def generate_rbox(im_size, polys, tags):
"""
score map is (128, 128, 1) with shrinked poly
poly mask is (128, 128, 1) with differnt colors
geo map is (128, 128, 5) with
"""
h, w = im_size
poly_mask = np.zeros((h, w), dtype=np.uint8)
score_map = np.zeros((h, w), dtype=np.uint8)
geo_map = np.zeros((h, w, 5), dtype=np.float32)
# mask used during traning, to ignore some hard areas
training_mask = np.ones((h, w), dtype=np.uint8)
for poly_idx, poly_tag in enumerate(zip(polys, tags)):
poly = poly_tag[0]
tag = poly_tag[1]
poly = np.array(poly)
tag = np.array(tag)
r = [None, None, None, None]
for i in range(4):
r[i] = min(np.linalg.norm(poly[i] - poly[(i + 1) % 4]),
np.linalg.norm(poly[i] - poly[(i - 1) % 4]))
# score map
shrinked_poly = shrink_poly(poly.copy(),
r).astype(np.int32)[np.newaxis, :, :]
cv2.fillPoly(score_map, shrinked_poly, 1)
# use different color to draw poly mask
cv2.fillPoly(poly_mask, shrinked_poly, poly_idx + 1)
# if the poly is too small, then ignore it during training
poly_h = min(np.linalg.norm(poly[0] - poly[3]),
np.linalg.norm(poly[1] - poly[2]))
poly_w = min(np.linalg.norm(poly[0] - poly[1]),
np.linalg.norm(poly[2] - poly[3]))
# if min(poly_h, poly_w) < FLAGS.min_text_size:
if min(poly_h, poly_w) < 10:
cv2.fillPoly(training_mask,
poly.astype(np.int32)[np.newaxis, :, :], 0)
if tag:
cv2.fillPoly(training_mask,
poly.astype(np.int32)[np.newaxis, :, :], 0)
xy_in_poly = np.argwhere(poly_mask == (poly_idx + 1))
# if geometry == 'RBOX':
# 对任意两个顶点的组合生成一个平行四边形
fitted_parallelograms = []
for i in range(4):
p0 = poly[i]
p1 = poly[(i + 1) % 4]
p2 = poly[(i + 2) % 4]
p3 = poly[(i + 3) % 4]
# fit_line ([x1, x2], [y1, y2]) return k, -1, b just a line
edge = fit_line([p0[0], p1[0]], [p0[1], p1[1]]) # p0, p1
backward_edge = fit_line([p0[0], p3[0]], [p0[1], p3[1]]) # p0, p3
forward_edge = fit_line([p1[0], p2[0]], [p1[1], p2[1]]) # p1, p2
# select shorter line
if point_dist_to_line(p0, p1, p2) > point_dist_to_line(p0, p1, p3):
# 平行线经过p2
if edge[1] == 0: # verticle
edge_opposite = [1, 0, -p2[0]]
else:
edge_opposite = [edge[0], -1, p2[1] - edge[0] * p2[0]]
else:
# 经过p3
if edge[1] == 0:
edge_opposite = [1, 0, -p3[0]]
else:
edge_opposite = [edge[0], -1, p3[1] - edge[0] * p3[0]]
# move forward edge
new_p0 = p0
new_p1 = p1
new_p2 = p2
new_p3 = p3
new_p2 = line_cross_point(forward_edge, edge_opposite)
if point_dist_to_line(p1, new_p2, p0) > point_dist_to_line(
p1, new_p2, p3):
# across p0
if forward_edge[1] == 0:
forward_opposite = [1, 0, -p0[0]]
else:
forward_opposite = [
forward_edge[0], -1, p0[1] - forward_edge[0] * p0[0]
]
else:
# across p3
if forward_edge[1] == 0:
forward_opposite = [1, 0, -p3[0]]
else:
forward_opposite = [
forward_edge[0], -1, p3[1] - forward_edge[0] * p3[0]
]
new_p0 = line_cross_point(forward_opposite, edge)
new_p3 = line_cross_point(forward_opposite, edge_opposite)
fitted_parallelograms.append(
[new_p0, new_p1, new_p2, new_p3, new_p0])
# or move backward edge
new_p0 = p0
new_p1 = p1
new_p2 = p2
new_p3 = p3
