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do not store dpi on instance

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Konstantin Baierer 2020-11-24 14:06:20 +01:00
parent 43f5e7e4e4
commit bf209d1622
3 changed files with 240 additions and 236 deletions
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271
sbb_newspapers_org_image/eynollah.py
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@ -61,6 +61,8 @@ from .utils import (
seperate_lines_new_inside_teils2, seperate_lines_new_inside_teils2,
filter_small_drop_capitals_from_no_patch_layout, filter_small_drop_capitals_from_no_patch_layout,
find_num_col_deskew, find_num_col_deskew,
return_hor_spliter_by_index_for_without_verticals,
find_new_features_of_contoures,
) )
@ -2263,78 +2265,6 @@ class eynollah:
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_num_col_olddd(self, regions_without_seperators, sigma_, multiplier=3.8):
regions_without_seperators_0 = regions_without_seperators[:, :].sum(axis=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)
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
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 > 0.06 * 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]
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) / 2.0)
p_g_l = int(len(y) / 3.0)
p_g_u = len(y) - int(len(y) / 3.0)
diff_peaks = np.abs(np.diff(peaks_neg_fin))
diff_peaks_annormal = diff_peaks[diff_peaks < 30]
return interest_neg_fin
def return_deskew_slop(self, img_patch_org, sigma_des, main_page=False): def return_deskew_slop(self, img_patch_org, sigma_des, main_page=False):
@ -4161,23 +4091,6 @@ class eynollah:
return regions_without_seperators return regions_without_seperators
def return_regions_without_seperators_new(self, regions_pre, regions_only_text):
kernel = np.ones((5, 5), np.uint8)
regions_without_seperators = ((regions_pre[:, :] != 6) & (regions_pre[:, :] != 0) & (regions_pre[:, :] != 1) & (regions_pre[:, :] != 2)) * 1
# plt.imshow(regions_without_seperators)
# plt.show()
regions_without_seperators_n = ((regions_without_seperators[:, :] == 1) | (regions_only_text[:, :] == 1)) * 1
# regions_without_seperators=( (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))*1
regions_without_seperators_n = regions_without_seperators_n.astype(np.uint8)
regions_without_seperators_n = cv2.erode(regions_without_seperators_n, kernel, iterations=6)
return regions_without_seperators_n
def image_change_background_pixels_to_zero(self, image_page): def image_change_background_pixels_to_zero(self, image_page):
image_back_zero = np.zeros((image_page.shape[0], image_page.shape[1])) image_back_zero = np.zeros((image_page.shape[0], image_page.shape[1]))
@ -4373,86 +4286,6 @@ class eynollah:
return len(peaks_fin_true), peaks_fin_true return len(peaks_fin_true), peaks_fin_true
def return_hor_spliter_by_index_for_without_verticals(self, 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_by_vertical_lines(self, regions_without_seperators, multiplier=3.8): def find_num_col_by_vertical_lines(self, regions_without_seperators, multiplier=3.8):
regions_without_seperators_0 = regions_without_seperators[:, :, 0].sum(axis=0) regions_without_seperators_0 = regions_without_seperators[:, :, 0].sum(axis=0)
@ -4660,40 +4493,6 @@ class eynollah:
##print(len(peaks_neg_true)) ##print(len(peaks_neg_true))
return len(peaks_neg_true), peaks_neg_true return len(peaks_neg_true), peaks_neg_true
def find_new_features_of_contoures(self, 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))]
try:
x_min_main = np.array([np.min(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
