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I have tried to address the issues #163 and #161 . The changes have also improved marginal detection and enhanced the isolation of headers.

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vahidrezanezhad 2025-05-12 00:10:18 +02:00
parent 5d447abcc4
commit 02a679a145
3 changed files with 275 additions and 34 deletions
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1
requirements.txt
View file

@ -4,4 +4,5 @@ numpy <1.24.0
scikit-learn >= 0.23.2 scikit-learn >= 0.23.2
tensorflow < 2.13 tensorflow < 2.13
numba <= 0.58.1 numba <= 0.58.1
scikit-image
loky loky

14
src/eynollah/cli.py
View file

@ -235,6 +235,16 @@ def binarization(patches, model_dir, input_image, output_image, dir_in, dir_out)
"-ncl", "-ncl",
help="upper limit of columns in document image", help="upper limit of columns in document image",
) )
@click.option(
"--threshold_art_class_layout",
"-tharl",
help="threshold of artifical class in the case of layout detection",
)
@click.option(
"--threshold_art_class_textline",
"-thart",
help="threshold of artifical class in the case of textline detection",
)
@click.option( @click.option(
"--skip_layout_and_reading_order", "--skip_layout_and_reading_order",
"-slro/-noslro", "-slro/-noslro",
@ -248,7 +258,7 @@ def binarization(patches, model_dir, input_image, output_image, dir_in, dir_out)
help="Override log level globally to this", help="Override log level globally to this",
) )
def layout(image, out, overwrite, dir_in, model, save_images, save_layout, save_deskewed, save_all, extract_only_images, save_page, enable_plotting, allow_enhancement, curved_line, textline_light, full_layout, tables, right2left, input_binary, allow_scaling, headers_off, light_version, reading_order_machine_based, do_ocr, num_col_upper, num_col_lower, skip_layout_and_reading_order, ignore_page_extraction, log_level): def layout(image, out, overwrite, dir_in, model, save_images, save_layout, save_deskewed, save_all, extract_only_images, save_page, enable_plotting, allow_enhancement, curved_line, textline_light, full_layout, tables, right2left, input_binary, allow_scaling, headers_off, light_version, reading_order_machine_based, do_ocr, num_col_upper, num_col_lower, threshold_art_class_textline, threshold_art_class_layout, skip_layout_and_reading_order, ignore_page_extraction, log_level):
initLogging() initLogging()
if log_level: if log_level:
getLogger('eynollah').setLevel(getLevelName(log_level)) getLogger('eynollah').setLevel(getLevelName(log_level))
@ -298,6 +308,8 @@ def layout(image, out, overwrite, dir_in, model, save_images, save_layout, save_
num_col_upper=num_col_upper, num_col_upper=num_col_upper,
num_col_lower=num_col_lower, num_col_lower=num_col_lower,
skip_layout_and_reading_order=skip_layout_and_reading_order, skip_layout_and_reading_order=skip_layout_and_reading_order,
threshold_art_class_textline=threshold_art_class_textline,
threshold_art_class_layout=threshold_art_class_layout,
) )
if dir_in: if dir_in:
eynollah.run(dir_in=dir_in, overwrite=overwrite) eynollah.run(dir_in=dir_in, overwrite=overwrite)

294
src/eynollah/eynollah.py
View file

@ -30,7 +30,7 @@ import numpy as np
from scipy.signal import find_peaks from scipy.signal import find_peaks
from scipy.ndimage import gaussian_filter1d from scipy.ndimage import gaussian_filter1d
from numba import cuda from numba import cuda
from skimage.morphology import skeletonize
from ocrd import OcrdPage from ocrd import OcrdPage
from ocrd_utils import getLogger, tf_disable_interactive_logs from ocrd_utils import getLogger, tf_disable_interactive_logs
@ -200,6 +200,8 @@ class Eynollah:
do_ocr : bool = False, do_ocr : bool = False,
num_col_upper : Optional[int] = None, num_col_upper : Optional[int] = None,
num_col_lower : Optional[int] = None, num_col_lower : Optional[int] = None,
threshold_art_class_layout: Optional[float] = None,
threshold_art_class_textline: Optional[float] = None,
skip_layout_and_reading_order : bool = False, skip_layout_and_reading_order : bool = False,
logger : Optional[Logger] = None, logger : Optional[Logger] = None,
): ):
@ -237,6 +239,17 @@ class Eynollah:
self.num_col_lower = int(num_col_lower) self.num_col_lower = int(num_col_lower)
else: else:
self.num_col_lower = num_col_lower self.num_col_lower = num_col_lower
if threshold_art_class_layout:
