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| # 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 | |