new_p3 = line_cross_point(backward_edge, edge_opposite)
if point_dist_to_line(p0, p3, p1) > point_dist_to_line(p0, p3, p2):
# across p1
if backward_edge[1] == 0:
backward_opposite = [1, 0, -p1[0]]
else:
backward_opposite = [
backward_edge[0], -1, p1[1] - backward_edge[0] * p1[0]
]
else:
# across p2
if backward_edge[1] == 0:
backward_opposite = [1, 0, -p2[0]]
else:
backward_opposite = [
backward_edge[0], -1, p2[1] - backward_edge[0] * p2[0]
]
new_p1 = line_cross_point(backward_opposite, edge)
new_p2 = line_cross_point(backward_opposite, edge_opposite)
fitted_parallelograms.append(
[new_p0, new_p1, new_p2, new_p3, new_p0])
areas = [Polygon(t).area for t in fitted_parallelograms]
parallelogram = np.array(fitted_parallelograms[np.argmin(areas)][:-1],
dtype=np.float32)
# sort thie polygon
parallelogram_coord_sum = np.sum(parallelogram, axis=1)
min_coord_idx = np.argmin(parallelogram_coord_sum)
parallelogram = parallelogram[[
min_coord_idx, (min_coord_idx + 1) % 4, (min_coord_idx + 2) % 4,
(min_coord_idx + 3) % 4
]]
rectange = rectangle_from_parallelogram(parallelogram)
rectange, rotate_angle = sort_rectangle(rectange)
# print('parallel {} rectangle {}'.format(parallelogram, rectange))
p0_rect, p1_rect, p2_rect, p3_rect = rectange
# this is one area of many
"""
for y, x in xy_in_poly:
point = np.array([x, y], dtype=np.float32)
# top
geo_map[y, x, 0] = point_dist_to_line(p0_rect, p1_rect, point)
# right
geo_map[y, x, 1] = point_dist_to_line(p1_rect, p2_rect, point)
# down
geo_map[y, x, 2] = point_dist_to_line(p2_rect, p3_rect, point)
# left
geo_map[y, x, 3] = point_dist_to_line(p3_rect, p0_rect, point)
# angle
geo_map[y, x, 4] = rotate_angle
"""
gen_geo_map.gen_geo_map(geo_map, xy_in_poly, rectange, rotate_angle)
###sum up
# score_map , in shrinked poly is 1
# geo_map, corresponding to score map
# training map is less than geo_map
return score_map, geo_map, training_mask
def generate_rbox(im_size, polys, tags):
h, w = im_size
poly_mask = np.zeros((h, w), dtype=np.uint8)
score_map = np.zeros((h, w), dtype=np.uint8)
geo_map = np.zeros((h, w, 5), dtype=np.float32)
# mask used during traning, to ignore some hard areas
training_mask = np.ones((h, w), dtype=np.uint8)
for poly_idx, poly_tag in enumerate(zip(polys, tags)):
poly = poly_tag[0]
tag = poly_tag[1]
r = [None, None, None, None]
for i in range(4):
r[i] = min(np.linalg.norm(poly[i] - poly[(i + 1) % 4]),
np.linalg.norm(poly[i] - poly[(i - 1) % 4]))
# score map
shrinked_poly = shrink_poly(poly.copy(),
r).astype(np.int32)[np.newaxis, :, :]
cv2.fillPoly(score_map, shrinked_poly, 1)
cv2.fillPoly(poly_mask, shrinked_poly, poly_idx + 1)
# if the poly is too small, then ignore it during training
poly_h = min(np.linalg.norm(poly[0] - poly[3]),
np.linalg.norm(poly[1] - poly[2]))
poly_w = min(np.linalg.norm(poly[0] - poly[1]),
np.linalg.norm(poly[2] - poly[3]))
if min(poly_h, poly_w) < FLAGS.min_text_size:
cv2.fillPoly(training_mask,
poly.astype(np.int32)[np.newaxis, :, :], 0)
if tag:
cv2.fillPoly(training_mask,