self.threshold_art_class_layout = float(threshold_art_class_layout)
else:
self.threshold_art_class_layout = 0.1
if threshold_art_class_textline:
self.threshold_art_class_textline = float(threshold_art_class_textline)
else:
self.threshold_art_class_textline = 0.1
self.logger = logger if logger else getLogger('eynollah') self.logger = logger if logger else getLogger('eynollah')
# for parallelization of CPU-intensive tasks: # for parallelization of CPU-intensive tasks:
self.executor = ProcessPoolExecutor(max_workers=cpu_count(), timeout=1200) self.executor = ProcessPoolExecutor(max_workers=cpu_count(), timeout=1200)
@ -784,7 +797,7 @@ class Eynollah:
self, patches, img, model, self, patches, img, model,
n_batch_inference=1, marginal_of_patch_percent=0.1, n_batch_inference=1, marginal_of_patch_percent=0.1,
thresholding_for_some_classes_in_light_version=False, thresholding_for_some_classes_in_light_version=False,
thresholding_for_artificial_class_in_light_version=False): thresholding_for_artificial_class_in_light_version=False, threshold_art_class_textline=0.1):
self.logger.debug("enter do_prediction") self.logger.debug("enter do_prediction")
img_height_model = model.layers[-1].output_shape[1] img_height_model = model.layers[-1].output_shape[1]
@ -802,10 +815,13 @@ class Eynollah:
if thresholding_for_artificial_class_in_light_version: if thresholding_for_artificial_class_in_light_version:
seg_art = label_p_pred[0,:,:,2] seg_art = label_p_pred[0,:,:,2]
seg_art[seg_art<0.2] = 0 seg_art[seg_art<threshold_art_class_textline] = 0
seg_art[seg_art>0] =1 seg_art[seg_art>0] =1
skeleton_art = skeletonize(seg_art)
skeleton_art = skeleton_art*1
seg[seg_art==1]=2 seg[skeleton_art==1]=2
seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2) seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
prediction_true = resize_image(seg_color, img_h_page, img_w_page).astype(np.uint8) prediction_true = resize_image(seg_color, img_h_page, img_w_page).astype(np.uint8)
return prediction_true return prediction_true
@ -896,14 +912,17 @@ class Eynollah:
if thresholding_for_artificial_class_in_light_version: if thresholding_for_artificial_class_in_light_version:
seg_art = label_p_pred[:,:,:,2] seg_art = label_p_pred[:,:,:,2]
seg_art[seg_art<0.2] = 0 seg_art[seg_art<threshold_art_class_textline] = 0
seg_art[seg_art>0] =1 seg_art[seg_art>0] =1
seg[seg_art==1]=2 ##seg[seg_art==1]=2
indexer_inside_batch = 0 indexer_inside_batch = 0
for i_batch, j_batch in zip(list_i_s, list_j_s): for i_batch, j_batch in zip(list_i_s, list_j_s):
seg_in = seg[indexer_inside_batch] seg_in = seg[indexer_inside_batch]
if thresholding_for_artificial_class_in_light_version:
seg_in_art = seg_art[indexer_inside_batch]
index_y_u_in = list_y_u[indexer_inside_batch] index_y_u_in = list_y_u[indexer_inside_batch]
index_y_d_in = list_y_d[indexer_inside_batch] index_y_d_in = list_y_d[indexer_inside_batch]
@ -917,54 +936,107 @@ class Eynollah:
seg_in[0:-margin or None, seg_in[0:-margin or None,
0:-margin or None, 0:-margin or None,
np.newaxis] np.newaxis]
if thresholding_for_artificial_class_in_light_version:
prediction_true[index_y_d_in + 0:index_y_u_in - margin,
index_x_d_in + 0:index_x_u_in - margin, 1] = \
seg_in_art[0:-margin or None,
0:-margin or None]
elif i_batch == nxf - 1 and j_batch == nyf - 1: elif i_batch == nxf - 1 and j_batch == nyf - 1:
prediction_true[index_y_d_in + margin:index_y_u_in - 0, prediction_true[index_y_d_in + margin:index_y_u_in - 0,
index_x_d_in + margin:index_x_u_in - 0] = \ index_x_d_in + margin:index_x_u_in - 0] = \
seg_in[margin:, seg_in[margin:,
margin:, margin:,
np.newaxis] np.newaxis]
if thresholding_for_artificial_class_in_light_version:
prediction_true[index_y_d_in + margin:index_y_u_in - 0,
index_x_d_in + margin:index_x_u_in - 0, 1] = \
seg_in_art[margin:,
margin:]
elif i_batch == 0 and j_batch == nyf - 1: elif i_batch == 0 and j_batch == nyf - 1:
prediction_true[index_y_d_in + margin:index_y_u_in - 0, prediction_true[index_y_d_in + margin:index_y_u_in - 0,
index_x_d_in + 0:index_x_u_in - margin] = \ index_x_d_in + 0:index_x_u_in - margin] = \
seg_in[margin:, seg_in[margin:,
0:-margin or None, 0:-margin or None,
np.newaxis] np.newaxis]
if thresholding_for_artificial_class_in_light_version:
prediction_true[index_y_d_in + margin:index_y_u_in - 0,
index_x_d_in + 0:index_x_u_in - margin, 1] = \
seg_in_art[margin:,
0:-margin or None]