argmin_x_main = np.array([np.argmin(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 0, 0] for j in range(len(contours_main))])
y_corr_x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 0, 1] 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))])
except:
x_min_main = np.array([np.min(contours_main[j][:, 0]) for j in range(len(contours_main))])
argmin_x_main = np.array([np.argmin(contours_main[j][:, 0]) for j in range(len(contours_main))])
x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 0] for j in range(len(contours_main))])
y_corr_x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 1] for j in range(len(contours_main))])
x_max_main = np.array([np.max(contours_main[j][:, 0]) for j in range(len(contours_main))])
y_min_main = np.array([np.min(contours_main[j][:, 1]) for j in range(len(contours_main))])
y_max_main = np.array([np.max(contours_main[j][:, 1]) for j in range(len(contours_main))])
# dis_x=np.abs(x_max_main-x_min_main)
return cx_main, cy_main, x_min_main, x_max_main, y_min_main, y_max_main, y_corr_x_min_from_argmin
def return_points_with_boundies(self, peaks_neg_fin, first_point, last_point): def return_points_with_boundies(self, peaks_neg_fin, first_point, last_point):
peaks_neg_tot = [] peaks_neg_tot = []
@ -4753,7 +4552,7 @@ class eynollah:
peaks_neg_tot = self.return_points_with_boundies(peaks_neg_fin, 0, seperators_closeup_n[:, :, 0].shape[1]) peaks_neg_tot = self.return_points_with_boundies(peaks_neg_fin, 0, seperators_closeup_n[:, :, 0].shape[1])
start_index_of_hor, newest_peaks, arg_min_hor_sort, lines_length_dels, lines_indexes_deleted = self.return_hor_spliter_by_index_for_without_verticals(peaks_neg_tot, x_min_hor_some, x_max_hor_some) start_index_of_hor, newest_peaks, arg_min_hor_sort, lines_length_dels, lines_indexes_deleted = return_hor_spliter_by_index_for_without_verticals(peaks_neg_tot, x_min_hor_some, x_max_hor_some)
arg_org_hor_some_sort = arg_org_hor_some[arg_min_hor_sort] arg_org_hor_some_sort = arg_org_hor_some[arg_min_hor_sort]
@ -4906,7 +4705,7 @@ class eynollah:
peaks_neg_ch_tot = self.return_points_with_boundies(peaks_neg_ch, newest_peaks[j], newest_peaks[j + 1]) peaks_neg_ch_tot = self.return_points_with_boundies(peaks_neg_ch, newest_peaks[j], newest_peaks[j + 1])
ss_in_ch, nst_p_ch, arg_n_ch, lines_l_del_ch, lines_in_del_ch = self.return_hor_spliter_by_index_for_without_verticals(peaks_neg_ch_tot, x_min_ch, x_max_ch) ss_in_ch, nst_p_ch, arg_n_ch, lines_l_del_ch, lines_in_del_ch = return_hor_spliter_by_index_for_without_verticals(peaks_neg_ch_tot, x_min_ch, x_max_ch)
newest_y_spliter_ch_tot = [] newest_y_spliter_ch_tot = []
@ -5154,7 +4953,7 @@ class eynollah:
ret, thresh = cv2.threshold(imgray, 0, 255, 0) ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_cross, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) contours_cross, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cx_cross, cy_cross, _, _, _, _, _ = self.find_new_features_of_contoures(contours_cross) cx_cross, cy_cross, _, _, _, _, _ = find_new_features_of_contoures(contours_cross)
for ii in range(len(cx_cross)): for ii in range(len(cx_cross)):
sep_ver_hor[int(cy_cross[ii]) - 15 : int(cy_cross[ii]) + 15, int(cx_cross[ii]) + 5 : int(cx_cross[ii]) + 40] = 0 sep_ver_hor[int(cy_cross[ii]) - 15 : int(cy_cross[ii]) + 15, int(cx_cross[ii]) + 5 : int(cx_cross[ii]) + 40] = 0
@ -5341,7 +5140,7 @@ class eynollah:
ret, thresh = cv2.threshold(imgray, 0, 255, 0) ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_cross, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) contours_cross, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cx_cross, cy_cross, _, _, _, _, _ = self.find_new_features_of_contoures(contours_cross) cx_cross, cy_cross, _, _, _, _, _ = find_new_features_of_contoures(contours_cross)
