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find_num_col: add optional criterion - sum of vertical separators

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when searching for gaps between text regions, consider the vertical
separator mask (if given): add the vertical sum of vertical separators
to the peak scores (making column detection more robust if still slighly
skewed or partially obscured by multi-column regions, but fg seps are
present)
This commit is contained in:
Robert Sachunsky 2025-11-28 18:07:15 +01:00
parent 84d10962f3
commit 4dd40c542b
1 changed files with 19 additions and 10 deletions
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29
src/eynollah/utils/__init__.py
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@ -241,10 +241,13 @@ def find_num_col_deskew(regions_without_separators, sigma_, multiplier=3.8):
z = gaussian_filter1d(regions_without_separators_0, sigma_)
return np.std(z)
def find_num_col(regions_without_separators, num_col_classifier, tables, multiplier=3.8, unbalanced=False):
def find_num_col(regions_without_separators, num_col_classifier, tables, multiplier=3.8, unbalanced=False, vertical_separators=None):
if not regions_without_separators.any():
return 0, []
if vertical_separators is None:
vertical_separators = np.zeros_like(regions_without_separators)
regions_without_separators_0 = regions_without_separators.sum(axis=0)
vertical_separators_0 = vertical_separators.sum(axis=0)
# fig, (ax1, ax2) = plt.subplots(2, sharex=True)
# ax1.imshow(regions_without_separators, aspect="auto")
# ax2.plot(regions_without_separators_0)
@ -258,13 +261,12 @@ def find_num_col(regions_without_separators, num_col_classifier, tables, multipl
first_nonzero = first_nonzero + 50 #+ 200
last_offmargin = len(regions_without_separators_0) - 170 #370
first_offmargin = 170 #370
x = vertical_separators_0
y = regions_without_separators_0 # [first_nonzero:last_nonzero]
y_help = np.zeros(len(y) + 20)
y_help[10 : len(y) + 10] = y
x = np.arange(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
y_help = np.pad(y, (10, 10), constant_values=(0, 0))
zneg_rev = y.max() - y_help
zneg = np.pad(zneg_rev, (10, 10), constant_values=(0, 0))
x = gaussian_filter1d(x, sigma_)
z = gaussian_filter1d(y, sigma_)
zneg = gaussian_filter1d(zneg, sigma_)
@ -333,6 +335,7 @@ def find_num_col(regions_without_separators, num_col_classifier, tables, multipl
#np.mean(y[peaks_neg[0]:peaks_neg[-1]])-np.std(y[peaks_neg[0]:peaks_neg[-1]])/2.0
# extra criterion: fixed multiple of lowest gap height
# print("grenze", grenze, multiplier * (5 + np.min(interest_neg)))
grenze = min(grenze, multiplier * (5 + np.min(interest_neg)))
# print(interest_neg,'interest_neg')
@ -341,16 +344,22 @@ def find_num_col(regions_without_separators, num_col_classifier, tables, multipl
# print(dis_talaei,'dis_talaei')
# print(peaks_neg,'peaks_neg')
# fig, (ax1, ax2) = plt.subplots(2, sharex=True)
# ax1.imshow(regions_without_separators, aspect="auto")
# ax1.imshow(regions_without_separators + 5 * vertical_separators, aspect="auto")
# ax2.plot(z, color='red', label='z')
# ax2.plot(zneg[20:], color='blue', label='zneg')
# ax2.plot(x, color='green', label='vsep')
# ax2.scatter(peaks_neg, z[peaks_neg], color='red')
# ax2.scatter(peaks_neg, zneg[20:][peaks_neg], color='blue')
# ax2.axhline(min_peaks_pos, color='red', label="min_peaks_pos")
# ax2.axhline(grenze, color='blue', label="grenze")
# ax2.axhline(min_peaks_pos, color='red')
# ax2.axhline(grenze, color='blue')
# ax2.annotate("min_peaks_pos", xy=(0, min_peaks_pos), color='red')
# ax2.annotate("grenze", xy=(0, grenze), color='blue')
# ax2.text(0, grenze, "grenze")
# ax2.legend()
# plt.show()
# print("vsep", x[peaks_neg])
interest_neg = interest_neg - x[peaks_neg]
interest_neg_fin = interest_neg[(interest_neg < grenze)]
peaks_neg_fin = peaks_neg[(interest_neg < grenze)]