poly.astype(np.int32)[np.newaxis, :, :], 0)
xy_in_poly = np.argwhere(poly_mask == (poly_idx + 1))
# if geometry == 'RBOX':
# 对任意两个顶点的组合生成一个平行四边形 - generate a parallelogram for any combination of two vertices
fitted_parallelograms = []
for i in range(4):
p0 = poly[i]
p1 = poly[(i + 1) % 4]
p2 = poly[(i + 2) % 4]
p3 = poly[(i + 3) % 4]
edge = fit_line([p0[0], p1[0]], [p0[1], p1[1]])
backward_edge = fit_line([p0[0], p3[0]], [p0[1], p3[1]])
forward_edge = fit_line([p1[0], p2[0]], [p1[1], p2[1]])
if point_dist_to_line(p0, p1, p2) > point_dist_to_line(p0, p1, p3):
# 平行线经过p2 - parallel lines through p2
if edge[1] == 0:
edge_opposite = [1, 0, -p2[0]]
else:
edge_opposite = [edge[0], -1, p2[1] - edge[0] * p2[0]]
else:
# 经过p3 - after p3
if edge[1] == 0:
edge_opposite = [1, 0, -p3[0]]
else:
edge_opposite = [edge[0], -1, p3[1] - edge[0] * p3[0]]
# move forward edge
new_p0 = p0
new_p1 = p1
new_p2 = p2
new_p3 = p3
new_p2 = line_cross_point(forward_edge, edge_opposite)
if point_dist_to_line(p1, new_p2, p0) > point_dist_to_line(
p1, new_p2, p3):
# across p0
if forward_edge[1] == 0:
forward_opposite = [1, 0, -p0[0]]
else:
forward_opposite = [
forward_edge[0], -1, p0[1] - forward_edge[0] * p0[0]
]
else:
# across p3
if forward_edge[1] == 0:
forward_opposite = [1, 0, -p3[0]]
else:
forward_opposite = [
forward_edge[0], -1, p3[1] - forward_edge[0] * p3[0]
]
new_p0 = line_cross_point(forward_opposite, edge)
new_p3 = line_cross_point(forward_opposite, edge_opposite)
fitted_parallelograms.append(
[new_p0, new_p1, new_p2, new_p3, new_p0])
# or move backward edge
new_p0 = p0
new_p1 = p1
new_p2 = p2
new_p3 = p3
new_p3 = line_cross_point(backward_edge, edge_opposite)
if point_dist_to_line(p0, p3, p1) > point_dist_to_line(p0, p3, p2):
# across p1
if backward_edge[1] == 0:
backward_opposite = [1, 0, -p1[0]]
else:
backward_opposite = [
backward_edge[0], -1, p1[1] - backward_edge[0] * p1[0]
]
else:
# across p2
if backward_edge[1] == 0:
backward_opposite = [1, 0, -p2[0]]
else:
backward_opposite = [
backward_edge[0], -1, p2[1] - backward_edge[0] * p2[0]
]
new_p1 = line_cross_point(backward_opposite, edge)
new_p2 = line_cross_point(backward_opposite, edge_opposite)
fitted_parallelograms.append(
[new_p0, new_p1, new_p2, new_p3, new_p0])
areas = [Polygon(t).area for t in fitted_parallelograms]
parallelogram = np.array(fitted_parallelograms[np.argmin(areas)][:-1],
dtype=np.float32)
# sort thie polygon
parallelogram_coord_sum = np.sum(parallelogram, axis=1)
min_coord_idx = np.argmin(parallelogram_coord_sum)
parallelogram = parallelogram[[
min_coord_idx, (min_coord_idx + 1) % 4, (min_coord_idx + 2) % 4,
(min_coord_idx + 3) % 4
]]
rectange = rectangle_from_parallelogram(parallelogram)
rectange, rotate_angle = sort_rectangle(rectange)
p0_rect, p1_rect, p2_rect, p3_rect = rectange
gen_geo_map.gen_geo_map(geo_map, xy_in_poly, rectange, rotate_angle)
return score_map, geo_map, training_mask
def generate_rbox(im_size, polys, tags, min_text_size=10):
"""
Generate rbox.
