# copyright (c) 2021 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. import math import cv2 import numpy as np from skimage.morphology._skeletonize import thin from ppocr.utils.e2e_utils.extract_textpoint_fast import sort_and_expand_with_direction_v2 __all__ = ['PGProcessTrain'] class PGProcessTrain(object): def __init__(self, character_dict_path, max_text_length, max_text_nums, tcl_len, batch_size=14, use_resize=True, use_random_crop=False, min_crop_size=24, min_text_size=4, max_text_size=512, tcc_type='v3', **kwargs): self.tcl_len = tcl_len self.max_text_length = max_text_length self.max_text_nums = max_text_nums self.batch_size = batch_size if use_random_crop is True: self.min_crop_size = min_crop_size self.use_random_crop = use_random_crop self.min_text_size = min_text_size self.max_text_size = max_text_size self.use_resize = use_resize self.tcc_type = tcc_type self.Lexicon_Table = self.get_dict(character_dict_path) self.pad_num = len(self.Lexicon_Table) self.img_id = 0 def get_dict(self, character_dict_path): character_str = "" with open(character_dict_path, "rb") as fin: lines = fin.readlines() for line in lines: line = line.decode('utf-8').strip("\n").strip("\r\n") character_str += line dict_character = list(character_str) return dict_character def quad_area(self, poly): """ compute area of a polygon :param poly: :return: """ edge = [(poly[1][0] - poly[0][0]) * (poly[1][1] + poly[0][1]), (poly[2][0] - poly[1][0]) * (poly[2][1] + poly[1][1]), (poly[3][0] - poly[2][0]) * (poly[3][1] + poly[2][1]), (poly[0][0] - poly[3][0]) * (poly[0][1] + poly[3][1])] return np.sum(edge) / 2. def gen_quad_from_poly(self, poly): """ Generate min area quad from poly. """ point_num = poly.shape[0] min_area_quad = np.zeros((4, 2), dtype=np.float32) rect = cv2.minAreaRect(poly.astype( np.int32)) # (center (x,y), (width, height), angle of rotation) box = np.array(cv2.boxPoints(rect)) first_point_idx = 0 min_dist = 1e4 for i in range(4): dist = np.linalg.norm(box[(i + 0) % 4] - poly[0]) + \ np.linalg.norm(box[(i + 1) % 4] - poly[point_num // 2 - 1]) + \ np.linalg.norm(box[(i + 2) % 4] - poly[point_num // 2]) + \ np.linalg.norm(box[(i + 3) % 4] - poly[-1]) if dist < min_dist: min_dist = dist first_point_idx = i for i in range(4): min_area_quad[i] = box[(first_point_idx + i) % 4] return min_area_quad def check_and_validate_polys(self, polys, tags, im_size): """ check so that the text poly is in the same direction, and also filter some invalid polygons :param polys: :param tags: :return: """ (h, w) = im_size if polys.shape[0] == 0: return polys, np.array([]), np.array([]) polys[:, :, 0] = np.clip(polys[:, :, 0], 0, w - 1) polys[:, :, 1] = np.clip(polys[:, :, 1], 0, h - 1) validated_polys = [] validated_tags = [] hv_tags = [] for poly, tag in zip(polys, tags): quad = self.gen_quad_from_poly(poly) p_area = self.quad_area(quad) if abs(p_area) < 1: print('invalid poly') continue if p_area > 0: if tag == False: print('poly in wrong direction') tag = True # reversed cases should be ignore poly = poly[(0, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1), :] quad = quad[(0, 3, 2, 1), :] len_w = np.linalg.norm(quad[0] - quad[1]) + np.linalg.norm(quad[3] - quad[2]) len_h = np.linalg.norm(quad[0] - quad[3]) + np.linalg.norm(quad[1] - quad[2]) hv_tag = 1 if len_w * 2.0 < len_h: hv_tag = 0 validated_polys.append(poly) validated_tags.append(tag) hv_tags.append(hv_tag) return np.array(validated_polys), np.array(validated_tags), np.array( hv_tags) def crop_area(self, im, polys, tags, hv_tags, txts, crop_background=False, max_tries=25): """ make random crop from