# 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 ppocr.utils.utility import check_install 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]) check_install('lanms', 'lanms-neo') from lanms import merge_quadrangle_n9 as la_nms 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