale): 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 @@ -286,11 +304,11 @@ def compute_line_seeds(binary,bottom,top,colseps,scale): t = args.threshold vrange = int(args.vscale*scale) bmarked = maximum_filter(bottom==maximum_filter(bottom,(vrange,0)),(2,2)) - bmarked = bmarked*(bottom>t*amax(bottom)*t)*(1-colseps) + 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*amax(top)*t/2)*(1-colseps) + tmarked = tmarked*(top>t*np.amax(top)*t/2)*(1-colseps) tmarked = maximum_filter(tmarked,(1,20)) - seeds = zeros(binary.shape,'i') + 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] @@ -308,7 +326,6 @@ def compute_line_seeds(binary,bottom,top,colseps,scale): return seeds - ################################################################ ### The complete line segmentation process. ################################################################ @@ -319,12 +336,13 @@ def remove_hlines(binary,scale,maxsize=10): for i,b in enumerate(objects): if sl.width(b)>maxsize*scale: labels[b][labels[b]==i+1] = 0 - return array(labels!=0,'B') + 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 = array(binary,'B') + binary = np.array(binary,'B') # start by removing horizontal black lines, which only # interfere with the rest of the page segmentation @@ -346,12 +364,11 @@ def compute_segmentation(binary,scale): llabels = morph.propagate_labels(boxmap,seeds,conflict=0) if not args.quiet: print_info("spreading labels") spread = morph.spread_labels(seeds,maxdist=scale) - llabels = where(llabels>0,llabels,spread*binary) + llabels = np.where(llabels>0,llabels,spread*binary) segmentation = llabels*binary return segmentation - ################################################################ ### Processing each file. ################################################################ @@ -375,7 +392,7 @@ def process1(job): checktype(binary,ABINARY2) if not args.nocheck: - check = check_page(amax(binary)-binary) + 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 @@ -392,7 +409,7 @@ def process1(job): else: scale = args.scale print_info("scale %f" % (scale)) - if isnan(scale) or scale>1000.0: + if np.isnan(scale) or scale>1000.0: print_error("%s: bad scale (%g); skipping\n" % (fname, scale)) return if scaleargs.maxlines: - print_error("%s: too many lines %g" % (fname, amax(segmentation))) + 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" % amax(segmentation)) + if not args.quiet: print_info("number of lines %g" % np.amax(segmentation)) # compute the reading order @@ -417,8 +434,8 @@ def process1(job): # renumber the labels so that they conform to the specs - nlabels = amax(segmentation)+1 - renumber = zeros(nlabels,'i') + 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] @@ -443,6 +460,7 @@ if len(args.files)==1 and os.path.isdir(args.files[0]): else: files = args.files + def safe_process1(job): fname,i = job try: diff --git a/ocropus-gtedit b/ocropus-gtedit index 9e35ee03..4b1999af 100755 --- a/ocropus-gtedit +++ b/ocropus-gtedit @@ -2,11 +2,17 @@ from __future__ import print_function -from pylab import * -import argparse,codecs,re,os.path,ocrolib,base64 -from lxml import etree +import argparse +import codecs +import re +import os.path +import base64 import urllib2 +from lxml import etree + +import ocrolib + parser = argparse.ArgumentParser() subparsers = parser.add_subparsers(help="subcommands",dest="subparser_name") diff --git a/ocropus-hocr b/ocropus-hocr index fb62869e..43b7222f 100755 --- a/ocropus-hocr +++ b/ocropus-hocr @@ -2,8 +2,15 @@ import __builtin__ as python import random as pyrandom -import sys,os,re,glob,argparse,codecs -from pylab import median, imread +import sys +import os.path +import re +import glob +import argparse +import codecs + +import numpy as np +from matplotlib.pyplot import imread import ocrolib from ocrolib import hocr @@ -47,12 +54,12 @@ xhfiles = python.sum([glob.glob(d+"/??????.xheight") for d in dirs],[]) if len(xhfiles)>5: xheights = [float(ocrolib.read_text(f)) for f in xhfiles] if len(xheights)>0: - median_xheight = median(xheights) + median_xheight = np.median(xheights) else: lfiles = python.sum([glob.glob(d+"/??????.bin.png") for d in dirs],[]) pyrandom.shuffle(lfiles) if len(lfiles)>0: - median_xheight = 0.5*median([imread(f).shape[0] for f in lfiles[:100]]) + median_xheight = 0.5*np.median([imread(f).shape[0] for f in lfiles[:100]]) E("median_xheight",median_xheight) P(hocr.header()) @@ -127,7 +134,7 @@ for arg in args.files: if median_xheight is not None and os.path.exists(lbase+".xheight"): xheight = float(ocrolib.read_text(lbase+".xheight")) - perc = int(clip(xheight*100.0/median_xheight,30,300)) + perc = int(np.clip(xheight*100.0/median_xheight,30,300)) perc = 10*((perc+5)//10) if perc!