def draw_boxes( boxfilename, outdir=None) :
box_list = zonebox.load_boxes(boxfilename)
print box_list
# get the document id from the box file
# imgfilename = "IMAGEBIN/{0}BIN.TIF".format( box_list[0].document_id )
imgfilename = "IMAGEBIN/{0}BIN.png".format( box_list[0].document_id )
# write the output image to this file and perhaps to another directory
if not outdir is None :
outfilename = outdir + "/" + get_basename(imgfilename) + "_zoneboxes.png"
else :
outfilename = get_basename(imgfilename) + "_zoneboxes.png"
img = load_image(imgfilename)
draw = ImageDraw.Draw(img)
# draw the zone boxes onto the image
for box in box_list :
box.sanity()
# print box.document_id
# print box.corner_one
# print box.corner_two
upper_left = (box.corner_one["col"],box.corner_one["row"])
lower_right = (box.corner_two["col"],box.corner_two["row"])
draw.rectangle( (upper_left,lower_right), outline="red" )
img.save(outfilename)
print "wrote",outfilename
def draw_boxes(boxfilename, outdir=None):
box_list = zonebox.load_boxes(boxfilename)
print box_list
# get the document id from the box file
# imgfilename = "IMAGEBIN/{0}BIN.TIF".format( box_list[0].document_id )
imgfilename = "IMAGEBIN/{0}BIN.png".format(box_list[0].document_id)
# write the output image to this file and perhaps to another directory
if not outdir is None:
outfilename = outdir + "/" + get_basename(
imgfilename) + "_zoneboxes.png"
else:
outfilename = get_basename(imgfilename) + "_zoneboxes.png"
img = load_image(imgfilename)
draw = ImageDraw.Draw(img)
# draw the zone boxes onto the image
for box in box_list:
box.sanity()
# print box.document_id
# print box.corner_one
# print box.corner_two
upper_left = (box.corner_one["col"], box.corner_one["row"])
lower_right = (box.corner_two["col"], box.corner_two["row"])
draw.rectangle((upper_left, lower_right), outline="red")
img.save(outfilename)
print "wrote", outfilename
def main() :
from basename import get_basename
infilename = sys.argv[1]
ndata = imtools.load_image( infilename, mode="L", dtype="uint8" )
print ndata.shape
# aggressive median filter to smooth out as much noise as possible
if 1 :
print "filtering..."
fdata = scipy.ndimage.filters.median_filter( ndata, size=(5,5) )
else :
fdata = np.copy(ndata) # no smoothing
basename = get_basename(infilename)
global mkoutfilename
mkoutfilename = lambda s : "{0}_{1}.tif".format(basename,s)
imtools.clip_and_save( fdata, mkoutfilename("gray"))
np.save("gray.npy",fdata)
peaks_list, pixel_counts = find_histogram_peaks( fdata )
print "peaks=",peaks_list
print "counts=",pixel_counts
def make_sliding_strips_from_box( boxfilename ) :
basename = get_basename( boxfilename )
# create the output directory for all the files I'm about to create
output_dir = make_output_dir(basename)
# return
box_list = zonebox.load_boxes( boxfilename )
# load the image associated with this box list
# assume all the boxes have the same image name (they should)
imgfilename = "IMAGEBIN/{0}BIN.png".format( box_list[0].document_id )
# get the image as a numpy array
data = mkslices.load_image( imgfilename )
strip_list = make_all_strips_images( data, basename, output_dir )
#
# Now make the ground truth files for each strip
#
# convert the box list into a list of strips
box_strip_list = [ rects.Strip(box=box) for box in box_list ]
# slice up the ground truth into individual XML files
make_all_gtruth_xml( box_strip_list, data, output_dir, basename )
def main( infilename ) :
basename = get_basename(infilename)
# try to load previous file for speed
try :
ycc = np.load( basename+"_ycc.npy" )
except IOError as err :
# convert image to ycc
ycc = convert_image_to_ycc( infilename )
clip_and_save( convert_ycc_to_bgr(ycc),"out.tif")
# davep 09-Jan-2013 ; XXX stop after the conversion
return
gamma = 2.2
run_gamma( ycc, basename, gamma )
ycc_gamma = gamma_enhance( ycc, gamma )
ycc_contrast = contrast_enhance( ycc_gamma, 1.4 )
outfilename = "{0}_gamma_contrast.tif".format( basename )
clip_and_save( convert_ycc_to_rgb( ycc_contrast ), outfilename )
# run_contrast( ycc, basename, 2.2 )
# for contrast in np.linspace( 0, 2, num=30 ) :
# run_contrast( ycc, basename, contrast )
for b in np.linspace(-1,1) :
contrast = 1.0
brightness = int(b*127)
def make_sliding_strips_from_box(boxfilename):
basename = get_basename(boxfilename)
# create the output directory for all the files I'm about to create
output_dir = make_output_dir(basename)
# return
box_list = zonebox.load_boxes(boxfilename)
# load the image associated with this box list
# assume all the boxes have the same image name (they should)
imgfilename = "IMAGEBIN/{0}BIN.png".format(box_list[0].document_id)
# get the image as a numpy array
data = mkslices.load_image(imgfilename)
strip_list = make_all_strips_images(data, basename, output_dir)
#
# Now make the ground truth files for each strip
#
# convert the box list into a list of strips
box_strip_list = [rects.Strip(box=box) for box in box_list]
# slice up the ground truth into individual XML files
make_all_gtruth_xml(box_strip_list, data, output_dir, basename)
def run_image_with_gtruth(imgfilename, gtruth_xml_filename, output_dir):
basename = get_basename(imgfilename)
document_id = get_document_id_from_basename(basename)
out_imgfilename = os.path.join(output_dir, "{0}_{1}.png".format(basename, segmentation_algorithm))
xml_filename = os.path.join(output_dir, "{0}_{1}.xml".format(basename, segmentation_algorithm))
# inputs
# print "imgfilename=",imgfilename
# print "gtruth_xml_filename=",gtruth_xml_filename
# outputs
# print "xml_filename=",xml_filename
# print "out_imgfilename=",out_imgfilename
# sys.exit(0)
# segment the image
cmd = "{0} {1} {2}".format(segmentation_cmd, imgfilename, out_imgfilename)
# cmd = "./rast-ocropus {0} {1}".format( imgfilename, out_imgfilename )
signal.signal(signal.SIGALRM, handler)
# small optimization; if the result already exists, don't run it again
# (crash recovery)
print cmd
if os.path.exists(xml_filename):
print "{0} already exists so assume seg already run".format(xml_filename)
