qurator-spk
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eynollah
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Merge pull request #19 from qurator-spk/refactor-cntd

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Refactor cntd
pull/23/head
vahidrezanezhad 4 years ago committed by GitHub
parent 8ab50a5486 58c4403e13
commit 932c3fb479
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30 changed files with 2526 additions and 7352 deletions
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3
.github/workflows/test-eynollah.yml vendored
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@ -31,5 +31,6 @@ jobs:
run: | run: |
python -m pip install --upgrade pip python -m pip install --upgrade pip
pip install . pip install .
pip install -r requirements-test.txt
- name: Test with pytest - name: Test with pytest
run: echo success # make test run: make test

3
Makefile
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@ -1,3 +1,6 @@
EYNOLLAH_MODELS ?= $(PWD)/models_eynollah
export EYNOLLAH_MODELS
# BEGIN-EVAL makefile-parser --make-help Makefile # BEGIN-EVAL makefile-parser --make-help Makefile
help: help:

15
README.md
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@ -88,6 +88,21 @@ eynollah \
The tool does accept and works better on original images (RGB format) than binarized images. The tool does accept and works better on original images (RGB format) than binarized images.
### `--full-layout` vs `--no-full-layout`
Here are the difference in elements detected depending on the `--full-layout`/`--no-full-layout` command line flags:
| | `--full-layout` | `--no-full-layout` |
| --- | --- | --- |
| reading order | x | x |
| header regions | x | - |
| text regions | x | x |
| text regions / text line | x | x |
| drop-capitals | x | - |
| marginals | x | x |
| marginals / text line | x | x |
| image region | x | x |
### How to use ### How to use
First of all, this model makes use of up to 9 trained models which are responsible for different operations like size detection, column classification, image enhancement, page extraction, main layout detection, full layout detection and textline detection. But this does not mean that all 9 models are always required for every document. Based on the document characteristics and parameters specified, different scenarios can be applied. First of all, this model makes use of up to 9 trained models which are responsible for different operations like size detection, column classification, image enhancement, page extraction, main layout detection, full layout detection and textline detection. But this does not mean that all 9 models are always required for every document. Based on the document characteristics and parameters specified, different scenarios can be applied.

0
sbb_newspapers_org_image/.gitkeep → qurator/.gitkeep
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1
qurator/__init__.py
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@ -0,0 +1 @@
__import__("pkg_resources").declare_namespace(__name__)

0
sbb_newspapers_org_image/__init__.py → qurator/eynollah/__init__.py
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62
sbb_newspapers_org_image/cli.py → qurator/eynollah/cli.py
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@ -1,16 +1,23 @@
import sys
import click import click
from sbb_newspapers_org_image.eynollah import eynollah from ocrd_utils import initLogging, setOverrideLogLevel
from qurator.eynollah.eynollah import Eynollah
@click.command() @click.command()
@click.option( @click.option(
"--image", "-i", help="image filename", type=click.Path(exists=True, dir_okay=False) "--image",
"-i",
help="image filename",
type=click.Path(exists=True, dir_okay=False),
required=True,
) )
@click.option( @click.option(
"--out", "--out",
"-o", "-o",
help="directory to write output xml data", help="directory to write output xml data",
type=click.Path(exists=True, file_okay=False), type=click.Path(exists=True, file_okay=False),
required=True,
) )
@click.option( @click.option(
"--model", "--model",
@ -43,35 +50,47 @@ from sbb_newspapers_org_image.eynollah import eynollah
type=click.Path(exists=True, file_okay=False), type=click.Path(exists=True, file_okay=False),
) )
@click.option( @click.option(
"--allow_enhancement", "--enable-plotting/--disable-plotting",
"-ae", "-ep/-noep",
is_flag=True,
help="If set, will plot intermediary files and images",
)
@click.option(
"--allow-enhancement/--no-allow-enhancement",
"-ae/-noae",
is_flag=True, is_flag=True,
help="if this parameter set to true, this tool would check that input image need resizing and enhancement or not. If so output of resized and enhanced image and corresponding layout data will be written in out directory", help="if this parameter set to true, this tool would check that input image need resizing and enhancement or not. If so output of resized and enhanced image and corresponding layout data will be written in out directory",
) )
@click.option( @click.option(
"--curved_line", "--curved-line/--no-curvedline",
"-cl", "-cl/-nocl",
is_flag=True, is_flag=True,
help="if this parameter set to true, this tool will try to return contoure of textlines instead of rectabgle bounding box of textline. This should be taken into account that with this option the tool need more time to do process.", help="if this parameter set to true, this tool will try to return contoure of textlines instead of rectabgle bounding box of textline. This should be taken into account that with this option the tool need more time to do process.",
) )
@click.option( @click.option(
"--full_layout", "--full-layout/--no-full-layout",
"-fl", "-fl/-nofl",
is_flag=True, is_flag=True,
help="if this parameter set to true, this tool will try to return all elements of layout.", help="if this parameter set to true, this tool will try to return all elements of layout.",
) )
@click.option( @click.option(
"--allow_scaling", "--allow_scaling/--no-allow-scaling",
"-as", "-as/-noas",
is_flag=True, is_flag=True,
help="if this parameter set to true, this tool would check the scale and if needed it will scale it to perform better layout detection", help="if this parameter set to true, this tool would check the scale and if needed it will scale it to perform better layout detection",
) )
@click.option( @click.option(
"--headers_off", "--headers-off/--headers-on",
"-ho", "-ho/-noho",
is_flag=True, is_flag=True,
help="if this parameter set to true, this tool would ignore headers role in reading order", help="if this parameter set to true, this tool would ignore headers role in reading order",
) )
@click.option(
"--log-level",
"-l",
type=click.Choice(['OFF', 'DEBUG', 'INFO', 'WARN', 'ERROR']),
help="Override log level globally to this",
)
def main( def main(
image, image,
out, out,
@ -80,13 +99,24 @@ def main(
save_layout, save_layout,
save_deskewed, save_deskewed,
save_all, save_all,
enable_plotting,
allow_enhancement, allow_enhancement,
curved_line, curved_line,
full_layout, full_layout,
allow_scaling, allow_scaling,
headers_off, headers_off,
log_level
): ):
eynollah( if log_level:
setOverrideLogLevel(log_level)
initLogging()
if not enable_plotting and (save_layout or save_deskewed or save_all or save_images):
print("Error: You used one of -sl, -sd, -sa or -si but did not enable plotting with -ep")
sys.exit(1)
elif enable_plotting and not (save_layout or save_deskewed or save_all or save_images):
print("Error: You used -ep to enable plotting but set none of -sl, -sd, -sa or -si")
sys.exit(1)
eynollah = Eynollah(
image, image,
None, None,
out, out,
@ -95,13 +125,15 @@ def main(
save_layout, save_layout,
save_deskewed, save_deskewed,
save_all, save_all,
enable_plotting,
allow_enhancement, allow_enhancement,
curved_line, curved_line,
full_layout, full_layout,
allow_scaling, allow_scaling,
headers_off, headers_off,
).run() )
pcgts = eynollah.run()
eynollah.writer.write_pagexml(pcgts)
if __name__ == "__main__": if __name__ == "__main__":
main() main()

