# Copyright (c) OpenMMLab. All rights reserved. import cv2 import numpy as np import torch from lanms import merge_quadrangle_n9 as la_nms from mmcv.ops import RoIAlignRotated from mmocr.models.textdet.postprocess.utils import fill_hole from .utils import (euclidean_distance_matrix, feature_embedding, normalize_adjacent_matrix) class ProposalLocalGraphs: """Propose text components and generate local graphs for GCN to classify the k-nearest neighbors of a pivot in DRRG: Deep Relational Reasoning Graph Network for Arbitrary Shape Text Detection. [https://arxiv.org/abs/2003.07493]. This code was partially adapted from https://github.com/GXYM/DRRG licensed under the MIT license. Args: k_at_hops (tuple(int)): The number of i-hop neighbors, i = 1, 2. num_adjacent_linkages (int): The number of linkages when constructing adjacent matrix. node_geo_feat_len (int): The length of embedded geometric feature vector of a text component. pooling_scale (float): The spatial scale of rotated RoI-Align. pooling_output_size (tuple(int)): The output size of rotated RoI-Align. nms_thr (float): The locality-aware NMS threshold for text components. min_width (float): The minimum width of text components. max_width (float): The maximum width of text components. comp_shrink_ratio (float): The shrink ratio of text components. comp_w_h_ratio (float): The width to height ratio of text components. comp_score_thr (float): The score threshold of text component. text_region_thr (float): The threshold for text region probability map. center_region_thr (float): The threshold for text center region probability map. center_region_area_thr (int): The threshold for filtering small-sized text center region. """ def __init__(self, k_at_hops, num_adjacent_linkages, node_geo_feat_len, pooling_scale, pooling_output_size, nms_thr, min_width, max_width, comp_shrink_ratio, comp_w_h_ratio, comp_score_thr, text_region_thr, center_region_thr, center_region_area_thr): assert len(k_at_hops) == 2 assert isinstance(k_at_hops, tuple) assert isinstance(num_adjacent_linkages, int) assert isinstance(node_geo_feat_len, int) assert isinstance(pooling_scale, float) assert isinstance(pooling_output_size, tuple) assert isinstance(nms_thr, float) assert isinstance(min_width, float) assert isinstance(max_width, float) assert isinstance(comp_shrink_ratio, float) assert isinstance(comp_w_h_ratio, float) assert isinstance(comp_score_thr, float) assert isinstance(text_region_thr, float) assert isinstance(center_region_thr, float) assert isinstance(center_region_area_thr, int) self.k_at_hops = k_at_hops self.active_connection = num_adjacent_linkages self.local_graph_depth = len(self.k_at_hops) self.node_geo_feat_dim = node_geo_feat_len self.pooling = RoIAlignRotated(pooling_output_size, pooling_scale) self.nms_thr = nms_thr self.min_width = min_width self.max_width = max_width self.comp_shrink_ratio = comp_shrink_ratio self.comp_w_h_ratio = comp_w_h_ratio self.comp_score_thr = comp_score_thr self.text_region_thr = text_region_thr self.center_region_thr = center_region_thr self.center_region_area_thr = center_region_area_thr def propose_comps(self, score_map, top_height_map, bot_height_map, sin_map, cos_map, comp_score_thr, min_width, max_width, comp_shrink_ratio, comp_w_h_ratio): """Propose text components. Args: score_map (ndarray): The score map for NMS. top_height_map (ndarray): The predicted text height map from each pixel in text center region to top sideline. bot_height_map (ndarray): The predicted text height map from each pixel in text center region to bottom