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matching deskewed text region contours with predicted: improve

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- avoid duplicate and missing mappings by using a different approach:
  instead of just minimising the center distance for the N contours
  that we expect,
  1. get all N:M distances
  2. iterate over them from small to large
  3. continue adding correspondences until both every original contour
     and every deskewed contour have at least one match
  4. where one original matches multiple deskewed contours,
     join the latter polygons to map as single contour
  5. where one deskewed contour matches multiple originals,
     split the former by intersecting with each of the latter
     (after bringing them into the same coordinate space),
     so ultimately only the respective match gets assigned
This commit is contained in:
Robert Sachunsky 2025-10-06 12:58:24 +02:00
parent 2850fc6f8d
commit 1fa46303c0
2 changed files with 83 additions and 14 deletions
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82
src/eynollah/eynollah.py
View file

@ -33,6 +33,7 @@ from concurrent.futures import ProcessPoolExecutor
import xml.etree.ElementTree as ET import xml.etree.ElementTree as ET
import cv2 import cv2
import numpy as np import numpy as np
import shapely.affinity
from scipy.signal import find_peaks from scipy.signal import find_peaks
from scipy.ndimage import gaussian_filter1d from scipy.ndimage import gaussian_filter1d
from numba import cuda from numba import cuda
@ -83,6 +84,10 @@ from .utils.contour import (
return_parent_contours, return_parent_contours,
dilate_textregion_contours, dilate_textregion_contours,
dilate_textline_contours, dilate_textline_contours,
polygon2contour,
contour2polygon,
join_polygons,
make_intersection,
) )
from .utils.rotate import ( from .utils.rotate import (
rotate_image, rotate_image,
@ -4606,23 +4611,72 @@ class Eynollah:
p0 = np.dot(M_22, center0) # [2, 1] p0 = np.dot(M_22, center0) # [2, 1]
offset = p0 - center0_d # [2, 1] offset = p0 - center0_d # [2, 1]
# img2 = np.zeros(text_only_d.shape[:2], dtype=np.uint8) centers = np.dot(M_22, centers) - offset # [2,N]
contours_only_text_parent_d_ordered = [] # add dimension for area (so only contours of similar size will be considered close)
centers = np.append(centers, areas_cnt_text_parent[np.newaxis], axis=0)
centers_d = np.append(centers_d, areas_cnt_text_d[np.newaxis], axis=0)
dists = np.zeros((len(contours_only_text_parent), len(contours_only_text_parent_d)))
for i in range(len(contours_only_text_parent)): for i in range(len(contours_only_text_parent)):
p = np.dot(M_22, centers[:, i:i+1]) # [2, 1] dists[i] = np.linalg.norm(centers[:, i:i + 1] - centers_d, axis=0)
p -= offset corresp = np.zeros(dists.shape, dtype=bool)
# add dimension for area # keep searching next-closest until at least one correspondence on each side
#dists = np.linalg.norm(p - centers_d, axis=0) while not np.all(corresp.sum(axis=1)) and not np.all(corresp.sum(axis=0)):
diffs = (np.append(p, [[areas_cnt_text_parent[i]]], axis=0) - idx = np.nanargmin(dists)
np.append(centers_d, areas_cnt_text_d[np.newaxis], axis=0)) i, j = np.unravel_index(idx, dists.shape)
dists = np.linalg.norm(diffs, axis=0) dists[i, j] = np.nan
contours_only_text_parent_d_ordered.append( corresp[i, j] = True
contours_only_text_parent_d[np.argmin(dists)]) #print("original/deskewed adjacency", corresp.nonzero())
# cv2.fillPoly(img2, pts=[contours_only_text_parent_d[np.argmin(dists)]], color=i + 1) contours_only_text_parent_d_ordered = np.zeros_like(contours_only_text_parent)
contours_only_text_parent_d_ordered = contours_only_text_parent_d[np.argmax(corresp, axis=1)]
# img1 = np.zeros(text_only_d.shape[:2], dtype=np.uint8)
# for i in range(len(contours_only_text_parent)):
# cv2.fillPoly(img1, pts=[contours_only_text_parent_d_ordered[i]], color=i + 1)
# plt.subplot(2, 2, 1, title="direct corresp contours")
# plt.imshow(img1)
# img2 = np.zeros(text_only_d.shape[:2], dtype=np.uint8)
# join deskewed regions mapping to single original ones
for i in range(len(contours_only_text_parent)):
if np.count_nonzero(corresp[i]) > 1:
indices = np.flatnonzero(corresp[i])
#print("joining", indices)
polygons_d = [contour2polygon(contour)
for contour in contours_only_text_parent_d[indices]]
contour_d = polygon2contour(join_polygons(polygons_d))
contours_only_text_parent_d_ordered[i] = contour_d
# cv2.fillPoly(img2, pts=[contour_d], color=i + 1)
# plt.subplot(2, 2, 3, title="joined contours")
# plt.imshow(img2) # plt.imshow(img2)
# img3 = np.zeros(text_only_d.shape[:2], dtype=np.uint8)
# split deskewed regions mapping to multiple original ones
def deskew(polygon):
polygon = shapely.affinity.rotate(polygon, -slope_deskew, origin=center)
polygon = shapely.affinity.translate(polygon, *offset.squeeze())
return polygon
for j in range(len(contours_only_text_parent_d)):
if np.count_nonzero(corresp[:, j]) > 1:
indices = np.flatnonzero(corresp[:, j])
#print("splitting along", indices)
polygons = [deskew(contour2polygon(contour))
for contour in contours_only_text_parent[indices]]
polygon_d = contour2polygon(contours_only_text_parent_d[j])
polygons_d = [make_intersection(polygon_d, polygon)
for polygon in polygons]
# ignore where there is no actual overlap
indices = indices[np.flatnonzero(polygons_d)]
contours_d = [polygon2contour(polygon_d)
for polygon_d in polygons_d
if polygon_d]
contours_only_text_parent_d_ordered[indices] = contours_d
# cv2.fillPoly(img3, pts=contours_d, color=j + 1)
# plt.subplot(2, 2, 4, title="split contours")
# plt.imshow(img3)
# img4 = np.zeros(text_only_d.shape[:2], dtype=np.uint8)
# for i in range(len(contours_only_text_parent)):
# cv2.fillPoly(img4, pts=[contours_only_text_parent_d_ordered[i]], color=i + 1)
# plt.subplot(2, 2, 2, title="result contours")
# plt.imshow(img4)
# plt.show() # plt.show()
# rs: what about the remaining contours_only_text_parent_d?
# rs: what about duplicates?
else: else:
contours_only_text_parent_d_ordered = [] contours_only_text_parent_d_ordered = []
contours_only_text_parent_d = [] contours_only_text_parent_d = []

