Shortcuts

Source code for mmocr.datasets.pipelines.textdet_targets.textsnake_targets

# 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


[docs]@PIPELINES.register_module() 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)
[docs] 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
[docs] 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
[docs] 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
[docs] 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
[docs] 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
[docs] 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)
[docs] 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
[docs] 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
[docs] 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
Read the Docs v: v0.4.0
Versions
latest
stable
v0.4.0
v0.3.0
v0.2.1
v0.2.0
v0.1.0
Downloads
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.