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https://github.com/qurator-spk/eynollah.git
synced 2025-11-17 01:44:14 +01:00
improve rules governing column split
- reduce `sigma` for smoothing of input to `find_peaks` (so we get deeper gaps between columns) - allow column boundaries closer to the margins (50 instead of 100 or 200 px, 170 instead of 370 px) - allow column boundaries closer to each other (300 instead of 400 px) - add a secondary `grenze` criterion for depth of gap (relative to lowest minimum, if that is smaller than the old criterion relative to lowest maximum) - for calls to `find_num_col` within parts of a page, do allow unbalanced column boundaries
This commit is contained in:
Robert Sachunsky
parent
4abc2ff572
commit
4475183f08
1 changed files with 66 additions and 47 deletions
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@ -241,7 +241,7 @@ def find_num_col_deskew(regions_without_separators, sigma_, multiplier=3.8):
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z = gaussian_filter1d(regions_without_separators_0, sigma_)
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return np.std(z)
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def find_num_col(regions_without_separators, num_col_classifier, tables, multiplier=3.8):
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def find_num_col(regions_without_separators, num_col_classifier, tables, multiplier=3.8, unbalanced=False):
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if not regions_without_separators.any():
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return 0, []
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regions_without_separators_0 = regions_without_separators.sum(axis=0)
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@ -249,13 +249,15 @@ def find_num_col(regions_without_separators, num_col_classifier, tables, multipl
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# ax1.imshow(regions_without_separators, aspect="auto")
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# ax2.plot(regions_without_separators_0)
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# plt.show()
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sigma_ = 35 # 70#35
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sigma_ = 25 # 70#35
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meda_n_updown = regions_without_separators_0[::-1]
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first_nonzero = next((i for i, x in enumerate(regions_without_separators_0) if x), 0)
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last_nonzero = next((i for i, x in enumerate(meda_n_updown) if x), 0)
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last_nonzero = len(regions_without_separators_0) - last_nonzero
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last_nonzero = last_nonzero - 100
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first_nonzero = first_nonzero + 200
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last_nonzero = last_nonzero - 50 #- 100
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first_nonzero = first_nonzero + 50 #+ 200
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last_offmargin = len(regions_without_separators_0) - 170 #370
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first_offmargin = 170 #370
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y = regions_without_separators_0 # [first_nonzero:last_nonzero]
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y_help = np.zeros(len(y) + 20)
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y_help[10 : len(y) + 10] = y
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@ -285,26 +287,34 @@ def find_num_col(regions_without_separators, num_col_classifier, tables, multipl
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# ax2.axvline(last_nonzero, label="last nonzero")
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# ax2.text(first_nonzero, 0, "first nonzero", rotation=90)
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# ax2.text(last_nonzero, 0, "last nonzero", rotation=90)
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# ax2.axvline(370, label="first")
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# ax2.axvline(len(y) - 370, label="last")
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# ax2.text(370, 0, "first", rotation=90)
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# ax2.text(len(y) - 370, 0, "last", rotation=90)
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# ax2.axvline(first_offmargin, label="first offmargin")
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# ax2.axvline(last_offmargin, label="last offmargin")
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# ax2.text(first_offmargin, 0, "first offmargin", rotation=90)
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# ax2.text(last_offmargin, 0, "last offmargin", rotation=90)
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# plt.show()
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peaks_neg = peaks_neg - 10 - 10
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# print("raw peaks", peaks)
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peaks = peaks[(peaks > 0.06 * len(y)) &
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(peaks < 0.94 * len(y))]
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# print("non-marginal peaks", peaks)
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interest_pos = z[peaks]
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# print("interest_pos", interest_pos)
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interest_pos = interest_pos[interest_pos > 10]
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if not interest_pos.any():
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return 0, []
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# plt.plot(z)
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# plt.show()
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#print("raw peaks_neg", peaks_neg)
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peaks_neg = peaks_neg[(peaks_neg > first_nonzero) &
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(peaks_neg < last_nonzero)]
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peaks_neg = peaks_neg[(peaks_neg > 370) &
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(peaks_neg < len(y) - 370)]
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#print("non-zero peaks_neg", peaks_neg)
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peaks_neg = peaks_neg[(peaks_neg > first_offmargin) &
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(peaks_neg < last_offmargin)]
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#print("non-marginal peaks_neg", peaks_neg)
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interest_neg = z[peaks_neg]
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#print("interest_neg", interest_neg)
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if not interest_neg.any():
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return 0, []
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@ -317,10 +327,14 @@ def find_num_col(regions_without_separators, num_col_classifier, tables, multipl
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min_peaks_neg = 0 # np.min(interest_neg)
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# cutoff criterion: fixed fraction of lowest column height
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dis_talaei = (min_peaks_pos - min_peaks_neg) / multiplier
