# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This code is refer from:
https://github.com/open-mmlab/mmocr/blob/main/mmocr/datasets/pipelines/textdet_targets/drrg_targets.py
"""
import cv2
import numpy as np
from lanms import merge_quadrangle_n9 as la_nms
from numpy.linalg import norm
class DRRGTargets(object):
def __init__(self,
orientation_thr=2.0,
resample_step=8.0,
num_min_comps=9,
num_max_comps=600,
min_width=8.0,
max_width=24.0,
center_region_shrink_ratio=0.3,
comp_shrink_ratio=1.0,
comp_w_h_ratio=0.3,
text_comp_nms_thr=0.25,
min_rand_half_height=8.0,
max_rand_half_height=24.0,
jitter_level=0.2,
**kwargs):
super().__init__()
self.orientation_thr = orientation_thr
self.resample_step = resample_step
self.num_max_comps = num_max_comps
self.num_min_comps = num_min_comps
self.min_width = min_width
self.max_width = max_width
self.center_region_shrink_ratio = center_region_shrink_ratio
self.comp_shrink_ratio = comp_shrink_ratio
self.comp_w_h_ratio = comp_w_h_ratio
self.text_comp_nms_thr = text_comp_nms_thr
self.min_rand_half_height = min_rand_half_height
self.max_rand_half_height = max_rand_half_height
self.jitter_level = jitter_level
self.eps = 1e-8
def vector_angle(self, vec1, vec2):
if vec1.ndim > 1:
unit_vec1 = vec1 / (norm(vec1, axis=-1) + self.eps).reshape((-1, 1))
else:
unit_vec1 = vec1 / (norm(vec1, axis=-1) + self.eps)
if vec2.ndim > 1:
unit_vec2 = vec2 / (norm(vec2, axis=-1) + self.eps).reshape((-1, 1))
else:
unit_vec2 = vec2 / (norm(vec2, axis=-1) + self.eps)
return np.arccos(
np.clip(
np.sum(unit_vec1 * unit_vec2, axis=-1), -1.0, 1.0))
def vector_slope(self, vec):
assert len(vec) == 2
return abs(vec[1] / (vec[0] + self.eps))
def vector_sin(self, vec):
assert len(vec) == 2
return vec[1] / (norm(vec) + self.eps)
def vector_cos(self, vec):
assert len(vec) == 2
return vec[0] / (norm(vec) + self.eps)
def find_head_tail(self, points, orientation_thr):
assert points.ndim == 2
assert points.shape[0] >= 4
assert points.shape[1] == 2
assert isinstance(orientation_thr, float)
if len(points) > 4:
pad_points = np.vstack([points, points[0]])
edge_vec = pad_points[1:] - pad_points[:-1]
theta_sum = []
adjacent_vec_theta = []
for i, edge_vec1 in enumerate(edge_vec):
adjacent_ind = [x % len(edge_vec) for x in [i - 1, i + 1]]
adjacent_edge_vec = edge_vec[adjacent_ind]
temp_theta_sum = np.sum(
self.vector_angle(edge_vec1, adjacent_edge_vec))
temp_adjacent_theta = self.vector_angle(adjacent_edge_vec[0],
adjacent_edge_vec[1])
theta_sum.append(temp_theta_sum)
adjacent_vec_theta.append(temp_adjacent_theta)
theta_sum_score = np.array(theta_sum) / np.pi
adjacent_theta_score = np.array(adjacent_vec_theta) / np.pi
poly_center = np.mean(points, axis=0)
edge_dist = np.maximum(
norm(
pad_points[1:] - poly_center, axis=-1),
norm(
pad_points[:-1] - poly_center, axis=-1))
dist_score = edge_dist / (np.max(edge_dist) + self.eps)
position_score = np.zeros(len(edge_vec))
score = 0.5 * theta_sum_score + 0.15 * adjacent_theta_score
score += 0.35 * dist_score
if len(points) % 2 == 0:
position_score[(len(score) // 2 - 1)] += 1
position_score[-1] += 1
score += 0.1 * position_score