15
src/eynollah/utils/contour.py
View file

@ -335,6 +335,21 @@ def polygon2contour(polygon: Polygon) -> np.ndarray:
polygon = np.array(polygon.exterior.coords[:-1], dtype=int) polygon = np.array(polygon.exterior.coords[:-1], dtype=int)
return np.maximum(0, polygon).astype(np.uint)[:, np.newaxis] return np.maximum(0, polygon).astype(np.uint)[:, np.newaxis]
def make_intersection(poly1, poly2):
interp = poly1.intersection(poly2)
# post-process
if interp.is_empty or interp.area == 0.0:
return None
if interp.geom_type == 'GeometryCollection':
# heterogeneous result: filter zero-area shapes (LineString, Point)
interp = unary_union([geom for geom in interp.geoms if geom.area > 0])
if interp.geom_type == 'MultiPolygon':
# homogeneous result: construct convex hull to connect
interp = join_polygons(interp.geoms)
assert interp.geom_type == 'Polygon', interp.wkt
interp = make_valid(interp)
return interp
def make_valid(polygon: Polygon) -> Polygon: def make_valid(polygon: Polygon) -> Polygon:
"""Ensures shapely.geometry.Polygon object is valid by repeated rearrangement/simplification/enlargement.""" """Ensures shapely.geometry.Polygon object is valid by repeated rearrangement/simplification/enlargement."""
def isint(x): def isint(x):