=100: style += "font-size:%d%%;"%perc diff --git a/ocropus-linegen b/ocropus-linegen index 901093d9..46320e36 100755 --- a/ocropus-linegen +++ b/ocropus-linegen @@ -4,15 +4,23 @@ from __future__ import print_function import random as pyrandom -import glob,sys,os,re,codecs,traceback -from pylab import * +import glob +import sys +import os +import re +import codecs +import traceback +import argparse + +import numpy as np +import matplotlib.pyplot as plt from PIL import Image from PIL import ImageFont,ImageDraw from scipy.ndimage import filters,measurements,interpolation from scipy.misc import imsave + import ocrolib -import argparse parser = argparse.ArgumentParser(description = "Generate text line training data") parser.add_argument('-o','--base',default='linegen',help='output directory, default: %(default)s') parser.add_argument('-r','--distort',type=float,default=1.0) @@ -80,7 +88,9 @@ if args.fonts is not None: if pat=="": continue fonts += sorted(glob.glob(pat)) elif args.fontlist is not None: - fonts = re.split(r'\s*\n\s*',open(args.fontlist).read()) + with open(args.fontlist) as fh: + lines = (line.strip() for line in fh) + fonts = [line for line in lines if line] else: print("use -f or -F arguments to specify fonts") sys.exit(1) @@ -114,36 +124,41 @@ assert len(lines)>0 lines = list(set(lines)) print("got", len(lines), "unique lines") + def rgeometry(image,eps=0.03,delta=0.3): - m = array([[1+eps*randn(),0.0],[eps*randn(),1.0+eps*randn()]]) + m = np.array([[1+eps*np.random.randn(),0.0],[eps*np.random.randn(),1.0+eps*np.random.randn()]]) w,h = image.shape - c = array([w/2.0,h/2]) - d = c-dot(m,c)+array([randn()*delta,randn()*delta]) + c = np.array([w/2.0,h/2]) + d = c-np.dot(m,c)+np.array([np.random.randn()*delta,np.random.randn()*delta]) return interpolation.affine_transform(image,m,offset=d,order=1,mode='constant',cval=image[0,0]) + def rdistort(image,distort=3.0,dsigma=10.0,cval=0): h,w = image.shape - hs = randn(h,w) - ws = randn(h,w) + hs = np.random.randn(h,w) + ws = np.random.randn(h,w) hs = filters.gaussian_filter(hs,dsigma) ws = filters.gaussian_filter(ws,dsigma) - hs *= distort/amax(hs) - ws *= distort/amax(ws) + hs *= distort/np.amax(hs) + ws *= distort/np.amax(ws) def f(p): return (p[0]+hs[p[0],p[1]],p[1]+ws[p[0],p[1]]) return interpolation.geometric_transform(image,f,output_shape=(h,w), order=1,mode='constant',cval=cval) if args.debug_show: - ion(); gray() + plt.ion() + plt.gray() + base = args.base print("base", base) os.system("rm -rf "+base) os.mkdir(base) + def crop(image,pad=1): - [[r,c]] = measurements.find_objects(array(image==0,'i')) + [[r,c]] = measurements.find_objects(np.array(image==0,'i')) r0 = r.start r1 = r.stop c0 = c.start @@ -155,6 +170,7 @@ last_font = None last_size = None last_fontfile = None + def genline(text,fontfile=None,size=36,sigma=0.5,threshold=0.5): global image,draw,last_font,last_fontfile if last_fontfile!=fontfile or last_size!=size: @@ -166,13 +182,13 @@ def genline(text,fontfile=None,size=36,sigma=0.5,threshold=0.5): draw.rectangle((0,0,6000,6000),fill="white") # print("\t", size, font) draw.text((250,20),text,fill="black",font=font) - a = asarray(image,'f') - a = a*1.0/amax(a) + a = np.asarray(image,'f') + a = a*1.0/np.amax(a) if sigma>0.0: a = filters.gaussian_filter(a,sigma) - a += clip(randn(*a.shape)*0.2,-0.25,0.25) + a += np.clip(np.random.randn(*a.shape)*0.2,-0.25,0.25) a = rgeometry(a) - a = array(a>threshold,'f') + a = np.array(a>threshold,'f') a = crop(a,pad=3) # FIXME add grid warping here # clf(); ion(); gray(); imshow(a); ginput(1,0.1) @@ -194,8 +210,8 @@ for pageno,font in enumerate(fonts): lineno = 0 while lineno0: - image = rdistort(image,args.distort,args.dsigma,cval=amax(image)) + image = rdistort(image,args.distort,args.dsigma,cval=np.amax(image)) if args.display: - gray() - clf(); imshow(image); ginput(1,0.1) + plt.gray() + plt.clf() + plt.imshow(image) + plt.ginput(1,0.1) fname = pagedir+"/01%04d"%lineno imsave(fname+".bin.png",image) gt = ocrolib.normalize_text(line) diff --git a/ocropus-lpred b/ocropus-lpred index 48a12ac6..560bfead 100755 --- a/ocropus-lpred +++ b/ocropus-lpred @@ -2,26 +2,25 @@ from __future__ import print_function -import random as pyrandom -import re -from pylab import * -import os.path -import ocrolib import argparse -import matplotlib -import numpy +import sys + +import numpy as np +import matplotlib.pyplot as plt + +import ocrolib from ocrolib import lineest import ocrolib.lstm as lstm from ocrolib import edist -import traceback import clstm -ion() -matplotlib.rc('xtick',labelsize=7) -matplotlib.rc('ytick',labelsize=7) -matplotlib.rcParams.update({"font.size":7}) + +plt.ion() +plt.rc('xtick',labelsize=7) +plt.rc('ytick',labelsize=7) +plt.rcParams.update({"font.size":7}) -numpy.seterr(divide='raise',over='raise',invalid='raise',under='ignore') +np.seterr(divide='raise',over='raise',invalid='raise',under='ignore') parser = argparse.ArgumentParser("run an RNN recognizer") @@ -61,16 +60,16 @@ network = clstm.CNetwork(network) network.load(args.load) def preprocess(line): - lnorm.measure(amax(line)-line) - line = lnorm.normalize(line,cval=amax(line)) - if line.size<10 or amax(line)==amin(line): + lnorm.measure(np.amax(line)-line) + line = lnorm.normalize(line,cval=np.amax(line)) + if line.size<10 or np.amax(line)==np.amin(line): return None - line = line * 1.0/amax(line) - line = amax(line)-line + line = line * 1.0/np.amax(line) + line = np.amax(line)-line line = line.T if args.pad>0: w = line.shape[1] - line = vstack([zeros((args.pad,w)),line,zeros((args.pad,w))]) + line = np.vstack([np.zeros((args.pad,w)),line,np.zeros((args.pad,w))]) return line if args.eval: @@ -84,7 +83,7 @@ for trial in range(len(inputs)): line = ocrolib.read_image_gray(fname) line = preprocess(line) if line is None: continue - outputs = array(network.forward(line)) + outputs = np.array(network.forward(line)) result = lstm.translate_back(outputs) pred = "".join(codec.decode(result)) print("%s\t%s" % (fname, pred)) diff --git a/ocropus-ltrain b/ocropus-ltrain