# 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/fcenet_targets.py
"""
import cv2
import numpy as np
from numpy.fft import fft
from numpy.linalg import norm
import sys
def vector_slope(vec):
assert len(vec) == 2
return abs(vec[1] / (vec[0] + 1e-8))
class FCENetTargets:
"""Generate the ground truth targets of FCENet: Fourier Contour Embedding
for Arbitrary-Shaped Text Detection.
[https://arxiv.org/abs/2104.10442]
Args:
fourier_degree (int): The maximum Fourier transform degree k.
resample_step (float): The step size for resampling the text center
line (TCL). It's better not to exceed half of the minimum width.
center_region_shrink_ratio (float): The shrink ratio of text center
region.
level_size_divisors (tuple(int)): The downsample ratio on each level.
level_proportion_range (tuple(tuple(int))): The range of text sizes
assigned to each level.
"""
def __init__(self,
fourier_degree=5,
resample_step=4.0,
center_region_shrink_ratio=0.3,
level_size_divisors=(8, 16, 32),
level_proportion_range=((0, 0.25), (0.2, 0.65), (0.55, 1.0)),
orientation_thr=2.0,
**kwargs):
super().__init__()
assert isinstance(level_size_divisors, tuple)
assert isinstance(level_proportion_range, tuple)
assert len(level_size_divisors) == len(level_proportion_range)
self.fourier_degree = fourier_degree
self.resample_step = resample_step
self.center_region_shrink_ratio = center_region_shrink_ratio
self.level_size_divisors = level_size_divisors
self.level_proportion_range = level_proportion_range
self.orientation_thr = orientation_thr
def vector_angle(self, vec1, vec2):
if vec1.ndim > 1:
unit_vec1 = vec1 / (norm(vec1, axis=-1) + 1e-8).reshape((-1, 1))
else:
unit_vec1 = vec1 / (norm(vec1, axis=-1) + 1e-8)
if vec2.ndim > 1:
unit_vec2 = vec2 / (norm(vec2, axis=-1) + 1e-8).reshape((-1, 1))
else:
unit_vec2 = vec2 / (norm(vec2, axis=-1) + 1e-8)
return np.arccos(
np.clip(
np.sum(unit_vec1 * unit_vec2, axis=-1), -1.0, 1.0))
def resample_line(self, line, n):
"""Resample n points on a line.
Args:
line (ndarray): The points composing a line.
n (int): The resampled points number.
Returns:
resampled_line (ndarray): The points composing the resampled line.
"""
assert line.ndim == 2
assert line.shape[0] >= 2
assert line.shape[1] == 2
assert isinstance(n, int)
assert n > 0
length_list = [
norm(line[i + 1] - line[i]) for i in range(len(line) - 1)
]
total_length = sum(length_list)
length_cumsum = np.cumsum([0.0] + length_list)
delta_length = total_length / (float(n) + 1e-8)
current_edge_ind = 0
resampled_line = [line[0]]
for i in range(1, n):
current_line_len = i * delta_length
while current_edge_ind + 1 < len(length_cumsum) and current_line_len >= length_cumsum[current_edge_ind + 1]:
current_edge_ind += 1
current_edge_end_shift = current_line_len - length_cumsum[
current_edge_ind]
if current_edge_ind >= len(length_list):
break
end_shift_ratio = current_edge_end_shift / length_list[
current_edge_ind]
current_point = line[current_edge_ind] + (line[current_edge_ind + 1]
- line[current_edge_ind]
) * end_shift_ratio
resampled_line.append(current_point)
resampled_line.append(line[-1])
resampled_line = np.array(resampled_line)
return resampled_line
def reorder_poly_edge(self, points):
"""Get the respective points composing head edge, tail edge, top
sideline and bottom sideline.
Args:
points (ndarray): The points composing a text polygon.
Returns:
head_edge (ndarray): The two points composing the head edge of text
polygon.
tail_edge (ndarray): The two points composing the tail edge of text
polygon.
top_sideline (ndarray): The points composing top curved sideline of
text polygon.
bot_sideline (ndarray): The points composing bottom curved sideline
of text polygon.