for ii in range(len(cx_cross)): for ii in range(len(cx_cross)):
img_p_in[int(cy_cross[ii]) - 30 : int(cy_cross[ii]) + 30, int(cx_cross[ii]) + 5 : int(cx_cross[ii]) + 40, 0] = 0 img_p_in[int(cy_cross[ii]) - 30 : int(cy_cross[ii]) + 30, int(cx_cross[ii]) + 5 : int(cx_cross[ii]) + 40, 0] = 0
@ -5680,7 +5479,7 @@ class eynollah:
peaks_neg_tot = self.return_points_with_boundies(peaks_neg_fin, 0, regions_without_seperators[:, :].shape[1]) peaks_neg_tot = self.return_points_with_boundies(peaks_neg_fin, 0, regions_without_seperators[:, :].shape[1])
start_index_of_hor, newest_peaks, arg_min_hor_sort, lines_length_dels, lines_indexes_deleted = self.return_hor_spliter_by_index_for_without_verticals(peaks_neg_tot, x_min_hor_some, x_max_hor_some) start_index_of_hor, newest_peaks, arg_min_hor_sort, lines_length_dels, lines_indexes_deleted = return_hor_spliter_by_index_for_without_verticals(peaks_neg_tot, x_min_hor_some, x_max_hor_some)
arg_org_hor_some_sort = arg_org_hor_some[arg_min_hor_sort] arg_org_hor_some_sort = arg_org_hor_some[arg_min_hor_sort]
@ -5845,7 +5644,7 @@ class eynollah:
peaks_neg_ch_tot = self.return_points_with_boundies(peaks_neg_ch, newest_peaks[j], newest_peaks[j + 1]) peaks_neg_ch_tot = self.return_points_with_boundies(peaks_neg_ch, newest_peaks[j], newest_peaks[j + 1])
ss_in_ch, nst_p_ch, arg_n_ch, lines_l_del_ch, lines_in_del_ch = self.return_hor_spliter_by_index_for_without_verticals(peaks_neg_ch_tot, x_min_ch, x_max_ch) ss_in_ch, nst_p_ch, arg_n_ch, lines_l_del_ch, lines_in_del_ch = return_hor_spliter_by_index_for_without_verticals(peaks_neg_ch_tot, x_min_ch, x_max_ch)
newest_y_spliter_ch_tot = [] newest_y_spliter_ch_tot = []
@ -6266,8 +6065,8 @@ class eynollah:
# _,_,y_min_main_line ,y_max_main_line,x_min_main_line,x_max_main_line=find_new_features_of_contoures(contours_line) # _,_,y_min_main_line ,y_max_main_line,x_min_main_line,x_max_main_line=find_new_features_of_contoures(contours_line)
y_min_main_tab, y_max_main_tab = self.find_features_of_contoures(contours_tab) y_min_main_tab, y_max_main_tab = self.find_features_of_contoures(contours_tab)
cx_tab_m_text, cy_tab_m_text, x_min_tab_m_text, x_max_tab_m_text, y_min_tab_m_text, y_max_tab_m_text = self.find_new_features_of_contoures(contours_table_m_text) cx_tab_m_text, cy_tab_m_text, x_min_tab_m_text, x_max_tab_m_text, y_min_tab_m_text, y_max_tab_m_text = find_new_features_of_contoures(contours_table_m_text)
cx_tabl, cy_tabl, x_min_tabl, x_max_tabl, y_min_tabl, y_max_tabl, _ = self.find_new_features_of_contoures(contours_tab) cx_tabl, cy_tabl, x_min_tabl, x_max_tabl, y_min_tabl, y_max_tabl, _ = find_new_features_of_contoures(contours_tab)
if len(y_min_main_tab) > 0: if len(y_min_main_tab) > 0:
y_down_tabs = [] y_down_tabs = []
@ -8012,7 +7811,7 @@ class eynollah:
min_area_text = 0.00001 min_area_text = 0.00001
polygons_of_marginals = return_contours_of_interested_region(text_regions, pixel_img, min_area_text) polygons_of_marginals = return_contours_of_interested_region(text_regions, pixel_img, min_area_text)
cx_text_only, cy_text_only, x_min_text_only, x_max_text_only, y_min_text_only, y_max_text_only, y_cor_x_min_main = self.find_new_features_of_contoures(polygons_of_marginals) cx_text_only, cy_text_only, x_min_text_only, x_max_text_only, y_min_text_only, y_max_text_only, y_cor_x_min_main = find_new_features_of_contoures(polygons_of_marginals)
text_regions[(text_regions[:, :] == 4)] = 1 text_regions[(text_regions[:, :] == 4)] = 1
@ -8293,7 +8092,7 @@ class eynollah:
contours_only_text_parent=[contours_only_text_parent[jz] for jz in range(len(contours_only_text_parent)) if areas_cnt_text[jz]>0.00001] contours_only_text_parent=[contours_only_text_parent[jz] for jz in range(len(contours_only_text_parent)) if areas_cnt_text[jz]>0.00001]
""" """
cx_main, cy_main, x_min_main, x_max_main, y_min_main, y_max_main, y_corr_x_min_from_argmin = self.find_new_features_of_contoures(contours_only_text_parent) cx_main, cy_main, x_min_main, x_max_main, y_min_main, y_max_main, y_corr_x_min_from_argmin = find_new_features_of_contoures(contours_only_text_parent)
length_con = x_max_main - x_min_main length_con = x_max_main - x_min_main
height_con = y_max_main - y_min_main height_con = y_max_main - y_min_main
@ -8401,8 +8200,8 @@ class eynollah:
def do_order_of_regions(self, contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot): def do_order_of_regions(self, contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot):
if self.full_layout: if self.full_layout:
cx_text_only, cy_text_only, x_min_text_only, _, _, _, y_cor_x_min_main = self.find_new_features_of_contoures(contours_only_text_parent) cx_text_only, cy_text_only, x_min_text_only, _, _, _, y_cor_x_min_main = find_new_features_of_contoures(contours_only_text_parent)
cx_text_only_h, cy_text_only_h, x_min_text_only_h, _, _, _, y_cor_x_min_main_h = self.find_new_features_of_contoures(contours_only_text_parent_h) cx_text_only_h, cy_text_only_h, x_min_text_only_h, _, _, _, y_cor_x_min_main_h = find_new_features_of_contoures(contours_only_text_parent_h)
try: try:
arg_text_con = [] arg_text_con = []
@ -8568,7 +8367,7 @@ class eynollah:
return order_text_new, id_of_texts_tot return order_text_new, id_of_texts_tot
else: else:
cx_text_only, cy_text_only, x_min_text_only, _, _, _, y_cor_x_min_main = self.find_new_features_of_contoures(contours_only_text_parent) cx_text_only, cy_text_only, x_min_text_only, _, _, _, y_cor_x_min_main = find_new_features_of_contoures(contours_only_text_parent)
try: try:
arg_text_con = [] arg_text_con = []
@ -8684,9 +8483,9 @@ class eynollah:
def adhere_drop_capital_region_into_cprresponding_textline(self, text_regions_p, polygons_of_drop_capitals, contours_only_text_parent, contours_only_text_parent_h, all_box_coord, all_box_coord_h, all_found_texline_polygons, all_found_texline_polygons_h): def adhere_drop_capital_region_into_cprresponding_textline(self, text_regions_p, polygons_of_drop_capitals, contours_only_text_parent, contours_only_text_parent_h, all_box_coord, all_box_coord_h, all_found_texline_polygons, all_found_texline_polygons_h):
# print(np.shape(all_found_texline_polygons),np.shape(all_found_texline_polygons[3]),'all_found_texline_polygonsshape') # print(np.shape(all_found_texline_polygons),np.shape(all_found_texline_polygons[3]),'all_found_texline_polygonsshape')
# print(all_found_texline_polygons[3]) # print(all_found_texline_polygons[3])
cx_m, cy_m, _, _, _, _, _ = self.find_new_features_of_contoures(contours_only_text_parent) cx_m, cy_m, _, _, _, _, _ = find_new_features_of_contoures(contours_only_text_parent)
cx_h, cy_h, _, _, _, _, _ = self.find_new_features_of_contoures(contours_only_text_parent_h) cx_h, cy_h, _, _, _, _, _ = find_new_features_of_contoures(contours_only_text_parent_h)
cx_d, cy_d, _, _, y_min_d, y_max_d, _ = self.find_new_features_of_contoures(polygons_of_drop_capitals) cx_d, cy_d, _, _, y_min_d, y_max_d, _ = find_new_features_of_contoures(polygons_of_drop_capitals)
img_con_all = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1], 3)) img_con_all = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1], 3))
for j_cont in range(len(contours_only_text_parent)): for j_cont in range(len(contours_only_text_parent)):