index 41de9dd5..ae638e98 100755 --- a/ocropus-ltrain +++ b/ocropus-ltrain @@ -2,25 +2,26 @@ from __future__ import print_function -import random as pyrandom import re -from pylab import * -import os.path -import ocrolib import argparse -import matplotlib -import numpy +import traceback +import sys + +import numpy as np +import matplotlib.pyplot as plt + +import ocrolib from ocrolib import lineest import ocrolib.lstm as lstm -import traceback import clstm -ion() -matplotlib.rc('xtick',labelsize=7) -matplotlib.rc('ytick',labelsize=7) -matplotlib.rcParams.update({"font.size":7}) + +plt.ion() +plt.matplotlib.rc('xtick',labelsize=7) +plt.matplotlib.rc('ytick',labelsize=7) +plt.matplotlib.rcParams.update({"font.size":7}) -numpy.seterr(divide='raise',over='raise',invalid='raise',under='ignore') +np.seterr(divide='raise',over='raise',invalid='raise',under='ignore') parser = argparse.ArgumentParser("train an RNN recognizer") @@ -74,20 +75,22 @@ if args.load: network.load(args.load) network.setLearningRate(args.lrate,0.9) + def cleandisp(s): return re.sub('[$]',r'#',s) + def preprocess(line): - lnorm.measure(amax(line)-line) - line = lnorm.normalize(line,cval=amax(line)) - if line.size<10 or amax(line)==amin(line): + lnorm.measure(np.amax(line)-line) + line = lnorm.normalize(line,cval=np.amax(line)) + if line.size<10 or np.amax(line)==np.amin(line): return None - line = line * 1.0/amax(line) - line = amax(line)-line + line = line * 1.0/np.amax(line) + line = np.amax(line)-line line = line.T if args.pad>0: w = line.shape[1] - line = vstack([zeros((args.pad,w)),line,zeros((args.pad,w))]) + line = np.vstack([np.zeros((args.pad,w)),line,np.zeros((args.pad,w))]) return line for trial in range(args.start,args.ntrain): @@ -95,17 +98,17 @@ for trial in range(args.start,args.ntrain): network.save(args.output % trial) try: # fname = inputs[trial%len(inputs)] - fname = inputs[randint(0,len(inputs))] + fname = inputs[np.random.randint(0,len(inputs))] base,_ = ocrolib.allsplitext(fname) line = ocrolib.read_image_gray(fname) transcript = ocrolib.read_text(base+".gt.txt") print("#", trial, fname, line.shape) line = preprocess(line) if line is None: continue - cs = array(codec.encode(transcript),'i') - outputs = array(network.forward(line)) - targets = array(lstm.make_target(cs,network.noutput())) - aligned = array(lstm.ctc_align_targets(outputs,targets)) + cs = np.array(codec.encode(transcript),'i') + outputs = np.array(network.forward(line)) + targets = np.array(lstm.make_target(cs,network.noutput())) + aligned = np.array(lstm.ctc_align_targets(outputs,targets)) deltas = aligned-outputs network.backward(deltas) result = lstm.translate_back(outputs) @@ -116,17 +119,17 @@ for trial in range(args.start,args.ntrain): print(" ALN:", repr(gta[:len(transcript)+5])) print(" OUT:", repr(pred[:len(transcript)+5])) if trial%20==0: - clf() - subplot(311) - title(cleandisp(transcript)) - imshow(line.T,cmap=cm.gray,interpolation='bilinear') - subplot(312) - title(cleandisp(gta)) - imshow(aligned.T,cmap=cm.hot,interpolation='bilinear',aspect='auto') - subplot(313) - title(cleandisp(pred)) - imshow(outputs.T,cmap=cm.hot,interpolation='bilinear',aspect='auto') + plt.clf() + plt.subplot(311) + plt.title(cleandisp(transcript)) + plt.imshow(line.T,cmap=plt.cm.gray,interpolation='bilinear') + plt.subplot(312) + plt.title(cleandisp(gta)) + plt.imshow(aligned.T,cmap=plt.cm.hot,interpolation='bilinear',aspect='auto') + plt.subplot(313) + plt.title(cleandisp(pred)) + plt.imshow(outputs.T,cmap=plt.cm.hot,interpolation='bilinear',aspect='auto') tight_layout() - ginput(1,0.01) + plt.ginput(1,0.01) except Exception as e: print(e) diff --git a/ocropus-nlbin b/ocropus-nlbin index 1d05d786..59905cbd 100755 --- a/ocropus-nlbin +++ b/ocropus-nlbin @@ -2,15 +2,18 @@ from __future__ import print_function +import argparse +import os +import multiprocessing import sys -from pylab import * -from numpy.ctypeslib import ndpointer -import argparse,os,os.path +import codecs + +import numpy as np +import matplotlib.pyplot as plt from scipy.ndimage import filters,interpolation,morphology,measurements from scipy import stats -import multiprocessing + import ocrolib -import codecs utf8writer = codecs.getwriter('utf8') sys.stdout = utf8writer(sys.stdout) @@ -55,12 +58,14 @@ if len(args.files)<1: def print_info(*objs): print("INFO: ", " ".join(objs)) + def print_error(*objs): print("ERROR: ", " ".join(objs), file=sys.stderr) + def check_page(image): if len(image.shape)==3: return "input image is color image %s"%(image.shape,) - if mean(image)10000: return "image too tall for a page image %s"%(image.shape,) @@ -71,22 +76,108 @@ def check_page(image): def estimate_skew_angle(image,angles): estimates = [] for a in angles: - v = mean(interpolation.rotate(image,a,order=0,mode='constant'),axis=1) - v = var(v) + v = np.mean(interpolation.rotate(image,a,order=0,mode='constant'),axis=1) + v = np.var(v) estimates.append((v,a)) if args.debug>0: - plot([y for x,y in estimates],[x for x,y in estimates]) - ginput(1,args.debug) + plt.plot([y for x,y in estimates],[x for x,y in estimates]) + plt.ginput(1,args.debug) _,a = max(estimates) return a + def H(s): return s[0].stop-s[0].start def W(s): return s[1].stop-s[1].start def A(s): return W(s)*H(s) + def dshow(image,info): if args.debug<=0: return - ion(); gray(); imshow(image); title(info); ginput(1,args.debug) + plt.ion() + plt.gray() + plt.imshow(image) + plt.title(info) + plt.ginput(1,args.debug) + + +def normalize_raw_image(raw): + ''' perform image normalization ''' + image = raw-np.amin(raw) + if np.amax(image)==np.amin(image): + print_info("# image is empty: %s" % (fname)) + return None + image /= np.amax(image) + return image + + +def estimate_local_whitelevel(image, zoom=0.5, perc=80, range=20, debug=0): + '''flatten it by estimating the local whitelevel + zoom for page background estimation, smaller=faster, default: %(default)s + percentage for filters, default: %(default)s + range for filters, default: %(default)s + ''' + m = interpolation.zoom(image,zoom) + m = filters.percentile_filter(m,perc,size=(range,2)) + m = filters.percentile_filter(m,perc,size=(2,range)) + m = interpolation.zoom(m,1.0/zoom) + if debug>0: + plt.clf() + plt.imshow(m,vmin=0,vmax=1) + plt.ginput(1,debug) + w,h = np.minimum(np.array(image.shape),np.array(m.shape)) + flat = np.clip(image[:w,:h]-m[:w,:h]+1,0,1) + if debug>0: + plt.clf() + plt.imshow(flat,vmin=0,vmax=1) + plt.ginput(1,debug) + return flat + + +def estimate_skew(flat, bignore=0.1, maxskew=2, skewsteps=8): + ''' estimate skew angle and rotate''' + d0,d1 = flat.shape + o0,o1 = int(bignore*d0),int(bignore*d1) # border ignore + flat = np.amax(flat)-flat + flat -= np.amin(flat) + est = flat[o0:d0-o0,o1:d1-o1] + ma = maxskew + ms = int(2*maxskew*skewsteps) + # print(linspace(-ma,ma,ms+1)) + angle = estimate_skew_angle(est,np.linspace(-ma,ma,ms+1)) + flat = interpolation.rotate(flat,angle,mode='constant',reshape=0) + flat = np.amax(flat)-flat + return flat, angle + + + +def estimate_thresholds(flat, bignore=0.1, escale=1.0, lo=5, hi=90, debug=0): + '''# estimate low and high thresholds + ignore this much of the border for threshold estimation, default: %(default)s + scale for estimating a mask over the text region, default: %(default)s + lo percentile for black estimation, default: %(default)s + hi percentile for white estimation, default: %(default)s + ''' + d0,d1 = flat.shape + o0,o1 = int(bignore*d0),int(bignore*d1) + est = flat[o0:d0-o0,o1:d1-o1] + if escale>0: + # by default, we use only regions that contain + # significant variance; this makes the percentile + # based low and high estimates more reliable + e = escale + v = est-filters.gaussian_filter(est,e*20.0) + v = filters.gaussian_filter(v**2,e*20.0)**0.5 + v = (v>0.3*np.amax(v)) + v = morphology.binary_dilation(v,structure=np.ones((int(e*50),1))) + v = morphology.binary_dilation(v,structure=np.ones((1,int(e*50)))) + if debug>0: + plt.imshow(v) + plt.ginput(1,debug) + est = est[v] + lo = stats.scoreatpercentile(est.ravel(),lo) + hi = stats.scoreatpercentile(est.ravel(),hi) + return lo, hi + def process1(job): fname,i = job @@ -95,14 +186,10 @@ def process1(job): raw = ocrolib.read_image_gray(fname) dshow(raw,"input") # perform image normalization - image = raw-amin(raw) - if amax(image)==amin(image): - print_info("# image is empty: %s" % (fname)) - return - image /= amax(image) + image = normalize_raw_image(raw) if not args.nocheck: - check = check_page(amax(image)-image) + check = check_page(np.amax(image)-image) if check is not None: print_error(fname+"SKIPPED"+check+"(use -n to disable this check)") return @@ -111,7 +198,7 @@ def process1(job): if args.gray: extreme = 0 else: - extreme = (sum(image<0.05)+sum(image>0.95))*1.0/prod(image.shape) + extreme = (np.sum(image<0.05)+np.sum(image>0.95))*1.0/np.prod(image.shape) if extreme>0.95: comment = "no-normalization" flat = image @@ -119,62 +206,36 @@ def process1(job): comment = "" # if not, we need to flatten it by estimating the local whitelevel if args.parallel<2: print_info("flattening") - m = interpolation.zoom(image,args.zoom) - m = filters.percentile_filter(m,args.perc,size=(args.range,2)) - m = filters.percentile_filter(m,args.perc,size=(2,args.range)) - m = interpolation.zoom(m,1.0/args.zoom) - if args.debug>0: clf(); imshow(m,vmin=0,vmax=1); ginput(1,args.debug) - w,h = minimum(array(image.shape),array(m.shape)) - flat = clip(image[:w,:h]-m[:w,:h]+1,0,1) - if args.debug>0: clf(); imshow(flat,vmin=0,vmax=1); ginput(1,args.debug) + flat = estimate_local_whitelevel(image, args.zoom, args.perc, args.range, args.debug) # estimate skew angle and rotate if args.maxskew>0: if args.parallel<2: print_info("estimating skew angle") - d0,d1 = flat.shape - o0,o1 = int(args.bignore*d0),int(args.bignore*d1) - flat = amax(flat)-flat - flat -= amin(flat) - est = flat[o0:d0-o0,o1:d1-o1] - ma = args.maxskew - ms = int(2*args.maxskew*args.skewsteps) - angle = estimate_skew_angle(est,linspace(-ma,ma,ms+1)) - flat = interpolation.rotate(flat,angle,mode='constant',reshape=0) - flat = amax(flat)-flat + flat, angle = estimate_skew(flat, args.bignore, args.maxskew, args.skewsteps) else: angle = 0 # estimate low and high thresholds if args.parallel<2: print_info("estimating thresholds") - d0,d1 = flat.shape - o0,o1 = int(args.bignore*d0),int(args.bignore*d1) - est = flat[o0:d0-o0,o1:d1-o1] - if args.escale>0: - # by default, we use only regions that contain - # significant variance; this makes the percentile - # based low and high estimates more reliable - e = args.escale - v = est-filters.gaussian_filter(est,e*20.0) - v = filters.gaussian_filter(v**2,e*20.0)**0.5 - v = (v>0.3*amax(v)) - v = morphology.binary_dilation(v,structure=ones((int(e*50),1))) - v = morphology.binary_dilation(v,structure=ones((1,int(e*50)))) - if args.debug>0: imshow(v); ginput(1,args.debug) - est = est[v] - lo = stats.scoreatpercentile(est.ravel(),args.lo) - hi = stats.scoreatpercentile(est.ravel(),args.hi) + lo, hi = estimate_thresholds(flat, args.bignore, args.escale, args.lo, args.hi, args.debug) # rescale the image to get the gray scale image if args.parallel<2: print_info("rescaling") flat -= lo flat /= (hi-lo) - flat = clip(flat,0,1) - if args.debug>0: imshow(flat,vmin=0,vmax=1); ginput(1,args.debug) + flat = np.clip(flat,0,1) + if args.debug>0: + plt.imshow(flat,vmin=0,vmax=1) + plt.ginput(1,args.debug) bin = 1*(flat>args.threshold) # output the normalized grayscale and the thresholded images print_info("%s lo-hi (%.2f %.2f) angle %4.1f %s" % (fname, lo, hi, angle, comment)) if args.parallel<2: print_info("writing") - if args.debug>0 or args.show: clf(); gray();imshow(bin); ginput(1,max(0.1,args.debug)) + if args.debug>0 or args.show: + plt.clf() + plt.gray() + plt.imshow(bin) + plt.ginput(1,max(0.1,args.debug)) if args.output: if args.rawcopy: ocrolib.write_image_gray(args.output+"/%04d.raw.png"%i,raw) ocrolib.write_image_binary(args.output+"/%04d.bin.png"%i,bin) diff --git a/ocropus-rpred b/ocropus-rpred index e7346de9..8ebbfb20 100755 --- a/ocropus-rpred +++ b/ocropus-rpred @@ -2,21 +2,23 @@ from __future__ import print_function -import sys import traceback import codecs -from pylab import * import os.path -import ocrolib import argparse -import matplotlib +import sys from multiprocessing import Pool -from ocrolib import edist -from ocrolib.exceptions import FileNotFound, OcropusException from collections import Counter -from ocrolib import lstm + +import matplotlib.pyplot as plt +import numpy as np from scipy.ndimage import measurements +import ocrolib +from ocrolib import lstm +from ocrolib import edist +from ocrolib.exceptions import FileNotFound, OcropusException + utf8writer = codecs.getwriter('utf8') sys.stdout = utf8writer(sys.stdout) sys.stderr = utf8writer(sys.stderr) @@ -72,22 +74,25 @@ parser.add_argument("files",nargs="+", help="input files; glob and @ expansion performed") args = parser.parse_args() + def print_info(*objs): print("INFO: ", " ".join(objs)) + def print_error(*objs): print("ERROR: ", " ".join(objs), file=sys.stderr) + def check_line(image): if len(image.shape)==3: return "input image is color image %s"%(image.shape,) - if mean(image)200: return "image too tall for a text line %s"%(image.shape,) if w<1.5*h: return "line too short %s"%(image.shape,) if w>4000: return "line too long %s"%(image.shape,) ratio = w*1.0/h - _,ncomps = measurements.label(image>mean(image)) + _,ncomps = measurements.label(image>np.mean(image)) lo = int(0.5*ratio+0.5) hi = int(4*ratio)+1 if ncomps=%d)"%(ncomps,lo) @@ -116,18 +121,18 @@ if args.show>=0 or args.save is not None: # load the network used for classification try: - network = ocrolib.load_object(args.model,verbose=1) - for x in network.walk(): x.postLoad() - for x in network.walk(): - if isinstance(x,lstm.LSTM): - x.allocate(5000) + network = ocrolib.load_object(args.model,verbose=1) + for x in network.walk(): x.postLoad() + for x in network.walk(): + if isinstance(x,lstm.LSTM): + x.allocate(5000) except FileNotFound: - print_error("") - print_error("Cannot find OCR model file:" + args.model) - print_error("Download a model and put it into:" + ocrolib.default.modeldir) - print_error("(Or override the location with OCROPUS_DATA.)") - print_error("") - sys.exit(1) + print_error("") + print_error("Cannot find OCR model file:" + args.model) + print_error("Download a model and put it into:" + ocrolib.default.modeldir) + print_error("(Or override the location with OCROPUS_DATA.)") + print_error("") + sys.exit(1) # get the line normalizer from the loaded network, or optionally # let the user override it (this is not very useful) @@ -137,6 +142,7 @@ lnorm = getattr(network,"lnorm",None) if args.height>0: lnorm.setHeight(args.height) + # process one file def process1(arg): @@ -144,21 +150,21 @@ def process1(arg): base,_ = ocrolib.allsplitext(fname) line = ocrolib.read_image_gray(fname) raw_line = line.copy() - if prod(line.shape)==0: return None - if amax(line)==amin(line): return None + if np.prod(line.shape)==0: return None + if np.amax(line)==np.amin(line): return None if not args.nocheck: - check = check_line(amax(line)-line) + check = check_line(np.amax(line)-line) if check is not None: print_error("%s SKIPPED %s (use -n to disable this check)" % (fname, check)) return (0,[],0,trial,fname) if not args.nolineest: assert "dew.png" not in fname,"don't dewarp dewarped images" - temp = amax(line)-line - temp = temp*1.0/amax(temp) + temp = np.amax(line)-line + temp = temp*1.0/np.amax(temp) lnorm.measure(temp) - line = lnorm.normalize(line,cval=amax(line)) + line = lnorm.normalize(line,cval=np.amax(line)) else: assert "dew.png" in fname,"only apply to dewarped images" @@ -225,44 +231,45 @@ def process1(arg): ocrolib.write_text(base+".txt",pred) if args.show>0 or args.save is not None: - ion() - matplotlib.rc('xtick',labelsize=7) - matplotlib.rc('ytick',labelsize=7) - matplotlib.rcParams.update({"font.size":7}) + plt.ion() + plt.rc('xtick',labelsize=7) + plt.rc('ytick',labelsize=7) + plt.rcParams.update({"font.size":7}) if os.path.exists(base+".gt.txt"): transcript = ocrolib.read_text(base+".gt.txt") transcript = ocrolib.normalize_text(transcript) else: transcript = pred pred2 = network.trainString(line,transcript,update=0) - figure("result",figsize=(1400//75,800//75),dpi=75) - clf() - subplot(311) - imshow(line.T,cmap=cm.gray) - title(transcript) - subplot(312) - gca().set_xticks([]) - imshow(network.outputs.T[1:],vmin=0,cmap=cm.hot) - title(pred[:80]) - subplot(313) - plot(network.outputs[:,0],color='yellow',linewidth=3,alpha=0.5) - plot(network.outputs[:,1],color='green',linewidth=3,alpha=0.5) - plot(amax(network.outputs[:,2:],axis=1),color='blue',linewidth=3,alpha=0.5) - plot(network.aligned[:,0],color='orange',linestyle='dashed',alpha=0.7) - plot(network.aligned[:,1],color='green',linestyle='dashed',alpha=0.5) - plot(amax(network.aligned[:,2:],axis=1),color='blue',linestyle='dashed',alpha=0.5) + plt.figure("result",figsize=(1400//75,800//75),dpi=75) + plt.clf() + plt.subplot(311) + plt.imshow(line.T,cmap=plt.cm.gray) + plt.title(transcript) + plt.subplot(312) + plt.gca().set_xticks([]) + plt.imshow(network.outputs.T[1:],vmin=0,cmap=plt.cm.hot) + plt.title(pred[:80]) + plt.subplot(313) + plt.plot(network.outputs[:,0],color='yellow',linewidth=3,alpha=0.5) + plt.plot(network.outputs[:,1],color='green',linewidth=3,alpha=0.5) + plt.plot(np.amax(network.outputs[:,2:],axis=1),color='blue',linewidth=3,alpha=0.5) + plt.plot(network.aligned[:,0],color='orange',linestyle='dashed',alpha=0.7) + plt.plot(network.aligned[:,1],color='green',linestyle='dashed',alpha=0.5) + plt.plot(np.amax(network.aligned[:,2:],axis=1),color='blue',linestyle='dashed',alpha=0.5) if args.save is not None: - draw() + plt.draw() savename = args.save if "%" in savename: savename = savename%trial print_info("saving "+savename) - savefig(savename,bbox_inches=0) + plt.savefig(savename,bbox_inches=0) if trial==len(inputs)-1: - ginput(1,99999999) + plt.ginput(1,99999999) else: - ginput(1,args.show) + plt.ginput(1,args.show) return None + def safe_process1(arg): trial,fname = arg try: @@ -288,7 +295,7 @@ elif args.parallel==1: else: pool = Pool(processes=args.parallel) result = [] - for r in pool.imap_unordered(safe_process1,enumerate(inputs)): + for r in pool.imap_unordered(safe_process1,enumerate(inputs)): result.append(r) if not args.quiet and len(result)%100==0: sys.stderr.write("==== %d of %d\n"%(len(result),len(inputs))) diff --git a/ocropus-rtrain b/ocropus-rtrain index e545ad8a..1edbff2f 100755 --- a/ocropus-rtrain +++ b/ocropus-rtrain @@ -2,24 +2,26 @@ from __future__ import print_function -import sys import random as pyrandom import re -from pylab import * import os.path -import ocrolib -import argparse -import matplotlib -import numpy -from ocrolib import lineest -import ocrolib.lstm as lstm import traceback +import argparse +import sys import codecs +import numpy as np +import matplotlib.pyplot as plt + +import ocrolib +import ocrolib.lstm as lstm + +from ocrolib import lineest + utf8writer = codecs.getwriter('utf8') sys.stdout = utf8writer(sys.stdout) -numpy.seterr(divide='raise',over='raise',invalid='raise',under='ignore') +np.seterr(divide='raise',over='raise',invalid='raise',under='ignore') parser = argparse.ArgumentParser("train an RNN recognizer") @@ -123,9 +125,9 @@ print("# tests", len(tests) if tests is not None else "None") # load the line normalizer if args.lineest=="center": - lnorm = lineest.CenterNormalizer() + lnorm = lineest.CenterNormalizer() else: - raise Exception(args.lineest+": unknown line normalizer") + raise Exception(args.lineest+": unknown line normalizer") lnorm.setHeight(args.height) # The `codec` maps between strings and arrays of integers. @@ -154,6 +156,7 @@ else: print("[" + s[:20], "...", s[-20:] + "]") codec = lstm.Codec().init(charset) + # Load an existing network or construct a new one # Somewhat convoluted logic for dealing with old style Python # modules and new style C++ LSTM networks. @@ -169,6 +172,7 @@ def save_lstm(fname,network): if args.strip: for x in network.walk(): x.postLoad() + def load_lstm(fname): if args.clstm: network = lstm.SeqRecognizer(args.height,args.hiddensize, @@ -216,40 +220,42 @@ if args.updates: network.lstm.verbose = 1 # used for plotting -ion() -matplotlib.rc('xtick',labelsize=7) -matplotlib.rc('ytick',labelsize=7) -matplotlib.rcParams.update({"font.size":7}) +plt.ion() +plt.rc('xtick',labelsize=7) +plt.rc('ytick',labelsize=7) +plt.rcParams.update({"font.size":7}) + def cleandisp(s): return re.sub('[$]',r'#',s) + def plot_network_info(network,transcript,pred,gta): - subplot(511) - imshow(line.T,cmap=cm.gray) - title(cleandisp(transcript)) - subplot(512) - gca().set_xticks([]) - imshow(network.outputs.T[1:],vmin=0,cmap=cm.hot) - title(cleandisp(pred[:len(transcript)])) - subplot(513) - imshow(network.aligned.T[1:],vmin=0,cmap=cm.hot) - title(cleandisp(gta[:len(transcript)])) - subplot(514) - plot(network.outputs[:,0],color='yellow',linewidth=3,alpha=0.5) - plot(network.outputs[:,1],color='green',linewidth=3,alpha=0.5) - plot(amax(network.outputs[:,2:],axis=1),color='blue',linewidth=3,alpha=0.5) - plot(network.aligned[:,0],color='orange',linestyle='dashed',alpha=0.7) - plot(network.aligned[:,1],color='green',linestyle='dashed',alpha=0.5) - plot(amax(network.aligned[:,2:],axis=1),color='blue',linestyle='dashed',alpha=0.5) - subplot(515) - gca().set_yscale('log') + plt.subplot(511) + plt.imshow(line.T,cmap=plt.cm.gray