def go(argv):
im = grayscale(lib.imread(argv[1]))
lib.debug = True
lib.debug_prefix = ['binarize']
# lib.debug_imwrite('gradient2.png', gradient2(im))
lib.debug_imwrite('sauvola_noisy.png', sauvola_noisy(im, k=0.1))
# lib.debug_imwrite('adaptive_otsu.png', binarize(im, algorithm=adaptive_otsu))
# lib.debug_imwrite('ng2014.png', binarize(im, algorithm=ntirogiannis2014))
# lib.debug_imwrite('yan.png', binarize(im, algorithm=yan))
lib.debug_imwrite('sauvola.png', sauvola(im, k=0.1))
def run(args):
if args.single_file:
lib.debug = True
im = lib.imread(args.single_file)
_, out_images = process_image(im, dpi=args.dpi)
for idx, outimg in enumerate(out_images):
cv2.imwrite('out{}.png'.format(idx), outimg)
return
if args.concurrent:
pool = Pool(cpu_count())
map_fn = pool.map
else:
map_fn = map
files = []
accumulate_paths(args.indirs, files)
files = sorted_numeric(list(set(files)))
print('Files:', files)
for p in files:
d = os.path.dirname(p)
if not os.path.isdir(join(args.outdir, d)):
os.makedirs(join(args.outdir, d))
outfiles = map_fn(
process_file,
list(zip(files, [args.outdir] * len(files), [args.dpi] * len(files))))
outfiles = sum(outfiles, [])
outfiles.sort(key=lambda f: list(map(int, re.findall('[0-9]+', f))))
# outtif = join(args.outdir, 'out.tif')
outpdfpath = join(args.outdir, 'out.pdf')
if not isfile(outpdfpath):
print('making pdf:', outpdfpath)
pdf = FPDF(unit='in', format='Letter')
pdf.set_margins(0, 0, 0)
for outfile in outfiles:
print('.', end='')
sys.stdout.flush()
pdf.add_page()
im = cv2.imread(outfile)
inches_w = float(im.shape[1]) / args.dpi
inches_h = float(im.shape[0]) / args.dpi
x = (8.5 - inches_w) / 2
y = (11.0 - inches_h) / 2
pdf.image(outfile, w=inches_w, x=x, y=y)
print()
pdf.output(name=outpdfpath)
def process_file(file_args):
(inpath, outdir, dpi) = file_args
outfiles = glob.glob('{}/{}_*{}'.format(outdir, inpath[:-4], extension))
if outfiles:
print('skipping', inpath)
return outfiles
else:
print('processing', inpath)
original = lib.imread(inpath)
dpi, out_images = process_image(original, dpi=dpi)
for idx, outimg in enumerate(out_images):
outfile = '{}/{}_{}{}'.format(outdir, inpath[:-4], idx, extension)
print(' writing', outfile)
cv2.imwrite(outfile, outimg)
outfiles.append(outfile)
return outfiles
def run(args):
if args.single_file:
lib.debug = True
im = lib.imread(args.single_file)
_, out_images = process_image(im, dpi=args.dpi)
for idx, outimg in enumerate(out_images):
cv2.imwrite('out{}.png'.format(idx), outimg)
return
if args.concurrent:
pool = Pool(cpu_count())
map_fn = pool.map
else:
map_fn = map
files = []
accumulate_paths(args.indirs, files)
files = sorted_numeric(list(set(files)))
print('Files:', files)
for p in files:
d = os.path.dirname(p)
if not os.path.isdir(join(args.outdir, d)):
os.makedirs(join(args.outdir, d))
outfiles = map_fn(process_file, list(zip(files,
[args.outdir] * len(files),
[args.dpi] * len(files))))
outfiles = sum(outfiles, [])
outfiles.sort(key=lambda f: list(map(int, re.findall('[0-9]+', f))))
outtif = join(args.outdir, 'out.tif')
outpdf = join(args.outdir, 'out.pdf')
if not isfile(outpdf):
if not isfile(outtif):
print('making tif:', outtif)
check_call(['tiffcp'] + outfiles + [outtif])
print('making pdf:', outpdf)
check_call([
'tiff2pdf', '-z', '-p', 'letter',
'-o', outpdf, outtif
])
def process_file(file_args):
(inpath, outdir, dpi) = file_args
outfiles = glob.glob('{}/{}_*{}'.format(outdir, inpath[:-4], extension))
if outfiles:
print('skipping', inpath)
if dpi is not None:
for outfile in outfiles:
check_call(['tiffset', '-s', '282', str(dpi), outfile])
check_call(['tiffset', '-s', '283', str(dpi), outfile])
return outfiles
else:
print('processing', inpath)
original = lib.imread(inpath)
dpi, out_images = process_image(original, dpi=dpi)
for idx, outimg in enumerate(out_images):
outfile = '{}/{}_{}{}'.format(outdir, inpath[:-4], idx, extension)
print(' writing', outfile)
cv2.imwrite(outfile, outimg)
check_call(['tiffset', '-s', '282', str(dpi), outfile])
check_call(['tiffset', '-s', '283', str(dpi), outfile])
outfiles.append(outfile)
return outfiles
def add_text(img, H=100, D=80, Min=0, Max=100):
if D > Max:
color = (0, 0, 255)
elif D < Min:
color = (0, 255, 0)
else:
color = (0, 255 * (Max - D) / (Max - Min),
255 - 255 * (Max - D) / (Max - Min))
text = 'Height: ' + str(H)
cv2.putText(img, text, (75, 120), cv2.FONT_HERSHEY_DUPLEX, 0.3, color, 1)
text = 'Distance: ' + str(D)
cv2.putText(img, text, (75, 130), cv2.FONT_HERSHEY_DUPLEX, 0.3, color, 1)
return img
img_path = raw_input("image name:")
img_path = '../img/' + img_path + '.jpg'
img = imread(img_path)
# detect circles
img_ = rgb2blue(img)
circles = detect_circles(img_, 0)
circle = best_circle(circles, img_)
img = plot_arrow(img, (circle[0], circle[1]))
img = add_text(img)
img = cv2.resize(img, (750, 750))
cv2.imshow('haha', img)
cv2.waitKey(0)