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qurator/eynollah/eynollah.py
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qurator/eynollah/plot.py
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import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import numpy as np
import os.path
import cv2
from scipy.ndimage import gaussian_filter1d
from .utils import crop_image_inside_box
from .utils.rotate import rotyate_image_different
from .utils.resize import resize_image
class EynollahPlotter():
"""
Class collecting all the plotting and image writing methods
"""
def __init__(
self,
*,
dir_of_all,
dir_of_deskewed,
dir_of_layout,
dir_of_cropped_images,
image_filename,
image_filename_stem,
image_org=None,
scale_x=1,
scale_y=1,
):
self.dir_of_all = dir_of_all
self.dir_of_layout = dir_of_layout
self.dir_of_cropped_images = dir_of_cropped_images
self.dir_of_deskewed = dir_of_deskewed
self.image_filename = image_filename
self.image_filename_stem = image_filename_stem
# XXX TODO hacky these cannot be set at init time
self.image_org = image_org
self.scale_x = scale_x
self.scale_y = scale_y
def save_plot_of_layout_main(self, text_regions_p, image_page):
if self.dir_of_layout is not None:
values = np.unique(text_regions_p[:, :])
# pixels=['Background' , 'Main text' , 'Heading' , 'Marginalia' ,'Drop capitals' , 'Images' , 'Seperators' , 'Tables', 'Graphics']
pixels=['Background' , 'Main text' , 'Image' , 'Separator','Marginalia']
values_indexes = [0, 1, 2, 3, 4]
plt.figure(figsize=(40, 40))
plt.rcParams["font.size"] = "40"
im = plt.imshow(text_regions_p[:, :])
colors = [im.cmap(im.norm(value)) for value in values]
patches = [mpatches.Patch(color=colors[np.where(values == i)[0][0]], label="{l}".format(l=pixels[int(np.where(values_indexes == i)[0][0])])) for i in values]
plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0, fontsize=40)
plt.savefig(os.path.join(self.dir_of_layout, self.image_filename_stem + "_layout_main.png"))
def save_plot_of_layout_main_all(self, text_regions_p, image_page):
if self.dir_of_all is not None:
values = np.unique(text_regions_p[:, :])
# pixels=['Background' , 'Main text' , 'Heading' , 'Marginalia' ,'Drop capitals' , 'Images' , 'Seperators' , 'Tables', 'Graphics']
pixels=['Background' , 'Main text' , 'Image' , 'Separator','Marginalia']
values_indexes = [0, 1, 2, 3, 4]
plt.figure(figsize=(80, 40))
plt.rcParams["font.size"] = "40"
plt.subplot(1, 2, 1)
plt.imshow(image_page)
plt.subplot(1, 2, 2)
im = plt.imshow(text_regions_p[:, :])
colors = [im.cmap(im.norm(value)) for value in values]
patches = [mpatches.Patch(color=colors[np.where(values == i)[0][0]], label="{l}".format(l=pixels[int(np.where(values_indexes == i)[0][0])])) for i in values]
plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0, fontsize=60)
plt.savefig(os.path.join(self.dir_of_all, self.image_filename_stem + "_layout_main_and_page.png"))
def save_plot_of_layout(self, text_regions_p, image_page):
if self.dir_of_layout is not None:
values = np.unique(text_regions_p[:, :])
# pixels=['Background' , 'Main text' , 'Heading' , 'Marginalia' ,'Drop capitals' , 'Images' , 'Seperators' , 'Tables', 'Graphics']
pixels = ["Background", "Main text", "Header", "Marginalia", "Drop capital", "Image", "Separator"]
values_indexes = [0, 1, 2, 8, 4, 5, 6]
plt.figure(figsize=(40, 40))
plt.rcParams["font.size"] = "40"
im = plt.imshow(text_regions_p[:, :])
colors = [im.cmap(im.norm(value)) for value in values]
patches = [mpatches.Patch(color=colors[np.where(values == i)[0][0]], label="{l}".format(l=pixels[int(np.where(values_indexes == i)[0][0])])) for i in values]
plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0, fontsize=40)
plt.savefig(os.path.join(self.dir_of_layout, self.image_filename_stem + "_layout.png"))
def save_plot_of_layout_all(self, text_regions_p, image_page):
if self.dir_of_all is not None:
values = np.unique(text_regions_p[:, :])
# pixels=['Background' , 'Main text' , 'Heading' , 'Marginalia' ,'Drop capitals' , 'Images' , 'Seperators' , 'Tables', 'Graphics']
pixels = ["Background", "Main text", "Header", "Marginalia", "Drop capital", "Image", "Separator"]
values_indexes = [0, 1, 2, 8, 4, 5, 6]
plt.figure(figsize=(80, 40))
plt.rcParams["font.size"] = "40"
plt.subplot(1, 2, 1)
plt.imshow(image_page)
plt.subplot(1, 2, 2)
im = plt.imshow(text_regions_p[:, :])
colors = [im.cmap(im.norm(value)) for value in values]
patches = [mpatches.Patch(color=colors[np.where(values == i)[0][0]], label="{l}".format(l=pixels[int(np.where(values_indexes == i)[0][0])])) for i in values]
plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0, fontsize=60)
plt.savefig(os.path.join(self.dir_of_all, self.image_filename_stem + "_layout_and_page.png"))
def save_plot_of_textlines(self, textline_mask_tot_ea, image_page):
if self.dir_of_all is not None:
values = np.unique(textline_mask_tot_ea[:, :])
pixels = ["Background", "Textlines"]
values_indexes = [0, 1]
plt.figure(figsize=(80, 40))
plt.rcParams["font.size"] = "40"
plt.subplot(1, 2, 1)
plt.imshow(image_page)
plt.subplot(1, 2, 2)
im = plt.imshow(textline_mask_tot_ea[:, :])
colors = [im.cmap(im.norm(value)) for value in values]
patches = [mpatches.Patch(color=colors[np.where(values == i)[0][0]], label="{l}".format(l=pixels[int(np.where(values_indexes == i)[0][0])])) for i in values]
plt.legend(handles=patches, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0, fontsize=60)
plt.savefig(os.path.join(self.dir_of_all, self.image_filename_stem + "_textline_and_page.png"))
def save_deskewed_image(self, slope_deskew):
if self.dir_of_all is not None:
cv2.imwrite(os.path.join(self.dir_of_all, self.image_filename_stem + "_org.png"), self.image_org)
if self.dir_of_deskewed is not None:
img_rotated = rotyate_image_different(self.image_org, slope_deskew)
cv2.imwrite(os.path.join(self.dir_of_deskewed, self.image_filename_stem + "_deskewed.png"), img_rotated)
def save_page_image(self, image_page):
if self.dir_of_all is not None:
cv2.imwrite(os.path.join(self.dir_of_all, self.image_filename_stem + "_page.png"), image_page)
def save_plot_of_textline_density(self, img_patch_org):
if self.dir_of_all is not None:
plt.figure(figsize=(80,40))
plt.rcParams['font.size']='50'
plt.subplot(1,2,1)
plt.imshow(img_patch_org)
plt.subplot(1,2,2)
plt.plot(gaussian_filter1d(img_patch_org.sum(axis=1), 3),np.array(range(len(gaussian_filter1d(img_patch_org.sum(axis=1), 3)))),linewidth=8)
plt.xlabel('Density of textline prediction in direction of X axis',fontsize=60)
plt.ylabel('Height',fontsize=60)
plt.yticks([0,len(gaussian_filter1d(img_patch_org.sum(axis=1), 3))])
plt.gca().invert_yaxis()
plt.savefig(os.path.join(self.dir_of_all, self.image_filename_stem+'_density_of_textline.png'))
def save_plot_of_rotation_angle(self, angels, var_res):
if self.dir_of_all is not None:
plt.figure(figsize=(60,30))
plt.rcParams['font.size']='50'
plt.plot(angels,np.array(var_res),'-o',markersize=25,linewidth=4)
plt.xlabel('angle',fontsize=50)
plt.ylabel('variance of sum of rotated textline in direction of x axis',fontsize=50)
plt.plot(angels[np.argmax(var_res)],var_res[np.argmax(np.array(var_res))] ,'*',markersize=50,label='Angle of deskewing=' +str("{:.2f}".format(angels[np.argmax(var_res)]))+r'$\degree$')
plt.legend(loc='best')
plt.savefig(os.path.join(self.dir_of_all, self.image_filename_stem+'_rotation_angle.png'))
def write_images_into_directory(self, img_contoures, image_page):
if self.dir_of_cropped_images is not None:
index = 0
for cont_ind in img_contoures:
x, y, w, h = cv2.boundingRect(cont_ind)
box = [x, y, w, h]
croped_page, page_coord = crop_image_inside_box(box, image_page)
croped_page = resize_image(croped_page, int(croped_page.shape[0] / self.scale_y), int(croped_page.shape[1] / self.scale_x))
path = os.path.join(self.dir_of_cropped_images, self.image_filename_stem + "_" + str(index) + ".jpg")
cv2.imwrite(path, croped_page)
index += 1