"""
h, w = im_size
poly_mask = np.zeros((h, w), dtype=np.uint8)
score_map = np.zeros((h, w), dtype=np.uint8)
geo_map = np.zeros((h, w, 5), dtype=np.float32)
# mask used during traning, to ignore some hard areas
training_mask = np.ones((h, w), dtype=np.uint8)
for poly_idx, poly_tag in enumerate(zip(polys, tags)):
poly = poly_tag[0]
tag = poly_tag[1]
r = [None, None, None, None]
for i in range(4):
r[i] = min(np.linalg.norm(poly[i] - poly[(i + 1) % 4]),
np.linalg.norm(poly[i] - poly[(i - 1) % 4]))
# score map
shrinked_poly = shrink_poly(poly.copy(),
r).astype(np.int32)[np.newaxis, :, :]
cv2.fillPoly(score_map, shrinked_poly, 1)
cv2.fillPoly(poly_mask, shrinked_poly, poly_idx + 1)
# if the poly is too small, then ignore it during training
poly_h = min(np.linalg.norm(poly[0] - poly[3]),
np.linalg.norm(poly[1] - poly[2]))
poly_w = min(np.linalg.norm(poly[0] - poly[1]),
np.linalg.norm(poly[2] - poly[3]))
if min(poly_h, poly_w) < min_text_size:
cv2.fillPoly(training_mask,
poly.astype(np.int32)[np.newaxis, :, :], 0)
if tag:
cv2.fillPoly(training_mask,
poly.astype(np.int32)[np.newaxis, :, :], 0)
xy_in_poly = np.argwhere(poly_mask == (poly_idx + 1))
# if geometry == 'RBOX':
# Generate a parallelogram for each pair of vertices.
fitted_parallelograms = []
for i in range(4):
p0 = poly[i]
p1 = poly[(i + 1) % 4]
p2 = poly[(i + 2) % 4]
p3 = poly[(i + 3) % 4]
edge = fit_line([p0[0], p1[0]], [p0[1], p1[1]])
backward_edge = fit_line([p0[0], p3[0]], [p0[1], p3[1]])
forward_edge = fit_line([p1[0], p2[0]], [p1[1], p2[1]])
if point_dist_to_line(p0, p1, p2) > point_dist_to_line(p0, p1, p3):
# Pass through p2
if edge[1] == 0:
edge_opposite = [1, 0, -p2[0]]
else:
edge_opposite = [edge[0], -1, p2[1] - edge[0] * p2[0]]
else:
# Pass through p3
if edge[1] == 0:
edge_opposite = [1, 0, -p3[0]]
else:
edge_opposite = [edge[0], -1, p3[1] - edge[0] * p3[0]]
# Move forward edge
new_p0 = p0
new_p1 = p1
new_p2 = p2
new_p3 = p3
new_p2 = line_cross_point(forward_edge, edge_opposite)
if point_dist_to_line(p1, new_p2, p0) > point_dist_to_line(
p1, new_p2, p3):
# Pass through p0
if forward_edge[1] == 0:
forward_opposite = [1, 0, -p0[0]]
else:
forward_opposite = [
forward_edge[0], -1, p0[1] - forward_edge[0] * p0[0]
]
else:
# Pass through p3
if forward_edge[1] == 0:
forward_opposite = [1, 0, -p3[0]]
else:
forward_opposite = [
forward_edge[0], -1, p3[1] - forward_edge[0] * p3[0]
]
new_p0 = line_cross_point(forward_opposite, edge)
new_p3 = line_cross_point(forward_opposite, edge_opposite)
fitted_parallelograms.append(
[new_p0, new_p1, new_p2, new_p3, new_p0])
# or move backward edge
new_p0 = p0
new_p1 = p1
new_p2 = p2
new_p3 = p3
new_p3 = line_cross_point(backward_edge, edge_opposite)
if point_dist_to_line(p0, p3, p1) > point_dist_to_line(p0, p3, p2):
# Pass through p1
if backward_edge[1] == 0:
backward_opposite = [1, 0, -p1[0]]
else:
backward_opposite = [
backward_edge[0], -1, p1[1] - backward_edge[0] * p1[0]
]
else:
# Pass through p2
if backward_edge[1] == 0:
backward_opposite = [1, 0, -p2[0]]
else:
backward_opposite = [
backward_edge[0], -1, p2[1] - backward_edge[0] * p2[0]
]
new_p1 = line_cross_point(backward_opposite, edge)
new_p2 = line_cross_point(backward_opposite, edge_opposite)