the input image :param im: :param polys: [b,4,2] :param tags: :param crop_background: :param max_tries: 50 -> 25 :return: """ h, w, _ = im.shape pad_h = h // 10 pad_w = w // 10 h_array = np.zeros((h + pad_h * 2), dtype=np.int32) w_array = np.zeros((w + pad_w * 2), dtype=np.int32) for poly in polys: poly = np.round(poly, decimals=0).astype(np.int32) minx = np.min(poly[:, 0]) maxx = np.max(poly[:, 0]) w_array[minx + pad_w:maxx + pad_w] = 1 miny = np.min(poly[:, 1]) maxy = np.max(poly[:, 1]) h_array[miny + pad_h:maxy + pad_h] = 1 # ensure the cropped area not across a text h_axis = np.where(h_array == 0)[0] w_axis = np.where(w_array == 0)[0] if len(h_axis) == 0 or len(w_axis) == 0: return im, polys, tags, hv_tags, txts for i in range(max_tries): xx = np.random.choice(w_axis, size=2) xmin = np.min(xx) - pad_w xmax = np.max(xx) - pad_w xmin = np.clip(xmin, 0, w - 1) xmax = np.clip(xmax, 0, w - 1) yy = np.random.choice(h_axis, size=2) ymin = np.min(yy) - pad_h ymax = np.max(yy) - pad_h ymin = np.clip(ymin, 0, h - 1) ymax = np.clip(ymax, 0, h - 1) if xmax - xmin < self.min_crop_size or \ ymax - ymin < self.min_crop_size: continue if polys.shape[0] != 0: poly_axis_in_area = (polys[:, :, 0] >= xmin) & (polys[:, :, 0] <= xmax) \ & (polys[:, :, 1] >= ymin) & (polys[:, :, 1] <= ymax) selected_polys = np.where( np.sum(poly_axis_in_area, axis=1) == 4)[0] else: selected_polys = [] if len(selected_polys) == 0: # no text in this area if crop_background: txts_tmp = [] for selected_poly in selected_polys: txts_tmp.append(txts[selected_poly]) txts = txts_tmp return im[ymin: ymax + 1, xmin: xmax + 1, :], \ polys[selected_polys], tags[selected_polys], hv_tags[selected_polys], txts else: continue im = im[ymin:ymax + 1, xmin:xmax + 1, :] polys = polys[selected_polys] tags = tags[selected_polys] hv_tags = hv_tags[selected_polys] txts_tmp = [] for selected_poly in selected_polys: txts_tmp.append(txts[selected_poly]) txts = txts_tmp polys[:, :, 0] -= xmin polys[:, :, 1] -= ymin return im, polys, tags, hv_tags, txts return im, polys, tags, hv_tags, txts def fit_and_gather_tcl_points_v2(self, min_area_quad, poly, max_h, max_w, fixed_point_num=64, img_id=0, reference_height=3): """ Find the center point of poly as key_points, then fit and gather. """ key_point_xys = [] point_num = poly.shape[0] for idx in range(point_num // 2): center_point = (poly[idx] + poly[point_num - 1 - idx]) / 2.0 key_point_xys.append(center_point) tmp_image = np.zeros( shape=( max_h, max_w, ), dtype='float32') cv2.polylines(tmp_image, [np.array(key_point_xys).astype('int32')], False, 1.0) ys, xs = np.where(tmp_image > 0) xy_text = np.array(list(zip(xs, ys)), dtype='float32') left_center_pt = ( (min_area_quad[0] - min_area_quad[1]) / 2.0).reshape(1, 2) right_center_pt = ( (min_area_quad[1] - min_area_quad[2]) / 2.0).reshape(1, 2) proj_unit_vec = (right_center_pt - left_center_pt) / ( np.linalg.norm(right_center_pt - left_center_pt) + 1e-6) proj_unit_vec_tile = np.tile(proj_unit_vec, (xy_text.shape[0], 1)) # (n, 2) left_center_pt_tile = np.tile(left_center_pt, (xy_text.shape[0], 1)) # (n, 2) xy_text_to_left_center = xy_text - left_center_pt_tile proj_value = np.sum(xy_text_to_left_center * proj_unit_vec_tile, axis=1) xy_text = xy_text[np.argsort(proj_value)] # convert to np and keep the num of point not greater then fixed_point_num pos_info = np.array(xy_text).reshape(-1, 2)[:, ::-1] # xy-> yx point_num = len(pos_info) if point_num > fixed_point_num: keep_ids = [ int((point_num * 1.0 / fixed_point_num) * x) for x in range(fixed_point_num) ] pos_info = pos_info[keep_ids, :] keep = int(min(len(pos_info), fixed_point_num)) if np.random.rand() < 0.2 and reference_height >= 3: dl = (np.random.rand(keep) - 0.5) * reference_height * 0.3 random_float = np.array([1, 0]).reshape([1, 2]) * dl.reshape( [keep, 1]) pos_info += random_float pos_info[:, 0] = np.clip(pos_info[:, 0], 0, max_h - 1) pos_info[:, 1] = np.clip(pos_info[:, 1], 0, max_w - 1) # padding to fixed length pos_l = np.zeros((self.tcl_len, 3), dtype=np.int32) pos_l[:, 0] = np.ones((self.tcl_len, )) * img_id pos_m = np.zeros((self.tcl_len, 1), dtype=np.float32) pos_l[:keep, 1:] = np.round(pos_info).astype(np.int32) pos_m[:keep] = 1.0 return pos_l, pos_m def fit_and_gather_tcl_points_v3(self, min_area_quad, poly, max_h, max_w, fixed_point_num=64, img_id=0, reference_height=3): """ Find the center point of poly as key_points, then fit and gather. """ det_mask = np.zeros((int(max_h / self.ds_ratio), int(max_w / self.ds_ratio))).astype(np.float32) # score_big_map cv2.fillPoly(det_mask, np.round(poly / self.ds_ratio).astype(np.int32), 1.0) det_mask = cv2.resize( det_mask, dsize=None, fx=self.ds_ratio, fy=self.ds_ratio) det_mask = np.array(det_mask > 1e-3, dtype='float32') f_direction = self.f_direction skeleton_map = thin(det_mask.astype(np.uint8)) instance_count, instance_label_map = cv2.connectedComponents( skeleton_map.astype(np.uint8), connectivity=8) ys, xs = np.where(instance_label_map == 1) pos_list = list(zip(ys, xs)) if len(pos_list) < 3: return None pos_list_sorted = sort_and_expand_with_direction_v2( pos_list, f_direction, det_mask) pos_list_sorted = np.array(pos_list_sorted) length = len(pos_list_sorted) - 1 insert_num = 0 for index in range(length): stride_y = np.abs(pos_list_sorted[index + insert_num][0] - pos_list_sorted[index + 1 + insert_num][0]) stride_x = np.abs(pos_list_sorted[index + insert_num][1] - pos_list_sorted[index + 1 + insert_num][1]) max_points = int(max(stride_x, stride_y)) stride = (pos_list_sorted[index + insert_num] - pos_list_sorted[index + 1 + insert_num]) / (max_points) insert_num_temp = max_points - 1 for i in range(int(insert_num_temp)): insert_value = pos_list_sorted[index + insert_num] - (i + 1 ) * stride insert_index = index + i + 1 + insert_num pos_list_sorted = np.insert( pos_list_sorted, insert_index, insert_value, axis=0) insert_num += insert_num_temp pos_info = np.array(pos_list_sorted).reshape(-1, 2).astype( np.float32) # xy-> yx point_num = len(pos_info) if point_num > fixed_point_num: keep_ids = [ int((point_num * 1.0 / fixed_point_num) * x) for x in range(fixed_point_num) ] pos_info = pos_info[keep_ids, :] keep = int(min(len(pos_info), fixed_point_num)) reference_width = (np.abs(poly[0, 0, 0] - poly[-1, 1, 0]) + np.abs(poly[0, 3, 0] - poly[-1, 2, 0])) // 2 if np.random.rand() < 1: dh = (np.random.rand(keep) - 0.5) * reference_height offset = np.random.rand() - 0.5 dw = np.array([[0, offset * reference_width * 0.2]]) random_float_h = np.array([1, 0]).reshape([1, 2]) * dh.reshape( [keep, 1]) random_float_w = dw.repeat(keep, axis=0) pos_info += random_float_h pos_info += random_float_w pos_info[:, 0] = np.clip(pos_info[:, 0], 0, max_h - 1) pos_info[:, 1] = np.clip(pos_info[:, 1], 0, max_w - 1) # padding to fixed length pos_l = np.zeros((self.tcl_len, 3), dtype=np.int32) pos_l[:, 0] = np.ones((self.tcl_len, )) * img_id pos_m = np.zeros((self.tcl_len, 1), dtype=np.float32) pos_l[:keep, 1:] = np.round(pos_info).astype(np.int32) pos_m[:keep] = 1.0 return pos_l, pos_m def generate_direction_map(self, poly_quads, n_char, direction_map): """ """ width_list = [] height_list = [] for quad in poly_quads: quad_w = (np.linalg.norm(quad[0] - quad[1]) + np.linalg.norm(quad[2] - quad[3])) / 2.0 quad_h = (np.linalg.norm(quad[0] - quad[3]) + np.linalg.norm(quad[2] - quad[1])) / 2.0 width_list.append(quad_w) height_list.append(quad_h) norm_width = max(sum(width_list) / n_char, 1.0) average_height = max(sum(height_list) / len(height_list), 1.0) k = 1 for quad in poly_quads: direct_vector_full = ( (quad[1] + quad[2]) - (quad[0] + quad[3])) / 2.0 direct_vector = direct_vector_full / ( np.linalg.norm(direct_vector_full) + 1e-6) * norm_width direction_label = tuple( map(float, [direct_vector[0], direct_vector[1], 1.0 / average_height])) cv2.fillPoly(direction_map, quad.round().astype(np.int32)[np.newaxis, :, :], direction_label) k += 1 return direction_map def calculate_average_height(self, poly_quads): """ """ height_list = [] for quad in poly_quads: quad_h = (np.linalg.norm(quad[0] - quad[3]) + np.linalg.norm(quad[2] - quad[1])) / 2.0 height_list.append(quad_h) average_height = max(sum(height_list) / len(height_list), 1.0) return average_height def generate_tcl_ctc_label(self, h, w, polys, tags, text_strs, ds_ratio, tcl_ratio=0.3, shrink_ratio_of_width=0.15): """ Generate polygon. """ self.ds_ratio = ds_ratio score_map_big = np.zeros( ( h, w, ), dtype=np.float32) h, w = int(h * ds_ratio), int(w * ds_ratio) polys = polys * ds_ratio score_map = np.zeros( ( h, w, ), dtype=np.float32) score_label_map = np.zeros( ( h, w, ), dtype=np.float32) tbo_map = np.zeros((h, w, 5), dtype=np.float32) training_mask = np.ones( ( h, w, ), dtype=np.float32) direction_map = np.ones((h, w, 3)) * np.array([0, 0, 1]).reshape( [1, 1, 3]).astype(np.float32) label_idx = 0 score_label_map_text_label_list = [] pos_list, pos_mask, label_list = [], [], [] for poly_idx, poly_tag in enumerate(zip(polys, tags)): poly = poly_tag[0] tag = poly_tag[1] # generate min_area_quad min_area_quad, center_point = self.gen_min_area_quad_from_poly(poly) min_area_quad_h = 0.5 * ( np.linalg.norm(min_area_quad[0] - min_area_quad[3]) + np.linalg.norm(min_area_quad[1] - min_area_quad[2])) min_area_quad_w = 0.5 * ( np.linalg.norm(min_area_quad[0] - min_area_quad[1]) + np.linalg.norm(min_area_quad[2] - min_area_quad[3])) if min(min_area_quad_h, min_area_quad_w) < self.min_text_size * ds_ratio \ or min(min_area_quad_h, min_area_quad_w) > self.max_text_size * ds_ratio: continue if tag: cv2.fillPoly(training_mask, poly.astype(np.int32)[np.newaxis, :, :], 0.15) else: text_label = text_strs[poly_idx] text_label = self.prepare_text_label(text_label, self.Lexicon_Table) text_label_index_list = [[self.Lexicon_Table.index(c_)] for c_ in text_label if c_ in self.Lexicon_Table] if len(text_label_index_list) < 1: continue tcl_poly = self.poly2tcl(poly, tcl_ratio) tcl_quads = self.poly2quads(tcl_poly) poly_quads = self.poly2quads(poly) stcl_quads, quad_index = self.shrink_poly_along_width( tcl_quads, shrink_ratio_of_width=shrink_ratio_of_width, expand_height_ratio=1.0 / tcl_ratio) cv2.fillPoly(score_map, np.round(stcl_quads).astype(np.int32), 1.0) cv2.fillPoly(score_map_big, np.round(stcl_quads / ds_ratio).astype(np.int32), 1.0) for idx, quad in enumerate(stcl_quads): quad_mask = np.zeros((h, w), dtype=np.float32) quad_mask = cv2.fillPoly( quad_mask, np.round(quad[np.newaxis, :, :]).astype(np.int32), 1.0) tbo_map = self.gen_quad_tbo(poly_quads[quad_index[idx]], quad_mask, tbo_map) # score label map and score_label_map_text_label_list for refine if label_idx == 0: text_pos_list_ = [[len(self.Lexicon_Table)], ] score_label_map_text_label_list.append(text_pos_list_) label_idx += 1 cv2.fillPoly(score_label_map, np.round(poly_quads).astype(np.int32), label_idx) score_label_map_text_label_list.append(text_label_index_list) # direction