422
sbb_newspapers_org_image/utils/__init__.py → qurator/eynollah/utils/__init__.py
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@ -299,24 +299,6 @@ def crop_image_inside_box(box, img_org_copy):
image_box = img_org_copy[box[1] : box[1] + box[3], box[0] : box[0] + box[2]] image_box = img_org_copy[box[1] : box[1] + box[3], box[0] : box[0] + box[2]]
return image_box, [box[1], box[1] + box[3], box[0], box[0] + box[2]] return image_box, [box[1], box[1] + box[3], box[0], box[0] + box[2]]
def otsu_copy(img):
img_r = np.zeros(img.shape)
img1 = img[:, :, 0]
img2 = img[:, :, 1]
img3 = img[:, :, 2]
# print(img.min())
# print(img[:,:,0].min())
# blur = cv2.GaussianBlur(img,(5,5))
# ret3,th3 = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
retval1, threshold1 = cv2.threshold(img1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
retval2, threshold2 = cv2.threshold(img2, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
retval3, threshold3 = cv2.threshold(img3, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
img_r[:, :, 0] = threshold1
img_r[:, :, 1] = threshold1
img_r[:, :, 2] = threshold1
return img_r
def otsu_copy_binary(img): def otsu_copy_binary(img):
img_r = np.zeros((img.shape[0], img.shape[1], 3)) img_r = np.zeros((img.shape[0], img.shape[1], 3))
img1 = img[:, :, 0] img1 = img[:, :, 0]
@ -373,241 +355,41 @@ def boosting_headers_by_longshot_region_segmentation(textregion_pre_p, textregio
def find_num_col_deskew(regions_without_seperators, sigma_, multiplier=3.8): def find_num_col_deskew(regions_without_seperators, sigma_, multiplier=3.8):
regions_without_seperators_0 = regions_without_seperators[:,:].sum(axis=1) regions_without_seperators_0 = regions_without_seperators[:,:].sum(axis=1)
z = gaussian_filter1d(regions_without_seperators_0, sigma_)
return np.std(z)
##meda_n_updown=regions_without_seperators_0[len(regions_without_seperators_0)::-1]
##first_nonzero=(next((i for i, x in enumerate(regions_without_seperators_0) if x), 0))
##last_nonzero=(next((i for i, x in enumerate(meda_n_updown) if x), 0))
##last_nonzero=len(regions_without_seperators_0)-last_nonzero
y=regions_without_seperators_0#[first_nonzero:last_nonzero]
##y_help=np.zeros(len(y)+20)
##y_help[10:len(y)+10]=y
##x=np.array( range(len(y)) )
##zneg_rev=-y_help+np.max(y_help)
##zneg=np.zeros(len(zneg_rev)+20)
##zneg[10:len(zneg_rev)+10]=zneg_rev
z=gaussian_filter1d(y, sigma_)
###zneg= gaussian_filter1d(zneg, sigma_)
###peaks_neg, _ = find_peaks(zneg, height=0)
###peaks, _ = find_peaks(z, height=0)
###peaks_neg=peaks_neg-10-10
####print(np.std(z),'np.std(z)np.std(z)np.std(z)')
#####plt.plot(z)
#####plt.show()
#####plt.imshow(regions_without_seperators)
#####plt.show()
###"""
###last_nonzero=last_nonzero-0#100
###first_nonzero=first_nonzero+0#+100
###peaks_neg=peaks_neg[(peaks_neg>first_nonzero) & (peaks_neg<last_nonzero)]
###peaks=peaks[(peaks>.06*regions_without_seperators.shape[1]) & (peaks<0.94*regions_without_seperators.shape[1])]
###"""
###interest_pos=z[peaks]
###interest_pos=interest_pos[interest_pos>10]
###interest_neg=z[peaks_neg]
###min_peaks_pos=np.mean(interest_pos)
###min_peaks_neg=0#np.min(interest_neg)
###dis_talaei=(min_peaks_pos-min_peaks_neg)/multiplier
####print(interest_pos)
###grenze=min_peaks_pos-dis_talaei#np.mean(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])-np.std(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])/2.0
###interest_neg_fin=interest_neg[(interest_neg<grenze)]
###peaks_neg_fin=peaks_neg[(interest_neg<grenze)]
###interest_neg_fin=interest_neg[(interest_neg<grenze)]
###"""
###if interest_neg[0]<0.1:
###interest_neg=interest_neg[1:]
###if interest_neg[len(interest_neg)-1]<0.1:
###interest_neg=interest_neg[:len(interest_neg)-1]
###min_peaks_pos=np.min(interest_pos)
###min_peaks_neg=0#np.min(interest_neg)
###dis_talaei=(min_peaks_pos-min_peaks_neg)/multiplier
###grenze=min_peaks_pos-dis_talaei#np.mean(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])-np.std(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])/2.0
###"""
####interest_neg_fin=interest_neg#[(interest_neg<grenze)]
####peaks_neg_fin=peaks_neg#[(interest_neg<grenze)]
####interest_neg_fin=interest_neg#[(interest_neg<grenze)]
###num_col=(len(interest_neg_fin))+1
###p_l=0
###p_u=len(y)-1
###p_m=int(len(y)/2.)
###p_g_l=int(len(y)/3.)
###p_g_u=len(y)-int(len(y)/3.)
###diff_peaks=np.abs( np.diff(peaks_neg_fin) )
###diff_peaks_annormal=diff_peaks[diff_peaks<30]
#print(len(interest_neg_fin),np.mean(interest_neg_fin))
return np.std(z)#interest_neg_fin,np.std(z)
def return_hor_spliter_by_index_for_without_verticals(peaks_neg_fin_t, x_min_hor_some, x_max_hor_some):
# print(peaks_neg_fin_t,x_min_hor_some,x_max_hor_some)
arg_min_hor_sort = np.argsort(x_min_hor_some)
x_min_hor_some_sort = np.sort(x_min_hor_some)
x_max_hor_some_sort = x_max_hor_some[arg_min_hor_sort]
arg_minmax = np.array(range(len(peaks_neg_fin_t)))
indexer_lines = []
indexes_to_delete = []
indexer_lines_deletions_len = []
indexr_uniq_ind = []
for i in range(len(x_min_hor_some_sort)):
min_h = peaks_neg_fin_t - x_min_hor_some_sort[i]
max_h = peaks_neg_fin_t - x_max_hor_some_sort[i]
min_h[0] = min_h[0] # +20
max_h[len(max_h) - 1] = max_h[len(max_h) - 1] - 20
min_h_neg = arg_minmax[(min_h < 0)]
min_h_neg_n = min_h[min_h < 0]
try:
min_h_neg = [min_h_neg[np.argmax(min_h_neg_n)]]
except:
min_h_neg = []
max_h_neg = arg_minmax[(max_h > 0)]
max_h_neg_n = max_h[max_h > 0]
if len(max_h_neg_n) > 0:
max_h_neg = [max_h_neg[np.argmin(max_h_neg_n)]]
else:
max_h_neg = []
if len(min_h_neg) > 0 and len(max_h_neg) > 0:
deletions = list(range(min_h_neg[0] + 1, max_h_neg[0]))
unique_delets_int = []
# print(deletions,len(deletions),'delii')
if len(deletions) > 0:
for j in range(len(deletions)):
indexes_to_delete.append(deletions[j])
# print(deletions,indexes_to_delete,'badiii')
unique_delets = np.unique(indexes_to_delete)
# print(min_h_neg[0],unique_delets)
unique_delets_int = unique_delets[unique_delets < min_h_neg[0]]
indexer_lines_deletions_len.append(len(deletions))
indexr_uniq_ind.append([deletions])
else:
indexer_lines_deletions_len.append(0)
indexr_uniq_ind.append(-999)
index_line_true = min_h_neg[0] - len(unique_delets_int)
# print(index_line_true)
if index_line_true > 0 and min_h_neg[0] >= 2:
index_line_true = index_line_true
else:
index_line_true = min_h_neg[0]
indexer_lines.append(index_line_true)
if len(unique_delets_int) > 0:
for dd in range(len(unique_delets_int)):
indexes_to_delete.append(unique_delets_int[dd])
else:
indexer_lines.append(-999)
indexer_lines_deletions_len.append(-999)
indexr_uniq_ind.append(-999)
peaks_true = []
for m in range(len(peaks_neg_fin_t)):
if m in indexes_to_delete:
pass
else:
peaks_true.append(peaks_neg_fin_t[m])
return indexer_lines, peaks_true, arg_min_hor_sort, indexer_lines_deletions_len, indexr_uniq_ind
def find_num_col(regions_without_seperators, multiplier=3.8): def find_num_col(regions_without_seperators, multiplier=3.8):
regions_without_seperators_0 = regions_without_seperators[:, :].sum(axis=0) regions_without_seperators_0 = regions_without_seperators[:, :].sum(axis=0)
##plt.plot(regions_without_seperators_0) ##plt.plot(regions_without_seperators_0)
##plt.show() ##plt.show()
sigma_ = 35 # 70#35 sigma_ = 35 # 70#35
meda_n_updown = regions_without_seperators_0[len(regions_without_seperators_0) :: -1] meda_n_updown = regions_without_seperators_0[len(regions_without_seperators_0) :: -1]
first_nonzero = next((i for i, x in enumerate(regions_without_seperators_0) if x), 0) first_nonzero = next((i for i, x in enumerate(regions_without_seperators_0) if x), 0)
last_nonzero = next((i for i, x in enumerate(meda_n_updown) if x), 0) last_nonzero = next((i for i, x in enumerate(meda_n_updown) if x), 0)
# print(last_nonzero)
# print(isNaN(last_nonzero))
# last_nonzero=0#halalikh
last_nonzero = len(regions_without_seperators_0) - last_nonzero last_nonzero = len(regions_without_seperators_0) - last_nonzero
y = regions_without_seperators_0 # [first_nonzero:last_nonzero] y = regions_without_seperators_0 # [first_nonzero:last_nonzero]
y_help = np.zeros(len(y) + 20) y_help = np.zeros(len(y) + 20)
y_help[10 : len(y) + 10] = y y_help[10 : len(y) + 10] = y
x = np.array(range(len(y))) x = np.array(range(len(y)))
zneg_rev = -y_help + np.max(y_help) zneg_rev = -y_help + np.max(y_help)
zneg = np.zeros(len(zneg_rev) + 20) zneg = np.zeros(len(zneg_rev) + 20)
zneg[10 : len(zneg_rev) + 10] = zneg_rev zneg[10 : len(zneg_rev) + 10] = zneg_rev
z = gaussian_filter1d(y, sigma_) z = gaussian_filter1d(y, sigma_)
zneg = gaussian_filter1d(zneg, sigma_) zneg = gaussian_filter1d(zneg, sigma_)
peaks_neg, _ = find_peaks(zneg, height=0) peaks_neg, _ = find_peaks(zneg, height=0)
peaks, _ = find_peaks(z, height=0) peaks, _ = find_peaks(z, height=0)
peaks_neg = peaks_neg - 10 - 10 peaks_neg = peaks_neg - 10 - 10
last_nonzero = last_nonzero - 100 last_nonzero = last_nonzero - 100
first_nonzero = first_nonzero + 200 first_nonzero = first_nonzero + 200
peaks_neg = peaks_neg[(peaks_neg > first_nonzero) & (peaks_neg < last_nonzero)] peaks_neg = peaks_neg[(peaks_neg > first_nonzero) & (peaks_neg < last_nonzero)]
peaks = peaks[(peaks > 0.06 * regions_without_seperators.shape[1]) & (peaks < 0.94 * regions_without_seperators.shape[1])] peaks = peaks[(peaks > 0.06 * regions_without_seperators.shape[1]) & (peaks < 0.94 * regions_without_seperators.shape[1])]
peaks_neg = peaks_neg[(peaks_neg > 370) & (peaks_neg < (regions_without_seperators.shape[1] - 370))] peaks_neg = peaks_neg[(peaks_neg > 370) & (peaks_neg < (regions_without_seperators.shape[1] - 370))]
# print(peaks)
interest_pos = z[peaks] interest_pos = z[peaks]
interest_pos = interest_pos[interest_pos > 10] interest_pos = interest_pos[interest_pos > 10]
# plt.plot(z) # plt.plot(z)
# plt.show() # plt.show()
interest_neg = z[peaks_neg] interest_neg = z[peaks_neg]
@ -621,9 +403,7 @@ def find_num_col(regions_without_seperators, multiplier=3.8):
min_peaks_neg = 0 # np.min(interest_neg) min_peaks_neg = 0 # np.min(interest_neg)
# print(np.min(interest_pos),np.max(interest_pos),np.max(interest_pos)/np.min(interest_pos),'minmax') # print(np.min(interest_pos),np.max(interest_pos),np.max(interest_pos)/np.min(interest_pos),'minmax')
# $print(min_peaks_pos)
dis_talaei = (min_peaks_pos - min_peaks_neg) / multiplier dis_talaei = (min_peaks_pos - min_peaks_neg) / multiplier
# print(interest_pos)
grenze = min_peaks_pos - dis_talaei # np.mean(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])-np.std(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])/2.0 grenze = min_peaks_pos - dis_talaei # np.mean(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])-np.std(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])/2.0
# print(interest_neg,'interest_neg') # print(interest_neg,'interest_neg')
@ -650,15 +430,11 @@ def find_num_col(regions_without_seperators, multiplier=3.8):
if (peaks_neg_fin[0] > p_g_u and peaks_neg_fin[1] > p_g_u) or (peaks_neg_fin[0] < p_g_l and peaks_neg_fin[1] < p_g_l) or ((peaks_neg_fin[0] + 200) < p_m and peaks_neg_fin[1] < p_m) or ((peaks_neg_fin[0] - 200) > p_m and peaks_neg_fin[1] > p_m): if (peaks_neg_fin[0] > p_g_u and peaks_neg_fin[1] > p_g_u) or (peaks_neg_fin[0] < p_g_l and peaks_neg_fin[1] < p_g_l) or ((peaks_neg_fin[0] + 200) < p_m and peaks_neg_fin[1] < p_m) or ((peaks_neg_fin[0] - 200) > p_m and peaks_neg_fin[1] > p_m):
