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eynollah
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implent_law_head_main_not_parallel is unused

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pull/19/head
Konstantin Baierer 3 years ago
parent d7d388671d
commit 133982380f
3 changed files with 94 additions and 94 deletions
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2
sbb_newspapers_org_image/eynollah.py
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@ -1,5 +1,6 @@
# pylint: disable=no-member,invalid-name,line-too-long,missing-function-docstring
# pylint: disable=too-many-locals,wrong-import-position,too-many-lines
# pylint: disable=too-many-public-methods
"""
tool to extract table form data from alto xml data
"""
@ -74,7 +75,6 @@ from .utils import (
small_textlines_to_parent_adherence2,
order_and_id_of_texts,
order_of_regions,
implent_law_head_main_not_parallel,
find_number_of_columns_in_document,
return_boxes_of_images_by_order_of_reading_new,
)

93
sbb_newspapers_org_image/unused.py
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@ -3252,3 +3252,96 @@ def return_hor_spliter_by_index(peaks_neg_fin_t, x_min_hor_some, x_max_hor_some)
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 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

93
sbb_newspapers_org_image/utils/__init__.py
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@ -1202,99 +1202,6 @@ def order_of_regions(textline_mask, contours_main, contours_header, y_ref):
return final_indexers_sorted, matrix_of_orders, final_types, final_index_type
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 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=img_p_in_ver.astype(np.uint8)

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