elif i_batch == nxf - 1 and j_batch == 0: elif i_batch == nxf - 1 and j_batch == 0:
prediction_true[index_y_d_in + 0:index_y_u_in - margin, prediction_true[index_y_d_in + 0:index_y_u_in - margin,
index_x_d_in + margin:index_x_u_in - 0] = \ index_x_d_in + margin:index_x_u_in - 0] = \
seg_in[0:-margin or None, seg_in[0:-margin or None,
margin:, margin:,
np.newaxis] np.newaxis]
if thresholding_for_artificial_class_in_light_version:
prediction_true[index_y_d_in + 0:index_y_u_in - margin,
index_x_d_in + margin:index_x_u_in - 0, 1] = \
seg_in_art[0:-margin or None,
margin:]
elif i_batch == 0 and j_batch != 0 and j_batch != nyf - 1: elif i_batch == 0 and j_batch != 0 and j_batch != nyf - 1:
prediction_true[index_y_d_in + margin:index_y_u_in - margin, prediction_true[index_y_d_in + margin:index_y_u_in - margin,
index_x_d_in + 0:index_x_u_in - margin] = \ index_x_d_in + 0:index_x_u_in - margin] = \
seg_in[margin:-margin or None, seg_in[margin:-margin or None,
0:-margin or None, 0:-margin or None,
np.newaxis] np.newaxis]
if thresholding_for_artificial_class_in_light_version:
prediction_true[index_y_d_in + margin:index_y_u_in - margin,
index_x_d_in + 0:index_x_u_in - margin, 1] = \
seg_in_art[margin:-margin or None,
0:-margin or None]
elif i_batch == nxf - 1 and j_batch != 0 and j_batch != nyf - 1: elif i_batch == nxf - 1 and j_batch != 0 and j_batch != nyf - 1:
prediction_true[index_y_d_in + margin:index_y_u_in - margin, prediction_true[index_y_d_in + margin:index_y_u_in - margin,
index_x_d_in + margin:index_x_u_in - 0] = \ index_x_d_in + margin:index_x_u_in - 0] = \
seg_in[margin:-margin or None, seg_in[margin:-margin or None,
margin:, margin:,
np.newaxis] np.newaxis]
if thresholding_for_artificial_class_in_light_version:
prediction_true[index_y_d_in + margin:index_y_u_in - margin,
index_x_d_in + margin:index_x_u_in - 0, 1] = \
seg_in_art[margin:-margin or None,
margin:]
elif i_batch != 0 and i_batch != nxf - 1 and j_batch == 0: elif i_batch != 0 and i_batch != nxf - 1 and j_batch == 0:
prediction_true[index_y_d_in + 0:index_y_u_in - margin, prediction_true[index_y_d_in + 0:index_y_u_in - margin,
index_x_d_in + margin:index_x_u_in - margin] = \ index_x_d_in + margin:index_x_u_in - margin] = \
seg_in[0:-margin or None, seg_in[0:-margin or None,
margin:-margin or None, margin:-margin or None,
np.newaxis] np.newaxis]
if thresholding_for_artificial_class_in_light_version:
prediction_true[index_y_d_in + 0:index_y_u_in - margin,
index_x_d_in + margin:index_x_u_in - margin, 1] = \
seg_in_art[0:-margin or None,
margin:-margin or None]
elif i_batch != 0 and i_batch != nxf - 1 and j_batch == nyf - 1: elif i_batch != 0 and i_batch != nxf - 1 and j_batch == nyf - 1:
prediction_true[index_y_d_in + margin:index_y_u_in - 0, prediction_true[index_y_d_in + margin:index_y_u_in - 0,
index_x_d_in + margin:index_x_u_in - margin] = \ index_x_d_in + margin:index_x_u_in - margin] = \
seg_in[margin:, seg_in[margin:,
margin:-margin or None, margin:-margin or None,
np.newaxis] np.newaxis]
if thresholding_for_artificial_class_in_light_version:
prediction_true[index_y_d_in + margin:index_y_u_in - 0,
index_x_d_in + margin:index_x_u_in - margin, 1] = \
seg_in_art[margin:,
margin:-margin or None]
else: else:
prediction_true[index_y_d_in + margin:index_y_u_in - margin, prediction_true[index_y_d_in + margin:index_y_u_in - margin,
index_x_d_in + margin:index_x_u_in - margin] = \ index_x_d_in + margin:index_x_u_in - margin] = \
seg_in[margin:-margin or None, seg_in[margin:-margin or None,
margin:-margin or None, margin:-margin or None,
np.newaxis] np.newaxis]
if thresholding_for_artificial_class_in_light_version:
prediction_true[index_y_d_in + margin:index_y_u_in - margin,
index_x_d_in + margin:index_x_u_in - margin, 1] = \
seg_in_art[margin:-margin or None,
margin:-margin or None]
indexer_inside_batch += 1 indexer_inside_batch += 1
@ -979,6 +1051,19 @@ class Eynollah:
img_patch[:] = 0 img_patch[:] = 0
prediction_true = prediction_true.astype(np.uint8) prediction_true = prediction_true.astype(np.uint8)
if thresholding_for_artificial_class_in_light_version:
kernel_min = np.ones((3, 3), np.uint8)
prediction_true[:,:,0][prediction_true[:,:,0]==2] = 0
skeleton_art = skeletonize(prediction_true[:,:,1])
skeleton_art = skeleton_art*1
skeleton_art = skeleton_art.astype('uint8')
skeleton_art = cv2.dilate(skeleton_art, kernel_min, iterations=1)