pad_score = np.concatenate([score, score])
score_matrix = np.zeros((len(score), len(score) - 3))
x = np.arange(len(score) - 3) / float(len(score) - 4)
gaussian = 1. / (np.sqrt(2. * np.pi) * 0.5) * np.exp(-np.power(
(x - 0.5) / 0.5, 2.) / 2)
gaussian = gaussian / np.max(gaussian)
for i in range(len(score)):
score_matrix[i, :] = score[i] + pad_score[(i + 2):(i + len(
score) - 1)] * gaussian * 0.3
head_start, tail_increment = np.unravel_index(score_matrix.argmax(),
score_matrix.shape)
tail_start = (head_start + tail_increment + 2) % len(points)
head_end = (head_start + 1) % len(points)
tail_end = (tail_start + 1) % len(points)
if head_end > tail_end:
head_start, tail_start = tail_start, head_start
head_end, tail_end = tail_end, head_end
head_inds = [head_start, head_end]
tail_inds = [tail_start, tail_end]
else:
if self.vector_slope(points[1] - points[0]) + self.vector_slope(
points[3] - points[2]) < self.vector_slope(points[
2] - points[1]) + self.vector_slope(points[0] - points[
3]):
horizontal_edge_inds = [[0, 1], [2, 3]]
vertical_edge_inds = [[3, 0], [1, 2]]
else:
horizontal_edge_inds = [[3, 0], [1, 2]]
vertical_edge_inds = [[0, 1], [2, 3]]
vertical_len_sum = norm(points[vertical_edge_inds[0][0]] - points[
vertical_edge_inds[0][1]]) + norm(points[vertical_edge_inds[1][
0]] - points[vertical_edge_inds[1][1]])
horizontal_len_sum = norm(points[horizontal_edge_inds[0][
0]] - points[horizontal_edge_inds[0][1]]) + norm(points[
horizontal_edge_inds[1][0]] - points[horizontal_edge_inds[1]
[1]])
if vertical_len_sum > horizontal_len_sum * orientation_thr:
head_inds = horizontal_edge_inds[0]
tail_inds = horizontal_edge_inds[1]
else:
head_inds = vertical_edge_inds[0]
tail_inds = vertical_edge_inds[1]
return head_inds, tail_inds
def reorder_poly_edge(self, points):
assert points.ndim == 2
assert points.shape[0] >= 4
assert points.shape[1] == 2
head_inds, tail_inds = self.find_head_tail(points, self.orientation_thr)
head_edge, tail_edge = points[head_inds], points[tail_inds]
pad_points = np.vstack([points, points])
if tail_inds[1] < 1:
tail_inds[1] = len(points)
sideline1 = pad_points[head_inds[1]:tail_inds[1]]
sideline2 = pad_points[tail_inds[1]:(head_inds[1] + len(points))]
sideline_mean_shift = np.mean(
sideline1, axis=0) - np.mean(
sideline2, axis=0)
if sideline_mean_shift[1] > 0:
top_sideline, bot_sideline = sideline2, sideline1
else:
top_sideline, bot_sideline = sideline1, sideline2
return head_edge, tail_edge, top_sideline, bot_sideline
def cal_curve_length(self, line):
assert line.ndim == 2
assert len(line) >= 2
edges_length = np.sqrt((line[1:, 0] - line[:-1, 0])**2 + (line[
1:, 1] - line[:-1, 1])**2)
total_length = np.sum(edges_length)
return edges_length, total_length
def resample_line(self, line, n):
assert line.ndim == 2
assert line.shape[0] >= 2
assert line.shape[1] == 2
assert isinstance(n, int)
assert n > 2
edges_length, total_length = self.cal_curve_length(line)
t_org = np.insert(np.cumsum(edges_length), 0, 0)
unit_t = total_length / (n - 1)
t_equidistant = np.arange(1, n - 1, dtype=np.float32) * unit_t
edge_ind = 0
points = [line[0]]
for t in t_equidistant:
while edge_ind < len(edges_length) - 1 and t > t_org[edge_ind + 1]:
edge_ind += 1
t_l, t_r = t_org[edge_ind], t_org[edge_ind + 1]
weight = np.array(
[t_r - t, t - t_l], dtype=np.float32) / (t_r - t_l + self.eps)
p_coords = np.dot(weight, line[[edge_ind, edge_ind + 1]])
points.append(p_coords)
points.append(line[-1])
resampled_line = np.vstack(points)
return resampled_line
def resample_sidelines(self, sideline1, sideline2, resample_step):
assert sideline1.ndim == sideline2.ndim == 2
assert sideline1.shape[1] == sideline2.shape[1] == 2
assert sideline1.shape[0] >= 2
assert sideline2.shape[0] >= 2
assert isinstance(resample_step, float)
_, length1 = self.cal_curve_length(sideline1)
_, length2 = self.cal_curve_length(sideline2)
avg_length = (length1 + length2) / 2
resample_point_num = max(int(float(avg_length) / resample_step) + 1, 3)
resampled_line1 = self.resample_line(sideline1, resample_point_num)
resampled_line2 = self.resample_line(sideline2, resample_point_num)
return resampled_line1, resampled_line2
def dist_point2line(self, point, line):
assert isinstance(line, tuple)
point1, point2 = line
d = abs(np.cross(point2 - point1, point - point1)) / (
norm(point2 - point1) + 1e-8)
return d
def draw_center_region_maps(self, top_line, bot_line, center_line,
center_region_mask, top_height_map,
bot_height_map, sin_map, cos_map,
region_shrink_ratio):
assert top_line.shape == bot_line.shape == center_line.shape
assert (center_region_mask.shape == top_height_map.shape ==
bot_height_map.shape == sin_map.shape == cos_map.shape)
assert isinstance(region_shrink_ratio, float)
h, w = center_region_mask.shape
for i in range(0, len(center_line) - 1):
top_mid_point = (top_line[i] + top_line[i + 1]) / 2
bot_mid_point = (bot_line[i] + bot_line[i + 1]) / 2
sin_theta = self.vector_sin(top_mid_point - bot_mid_point)
cos_theta = self.vector_cos(top_mid_point - bot_mid_point)
tl = center_line[i] + (top_line[i] - center_line[i]
) * region_shrink_ratio
tr = center_line[i + 1] + (top_line[i + 1] - center_line[i + 1]
) * region_shrink_ratio
br = center_line[i + 1] + (bot_line[i + 1] - center_line[i + 1]
) * region_shrink_ratio
bl = center_line[i] + (bot_line[i] - center_line[i]
) * region_shrink_ratio
current_center_box = np.vstack([tl, tr, br, bl]).astype(np.int32)
cv2.fillPoly(center_region_mask, [current_center_box], color=1)
cv2.fillPoly(sin_map, [current_center_box], color=sin_theta)
cv2.fillPoly(cos_map, [current_center_box], color=cos_theta)
current_center_box[:, 0] = np.clip(current_center_box[:, 0], 0,
w - 1)
current_center_box[:, 1] = np.clip(current_center_box[:, 1], 0,
h - 1)
min_coord = np.min(current_center_box, axis=0).astype(np.int32)
max_coord = np.max(current_center_box, axis=0).astype(np.int32)
current_center_box = current_center_box - min_coord
box_sz = (max_coord - min_coord + 1)
center_box_mask = np.zeros((box_sz[1], box_sz[0]), dtype=np.uint8)
cv2.fillPoly(center_box_mask, [current_center_box], color=1)
inds = np.argwhere(center_box_mask > 0)
inds = inds + (min_coord[1], min_coord[0])
inds_xy = np.fliplr(inds)
top_height_map[(inds[:, 0], inds[:, 1])] = self.dist_point2line(
