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get_marginals(): reduce indentation
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Robert Sachunsky
parent
c18deb0722
commit
9571ce3474
1 changed files with 109 additions and 102 deletions
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@ -58,117 +58,124 @@ def get_marginals(text_mask, early_layout, num_col, slope_deskew,
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# plt.legend()
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# plt.show()
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if max_text_thickness_percent >= 14:
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text_mask_d_y_rev = np.max(text_mask_d_y) - text_mask_d_y
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if max_text_thickness_percent < 14:
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return
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region_sum_0 = gaussian_filter1d(text_mask_d_y, 1)
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first_nonzero = region_sum_0.nonzero()[0][0] # outer left
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last_nonzero = region_sum_0.nonzero()[0][-1] # outer right
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mid_point = 0.5 * (last_nonzero + first_nonzero)
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one_third_right = (last_nonzero - mid_point) / 3.0
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one_third_left = (mid_point - first_nonzero) / 3.0
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text_mask_d_y_rev = np.max(text_mask_d_y) - text_mask_d_y
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region_sum_0 = gaussian_filter1d(text_mask_d_y, 1)
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first_nonzero = region_sum_0.nonzero()[0][0] # outer left
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last_nonzero = region_sum_0.nonzero()[0][-1] # outer right
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mid_point = 0.5 * (last_nonzero + first_nonzero)
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one_third_right = (last_nonzero - mid_point) / 3.0
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one_third_left = (mid_point - first_nonzero) / 3.0
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# rs: constrain the distance at least 2 characters at 12pt, retrieve height and prominence
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peaks, props = find_peaks(text_mask_d_y_rev, height=0, prominence=0, distance=30)
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peaks_orig = np.copy(peaks)
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# rs: also calculate the product of prominence and height (for final selection)
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scores = np.zeros(peaks.max() + 1)
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scores[peaks] = props['prominences'] * props['peak_heights']
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# rs: constrain the distance at least 2 characters at 12pt, retrieve height and prominence
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peaks, props = find_peaks(text_mask_d_y_rev, height=0, prominence=0, distance=30)
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peaks_orig = np.copy(peaks)
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# rs: also calculate the product of prominence and height (for final selection)
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scores = np.zeros(peaks.max() + 1)
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scores[peaks] = props['prominences'] * props['peak_heights']
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peaks = peaks[(peaks > first_nonzero) & (peaks < last_nonzero)]
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peaks = peaks[region_sum_0[peaks] < min_text_thickness]
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peaks = peaks[(peaks > first_nonzero) & (peaks < last_nonzero)]
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peaks = peaks[region_sum_0[peaks] < min_text_thickness]
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if num_col == 1:
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peaks_right = peaks[peaks > mid_point]
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peaks_left = peaks[peaks < mid_point]
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if num_col == 2:
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peaks_right = peaks[peaks > mid_point + one_third_right]
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peaks_left = peaks[peaks < mid_point - one_third_left]
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if num_col == 1:
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peaks_right = peaks[peaks > mid_point]
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peaks_left = peaks[peaks < mid_point]
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elif num_col == 2:
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peaks_right = peaks[peaks > mid_point + one_third_right]
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peaks_left = peaks[peaks < mid_point - one_third_left]
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else:
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# should not happen, anyway
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return
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if len(peaks_left) == 0:
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if len(peaks_right) == 0:
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# plt.figure()
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# ax1 = plt.subplot(2, 1, 1, title='text_mask_d (deskewed text+sep mask)')
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# ax1.imshow(text_mask_d, aspect='auto')
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# ax1.vlines([first_nonzero], 0, height, label='first_nonzero', colors='r')
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# ax1.vlines([last_nonzero], 0, height, label='last_nonzero', colors='r')
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# ax1.vlines(peaks_left, 0, height, label='peaks_left', colors='orange')
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# ax1.vlines(peaks_right, 0, height, label='peaks_right', colors='orange')
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# ax2 = plt.subplot(2, 1, 2, title='text_mask_d_y (smoothed)', sharex=ax1)
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# ax2.plot(list(range(width)), region_sum_0)
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# ax2.hlines(min_text_thickness, 0, width, colors='g',
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# label='min_text_thickness=%d' % min_text_thickness)
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# ax2.scatter(peaks_orig, region_sum_0[peaks_orig], label='peaks')
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# plt.legend()