else:
global running_filename
running_filename = imgfilename
signal.alarm(5)
try:
result = subprocess.check_output(cmd.split())
signal.alarm(0)
except subprocess.CalledProcessError:
signal.alarm(0)
return {"output_image_file": out_imgfilename, "output_xml_file": "failed", "metric": 0}
# write the XML results
with open(xml_filename, "w") as outfile:
print >> outfile, result
print "wrote", xml_filename
# run the compare
cmd = "./runZoneComp -g {0} -d {1}".format(gtruth_xml_filename, xml_filename)
print cmd
try:
result = subprocess.check_output(cmd.split())
except subprocess.CalledProcessError:
return {"output_image_file": out_imgfilename, "output_xml_file": "failed", "metric": 0}
# get the segmentation metric from the output
metric = parse_runZoneComp_result(result)
print "metric={0}".format(metric)
return {"output_image_file": out_imgfilename, "output_xml_file": xml_filename, "metric": metric}
def main_2():
def make_outdir(img_filename):
# split a png filename into an appropriate output path
pathname, filename = os.path.split(img_filename)
# print pathname
# print filename
dirlist = pathname.split(os.sep)
# print dirlist, dirlist[1:]
outdir = os.path.join(*dirlist[1:])
# print outdir
return outdir
output_dir_base = "600_winder_{0}".format(runseg.segmentation_algorithm)
input_dirname = "600_winder"
img_filelist, xml_filelist = load_image_and_xml_list(input_dirname)
output_dir_hash = {}
for i, x in zip(img_filelist, xml_filelist):
od = make_outdir(i)
if not od in output_dir_hash:
output_dir_hash[od] = []
output_dir_hash[od].append(get_basename(i))
pklname = "{0}_output_dir_hash.pkl".format(input_dirname)
f = open(pklname, "wb")
pickle.dump(output_dir_hash, f)
f.close()
# print "wrote",pklname
# fname_fmt = "{0}_{1}".format( input_dirname
for dirname in output_dir_hash.keys():
filelist = [
os.path.join(input_dirname, dirname, f)
for f in output_dir_hash[dirname]
]
img_filelist = [s + ".png" for s in filelist]
xml_filelist = [s + ".xml" for s in filelist]
output_basename = os.path.split(dirname)[-1]
print img_filelist[0]
print xml_filelist[0]
output_dir = os.path.join(output_dir_base, dirname)
print "outputdir=", output_dir
print output_basename
if not os.path.exists(output_dir):
os.makedirs(output_dir)
runseg.run_file_list(img_filelist, xml_filelist, output_dir,
output_basename)
sys.exit(0)
def main_2() :
def make_outdir(img_filename) :
# split a png filename into an appropriate output path
pathname,filename = os.path.split(img_filename)
# print pathname
# print filename
dirlist = pathname.split(os.sep)
# print dirlist, dirlist[1:]
outdir = os.path.join(*dirlist[1:])
# print outdir
return outdir
output_dir_base = "600_winder_{0}".format(runseg.segmentation_algorithm)
input_dirname = "600_winder"
img_filelist, xml_filelist = load_image_and_xml_list( input_dirname )
output_dir_hash = {}
for i,x in zip(img_filelist,xml_filelist) :
od = make_outdir(i)
if not od in output_dir_hash :
output_dir_hash[od] = []
output_dir_hash[od].append( get_basename(i) )
pklname = "{0}_output_dir_hash.pkl".format(input_dirname)
f = open( pklname, "wb" )
pickle.dump(output_dir_hash,f)
f.close()
# print "wrote",pklname
# fname_fmt = "{0}_{1}".format( input_dirname
for dirname in output_dir_hash.keys() :
filelist = [ os.path.join(input_dirname,dirname,f) for f in
output_dir_hash[dirname] ]
img_filelist = [ s+".png" for s in filelist ]
xml_filelist = [ s+".xml" for s in filelist ]
output_basename = os.path.split(dirname)[-1]
print img_filelist[0]
print xml_filelist[0]
output_dir = os.path.join(output_dir_base,dirname)
print "outputdir=",output_dir
print output_basename
if not os.path.exists(output_dir) :
os.makedirs(output_dir)
runseg.run_file_list( img_filelist, xml_filelist, output_dir, output_basename )
sys.exit(0)
def main():
import imtools
from basename import get_basename
infilename = sys.argv[1]
basename = get_basename(infilename)
outfilename = "{0}_gray.tif".format(basename)
ndata = imtools.load_image(infilename,dtype="uint8")
gray = togray(ndata)
imtools.clip_and_save(gray,outfilename)
def sanity_check_images(document_id):
img_file_list, xml_file_list = get_file_lists(document_id)
for imgfilename, xmlfilename in zip(img_file_list, xml_file_list):
basename = get_basename(imgfilename)
document_id = get_document_id_from_basename(basename)
print imgfilename, xmlfilename, document_id
img = Image.open(imgfilename)
img.load()
xmlfile = open(xmlfilename, "r")
xmlfile.close()
del img
def sanity_check_images(document_id):
img_file_list, xml_file_list = get_file_lists(document_id)
for imgfilename, xmlfilename in zip(img_file_list, xml_file_list):
basename = get_basename(imgfilename)
document_id = get_document_id_from_basename(basename)
print imgfilename, xmlfilename, document_id
img = Image.open(imgfilename)
img.load()
xmlfile = open(xmlfilename, "r")
xmlfile.close()
del img
def save_results(data):
# get an output filename by parsing the first filename in the list
basename = get_basename(data[0][0])
document_id = get_document_id_from_basename(basename)
print document_id
output_filename = output_dir + document_id + ".dat"
# get just the valid metrics
metric_list = []
num_failures = 0
for d in data:
try:
metric = float(d[1])
except ValueError:
# skip
metric = 0
num_failures += 1
metric_list.append(metric)
print metric_list
metric_data = np.asarray(metric_list, dtype="float")
outfile = open(output_filename, "w")
# write some overall statistics
print >> outfile, "# id={0}".format(document_id)
print >> outfile, "# mean={0}".format(np.mean(metric_data))
print >> outfile, "# median={0}".format(np.median(metric_data))
print >> outfile, "# stddev={0}".format(np.std(metric_data))
print >> outfile, "# num_failures={0}".format(num_failures)
for d in data:
basename = get_basename(d[0])
print >> outfile, basename, d[1]
outfile.close()
def save_results( data ) :
# get an output filename by parsing the first filename in the list
basename = get_basename( data[0][0] )
document_id = get_document_id_from_basename( basename )
