qurator-spk/eynollah
1
0
Fork 0
mirror of https://github.com/qurator-spk/eynollah.git synced 2026-04-30 19:22:03 +02:00
Code Issues Projects Releases Wiki Activity

find_number_of_columns_in_document(): pass in (reuse) masks

Browse source
This commit is contained in:
Robert Sachunsky 2026-04-29 14:03:26 +02:00
parent da9e00cfe5
commit 63df9be4db
2 changed files with 28 additions and 48 deletions
Download patch file Download diff file Expand all files Collapse all files

10
src/eynollah/eynollah.py
View file

@ -1725,16 +1725,18 @@ class Eynollah:
contours_h=None, contours_h=None,
label_seps_fl=6, label_seps_fl=6,
): ):
_, _, matrix_of_seps_ch, splitter_y_new, _ = find_number_of_columns_in_document(
text_regions_p, num_col_classifier, self.tables, label_seps_fl, contours_h=contours_h)
if not erosion_hurts: if not erosion_hurts:
regions_without_separators = regions_without_separators.astype(np.uint8) regions_without_separators = regions_without_separators.astype(np.uint8)
regions_without_separators = cv2.erode(regions_without_separators, KERNEL, iterations=6) regions_without_separators = cv2.erode(regions_without_separators, KERNEL, iterations=6)
separator_mask = text_regions_p == label_seps_fl
_, _, matrix_of_seps_ch, splitter_y_new = find_number_of_columns_in_document(
regions_without_separators, separator_mask, num_col_classifier, self.tables,
contours_h=contours_h)
boxes, _ = return_boxes_of_images_by_order_of_reading_new( boxes, _ = return_boxes_of_images_by_order_of_reading_new(
splitter_y_new, regions_without_separators, splitter_y_new, regions_without_separators,
text_regions_p == label_seps_fl, matrix_of_seps_ch, separator_mask, matrix_of_seps_ch,
num_col_classifier, erosion_hurts, self.tables, self.right2left, num_col_classifier, erosion_hurts, self.tables, self.right2left,
logger=self.logger) logger=self.logger)
return boxes return boxes