@ -8751,9 +8550,9 @@ class eynollah:
region_final = region_with_intersected_drop[np.argmax(sum_pixels_of_intersection)] - 1 region_final = region_with_intersected_drop[np.argmax(sum_pixels_of_intersection)] - 1
# print(region_final,'region_final') # print(region_final,'region_final')
# cx_t,cy_t ,_, _, _ ,_,_=self.find_new_features_of_contoures(all_found_texline_polygons[int(region_final)]) # cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
try: try:
cx_t, cy_t, _, _, _, _, _ = self.find_new_features_of_contoures(all_found_texline_polygons[int(region_final)]) cx_t, cy_t, _, _, _, _, _ = find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
# print(all_box_coord[j_cont]) # print(all_box_coord[j_cont])
# print(cx_t) # print(cx_t)
# print(cy_t) # print(cy_t)
@ -8805,9 +8604,9 @@ class eynollah:
# areas_main=np.array([cv2.contourArea(all_found_texline_polygons[int(region_final)][0][j] ) for j in range(len(all_found_texline_polygons[int(region_final)]))]) # areas_main=np.array([cv2.contourArea(all_found_texline_polygons[int(region_final)][0][j] ) for j in range(len(all_found_texline_polygons[int(region_final)]))])
# cx_t,cy_t ,_, _, _ ,_,_=self.find_new_features_of_contoures(all_found_texline_polygons[int(region_final)]) # cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
cx_t, cy_t, _, _, _, _, _ = self.find_new_features_of_contoures(all_found_texline_polygons[int(region_final)]) cx_t, cy_t, _, _, _, _, _ = find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
# print(all_box_coord[j_cont]) # print(all_box_coord[j_cont])
# print(cx_t) # print(cx_t)
# print(cy_t) # print(cy_t)
@ -8855,7 +8654,7 @@ class eynollah:
# print(cx_t,'print') # print(cx_t,'print')
try: try:
# print(all_found_texline_polygons[j_cont][0]) # print(all_found_texline_polygons[j_cont][0])
cx_t, cy_t, _, _, _, _, _ = self.find_new_features_of_contoures(all_found_texline_polygons[int(region_final)]) cx_t, cy_t, _, _, _, _, _ = find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
# print(all_box_coord[j_cont]) # print(all_box_coord[j_cont])
# print(cx_t) # print(cx_t)
# print(cy_t) # print(cy_t)
@ -8902,7 +8701,7 @@ class eynollah:
else: else:
pass pass
##cx_t,cy_t ,_, _, _ ,_,_=self.find_new_features_of_contoures(all_found_texline_polygons[int(region_final)]) ##cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
###print(all_box_coord[j_cont]) ###print(all_box_coord[j_cont])
###print(cx_t) ###print(cx_t)
###print(cy_t) ###print(cy_t)
@ -8956,9 +8755,9 @@ class eynollah:
region_final = region_with_intersected_drop[np.argmax(sum_pixels_of_intersection)] - 1 region_final = region_with_intersected_drop[np.argmax(sum_pixels_of_intersection)] - 1
# print(region_final,'region_final') # print(region_final,'region_final')
# cx_t,cy_t ,_, _, _ ,_,_=self.find_new_features_of_contoures(all_found_texline_polygons[int(region_final)]) # cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
try: try:
cx_t, cy_t, _, _, _, _, _ = self.find_new_features_of_contoures(all_found_texline_polygons[int(region_final)]) cx_t, cy_t, _, _, _, _, _ = find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
# print(all_box_coord[j_cont]) # print(all_box_coord[j_cont])
# print(cx_t) # print(cx_t)
# print(cy_t) # print(cy_t)
@ -9010,12 +8809,12 @@ class eynollah:
# areas_main=np.array([cv2.contourArea(all_found_texline_polygons[int(region_final)][0][j] ) for j in range(len(all_found_texline_polygons[int(region_final)]))]) # areas_main=np.array([cv2.contourArea(all_found_texline_polygons[int(region_final)][0][j] ) for j in range(len(all_found_texline_polygons[int(region_final)]))])
# cx_t,cy_t ,_, _, _ ,_,_=self.find_new_features_of_contoures(all_found_texline_polygons[int(region_final)]) # cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
# print(cx_t,'print') # print(cx_t,'print')
try: try:
# print(all_found_texline_polygons[j_cont][0]) # print(all_found_texline_polygons[j_cont][0])
cx_t, cy_t, _, _, _, _, _ = self.find_new_features_of_contoures(all_found_texline_polygons[int(region_final)]) cx_t, cy_t, _, _, _, _, _ = find_new_features_of_contoures(all_found_texline_polygons[int(region_final)])
# print(all_box_coord[j_cont]) # print(all_box_coord[j_cont])
# print(cx_t) # print(cx_t)
# print(cy_t) # print(cy_t)
@ -9081,7 +8880,7 @@ class eynollah:
#####try: #####try:
#####if len(contours_new_parent)==1: #####if len(contours_new_parent)==1:
######print(all_found_texline_polygons[j_cont][0]) ######print(all_found_texline_polygons[j_cont][0])
#####cx_t,cy_t ,_, _, _ ,_,_=self.find_new_features_of_contoures(all_found_texline_polygons[j_cont]) #####cx_t,cy_t ,_, _, _ ,_,_= find_new_features_of_contoures(all_found_texline_polygons[j_cont])
######print(all_box_coord[j_cont]) ######print(all_box_coord[j_cont])
######print(cx_t) ######print(cx_t)
######print(cy_t) ######print(cy_t)
@ -9594,8 +9393,8 @@ class eynollah:
contours_only_text_parent = list(np.array(contours_only_text_parent)[index_con_parents]) contours_only_text_parent = list(np.array(contours_only_text_parent)[index_con_parents])
areas_cnt_text_parent = list(np.array(areas_cnt_text_parent)[index_con_parents]) areas_cnt_text_parent = list(np.array(areas_cnt_text_parent)[index_con_parents])
cx_bigest_big, cy_biggest_big, _, _, _, _, _ = self.find_new_features_of_contoures([contours_biggest]) cx_bigest_big, cy_biggest_big, _, _, _, _, _ = find_new_features_of_contoures([contours_biggest])
cx_bigest, cy_biggest, _, _, _, _, _ = self.find_new_features_of_contoures(contours_only_text_parent) cx_bigest, cy_biggest, _, _, _, _, _ = find_new_features_of_contoures(contours_only_text_parent)
contours_only_text_d, hir_on_text_d = return_contours_of_image(text_only_d) contours_only_text_d, hir_on_text_d = return_contours_of_image(text_only_d)
contours_only_text_parent_d = return_parent_contours(contours_only_text_d, hir_on_text_d) contours_only_text_parent_d = return_parent_contours(contours_only_text_d, hir_on_text_d)
@ -9606,8 +9405,8 @@ class eynollah:
contours_biggest_d = contours_only_text_parent_d[np.argmax(areas_cnt_text_d)] contours_biggest_d = contours_only_text_parent_d[np.argmax(areas_cnt_text_d)]
cx_bigest_d_big, cy_biggest_d_big, _, _, _, _, _ = self.find_new_features_of_contoures([contours_biggest_d]) cx_bigest_d_big, cy_biggest_d_big, _, _, _, _, _ = find_new_features_of_contoures([contours_biggest_d])
cx_bigest_d, cy_biggest_d, _, _, _, _, _ = self.find_new_features_of_contoures(contours_only_text_parent_d) cx_bigest_d, cy_biggest_d, _, _, _, _, _ = find_new_features_of_contoures(contours_only_text_parent_d)
(h, w) = text_only.shape[:2] (h, w) = text_only.shape[:2]
center = (w // 2.0, h // 2.0) center = (w // 2.0, h // 2.0)
@ -9665,8 +9464,8 @@ class eynollah:
contours_only_text_parent = list(np.array(contours_only_text_parent)[index_con_parents]) contours_only_text_parent = list(np.array(contours_only_text_parent)[index_con_parents])
areas_cnt_text_parent = list(np.array(areas_cnt_text_parent)[index_con_parents]) areas_cnt_text_parent = list(np.array(areas_cnt_text_parent)[index_con_parents])
cx_bigest_big, cy_biggest_big, _, _, _, _, _ = self.find_new_features_of_contoures([contours_biggest]) cx_bigest_big, cy_biggest_big, _, _, _, _, _ = find_new_features_of_contoures([contours_biggest])