"""
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 find_head_tail(self, points, orientation_thr):
"""Find the head edge and tail edge of a text polygon.
Args:
points (ndarray): The points composing a text polygon.
orientation_thr (float): The threshold for distinguishing between
head edge and tail edge among the horizontal and vertical edges
of a quadrangle.
Returns:
head_inds (list): The indexes of two points composing head edge.
tail_inds (list): The indexes of two points composing tail edge.
"""
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)
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 vector_slope(points[1] - points[0]) + vector_slope(
points[3] - points[2]) < vector_slope(points[
2] - points[1]) + 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 resample_sidelines(self, sideline1, sideline2, resample_step):
"""Resample two sidelines to be of the same points number according to
step size.
Args:
sideline1 (ndarray): The points composing a sideline of a text
polygon.
sideline2 (ndarray): The points composing another sideline of a
text polygon.
resample_step (float): The resampled step size.
Returns:
resampled_line1 (ndarray): The resampled line 1.
resampled_line2 (ndarray): The resampled line 2.
"""
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 = sum([
norm(sideline1[i + 1] - sideline1[i])
for i in range(len(sideline1) - 1)
])
length2 = sum([
norm(sideline2[i + 1] - sideline2[i])
for i in range(len(sideline2) - 1)
])
total_length = (length1 + length2) / 2
resample_point_num = max(int(float(total_length) / resample_step), 1)
resampled_line1 = self.resample_line(sideline1, resample_point_num)
resampled_line2 = self.resample_line(sideline2, resample_point_num)
return resampled_line1, resampled_line2
def generate_center_region_mask(self, img_size, text_polys):
"""Generate text center region mask.
Args:
img_size (tuple): The image size of (height, width).
text_polys (list[list[ndarray]]): The list of text polygons.
Returns:
center_region_mask (ndarray): The text center region mask.
"""
assert isinstance(img_size, tuple)
# assert check_argument.is_2dlist(text_polys)
h, w = img_size
center_region_mask = np.zeros((h, w), np.uint8)
center_region_boxes = []
for poly in text_polys:
# assert len(poly) == 1
polygon_points = poly.reshape(-1, 2)
_, _, 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]
if len(resampled_top_line) != len(resampled_bot_line):
continue
center_line = (resampled_top_line + resampled_bot_line) / 2
line_head_shrink_len = norm(resampled_top_line[0] -
resampled_bot_line[0]) / 4.0
line_tail_shrink_len = norm(resampled_top_line[-1] -
resampled_bot_line[-1]) / 4.0
head_shrink_num = int(line_head_shrink_len // self.resample_step)
tail_shrink_num = int(line_tail_shrink_len // self.resample_step)
if len(center_line) > head_shrink_num + tail_shrink_num + 2:
center_line = center_line[head_shrink_num:len(center_line) -
tail_shrink_num]
resampled_top_line = resampled_top_line[head_shrink_num:len(
resampled_top_line) - tail_shrink_num]
resampled_bot_line = resampled_bot_line[head_shrink_num:len(
resampled_bot_line) - tail_shrink_num]
for i in range(0, len(center_line) - 1):
tl = center_line[i] + (resampled_top_line[i] - center_line[i]
) * self.center_region_shrink_ratio
tr = center_line[i + 1] + (resampled_top_line[i + 1] -
center_line[i + 1]
) * self.center_region_shrink_ratio
br = center_line[i + 1] + (resampled_bot_line[i + 1] -
center_line[i + 1]
) * self.center_region_shrink_ratio
bl = center_line[i] + (resampled_bot_line[i] - center_line[i]
) * self.center_region_shrink_ratio
current_center_box = np.vstack([tl, tr, br,
bl]).astype(np.int32)
center_region_boxes.append(current_center_box)
cv2.fillPoly(center_region_mask, center_region_boxes, 1)
return center_region_mask
def resample_polygon(self, polygon, n=400):
"""Resample one polygon with n points on its boundary.
Args:
polygon (list[float]): The input polygon.
n (int): The number of resampled points.
Returns:
resampled_polygon (list[float]): The resampled polygon.