print document_id
output_filename = output_dir + document_id + ".dat"
# get just the valid metrics
metric_list = []
num_failures = 0
for d in data :
try :
metric = float(d[1])
except ValueError :
# skip
metric = 0
num_failures += 1
metric_list.append( metric )
print metric_list
metric_data = np.asarray( metric_list, dtype="float")
outfile = open(output_filename,"w")
# write some overall statistics
print >>outfile, "# id={0}".format( document_id )
print >>outfile, "# mean={0}".format( np.mean(metric_data) )
print >>outfile, "# median={0}".format( np.median(metric_data) )
print >>outfile, "# stddev={0}".format( np.std(metric_data) )
print >>outfile, "# num_failures={0}".format( num_failures)
for d in data :
basename = get_basename(d[0])
print >>outfile, basename,d[1]
outfile.close()
def run_all_uwiii():
# run all UW-III images
output_dir = "fullpage/"
result_list = []
for imgfilename in sys.argv[1:]:
basename = get_basename(imgfilename)
document_id = get_document_id_from_basename(basename)
# zone box files use "ZONE" instead of "BIN"
# e.g.,
# A00ABIN_300_010_2990.png -> A00AZONE_300_010_2990.xml
gtruth_xml_filename = uwiii_xmldir + "{0}.xml".format(
basename.replace("BIN", "ZONE"))
# print basename, document_id, gtruth_xml_filename
result = run_image_with_gtruth(imgfilename, gtruth_xml_filename)
result_list.append(result)
# save pickled file so can do interesting things with the results later
# (especially if we crash)
output = open("uwiii.pkl", "wb")
pickle.dump(result_list, output)
output.close()
xmlfilename = result["output_xml_file"]
# did we crash? if so don't bother trying to draw the XML
if xmlfilename == "failed":
print "{0} failed!".format(imgfilename)
continue
# draw the resulting XML onto the original input image
out_imgfilename = output_dir + "{0}_zones.png".format(document_id)
if os.path.exists(out_imgfilename):
print "{0} already exists; not redrawing".format(out_imgfilename)
continue
drawxml.draw_zones(xmlfilename, imgfilename, out_imgfilename)
print "wrote", out_imgfilename
outfile = open("uwiii.dat", "w")
for result in result_list:
print >> outfile, "{0} {1} {2}".format(result["output_image_file"],
result["output_xml_file"],
result["metric"])
outfile.close()
def get_winder_class_results(class_dir) :
# load all .dat files corresponding to each class of image in the Winder
# data set
# gather all the datafiles from the fullpage winder
fullpage_winder = datfile.find_all(class_dir)
class_data = []
class_names = []
for datfilename in fullpage_winder :
ndata = datfile.load(datfilename)
basename = get_basename(datfilename)
class_data.append( np.mean(ndata) )
class_names.append( basename.replace( "_"," ") )
return (class_data,class_names)
def main() :
infilename = sys.argv[1]
ndata = imtools.load_image(infilename,dtype="uint8",mode="L")
# aggressive median filter to smooth out as much noise as possible
fdata = scipy.ndimage.filters.median_filter( ndata, size=(5,5) )
basename = get_basename(infilename)
global mkoutfilename
mkoutfilename = lambda s : "{0}_{1}.tif".format(basename,s)
is_straight = straightness_test( fdata, 126, 146, 60 )
if is_straight :
print "is straight enough"
else :
print "is NOT straight enough"
def load_image_and_xml_list(dirname):
# sweep a dir; load the list of .png and corresponding ground truth .xml
# files. Store in a .pkl as cache
pklfilename = dirname + ".pkl"
test = 0
if os.path.exists(pklfilename):
pfile = open(pklfilename, "rb")
img_filelist, xml_filelist = pickle.load(pfile)
pfile.close()
print "loaded from pickle"
return img_filelist, xml_filelist
img_filelist = []
xml_filelist = []
for root, dirs, files in os.walk(dirname):
for f in files:
path = os.path.join(root, f)
if path.endswith(".png"):
imgfilename = path
basename = get_basename(imgfilename)
if test:
img = Image.open(path)
img.load()
del img
# look for the xmlfile
xmlfilename = path.replace(".png", ".xml")
if test:
f = open(xmlfilename, "r")
f.close()
print imgfilename, xmlfilename
img_filelist.append(imgfilename)
xml_filelist.append(xmlfilename)
pfile = open(pklfilename, "wb")
pickle.dump((img_filelist, xml_filelist), pfile)
pfile.close()
return img_filelist, xml_filelist
def run_all_uwiii():
# run all UW-III images
output_dir = "fullpage/"
result_list = []
for imgfilename in sys.argv[1:]:
basename = get_basename(imgfilename)
document_id = get_document_id_from_basename(basename)
# zone box files use "ZONE" instead of "BIN"
# e.g.,
# A00ABIN_300_010_2990.png -> A00AZONE_300_010_2990.xml
gtruth_xml_filename = uwiii_xmldir + "{0}.xml".format(basename.replace("BIN", "ZONE"))
# print basename, document_id, gtruth_xml_filename
result = run_image_with_gtruth(imgfilename, gtruth_xml_filename)
result_list.append(result)
# save pickled file so can do interesting things with the results later
# (especially if we crash)
output = open("uwiii.pkl", "wb")
pickle.dump(result_list, output)
output.close()
xmlfilename = result["output_xml_file"]
# did we crash? if so don't bother trying to draw the XML
if xmlfilename == "failed":
print "{0} failed!".format(imgfilename)
continue
# draw the resulting XML onto the original input image
out_imgfilename = output_dir + "{0}_zones.png".format(document_id)
if os.path.exists(out_imgfilename):
print "{0} already exists; not redrawing".format(out_imgfilename)
continue
drawxml.draw_zones(xmlfilename, imgfilename, out_imgfilename)
print "wrote", out_imgfilename
outfile = open("uwiii.dat", "w")
for result in result_list:
print >> outfile, "{0} {1} {2}".format(result["output_image_file"], result["output_xml_file"], result["metric"])
outfile.close()
def load_image_and_xml_list( dirname ) :
# sweep a dir; load the list of .png and corresponding ground truth .xml
# files. Store in a .pkl as cache
pklfilename = dirname+".pkl"
test = 0
if os.path.exists(pklfilename) :
pfile=open(pklfilename,"rb")
img_filelist,xml_filelist = pickle.load(pfile)
pfile.close()
print "loaded from pickle"
return img_filelist,xml_filelist
img_filelist = []
xml_filelist = []