cx_bigest, cy_biggest, _, _, _, _, _ = self.find_new_features_of_contoures(contours_only_text_parent) cx_bigest, cy_biggest, _, _, _, _, _ = find_new_features_of_contoures(contours_only_text_parent)
# print(areas_cnt_text_parent,'areas_cnt_text_parent') # print(areas_cnt_text_parent,'areas_cnt_text_parent')
###index_con_parents_d=np.argsort(areas_cnt_text_parent_d) ###index_con_parents_d=np.argsort(areas_cnt_text_parent_d)

90
sbb_newspapers_org_image/unused.py
View file

@ -278,3 +278,93 @@ def get_all_image_patches_coordination(self, image_page):
_, crop_coor = crop_image_inside_box(self.boxes[jk], image_page) _, crop_coor = crop_image_inside_box(self.boxes[jk], image_page)
self.all_box_coord.append(crop_coor) self.all_box_coord.append(crop_coor)
def find_num_col_olddd(self, regions_without_seperators, sigma_, multiplier=3.8):
regions_without_seperators_0 = regions_without_seperators[:, :].sum(axis=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)
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
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 > 0.06 * 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]
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) / 2.0)
p_g_l = int(len(y) / 3.0)
p_g_u = len(y) - int(len(y) / 3.0)
diff_peaks = np.abs(np.diff(peaks_neg_fin))
diff_peaks_annormal = diff_peaks[diff_peaks < 30]
return interest_neg_fin
def return_regions_without_seperators_new(self, regions_pre, regions_only_text):
kernel = np.ones((5, 5), np.uint8)
regions_without_seperators = ((regions_pre[:, :] != 6) & (regions_pre[:, :] != 0) & (regions_pre[:, :] != 1) & (regions_pre[:, :] != 2)) * 1
# plt.imshow(regions_without_seperators)
# plt.show()
regions_without_seperators_n = ((regions_without_seperators[:, :] == 1) | (regions_only_text[:, :] == 1)) * 1
# regions_without_seperators=( (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))*1
regions_without_seperators_n = regions_without_seperators_n.astype(np.uint8)
regions_without_seperators_n = cv2.erode(regions_without_seperators_n, kernel, iterations=6)
return regions_without_seperators_n

115
sbb_newspapers_org_image/utils.py
View file

@ -1554,3 +1554,118 @@ def find_num_col_deskew(regions_without_seperators, sigma_, multiplier=3.8):
# print(len(interest_neg_fin),np.mean(interest_neg_fin)) # print(len(interest_neg_fin),np.mean(interest_neg_fin))
return interest_neg_fin, np.std(z) return 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_new_features_of_contoures(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))]
try:
x_min_main = np.array([np.min(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
argmin_x_main = np.array([np.argmin(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 0, 0] for j in range(len(contours_main))])
y_corr_x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 0, 1] 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))])
except:
x_min_main = np.array([np.min(contours_main[j][:, 0]) for j in range(len(contours_main))])
argmin_x_main = np.array([np.argmin(contours_main[j][:, 0]) for j in range(len(contours_main))])
x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 0] for j in range(len(contours_main))])
y_corr_x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 1] for j in range(len(contours_main))])
x_max_main = np.array([np.max(contours_main[j][:, 0]) for j in range(len(contours_main))])
y_min_main = np.array([np.min(contours_main[j][:, 1]) for j in range(len(contours_main))])
y_max_main = np.array([np.max(contours_main[j][:, 1]) for j in range(len(contours_main))])
# dis_x=np.abs(x_max_main-x_min_main)
return cx_main, cy_main, x_min_main, x_max_main, y_min_main, y_max_main, y_corr_x_min_from_argmin