"""
length = []
for i in range(len(polygon)):
p1 = polygon[i]
if i == len(polygon) - 1:
p2 = polygon[0]
else:
p2 = polygon[i + 1]
length.append(((p1[0] - p2[0])**2 + (p1[1] - p2[1])**2)**0.5)
total_length = sum(length)
n_on_each_line = (np.array(length) / (total_length + 1e-8)) * n
n_on_each_line = n_on_each_line.astype(np.int32)
new_polygon = []
for i in range(len(polygon)):
num = n_on_each_line[i]
p1 = polygon[i]
if i == len(polygon) - 1:
p2 = polygon[0]
else:
p2 = polygon[i + 1]
if num == 0:
continue
dxdy = (p2 - p1) / num
for j in range(num):
point = p1 + dxdy * j
new_polygon.append(point)
return np.array(new_polygon)
def normalize_polygon(self, polygon):
"""Normalize one polygon so that its start point is at right most.
Args:
polygon (list[float]): The origin polygon.
Returns:
new_polygon (lost[float]): The polygon with start point at right.
"""
temp_polygon = polygon - polygon.mean(axis=0)
x = np.abs(temp_polygon[:, 0])
y = temp_polygon[:, 1]
index_x = np.argsort(x)
index_y = np.argmin(y[index_x[:8]])
index = index_x[index_y]
new_polygon = np.concatenate([polygon[index:], polygon[:index]])
return new_polygon
def poly2fourier(self, polygon, fourier_degree):
"""Perform Fourier transformation to generate Fourier coefficients ck
from polygon.
Args:
polygon (ndarray): An input polygon.
fourier_degree (int): The maximum Fourier degree K.
Returns:
c (ndarray(complex)): Fourier coefficients.
"""
points = polygon[:, 0] + polygon[:, 1] * 1j
c_fft = fft(points) / len(points)
c = np.hstack((c_fft[-fourier_degree:], c_fft[:fourier_degree + 1]))
return c
def clockwise(self, c, fourier_degree):
"""Make sure the polygon reconstructed from Fourier coefficients c in
the clockwise direction.
Args:
polygon (list[float]): The origin polygon.
Returns:
new_polygon (lost[float]): The polygon in clockwise point order.
"""
if np.abs(c[fourier_degree + 1]) > np.abs(c[fourier_degree - 1]):
return c
elif np.abs(c[fourier_degree + 1]) < np.abs(c[fourier_degree - 1]):
return c[::-1]
else:
if np.abs(c[fourier_degree + 2]) > np.abs(c[fourier_degree - 2]):
return c
else:
return c[::-1]
def cal_fourier_signature(self, polygon, fourier_degree):
"""Calculate Fourier signature from input polygon.
Args:
polygon (ndarray): The input polygon.
fourier_degree (int): The maximum Fourier degree K.
Returns:
fourier_signature (ndarray): An array shaped (2k+1, 2) containing
real part and image part of 2k+1 Fourier coefficients.
"""
resampled_polygon = self.resample_polygon(polygon)
resampled_polygon = self.normalize_polygon(resampled_polygon)
fourier_coeff = self.poly2fourier(resampled_polygon, fourier_degree)
fourier_coeff = self.clockwise(fourier_coeff, fourier_degree)
real_part = np.real(fourier_coeff).reshape((-1, 1))
image_part = np.imag(fourier_coeff).reshape((-1, 1))
fourier_signature = np.hstack([real_part, image_part])
return fourier_signature
def generate_fourier_maps(self, img_size, text_polys):
"""Generate Fourier coefficient maps.
Args:
img_size (tuple): The image size of (height, width).
text_polys (list[list[ndarray]]): The list of text polygons.
Returns:
fourier_real_map (ndarray): The Fourier coefficient real part maps.
fourier_image_map (ndarray): The Fourier coefficient image part
maps.
