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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import cv2 | |
| import numpy as np | |
| from mmdet.core import BitmapMasks | |
| from mmdet.datasets.builder import PIPELINES | |
| from numpy.linalg import norm | |
| import mmocr.utils.check_argument as check_argument | |
| from . import BaseTextDetTargets | |
| class TextSnakeTargets(BaseTextDetTargets): | |
| """Generate the ground truth targets of TextSnake: TextSnake: A Flexible | |
| Representation for Detecting Text of Arbitrary Shapes. | |
| [https://arxiv.org/abs/1807.01544]. This was partially adapted from | |
| https://github.com/princewang1994/TextSnake.pytorch. | |
| Args: | |
| orientation_thr (float): The threshold for distinguishing between | |
| head edge and tail edge among the horizontal and vertical edges | |
| of a quadrangle. | |
| """ | |
| def __init__(self, | |
| orientation_thr=2.0, | |
| resample_step=4.0, | |
| center_region_shrink_ratio=0.3): | |
| super().__init__() | |
| self.orientation_thr = orientation_thr | |
| self.resample_step = resample_step | |
| self.center_region_shrink_ratio = center_region_shrink_ratio | |
| 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): | |
| """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) + 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): | |
| """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 cal_curve_length(self, line): | |
| """Calculate the length of each edge on the discrete curve and the sum. | |
| Args: | |
| line (ndarray): The points composing a discrete curve. | |
| Returns: | |
| tuple: Returns (edges_length, total_length). | |
| - | edge_length (ndarray): The length of each edge on the | |
| discrete curve. | |
| - | total_length (float): The total length of the discrete | |
| curve. | |
| """ | |
| 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): | |
| """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 > 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): | |
| """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 = 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 draw_center_region_maps(self, top_line, bot_line, center_line, | |
| center_region_mask, radius_map, sin_map, | |
| cos_map, region_shrink_ratio): | |
| """Draw attributes on text center region. | |
| Args: | |
| top_line (ndarray): The points composing top curved sideline of | |
| text polygon. | |
| bot_line (ndarray): The points composing bottom curved sideline | |
| of text polygon. | |
| center_line (ndarray): The points composing the center line of text | |
| instance. | |
| center_region_mask (ndarray): The text center region mask. | |
| radius_map (ndarray): The map where the distance from point to | |
| sidelines will be drawn on for each pixel in text center | |
| region. | |
| sin_map (ndarray): The map where vector_sin(theta) will be drawn | |
| on text center regions. Theta is the angle between tangent | |
| line and vector (1, 0). | |
| cos_map (ndarray): The map where vector_cos(theta) will be drawn on | |
| text center regions. Theta is the angle between tangent line | |
| and vector (1, 0). | |
| region_shrink_ratio (float): The shrink ratio of text center. | |
| """ | |
| assert top_line.shape == bot_line.shape == center_line.shape | |
| assert (center_region_mask.shape == radius_map.shape == sin_map.shape | |
| == cos_map.shape) | |
| assert isinstance(region_shrink_ratio, float) | |
| 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 | |
| radius = norm(top_mid_point - bot_mid_point) / 2 | |
| text_direction = center_line[i + 1] - center_line[i] | |
| sin_theta = self.vector_sin(text_direction) | |
| cos_theta = self.vector_cos(text_direction) | |
| 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) | |
| cv2.fillPoly(radius_map, [current_center_box], color=radius) | |
| def generate_center_mask_attrib_maps(self, img_size, text_polys): | |
| """Generate text center region mask and geometric attribute maps. | |
| 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. | |
| radius_map (ndarray): The distance map from each pixel in text | |
| center region to top sideline. | |
| 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). | |
| """ | |
| 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) | |
| radius_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: | |
| assert len(poly) == 1 | |
| text_instance = [[poly[0][i], poly[0][i + 1]] | |
| for i in range(0, len(poly[0]), 2)] | |
| polygon_points = np.array(text_instance).reshape(-1, 2) | |
| n = len(polygon_points) | |
| keep_inds = [] | |
| for i in range(n): | |
| if norm(polygon_points[i] - | |
| polygon_points[(i + 1) % n]) > 1e-5: | |
| keep_inds.append(i) | |
| polygon_points = polygon_points[keep_inds] | |
| _, _, 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]) > 0.9: | |
| 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 = 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] | |
| self.draw_center_region_maps(resampled_top_line, | |
| resampled_bot_line, center_line, | |
| center_region_mask, radius_map, | |
| sin_map, cos_map, | |
| self.center_region_shrink_ratio) | |
| return center_region_mask, radius_map, sin_map, cos_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) | |
| assert check_argument.is_2dlist(text_polys) | |
| h, w = img_size | |
| text_region_mask = np.zeros((h, w), dtype=np.uint8) | |
| for poly in text_polys: | |
| assert len(poly) == 1 | |
| text_instance = [[poly[0][i], poly[0][i + 1]] | |
| for i in range(0, len(poly[0]), 2)] | |
| polygon = np.array( | |
| text_instance, dtype=np.int32).reshape((1, -1, 2)) | |
| cv2.fillPoly(text_region_mask, polygon, 1) | |
| return text_region_mask | |
| def generate_targets(self, results): | |
| """Generate the gt targets for TextSnake. | |
| Args: | |
| results (dict): The input result dictionary. | |
| Returns: | |
| results (dict): The output result dictionary. | |
| """ | |
| assert isinstance(results, dict) | |
| polygon_masks = results['gt_masks'].masks | |
| polygon_masks_ignore = results['gt_masks_ignore'].masks | |
| h, w, _ = results['img_shape'] | |
| gt_text_mask = self.generate_text_region_mask((h, w), polygon_masks) | |
| gt_mask = self.generate_effective_mask((h, w), polygon_masks_ignore) | |
| (gt_center_region_mask, gt_radius_map, gt_sin_map, | |
| gt_cos_map) = self.generate_center_mask_attrib_maps((h, w), | |
| polygon_masks) | |
| results['mask_fields'].clear() # rm gt_masks encoded by polygons | |
| mapping = { | |
| 'gt_text_mask': gt_text_mask, | |
| 'gt_center_region_mask': gt_center_region_mask, | |
| 'gt_mask': gt_mask, | |
| 'gt_radius_map': gt_radius_map, | |
| 'gt_sin_map': gt_sin_map, | |
| 'gt_cos_map': gt_cos_map | |
| } | |
| for key, value in mapping.items(): | |
| value = value if isinstance(value, list) else [value] | |
| results[key] = BitmapMasks(value, h, w) | |
| results['mask_fields'].append(key) | |
| return results | |