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# plt.show()
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return
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point_right = peaks_right[np.argmax(scores[peaks_right])]
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#point_left = first_nonzero
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point_left = 0
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elif len(peaks_right) == 0:
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point_left = peaks_left[np.argmax(scores[peaks_left])]
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#point_right = last_nonzero
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point_right = width - 1
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elif scores[peaks_left].max() < scores[peaks_right].max():
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point_right = peaks_right[np.argmax(scores[peaks_right])]
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#point_left = first_nonzero
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point_left = 0
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else:
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point_left = peaks_left[np.argmax(scores[peaks_left])]
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#point_right = last_nonzero
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point_right = 0
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main_mask_d[:, point_left: point_right] = 1
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if not np.any(main_mask_d):
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if len(peaks_left) == 0:
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if len(peaks_right) == 0:
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# plt.figure()
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# ax1 = plt.subplot(2, 1, 1, title='text_mask_d (deskewed text+sep mask)')
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# ax1.imshow(text_mask_d, aspect='auto')
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# ax1.vlines([first_nonzero], 0, height, label='first_nonzero', colors='r')
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# ax1.vlines([last_nonzero], 0, height, label='last_nonzero', colors='r')
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# ax1.vlines(peaks_left, 0, height, label='peaks_left', colors='orange')
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# ax1.vlines(peaks_right, 0, height, label='peaks_right', colors='orange')
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# ax2 = plt.subplot(2, 1, 2, title='text_mask_d_y (smoothed)', sharex=ax1)
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# ax2.plot(list(range(width)), region_sum_0)
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# ax2.hlines(min_text_thickness, 0, width, colors='g',
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# label='min_text_thickness=%d' % min_text_thickness)
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# ax2.scatter(peaks_orig, region_sum_0[peaks_orig], label='peaks')
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# plt.legend()
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# plt.show()
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return
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point_right = peaks_right[np.argmax(scores[peaks_right])]
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#point_left = first_nonzero
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point_left = 0
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elif len(peaks_right) == 0:
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point_left = peaks_left[np.argmax(scores[peaks_left])]
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#point_right = last_nonzero
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point_right = width - 1
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elif scores[peaks_left].max() < scores[peaks_right].max():
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point_right = peaks_right[np.argmax(scores[peaks_right])]
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#point_left = first_nonzero
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point_left = 0
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else:
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point_left = peaks_left[np.argmax(scores[peaks_left])]
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#point_right = last_nonzero
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point_right = 0
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# plt.figure()
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# ax1 = plt.subplot(2, 2, 1)
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# ax1.title.set_text('text_mask_d (deskewed text+table mask)')
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# ax1.imshow(text_mask_d)
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# ax1.vlines(peaks_left, 0, height, label='peaks_left', colors='b')
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# ax1.vlines(peaks_right, 0, height, label='peaks_right', colors='b')
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# ax1.vlines([first_nonzero], 0, height, label='first_nonzero', colors='g')
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# ax1.vlines([last_nonzero], 0, height, label='last_nonzero', colors='g')
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# ax1.vlines([point_left], 0, height, label='point_left', colors='r')
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# ax1.vlines([point_right], 0, height, label='point_right', colors='r')
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# ax2 = plt.subplot(2, 2, 2, title='main_mask_d (deskewed main mask)', sharey=ax1)
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# ax2.imshow(main_mask_d)
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# ax3 = plt.subplot(2, 2, 3, title='text_mask_d_y (projection for minima)', sharex=ax1)
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# ax3.plot(list(range(width)), text_mask_d_y)
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# ax3.set_aspect('auto')
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# ax4 = plt.subplot(2, 2, 4, title='early_layout (undeskewed labels)')
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# ax4.imshow(early_layout)
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# plt.legend()
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# plt.show()
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main_mask_d[:, point_left: point_right] = 1
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if not np.any(main_mask_d):
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return
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# rs: rotate back (into undeskewed/original shape as early_layout input):
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main_mask = rotate_image(main_mask_d, -slope_deskew)