num_col = 1 num_col = 1
peaks_neg_fin = [] peaks_neg_fin = []
else:
pass
if num_col == 2: if num_col == 2:
if (peaks_neg_fin[0] > p_g_u) or (peaks_neg_fin[0] < p_g_l): if (peaks_neg_fin[0] > p_g_u) or (peaks_neg_fin[0] < p_g_l):
num_col = 1 num_col = 1
peaks_neg_fin = [] peaks_neg_fin = []
else:
pass
##print(len(peaks_neg_fin)) ##print(len(peaks_neg_fin))
@ -673,7 +449,7 @@ def find_num_col(regions_without_seperators, multiplier=3.8):
for i in range(len(peaks_neg_fin)): for i in range(len(peaks_neg_fin)):
if i == 0: if i == 0:
forest.append(peaks_neg_fin[i]) forest.append(peaks_neg_fin[i])
if i < (len(peaks_neg_fin) - 1): if i < len(peaks_neg_fin) - 1:
if diff_peaks[i] <= cut_off: if diff_peaks[i] <= cut_off:
forest.append(peaks_neg_fin[i + 1]) forest.append(peaks_neg_fin[i + 1])
if diff_peaks[i] > cut_off: if diff_peaks[i] > cut_off:
@ -687,7 +463,7 @@ def find_num_col(regions_without_seperators, multiplier=3.8):
if not isNaN(forest[np.argmin(z[forest])]): if not isNaN(forest[np.argmin(z[forest])]):
peaks_neg_true.append(forest[np.argmin(z[forest])]) peaks_neg_true.append(forest[np.argmin(z[forest])])
num_col = (len(peaks_neg_true)) + 1 num_col = len(peaks_neg_true) + 1
p_l = 0 p_l = 0
p_u = len(y) - 1 p_u = len(y) - 1
p_m = int(len(y) / 2.0) p_m = int(len(y) / 2.0)
@ -706,15 +482,11 @@ def find_num_col(regions_without_seperators, multiplier=3.8):
peaks_neg_true = [peaks_neg_true[0]] peaks_neg_true = [peaks_neg_true[0]]
elif (peaks_neg_true[1] < p_g_u and peaks_neg_true[1] > p_g_l) and (peaks_neg_true[0] < p_quarter): elif (peaks_neg_true[1] < p_g_u and peaks_neg_true[1] > p_g_l) and (peaks_neg_true[0] < p_quarter):
peaks_neg_true = [peaks_neg_true[1]] peaks_neg_true = [peaks_neg_true[1]]
else:
pass
if num_col == 2: if num_col == 2:
if (peaks_neg_true[0] > p_g_u) or (peaks_neg_true[0] < p_g_l): if (peaks_neg_true[0] > p_g_u) or (peaks_neg_true[0] < p_g_l):
num_col = 1 num_col = 1
peaks_neg_true = [] peaks_neg_true = []
else:
pass
diff_peaks_annormal = diff_peaks[diff_peaks < 360] diff_peaks_annormal = diff_peaks[diff_peaks < 360]
@ -732,9 +504,7 @@ def find_num_col(regions_without_seperators, multiplier=3.8):
else: else:
peaks_neg_fin_new.append(peaks_neg_fin[ii + 1]) peaks_neg_fin_new.append(peaks_neg_fin[ii + 1])
elif (ii - 1) in arg_help_ann: elif (ii - 1) not in arg_help_ann:
pass
else:
peaks_neg_fin_new.append(peaks_neg_fin[ii]) peaks_neg_fin_new.append(peaks_neg_fin[ii])
else: else:
peaks_neg_fin_new = peaks_neg_fin peaks_neg_fin_new = peaks_neg_fin
@ -948,28 +718,6 @@ def find_num_col_by_vertical_lines(regions_without_seperators, multiplier=3.8):
# print(peaks,'peaksnew') # print(peaks,'peaksnew')
return peaks return peaks
def delete_seperator_around(spliter_y, peaks_neg, image_by_region):
# format of subboxes box=[x1, x2 , y1, y2]
if len(image_by_region.shape) == 3:
for i in range(len(spliter_y) - 1):
for j in range(1, len(peaks_neg[i]) - 1):
image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 0][image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 0] == 6] = 0
image_by_region[spliter_y[i] : spliter_y[i + 1], peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 0][image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 1] == 6] = 0
image_by_region[spliter_y[i] : spliter_y[i + 1], peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 0][image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 2] == 6] = 0
image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 0][image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 0] == 7] = 0
image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 0][image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 1] == 7] = 0
image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 0][image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j]), 2] == 7] = 0
else:
for i in range(len(spliter_y) - 1):
for j in range(1, len(peaks_neg[i]) - 1):
image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j])][image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j])] == 6] = 0
image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j])][image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j])] == 7] = 0
return image_by_region
def return_regions_without_seperators(regions_pre): def return_regions_without_seperators(regions_pre):
kernel = np.ones((5, 5), np.uint8) kernel = np.ones((5, 5), np.uint8)
regions_without_seperators = ((regions_pre[:, :] != 6) & (regions_pre[:, :] != 0)) * 1 regions_without_seperators = ((regions_pre[:, :] != 6) & (regions_pre[:, :] != 0)) * 1
@ -1432,166 +1180,6 @@ def order_of_regions(textline_mask, contours_main, contours_header, y_ref):
return final_indexers_sorted, matrix_of_orders, final_types, final_index_type return final_indexers_sorted, matrix_of_orders, final_types, final_index_type
def implent_law_head_main_not_parallel(text_regions):
# print(text_regions.shape)
text_indexes = [1, 2] # 1: main text , 2: header , 3: comments
for t_i in text_indexes:
textline_mask = text_regions[:, :] == t_i
textline_mask = textline_mask * 255.0
textline_mask = textline_mask.astype(np.uint8)
textline_mask = np.repeat(textline_mask[:, :, np.newaxis], 3, axis=2)
kernel = np.ones((5, 5), np.uint8)
# print(type(textline_mask),np.unique(textline_mask),textline_mask.shape)
imgray = cv2.cvtColor(textline_mask, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
if t_i == 1:
contours_main, hirarchy = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# print(type(contours_main))
areas_main = np.array([cv2.contourArea(contours_main[j]) for j in range(len(contours_main))])
M_main = [cv2.moments(contours_main[j]) for j in range(len(contours_main))]
cx_main = [(M_main[j]["m10"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
x_min_main = np.array([np.min(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
x_max_main = np.array([np.max(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
y_min_main = np.array([np.min(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
y_max_main = np.array([np.max(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
# print(contours_main[0],np.shape(contours_main[0]),contours_main[0][:,0,0])
elif t_i == 2:
contours_header, hirarchy = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# print(type(contours_header))
areas_header = np.array([cv2.contourArea(contours_header[j]) for j in range(len(contours_header))])
M_header = [cv2.moments(contours_header[j]) for j in range(len(contours_header))]
cx_header = [(M_header[j]["m10"] / (M_header[j]["m00"] + 1e-32)) for j in range(len(M_header))]
cy_header = [(M_header[j]["m01"] / (M_header[j]["m00"] + 1e-32)) for j in range(len(M_header))]
x_min_header = np.array([np.min(contours_header[j][:, 0, 0]) for j in range(len(contours_header))])
x_max_header = np.array([np.max(contours_header[j][:, 0, 0]) for j in range(len(contours_header))])
y_min_header = np.array([np.min(contours_header[j][:, 0, 1]) for j in range(len(contours_header))])
y_max_header = np.array([np.max(contours_header[j][:, 0, 1]) for j in range(len(contours_header))])
args = np.array(range(1, len(cy_header) + 1))
args_main = np.array(range(1, len(cy_main) + 1))
for jj in range(len(contours_main)):
headers_in_main = [(cy_header > y_min_main[jj]) & ((cy_header < y_max_main[jj]))]
mains_in_main = [(cy_main > y_min_main[jj]) & ((cy_main < y_max_main[jj]))]
args_log = args * headers_in_main
res = args_log[args_log > 0]
res_true = res - 1
args_log_main = args_main * mains_in_main
res_main = args_log_main[args_log_main > 0]
res_true_main = res_main - 1
if len(res_true) > 0:
sum_header = np.sum(areas_header[res_true])
sum_main = np.sum(areas_main[res_true_main])
if sum_main > sum_header:
cnt_int = [contours_header[j] for j in res_true]
text_regions = cv2.fillPoly(text_regions, pts=cnt_int, color=(1, 1, 1))
else:
cnt_int = [contours_main[j] for j in res_true_main]
text_regions = cv2.fillPoly(text_regions, pts=cnt_int, color=(2, 2, 2))
for jj in range(len(contours_header)):
main_in_header = [(cy_main > y_min_header[jj]) & ((cy_main < y_max_header[jj]))]
header_in_header = [(cy_header > y_min_header[jj]) & ((cy_header < y_max_header[jj]))]
args_log = args_main * main_in_header
res = args_log[args_log > 0]
res_true = res - 1
args_log_header = args * header_in_header
res_header = args_log_header[args_log_header > 0]
res_true_header = res_header - 1
if len(res_true) > 0:
sum_header = np.sum(areas_header[res_true_header])
sum_main = np.sum(areas_main[res_true])
if sum_main > sum_header:
cnt_int = [contours_header[j] for j in res_true_header]
text_regions = cv2.fillPoly(text_regions, pts=cnt_int, color=(1, 1, 1))
else:
cnt_int = [contours_main[j] for j in res_true]
text_regions = cv2.fillPoly(text_regions, pts=cnt_int, color=(2, 2, 2))
return text_regions
def return_hor_spliter_by_index(peaks_neg_fin_t, x_min_hor_some, x_max_hor_some):
arg_min_hor_sort = np.argsort(x_min_hor_some)
x_min_hor_some_sort = np.sort(x_min_hor_some)
x_max_hor_some_sort = x_max_hor_some[arg_min_hor_sort]
arg_minmax = np.array(range(len(peaks_neg_fin_t)))
indexer_lines = []
indexes_to_delete = []
indexer_lines_deletions_len = []
indexr_uniq_ind = []
for i in range(len(x_min_hor_some_sort)):
min_h = peaks_neg_fin_t - x_min_hor_some_sort[i]
max_h = peaks_neg_fin_t - x_max_hor_some_sort[i]
min_h[0] = min_h[0] # +20
max_h[len(max_h) - 1] = max_h[len(max_h) - 1] ##-20
min_h_neg = arg_minmax[(min_h < 0) & (np.abs(min_h) < 360)]
max_h_neg = arg_minmax[(max_h >= 0) & (np.abs(max_h) < 360)]
if len(min_h_neg) > 0 and len(max_h_neg) > 0:
deletions = list(range(min_h_neg[0] + 1, max_h_neg[0]))
unique_delets_int = []
# print(deletions,len(deletions),'delii')
if len(deletions) > 0:
# print(deletions,len(deletions),'delii2')
for j in range(len(deletions)):
indexes_to_delete.append(deletions[j])
# print(deletions,indexes_to_delete,'badiii')
unique_delets = np.unique(indexes_to_delete)
# print(min_h_neg[0],unique_delets)
unique_delets_int = unique_delets[unique_delets < min_h_neg[0]]
indexer_lines_deletions_len.append(len(deletions))
indexr_uniq_ind.append([deletions])
else:
indexer_lines_deletions_len.append(0)
indexr_uniq_ind.append(-999)
index_line_true = min_h_neg[0] - len(unique_delets_int)
# print(index_line_true)
if index_line_true > 0 and min_h_neg[0] >= 2:
index_line_true = index_line_true
else:
index_line_true = min_h_neg[0]
indexer_lines.append(index_line_true)
if len(unique_delets_int) > 0:
for dd in range(len(unique_delets_int)):
indexes_to_delete.append(unique_delets_int[dd])
else:
indexer_lines.append(-999)
indexer_lines_deletions_len.append(-999)
indexr_uniq_ind.append(-999)
peaks_true = []
for m in range(len(peaks_neg_fin_t)):
if m in indexes_to_delete:
pass
else:
peaks_true.append(peaks_neg_fin_t[m])
return indexer_lines, peaks_true, arg_min_hor_sort, indexer_lines_deletions_len, indexr_uniq_ind
def combine_hor_lines_and_delete_cross_points_and_get_lines_features_back_new(img_p_in_ver, img_in_hor,num_col_classifier): def combine_hor_lines_and_delete_cross_points_and_get_lines_features_back_new(img_p_in_ver, img_in_hor,num_col_classifier):
#img_p_in_ver = cv2.erode(img_p_in_ver, self.kernel, iterations=2) #img_p_in_ver = cv2.erode(img_p_in_ver, self.kernel, iterations=2)
img_p_in_ver=img_p_in_ver.astype(np.uint8) img_p_in_ver=img_p_in_ver.astype(np.uint8)

