def binarize(image_filepath):
raw = ocrolib.read_image_gray(image_filepath)
# Perform image normalization.
image = normalize_raw_image(raw)
threshold = 0.5 # Threshold, determines lightness.
zoom = 0.5 # Zoom for page background estimation, smaller=faster.
escale = 1.0 # Scale for estimating a mask over the text region.
bignore = 0.1 # Ignore this much of the border for threshold estimation.
perc = 80 # Percentage for filters.
range = 20 # Range for filters.
maxskew = 2 # Skew angle estimation parameters (degrees).
lo = 5 # Percentile for black estimation.
hi = 90 # Percentile for white estimation.
skewsteps = 8 # Steps for skew angle estimation (per degree).
debug = 0 # Display intermediate results.
# Flatten it by estimating the local whitelevel.
flat = estimate_local_whitelevel(image, zoom, perc, range, debug)
# Estimate skew angle and rotate.
flat, angle = estimate_skew(flat, bignore, maxskew, skewsteps, debug)
# Estimate low and high thresholds.
lo, hi = estimate_thresholds(flat, bignore, escale, lo, hi, debug)
# Rescale the image to get the gray scale image.
flat -= lo
flat /= (hi - lo)
flat = np.clip(flat, 0, 1)
bin = 1 * (flat > threshold)
if False:
# Output the normalized grayscale and the thresholded images.
ocrolib.write_image_binary('./ocropy_test.bin.png', bin)
ocrolib.write_image_gray('./ocropy_test.nrm.png', flat)
return bin, flat
def process1(job):
fname, i = job
print_info("# %s" % (fname))
if args.parallel < 2: print_info("=== %s %-3d" % (fname, i))
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)
if not args.nocheck:
check = check_page(amax(image) - image)
if check is not None:
print_error(fname + " SKIPPED. " + check +
" (use -n to disable this check)")
return
# check whether the image is already effectively binarized
if args.gray:
extreme = 0
else:
extreme = (sum(image < 0.05) + sum(image > 0.95)) * 1.0 / prod(
image.shape)
if extreme > 0.95:
comment = "no-normalization"
flat = image
else:
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)
# 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)
# 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)
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))
print_info("%s lo-hi (%.2f %.2f) %s" % (fname, lo, hi, 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))
base, _ = ocrolib.allsplitext(fname)
ocrolib.write_image_binary(base + ".bin.png", bin)
ocrolib.write_image_gray(base + ".nrm.png", flat)
#print("########### File path : ", base+".nrm.png")
#write_to_xml(base+".bin.png")
return base + ".bin.png"
def analyze_page_layout(binary, gray, rgb=None):
hscale = 1.0 # Non-standard scaling of horizontal parameters.
vscale = 1.0 # Non-standard scaling of vertical parameters.
threshold = 0.2 # baseline threshold.
usegauss = True # Use gaussian instead of uniform.
maxseps = 0 # Maximum black column separators.
sepwiden = 10 # Widen black separators (to account for warping).
blackseps = True
maxcolseps = 3 # Maximum # whitespace column separators.
csminheight = 10 # Minimum column height (units=scale).
noise = 8 # Noise threshold for removing small components from lines.
gray_output = True # Output grayscale lines as well, which are extracted from the grayscale version of the pages.
pad = 3 # Padding for extracted lines.
expand = 3 # Expand mask for grayscale extraction.
if False:
bin_image_filepath = './ocropy_test.bin.png'
gray_image_filepath = './ocropy_test.nrm.png'
binary = ocrolib.read_image_binary(bin_image_filepath)
gray = ocrolib.read_image_gray(gray_image_filepath)
binary = 1 - binary # Invert.
scale = psegutils.estimate_scale(binary)
segmentation = compute_segmentation(binary,
scale,
blackseps,
maxseps,
maxcolseps,
csminheight,
sepwiden,
usegauss,
vscale,
hscale,
threshold,
quiet=True)
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] # Image.