fitted_parallelograms.append(
[new_p0, new_p1, new_p2, new_p3, new_p0])
areas = [Polygon(t).area for t in fitted_parallelograms]
parallelogram = np.array(fitted_parallelograms[np.argmin(areas)][:-1],
dtype=np.float32)
# sort the polygon
parallelogram_coord_sum = np.sum(parallelogram, axis=1)
min_coord_idx = np.argmin(parallelogram_coord_sum)
parallelogram = parallelogram[[min_coord_idx, \
(min_coord_idx + 1) % 4, \
(min_coord_idx + 2) % 4, \
(min_coord_idx + 3) % 4]]
rectange = rectangle_from_parallelogram(parallelogram)
rectange, rotate_angle = sort_rectangle(rectange)
p0_rect, p1_rect, p2_rect, p3_rect = rectange
# for y, x in xy_in_poly:
# point = np.array([x, y], dtype=np.float32)
# # top
# geo_map[y, x, 0] = point_dist_to_line(p0_rect, p1_rect, point)
# # right
# geo_map[y, x, 1] = point_dist_to_line(p1_rect, p2_rect, point)
# # down
# geo_map[y, x, 2] = point_dist_to_line(p2_rect, p3_rect, point)
# # left
# geo_map[y, x, 3] = point_dist_to_line(p3_rect, p0_rect, point)
# # geo_map[y, x, 0] = abs(point[1] - p1_rect[1])
# # geo_map[y, x, 1] = abs(point[0] - p2_rect[0])
# # geo_map[y, x, 2] = abs(point[1] - p3_rect[1])
# # geo_map[y, x, 3] = abs(point[0] - p0_rect[0])
# # angle
# geo_map[y, x, 4] = rotate_angle
gen_geo_map.gen_geo_map(geo_map, xy_in_poly, rectange,
rotate_angle) ## 用cython编写预处理，实现加速
return score_map, geo_map, training_mask
def generate_rbox(FLAGS, im_size, polys, tags):
h, w = im_size
shrinked_poly_mask = np.zeros((h, w), dtype=np.uint8)
orig_poly_mask = np.zeros((h, w), dtype=np.uint8)
score_map = np.zeros((h, w), dtype=np.uint8)
geo_map = np.zeros((h, w, 5), dtype=np.float32)
# mask used during traning, to ignore some hard areas
overly_small_text_region_training_mask = np.ones((h, w), dtype=np.uint8)
for poly_idx, poly_data in enumerate(zip(polys, tags)):
poly = poly_data[0]
tag = poly_data[1]
r = [None, None, None, None]
for i in range(4):
r[i] = min(np.linalg.norm(poly[i] - poly[(i + 1) % 4]),
np.linalg.norm(poly[i] - poly[(i - 1) % 4]))
# score map
shrinked_poly = shrink_poly(poly.copy(),
r).astype(np.int32)[np.newaxis, :, :]
cv2.fillPoly(score_map, shrinked_poly, 1)
cv2.fillPoly(shrinked_poly_mask, shrinked_poly, poly_idx + 1)
cv2.fillPoly(orig_poly_mask,
poly.astype(np.int32)[np.newaxis, :, :], 1)
# if the poly is too small, then ignore it during training
poly_h = min(np.linalg.norm(poly[0] - poly[3]),
np.linalg.norm(poly[1] - poly[2]))
poly_w = min(np.linalg.norm(poly[0] - poly[1]),
np.linalg.norm(poly[2] - poly[3]))
if min(poly_h, poly_w) < FLAGS.min_text_size:
cv2.fillPoly(overly_small_text_region_training_mask,
poly.astype(np.int32)[np.newaxis, :, :], 0)
if tag:
cv2.fillPoly(overly_small_text_region_training_mask,
poly.astype(np.int32)[np.newaxis, :, :], 0)
xy_in_poly = np.argwhere(shrinked_poly_mask == (poly_idx + 1))
# if geometry == 'RBOX':
# generate a parallelogram for any combination of two vertices
fitted_parallelograms = []
for i in range(4):
p0 = poly[i]
p1 = poly[(i + 1) % 4]
p2 = poly[(i + 2) % 4]
p3 = poly[(i + 3) % 4]
edge = fit_line([p0[0], p1[0]], [p0[1], p1[1]])
backward_edge = fit_line([p0[0], p3[0]], [p0[1], p3[1]])
forward_edge = fit_line([p1[0], p2[0]], [p1[1], p2[1]])
if point_dist_to_line(p0, p1, p2) > point_dist_to_line(p0, p1, p3):