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min_area_text = 0.00001
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main_contour = return_contours_of_interested_region(main_mask, 1, min_area_text)[0]
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text_contours = return_contours_of_interested_region(early_layout, label_text, min_area_text)
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cx_text, cy_text = find_center_of_contours(text_contours)
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# plt.figure()
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# ax1 = plt.subplot(2, 2, 1)
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# ax1.title.set_text('text_mask_d (deskewed text+table mask)')
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# ax1.imshow(text_mask_d)
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# ax1.vlines(peaks_left, 0, height, label='peaks_left', colors='b')
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# ax1.vlines(peaks_right, 0, height, label='peaks_right', colors='b')
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# ax1.vlines([first_nonzero], 0, height, label='first_nonzero', colors='g')
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# ax1.vlines([last_nonzero], 0, height, label='last_nonzero', colors='g')
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# ax1.vlines([point_left], 0, height, label='point_left', colors='r')
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# ax1.vlines([point_right], 0, height, label='point_right', colors='r')
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# ax2 = plt.subplot(2, 2, 2, title='main_mask_d (deskewed main mask)', sharey=ax1)
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# ax2.imshow(main_mask_d)
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# ax3 = plt.subplot(2, 2, 3, title='text_mask_d_y (projection for minima)', sharex=ax1)
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# ax3.plot(list(range(width)), text_mask_d_y)
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# ax3.set_aspect('auto')
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# ax4 = plt.subplot(2, 2, 4, title='early_layout (undeskewed labels)')
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# ax4.imshow(early_layout)
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# plt.legend()
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# plt.show()
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marg_contours = []
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for i, contour in enumerate(text_contours):
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if -1 == cv2.pointPolygonTest(main_contour,
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(cx_text[i],
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cy_text[i]),
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False):
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marg_contours.append(contour)
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# rs: rotate back (into undeskewed/original shape as early_layout input):
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main_mask = rotate_image(main_mask_d, -slope_deskew)
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# early_layout_orig = np.copy(early_layout)
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early_layout = cv2.fillPoly(early_layout, pts=marg_contours, color=label_marg)
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min_area_text = 0.00001
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main_contour = return_contours_of_interested_region(main_mask, 1, min_area_text)[0]
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text_contours = return_contours_of_interested_region(early_layout, label_text, min_area_text)
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cx_text, cy_text = find_center_of_contours(text_contours)
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# plt.figure()
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# ax1 = plt.subplot(2, 2, 1, title='main_mask_d (deskewed main mask)')
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# plt.imshow(main_mask_d)
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# ax2 = plt.subplot(2, 2, 2, title='main_mask (undeskewed main mask)')
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# plt.imshow(main_mask)
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# ax3 = plt.subplot(2, 2, 3, title='early_layout (undeskewed labels original)')
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# plt.imshow(early_layout_orig)
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# ax4 = plt.subplot(2, 2, 4, title='early_layout (undeskewed labels split)')
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# plt.imshow(early_layout)
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# plt.show()
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marg_contours = []
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for i, contour in enumerate(text_contours):
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if -1 == cv2.pointPolygonTest(main_contour,
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(cx_text[i],
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cy_text[i]),
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False):
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marg_contours.append(contour)
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# early_layout_orig = np.copy(early_layout)
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early_layout = cv2.fillPoly(early_layout, pts=marg_contours, color=label_marg)
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# plt.figure()
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# ax1 = plt.subplot(2, 2, 1, title='main_mask_d (deskewed main mask)')
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# plt.imshow(main_mask_d)
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# ax2 = plt.subplot(2, 2, 2, title='main_mask (undeskewed main mask)')
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# plt.imshow(main_mask)
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# ax3 = plt.subplot(2, 2, 3, title='early_layout (undeskewed labels original)')
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# plt.imshow(early_layout_orig)
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# ax4 = plt.subplot(2, 2, 4, title='early_layout (undeskewed labels split)')
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# plt.imshow(early_layout)
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# plt.show()
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# if there was no main text, then relabel marginalia as main
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if not np.any(early_layout == label_text):
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early_layout[early_layout == label_marg] = label_text
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