prediction_true[:,:,0][skeleton_art==1]=2
#del model #del model
gc.collect() gc.collect()
return prediction_true return prediction_true
@ -1117,7 +1202,7 @@ class Eynollah:
self, patches, img, model, self, patches, img, model,
n_batch_inference=1, marginal_of_patch_percent=0.1, n_batch_inference=1, marginal_of_patch_percent=0.1,
thresholding_for_some_classes_in_light_version=False, thresholding_for_some_classes_in_light_version=False,
thresholding_for_artificial_class_in_light_version=False): thresholding_for_artificial_class_in_light_version=False, threshold_art_class_textline=0.1, threshold_art_class_layout=0.1):
self.logger.debug("enter do_prediction_new_concept") self.logger.debug("enter do_prediction_new_concept")
img_height_model = model.layers[-1].output_shape[1] img_height_model = model.layers[-1].output_shape[1]
@ -1132,19 +1217,28 @@ class Eynollah:
label_p_pred = model.predict(img[np.newaxis], verbose=0) label_p_pred = model.predict(img[np.newaxis], verbose=0)
seg = np.argmax(label_p_pred, axis=3)[0] seg = np.argmax(label_p_pred, axis=3)[0]
if thresholding_for_artificial_class_in_light_version:
#seg_text = label_p_pred[0,:,:,1]
#seg_text[seg_text<0.2] =0
#seg_text[seg_text>0] =1
#seg[seg_text==1]=1
seg_art = label_p_pred[0,:,:,4]
seg_art[seg_art<0.2] =0
seg_art[seg_art>0] =1
seg[seg_art==1]=4
seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2) seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
prediction_true = resize_image(seg_color, img_h_page, img_w_page).astype(np.uint8) prediction_true = resize_image(seg_color, img_h_page, img_w_page).astype(np.uint8)
if thresholding_for_artificial_class_in_light_version:
kernel_min = np.ones((3, 3), np.uint8)
seg_art = label_p_pred[0,:,:,4]
seg_art[seg_art<threshold_art_class_layout] =0
seg_art[seg_art>0] =1
#seg[seg_art==1]=4
seg_art = resize_image(seg_art, img_h_page, img_w_page).astype(np.uint8)
prediction_true[:,:,0][prediction_true[:,:,0]==4] = 0
skeleton_art = skeletonize(seg_art)
skeleton_art = skeleton_art*1
skeleton_art = skeleton_art.astype('uint8')
skeleton_art = cv2.dilate(skeleton_art, kernel_min, iterations=1)
prediction_true[:,:,0][skeleton_art==1] = 4
return prediction_true , resize_image(label_p_pred[0, :, :, 1] , img_h_page, img_w_page) return prediction_true , resize_image(label_p_pred[0, :, :, 1] , img_h_page, img_w_page)
if img.shape[0] < img_height_model: if img.shape[0] < img_height_model:
@ -1217,26 +1311,29 @@ class Eynollah:
if thresholding_for_some_classes_in_light_version: if thresholding_for_some_classes_in_light_version:
seg_art = label_p_pred[:,:,:,4] seg_art = label_p_pred[:,:,:,4]
seg_art[seg_art<0.2] =0 seg_art[seg_art<threshold_art_class_layout] =0
seg_art[seg_art>0] =1 seg_art[seg_art>0] =1
seg_line = label_p_pred[:,:,:,3] seg_line = label_p_pred[:,:,:,3]
seg_line[seg_line>0.5] =1#seg_line[seg_line>0.1] =1 seg_line[seg_line>0.5] =1#seg_line[seg_line>0.1] =1
seg_line[seg_line<1] =0 seg_line[seg_line<1] =0
seg[seg_art==1]=4 ##seg[seg_art==1]=4
seg[(seg_line==1) & (seg==0)]=3 seg[(seg_line==1) & (seg==0)]=3
if thresholding_for_artificial_class_in_light_version: if thresholding_for_artificial_class_in_light_version:
seg_art = label_p_pred[:,:,:,2] seg_art = label_p_pred[:,:,:,2]
seg_art[seg_art<0.2] = 0 seg_art[seg_art<threshold_art_class_textline] = 0
seg_art[seg_art>0] =1 seg_art[seg_art>0] =1
seg[seg_art==1]=2 ##seg[seg_art==1]=2
indexer_inside_batch = 0 indexer_inside_batch = 0
for i_batch, j_batch in zip(list_i_s, list_j_s): for i_batch, j_batch in zip(list_i_s, list_j_s):
seg_in = seg[indexer_inside_batch] seg_in = seg[indexer_inside_batch]
if thresholding_for_artificial_class_in_light_version or thresholding_for_some_classes_in_light_version:
seg_in_art = seg_art[indexer_inside_batch]
index_y_u_in = list_y_u[indexer_inside_batch] index_y_u_in = list_y_u[indexer_inside_batch]
index_y_d_in = list_y_d[indexer_inside_batch] index_y_d_in = list_y_d[indexer_inside_batch]
@ -1255,6 +1352,12 @@ class Eynollah:
label_p_pred[0, 0:-margin or None, label_p_pred[0, 0:-margin or None,
0:-margin or None, 0:-margin or None,
1] 1]
if thresholding_for_artificial_class_in_light_version or thresholding_for_some_classes_in_light_version:
prediction_true[index_y_d_in + 0:index_y_u_in - margin,
index_x_d_in + 0:index_x_u_in - margin, 1] = \
seg_in_art[0:-margin or None,
0:-margin or None]