sideline. sin_map (ndarray): The predicted sin(theta) map. cos_map (ndarray): The predicted cos(theta) map. comp_score_thr (float): The score threshold of text component. min_width (float): The minimum width of text components. max_width (float): The maximum width of text components. comp_shrink_ratio (float): The shrink ratio of text components. comp_w_h_ratio (float): The width to height ratio of text components. Returns: text_comps (ndarray): The text components. """ comp_centers = np.argwhere(score_map > comp_score_thr) comp_centers = comp_centers[np.argsort(comp_centers[:, 0])] y = comp_centers[:, 0] x = comp_centers[:, 1] top_height = top_height_map[y, x].reshape((-1, 1)) * comp_shrink_ratio bot_height = bot_height_map[y, x].reshape((-1, 1)) * comp_shrink_ratio sin = sin_map[y, x].reshape((-1, 1)) cos = cos_map[y, x].reshape((-1, 1)) top_mid_pts = comp_centers + np.hstack( [top_height * sin, top_height * cos]) bot_mid_pts = comp_centers - np.hstack( [bot_height * sin, bot_height * cos]) width = (top_height + bot_height) * comp_w_h_ratio width = np.clip(width, min_width, max_width) r = width / 2 tl = top_mid_pts[:, ::-1] - np.hstack([-r * sin, r * cos]) tr = top_mid_pts[:, ::-1] + np.hstack([-r * sin, r * cos]) br = bot_mid_pts[:, ::-1] + np.hstack([-r * sin, r * cos]) bl = bot_mid_pts[:, ::-1] - np.hstack([-r * sin, r * cos]) text_comps = np.hstack([tl, tr, br, bl]).astype(np.float32) score = score_map[y, x].reshape((-1, 1)) text_comps = np.hstack([text_comps, score]) return text_comps def propose_comps_and_attribs(self, text_region_map, center_region_map, top_height_map, bot_height_map, sin_map, cos_map): """Generate text components and attributes. Args: text_region_map (ndarray): The predicted text region probability map. center_region_map (ndarray): The predicted text center region probability map. top_height_map (ndarray): The predicted text height map from each pixel in text center region to top sideline. bot_height_map (ndarray): The predicted text height map from each pixel in text center region to bottom sideline. sin_map (ndarray): The predicted sin(theta) map. cos_map (ndarray): The predicted cos(theta) map. Returns: comp_attribs (ndarray): The text component attributes. text_comps (ndarray): The text components. """ assert (text_region_map.shape == center_region_map.shape == top_height_map.shape == bot_height_map.shape == sin_map.shape == cos_map.shape) text_mask = text_region_map > self.text_region_thr center_region_mask = (center_region_map > self.center_region_thr) * text_mask scale = np.sqrt(1.0 / (sin_map**2 + cos_map**2 + 1e-8)) sin_map, cos_map = sin_map * scale, cos_map * scale center_region_mask = fill_hole(center_region_mask) center_region_contours, _ = cv2.findContours( center_region_mask.astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) mask_sz = center_region_map.shape comp_list = [] for contour in center_region_contours: current_center_mask = np.zeros(mask_sz) cv2.drawContours(current_center_mask, [contour], -1, 1, -1) if current_center_mask.sum() <= self.center_region_area_thr: continue score_map = text_region_map * current_center_mask text_comps = self.propose_comps(score_map, top_height_map, bot_height_map, sin_map, cos_map, self.comp_score_thr, self.min_width, self.max_width, self.comp_shrink_ratio, self.comp_w_h_ratio) text_comps = la_nms(text_comps, self.nms_thr) text_comp_mask = np.zeros(mask_sz) text_comp_boxes = text_comps[:, :8].reshape( (-1, 4, 2)).astype(np.int32) cv2.drawContours(text_comp_mask, text_comp_boxes, -1, 1, -1) if (text_comp_mask * text_mask).sum() < text_comp_mask.sum() * 0.5: continue if text_comps.shape[-1] > 0: comp_list.append(text_comps) if len(comp_list) <= 0: return None, None text_comps = np.vstack(comp_list) text_comp_boxes = text_comps[:, :8].reshape((-1, 4, 2)) centers = np.mean(text_comp_boxes, axis=1).astype(np.int32) x = centers[:, 0] y = centers[:, 1] scores = [] for text_comp_box in text_comp_boxes: text_comp_box[:, 0] = np.clip(text_comp_box[:, 0], 0, mask_sz[1] - 1) text_comp_box[:, 1] = np.clip(text_comp_box[:, 1], 0, mask_sz[0] - 1) min_coord = np.min(text_comp_box, axis=0).astype(np.int32) max_coord = np.max(text_comp_box, axis=0).astype(np.int32) text_comp_box = text_comp_box - min_coord box_sz = (max_coord - min_coord + 1) temp_comp_mask = np.zeros((box_sz[1], box_sz[0]), dtype=np.uint8) cv2.fillPoly(temp_comp_mask, [text_comp_box.astype(np.int32)], 1) temp_region_patch = text_region_map[min_coord[1]:(max_coord[1] + 1), min_coord[0]:(max_coord[0] + 1)] score = cv2.mean(temp_region_patch, temp_comp_mask)[0] scores.append(score) scores = np.array(scores).reshape((-1, 1)) text_comps = np.hstack([text_comps[:, :-1], scores]) h = top_height_map[y, x].reshape( (-1, 1)) + bot_height_map[y, x].reshape((-1, 1)) w = np.clip(h * self.comp_w_h_ratio, self.min_width, self.max_width) sin = sin_map[y, x].reshape((-1, 1)) cos = cos_map[y, x].reshape((-1, 1)) x = x.reshape((-1, 1)) y = y.reshape((-1, 1)) comp_attribs = np.hstack([x, y, h, w, cos, sin]) return comp_attribs, text_comps def generate_local_graphs(self, sorted_dist_inds, node_feats): """Generate local graphs and graph convolution network input data. Args: sorted_dist_inds (ndarray): The node indices sorted according to the Euclidean distance. node_feats (tensor): The features of nodes in graph. Returns: local_graphs_node_feats (tensor): The features of nodes in local graphs. adjacent_matrices (tensor): The adjacent matrices. pivots_knn_inds (tensor): The k-nearest neighbor indices in local graphs. pivots_local_graphs (tensor): The indices of nodes in local graphs. """ assert sorted_dist_inds.ndim == 2 assert (sorted_dist_inds.shape[0] == sorted_dist_inds.shape[1] == node_feats.shape[0]) knn_graph = sorted_dist_inds[:, 1:self.k_at_hops[0] + 1] pivot_local_graphs = [] pivot_knns = [] device = node_feats.device for pivot_ind, knn in enumerate(knn_graph): local_graph_neighbors = set(knn) for neighbor_ind in knn: local_graph_neighbors.update( set(sorted_dist_inds[neighbor_ind, 1:self.k_at_hops[1] + 1])) local_graph_neighbors.discard(pivot_ind) pivot_local_graph = list(local_graph_neighbors) pivot_local_graph.insert(0, pivot_ind) pivot_knn = [pivot_ind] + list(knn) pivot_local_graphs.append(pivot_local_graph) pivot_knns.append(pivot_knn) num_max_nodes = max([ len(pivot_local_graph) for pivot_local_graph in pivot_local_graphs ]) local_graphs_node_feat = [] adjacent_matrices = [] pivots_knn_inds = [] pivots_local_graphs = [] for graph_ind, pivot_knn in enumerate(pivot_knns): pivot_local_graph = pivot_local_graphs[graph_ind] num_nodes = len(pivot_local_graph) pivot_ind = pivot_local_graph[0] node2ind_map = {j: i for i, j in enumerate(pivot_local_graph)} knn_inds = torch.tensor([node2ind_map[i] for i in pivot_knn[1:]]).long().to(device) pivot_feats = node_feats[pivot_ind] normalized_feats = node_feats[pivot_local_graph] - pivot_feats adjacent_matrix = np.zeros((num_nodes, num_nodes)) for node in pivot_local_graph: neighbors = sorted_dist_inds[node, 1:self.active_connection + 1] for neighbor in neighbors: if neighbor in pivot_local_graph: adjacent_matrix[node2ind_map[node], node2ind_map[neighbor]] = 