60
sbb_newspapers_org_image/utils/contour.py → qurator/eynollah/utils/contour.py
Unescape Escape View File

@ -26,39 +26,6 @@ def find_contours_mean_y_diff(contours_main):
cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))] cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
return np.mean(np.diff(np.sort(np.array(cy_main)))) return np.mean(np.diff(np.sort(np.array(cy_main))))
def find_features_of_contours(contours_main):
areas_main = np.array([cv2.contourArea(contours_main[j]) for j in range(len(contours_main))])
M_main = [cv2.moments(contours_main[j]) for j in range(len(contours_main))]
cx_main = [(M_main[j]["m10"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
x_min_main = np.array([np.min(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
x_max_main = np.array([np.max(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
y_min_main = np.array([np.min(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
y_max_main = np.array([np.max(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
return y_min_main, y_max_main, areas_main
def return_contours_of_interested_region_and_bounding_box(region_pre_p, pixel):
# pixels of images are identified by 5
cnts_images = (region_pre_p[:, :, 0] == pixel) * 1
cnts_images = cnts_images.astype(np.uint8)
cnts_images = np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2)
imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_imgs, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours_imgs = return_parent_contours(contours_imgs, hiearchy)
contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=1, min_area=0.0003)
boxes = []
for jj in range(len(contours_imgs)):
x, y, w, h = cv2.boundingRect(contours_imgs[jj])
boxes.append([int(x), int(y), int(w), int(h)])
return contours_imgs, boxes
def get_text_region_boxes_by_given_contours(contours): def get_text_region_boxes_by_given_contours(contours):
@ -76,7 +43,6 @@ def get_text_region_boxes_by_given_contours(contours):
def filter_contours_area_of_image(image, contours, hirarchy, max_area, min_area): def filter_contours_area_of_image(image, contours, hirarchy, max_area, min_area):
found_polygons_early = list() found_polygons_early = list()
jv = 0 jv = 0
for c in contours: for c in contours:
if len(c) < 3: # A polygon cannot have less than 3 points if len(c) < 3: # A polygon cannot have less than 3 points
@ -89,23 +55,6 @@ def filter_contours_area_of_image(image, contours, hirarchy, max_area, min_area)
jv += 1 jv += 1
return found_polygons_early return found_polygons_early
def filter_contours_area_of_image_interiors(image, contours, hirarchy, max_area, min_area):
found_polygons_early = list()
jv = 0
for c in contours:
if len(c) < 3: # A polygon cannot have less than 3 points
continue
polygon = geometry.Polygon([point[0] for point in c])
area = polygon.area
if area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod(image.shape[:2]) and hirarchy[0][jv][3] != -1:
# print(c[0][0][1])
found_polygons_early.append(np.array([point for point in polygon.exterior.coords], dtype=np.uint))
jv += 1
return found_polygons_early
def filter_contours_area_of_image_tables(image, contours, hirarchy, max_area, min_area): def filter_contours_area_of_image_tables(image, contours, hirarchy, max_area, min_area):
found_polygons_early = list() found_polygons_early = list()
@ -236,15 +185,6 @@ def return_contours_of_interested_textline(region_pre_p, pixel):
contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=1, min_area=0.000000003) contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=1, min_area=0.000000003)
return contours_imgs return contours_imgs
def return_bonding_box_of_contours(cnts):
boxes_tot = []
for i in range(len(cnts)):
x, y, w, h = cv2.boundingRect(cnts[i])
box = [x, y, w, h]
boxes_tot.append(box)
return boxes_tot
def return_contours_of_image(image): def return_contours_of_image(image):
if len(image.shape) == 2: if len(image.shape) == 2:

0
sbb_newspapers_org_image/utils/drop_capitals.py → qurator/eynollah/utils/drop_capitals.py
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0
sbb_newspapers_org_image/utils/is_nan.py → qurator/eynollah/utils/is_nan.py
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0
sbb_newspapers_org_image/utils/marginals.py → qurator/eynollah/utils/marginals.py
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24
qurator/eynollah/utils/pil_cv2.py
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@ -0,0 +1,24 @@
from PIL import Image
import numpy as np
from ocrd_models import OcrdExif
from cv2 import COLOR_GRAY2BGR, COLOR_RGB2BGR, cvtColor, imread
# from sbb_binarization
def cv2pil(img):
return Image.fromarray(img.astype('uint8'))
def pil2cv(img):
# from ocrd/workspace.py
color_conversion = COLOR_GRAY2BGR if img.mode in ('1', 'L') else COLOR_RGB2BGR
pil_as_np_array = np.array(img).astype('uint8') if img.mode == '1' else np.array(img)
return cvtColor(pil_as_np_array, color_conversion)
def check_dpi(image_filename):
exif = OcrdExif(Image.open(image_filename))
print(exif.to_xml())
resolution = exif.resolution
if exif.resolutionUnit == 'cm':
resolution /= 2.54
return int(resolution)