inds_xy, (top_line[i], top_line[i + 1]))
bot_height_map[(inds[:, 0], inds[:, 1])] = self.dist_point2line(
inds_xy, (bot_line[i], bot_line[i + 1]))
def generate_center_mask_attrib_maps(self, img_size, text_polys):
assert isinstance(img_size, tuple)
h, w = img_size
center_lines = []
center_region_mask = np.zeros((h, w), np.uint8)
top_height_map = np.zeros((h, w), dtype=np.float32)
bot_height_map = np.zeros((h, w), dtype=np.float32)
sin_map = np.zeros((h, w), dtype=np.float32)
cos_map = np.zeros((h, w), dtype=np.float32)
for poly in text_polys:
polygon_points = poly
_, _, top_line, bot_line = self.reorder_poly_edge(polygon_points)
resampled_top_line, resampled_bot_line = self.resample_sidelines(
top_line, bot_line, self.resample_step)
resampled_bot_line = resampled_bot_line[::-1]
center_line = (resampled_top_line + resampled_bot_line) / 2
if self.vector_slope(center_line[-1] - center_line[0]) > 2:
if (center_line[-1] - center_line[0])[1] < 0:
center_line = center_line[::-1]
resampled_top_line = resampled_top_line[::-1]
resampled_bot_line = resampled_bot_line[::-1]
else:
if (center_line[-1] - center_line[0])[0] < 0:
center_line = center_line[::-1]
resampled_top_line = resampled_top_line[::-1]
resampled_bot_line = resampled_bot_line[::-1]
line_head_shrink_len = np.clip(
(norm(top_line[0] - bot_line[0]) * self.comp_w_h_ratio),
self.min_width, self.max_width) / 2
line_tail_shrink_len = np.clip(
(norm(top_line[-1] - bot_line[-1]) * self.comp_w_h_ratio),
self.min_width, self.max_width) / 2
num_head_shrink = int(line_head_shrink_len // self.resample_step)
num_tail_shrink = int(line_tail_shrink_len // self.resample_step)
if len(center_line) > num_head_shrink + num_tail_shrink + 2:
center_line = center_line[num_head_shrink:len(center_line) -
num_tail_shrink]
resampled_top_line = resampled_top_line[num_head_shrink:len(
resampled_top_line) - num_tail_shrink]
resampled_bot_line = resampled_bot_line[num_head_shrink:len(
resampled_bot_line) - num_tail_shrink]
center_lines.append(center_line.astype(np.int32))
self.draw_center_region_maps(
resampled_top_line, resampled_bot_line, center_line,
center_region_mask, top_height_map, bot_height_map, sin_map,
cos_map, self.center_region_shrink_ratio)
return (center_lines, center_region_mask, top_height_map,
bot_height_map, sin_map, cos_map)
def generate_rand_comp_attribs(self, num_rand_comps, center_sample_mask):
assert isinstance(num_rand_comps, int)
assert num_rand_comps > 0
assert center_sample_mask.ndim == 2
h, w = center_sample_mask.shape
max_rand_half_height = self.max_rand_half_height
min_rand_half_height = self.min_rand_half_height
max_rand_height = max_rand_half_height * 2
max_rand_width = np.clip(max_rand_height * self.comp_w_h_ratio,
self.min_width, self.max_width)
margin = int(
np.sqrt((max_rand_height / 2)**2 + (max_rand_width / 2)**2)) + 1
if 2 * margin + 1 > min(h, w):
assert min(h, w) > (np.sqrt(2) * (self.min_width + 1))
max_rand_half_height = max(min(h, w) / 4, self.min_width / 2 + 1)
min_rand_half_height = max(max_rand_half_height / 4,
self.min_width / 2)
max_rand_height = max_rand_half_height * 2