elif i_batch == nxf - 1 and j_batch == nyf - 1: elif i_batch == nxf - 1 and j_batch == nyf - 1:
prediction_true[index_y_d_in + margin:index_y_u_in - 0, prediction_true[index_y_d_in + margin:index_y_u_in - 0,
index_x_d_in + margin:index_x_u_in - 0] = \ index_x_d_in + margin:index_x_u_in - 0] = \
@ -1266,6 +1369,12 @@ class Eynollah:
label_p_pred[0, margin:, label_p_pred[0, margin:,
margin:, margin:,
1] 1]
if thresholding_for_artificial_class_in_light_version or thresholding_for_some_classes_in_light_version:
prediction_true[index_y_d_in + margin:index_y_u_in - 0,
index_x_d_in + margin:index_x_u_in - 0, 1] = \
seg_in_art[margin:,
margin:]
elif i_batch == 0 and j_batch == nyf - 1: elif i_batch == 0 and j_batch == nyf - 1:
prediction_true[index_y_d_in + margin:index_y_u_in - 0, prediction_true[index_y_d_in + margin:index_y_u_in - 0,
index_x_d_in + 0:index_x_u_in - margin] = \ index_x_d_in + 0:index_x_u_in - margin] = \
@ -1277,6 +1386,13 @@ class Eynollah:
label_p_pred[0, margin:, label_p_pred[0, margin:,
0:-margin or None, 0:-margin or None,
1] 1]
if thresholding_for_artificial_class_in_light_version or thresholding_for_some_classes_in_light_version:
prediction_true[index_y_d_in + margin:index_y_u_in - 0,
index_x_d_in + 0:index_x_u_in - margin, 1] = \
seg_in_art[margin:,
0:-margin or None]
elif i_batch == nxf - 1 and j_batch == 0: elif i_batch == nxf - 1 and j_batch == 0:
prediction_true[index_y_d_in + 0:index_y_u_in - margin, prediction_true[index_y_d_in + 0:index_y_u_in - margin,
index_x_d_in + margin:index_x_u_in - 0] = \ index_x_d_in + margin:index_x_u_in - 0] = \
@ -1288,6 +1404,12 @@ class Eynollah:
label_p_pred[0, 0:-margin or None, label_p_pred[0, 0:-margin or None,
margin:, margin:,
1] 1]
if thresholding_for_artificial_class_in_light_version or thresholding_for_some_classes_in_light_version:
prediction_true[index_y_d_in + 0:index_y_u_in - margin,
index_x_d_in + margin:index_x_u_in - 0, 1] = \
seg_in_art[0:-margin or None,
margin:]
elif i_batch == 0 and j_batch != 0 and j_batch != nyf - 1: elif i_batch == 0 and j_batch != 0 and j_batch != nyf - 1:
prediction_true[index_y_d_in + margin:index_y_u_in - margin, prediction_true[index_y_d_in + margin:index_y_u_in - margin,
index_x_d_in + 0:index_x_u_in - margin] = \ index_x_d_in + 0:index_x_u_in - margin] = \
@ -1299,6 +1421,11 @@ class Eynollah:
label_p_pred[0, margin:-margin or None, label_p_pred[0, margin:-margin or None,
0:-margin or None, 0:-margin or None,
1] 1]
if thresholding_for_artificial_class_in_light_version or thresholding_for_some_classes_in_light_version:
prediction_true[index_y_d_in + margin:index_y_u_in - margin,
index_x_d_in + 0:index_x_u_in - margin, 1] = \
seg_in_art[margin:-margin or None,
0:-margin or None]
elif i_batch == nxf - 1 and j_batch != 0 and j_batch != nyf - 1: elif i_batch == nxf - 1 and j_batch != 0 and j_batch != nyf - 1:
prediction_true[index_y_d_in + margin:index_y_u_in - margin, prediction_true[index_y_d_in + margin:index_y_u_in - margin,
index_x_d_in + margin:index_x_u_in - 0] = \ index_x_d_in + margin:index_x_u_in - 0] = \
@ -1310,6 +1437,11 @@ class Eynollah:
label_p_pred[0, margin:-margin or None, label_p_pred[0, margin:-margin or None,
margin:, margin:,
1] 1]
if thresholding_for_artificial_class_in_light_version or thresholding_for_some_classes_in_light_version:
prediction_true[index_y_d_in + margin:index_y_u_in - margin,
index_x_d_in + margin:index_x_u_in - 0, 1] = \
seg_in_art[margin:-margin or None,
margin:]
elif i_batch != 0 and i_batch != nxf - 1 and j_batch == 0: elif i_batch != 0 and i_batch != nxf - 1 and j_batch == 0:
prediction_true[index_y_d_in + 0:index_y_u_in - margin, prediction_true[index_y_d_in + 0:index_y_u_in - margin,
index_x_d_in + margin:index_x_u_in - margin] = \ index_x_d_in + margin:index_x_u_in - margin] = \
@ -1321,6 +1453,11 @@ class Eynollah:
label_p_pred[0, 0:-margin or None, label_p_pred[0, 0:-margin or None,
margin:-margin or None, margin:-margin or None,
1] 1]
if thresholding_for_artificial_class_in_light_version or thresholding_for_some_classes_in_light_version:
prediction_true[index_y_d_in + 0:index_y_u_in - margin,
index_x_d_in + margin:index_x_u_in - margin, 1] = \
seg_in_art[0:-margin or None,
margin:-margin or None]
elif i_batch != 0 and i_batch != nxf - 1 and j_batch == nyf - 1: elif i_batch != 0 and i_batch != nxf - 1 and j_batch == nyf - 1:
prediction_true[index_y_d_in + margin:index_y_u_in - 0, prediction_true[index_y_d_in + margin:index_y_u_in - 0,
index_x_d_in + margin:index_x_u_in - margin] = \ index_x_d_in + margin:index_x_u_in - margin] = \
@ -1332,6 +1469,11 @@ class Eynollah:
label_p_pred[0, margin:, label_p_pred[0, margin:,
margin:-margin or None, margin:-margin or None,
1] 1]
if thresholding_for_artificial_class_in_light_version or thresholding_for_some_classes_in_light_version:
prediction_true[index_y_d_in + margin:index_y_u_in - 0,
index_x_d_in + margin:index_x_u_in - margin, 1] = \
seg_in_art[margin:,
margin:-margin or None]
else: else:
prediction_true[index_y_d_in + margin:index_y_u_in - margin, prediction_true[index_y_d_in + margin:index_y_u_in - margin,
index_x_d_in + margin:index_x_u_in - margin] = \ index_x_d_in + margin:index_x_u_in - margin] = \
@ -1343,6 +1485,11 @@ class Eynollah:
label_p_pred[0, margin:-margin or None, label_p_pred[0, margin:-margin or None,
margin:-margin or None, margin:-margin or None,
1] 1]
if thresholding_for_artificial_class_in_light_version or thresholding_for_some_classes_in_light_version:
prediction_true[index_y_d_in + margin:index_y_u_in - margin,
index_x_d_in + margin:index_x_u_in - margin, 1] = \
seg_in_art[margin:-margin or None,
margin:-margin or None]
indexer_inside_batch += 1 indexer_inside_batch += 1
list_i_s = [] list_i_s = []
@ -1356,6 +1503,32 @@ class Eynollah:
img_patch[:] = 0 img_patch[:] = 0
prediction_true = prediction_true.astype(np.uint8) prediction_true = prediction_true.astype(np.uint8)
if thresholding_for_artificial_class_in_light_version:
kernel_min = np.ones((3, 3), np.uint8)
prediction_true[:,:,0][prediction_true[:,:,0]==2] = 0
skeleton_art = skeletonize(prediction_true[:,:,1])
skeleton_art = skeleton_art*1
skeleton_art = skeleton_art.astype('uint8')
skeleton_art = cv2.dilate(skeleton_art, kernel_min, iterations=1)
prediction_true[:,:,0][skeleton_art==1]=2
if thresholding_for_some_classes_in_light_version:
kernel_min = np.ones((3, 3), np.uint8)
prediction_true[:,:,0][prediction_true[:,:,0]==4] = 0
skeleton_art = skeletonize(prediction_true[:,:,1])
skeleton_art = skeleton_art*1
skeleton_art = skeleton_art.astype('uint8')
skeleton_art = cv2.dilate(skeleton_art, kernel_min, iterations=1)
prediction_true[:,:,0][skeleton_art==1]=4
gc.collect() gc.collect()
return prediction_true, confidence_matrix return prediction_true, confidence_matrix
@ -1608,7 +1781,7 @@ class Eynollah:
prediction_textline = self.do_prediction( prediction_textline = self.do_prediction(
use_patches, img, self.model_textline, use_patches, img, self.model_textline,
marginal_of_patch_percent=0.15, n_batch_inference=3, marginal_of_patch_percent=0.15, n_batch_inference=3,
thresholding_for_artificial_class_in_light_version=self.textline_light) thresholding_for_artificial_class_in_light_version=self.textline_light, threshold_art_class_textline=self.threshold_art_class_textline)
#if not self.textline_light: #if not self.textline_light:
#if num_col_classifier==1: #if num_col_classifier==1:
#prediction_textline_nopatch = self.do_prediction(False, img, self.model_textline) #prediction_textline_nopatch = self.do_prediction(False, img, self.model_textline)
@ -1622,7 +1795,55 @@ class Eynollah:
textline_mask_tot_ea_art = textline_mask_tot_ea_art.astype('uint8') textline_mask_tot_ea_art = textline_mask_tot_ea_art.astype('uint8')
#textline_mask_tot_ea_art = cv2.dilate(textline_mask_tot_ea_art, KERNEL, iterations=1) #textline_mask_tot_ea_art = cv2.dilate(textline_mask_tot_ea_art, KERNEL, iterations=1)
prediction_textline[:,:][textline_mask_tot_ea_art[:,:]==1]=2 prediction_textline[:,:][textline_mask_tot_ea_art[:,:]==1]=2
"""
else:
textline_mask_tot_ea_art = textline_mask_tot_ea_art.astype('uint8')
hor_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (8, 1))
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
##cv2.imwrite('textline_mask_tot_ea_art.png', textline_mask_tot_ea_art)
textline_mask_tot_ea_art = cv2.dilate(textline_mask_tot_ea_art, hor_kernel, iterations=1)
###cv2.imwrite('dil_textline_mask_tot_ea_art.png', dil_textline_mask_tot_ea_art)
textline_mask_tot_ea_art = textline_mask_tot_ea_art.astype('uint8')
#print(np.shape(dil_textline_mask_tot_ea_art), np.unique(dil_textline_mask_tot_ea_art), 'dil_textline_mask_tot_ea_art')
tsk = time.time()
skeleton_art_textline = skeletonize(textline_mask_tot_ea_art[:,:,0])