66
src/eynollah/utils/__init__.py
View file

@ -705,20 +705,6 @@ def find_num_col_by_vertical_lines(regions_without_separators, multiplier=3.8):
# plt.show() # plt.show()
return peaks return peaks
def return_regions_without_separators(regions_pre, label_seps=6):
kernel = np.ones((5, 5), np.uint8)
regions_without_separators = ((regions_pre[:, :] != label_seps) &
(regions_pre[:, :] != 0))
# regions_without_separators=( (image_regions_eraly_p[:,:,:]!=6) &
# (image_regions_eraly_p[:,:,:]!=0) &
# (image_regions_eraly_p[:,:,:]!=5) &
# (image_regions_eraly_p[:,:,:]!=8) &
# (image_regions_eraly_p[:,:,:]!=7))
regions_without_separators = cv2.erode(regions_without_separators.astype(np.uint8), kernel, iterations=6)
return regions_without_separators
def put_drop_out_from_only_drop_model(layout_no_patch, layout1): def put_drop_out_from_only_drop_model(layout_no_patch, layout1):
if layout_no_patch.ndim == 3: if layout_no_patch.ndim == 3:
layout_no_patch = layout_no_patch[:, :, 0] layout_no_patch = layout_no_patch[:, :, 0]
@ -1292,10 +1278,10 @@ def return_points_with_boundies(peaks_neg_fin, first_point, last_point):
return peaks_neg_tot return peaks_neg_tot
def find_number_of_columns_in_document( def find_number_of_columns_in_document(
region_pre_p: np.ndarray, regions_without_separators: np.ndarray,
separator_mask: np.ndarray,
num_col_classifier: int, num_col_classifier: int,
tables: bool, tables: bool,
label_seps: int,
contours_h: List[np.ndarray] = None, contours_h: List[np.ndarray] = None,
logger=None logger=None
) -> Tuple[int, List[int], np.ndarray, List[int], np.ndarray]: ) -> Tuple[int, List[int], np.ndarray, List[int], np.ndarray]:
@ -1303,10 +1289,10 @@ def find_number_of_columns_in_document(
Extract vertical and horizontal separators, vertical splits and horizontal column boundaries on page. Extract vertical and horizontal separators, vertical splits and horizontal column boundaries on page.
Arguments: Arguments:
* region_pre_p: segmentation map of the page * regions_without_separators: mask of (non-separator) region labels
* separator_mask: mask of (separator-only) region labels
* num_col_classifier: predicted (expected) number of columns of the page * num_col_classifier: predicted (expected) number of columns of the page
* tables: whether tables may be present * tables: whether tables may be present
* label_seps: segmentation map class label for separators
* contours_h: polygons of potential headings (serving as additional horizontal separators) * contours_h: polygons of potential headings (serving as additional horizontal separators)
* logger * logger
@ -1315,25 +1301,20 @@ def find_number_of_columns_in_document(
* the x coordinates of the column boundaries * the x coordinates of the column boundaries
* an array of the separators (bounding boxes and types) * an array of the separators (bounding boxes and types)
* the y coordinates of the page splits * the y coordinates of the page splits
* a mask of the separators
""" """
if logger is None: if logger is None:
logger = getLogger(__package__) logger = getLogger(__package__)
separators_closeup = 1 * (region_pre_p == label_seps) height, width = separator_mask.shape
separators_closeup = separator_mask.astype(np.uint8)
separators_closeup[0:110] = 0 separators_closeup[0:110] = 0
separators_closeup[-150:] = 0 separators_closeup[-150:] = 0
kernel = np.ones((5,5),np.uint8) kernel = np.ones((5,5),np.uint8)
separators_closeup = separators_closeup.astype(np.uint8)
separators_closeup = cv2.morphologyEx(separators_closeup, cv2.MORPH_CLOSE, kernel, iterations=1) separators_closeup = cv2.morphologyEx(separators_closeup, cv2.MORPH_CLOSE, kernel, iterations=1)
separators_closeup_n = separators_closeup.astype(np.uint8) # to be returned
separators_closeup_n_binary = separators_closeup_n.copy()
# find horizontal lines by contour properties # find horizontal lines by contour properties
contours_sep_e, _ = cv2.findContours(separators_closeup_n_binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) contours_sep_e, _ = cv2.findContours(separators_closeup, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cnts_hor_e = [] cnts_hor_e = []
for cnt in contours_sep_e: for cnt in contours_sep_e:
max_xe = cnt[:, 0, 0].max() max_xe = cnt[:, 0, 0].max()
@ -1347,8 +1328,8 @@ def find_number_of_columns_in_document(
cnts_hor_e.append(cnt) cnts_hor_e.append(cnt)
# delete horizontal contours (leaving only the edges) # delete horizontal contours (leaving only the edges)
separators_closeup_n_binary = cv2.fillPoly(separators_closeup_n_binary, pts=cnts_hor_e, color=0) separators_closeup = cv2.fillPoly(separators_closeup, pts=cnts_hor_e, color=0)
edges = cv2.adaptiveThreshold(separators_closeup_n_binary * 255, 255, edges = cv2.adaptiveThreshold(separators_closeup * 255, 255,
cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 15, -2) cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 15, -2)
horizontal = np.copy(edges) horizontal = np.copy(edges)
vertical = np.copy(edges) vertical = np.copy(edges)
@ -1453,31 +1434,28 @@ def find_number_of_columns_in_document(
matrix_of_seps_ch, matrix_l_n, axis=0) matrix_of_seps_ch, matrix_l_n, axis=0)
# ensure no seps are out of bounds # ensure no seps are out of bounds
matrix_of_seps_ch[:, 1] = np.maximum(np.minimum(matrix_of_seps_ch[:, 1], region_pre_p.shape[1]), 0) matrix_of_seps_ch[:, 1] = np.maximum(np.minimum(matrix_of_seps_ch[:, 1], width), 0)
matrix_of_seps_ch[:, 2] = np.maximum(matrix_of_seps_ch[:, 2], 0) matrix_of_seps_ch[:, 2] = np.maximum(matrix_of_seps_ch[:, 2], 0)
matrix_of_seps_ch[:, 3] = np.minimum(matrix_of_seps_ch[:, 3], region_pre_p.shape[1]) matrix_of_seps_ch[:, 3] = np.minimum(matrix_of_seps_ch[:, 3], width)
matrix_of_seps_ch[:, 5] = np.maximum(np.minimum(matrix_of_seps_ch[:, 5], region_pre_p.shape[0]), 0) matrix_of_seps_ch[:, 5] = np.maximum(np.minimum(matrix_of_seps_ch[:, 5], height), 0)
matrix_of_seps_ch[:, 6] = np.maximum(matrix_of_seps_ch[:, 6], 0) matrix_of_seps_ch[:, 6] = np.maximum(matrix_of_seps_ch[:, 6], 0)
matrix_of_seps_ch[:, 7] = np.minimum(matrix_of_seps_ch[:, 7], region_pre_p.shape[0]) matrix_of_seps_ch[:, 7] = np.minimum(matrix_of_seps_ch[:, 7], height)
cy_seps_splitters=cy_seps_hor[(x_min_seps_hor<=.16*region_pre_p.shape[1]) & cy_seps_splitters=cy_seps_hor[(x_min_seps_hor <= .16 * width) &
(x_max_seps_hor>=.84*region_pre_p.shape[1])] (x_max_seps_hor >= .84 * width)]
cy_seps_splitters = np.append(cy_seps_splitters, special_separators) cy_seps_splitters = np.append(cy_seps_splitters, special_separators)
if contours_h is not None: if contours_h is not None:
y_min_splitters_head = y_min_head[(x_min_head<=.16*region_pre_p.shape[1]) & y_min_splitters_head = y_min_head[(x_min_head <= .16 * width) &
(x_max_head>=.84*region_pre_p.shape[1])] (x_max_head >= .84 * width)]
y_max_splitters_head = y_max_head[(x_min_head<=.16*region_pre_p.shape[1]) & y_max_splitters_head = y_max_head[(x_min_head <= .16 * width) &
(x_max_head>=.84*region_pre_p.shape[1])] (x_max_head >= .84 * width)]
cy_seps_splitters = np.append(cy_seps_splitters, y_min_splitters_head) cy_seps_splitters = np.append(cy_seps_splitters, y_min_splitters_head)
cy_seps_splitters = np.append(cy_seps_splitters, y_max_splitters_head) cy_seps_splitters = np.append(cy_seps_splitters, y_max_splitters_head)
cy_seps_splitters = np.sort(cy_seps_splitters).astype(int) cy_seps_splitters = np.sort(cy_seps_splitters).astype(int)
splitter_y_new = [0] + list(cy_seps_splitters) + [region_pre_p.shape[0]] splitter_y_new = [0] + list(cy_seps_splitters) + [height]
big_part = 22 * region_pre_p.shape[0] // 100 # percent height big_part = 22 * height // 100 # percent height
regions_without_separators = return_regions_without_separators(
region_pre_p, label_seps=label_seps)
num_col_fin=0 num_col_fin=0
peaks_neg_fin_fin=[] peaks_neg_fin_fin=[]
@ -1506,7 +1484,7 @@ def find_number_of_columns_in_document(
peaks_neg_fin=peaks_neg_fin[peaks_neg_fin<=(vertical.shape[1]-500)] peaks_neg_fin=peaks_neg_fin[peaks_neg_fin<=(vertical.shape[1]-500)]
peaks_neg_fin_fin=peaks_neg_fin[:] peaks_neg_fin_fin=peaks_neg_fin[:]
return num_col_fin, peaks_neg_fin_fin, matrix_of_seps_ch, splitter_y_new, separators_closeup_n return num_col_fin, peaks_neg_fin_fin, matrix_of_seps_ch, splitter_y_new
def return_boxes_of_images_by_order_of_reading_new( def return_boxes_of_images_by_order_of_reading_new(
splitter_y_new, splitter_y_new,