# parallel lines through p2
if edge[1] == 0:
edge_opposite = [1, 0, -p2[0]]
else:
edge_opposite = [edge[0], -1, p2[1] - edge[0] * p2[0]]
else:
# after p3
if edge[1] == 0:
edge_opposite = [1, 0, -p3[0]]
else:
edge_opposite = [edge[0], -1, p3[1] - edge[0] * p3[0]]
# move forward edge
new_p0 = p0
new_p1 = p1
new_p2 = p2
new_p3 = p3
new_p2 = line_cross_point(FLAGS, forward_edge, edge_opposite)
if point_dist_to_line(p1, new_p2, p0) > point_dist_to_line(
p1, new_p2, p3):
# across p0
if forward_edge[1] == 0:
forward_opposite = [1, 0, -p0[0]]
else:
forward_opposite = [
forward_edge[0], -1, p0[1] - forward_edge[0] * p0[0]
]
else:
# across p3
if forward_edge[1] == 0:
forward_opposite = [1, 0, -p3[0]]
else:
forward_opposite = [
forward_edge[0], -1, p3[1] - forward_edge[0] * p3[0]
]
new_p0 = line_cross_point(FLAGS, forward_opposite, edge)
new_p3 = line_cross_point(FLAGS, forward_opposite, edge_opposite)
fitted_parallelograms.append(
[new_p0, new_p1, new_p2, new_p3, new_p0])
# or move backward edge
new_p0 = p0
new_p1 = p1
new_p2 = p2
new_p3 = p3
new_p3 = line_cross_point(FLAGS, backward_edge, edge_opposite)
if point_dist_to_line(p0, p3, p1) > point_dist_to_line(p0, p3, p2):
# across p1
if backward_edge[1] == 0:
backward_opposite = [1, 0, -p1[0]]
else:
backward_opposite = [
backward_edge[0], -1, p1[1] - backward_edge[0] * p1[0]
]
else:
# across p2
if backward_edge[1] == 0:
backward_opposite = [1, 0, -p2[0]]
else:
backward_opposite = [
backward_edge[0], -1, p2[1] - backward_edge[0] * p2[0]
]
new_p1 = line_cross_point(FLAGS, backward_opposite, edge)
new_p2 = line_cross_point(FLAGS, backward_opposite, edge_opposite)
fitted_parallelograms.append(
[new_p0, new_p1, new_p2, new_p3, new_p0])
areas = [Polygon(t).area for t in fitted_parallelograms]
parallelogram = np.array(fitted_parallelograms[np.argmin(areas)][:-1],
dtype=np.float32)
# sort thie polygon
parallelogram_coord_sum = np.sum(parallelogram, axis=1)
min_coord_idx = np.argmin(parallelogram_coord_sum)
parallelogram = parallelogram[[
min_coord_idx, (min_coord_idx + 1) % 4, (min_coord_idx + 2) % 4,
(min_coord_idx + 3) % 4
]]
rectangle = rectangle_from_parallelogram(FLAGS, parallelogram)
rectangle, rotate_angle = sort_rectangle(rectangle)
#rectangle, rotate_angle, is_right = sort_rectangle(rectangle)
#if not is_right:
# cv2.fillPoly(score_map, ex_poly, 2)
# continue
p0_rect, p1_rect, p2_rect, p3_rect = rectangle
# for y, x in xy_in_poly:
# point = np.array([x, y], dtype=np.float32)
# # top
# geo_map[y, x, 0] = point_dist_to_line(p0_rect, p1_rect, point)
# # right
# geo_map[y, x, 1] = point_dist_to_line(p1_rect, p2_rect, point)
# # down
# geo_map[y, x, 2] = point_dist_to_line(p2_rect, p3_rect, point)
# # left
# geo_map[y, x, 3] = point_dist_to_line(p3_rect, p0_rect, point)
# # angle
# geo_map[y, x, 4] = rotate_angle
#geo_map = caclulate_geo_map(geo_map, xy_in_poly, rectangle, rotate_angle)
gen_geo_map.gen_geo_map(geo_map, xy_in_poly, rectangle, rotate_angle)
shrinked_poly_mask = (shrinked_poly_mask > 0).astype('uint8')
text_region_boundary_training_mask = 1 - (orig_poly_mask -
shrinked_poly_mask)
return score_map, geo_map, overly_small_text_region_training_mask, text_region_boundary_training_mask
