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eynollah
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binarization as a standalone command

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pull/138/head^2
vahidrezanezhad 2 months ago
parent f93fa12441
commit 70772d4104
3 changed files with 418 additions and 3 deletions
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33
qurator/eynollah/cli.py
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@ -2,6 +2,7 @@ import sys
import click
from ocrd_utils import initLogging, setOverrideLogLevel
from qurator.eynollah.eynollah import Eynollah
from qurator.eynollah.sbb_binarize import SbbBinarizer
@click.group()
def main():
@ -48,6 +49,38 @@ def main():
def machine_based_reading_order(dir_xml, dir_out_modal_image, dir_out_classes, input_height, input_width, min_area_size):
xml_files_ind = os.listdir(dir_xml)
@main.command()
@click.option('--patches/--no-patches', default=True, help='by enabling this parameter you let the model to see the image in patches.')
@click.option('--model_dir', '-m', type=click.Path(exists=True, file_okay=False), required=True, help='directory containing models for prediction')
@click.argument('input_image')
@click.argument('output_image')
@click.option(
"--dir_in",
"-di",
help="directory of images",
type=click.Path(exists=True, file_okay=False),
)
@click.option(
"--dir_out",
"-do",
help="directory where the binarized images will be written",
type=click.Path(exists=True, file_okay=False),
)
def binarization(patches, model_dir, input_image, output_image, dir_in, dir_out):
if not dir_out and (dir_in):
print("Error: You used -di but did not set -do")
sys.exit(1)
elif dir_out and not (dir_in):
print("Error: You used -do to write out binarized images but have not set -di")
sys.exit(1)
SbbBinarizer(model_dir).run(image_path=input_image, use_patches=patches, save=output_image, dir_in=dir_in, dir_out=dir_out)
@main.command()
@click.option(

5
qurator/eynollah/eynollah.py
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@ -240,7 +240,6 @@ class Eynollah:
pcgts=pcgts)
self.logger = logger if logger else getLogger('eynollah')
self.dir_models = dir_models
self.model_dir_of_enhancement = dir_models + "/eynollah-enhancement_20210425"
self.model_dir_of_binarization = dir_models + "/eynollah-binarization_20210425"
self.model_dir_of_col_classifier = dir_models + "/eynollah-column-classifier_20210425"
@ -4769,9 +4768,9 @@ class Eynollah:
textline_mask_tot_ea_deskew = resize_image(textline_mask_tot_ea,img_h_new, img_w_new )
slope_deskew, slope_first = self.run_deskew(textline_mask_tot_ea_deskew)
slope_deskew, slope_first = 0, 0#self.run_deskew(textline_mask_tot_ea_deskew)
else:
slope_deskew, slope_first = self.run_deskew(textline_mask_tot_ea)
slope_deskew, slope_first = 0, 0#self.run_deskew(textline_mask_tot_ea)
#print("text region early -2,5 in %.1fs", time.time() - t0)
#self.logger.info("Textregion detection took %.1fs ", time.time() - t1t)
num_col, num_col_classifier, img_only_regions, page_coord, image_page, mask_images, mask_lines, text_regions_p_1, cont_page, table_prediction, textline_mask_tot_ea, img_bin_light = \

383
qurator/eynollah/sbb_binarize.py
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@ -0,0 +1,383 @@
"""
Tool to load model and binarize a given image.
"""
import sys
from glob import glob
from os import environ, devnull
from os.path import join
from warnings import catch_warnings, simplefilter
import os
import numpy as np
from PIL import Image
import cv2
environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
stderr = sys.stderr
sys.stderr = open(devnull, 'w')
import tensorflow as tf
from tensorflow.keras.models import load_model
from tensorflow.python.keras import backend as tensorflow_backend
sys.stderr = stderr
import logging
def resize_image(img_in, input_height, input_width):
return cv2.resize(img_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST)
class SbbBinarizer:
def __init__(self, model_dir, logger=None):
self.model_dir = model_dir
self.log = logger if logger else logging.getLogger('SbbBinarizer')
self.start_new_session()
self.model_files = glob(self.model_dir+"/*/", recursive = True)
self.models = []
for model_file in self.model_files:
self.models.append(self.load_model(model_file))
def start_new_session(self):
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
self.session = tf.compat.v1.Session(config=config) # tf.InteractiveSession()
tensorflow_backend.set_session(self.session)
def end_session(self):
tensorflow_backend.clear_session()
self.session.close()
del self.session
def load_model(self, model_name):
model = load_model(join(self.model_dir, model_name), compile=False)
model_height = model.layers[len(model.layers)-1].output_shape[1]
model_width = model.layers[len(model.layers)-1].output_shape[2]
n_classes = model.layers[len(model.layers)-1].output_shape[3]
return model, model_height, model_width, n_classes
def predict(self, model_in, img, use_patches, n_batch_inference=5):
tensorflow_backend.set_session(self.session)
model, model_height, model_width, n_classes = model_in
img_org_h = img.shape[0]
img_org_w = img.shape[1]
if img.shape[0] < model_height and img.shape[1] >= model_width:
img_padded = np.zeros(( model_height, img.shape[1], img.shape[2] ))
index_start_h = int( abs( img.shape[0] - model_height) /2.)