lines = [lines[i] for i in lsort]
# Visualize bounding boxes.
if False:
if rgb is not None:
# REF [function] >> extract_masked() in ${OCROPY_HOME}/ocrolib/psegutils.py.
for l in lines:
y0, x0, y1, x1 = [
int(x) for x in [
l.bounds[0].start, l.bounds[1].start, l.bounds[0].stop,
l.bounds[1].stop
]
]
cv2.rectangle(rgb, (x0, y0), (x1, y1), (0, 0, 255), 1,
cv2.LINE_AA)
cv2.imshow('Image', rgb)
cv2.waitKey(0)
# Output everything.
if False:
if not os.path.exists(outputdir):
os.mkdir(outputdir)
ocrolib.write_page_segmentation("%s.pseg.png" % outputdir,
segmentation)
cleaned = ocrolib.remove_noise(binary, noise)
for i, l in enumerate(lines):
binline = psegutils.extract_masked(1 - cleaned,
l,
pad=pad,
expand=expand) # Image.
ocrolib.write_image_binary(
"%s/01%04x.bin.png" % (outputdir, i + 1), binline)
if gray_output:
grayline = psegutils.extract_masked(gray,
l,
pad=pad,
expand=expand) # Image.
ocrolib.write_image_gray(
"%s/01%04x.nrm.png" % (outputdir, i + 1), grayline)
def binarize(inFile, binFile, grayFile):
print("binarize: inFile=%s binFile=%s grayFile=%s" %
(inFile, binFile, grayFile))
fname = inFile
raw = ocrolib.read_image_gray(inFile)
# perform image normalization
image = normalize_raw_image(raw)
if image is None:
print("!! # image is empty: %s" % (inFile))
assert False
return False
check = check_page(np.amax(image) - image)
if check is not None:
print(inFile + " SKIPPED " + check + "(use -n to disable this check)")
# assert False
return False
# check whether the image is already effectively binarized
extreme = (np.sum(image < 0.05) + np.sum(image > 0.95)) / np.prod(
image.shape)
if extreme > 0.95:
comment = "no-normalization"
flat = image
else:
comment = ""
# if not, we need to flatten it by estimating the local whitelevel
print("flattening")
flat = estimate_local_whitelevel(image, zoom, perc, size)
print("comment=%r extreme=%s" % (comment, extreme))
print("image=%s" % desc(image))
print(" flat=%s" % desc(flat))
# assert False
# estimate skew angle and rotate
# print("estimating skew angle")
# flat, angle = estimate_skew(flat, args.bignore, args.maxskew, args.skewsteps)
angle = 0.0
# estimate low and high thresholds
print("estimating thresholds")
lo, hi, ok = estimate_thresholds(flat, bignore, escale, defLo, defHi)
if not ok:
return False
print("lo=%5.3f (%g)" % (lo, defLo))
print("hi=%5.3f (%g)" % (hi, defHi))
# rescale the image to get the gray scale image
print("rescaling")
flat -= lo
flat /= (hi - lo)
flat = np.clip(flat, 0, 1)
bin = flat > threshold
# output the normalized grayscale and the thresholded images
print("%s lo-hi (%.2f %.2f) angle %4.1f %s" %
(fname, lo, hi, angle, comment))
print("##1 flat=%s" % desc(flat))
print("##2 bin=%s" % desc(bin))
print("writing %s" % binFile)
ocrolib.write_image_binary(binFile, bin)
ocrolib.write_image_gray(grayFile, flat)
return True
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("cannot open either", base + ".bin.png", "or", fname)
return
checktype(binary, ABINARY2)
if not args.nocheck:
check = check_page(amax(binary) - binary)
if check is not None:
print(fname, "SKIPPED", check, "(use -n to disable this 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("scale", scale)
if isnan(scale) or scale > 1000.0:
sys.stderr.write("%s: bad scale (%g); skipping\n" % (fname, scale))
return
if scale < args.minscale:
sys.stderr.write("%s: scale (%g) less than --minscale; skipping\n" %
(fname, scale))
return
# find columns and text lines
if not args.quiet:
print("computing segmentation")
segmentation = compute_segmentation(binary, scale)
if amax(segmentation) > args.maxlines:
print(fname, ": too many lines", amax(segmentation))
return
if not args.quiet:
print("number of lines", amax(segmentation))
# compute the reading order
if not args.quiet:
print("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 = amax(segmentation) + 1
renumber = 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("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("%6d" % i, fname, "%4.1f" % scale, len(lines))