max_rand_width = np.clip(max_rand_height * self.comp_w_h_ratio,
self.min_width, self.max_width)
margin = int(
np.sqrt((max_rand_height / 2)**2 + (max_rand_width / 2)**2)) + 1
inner_center_sample_mask = np.zeros_like(center_sample_mask)
inner_center_sample_mask[margin:h - margin, margin:w - margin] = \
center_sample_mask[margin:h - margin, margin:w - margin]
kernel_size = int(np.clip(max_rand_half_height, 7, 21))
inner_center_sample_mask = cv2.erode(
inner_center_sample_mask,
np.ones((kernel_size, kernel_size), np.uint8))
center_candidates = np.argwhere(inner_center_sample_mask > 0)
num_center_candidates = len(center_candidates)
sample_inds = np.random.choice(num_center_candidates, num_rand_comps)
rand_centers = center_candidates[sample_inds]
rand_top_height = np.random.randint(
min_rand_half_height,
max_rand_half_height,
size=(len(rand_centers), 1))
rand_bot_height = np.random.randint(
min_rand_half_height,
max_rand_half_height,
size=(len(rand_centers), 1))
rand_cos = 2 * np.random.random(size=(len(rand_centers), 1)) - 1
rand_sin = 2 * np.random.random(size=(len(rand_centers), 1)) - 1
scale = np.sqrt(1.0 / (rand_cos**2 + rand_sin**2 + 1e-8))
rand_cos = rand_cos * scale
rand_sin = rand_sin * scale
height = (rand_top_height + rand_bot_height)
width = np.clip(height * self.comp_w_h_ratio, self.min_width,
self.max_width)
rand_comp_attribs = np.hstack([
rand_centers[:, ::-1], height, width, rand_cos, rand_sin,
np.zeros_like(rand_sin)
]).astype(np.float32)
return rand_comp_attribs
def jitter_comp_attribs(self, comp_attribs, jitter_level):
"""Jitter text components attributes.
Args:
comp_attribs (ndarray): The text component attributes.
jitter_level (float): The jitter level of text components
attributes.
Returns:
jittered_comp_attribs (ndarray): The jittered text component
attributes (x, y, h, w, cos, sin, comp_label).
"""
assert comp_attribs.shape[1] == 7
assert comp_attribs.shape[0] > 0
assert isinstance(jitter_level, float)
x = comp_attribs[:, 0].reshape((-1, 1))
y = comp_attribs[:, 1].reshape((-1, 1))
h = comp_attribs[:, 2].reshape((-1, 1))
w = comp_attribs[:, 3].reshape((-1, 1))
cos = comp_attribs[:, 4].reshape((-1, 1))
sin = comp_attribs[:, 5].reshape((-1, 1))
comp_labels = comp_attribs[:, 6].reshape((-1, 1))
x += (np.random.random(size=(len(comp_attribs), 1)) - 0.5) * (
h * np.abs(cos) + w * np.abs(sin)) * jitter_level
y += (np.random.random(size=(len(comp_attribs), 1)) - 0.5) * (
h * np.abs(sin) + w * np.abs(cos)) * jitter_level
h += (np.random.random(size=(len(comp_attribs), 1)) - 0.5
) * h * jitter_level
w += (np.random.random(size=(len(comp_attribs), 1)) - 0.5
) * w * jitter_level
cos += (np.random.random(size=(len(comp_attribs), 1)) - 0.5
) * 2 * jitter_level
sin += (np.random.random(size=(len(comp_attribs), 1)) - 0.5
) * 2 * jitter_level
scale = np.sqrt(1.0 / (cos**2 + sin**2 + 1e-8))
cos = cos * scale
sin = sin * scale
jittered_comp_attribs = np.hstack([x, y, h, w, cos, sin, comp_labels])
return jittered_comp_attribs
def generate_comp_attribs(self, center_lines, text_mask, center_region_mask,
top_height_map, bot_height_map, sin_map, cos_map):
"""Generate text component attributes.