) + plt.title(cleandisp(transcript)) + plt.subplot(512) + plt.gca().set_xticks([]) + plt.imshow(network.outputs.T[1:],vmin=0,cmap=plt.cm.hot) + plt.title(cleandisp(pred[:len(transcript)])) + plt.subplot(513) + plt.imshow(network.aligned.T[1:],vmin=0,cmap=plt.cm.hot) + plt.title(cleandisp(gta[:len(transcript)])) + plt.subplot(514) + plt.plot(network.outputs[:,0],color='yellow',linewidth=3,alpha=0.5) + plt.plot(network.outputs[:,1],color='green',linewidth=3,alpha=0.5) + plt.plot(np.amax(network.outputs[:,2:],axis=1),color='blue',linewidth=3,alpha=0.5) + plt.plot(network.aligned[:,0],color='orange',linestyle='dashed',alpha=0.7) + plt.plot(network.aligned[:,1],color='green',linestyle='dashed',alpha=0.5) + plt.plot(np.amax(network.aligned[:,2:],axis=1),color='blue',linestyle='dashed',alpha=0.5) + plt.subplot(515) + plt.gca().set_yscale('log') r = 10000 errs = network.errors(range=r,smooth=100) - xs = arange(len(errs))+network.last_trial-len(errs) - plot(xs,errs,color='black') - plot(xs,network.errors(range=r),color='black',alpha=0.4) - plot(xs,network.cerrors(range=r,smooth=100),color='red',linestyle='dashed') + xs = np.arange(len(errs))+network.last_trial-len(errs) + plt.plot(xs,errs,color='black') + plt.plot(xs,network.errors(range=r),color='black',alpha=0.4) + plt.plot(xs,network.cerrors(range=r,smooth=100),color='red',linestyle='dashed') start = args.start if args.start>=0 else network.last_trial @@ -275,21 +281,21 @@ for trial in range(start,args.ntrain): if not args.nolineest: assert "dew.png" not in fname,"don't dewarp already dewarped lines" - network.lnorm.measure(amax(line)-line) - line = network.lnorm.normalize(line,cval=amax(line)) + network.lnorm.measure(np.amax(line)-line) + line = network.lnorm.normalize(line,cval=np.amax(line)) else: assert "dew.png" in fname,"input must already be dewarped" - if line.size<10 or amax(line)==amin(line): + if line.size<10 or np.amax(line)==np.amin(line): print("EMPTY-INPUT") continue - line = line * 1.0/amax(line) - line = amax(line)-line + line = line * 1.0/np.amax(line) + line = np.amax(line)-line line = line.T if args.pad>0: w = line.shape[1] - line = vstack([zeros((args.pad,w)),line,zeros((args.pad,w))]) - cs = array(codec.encode(transcript),'i') + line = np.vstack([np.zeros((args.pad,w)),line,np.zeros((args.pad,w))]) + cs = np.array(codec.encode(transcript),'i') try: pcs = network.trainSequence(line,cs,update=do_update,key=fname) except FloatingPointError as e: @@ -318,12 +324,11 @@ for trial in range(start,args.ntrain): last_save = ofile if do_display: - figure("training",figsize=(1400//75,800//75),dpi=75) - clf() - gcf().canvas.set_window_title(args.output) + plt.figure("training",figsize=(1400//75,800//75),dpi=75) + plt.clf() + plt.gcf().canvas.set_window_title(args.output) plot_network_info(network,transcript,pred,gta) - ginput(1,0.01) + plt.ginput(1,0.01) if args.movie is not None: - draw() - savefig("%s-%08d.png"%(args.movie,trial),bbox_inches=0) - + plt.draw() + plt.savefig("%s-%08d.png"%(args.movie,trial),bbox_inches=0) diff --git a/ocropus-visualize-results b/ocropus-visualize-results index 4546b30d..45231685 100755 --- a/ocropus-visualize-results +++ b/ocropus-visualize-results @@ -3,20 +3,22 @@ from __future__ import print_function import glob +import sys +import os +import signal +import argparse + import matplotlib matplotlib.use("AGG") -import sys,os,signal +import matplotlib.pyplot as plt +import numpy as np from scipy.ndimage import interpolation -import pylab -from pylab import * + import ocrolib from ocrolib import morph -from scipy.misc import imsave signal.signal(signal.SIGINT,lambda *args:sys.exit(1)) -# these options control alignment -import argparse parser = argparse.ArgumentParser(description = """ Generate HTML for debugging a book directory. @@ -27,6 +29,7 @@ parser.add_argument("book",default="book") parser.add_argument("-N","--npages",type=int,default=100000,help="max number of pages, default: %(default)s") args = parser.parse_args() + def write_cseg(stream,cseg_file): cseg = ocrolib.read_line_segmentation(cseg_file) cseg = ocrolib.read_line_segmentation(cseg_file) @@ -34,11 +37,12 @@ def write_cseg(stream,cseg_file): stream.write("") for i,c in enumerate(csegs): out = ".__"+cseg_file+"_%03d.png"%i - pylab.imsave(out,amax(c.img)-c.img,cmap=cm.gray) + plt.imsave(out,np.amax(c.img)-c.img,cmap=plt.cm.gray) stream.write(""%(out,max(2,c.img.shape[0]/2))) stream.write("
") stream.write("\n") + def genpage(d): print("===", d) here = os.getcwd() @@ -66,17 +70,16 @@ def genpage(d): stream.write("%s"%(img,img)) stream.write("") stream.write("

\n") - # if os.path.exists(cseg): stream.write("
\n"%cseg) cseg = ocrolib.fvariant(img,"cseg") if os.path.exists(cseg): write_cseg(stream,cseg) rseg_file = ocrolib.fvariant(img,"rseg") if os.path.exists(rseg_file): rseg = ocrolib.read_line_segmentation(rseg_file) - figure(figsize=(20,1),dpi=150) + plt.figure(figsize=(20,1),dpi=150) morph.showlabels(rseg) figfile = ".__"+rseg_file+"_.png" - savefig(figfile) + plt.savefig(figfile) stream.write("
\n"%figfile) stream.write("


\n") finally: @@ -89,10 +92,10 @@ with open("index.html","w") as stream: if os.path.exists(d+".bin.png"): image = ocrolib.read_image_gray(d+".bin.png") else: - image = zeros((300,300)) + image = np.zeros((300,300)) out = ".__"+d+".png" image = interpolation.zoom(image,(0.125,0.125),order=1) - imsave(out,image) + plt.imsave(out,image,cmap=plt.cm.gray) stream.write("\n") stream.write("\n") stream.write("