for root,dirs,files in os.walk(dirname) :
for f in files :
path=os.path.join(root,f)
if path.endswith(".png") :
imgfilename = path
basename = get_basename(imgfilename)
if test :
img = Image.open(path)
img.load()
del img
# look for the xmlfile
xmlfilename = path.replace(".png",".xml")
if test:
f = open(xmlfilename,"r")
f.close()
print imgfilename, xmlfilename
img_filelist.append( imgfilename )
xml_filelist.append( xmlfilename )
pfile = open(pklfilename,"wb")
pickle.dump((img_filelist,xml_filelist),pfile)
pfile.close()
return img_filelist,xml_filelist
def run_file_list(img_filelist, xml_filelist, output_dir, output_basename):
result_list = []
pickle_filename = os.path.join(output_dir, output_basename + ".pkl")
dat_filename = os.path.join(output_dir, output_basename + ".dat")
for imgfilename, gtruth_xml_filename in zip(img_filelist, xml_filelist):
basename = get_basename(imgfilename)
document_id = get_document_id_from_basename(basename)
result = run_image_with_gtruth(imgfilename, gtruth_xml_filename,
output_dir)
result_list.append(result)
# save pickled file so can do interesting things with the results later
# (especially if we crash)
output = open(pickle_filename, "wb")
pickle.dump(result_list, output)
output.close()
xmlfilename = result["output_xml_file"]
# did we crash? if so don't bother trying to draw the XML
if xmlfilename == "failed":
print "{0} failed!".format(imgfilename)
continue
# draw the resulting XML onto the original input image
out_imgfilename = os.path.join(output_dir,
"{0}_zones.png".format(basename))
if os.path.exists(out_imgfilename):
print "{0} already exists; not redrawing".format(out_imgfilename)
continue
drawxml.draw_zones(xmlfilename, imgfilename, out_imgfilename)
print "wrote", out_imgfilename
outfile = open(dat_filename, "w")
for result in result_list:
print >> outfile, "{0} {1} {2}".format(result["output_image_file"],
result["output_xml_file"],
result["metric"])
outfile.close()
def awinder():
# slice up Amy Winder's images
output_dir_base = "{0}_winder/".format(num_rows_in_strip)
# output_dir_base = "300_winder/"
for imgfilename in sys.argv[1:]:
basename = get_basename(imgfilename)
input_path = os.path.dirname(imgfilename)
# get rid of the trailing "/png" component (it's annoying)
input_path = input_path.replace("/png", "/")
output_dir = output_dir_base + input_path + basename
print output_dir
if os.path.exists(output_dir):
print "{0} exists so assume files are OK".format(output_dir)
continue
else:
os.makedirs(output_dir)
make_sliding_strips_from_image(imgfilename, output_dir)
xmlfilename = imgfilename.replace(".png", ".xml")
xmlfilename = xmlfilename.replace("png", "gTruth")
print xmlfilename
try:
zone_list = gtruthxml.parse_xml(xmlfilename)
except IOError, e:
if e.errno == 2:
# some of the files are living in a ./xml subdir
xmlfilename = imgfilename.replace(".png", ".xml")
xmlfilename = xmlfilename.replace("png", "gTruth/xml")
zone_list = gtruthxml.parse_xml(xmlfilename)
else:
raise
print zone_list
# get the image as a numpy array
data = mkslices.load_image(imgfilename)
# slice up the ground truth into individual XML files
make_all_gtruth_xml(zone_list, data, output_dir, basename)
def awinder( ) :
# slice up Amy Winder's images
output_dir_base = "{0}_winder/".format(num_rows_in_strip)
# output_dir_base = "300_winder/"
for imgfilename in sys.argv[1:] :
basename = get_basename(imgfilename)
input_path = os.path.dirname(imgfilename)
# get rid of the trailing "/png" component (it's annoying)
input_path = input_path.replace( "/png", "/" )
output_dir = output_dir_base + input_path + basename
print output_dir
if os.path.exists(output_dir) :
print "{0} exists so assume files are OK".format(output_dir)
continue
else:
os.makedirs(output_dir)
make_sliding_strips_from_image( imgfilename, output_dir )
xmlfilename = imgfilename.replace(".png",".xml")
xmlfilename = xmlfilename.replace("png","gTruth")
print xmlfilename
try :
zone_list = gtruthxml.parse_xml( xmlfilename )
except IOError,e:
if e.errno==2 :
# some of the files are living in a ./xml subdir
xmlfilename = imgfilename.replace(".png",".xml")
xmlfilename = xmlfilename.replace("png","gTruth/xml")
zone_list = gtruthxml.parse_xml( xmlfilename )
else :
raise
print zone_list
# get the image as a numpy array
data = mkslices.load_image( imgfilename )
# slice up the ground truth into individual XML files
make_all_gtruth_xml( zone_list, data, output_dir, basename )
def run_file_list(img_filelist, xml_filelist, output_dir, output_basename):
result_list = []
pickle_filename = os.path.join(output_dir, output_basename + ".pkl")
dat_filename = os.path.join(output_dir, output_basename + ".dat")
for imgfilename, gtruth_xml_filename in zip(img_filelist, xml_filelist):
basename = get_basename(imgfilename)
document_id = get_document_id_from_basename(basename)
result = run_image_with_gtruth(imgfilename, gtruth_xml_filename, output_dir)
result_list.append(result)
# save pickled file so can do interesting things with the results later
# (especially if we crash)
output = open(pickle_filename, "wb")
pickle.dump(result_list, output)
output.close()
xmlfilename = result["output_xml_file"]
# did we crash? if so don't bother trying to draw the XML
if xmlfilename == "failed":
print "{0} failed!".format(imgfilename)
continue
# draw the resulting XML onto the original input image
out_imgfilename = os.path.join(output_dir, "{0}_zones.png".format(basename))
if os.path.exists(out_imgfilename):
print "{0} already exists; not redrawing".format(out_imgfilename)
continue
drawxml.draw_zones(xmlfilename, imgfilename, out_imgfilename)
print "wrote", out_imgfilename
outfile = open(dat_filename, "w")
for result in result_list:
print >> outfile, "{0} {1} {2}".format(result["output_image_file"], result["output_xml_file"], result["metric"])
outfile.close()
def load_image( imgfilename ) :
img = Image.open(imgfilename)
img.load()
if img.mode == "RGB" :
errmsg="mode={0}; cowardly refusing a non-gray image".format( img.mode )
raise Exception( errmsg )