skeleton_art_textline = skeleton_art_textline*1
skeleton_art_textline = skeleton_art_textline.astype('uint8')
skeleton_art_textline = cv2.dilate(skeleton_art_textline, kernel, iterations=1)
#print(np.unique(skeleton_art_textline), np.shape(skeleton_art_textline))
#print(skeleton_art_textline, np.unique(skeleton_art_textline))
#cv2.imwrite('skeleton_art_textline.png', skeleton_art_textline)
prediction_textline[:,:,0][skeleton_art_textline[:,:]==1]=2
#cv2.imwrite('prediction_textline1.png', prediction_textline[:,:,0])
##hor_kernel2 = cv2.getStructuringElement(cv2.MORPH_RECT, (4, 1))
##ver_kernel2 = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 3))
##textline_mask_tot_ea_main = (prediction_textline[:,:]==1)*1
##textline_mask_tot_ea_main = textline_mask_tot_ea_main.astype('uint8')
##dil_textline_mask_tot_ea_main = cv2.erode(textline_mask_tot_ea_main, ver_kernel2, iterations=1)
##dil_textline_mask_tot_ea_main = cv2.dilate(textline_mask_tot_ea_main, hor_kernel2, iterations=1)
##dil_textline_mask_tot_ea_main = cv2.dilate(textline_mask_tot_ea_main, ver_kernel2, iterations=1)
##prediction_textline[:,:][dil_textline_mask_tot_ea_main[:,:]==1]=1
"""
textline_mask_tot_ea_lines = (prediction_textline[:,:]==1)*1 textline_mask_tot_ea_lines = (prediction_textline[:,:]==1)*1
textline_mask_tot_ea_lines = textline_mask_tot_ea_lines.astype('uint8') textline_mask_tot_ea_lines = textline_mask_tot_ea_lines.astype('uint8')
if not self.textline_light: if not self.textline_light:
@ -1631,10 +1852,15 @@ class Eynollah:
prediction_textline[:,:][textline_mask_tot_ea_lines[:,:]==1]=1 prediction_textline[:,:][textline_mask_tot_ea_lines[:,:]==1]=1
if not self.textline_light: if not self.textline_light:
prediction_textline[:,:][old_art[:,:]==1]=2 prediction_textline[:,:][old_art[:,:]==1]=2
#cv2.imwrite('prediction_textline2.png', prediction_textline[:,:,0])
prediction_textline_longshot = self.do_prediction(False, img, self.model_textline) prediction_textline_longshot = self.do_prediction(False, img, self.model_textline)
prediction_textline_longshot_true_size = resize_image(prediction_textline_longshot, img_h, img_w) prediction_textline_longshot_true_size = resize_image(prediction_textline_longshot, img_h, img_w)
#cv2.imwrite('prediction_textline.png', prediction_textline[:,:,0])
#sys.exit()
self.logger.debug('exit textline_contours') self.logger.debug('exit textline_contours')
return ((prediction_textline[:, :, 0]==1).astype(np.uint8), return ((prediction_textline[:, :, 0]==1).astype(np.uint8),
(prediction_textline_longshot_true_size[:, :, 0]==1).astype(np.uint8)) (prediction_textline_longshot_true_size[:, :, 0]==1).astype(np.uint8))
@ -1840,7 +2066,7 @@ class Eynollah:
textline_mask_tot_ea = resize_image(textline_mask_tot_ea,img_height_h, img_width_h ) textline_mask_tot_ea = resize_image(textline_mask_tot_ea,img_height_h, img_width_h )
#print(self.image_org.shape) #print(self.image_org.shape)
#cv2.imwrite('out_13.png', self.image_page_org_size) #cv2.imwrite('textline.png', textline_mask_tot_ea)
#plt.imshwo(self.image_page_org_size) #plt.imshwo(self.image_page_org_size)
#plt.show() #plt.show()
@ -1852,13 +2078,13 @@ class Eynollah:
img_resized.shape[1], img_resized.shape[0], num_col_classifier) img_resized.shape[1], img_resized.shape[0], num_col_classifier)
prediction_regions_org, confidence_matrix = self.do_prediction_new_concept( prediction_regions_org, confidence_matrix = self.do_prediction_new_concept(
True, img_resized, self.model_region_1_2, n_batch_inference=1, True, img_resized, self.model_region_1_2, n_batch_inference=1,
thresholding_for_some_classes_in_light_version=True) thresholding_for_some_classes_in_light_version=True, threshold_art_class_layout=self.threshold_art_class_layout)
else: else:
prediction_regions_org = np.zeros((self.image_org.shape[0], self.image_org.shape[1], 3)) prediction_regions_org = np.zeros((self.image_org.shape[0], self.image_org.shape[1], 3))
confidence_matrix = np.zeros((self.image_org.shape[0], self.image_org.shape[1])) confidence_matrix = np.zeros((self.image_org.shape[0], self.image_org.shape[1]))
prediction_regions_page, confidence_matrix_page = self.do_prediction_new_concept( prediction_regions_page, confidence_matrix_page = self.do_prediction_new_concept(
False, self.image_page_org_size, self.model_region_1_2, n_batch_inference=1, False, self.image_page_org_size, self.model_region_1_2, n_batch_inference=1,