1 adjacent_matrix[node2ind_map[neighbor], node2ind_map[node]] = 1 adjacent_matrix = normalize_adjacent_matrix(adjacent_matrix) pad_adjacent_matrix = torch.zeros((num_max_nodes, num_max_nodes), dtype=torch.float, device=device) pad_adjacent_matrix[:num_nodes, :num_nodes] = torch.from_numpy( adjacent_matrix) pad_normalized_feats = torch.cat([ normalized_feats, torch.zeros( (num_max_nodes - num_nodes, normalized_feats.shape[1]), dtype=torch.float, device=device) ], dim=0) local_graph_nodes = torch.tensor(pivot_local_graph) local_graph_nodes = torch.cat([ local_graph_nodes, torch.zeros(num_max_nodes - num_nodes, dtype=torch.long) ], dim=-1) local_graphs_node_feat.append(pad_normalized_feats) adjacent_matrices.append(pad_adjacent_matrix) pivots_knn_inds.append(knn_inds) pivots_local_graphs.append(local_graph_nodes) local_graphs_node_feat = torch.stack(local_graphs_node_feat, 0) adjacent_matrices = torch.stack(adjacent_matrices, 0) pivots_knn_inds = torch.stack(pivots_knn_inds, 0) pivots_local_graphs = torch.stack(pivots_local_graphs, 0) return (local_graphs_node_feat, adjacent_matrices, pivots_knn_inds, pivots_local_graphs) def __call__(self, preds, feat_maps): """Generate local graphs and graph convolutional network input data. Args: preds (tensor): The predicted maps. feat_maps (tensor): The feature maps to extract content feature of text components. Returns: none_flag (bool): The flag showing whether the number of proposed text components is 0. local_graphs_node_feats (tensor): The features of nodes in local graphs. adjacent_matrices (tensor): The adjacent matrices. pivots_knn_inds (tensor): The k-nearest neighbor indices in local graphs. pivots_local_graphs (tensor): The indices of nodes in local graphs. text_comps (ndarray): The predicted text components. """ if preds.ndim == 4: assert preds.shape[0] == 1 preds = torch.squeeze(preds) pred_text_region = torch.sigmoid(preds[0]).data.cpu().numpy() pred_center_region = torch.sigmoid(preds[1]).data.cpu().numpy() pred_sin_map = preds[2].data.cpu().numpy() pred_cos_map = preds[3].data.cpu().numpy() pred_top_height_map = preds[4].data.cpu().numpy() pred_bot_height_map = preds[5].data.cpu().numpy() device = preds.device comp_attribs, text_comps = self.propose_comps_and_attribs( pred_text_region, pred_center_region, pred_top_height_map, pred_bot_height_map, pred_sin_map, pred_cos_map) if comp_attribs is None or len(comp_attribs) < 2: none_flag = True return none_flag, (0, 0, 0, 0, 0) comp_centers = comp_attribs[:, 0:2] distance_matrix = euclidean_distance_matrix(comp_centers, comp_centers) geo_feats = feature_embedding(comp_attribs, self.node_geo_feat_dim) geo_feats = torch.from_numpy(geo_feats).to(preds.device) batch_id = np.zeros((comp_attribs.shape[0], 1), dtype=np.float32) comp_attribs = comp_attribs.astype(np.float32) angle = np.arccos(comp_attribs[:, -2]) * np.sign(comp_attribs[:, -1]) angle = angle.reshape((-1, 1)) rotated_rois = np.hstack([batch_id, comp_attribs[:, :-2], angle]) rois = torch.from_numpy(rotated_rois).to(device) content_feats = self.pooling(feat_maps, rois) content_feats = content_feats.view(content_feats.shape[0], -1).to(device) node_feats = torch.cat([content_feats, geo_feats], dim=-1) sorted_dist_inds = np.argsort(distance_matrix, axis=1) (local_graphs_node_feat, adjacent_matrices, pivots_knn_inds, pivots_local_graphs) = self.generate_local_graphs( sorted_dist_inds, node_feats) none_flag = False return none_flag, (local_graphs_node_feat, adjacent_matrices, pivots_knn_inds, pivots_local_graphs, text_comps)