index_start_w = 0
img_padded [ index_start_h: index_start_h+img.shape[0], :, : ] = img[:,:,:]
elif img.shape[0] >= model_height and img.shape[1] < model_width:
img_padded = np.zeros(( img.shape[0], model_width, img.shape[2] ))
index_start_h = 0
index_start_w = int( abs( img.shape[1] - model_width) /2.)
img_padded [ :, index_start_w: index_start_w+img.shape[1], : ] = img[:,:,:]
elif img.shape[0] < model_height and img.shape[1] < model_width:
img_padded = np.zeros(( model_height, model_width, img.shape[2] ))
index_start_h = int( abs( img.shape[0] - model_height) /2.)
index_start_w = int( abs( img.shape[1] - model_width) /2.)
img_padded [ index_start_h: index_start_h+img.shape[0], index_start_w: index_start_w+img.shape[1], : ] = img[:,:,:]
else:
index_start_h = 0
index_start_w = 0
img_padded = np.copy(img)
img = np.copy(img_padded)
if use_patches:
margin = int(0.1 * model_width)
width_mid = model_width - 2 * margin
height_mid = model_height - 2 * margin
img = img / float(255.0)
img_h = img.shape[0]
img_w = img.shape[1]
prediction_true = np.zeros((img_h, img_w, 3))
mask_true = np.zeros((img_h, img_w))
nxf = img_w / float(width_mid)
nyf = img_h / float(height_mid)
if nxf > int(nxf):
nxf = int(nxf) + 1
else:
nxf = int(nxf)
if nyf > int(nyf):
nyf = int(nyf) + 1
else:
nyf = int(nyf)
list_i_s = []
list_j_s = []
list_x_u = []
list_x_d = []
list_y_u = []
list_y_d = []
batch_indexer = 0
img_patch = np.zeros((n_batch_inference, model_height, model_width,3))
for i in range(nxf):
for j in range(nyf):
if i == 0:
index_x_d = i * width_mid
index_x_u = index_x_d + model_width
elif i > 0:
index_x_d = i * width_mid
index_x_u = index_x_d + model_width
if j == 0:
index_y_d = j * height_mid
index_y_u = index_y_d + model_height
elif j > 0:
index_y_d = j * height_mid
index_y_u = index_y_d + model_height
if index_x_u > img_w:
index_x_u = img_w
index_x_d = img_w - model_width
if index_y_u > img_h:
index_y_u = img_h
index_y_d = img_h - model_height
list_i_s.append(i)
list_j_s.append(j)
list_x_u.append(index_x_u)
list_x_d.append(index_x_d)
list_y_d.append(index_y_d)
list_y_u.append(index_y_u)
img_patch[batch_indexer,:,:,:] = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
batch_indexer = batch_indexer + 1
if batch_indexer == n_batch_inference:
label_p_pred = model.predict(img_patch,verbose=0)
seg = np.argmax(label_p_pred, axis=3)
#print(seg.shape, len(seg), len(list_i_s))
indexer_inside_batch = 0
for i_batch, j_batch in zip(list_i_s, list_j_s):
seg_in = seg[indexer_inside_batch,:,:]
seg_color = np.repeat(seg_in[:, :, np.newaxis], 3, axis=2)
index_y_u_in = list_y_u[indexer_inside_batch]
index_y_d_in = list_y_d[indexer_inside_batch]
index_x_u_in = list_x_u[indexer_inside_batch]
index_x_d_in = list_x_d[indexer_inside_batch]
if i_batch == 0 and j_batch == 0:
seg_color = seg_color[0 : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
elif i_batch == nxf - 1 and j_batch == nyf - 1:
seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - 0, :]
prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
elif i_batch == 0 and j_batch == nyf - 1:
seg_color = seg_color[margin : seg_color.shape[0] - 0, 0 : seg_color.shape[1] - margin, :]
prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
elif i_batch == nxf - 1 and j_batch == 0:
seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
elif i_batch == 0 and j_batch != 0 and j_batch != nyf - 1:
seg_color = seg_color[margin : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
elif i_batch == nxf - 1 and j_batch != 0 and j_batch != nyf - 1:
seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
elif i_batch != 0 and i_batch != nxf - 1 and j_batch == 0:
seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
elif i_batch != 0 and i_batch != nxf - 1 and j_batch == nyf - 1:
seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - margin, :]
prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
else:
seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
indexer_inside_batch = indexer_inside_batch +1
list_i_s = []
list_j_s = []
list_x_u = []
list_x_d = []
list_y_u = []
list_y_d = []
batch_indexer = 0
img_patch = np.zeros((n_batch_inference, model_height, model_width,3))
elif i==(nxf-1) and j==(nyf-1):
label_p_pred = model.predict(img_patch,verbose=0)
seg = np.argmax(label_p_pred, axis=3)
#print(seg.shape, len(seg), len(list_i_s))
indexer_inside_batch = 0
