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do_work_of_slopes_new_curved: improve deskewing…
- return early if textline mask is empty
- intersect textline mask with parent mask
(so neighbouring, truncated textlines
will not interfere)
- fix bug when resulting angle is small:
rather, compare with page angle
- if there is more than 1 line in the region,
* use median instead of mean to estimate y_diff
* if height dominates over width and x_diff
over y_diff, then assume 90°: transpose image,
deskew on that, then add 90° to result
- otherwise instead of just using page angle,
try to estimate single-line angle by approximating
slope of linear x-y regression on mask image;
again, if height dominates over width, then
assume +90° and use transposed image
- drop unused `scale` param
This commit is contained in:
Robert Sachunsky
parent
97d9b0ea50
commit
0dce1f24d2
2 changed files with 54 additions and 23 deletions
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@ -1034,14 +1034,13 @@ class Eynollah:
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slopes)
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def get_slopes_and_deskew_new_curved(self, contours_par, textline_mask_tot, boxes,
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num_col, scale_par, slope_deskew, name):
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num_col, slope_deskew, name):
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if not len(contours_par):
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return [], [], []
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self.logger.debug("enter get_slopes_and_deskew_new_curved")
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results = map(partial(do_work_of_slopes_new_curved,
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textline_mask_tot_ea=textline_mask_tot,
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num_col=num_col,
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scale_par=scale_par,
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slope_deskew=slope_deskew,
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MAX_SLOPE=MAX_SLOPE,
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KERNEL=KERNEL,
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@ -2503,20 +2502,19 @@ class Eynollah:
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all_found_textline_polygons_marginals = dilate_textline_contours(
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all_found_textline_polygons_marginals)
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else:
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scale_param = 1
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textline_mask_tot_ea_erode = cv2.erode(textline_mask_tot_ea, kernel=KERNEL, iterations=2)
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all_found_textline_polygons, \
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all_box_coord, slopes = self.get_slopes_and_deskew_new_curved(
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polygons_of_textregions, textline_mask_tot_ea_erode,
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boxes_text,
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num_col_classifier, scale_param, slope_deskew, image['name'])
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num_col_classifier, slope_deskew, image['name'])
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all_found_textline_polygons = small_textlines_to_parent_adherence2(
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all_found_textline_polygons, textline_mask_tot_ea, num_col_classifier)
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all_found_textline_polygons_marginals, \
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all_box_coord_marginals, slopes_marginals = self.get_slopes_and_deskew_new_curved(
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polygons_of_marginals, textline_mask_tot_ea_erode,
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boxes_marginals,
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num_col_classifier, scale_param, slope_deskew, image['name'])
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num_col_classifier, slope_deskew, image['name'])
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all_found_textline_polygons_marginals = small_textlines_to_parent_adherence2(
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all_found_textline_polygons_marginals, textline_mask_tot_ea, num_col_classifier)
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(polygons_of_textregions,
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@ -6,6 +6,7 @@ import cv2
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from scipy.signal import find_peaks
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from scipy.ndimage import gaussian_filter1d
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from .rotate import rotate_image
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from scipy.stats import linregress
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from .resize import resize_image
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from .contour import (
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return_parent_contours,
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@ -13,6 +14,7 @@ from .contour import (
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return_contours_of_image,
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filter_contours_area_of_image,
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return_contours_of_interested_textline,
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find_center_of_contours,
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find_contours_mean_y_diff,
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)
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from . import (
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@ -1585,8 +1587,10 @@ def get_smallest_skew(img, sigma_des, angles, logger=None, plotter=None, name=No
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def do_work_of_slopes_new_curved(
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box_text, contour_par,
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textline_mask_tot_ea=None,
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num_col=1, scale_par=1.0, slope_deskew=0.0,
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logger=None, MAX_SLOPE=999, KERNEL=None, plotter=None, name=None
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num_col=1, slope_deskew=0.0,
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logger=None, MAX_SLOPE=999,
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KERNEL=None, plotter=None,
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name=None,
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):
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if KERNEL is None:
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KERNEL = np.ones((5, 5), np.uint8)
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@ -1595,38 +1599,67 @@ def do_work_of_slopes_new_curved(
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logger.debug("enter do_work_of_slopes_new_curved")
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x, y, w, h = box_text
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all_text_region_raw = textline_mask_tot_ea[y: y + h, x: x + w].astype(np.uint8)
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img_int_p = all_text_region_raw[:, :]
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mask_parent = np.zeros((h, w), dtype=np.uint8)
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mask_parent = cv2.fillPoly(mask_parent, pts=[contour_par - [x, y]], color=1)
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all_text_region_raw = textline_mask_tot_ea[y: y + h, x: x + w] * mask_parent
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if not np.any(all_text_region_raw):
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return [], slope_deskew
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img_int_p = np.copy(all_text_region_raw)
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# img_int_p=cv2.erode(img_int_p,KERNEL,iterations = 2)
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# plt.imshow(img_int_p)
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# plt.show()
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if not np.prod(img_int_p.shape) or img_int_p.shape[0] / img_int_p.shape[1] < 0.1:
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slope = 0
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slope_for_all = slope_deskew
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slope = slope_deskew
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else:
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try:
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textline_con, hierarchy = return_contours_of_image(img_int_p)
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textline_con_fil = filter_contours_area_of_image(img_int_p, textline_con,
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hierarchy,
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max_area=1, min_area=0.0008)
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y_diff_mean = find_contours_mean_y_diff(textline_con_fil) if len(textline_con_fil) > 1 else np.NaN
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if np.isnan(y_diff_mean):
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slope_for_all = MAX_SLOPE
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if len(textline_con_fil) > 1:
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cx, cy = find_center_of_contours(textline_con_fil)
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y_diff_mean = np.median(np.diff(np.sort(np.array(cy))))
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x_diff_mean = np.median(np.diff(np.sort(np.array(cx))))
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if h > w and x_diff_mean / w > 2 * y_diff_mean / h:
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# print(len(textline_con_fil), "transposed", x_diff_mean, y_diff_mean)
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transposed = True
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img_int_p = img_int_p.T
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sigma = x_diff_mean
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else:
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transposed = False
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sigma = y_diff_mean
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slope = return_deskew_slop(img_int_p, max(1.0, 0.1 * sigma),
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logger=logger,
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name=name,
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plotter=plotter)
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if transposed:
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slope = -90 - slope if slope < 0 else 90 - slope
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if abs(slope - slope_deskew) < 0.5:
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slope = slope_deskew
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else:
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sigma_des = max(1, int(y_diff_mean * (4.0 / 40.0)))
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img_int_p[img_int_p > 0] = 1
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slope_for_all = return_deskew_slop(img_int_p, sigma_des, logger=logger, name=name, plotter=plotter)
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if abs(slope_for_all) < 0.5:
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slope_for_all = slope_deskew
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if h > 3 * w:
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# print(1, "transposed", h, w)
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transposed = True
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img_int_p = img_int_p.T
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else:
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transposed = False
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# do linear regression on mask to get slope
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img_ys, img_xs = img_int_p.nonzero()
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regression = linregress(x=img_xs, y=img_ys)
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slope = 180 / np.pi * np.arctan(regression.slope)
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# print(regression, regression.stderr)
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if transposed:
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slope = 90 - slope
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if regression.stderr > 0.005:
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slope = slope_deskew
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except:
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logger.exception("cannot determine angle of contours")
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slope_for_all = MAX_SLOPE
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slope = slope_deskew
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if slope_for_all == MAX_SLOPE:
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slope_for_all = slope_deskew
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slope = slope_for_all
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# print(slope, slope_deskew)
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crop_coor = box2rect(box_text)
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