") stream.write(""%(d,out)) @@ -108,4 +111,3 @@ with open("index.html","w") as stream: if count>=10: break stream.write("
\n") - diff --git a/run-coverage b/run-coverage index a44dd0ae..3ef88506 100755 --- a/run-coverage +++ b/run-coverage @@ -11,8 +11,8 @@ except ImportError: exit(1)" rm -rf .coverage* -PATH=$PWD:$PATH $COVERAGE run -p --include=$PWD/**/* ./tests/run-unit -PATH=$PWD:$PATH RUNNER="$COVERAGE run -p --include=$PWD/**/*" ./run-test-ci +PATH=$PWD:$PATH $COVERAGE run -p --include=$PWD/ocropus-*,$PWD/**/* ./tests/run-unit +PATH=$PWD:$PATH RUNNER="$COVERAGE run -p --include=$PWD/ocropus-*,$PWD/**/*" ./run-test-ci $COVERAGE combine rm -rf htmlcov $COVERAGE html diff --git a/run-test-ci b/run-test-ci index cd8b6c49..1b3f70e5 100755 --- a/run-test-ci +++ b/run-test-ci @@ -13,27 +13,47 @@ RUNNER="${RUNNER:-python2}" test_page() { set -x $RUNNER $BASE/ocropus-nlbin "$BASE/tests/testpage.png" -o temp - $RUNNER $BASE/ocropus-gpageseg 'temp/????.bin.png' + $RUNNER $BASE/ocropus-gpageseg 'temp/????.bin.png' --gray $RUNNER $BASE/ocropus-rpred --parallel=0 --nocheck 'temp/0001/01000?.bin.png' $RUNNER $BASE/ocropus-dewarp 'temp/0001/01001?.bin.png' $RUNNER $BASE/ocropus-hocr 'temp/????.bin.png' -o temp.html 2>/dev/null $RUNNER $BASE/ocropus-visualize-results temp $RUNNER $BASE/ocropus-gtedit html temp/????/??????.bin.png -o temp-correction.html + $RUNNER $BASE/ocropus-gtedit extract temp-correction.html set +x } test_conf() { local TESTIMAGE=0079-01000d - mkdir -p temp cp "$BASE/tests/$TESTIMAGE"* temp set -x $RUNNER $BASE/ocropus-rpred temp/$TESTIMAGE.png + # test different parameters of ocropus-errs $RUNNER $BASE/ocropus-errs temp/$TESTIMAGE.gt.txt + $RUNNER $BASE/ocropus-errs temp/$TESTIMAGE.gt.txt --erroronly + # test different parameters of ocropus-econf $RUNNER $BASE/ocropus-econf temp/$TESTIMAGE.gt.txt + $RUNNER $BASE/ocropus-econf temp/$TESTIMAGE.gt.txt --perfile temp/$TESTIMAGE.perfile.econf.txt + $RUNNER $BASE/ocropus-econf temp/$TESTIMAGE.gt.txt --allconf temp/$TESTIMAGE.allconf.econf.txt + # test with empty gt file, no txt file + echo '' > temp/econf-file.gt.txt + $RUNNER $BASE/ocropus-econf temp/econf-file.gt.txt + # test with empty gt file, existing txt file + echo '0' > temp/econf-file.txt + $RUNNER $BASE/ocropus-econf temp/econf-file.gt.txt + # test on files withouth "gt" in their file endings + $RUNNER $BASE/ocropus-errs temp/$TESTIMAGE.txt + $RUNNER $BASE/ocropus-econf temp/$TESTIMAGE.txt + # test on files where there is no corresponding txt file + cp temp/$TESTIMAGE.gt.txt temp/$TESTIMAGE.copy.gt.txt + $RUNNER $BASE/ocropus-errs temp/$TESTIMAGE.copy.gt.txt + $RUNNER $BASE/ocropus-econf temp/$TESTIMAGE.copy.gt.txt } test_linegen() { $RUNNER $BASE/ocropus-linegen -m 3 -t $BASE/tests/tomsawyer.txt -f $BASE/tests/DejaVuSans.ttf + $RUNNER $BASE/ocropus-linegen -m 3 -t $BASE/tests/tomsawyer.txt -f $BASE/tests/DejaVuSans.ttf --degradations med --numdir + $RUNNER $BASE/ocropus-linegen -m 3 -t $BASE/tests/tomsawyer.txt -f $BASE/tests/DejaVuSans.ttf --degradations hi --sizes 40,50,60,70 } test_rtrain() { @@ -41,8 +61,50 @@ test_rtrain() { $RUNNER $BASE/ocropus-rtrain 'book/*/*.bin.png' -N 5 -o ci-test-model } +test_nlbin() { + local TESTIMAGE=0071-010012.png + cp $BASE/tests/$TESTIMAGE temp + $RUNNER $BASE/ocropus-nlbin temp/$TESTIMAGE + $RUNNER $BASE/ocropus-nlbin temp/$TESTIMAGE -n + $RUNNER $BASE/ocropus-nlbin temp/$TESTIMAGE -n --gray +} + +test_gpageseg() { + local TESTIMAGE=text-near-edge.bin.png + cp $BASE/tests/$TESTIMAGE temp + $RUNNER $BASE/ocropus-gpageseg temp/$TESTIMAGE + $RUNNER $BASE/ocropus-gpageseg temp/$TESTIMAGE -n + $RUNNER $BASE/ocropus-gpageseg temp/$TESTIMAGE -n --maxseps 3 + $RUNNER $BASE/ocropus-gpageseg temp/$TESTIMAGE -n -b + $RUNNER $BASE/ocropus-gpageseg temp/$TESTIMAGE -n --usegauss +} + +test_rpred() { + local TESTIMAGE=0079-01000d + cp "$BASE/tests/$TESTIMAGE"* temp + $RUNNER $BASE/ocropus-rpred temp/$TESTIMAGE.png --llocs --alocs --probabilities + $RUNNER $BASE/ocropus-rpred temp/$TESTIMAGE.png --estrate +} + +test_gtedit() { + set -x + mkdir -p temp/9999 + echo '0' > temp/9999/000000.gt.txt + echo 'basel' > temp/9999/000001.gt.txt + echo '2' > temp/9999/000002.gt.txt + $RUNNER $BASE/ocropus-gtedit text temp/9999/*.gt.txt -o temp/gtedit-together.gt.txt + $RUNNER $BASE/ocropus-gtedit org temp/9999/*.gt.txt -o temp/gtedit-together.org.gt.txt + mkdir -p temp/out + $RUNNER $BASE/ocropus-gtedit write temp/gtedit-together.gt.txt temp/out -x .copy.gt.txt +} + rm -rf temp +mkdir -p temp test_page test_conf test_linegen test_rtrain +test_nlbin +test_gpageseg +test_rpred +test_gtedit diff --git a/setup.py b/setup.py index 2ec5832e..09f93315 100644 --- a/setup.py +++ b/setup.py @@ -2,14 +2,14 @@ from __future__ import print_function -import sys,time,urllib,traceback,glob,os,os.path +import sys +import glob +import os.path +from distutils.core import setup assert sys.version_info[0]==2 and sys.version_info[1]>=7,\ "you must install and use OCRopus with Python version 2.7 or later, but not Python 3.x" -from distutils.core import setup #, Extension, Command -#from distutils.command.install_data import install_data - if not os.path.exists("models/en-default.pyrnn.gz"): print() print("You should download the default model 'en-default.pyrnn.gz'")