0
sbb_newspapers_org_image/utils/resize.py → qurator/eynollah/utils/resize.py
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0
sbb_newspapers_org_image/utils/rotate.py → qurator/eynollah/utils/rotate.py
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157
sbb_newspapers_org_image/utils/separate_lines.py → qurator/eynollah/utils/separate_lines.py
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@ -1,4 +1,3 @@
import matplotlib.pyplot as plt
import numpy as np import numpy as np
import cv2 import cv2
from scipy.signal import find_peaks from scipy.signal import find_peaks
@ -14,31 +13,8 @@ from .contour import (
) )
from .is_nan import isNaN from .is_nan import isNaN
from . import ( from . import (
boosting_headers_by_longshot_region_segmentation,
crop_image_inside_box,
find_features_of_lines,
find_num_col,
find_num_col_by_vertical_lines,
find_num_col_deskew, find_num_col_deskew,
find_num_col_only_image,
isNaN, isNaN,
otsu_copy,
otsu_copy_binary,
return_hor_spliter_by_index_for_without_verticals,
delete_seperator_around,
return_regions_without_seperators,
put_drop_out_from_only_drop_model,
putt_bb_of_drop_capitals_of_model_in_patches_in_layout,
check_any_text_region_in_model_one_is_main_or_header,
small_textlines_to_parent_adherence2,
order_and_id_of_texts,
order_of_regions,
implent_law_head_main_not_parallel,
return_hor_spliter_by_index,
combine_hor_lines_and_delete_cross_points_and_get_lines_features_back_new,
return_points_with_boundies,
find_number_of_columns_in_document,
return_boxes_of_images_by_order_of_reading_new,
) )
def dedup_separate_lines(img_patch, contour_text_interest, thetha, axis): def dedup_separate_lines(img_patch, contour_text_interest, thetha, axis):
@ -1395,7 +1371,7 @@ def seperate_lines_vertical_cont(img_patch, contour_text_interest, thetha, box_i
return None, cont_final return None, cont_final
def textline_contours_postprocessing(textline_mask, slope, contour_text_interest, box_ind, slope_first, add_boxes_coor_into_textlines=False): def textline_contours_postprocessing(textline_mask, slope, contour_text_interest, box_ind, add_boxes_coor_into_textlines=False):
textline_mask = np.repeat(textline_mask[:, :, np.newaxis], 3, axis=2) * 255 textline_mask = np.repeat(textline_mask[:, :, np.newaxis], 3, axis=2) * 255
textline_mask = textline_mask.astype(np.uint8) textline_mask = textline_mask.astype(np.uint8)
@ -1485,7 +1461,7 @@ def textline_contours_postprocessing(textline_mask, slope, contour_text_interest
return contours_rotated_clean return contours_rotated_clean
def seperate_lines_new2(img_path, thetha, num_col, slope_region, dir_of_all, f_name): def seperate_lines_new2(img_path, thetha, num_col, slope_region, plotter=None):
if num_col == 1: if num_col == 1:
num_patches = int(img_path.shape[1] / 200.0) num_patches = int(img_path.shape[1] / 200.0)
@ -1536,7 +1512,7 @@ def seperate_lines_new2(img_path, thetha, num_col, slope_region, dir_of_all, f_n
sigma = 2 sigma = 2
try: try:
slope_xline = return_deskew_slop(img_xline, sigma, dir_of_all=dir_of_all, f_name=f_name) slope_xline = return_deskew_slop(img_xline, sigma, plotter=plotter)
except: except:
slope_xline = 0 slope_xline = 0
@ -1593,29 +1569,10 @@ def seperate_lines_new2(img_path, thetha, num_col, slope_region, dir_of_all, f_n
# plt.show() # plt.show()
return img_patch_ineterst_revised return img_patch_ineterst_revised
def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=None, f_name=None): def return_deskew_slop(img_patch_org, sigma_des, main_page=False, plotter=None):
if main_page and plotter:
if main_page and dir_of_all is not None: plotter.save_plot_of_textline_density(img_patch_org)
plt.figure(figsize=(80,40))
plt.rcParams['font.size']='50'
plt.subplot(1,2,1)
plt.imshow(img_patch_org)
plt.subplot(1,2,2)
plt.plot(gaussian_filter1d(img_patch_org.sum(axis=1), 3),np.array(range(len(gaussian_filter1d(img_patch_org.sum(axis=1), 3)))),linewidth=8)
plt.xlabel('Density of textline prediction in direction of X axis',fontsize=60)
plt.ylabel('Height',fontsize=60)
plt.yticks([0,len(gaussian_filter1d(img_patch_org.sum(axis=1), 3))])
plt.gca().invert_yaxis()
plt.savefig(os.path.join(dir_of_all, f_name+'_density_of_textline.png'))
#print(np.max(img_patch_org.sum(axis=0)) ,np.max(img_patch_org.sum(axis=1)),'axislar')
#img_patch_org=resize_image(img_patch_org,int(img_patch_org.shape[0]*2.5),int(img_patch_org.shape[1]/2.5))
#print(np.max(img_patch_org.sum(axis=0)) ,np.max(img_patch_org.sum(axis=1)),'axislar2')
img_int=np.zeros((img_patch_org.shape[0],img_patch_org.shape[1])) img_int=np.zeros((img_patch_org.shape[0],img_patch_org.shape[1]))
img_int[:,:]=img_patch_org[:,:]#img_patch_org[:,:,0] img_int[:,:]=img_patch_org[:,:]#img_patch_org[:,:,0]
@ -1647,53 +1604,23 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=Non
#plt.show() #plt.show()
angels=np.array([-45, 0 , 45 , 90 , ])#np.linspace(-12,12,100)#np.array([0 , 45 , 90 , -45]) angels=np.array([-45, 0 , 45 , 90 , ])#np.linspace(-12,12,100)#np.array([0 , 45 , 90 , -45])
#res=[]
#num_of_peaks=[]
#index_cor=[]
var_res=[] var_res=[]
#indexer=0
for rot in angels: for rot in angels:
img_rot=rotate_image(img_resized,rot) img_rot=rotate_image(img_resized,rot)
#plt.imshow(img_rot) #plt.imshow(img_rot)
#plt.show() #plt.show()
img_rot[img_rot!=0]=1 img_rot[img_rot!=0]=1
#res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
#neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 ) #neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
#print(var_spectrum,'var_spectrum') #print(var_spectrum,'var_spectrum')
try: try:
var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3 ) var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3 )
##print(rot,var_spectrum,'var_spectrum') ##print(rot,var_spectrum,'var_spectrum')
#res_me=np.mean(neg_peaks)
#if res_me==0:
#res_me=1000000000000000000000
#else:
#pass
#res_num=len(neg_peaks)
except: except:
#res_me=1000000000000000000000
#res_num=0
var_spectrum=0 var_spectrum=0
#if self.isNaN(res_me):
#pass
#else:
#res.append( res_me )
#var_res.append(var_spectrum)
#num_of_peaks.append( res_num )
#index_cor.append(indexer)
#indexer=indexer+1
var_res.append(var_spectrum) var_res.append(var_spectrum)
#index_cor.append(indexer)
#indexer=indexer+1
try: try:
var_res=np.array(var_res) var_res=np.array(var_res)
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin] ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
except: except:
ang_int=0 ang_int=0
@ -1701,32 +1628,19 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=Non
angels=np.linspace(ang_int-22.5,ang_int+22.5,100) angels=np.linspace(ang_int-22.5,ang_int+22.5,100)
#res=[]
#num_of_peaks=[]
#index_cor=[]
var_res=[] var_res=[]
for rot in angels: for rot in angels:
img_rot=rotate_image(img_resized,rot) img_rot=rotate_image(img_resized,rot)
##plt.imshow(img_rot) ##plt.imshow(img_rot)
##plt.show() ##plt.show()
img_rot[img_rot!=0]=1 img_rot[img_rot!=0]=1
#res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
try: try:
var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3 ) var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3 )
except: except:
var_spectrum=0 var_spectrum=0
var_res.append(var_spectrum) var_res.append(var_spectrum)
try: try:
var_res=np.array(var_res) var_res=np.array(var_res)
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin] ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
except: except:
ang_int=0 ang_int=0
@ -1745,9 +1659,6 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=Non
#plt.imshow(img_rot) #plt.imshow(img_rot)
#plt.show() #plt.show()
img_rot[img_rot!=0]=1 img_rot[img_rot!=0]=1
#res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
#neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 ) #neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
#print(var_spectrum,'var_spectrum') #print(var_spectrum,'var_spectrum')
try: try:
@ -1759,51 +1670,30 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=Non
var_res.append(var_spectrum) var_res.append(var_spectrum)
if dir_of_all is not None: if plotter:
#print('galdi?') plotter.save_plot_of_rotation_angle(angels, var_res)
plt.figure(figsize=(60,30))
plt.rcParams['font.size']='50'
plt.plot(angels,np.array(var_res),'-o',markersize=25,linewidth=4)
plt.xlabel('angle',fontsize=50)
plt.ylabel('variance of sum of rotated textline in direction of x axis',fontsize=50)
plt.plot(angels[np.argmax(var_res)],var_res[np.argmax(np.array(var_res))] ,'*',markersize=50,label='Angle of deskewing=' +str("{:.2f}".format(angels[np.argmax(var_res)]))+r'$\degree$')
plt.legend(loc='best')
plt.savefig(os.path.join(dir_of_all,f_name+'_rotation_angle.png'))
try: try:
var_res=np.array(var_res) var_res=np.array(var_res)
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin] ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
except: except:
ang_int=0 ang_int=0
early_slope_edge=11 early_slope_edge=11
if abs(ang_int)>early_slope_edge and ang_int<0: if abs(ang_int)>early_slope_edge and ang_int<0:
angels=np.linspace(-90,-12,100) angels=np.linspace(-90,-12,100)
var_res=[] var_res=[]
for rot in angels: for rot in angels:
img_rot=rotate_image(img_resized,rot) img_rot=rotate_image(img_resized,rot)
##plt.imshow(img_rot) ##plt.imshow(img_rot)
##plt.show() ##plt.show()
img_rot[img_rot!=0]=1 img_rot[img_rot!=0]=1
#res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
try: try:
var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3 ) var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3 )
except: except:
var_spectrum=0 var_spectrum=0
var_res.append(var_spectrum) var_res.append(var_spectrum)
try: try:
var_res=np.array(var_res) var_res=np.array(var_res)
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin] ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
except: except:
ang_int=0 ang_int=0
@ -1811,67 +1701,47 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=Non
elif abs(ang_int)>early_slope_edge and ang_int>0: elif abs(ang_int)>early_slope_edge and ang_int>0:
angels=np.linspace(90,12,100) angels=np.linspace(90,12,100)
var_res=[] var_res=[]
for rot in angels: for rot in angels:
img_rot=rotate_image(img_resized,rot) img_rot=rotate_image(img_resized,rot)
##plt.imshow(img_rot) ##plt.imshow(img_rot)
##plt.show() ##plt.show()
img_rot[img_rot!=0]=1 img_rot[img_rot!=0]=1
#res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
try: try:
var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3 ) var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3 )
#print(indexer,'indexer') #print(indexer,'indexer')
except: except:
var_spectrum=0 var_spectrum=0
var_res.append(var_spectrum) var_res.append(var_spectrum)
try: try:
var_res=np.array(var_res) var_res=np.array(var_res)
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin] ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
except: except:
ang_int=0 ang_int=0
else: else:
angels=np.linspace(-25,25,60) angels=np.linspace(-25,25,60)
var_res=[] var_res=[]
indexer=0 indexer=0
for rot in angels: for rot in angels:
img_rot=rotate_image(img_resized,rot) img_rot=rotate_image(img_resized,rot)
#plt.imshow(img_rot) #plt.imshow(img_rot)
#plt.show() #plt.show()
img_rot[img_rot!=0]=1 img_rot[img_rot!=0]=1
#res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
#neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 ) #neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
#print(var_spectrum,'var_spectrum') #print(var_spectrum,'var_spectrum')
try: try:
var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3 ) var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3 )
except: except:
var_spectrum=0 var_spectrum=0
var_res.append(var_spectrum) var_res.append(var_spectrum)
try: try:
var_res=np.array(var_res) var_res=np.array(var_res)
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin] ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
except: except:
ang_int=0 ang_int=0
#plt.plot(var_res) #plt.plot(var_res)
#plt.show() #plt.show()
##plt.plot(mom3_res) ##plt.plot(mom3_res)
##plt.show() ##plt.show()
#print(ang_int,'ang_int111') #print(ang_int,'ang_int111')
@ -1888,20 +1758,14 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=Non
##plt.imshow(img_rot) ##plt.imshow(img_rot)
##plt.show() ##plt.show()
img_rot[img_rot!=0]=1 img_rot[img_rot!=0]=1
#res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
try: try:
var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3 ) var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3 )
except: except:
var_spectrum=0 var_spectrum=0
var_res.append(var_spectrum) var_res.append(var_spectrum)
try: try:
var_res=np.array(var_res) var_res=np.array(var_res)
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin] ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
except: except:
ang_int=0 ang_int=0
@ -1918,7 +1782,6 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=Non
##plt.imshow(img_rot) ##plt.imshow(img_rot)
##plt.show() ##plt.show()
img_rot[img_rot!=0]=1 img_rot[img_rot!=0]=1
#res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
try: try:
var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3 ) var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3 )
#print(indexer,'indexer') #print(indexer,'indexer')
@ -1926,12 +1789,8 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=Non
var_spectrum=0 var_spectrum=0
var_res.append(var_spectrum) var_res.append(var_spectrum)
try: try:
var_res=np.array(var_res) var_res=np.array(var_res)
ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin] ang_int=angels[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
except: except:
ang_int=0 ang_int=0

62
qurator/eynollah/utils/xml.py
Unescape Escape View File

@ -0,0 +1,62 @@
from lxml import etree as ET
NAMESPACES = {}
NAMESPACES['page'] = "http://schema.primaresearch.org/PAGE/gts/pagecontent/2019-07-15"
NAMESPACES['xsi'] = "http://www.w3.org/2001/XMLSchema-instance"
NAMESPACES[None] = NAMESPACES['page']
def create_page_xml(imageFilename, height, width):
pcgts = ET.Element("PcGts", nsmap=NAMESPACES)
pcgts.set("{%s}schemaLocation" % NAMESPACES['xsi'], NAMESPACES['page'])
metadata = ET.SubElement(pcgts, "Metadata")
author = ET.SubElement(metadata, "Creator")
author.text = "SBB_QURATOR"
created = ET.SubElement(metadata, "Created")
created.text = "2019-06-17T18:15:12"
changetime = ET.SubElement(metadata, "LastChange")
changetime.text = "2019-06-17T18:15:12"
page = ET.SubElement(pcgts, "Page")
page.set("imageFilename", imageFilename)
page.set("imageHeight", str(height))
page.set("imageWidth", str(width))
page.set("type", "content")
page.set("readingDirection", "left-to-right")
page.set("textLineOrder", "top-to-bottom")
return pcgts, page
def add_textequiv(parent, text=''):
textequiv = ET.SubElement(parent, 'TextEquiv')
unireg = ET.SubElement(textequiv, 'Unicode')
unireg.text = text
def xml_reading_order(page, order_of_texts, id_of_texts, id_of_marginalia, found_polygons_marginals):
"""
XXX side-effect: extends id_of_marginalia
"""
region_order = ET.SubElement(page, 'ReadingOrder')
region_order_sub = ET.SubElement(region_order, 'OrderedGroup')
region_order_sub.set('id', "ro357564684568544579089")
indexer_region = 0
for vj in order_of_texts:
name = "coord_text_%s" % vj
name = ET.SubElement(region_order_sub, 'RegionRefIndexed')
name.set('index', str(indexer_region))
name.set('regionRef', id_of_texts[vj])
indexer_region += 1
for vm in range(len(found_polygons_marginals)):
id_of_marginalia.append('r%s' % indexer_region)
name = "coord_text_%s" % indexer_region
name = ET.SubElement(region_order_sub, 'RegionRefIndexed')
name.set('index', str(indexer_region))
name.set('regionRef', 'r%s' % indexer_region)
indexer_region += 1
return id_of_marginalia