Args:
center_lines (list[ndarray]): The list of text center lines .
text_mask (ndarray): The text region mask.
center_region_mask (ndarray): The text center region mask.
top_height_map (ndarray): The map on which the distance from points
to top side lines will be drawn for each pixel in text center
regions.
bot_height_map (ndarray): The map on which the distance from points
to bottom side lines will be drawn for each pixel in text
center regions.
sin_map (ndarray): The sin(theta) map where theta is the angle
between vector (top point - bottom point) and vector (1, 0).
cos_map (ndarray): The cos(theta) map where theta is the angle
between vector (top point - bottom point) and vector (1, 0).
Returns:
pad_comp_attribs (ndarray): The padded text component attributes
of a fixed size.
"""
assert isinstance(center_lines, list)
assert (
text_mask.shape == center_region_mask.shape == top_height_map.shape
== bot_height_map.shape == sin_map.shape == cos_map.shape)
center_lines_mask = np.zeros_like(center_region_mask)
cv2.polylines(center_lines_mask, center_lines, 0, 1, 1)
center_lines_mask = center_lines_mask * center_region_mask
comp_centers = np.argwhere(center_lines_mask > 0)
y = comp_centers[:, 0]
x = comp_centers[:, 1]
top_height = top_height_map[y, x].reshape(
(-1, 1)) * self.comp_shrink_ratio
bot_height = bot_height_map[y, x].reshape(
(-1, 1)) * self.comp_shrink_ratio
sin = sin_map[y, x].reshape((-1, 1))
cos = cos_map[y, x].reshape((-1, 1))
top_mid_points = comp_centers + np.hstack(
[top_height * sin, top_height * cos])
bot_mid_points = comp_centers - np.hstack(
[bot_height * sin, bot_height * cos])
width = (top_height + bot_height) * self.comp_w_h_ratio
width = np.clip(width, self.min_width, self.max_width)
r = width / 2
tl = top_mid_points[:, ::-1] - np.hstack([-r * sin, r * cos])
tr = top_mid_points[:, ::-1] + np.hstack([-r * sin, r * cos])
br = bot_mid_points[:, ::-1] + np.hstack([-r * sin, r * cos])
bl = bot_mid_points[:, ::-1] - np.hstack([-r * sin, r * cos])
text_comps = np.hstack([tl, tr, br, bl]).astype(np.float32)
score = np.ones((text_comps.shape[0], 1), dtype=np.float32)
text_comps = np.hstack([text_comps, score])
text_comps = la_nms(text_comps, self.text_comp_nms_thr)
if text_comps.shape[0] >= 1:
img_h, img_w = center_region_mask.shape
text_comps[:, 0:8:2] = np.clip(text_comps[:, 0:8:2], 0, img_w - 1)
text_comps[:, 1:8:2] = np.clip(text_comps[:, 1:8:2], 0, img_h - 1)
comp_centers = np.mean(
text_comps[:, 0:8].reshape((-1, 4, 2)), axis=1).astype(np.int32)
x = comp_centers[:, 0]
y = comp_centers[:, 1]
height = (top_height_map[y, x] + bot_height_map[y, x]).reshape(
(-1, 1))
width = np.clip(height * self.comp_w_h_ratio, self.min_width,
self.max_width)
cos = cos_map[y, x].reshape((-1, 1))
sin = sin_map[y, x].reshape((-1, 1))
_, comp_label_mask = cv2.connectedComponents(
center_region_mask, connectivity=8)
comp_labels = comp_label_mask[y, x].reshape(
(-1, 1)).astype(np.float32)
x = x.reshape((-1, 1)).astype(np.float32)
y = y.reshape((-1, 1)).astype(np.float32)
comp_attribs = np.hstack(
[x, y, height, width, cos, sin, comp_labels])
comp_attribs = self.jitter_comp_attribs(comp_attribs,
self.jitter_level)