# single bit image? So much I don't know. I don know unless I convert to an
# 8bpp gray, the numpy conversion gets... weird.
if img.mode == "1" :
img2 = img.convert("L")
img = img2
del img2
basename = get_basename( imgfilename )
data = np.asarray(img,dtype="uint8")
print "shape=",data.shape
return data
def load_image(imgfilename):
img = Image.open(imgfilename)
img.load()
if img.mode == "RGB":
errmsg = "mode={0}; cowardly refusing a non-gray image".format(
img.mode)
raise Exception(errmsg)
# single bit image? So much I don't know. I don know unless I convert to an
# 8bpp gray, the numpy conversion gets... weird.
if img.mode == "1":
img2 = img.convert("L")
img = img2
del img2
basename = get_basename(imgfilename)
data = np.asarray(img, dtype="uint8")
print "shape=", data.shape
return data
def convert_image_to_ycc( infilename ) :
basename = get_basename(infilename)
img = Image.open( infilename )
img.load()
if img.mode != "RGB" :
print("{0} is not an RGB image.".format( infilename ))
sys.exit(1)
print("img size=",img.size)
rgb = np.asarray( img, dtype="float" )
print(rgb.shape, rgb.dtype)
# convert RGB -> YCC
# ycc = np.zeros_like( rgb )
# for row in range(rgb.shape[0]):
# ycc[row] = rgb[row] * rgb_to_ycc.T
ycc = convert_rgb_to_ycc( rgb )
np.save( basename+"_ycc", ycc )
save_ycc_as_image( ycc, basename )
return ycc
def draw_zones(xmlfilename, imgfilename, outfilename=None):
img = load_image(imgfilename)
draw = ImageDraw.Draw(img)
zone_list = gtruthxml.parse_xml(xmlfilename)
for zone in zone_list:
if zone.value == "Non-text":
color = "red"
else:
color = "green"
x1 = zone.rect[0].x
y1 = zone.rect[0].y
x2 = zone.rect[2].x
y2 = zone.rect[2].y
draw.rectangle(((x1, y1), (x2, y2)), outline=color)
if outfilename is None:
outfilename = get_basename(imgfilename) + "_zones.png"
img.save(outfilename)
return outfilename
def draw_zones( xmlfilename, imgfilename, outfilename=None ) :
img = load_image( imgfilename )
draw = ImageDraw.Draw(img)
zone_list = gtruthxml.parse_xml( xmlfilename )
for zone in zone_list :
if zone.value=="Non-text" :
color = "red"
else :
color = "green"
x1 = zone.rect[0].x
y1 = zone.rect[0].y
x2 = zone.rect[2].x
y2 = zone.rect[2].y
draw.rectangle( ((x1,y1),(x2,y2)), outline=color)
if outfilename is None :
outfilename = get_basename( imgfilename )+"_zones.png"
img.save(outfilename)
return outfilename
def run_image_with_gtruth(imgfilename, gtruth_xml_filename, output_dir):
basename = get_basename(imgfilename)
document_id = get_document_id_from_basename(basename)
out_imgfilename = os.path.join(
output_dir, "{0}_{1}.png".format(basename, segmentation_algorithm))
xml_filename = os.path.join(
output_dir, "{0}_{1}.xml".format(basename, segmentation_algorithm))
# inputs
# print "imgfilename=",imgfilename
# print "gtruth_xml_filename=",gtruth_xml_filename
# outputs
# print "xml_filename=",xml_filename
# print "out_imgfilename=",out_imgfilename
# sys.exit(0)
# segment the image
cmd = "{0} {1} {2}".format(segmentation_cmd, imgfilename, out_imgfilename)
# cmd = "./rast-ocropus {0} {1}".format( imgfilename, out_imgfilename )
signal.signal(signal.SIGALRM, handler)
# small optimization; if the result already exists, don't run it again
# (crash recovery)
print cmd
if os.path.exists(xml_filename):
print "{0} already exists so assume seg already run".format(
xml_filename)
else:
global running_filename
running_filename = imgfilename
signal.alarm(5)
try:
result = subprocess.check_output(cmd.split())
signal.alarm(0)
except subprocess.CalledProcessError:
signal.alarm(0)
return {
"output_image_file": out_imgfilename,
"output_xml_file": "failed",
"metric": 0
}
# write the XML results
with open(xml_filename, "w") as outfile:
print >> outfile, result
print "wrote", xml_filename
# run the compare
cmd = "./runZoneComp -g {0} -d {1}".format(gtruth_xml_filename,
xml_filename)
print cmd
try:
result = subprocess.check_output(cmd.split())
except subprocess.CalledProcessError:
return {
"output_image_file": out_imgfilename,
"output_xml_file": "failed",
"metric": 0
}
# get the segmentation metric from the output
metric = parse_runZoneComp_result(result)
print "metric={0}".format(metric)
return {
"output_image_file": out_imgfilename,
"output_xml_file": xml_filename,
"metric": metric
}
def run( imgfilename ) :
basename = get_basename(imgfilename)
document_id = get_document_id_from_basename( basename )
# destination for the output files
make_output_dir(document_id)
# stripnum = get_stripnum_from_filename( basename )
out_imgfilename = output_dir + "{0}_rast.png".format( basename )
xml_filename = output_dir + "{0}_rast.xml".format( basename )
input_dir = "{0}/{1}/".format( num_rows_in_strip, document_id )
# zone box files use "ZONE" instead of "BIN"
# e.g.,
# A00ABIN_300_010_2990.png -> A00AZONE_300_010_2990.xml
gtruth_xml_filename = input_dir + "{0}.xml".format( basename.replace("BIN","ZONE") )
print "imgfilename=",imgfilename
print "out_imgfilename=",out_imgfilename
print "xml_filename=",xml_filename
print "gtruth_xml_filename=",gtruth_xml_filename
# sys.exit(0)
# segment the image
cmd = "./rast-ocropus {0} {1}".format( imgfilename, out_imgfilename )
print cmd
try :
result = subprocess.check_output( cmd, shell=True )
except subprocess.CalledProcessError :
return (imgfilename,"failed")
# remove some clutter
os.unlink(out_imgfilename)
# write the XML results
with open(xml_filename,"w") as outfile :
print >>outfile, result
print "wrote", xml_filename
# run the compare
cmd = "runZoneComp -g {0} -d {1}".format( gtruth_xml_filename, xml_filename )
print cmd
result = subprocess.check_output( cmd, shell=True )
# get the segmentation metric from the output
metric = parse_runZoneComp_result( result )
print "metric={0}".format( metric )
# draw the experimental result onto the input image
out_imgfilename = output_dir + "{0}_rast_zone.png".format( basename )
fname = drawxml.draw_zones( xml_filename, imgfilename, out_imgfilename )
print "wrote", fname
# remove some clutter
# os.unlink(xml_filename)
return (imgfilename,metric)
def make_sliding_strips_from_image(imgfilename, output_dir):
basename = get_basename(imgfilename)
# get the image as a numpy array
data = mkslices.load_image(imgfilename)
strip_list = make_all_strips_images(data, basename, output_dir)
def test_get_basename():
assert get_basename('') == ''
assert get_basename('/') == ''
assert get_basename('/sort') == 'sort'
assert get_basename('sort') == 'sort'
assert get_basename('/usr/sort') == 'sort'
def main() :
infilename = sys.argv[1]
basename = get_basename(infilename)
global mkoutfilename
mkoutfilename = lambda s : "{0}_{1}.tif".format(basename,s)
ndata = imtools.load_image( infilename, mode="L", dtype="uint8" )
print ndata.dtype, ndata.shape
# # davep 19-Oct-2013 ; testing clip_rotated
# clip_rotated(ndata,2074)
# return
# # get rid of the obnoxious bezel shadow in my test image >:-(
# ndata = ndata[ bezel_row:, bezel_col: ]
# aggressive median filter to smooth out as much noise as possible
print "filtering..."