thresholding_for_artificial_class_in_light_version=True) thresholding_for_artificial_class_in_light_version=True, threshold_art_class_layout=self.threshold_art_class_layout)
ys = slice(*self.page_coord[0:2]) ys = slice(*self.page_coord[0:2])
xs = slice(*self.page_coord[2:4]) xs = slice(*self.page_coord[2:4])
prediction_regions_org[ys, xs] = prediction_regions_page prediction_regions_org[ys, xs] = prediction_regions_page
@ -1871,7 +2097,7 @@ class Eynollah:
img_resized.shape[1], img_resized.shape[0], new_h, num_col_classifier) img_resized.shape[1], img_resized.shape[0], new_h, num_col_classifier)
prediction_regions_org, confidence_matrix = self.do_prediction_new_concept( prediction_regions_org, confidence_matrix = self.do_prediction_new_concept(
True, img_resized, self.model_region_1_2, n_batch_inference=2, True, img_resized, self.model_region_1_2, n_batch_inference=2,
thresholding_for_some_classes_in_light_version=True) thresholding_for_some_classes_in_light_version=True, threshold_art_class_layout=self.threshold_art_class_layout)
###prediction_regions_org = self.do_prediction(True, img_bin, self.model_region, n_batch_inference=3, thresholding_for_some_classes_in_light_version=True) ###prediction_regions_org = self.do_prediction(True, img_bin, self.model_region, n_batch_inference=3, thresholding_for_some_classes_in_light_version=True)
#print("inside 3 ", time.time()-t_in) #print("inside 3 ", time.time()-t_in)
#plt.imshow(prediction_regions_org[:,:,0]) #plt.imshow(prediction_regions_org[:,:,0])
@ -3811,7 +4037,7 @@ class Eynollah:
if dilation_m1<6: if dilation_m1<6:
dilation_m1 = 6 dilation_m1 = 6
#print(dilation_m1, 'dilation_m1') #print(dilation_m1, 'dilation_m1')
dilation_m1 = 6 dilation_m1 = 4#6
dilation_m2 = int(dilation_m1/2.) +1 dilation_m2 = int(dilation_m1/2.) +1
for i in range(len(x_differential)): for i in range(len(x_differential)):
@ -4322,6 +4548,8 @@ class Eynollah:
cnt_clean_rot_raw, hir_on_cnt_clean_rot = return_contours_of_image(textline_mask_tot_ea) cnt_clean_rot_raw, hir_on_cnt_clean_rot = return_contours_of_image(textline_mask_tot_ea)
all_found_textline_polygons = filter_contours_area_of_image( all_found_textline_polygons = filter_contours_area_of_image(
textline_mask_tot_ea, cnt_clean_rot_raw, hir_on_cnt_clean_rot, max_area=1, min_area=0.00001) textline_mask_tot_ea, cnt_clean_rot_raw, hir_on_cnt_clean_rot, max_area=1, min_area=0.00001)
all_found_textline_polygons = all_found_textline_polygons[::-1]
all_found_textline_polygons=[ all_found_textline_polygons ] all_found_textline_polygons=[ all_found_textline_polygons ]
@ -4329,8 +4557,8 @@ class Eynollah:
all_found_textline_polygons) all_found_textline_polygons)
all_found_textline_polygons = self.filter_contours_inside_a_bigger_one( all_found_textline_polygons = self.filter_contours_inside_a_bigger_one(
all_found_textline_polygons, None, textline_mask_tot_ea, type_contour="textline") all_found_textline_polygons, None, textline_mask_tot_ea, type_contour="textline")
order_text_new = [0] order_text_new = [0]
slopes =[0] slopes =[0]
id_of_texts_tot =['region_0001'] id_of_texts_tot =['region_0001']
@ -4343,7 +4571,7 @@ class Eynollah:
polygons_lines_xml = [] polygons_lines_xml = []
contours_tables = [] contours_tables = []
ocr_all_textlines = None ocr_all_textlines = None
conf_contours_textregions =None conf_contours_textregions =[0]
pcgts = self.writer.build_pagexml_no_full_layout( pcgts = self.writer.build_pagexml_no_full_layout(
cont_page, page_coord, order_text_new, id_of_texts_tot, cont_page, page_coord, order_text_new, id_of_texts_tot,
all_found_textline_polygons, page_coord, polygons_of_images, polygons_of_marginals, all_found_textline_polygons, page_coord, polygons_of_images, polygons_of_marginals,
@ -4905,7 +5133,7 @@ class Eynollah_ocr:
self.b_s = int(batch_size) self.b_s = int(batch_size)
else: else:
self.model_ocr_dir = dir_models + "/model_step_1050000_ocr"#"/model_0_ocr_cnnrnn"#"/model_23_ocr_cnnrnn" self.model_ocr_dir = dir_models + "/model_ens_ocrcnn_125_225"#"/model_step_125000_ocr"#"/model_step_25000_ocr"#"/model_step_1050000_ocr"#"/model_0_ocr_cnnrnn"#"/model_23_ocr_cnnrnn"
model_ocr = load_model(self.model_ocr_dir , compile=False) model_ocr = load_model(self.model_ocr_dir , compile=False)
self.prediction_model = tf.keras.models.Model( self.prediction_model = tf.keras.models.Model(