if comp_attribs.shape[0] < self.num_min_comps:
num_rand_comps = self.num_min_comps - comp_attribs.shape[0]
rand_comp_attribs = self.generate_rand_comp_attribs(
num_rand_comps, 1 - text_mask)
comp_attribs = np.vstack([comp_attribs, rand_comp_attribs])
else:
comp_attribs = self.generate_rand_comp_attribs(self.num_min_comps,
1 - text_mask)
num_comps = (np.ones(
(comp_attribs.shape[0], 1),
dtype=np.float32) * comp_attribs.shape[0])
comp_attribs = np.hstack([num_comps, comp_attribs])
if comp_attribs.shape[0] > self.num_max_comps:
comp_attribs = comp_attribs[:self.num_max_comps, :]
comp_attribs[:, 0] = self.num_max_comps
pad_comp_attribs = np.zeros(
(self.num_max_comps, comp_attribs.shape[1]), dtype=np.float32)
pad_comp_attribs[:comp_attribs.shape[0], :] = comp_attribs
return pad_comp_attribs
def generate_text_region_mask(self, img_size, text_polys):
"""Generate text center region mask and geometry attribute maps.
Args:
img_size (tuple): The image size (height, width).
text_polys (list[list[ndarray]]): The list of text polygons.
Returns:
text_region_mask (ndarray): The text region mask.
"""
assert isinstance(img_size, tuple)
h, w = img_size
text_region_mask = np.zeros((h, w), dtype=np.uint8)
for poly in text_polys:
polygon = np.array(poly, dtype=np.int32).reshape((1, -1, 2))
cv2.fillPoly(text_region_mask, polygon, 1)
return text_region_mask
def generate_effective_mask(self, mask_size: tuple, polygons_ignore):
"""Generate effective mask by setting the ineffective regions to 0 and
effective regions to 1.
Args:
mask_size (tuple): The mask size.
polygons_ignore (list[[ndarray]]: The list of ignored text
polygons.
Returns:
mask (ndarray): The effective mask of (height, width).
"""
mask = np.ones(mask_size, dtype=np.uint8)
for poly in polygons_ignore:
instance = poly.astype(np.int32).reshape(1, -1, 2)
cv2.fillPoly(mask, instance, 0)
return mask
def generate_targets(self, data):
"""Generate the gt targets for DRRG.
Args:
data (dict): The input result dictionary.
Returns:
data (dict): The output result dictionary.
"""
assert isinstance(data, dict)
image = data['image']
polygons = data['polys']
ignore_tags = data['ignore_tags']
h, w, _ = image.shape
polygon_masks = []
polygon_masks_ignore = []
for tag, polygon in zip(ignore_tags, polygons):
if tag is True:
polygon_masks_ignore.append(polygon)
else:
polygon_masks.append(polygon)
gt_text_mask = self.generate_text_region_mask((h, w), polygon_masks)
gt_mask = self.generate_effective_mask((h, w), polygon_masks_ignore)
(center_lines, gt_center_region_mask, gt_top_height_map,
gt_bot_height_map, gt_sin_map,
gt_cos_map) = self.generate_center_mask_attrib_maps((h, w),
polygon_masks)
gt_comp_attribs = self.generate_comp_attribs(
center_lines, gt_text_mask, gt_center_region_mask,
gt_top_height_map, gt_bot_height_map, gt_sin_map, gt_cos_map)
mapping = {
'gt_text_mask': gt_text_mask,
'gt_center_region_mask': gt_center_region_mask,
'gt_mask': gt_mask,
'gt_top_height_map': gt_top_height_map,
'gt_bot_height_map': gt_bot_height_map,
'gt_sin_map': gt_sin_map,
'gt_cos_map': gt_cos_map
}
data.update(mapping)
data['gt_comp_attribs'] = gt_comp_attribs
return data
def __call__(self, data):
data = self.generate_targets(data)
return data