fdata = scipy.ndimage.filters.median_filter( ndata, size=(5,5) )
imtools.clip_and_save( fdata, mkoutfilename("gray"))
# find the optimum gray midpoint
print "finding boundaries..."
gray_low,gray_high = gray.calc_gray_boundaries(fdata)
# XXX temp debug ; leave here while working on gray background discovery
# return
gray1 = np.where(fdata>gray_low,fdata,0)
imtools.clip_and_save( gray1, mkoutfilename("gray1") )
gray2 = np.where(gray1<gray_high,gray1,0)
imtools.clip_and_save( gray2, mkoutfilename("gray2") )
nz = np.nonzero( gray2 )
np.save("nz.npy",nz)
# nz[0] is the rows
# nz[1] is the cols
min_row = np.min(nz[0])
max_row = np.max(nz[0])
min_col = np.min(nz[1])
max_col = np.max(nz[1])
print "min_row={0} max_row={1}".format(min_row,max_row)
print "min_col={0} max_col={1}".format(min_col,max_col)
# reload the original image
ndata = imtools.load_image(infilename,dtype="uint8")
# clip original image to bounding box
bbox = ndata[min_row:max_row, min_col:max_col]
imtools.clip_and_save(bbox,mkoutfilename("bbox"))
# clip gray2 to bbox
gray_bbox = gray2[min_row:max_row, min_col:max_col]
np.save("gray.npy",gray_bbox)
imtools.clip_and_save(gray_bbox,mkoutfilename("gray_bbox"))
# XXX temp debug ; stop here while working on skew
# draw_hypotenuse(infilename,nz)
# sys.exit(0)
# The q60 may not be heavily skewed. Sample the four corners of the
# bounding box. If the areas at the corners are mostly gray, call it good.
# Solves the problem of very, very small skew and the ragged edge of the
# Q60 causing the edges to land halfway down the target.
#
# Note we run the straightness test on the filtered (smoothed) data
is_straight = straight.straightness_test( fdata[min_row:max_row, min_col:max_col],
gray_low, gray_high )
if is_straight :
# close enough!
print "close enough!"
imtools.clip_and_save(bbox,mkoutfilename("q60"))
return
# davep 10-Oct-2013 ; stop here while testing straightness test
# assert 0
print "is not straight enough so lets rotate"
# davep 19-Sep-2013 ; new de-skew
rotate_direction = detect_rotate_direction( gray_bbox )
print "rotate={0}".format(rotate_direction)
rotation_angle, hyp_length = detect_rotation_angle( gray_bbox, rotate_direction )
print "angle={0}={1} hyp={2}".format(
rotation_angle,math.degrees(rotation_angle),hyp_length)
print "rotating radians={0} degrees={1}...".format(rotation_angle,
math.degrees(rotation_angle))
rot = scipy.ndimage.interpolation.rotate( bbox, math.degrees(rotation_angle) )
print "rot=",rot.shape
imtools.clip_and_save(rot,mkoutfilename("rot"))
clip_rotated(rot,hyp_length)
# # XXX temp debug ; leave here while working on new de-skew straightness test
sys.exit(0)