for i_batch, j_batch in zip(list_i_s, list_j_s):
seg_in = seg[indexer_inside_batch,:,:]
seg_color = np.repeat(seg_in[:, :, np.newaxis], 3, axis=2)
index_y_u_in = list_y_u[indexer_inside_batch]
index_y_d_in = list_y_d[indexer_inside_batch]
index_x_u_in = list_x_u[indexer_inside_batch]
index_x_d_in = list_x_d[indexer_inside_batch]
if i_batch == 0 and j_batch == 0:
seg_color = seg_color[0 : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
elif i_batch == nxf - 1 and j_batch == nyf - 1:
seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - 0, :]
prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
elif i_batch == 0 and j_batch == nyf - 1:
seg_color = seg_color[margin : seg_color.shape[0] - 0, 0 : seg_color.shape[1] - margin, :]
prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
elif i_batch == nxf - 1 and j_batch == 0:
seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
elif i_batch == 0 and j_batch != 0 and j_batch != nyf - 1:
seg_color = seg_color[margin : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
elif i_batch == nxf - 1 and j_batch != 0 and j_batch != nyf - 1:
seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
elif i_batch != 0 and i_batch != nxf - 1 and j_batch == 0:
seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
elif i_batch != 0 and i_batch != nxf - 1 and j_batch == nyf - 1:
seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - margin, :]
prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
else:
seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
indexer_inside_batch = indexer_inside_batch +1
list_i_s = []
list_j_s = []
list_x_u = []
list_x_d = []
list_y_u = []
list_y_d = []
batch_indexer = 0
img_patch = np.zeros((n_batch_inference, model_height, model_width,3))
prediction_true = prediction_true[index_start_h: index_start_h+img_org_h, index_start_w: index_start_w+img_org_w,:]
prediction_true = prediction_true.astype(np.uint8)
else:
img_h_page = img.shape[0]
img_w_page = img.shape[1]
img = img / float(255.0)
img = resize_image(img, model_height, model_width)
label_p_pred = model.predict(img.reshape(1, img.shape[0], img.shape[1], img.shape[2]))
seg = np.argmax(label_p_pred, axis=3)[0]
seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
prediction_true = resize_image(seg_color, img_h_page, img_w_page)
prediction_true = prediction_true.astype(np.uint8)
return prediction_true[:,:,0]
def run(self, image=None, image_path=None, save=None, use_patches=False, dir_in=None, dir_out=None):
print(dir_in,'dir_in')
if not dir_in:
if (image is not None and image_path is not None) or \
(image is None and image_path is None):
raise ValueError("Must pass either a opencv2 image or an image_path")
if image_path is not None:
image = cv2.imread(image_path)
img_last = 0
for n, (model, model_file) in enumerate(zip(self.models, self.model_files)):
self.log.info('Predicting with model %s [%s/%s]' % (model_file, n + 1, len(self.model_files)))
res = self.predict(model, image, use_patches)
img_fin = np.zeros((res.shape[0], res.shape[1], 3))
res[:, :][res[:, :] == 0] = 2
res = res - 1
res = res * 255
img_fin[:, :, 0] = res
img_fin[:, :, 1] = res
img_fin[:, :, 2] = res
img_fin = img_fin.astype(np.uint8)
img_fin = (res[:, :] == 0) * 255
img_last = img_last + img_fin
kernel = np.ones((5, 5), np.uint8)
img_last[:, :][img_last[:, :] > 0] = 255
img_last = (img_last[:, :] == 0) * 255
if save:
cv2.imwrite(save, img_last)
return img_last
else:
ls_imgs = os.listdir(dir_in)
for image_name in ls_imgs:
image_stem = image_name.split('.')[0]
print(image_name,'image_name')
image = cv2.imread(os.path.join(dir_in,image_name) )
img_last = 0
for n, (model, model_file) in enumerate(zip(self.models, self.model_files)):
self.log.info('Predicting with model %s [%s/%s]' % (model_file, n + 1, len(self.model_files)))
res = self.predict(model, image, use_patches)
img_fin = np.zeros((res.shape[0], res.shape[1], 3))
res[:, :][res[:, :] == 0] = 2
res = res - 1
res = res * 255
img_fin[:, :, 0] = res
img_fin[:, :, 1] = res
img_fin[:, :, 2] = res
img_fin = img_fin.astype(np.uint8)
img_fin = (res[:, :] == 0) * 255
img_last = img_last + img_fin
kernel = np.ones((5, 5), np.uint8)
img_last[:, :][img_last[:, :] > 0] = 255
img_last = (img_last[:, :] == 0) * 255
cv2.imwrite(os.path.join(dir_out,image_stem+'.png'), img_last)
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