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grenze = min_peaks_pos - dis_talaei
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#np.mean(y[peaks_neg[0]:peaks_neg[-1]])-np.std(y[peaks_neg[0]:peaks_neg[-1]])/2.0
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# extra criterion: fixed multiple of lowest gap height
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grenze = min(grenze, multiplier * (5 + np.min(interest_neg)))
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# print(interest_neg,'interest_neg')
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# print(grenze,'grenze')
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# print(min_peaks_pos,'min_peaks_pos')
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@ -356,7 +370,9 @@ def find_num_col(regions_without_separators, num_col_classifier, tables, multipl
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# print(peaks_neg_fin,'peaks_neg_fin')
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# print(num_col,'diz')
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# cancel if resulting split is highly unbalanced across available width
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if ((num_col == 3 and
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if unbalanced:
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pass
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elif ((num_col == 3 and
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((peaks_neg_fin[0] > 0.75 * len(y) and
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peaks_neg_fin[1] > 0.75 * len(y)) or
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(peaks_neg_fin[0] < 0.25 * len(y) and
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@ -376,7 +392,7 @@ def find_num_col(regions_without_separators, num_col_classifier, tables, multipl
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# filter out peaks that are too close (<400px) to each other:
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# among each group, pick the position with smallest amount of text
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diff_peaks = np.abs(np.diff(peaks_neg_fin))
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cut_off = 400
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cut_off = 300 #400
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peaks_neg_true = []
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forest = []
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# print(len(peaks_neg_fin),'len_')
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@ -401,7 +417,9 @@ def find_num_col(regions_without_separators, num_col_classifier, tables, multipl
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#print(peaks_neg_true, "peaks_neg_true")
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##print(num_col,'early')
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# cancel if resulting split is highly unbalanced across available width
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if ((num_col == 3 and
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if unbalanced:
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pass
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elif ((num_col == 3 and
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((peaks_neg_true[0] > 0.75 * len(y) and
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peaks_neg_true[1] > 0.75 * len(y)) or
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(peaks_neg_true[0] < 0.25 * len(y) and
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@ -415,13 +433,13 @@ def find_num_col(regions_without_separators, num_col_classifier, tables, multipl
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peaks_neg_true[0] < 0.25 * len(y)))):
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num_col = 1
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peaks_neg_true = []
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if (num_col == 3 and
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elif (num_col == 3 and
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(peaks_neg_true[0] < 0.75 * len(y) and
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peaks_neg_true[0] > 0.25 * len(y) and
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peaks_neg_true[1] > 0.80 * len(y))):
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num_col = 2
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peaks_neg_true = [peaks_neg_true[0]]
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if (num_col == 3 and
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elif (num_col == 3 and
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(peaks_neg_true[1] < 0.75 * len(y) and
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peaks_neg_true[1] > 0.25 * len(y) and
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peaks_neg_true[0] < 0.20 * len(y))):
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@ -1151,8 +1169,8 @@ def order_of_regions(textline_mask, contours_main, contours_head, y_ref, x_ref):
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##matrix_of_orders[:len_main,4]=final_indexers_sorted[:]
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# assert len(final_indexers_sorted) == len(contours_main) + len(contours_head)
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# assert not len(final_indexers_sorted) or max(final_index_type) == max(len(contours_main)
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assert len(set(final_indexers_sorted)) == len(contours_main) + len(contours_head)
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assert set(final_index_type) == set(range(len(contours_main))).union(range(len(contours_head)))
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return np.array(final_indexers_sorted), np.array(final_types), np.array(final_index_type)
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@ -1518,7 +1536,8 @@ def return_boxes_of_images_by_order_of_reading_new(
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regions_without_separators[top:bot],
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# we do not expect to get all columns in small parts (headings etc.):
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num_col_classifier if bot - top >= big_part else 1,
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tables, multiplier=6. if erosion_hurts else 7.)
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tables, multiplier=6. if erosion_hurts else 7.,
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unbalanced=True)
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except:
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peaks_neg_fin=[]
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num_col = 0
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@ -1534,7 +1553,7 @@ def return_boxes_of_images_by_order_of_reading_new(
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if len(peaks_neg_fin)==0:
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num_col, peaks_neg_fin = find_num_col(
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regions_without_separators[top:bot],
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num_col_classifier, tables, multiplier=3.)
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num_col_classifier, tables, multiplier=3., unbalanced=True)
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#print(peaks_neg_fin,'peaks_neg_fin')
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peaks_neg_fin_early = [0] + peaks_neg_fin + [width_tot-1]
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