272
qurator/eynollah/writer.py
Unescape Escape View File

@ -0,0 +1,272 @@
# pylint: disable=too-many-locals,wrong-import-position,too-many-lines,too-many-statements,chained-comparison,fixme,broad-except,c-extension-no-member
from pathlib import Path
import os.path
from .utils.xml import create_page_xml, add_textequiv, xml_reading_order
from ocrd_utils import getLogger
from lxml import etree as ET
import numpy as np
class EynollahXmlWriter():
def __init__(self, *, dir_out, image_filename, curved_line):
self.logger = getLogger('eynollah.writer')
self.dir_out = dir_out
self.image_filename = image_filename
self.image_filename_stem = Path(Path(image_filename).name).stem
self.curved_line = curved_line
self.scale_x = None # XXX set outside __init__
self.scale_y = None # XXX set outside __init__
self.height_org = None # XXX set outside __init__
self.width_org = None # XXX set outside __init__
def calculate_page_coords(self, cont_page):
self.logger.debug('enter calculate_page_coords')
points_page_print = ""
for _, contour in enumerate(cont_page[0]):
if len(contour) == 2:
points_page_print += str(int((contour[0]) / self.scale_x))
points_page_print += ','
points_page_print += str(int((contour[1]) / self.scale_y))
else:
points_page_print += str(int((contour[0][0]) / self.scale_x))
points_page_print += ','
points_page_print += str(int((contour[0][1] ) / self.scale_y))
points_page_print = points_page_print + ' '
return points_page_print[:-1]
def serialize_lines_in_marginal(self, marginal, all_found_texline_polygons_marginals, marginal_idx, page_coord, all_box_coord_marginals, slopes_marginals, id_indexer_l):
for j in range(len(all_found_texline_polygons_marginals[marginal_idx])):
textline = ET.SubElement(marginal, 'TextLine')
textline.set('id', 'l%s' % id_indexer_l)
id_indexer_l += 1
coord = ET.SubElement(textline, 'Coords')
add_textequiv(textline)
points_co = ''
for l in range(len(all_found_texline_polygons_marginals[marginal_idx][j])):
if not self.curved_line:
if len(all_found_texline_polygons_marginals[marginal_idx][j][l]) == 2:
points_co += str(int((all_found_texline_polygons_marginals[marginal_idx][j][l][0] + all_box_coord_marginals[marginal_idx][2] + page_coord[2]) / self.scale_x))
points_co += ','
points_co += str(int((all_found_texline_polygons_marginals[marginal_idx][j][l][1] + all_box_coord_marginals[marginal_idx][0] + page_coord[0]) / self.scale_y))
else:
points_co += str(int((all_found_texline_polygons_marginals[marginal_idx][j][l][0][0] + all_box_coord_marginals[marginal_idx][2] + page_coord[2]) / self.scale_x))
points_co += ','
points_co += str(int((all_found_texline_polygons_marginals[marginal_idx][j][l][0][1] + all_box_coord_marginals[marginal_idx][0] + page_coord[0])/self.scale_y))
if self.curved_line and np.abs(slopes_marginals[marginal_idx]) <= 45:
if len(all_found_texline_polygons_marginals[marginal_idx][j][l]) == 2:
points_co += str(int((all_found_texline_polygons_marginals[marginal_idx][j][l][0] + page_coord[2]) / self.scale_x))
points_co += ','
points_co += str(int((all_found_texline_polygons_marginals[marginal_idx][j][l][1] + page_coord[0]) / self.scale_y))
else:
points_co += str(int((all_found_texline_polygons_marginals[marginal_idx][j][l][0][0] + page_coord[2]) / self.scale_x))
points_co += ','
points_co += str(int((all_found_texline_polygons_marginals[marginal_idx][j][l][0][1] + page_coord[0]) / self.scale_y))
elif self.curved_line and np.abs(slopes_marginals[marginal_idx]) > 45:
if len(all_found_texline_polygons_marginals[marginal_idx][j][l]) == 2:
points_co += str(int((all_found_texline_polygons_marginals[marginal_idx][j][l][0] + all_box_coord_marginals[marginal_idx][2] + page_coord[2]) / self.scale_x))
points_co += ','
points_co += str(int((all_found_texline_polygons_marginals[marginal_idx][j][l][1] + all_box_coord_marginals[marginal_idx][0] + page_coord[0]) / self.scale_y))
else:
points_co += str(int((all_found_texline_polygons_marginals[marginal_idx][j][l][0][0] + all_box_coord_marginals[marginal_idx][2] + page_coord[2]) / self.scale_x))
points_co += ','
points_co += str(int((all_found_texline_polygons_marginals[marginal_idx][j][l][0][1] + all_box_coord_marginals[marginal_idx][0] + page_coord[0]) / self.scale_y))
if l < len(all_found_texline_polygons_marginals[marginal_idx][j]) - 1:
points_co += ' '
coord.set('points',points_co)
return id_indexer_l
def serialize_lines_in_region(self, textregion, all_found_texline_polygons, region_idx, page_coord, all_box_coord, slopes, id_indexer_l):
self.logger.debug('enter serialize_lines_in_region')
for j in range(len(all_found_texline_polygons[region_idx])):
textline = ET.SubElement(textregion, 'TextLine')
textline.set('id', 'l%s' % id_indexer_l)
id_indexer_l += 1
coord = ET.SubElement(textline, 'Coords')
add_textequiv(textline)
points_co = ''
for l in range(len(all_found_texline_polygons[region_idx][j])):
if not self.curved_line:
if len(all_found_texline_polygons[region_idx][j][l])==2:
textline_x_coord = max(0, int((all_found_texline_polygons[region_idx][j][l][0] + all_box_coord[region_idx][2] + page_coord[2]) / self.scale_x))
textline_y_coord = max(0, int((all_found_texline_polygons[region_idx][j][l][1] + all_box_coord[region_idx][0] + page_coord[0]) / self.scale_y))
else:
textline_x_coord = max(0, int((all_found_texline_polygons[region_idx][j][l][0][0] + all_box_coord[region_idx][2] + page_coord[2]) / self.scale_x))
textline_y_coord = max(0, int((all_found_texline_polygons[region_idx][j][l][0][1] + all_box_coord[region_idx][0] + page_coord[0]) / self.scale_y))
points_co += str(textline_x_coord)
points_co += ','
points_co += str(textline_y_coord)
if self.curved_line and np.abs(slopes[region_idx]) <= 45:
if len(all_found_texline_polygons[region_idx][j][l]) == 2:
points_co += str(int((all_found_texline_polygons[region_idx][j][l][0] + page_coord[2]) / self.scale_x))
points_co += ','
points_co += str(int((all_found_texline_polygons[region_idx][j][l][1] + page_coord[0]) / self.scale_y))
else:
points_co += str(int((all_found_texline_polygons[region_idx][j][l][0][0] + page_coord[2]) / self.scale_x))
points_co += ','
points_co += str(int((all_found_texline_polygons[region_idx][j][l][0][1] + page_coord[0])/self.scale_y))
elif self.curved_line and np.abs(slopes[region_idx]) > 45:
if len(all_found_texline_polygons[region_idx][j][l])==2:
points_co += str(int((all_found_texline_polygons[region_idx][j][l][0] + all_box_coord[region_idx][2]+page_coord[2])/self.scale_x))
points_co += ','
points_co += str(int((all_found_texline_polygons[region_idx][j][l][1] + all_box_coord[region_idx][0]+page_coord[0])/self.scale_y))
else:
points_co += str(int((all_found_texline_polygons[region_idx][j][l][0][0] + all_box_coord[region_idx][2]+page_coord[2])/self.scale_x))
points_co += ','
points_co += str(int((all_found_texline_polygons[region_idx][j][l][0][1] + all_box_coord[region_idx][0]+page_coord[0])/self.scale_y))
if l < len(all_found_texline_polygons[region_idx][j]) - 1:
points_co += ' '
coord.set('points',points_co)
return id_indexer_l
def write_pagexml(self, pcgts):
self.logger.info("filename stem: '%s'", self.image_filename_stem)
tree = ET.ElementTree(pcgts)
tree.write(os.path.join(self.dir_out, self.image_filename_stem) + ".xml")
def build_pagexml_no_full_layout(self, found_polygons_text_region, page_coord, order_of_texts, id_of_texts, all_found_texline_polygons, all_box_coord, found_polygons_text_region_img, found_polygons_marginals, all_found_texline_polygons_marginals, all_box_coord_marginals, slopes, slopes_marginals, cont_page):
self.logger.debug('enter build_pagexml_no_full_layout')
# create the file structure
pcgts, page = create_page_xml(self.image_filename, self.height_org, self.width_org)
page_print_sub = ET.SubElement(page, "Border")
coord_page = ET.SubElement(page_print_sub, "Coords")
coord_page.set('points', self.calculate_page_coords(cont_page))
id_of_marginalia = []
id_indexer = 0
id_indexer_l = 0
if len(found_polygons_text_region) > 0:
id_of_marginalia = xml_reading_order(page, order_of_texts, id_of_texts, id_of_marginalia, found_polygons_marginals)
for mm in range(len(found_polygons_text_region)):
textregion = ET.SubElement(page, 'TextRegion')
textregion.set('id', 'r%s' % id_indexer)
id_indexer += 1
textregion.set('type', 'paragraph')
coord_text = ET.SubElement(textregion, 'Coords')
coord_text.set('points', self.calculate_polygon_coords(found_polygons_text_region, mm, page_coord))
id_indexer_l = self.serialize_lines_in_region(textregion, all_found_texline_polygons, mm, page_coord, all_box_coord, slopes, id_indexer_l)
add_textequiv(textregion)
for mm in range(len(found_polygons_marginals)):
marginal = ET.SubElement(page, 'TextRegion')
marginal.set('id', id_of_marginalia[mm])
marginal.set('type', 'marginalia')
coord_text = ET.SubElement(marginal, 'Coords')
coord_text.set('points', self.calculate_polygon_coords(found_polygons_marginals, mm, page_coord))
id_indexer_l = self.serialize_lines_in_marginal(marginal, all_found_texline_polygons_marginals, mm, page_coord, all_box_coord_marginals, slopes_marginals, id_indexer_l)
id_indexer = len(found_polygons_text_region) + len(found_polygons_marginals)
for mm in range(len(found_polygons_text_region_img)):
textregion = ET.SubElement(page, 'ImageRegion')
textregion.set('id', 'r%s' % id_indexer)
id_indexer += 1
coord_text = ET.SubElement(textregion, 'Coords')
points_co = ''
for lmm in range(len(found_polygons_text_region_img[mm])):
points_co += str(int((found_polygons_text_region_img[mm][lmm,0,0] + page_coord[2]) / self.scale_x))
points_co += ','
points_co += str(int((found_polygons_text_region_img[mm][lmm,0,1] + page_coord[0]) / self.scale_y))
if lmm < len(found_polygons_text_region_img[mm]) - 1:
points_co += ' '
coord_text.set('points', points_co)
return pcgts
def build_pagexml_full_layout(self, found_polygons_text_region, found_polygons_text_region_h, page_coord, order_of_texts, id_of_texts, all_found_texline_polygons, all_found_texline_polygons_h, all_box_coord, all_box_coord_h, found_polygons_text_region_img, found_polygons_tables, found_polygons_drop_capitals, found_polygons_marginals, all_found_texline_polygons_marginals, all_box_coord_marginals, slopes, slopes_marginals, cont_page):
self.logger.debug('enter build_pagexml_full_layout')
# create the file structure
pcgts, page = create_page_xml(self.image_filename, self.height_org, self.width_org)
page_print_sub = ET.SubElement(page, "Border")
coord_page = ET.SubElement(page_print_sub, "Coords")
coord_page.set('points', self.calculate_page_coords(cont_page))
id_indexer = 0
id_indexer_l = 0
id_of_marginalia = []
if len(found_polygons_text_region) > 0:
id_of_marginalia = xml_reading_order(page, order_of_texts, id_of_texts, id_of_marginalia, found_polygons_marginals)
for mm in range(len(found_polygons_text_region)):
textregion=ET.SubElement(page, 'TextRegion')
textregion.set('id', 'r%s' % id_indexer)
id_indexer += 1
textregion.set('type', 'paragraph')
coord_text = ET.SubElement(textregion, 'Coords')
coord_text.set('points', self.calculate_polygon_coords(found_polygons_text_region, mm, page_coord))
id_indexer_l = self.serialize_lines_in_region(textregion, all_found_texline_polygons, mm, page_coord, all_box_coord, slopes, id_indexer_l)
add_textequiv(textregion)
self.logger.debug('len(found_polygons_text_region_h) %s', len(found_polygons_text_region_h))
if len(found_polygons_text_region_h) > 0:
for mm in range(len(found_polygons_text_region_h)):
textregion=ET.SubElement(page, 'TextRegion')
textregion.set('id', 'r%s' % id_indexer)
id_indexer += 1
textregion.set('type','header')
coord_text = ET.SubElement(textregion, 'Coords')
coord_text.set('points', self.calculate_polygon_coords(found_polygons_text_region_h, mm, page_coord))
id_indexer_l = self.serialize_lines_in_region(textregion, all_found_texline_polygons_h, mm, page_coord, all_box_coord_h, slopes, id_indexer_l)
add_textequiv(textregion)
if len(found_polygons_drop_capitals) > 0:
id_indexer = len(found_polygons_text_region) + len(found_polygons_text_region_h) + len(found_polygons_marginals)
for mm in range(len(found_polygons_drop_capitals)):
textregion=ET.SubElement(page, 'TextRegion')
textregion.set('id',' r%s' % id_indexer)
id_indexer += 1
textregion.set('type', 'drop-capital')
coord_text = ET.SubElement(textregion, 'Coords')
coord_text.set('points', self.calculate_polygon_coords(found_polygons_drop_capitals, mm, page_coord))
add_textequiv(textregion)
for mm in range(len(found_polygons_marginals)):
marginal = ET.SubElement(page, 'TextRegion')
add_textequiv(textregion)
marginal.set('id', id_of_marginalia[mm])
marginal.set('type', 'marginalia')
coord_text = ET.SubElement(marginal, 'Coords')
coord_text.set('points', self.calculate_polygon_coords(found_polygons_marginals, mm, page_coord))
id_indexer_l = self.serialize_lines_in_marginal(marginal, all_found_texline_polygons_marginals, mm, page_coord, all_box_coord_marginals, slopes_marginals, id_indexer_l)
id_indexer = len(found_polygons_text_region) + len(found_polygons_text_region_h) + len(found_polygons_marginals) + len(found_polygons_drop_capitals)
for mm in range(len(found_polygons_text_region_img)):
textregion=ET.SubElement(page, 'ImageRegion')
textregion.set('id', 'r%s' % id_indexer)
id_indexer += 1
coord_text = ET.SubElement(textregion, 'Coords')
coord_text.set('points', self.calculate_polygon_coords(found_polygons_text_region_img, mm, page_coord))
for mm in range(len(found_polygons_tables)):
textregion = ET.SubElement(page, 'TableRegion')
textregion.set('id', 'r%s' %id_indexer)
id_indexer += 1
coord_text = ET.SubElement(textregion, 'Coords')
coord_text.set('points', self.calculate_polygon_coords(found_polygons_tables, mm, page_coord))
return pcgts
def calculate_polygon_coords(self, contour_list, i, page_coord):
self.logger.debug('enter calculate_polygon_coords')
coords = ''
for j in range(len(contour_list[i])):
if len(contour_list[i][j]) == 2:
coords += str(int((contour_list[i][j][0] + page_coord[2]) / self.scale_x))
coords += ','
coords += str(int((contour_list[i][j][1] + page_coord[0]) / self.scale_y))
else:
coords += str(int((contour_list[i][j][0][0] + page_coord[2]) / self.scale_x))
coords += ','
coords += str(int((contour_list[i][j][0][1] + page_coord[0]) / self.scale_y))
if j < len(contour_list[i]) - 1:
coords=coords + ' '
return coords