# make a triangle so we can de-skew.
# p1 is the upper right of Q60
# p2 is the upper left
# p3 is the lower left
#
# Counter-clockwise rotated
#
# p1
# p2
#
#
# p3
#
#
# Clockwise rotate
#
# p2
#
# p1
# p3
#
#
# Right Triangles
#
# .--------------p1
# |
# |
# |
# p2
#
#
# p2-------------.
# |
# |
# |
# p1
# if the target is pushed against a scanner edge, there will be multiple
# locations at the min/max
rows_at_min = np.where(nz[0]==min_row)[0]
rows_at_max = np.where(nz[0]==max_row)[0]
cols_at_min = np.where(nz[1]==min_col)[0]
cols_at_max = np.where(nz[1]==max_col)[0]
# Euclidean distance
dist = lambda P,Q : math.sqrt( (P[0]-Q[0])**2 + (P[1]-Q[1])**2 )
A = min_row, nz[1][rows_at_min[-1]]
# A = min_row, nz[1][np.argmin(nz[0])]
B = nz[0][cols_at_max[-1]], max_col
# B = nz[0][np.argmax(nz[1])], max_col
C = max_row, nz[1][np.argmax(nz[0])]
print "A={0} B={1} C={2}".format(A,B,C)
if dist(A,B) > dist(B,C) :
clockwise = False
p1 = B
p2 = A
p3 = nz[0][np.argmin(nz[1])],min_col
else :
clockwise = True
p1 = A
p2 = nz[0][np.argmin(nz[1])],min_col
p3 = C
print "p1=",p1
print "p2=",p2
print "p3=",p3
if clockwise :
print "rotate clockwise"
triangle = ndata[p1[0]:p2[0], p2[1]:p1[1]]
else :
print "rotate counter-clockwise"
triangle = ndata[p2[0]:p1[0], p2[1]:p1[1]]
imtools.clip_and_save(triangle,mkoutfilename("triangle"))
tri_width = abs(p1[1] - p2[1])
tri_height = abs(p2[0] - p1[0])
print "tri_width={0} tri_height={1}".format(tri_width,tri_height)
# length of upper edge (hypotenuse) is Euclidean distance between the two
# points
hyp_len = math.sqrt( (p1[0]-p2[0])**2 + (p1[1]-p2[1])**2 )
print "hyp_len=",hyp_len
if clockwise :
theta = math.acos( float(tri_width)/hyp_len )
theta_degrees = -math.degrees(theta)
else :
theta = math.acos( float(tri_height)/hyp_len )
theta_degrees = 90 - math.degrees(theta)
print "theta=",theta,theta_degrees
rot = scipy.ndimage.interpolation.rotate( bbox, theta_degrees)
print rot.shape
imtools.clip_and_save(rot,mkoutfilename("rot"))
q60_height = math.sqrt( (p3[0]-p2[0])**2 + (p3[1]-p2[1])**2 )
q60_width = math.sqrt( (p2[0]-p1[0])**2 + (p2[1]-p1[1])**2 )
print "q60_width={0} q60_height={1}".format(q60_width,q60_height)
center = rot.shape[0]/2,rot.shape[1]/2
print "center=",center
q60 = rot[ center[0]-q60_height/2 : center[0]+q60_height/2 ,
center[1]-q60_width/2 : center[1]+q60_width/2 ]
imtools.clip_and_save(q60,mkoutfilename("q60"))
def main() :
infilename = sys.argv[1]
basename = get_basename(infilename)
outfilename = "{0}_debezel.tif".format(basename)
debezel_done = debezel( infilename, outfilename )
def save_to_sqlite() :
winder_results, uwiii_results = load_all_results()
# pklname = "winder_results.pkl"
# with open(pklname,"rb") as f :
# winder_results = pickle.load(f)
# print "loaded",pklname
conn = sqlite3.connect("pageseg.db")
conn.text_factory = str
cur = conn.cursor()
cur.execute( "DROP TABLE pageseg" )
creat = """
CREATE TABLE IF NOT EXISTS pageseg
(filename text,
algorithm text,
stripsize text,
dataset text,
imgclass text,
metrics text)
"""
cur.execute( creat )
store_result_data_to_db( cur, winder_results )
store_result_data_to_db( cur, uwiii_results )
# my UW-III fullpage rast and voronoi are in separate datafiles
# I ran them on multiple machines, splitting the jobs by imgclass and by
# algorithm.
# The datafile contains lines like:
# fullpage_vor/A001BIN_vor.png fullpage_vor/A001BIN_vor.xml 0.0
file_list = ( "uwiii_fullpage_rast.dat", "uwiii_fullpage_vor.dat" )
for filename in [ os.path.join("uwiii_fullpage",f) for f in file_list ] :
# read all lines from the file into an array
with open( filename, "r" ) as infile :
lines = [ l.strip() for l in infile.readlines() ]
# parse the data into a dict we will put into the DB
for l in lines :
# line should look like:
# fullpage_vor/A001BIN_vor.png fullpage_vor/A001BIN_vor.xml 0.0
# split into space separated fields
fields = l.split()
# metric is the last field
data = np.array( fields[-1], dtype="float" )
# get the png filename; we'll use it to split into filename and
# algorithm
basename = get_basename(fields[0])
filename,algorithm = basename.split("_")
f = dict(db_fields)
f["filename"] = filename
f["algorithm"] = algorithm
f["stripsize"] = "fullpage"
f["dataset"] = "uwiii"
# first letter of the base e.g., W1U8BIN_vor
f["imgclass"] = basename[0]
cur.execute( "INSERT INTO pageseg VALUES(?,?,?,?,?,?)",
(f["filename"],f["algorithm"],f["stripsize"],
f["dataset"],f["imgclass"],data.tostring()))
conn.commit()
conn.close()
def run(imgfilename):
basename = get_basename(imgfilename)
document_id = get_document_id_from_basename(basename)
# destination for the output files
make_output_dir(document_id)
# stripnum = get_stripnum_from_filename( basename )
out_imgfilename = output_dir + "{0}_rast.png".format(basename)
xml_filename = output_dir + "{0}_rast.xml".format(basename)
input_dir = "{0}/{1}/".format(num_rows_in_strip, document_id)
# zone box files use "ZONE" instead of "BIN"
# e.g.,
# A00ABIN_300_010_2990.png -> A00AZONE_300_010_2990.xml
gtruth_xml_filename = input_dir + "{0}.xml".format(
basename.replace("BIN", "ZONE"))
print "imgfilename=", imgfilename
print "out_imgfilename=", out_imgfilename
print "xml_filename=", xml_filename
print "gtruth_xml_filename=", gtruth_xml_filename
# sys.exit(0)
# segment the image
cmd = "./rast-ocropus {0} {1}".format(imgfilename, out_imgfilename)
print cmd
try:
result = subprocess.check_output(cmd, shell=True)
except subprocess.CalledProcessError:
return (imgfilename, "failed")
# remove some clutter
os.unlink(out_imgfilename)
# write the XML results
with open(xml_filename, "w") as outfile:
print >> outfile, result
print "wrote", xml_filename
# run the compare
cmd = "runZoneComp -g {0} -d {1}".format(gtruth_xml_filename, xml_filename)
print cmd
result = subprocess.check_output(cmd, shell=True)
# get the segmentation metric from the output
metric = parse_runZoneComp_result(result)
print "metric={0}".format(metric)
# draw the experimental result onto the input image
out_imgfilename = output_dir + "{0}_rast_zone.png".format(basename)
fname = drawxml.draw_zones(xml_filename, imgfilename, out_imgfilename)
print "wrote", fname
# remove some clutter
# os.unlink(xml_filename)
return (imgfilename, metric)
def make_sliding_strips_from_image( imgfilename, output_dir ) :
basename = get_basename( imgfilename )
# get the image as a numpy array
data = mkslices.load_image( imgfilename )
strip_list = make_all_strips_images( data, basename, output_dir )
def save_to_sqlite():
winder_results, uwiii_results = load_all_results()
# pklname = "winder_results.pkl"
# with open(pklname,"rb") as f :
# winder_results = pickle.load(f)
# print "loaded",pklname
conn = sqlite3.connect("pageseg.db")
conn.text_factory = str
cur = conn.cursor()
cur.execute("DROP TABLE pageseg")
creat = """
CREATE TABLE IF NOT EXISTS pageseg
(filename text,
algorithm text,
stripsize text,
dataset text,
imgclass text,
metrics text)
"""
cur.execute(creat)
store_result_data_to_db(cur, winder_results)
store_result_data_to_db(cur, uwiii_results)