"""
assert isinstance(img_size, tuple)
h, w = img_size
k = self.fourier_degree
real_map = np.zeros((k * 2 + 1, h, w), dtype=np.float32)
imag_map = np.zeros((k * 2 + 1, h, w), dtype=np.float32)
for poly in text_polys:
mask = np.zeros((h, w), dtype=np.uint8)
polygon = np.array(poly).reshape((1, -1, 2))
cv2.fillPoly(mask, polygon.astype(np.int32), 1)
fourier_coeff = self.cal_fourier_signature(polygon[0], k)
for i in range(-k, k + 1):
if i != 0:
real_map[i + k, :, :] = mask * fourier_coeff[i + k, 0] + (
1 - mask) * real_map[i + k, :, :]
imag_map[i + k, :, :] = mask * fourier_coeff[i + k, 1] + (
1 - mask) * imag_map[i + k, :, :]
else:
yx = np.argwhere(mask > 0.5)
k_ind = np.ones((len(yx)), dtype=np.int64) * k
y, x = yx[:, 0], yx[:, 1]
real_map[k_ind, y, x] = fourier_coeff[k, 0] - x
imag_map[k_ind, y, x] = fourier_coeff[k, 1] - y
return real_map, imag_map
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.reshape(-1, 2).astype(np.int32).reshape(1, -1, 2)
cv2.fillPoly(mask, instance, 0)
return mask
def generate_level_targets(self, img_size, text_polys, ignore_polys):
"""Generate ground truth target on each level.
Args:
img_size (list[int]): Shape of input image.
text_polys (list[list[ndarray]]): A list of ground truth polygons.
ignore_polys (list[list[ndarray]]): A list of ignored polygons.
Returns:
level_maps (list(ndarray)): A list of ground target on each level.
"""
h, w = img_size
lv_size_divs = self.level_size_divisors
lv_proportion_range = self.level_proportion_range
lv_text_polys = [[] for i in range(len(lv_size_divs))]
lv_ignore_polys = [[] for i in range(len(lv_size_divs))]
level_maps = []
for poly in text_polys:
polygon = np.array(poly, dtype=np.int).reshape((1, -1, 2))
_, _, box_w, box_h = cv2.boundingRect(polygon)
proportion = max(box_h, box_w) / (h + 1e-8)
for ind, proportion_range in enumerate(lv_proportion_range):
if proportion_range[0] < proportion < proportion_range[1]:
lv_text_polys[ind].append(poly / lv_size_divs[ind])
for ignore_poly in ignore_polys:
polygon = np.array(ignore_poly, dtype=np.int).reshape((1, -1, 2))
_, _, box_w, box_h = cv2.boundingRect(polygon)
proportion = max(box_h, box_w) / (h + 1e-8)
for ind, proportion_range in enumerate(lv_proportion_range):
if proportion_range[0] < proportion < proportion_range[1]:
lv_ignore_polys[ind].append(ignore_poly / lv_size_divs[ind])
for ind, size_divisor in enumerate(lv_size_divs):
current_level_maps = []
level_img_size = (h // size_divisor, w // size_divisor)
text_region = self.generate_text_region_mask(
level_img_size, lv_text_polys[ind])[None]
current_level_maps.append(text_region)
center_region = self.generate_center_region_mask(
level_img_size, lv_text_polys[ind])[None]
current_level_maps.append(center_region)
effective_mask = self.generate_effective_mask(
level_img_size, lv_ignore_polys[ind])[None]
current_level_maps.append(effective_mask)
fourier_real_map, fourier_image_maps = self.generate_fourier_maps(
level_img_size, lv_text_polys[ind])
current_level_maps.append(fourier_real_map)
current_level_maps.append(fourier_image_maps)
level_maps.append(np.concatenate(current_level_maps))
return level_maps
def generate_targets(self, results):
"""Generate the ground truth targets for FCENet.
Args:
results (dict): The input result dictionary.
Returns:
results (dict): The output result dictionary.
"""
assert isinstance(results, dict)
image = results['image']
polygons = results['polys']
ignore_tags = results['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)
level_maps = self.generate_level_targets((h, w), polygon_masks,
polygon_masks_ignore)
mapping = {
'p3_maps': level_maps[0],
'p4_maps': level_maps[1],
'p5_maps': level_maps[2]
}
for key, value in mapping.items():
results[key] = value
return results
def __call__(self, results):
results = self.generate_targets(results)
return results