3636
sbb_newspapers_org_image/eynollah.py
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3029
sbb_newspapers_org_image/unused.py
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34
sbb_newspapers_org_image/utils/xml.py
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@ -1,34 +0,0 @@
from lxml import etree as ET
NAMESPACES = {}
NAMESPACES['page'] = "http://schema.primaresearch.org/PAGE/gts/pagecontent/2019-07-15"
NAMESPACES['xsi'] = "http://www.w3.org/2001/XMLSchema-instance"
NAMESPACES[None] = NAMESPACES['page']
def create_page_xml(imageFilename, height, width):
pcgts = ET.Element("PcGts", nsmap=NAMESPACES)
pcgts.set("{%s}schemaLocation" % NAMESPACES['xsi'], NAMESPACES['page'])
metadata = ET.SubElement(pcgts, "Metadata")
author = ET.SubElement(metadata, "Creator")
author.text = "SBB_QURATOR"
created = ET.SubElement(metadata, "Created")
created.text = "2019-06-17T18:15:12"
changetime = ET.SubElement(metadata, "LastChange")
changetime.text = "2019-06-17T18:15:12"
page = ET.SubElement(pcgts, "Page")
page.set("imageFilename", imageFilename)
page.set("imageHeight", str(height))
page.set("imageWidth", str(width))
page.set("type", "content")
page.set("readingDirection", "left-to-right")
page.set("textLineOrder", "top-to-bottom")
return pcgts, page

7
setup.py
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@ -10,12 +10,13 @@ setup(
author='Vahid Rezanezhad', author='Vahid Rezanezhad',
url='https://github.com/qurator-spk/eynollah', url='https://github.com/qurator-spk/eynollah',
license='Apache License 2.0', license='Apache License 2.0',
packages=find_packages(), namespace_packages=['qurator'],
packages=find_packages(exclude=['tests']),
install_requires=install_requires, install_requires=install_requires,
entry_points={ entry_points={
'console_scripts': [ 'console_scripts': [
'eynollah=sbb_newspapers_org_image.cli:main', 'eynollah=qurator.eynollah.cli:main',
# 'ocrd-eynollah=eynollah.ocrd_cli:cli', # 'ocrd-eynollah=qurator.eynollah.ocrd_cli:cli',
] ]
}, },
) )

0
tests/__init__.py
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54
tests/base.py
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@ -0,0 +1,54 @@
# pylint: disable=unused-import
from os.path import dirname, realpath
from os import chdir
import sys
import logging
import io
import collections
from unittest import TestCase as VanillaTestCase, skip, main as unittests_main
import pytest
from ocrd_utils import disableLogging, initLogging
def main(fn=None):
if fn:
sys.exit(pytest.main([fn]))
else:
unittests_main()
class TestCase(VanillaTestCase):
@classmethod
def setUpClass(cls):
chdir(dirname(realpath(__file__)) + '/..')
def setUp(self):
disableLogging()
initLogging()
class CapturingTestCase(TestCase):
"""
A TestCase that needs to capture stderr/stdout and invoke click CLI.
"""
@pytest.fixture(autouse=True)
def _setup_pytest_capfd(self, capfd):
self.capfd = capfd
def invoke_cli(self, cli, args):
"""
Substitution for click.CliRunner.invooke that works together nicely
with unittests/pytest capturing stdout/stderr.
"""
self.capture_out_err() # XXX snapshot just before executing the CLI
code = 0
sys.argv[1:] = args # XXX necessary because sys.argv reflects pytest args not cli args
try:
cli.main(args=args)
except SystemExit as e:
code = e.code
out, err = self.capture_out_err()
return code, out, err
def capture_out_err(self):
return self.capfd.readouterr()

10
tests/test_dpi.py
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@ -0,0 +1,10 @@
from pathlib import Path
from qurator.eynollah.utils.pil_cv2 import check_dpi
from tests.base import main
def test_dpi():
fpath = Path(__file__).parent.joinpath('resources', 'kant_aufklaerung_1784_0020.tif')
assert 300 == check_dpi(str(fpath))
if __name__ == '__main__':
main(__file__)

24
tests/test_run.py
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@ -0,0 +1,24 @@
from os import environ
from pathlib import Path
from ocrd_utils import pushd_popd
from tests.base import CapturingTestCase as TestCase, main
from qurator.eynollah.cli import main as eynollah_cli
testdir = Path(__file__).parent.resolve()
EYNOLLAH_MODELS = environ.get('EYNOLLAH_MODELS', str(testdir.joinpath('..', 'models_eynollah').resolve()))
class TestEynollahRun(TestCase):
def test_full_run(self):
with pushd_popd(tempdir=True) as tempdir:
code, out, err = self.invoke_cli(eynollah_cli, [
'-m', EYNOLLAH_MODELS,
'-i', str(testdir.joinpath('resources/kant_aufklaerung_1784_0020.tif')),
'-o', tempdir
])
print(code, out, err)
assert not code
if __name__ == '__main__':
main(__file__)

12
tests/test_smoke.py
Unescape Escape View File

@ -1,7 +1,7 @@
def test_utils_import(): def test_utils_import():
import sbb_newspapers_org_image.utils import qurator.eynollah.utils
import sbb_newspapers_org_image.utils.contour import qurator.eynollah.utils.contour
import sbb_newspapers_org_image.utils.drop_capitals import qurator.eynollah.utils.drop_capitals
import sbb_newspapers_org_image.utils.drop_capitals import qurator.eynollah.utils.drop_capitals
import sbb_newspapers_org_image.utils.is_nan import qurator.eynollah.utils.is_nan
import sbb_newspapers_org_image.utils.rotate import qurator.eynollah.utils.rotate

2
tests/test_xml.py
Unescape Escape View File

@ -1,5 +1,5 @@
from lxml import etree as ET from lxml import etree as ET
from sbb_newspapers_org_image.utils.xml import create_page_xml, NAMESPACES from qurator.eynollah.utils.xml import create_page_xml, NAMESPACES
def tostring(el): def tostring(el):
return ET.tostring(el).decode('utf-8') return ET.tostring(el).decode('utf-8')

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