# my UW-III fullpage rast and voronoi are in separate datafiles
# I ran them on multiple machines, splitting the jobs by imgclass and by
# algorithm.
# The datafile contains lines like:
# fullpage_vor/A001BIN_vor.png fullpage_vor/A001BIN_vor.xml 0.0
file_list = ("uwiii_fullpage_rast.dat", "uwiii_fullpage_vor.dat")
for filename in [os.path.join("uwiii_fullpage", f) for f in file_list]:
# read all lines from the file into an array
with open(filename, "r") as infile:
lines = [l.strip() for l in infile.readlines()]
# parse the data into a dict we will put into the DB
for l in lines:
# line should look like:
# fullpage_vor/A001BIN_vor.png fullpage_vor/A001BIN_vor.xml 0.0
# split into space separated fields
fields = l.split()
# metric is the last field
data = np.array(fields[-1], dtype="float")
# get the png filename; we'll use it to split into filename and
# algorithm
basename = get_basename(fields[0])
filename, algorithm = basename.split("_")
f = dict(db_fields)
f["filename"] = filename
f["algorithm"] = algorithm
f["stripsize"] = "fullpage"
f["dataset"] = "uwiii"
# first letter of the base e.g., W1U8BIN_vor
f["imgclass"] = basename[0]
cur.execute("INSERT INTO pageseg VALUES(?,?,?,?,?,?)",
(f["filename"], f["algorithm"], f["stripsize"],
f["dataset"], f["imgclass"], data.tostring()))
conn.commit()
conn.close()
def make_gtruth_slices(boxfilename):
basename = get_basename(boxfilename)
box_list = zonebox.load_boxes(boxfilename)
print "found", len(box_list), "boxes"
# load the image associated with this box list
# assume all the boxes have the same image name (they should)
imgfilename = "IMAGEBIN/{0}BIN.png".format(box_list[0].document_id)
img = drawboxes.load_image(imgfilename)
print img.mode, img.size
num_cols, num_rows = img.size
print "rows={0} cols={1}".format(num_rows, num_cols)
draw = ImageDraw.Draw(img)
# starting strip as wide as the iamge with our base number of rows
s = rects.Strip(width=num_cols, height=strip_rows)
box_strip_list = [rects.Strip(box=box) for box in box_list]
data = np.asarray(img, dtype="uint8")
print "shape=", data.shape
# draw the ground truth in blue as sanity check (should see no blue in the
# output image)
for box_strip in box_strip_list:
upper_left = box_strip.rect[0].x, box_strip.rect[0].y
lower_right = box_strip.rect[2].x, box_strip.rect[2].y
draw.rectangle((upper_left, lower_right), outline="blue")
# iterate the strip down the page, calculating all the box intersections
# for each strip
row = 0
strip_counter = 0
while row < num_rows:
print "strip=", s
upper_left = s.rect[0].x, s.rect[0].y
lower_right = s.rect[2].x, s.rect[2].y
draw.rectangle((upper_left, lower_right), outline="green")
# linear search all the boxes searching for those that match this strip
box_intersect_list = []
for box_strip in box_strip_list:
isect = rects.strip_intersect(box_strip, s)
if isect:
print 'isect=', isect
# PIL's Draw is x,y order
upper_left = isect.rect[0].x, isect.rect[0].y
lower_right = isect.rect[2].x, isect.rect[2].y
draw.rectangle((upper_left, lower_right), outline="red")
# adjust the intersections so the new ground truth of the box
# intersections starts at row=0 (making new images out of
# strips so need ground truth for each image strip)
for rect in isect.rect:
# subtract out the starting Y position of upper left
rect.y -= s.rect[0].y
print "adjusted isect=", isect
# save this intersection; we'll write to a new XML file
box_intersect_list.append(isect)
# save the intersections as XML
xmlfilename = "{0}_s{1}.xml".format(basename, strip_counter)
with open(xmlfilename, "w") as outfile:
zone2xml.write_boxlist_to_xml(outfile, box_intersect_list)
print "wrote", xmlfilename
s.next_strip()
row += strip_rows
strip_counter += 1
outfilename = "{0}_out.png".format(basename)
img.save(outfilename)
print "wrote", outfilename
def make_gtruth_slices( boxfilename ) :
basename = get_basename( boxfilename )
box_list = zonebox.load_boxes( boxfilename )
print "found",len(box_list),"boxes"
# load the image associated with this box list
# assume all the boxes have the same image name (they should)
imgfilename = "IMAGEBIN/{0}BIN.png".format( box_list[0].document_id )
img = drawboxes.load_image(imgfilename)
print img.mode, img.size
num_cols,num_rows = img.size
print "rows={0} cols={1}".format( num_rows, num_cols )
draw = ImageDraw.Draw(img)
# starting strip as wide as the iamge with our base number of rows
s = rects.Strip(width=num_cols, height=strip_rows )
box_strip_list = [ rects.Strip(box=box) for box in box_list ]
data = np.asarray(img,dtype="uint8")
print "shape=",data.shape
# draw the ground truth in blue as sanity check (should see no blue in the
# output image)
for box_strip in box_strip_list :
upper_left = box_strip.rect[0].x, box_strip.rect[0].y
lower_right = box_strip.rect[2].x, box_strip.rect[2].y
draw.rectangle( (upper_left,lower_right), outline="blue")
# iterate the strip down the page, calculating all the box intersections
# for each strip
row = 0
strip_counter = 0
while row < num_rows :
print "strip=",s
upper_left = s.rect[0].x, s.rect[0].y
lower_right = s.rect[2].x, s.rect[2].y
draw.rectangle( (upper_left,lower_right), outline="green" )
# linear search all the boxes searching for those that match this strip
box_intersect_list = []
for box_strip in box_strip_list :
isect = rects.strip_intersect( box_strip, s )
if isect :
print 'isect=',isect
# PIL's Draw is x,y order
upper_left = isect.rect[0].x, isect.rect[0].y
lower_right = isect.rect[2].x, isect.rect[2].y
draw.rectangle( (upper_left,lower_right), outline="red" )
# adjust the intersections so the new ground truth of the box
# intersections starts at row=0 (making new images out of
# strips so need ground truth for each image strip)
for rect in isect.rect :
# subtract out the starting Y position of upper left
rect.y -= s.rect[0].y
print "adjusted isect=",isect
# save this intersection; we'll write to a new XML file
box_intersect_list.append( isect )
# save the intersections as XML
xmlfilename = "{0}_s{1}.xml".format( basename, strip_counter )
with open(xmlfilename,"w") as outfile :
zone2xml.write_boxlist_to_xml( outfile, box_intersect_list )
print "wrote", xmlfilename
s.next_strip()
row += strip_rows
strip_counter += 1
outfilename = "{0}_out.png".format( basename )
img.save(outfilename)
print "wrote",outfilename