info, fix-me n_char = len(text_label_index_list) direction_map = self.generate_direction_map(poly_quads, n_char, direction_map) # pos info average_shrink_height = self.calculate_average_height( stcl_quads) if self.tcc_type == 'v3': self.f_direction = direction_map[:, :, :-1].copy() pos_res = self.fit_and_gather_tcl_points_v3( min_area_quad, stcl_quads, max_h=h, max_w=w, fixed_point_num=64, img_id=self.img_id, reference_height=average_shrink_height) if pos_res is None: continue pos_l, pos_m = pos_res[0], pos_res[1] elif self.tcc_type == 'v2': pos_l, pos_m = self.fit_and_gather_tcl_points_v2( min_area_quad, poly, max_h=h, max_w=w, fixed_point_num=64, img_id=self.img_id, reference_height=average_shrink_height) label_l = text_label_index_list if len(text_label_index_list) < 2: continue pos_list.append(pos_l) pos_mask.append(pos_m) label_list.append(label_l) # use big score_map for smooth tcl lines score_map_big_resized = cv2.resize( score_map_big, dsize=None, fx=ds_ratio, fy=ds_ratio) score_map = np.array(score_map_big_resized > 1e-3, dtype='float32') return score_map, score_label_map, tbo_map, direction_map, training_mask, \ pos_list, pos_mask, label_list, score_label_map_text_label_list def adjust_point(self, poly): """ adjust point order. """ point_num = poly.shape[0] if point_num == 4: len_1 = np.linalg.norm(poly[0] - poly[1]) len_2 = np.linalg.norm(poly[1] - poly[2]) len_3 = np.linalg.norm(poly[2] - poly[3]) len_4 = np.linalg.norm(poly[3] - poly[0]) if (len_1 + len_3) * 1.5 < (len_2 + len_4): poly = poly[[1, 2, 3, 0], :] elif point_num > 4: vector_1 = poly[0] - poly[1] vector_2 = poly[1] - poly[2] cos_theta = np.dot(vector_1, vector_2) / ( np.linalg.norm(vector_1) * np.linalg.norm(vector_2) + 1e-6) theta = np.arccos(np.round(cos_theta, decimals=4)) if abs(theta) > (70 / 180 * math.pi): index = list(range(1, point_num)) + [0] poly = poly[np.array(index), :] return poly def gen_min_area_quad_from_poly(self, poly): """ Generate min area quad from poly. """ point_num = poly.shape[0] min_area_quad = np.zeros((4, 2), dtype=np.float32) if point_num == 4: min_area_quad = poly center_point = np.sum(poly, axis=0) / 4 else: rect = cv2.minAreaRect(poly.astype( np.int32)) # (center (x,y), (width, height), angle of rotation) center_point = rect[0] box = np.array(cv2.boxPoints(rect)) first_point_idx = 0 min_dist = 1e4 for i in range(4): dist = np.linalg.norm(box[(i + 0) % 4] - poly[0]) + \ np.linalg.norm(box[(i + 1) % 4] - poly[point_num // 2 - 1]) + \ np.linalg.norm(box[(i + 2) % 4] - poly[point_num // 2]) + \ np.linalg.norm(box[(i + 3) % 4] - poly[-1]) if dist < min_dist: min_dist = dist first_point_idx = i for i in range(4): min_area_quad[i] = box[(first_point_idx + i) % 4] return min_area_quad, center_point def shrink_quad_along_width(self, quad, begin_width_ratio=0., end_width_ratio=1.): """ Generate shrink_quad_along_width. """ ratio_pair = np.array( [[begin_width_ratio], [end_width_ratio]], dtype=np.float32) p0_1 = quad[0] + (quad[1] - quad[0]) * ratio_pair p3_2 = quad[3] + (quad[2] - quad[3]) * ratio_pair return np.array([p0_1[0], p0_1[1], p3_2[1], p3_2[0]]) def shrink_poly_along_width(self, quads, shrink_ratio_of_width, expand_height_ratio=1.0): """ shrink poly with given length. """ upper_edge_list = [] def get_cut_info(edge_len_list, cut_len): for idx, edge_len in enumerate(edge_len_list): cut_len -= edge_len if cut_len <= 0.000001: ratio = (cut_len + edge_len_list[idx]) / edge_len_list[idx] return idx, ratio for quad in quads: upper_edge_len = np.linalg.norm(quad[0] - quad[1]) upper_edge_list.append(upper_edge_len) # length of left edge and right edge. left_length = np.linalg.norm(quads[0][0] - quads[0][ 3]) * expand_height_ratio right_length = np.linalg.norm(quads[-1][1] - quads[-1][ 2]) * expand_height_ratio shrink_length = min(left_length, right_length, sum(upper_edge_list)) * shrink_ratio_of_width # shrinking length upper_len_left = shrink_length upper_len_right = sum(upper_edge_list) - shrink_length left_idx, left_ratio = get_cut_info(upper_edge_list, upper_len_left) left_quad = self.shrink_quad_along_width( quads[left_idx], begin_width_ratio=left_ratio, end_width_ratio=1) right_idx, right_ratio = get_cut_info(upper_edge_list, upper_len_right) right_quad = self.shrink_quad_along_width( quads[right_idx], begin_width_ratio=0, end_width_ratio=right_ratio) out_quad_list = [] if left_idx == right_idx: out_quad_list.append( [left_quad[0], right_quad[1], right_quad[2], left_quad[3]]) else: out_quad_list.append(left_quad) for idx in range(left_idx + 1, right_idx): out_quad_list.append(quads[idx]) out_quad_list.append(right_quad) return np.array(out_quad_list), list(range(left_idx, right_idx + 1)) def prepare_text_label(self, label_str, Lexicon_Table): """ Prepare text lablel by given Lexicon_Table. """ if len(Lexicon_Table) == 36: return label_str.lower() else: return label_str def vector_angle(self, A, B): """ Calculate the angle between vector AB and x-axis positive direction. """ AB = np.array([B[1] - A[1], B[0] - A[0]]) return np.arctan2(*AB) def theta_line_cross_point(self, theta, point): """ Calculate the line through given point and angle in ax + by + c =0 form. """ x, y = point cos = np.cos(theta) sin = np.sin(theta) return [sin, -cos, cos * y - sin * x] def line_cross_two_point(self, A, B): """ Calculate the line through given point A and B in ax + by + c =0 form. """ angle = self.vector_angle(A, B) return self.theta_line_cross_point(angle, A) def average_angle(self, poly): """ Calculate the average angle between left and right edge in given poly. """ p0, p1, p2, p3 = poly angle30 = self.vector_angle(p3, p0) angle21 = self.vector_angle(p2, p1) return (angle30 + angle21) / 2 def line_cross_point(self, line1, line2): """ line1 and line2 in 0=ax+by+c form, compute the cross point of line1 and line2 """ a1, b1, c1 = line1 a2, b2, c2 = line2 d = a1 * b2 - a2 * b1 if d == 0: print('Cross point does not exist') return np.array([0, 0], dtype=np.float32) else: x = (b1 * c2 - b2 * c1) / d y = (a2 * c1 - a1 * c2) / d return np.array([x, y], dtype=np.float32) def quad2tcl(self, poly, ratio): """ Generate center line by poly clock-wise point. (4, 2) """ ratio_pair = np.array( [[0.5 - ratio / 2], [0.5 + ratio / 2]], dtype=np.float32) p0_3 = poly[0] + (poly[3] - poly[0]) * ratio_pair p1_2 = poly[1] + (poly[2] - poly[1]) * ratio_pair return np.array([p0_3[0], p1_2[0], p1_2[1], p0_3[1]]) def poly2tcl(self, poly, ratio): """ Generate center line by poly clock-wise point. """ ratio_pair = np.array( [[0.5 - ratio / 2], [0.5 + ratio / 2]], dtype=np.float32) tcl_poly = np.zeros_like(poly) point_num = poly.shape[0] for idx in range(point_num // 2): point_pair = poly[idx] + (poly[point_num - 1 - idx] - poly[idx] ) * ratio_pair tcl_poly[idx] = point_pair[0] tcl_poly[point_num - 1 - idx] = point_pair[1] return tcl_poly def gen_quad_tbo(self, quad, tcl_mask, tbo_map): """ Generate tbo_map for give quad. """ # upper and lower line function: ax + by + c = 0; up_line = self.line_cross_two_point(quad[0], quad[1]) lower_line = self.line_cross_two_point(quad[3], quad[2]) quad_h = 0.5 * (np.linalg.norm(quad[0] - quad[3]) + np.linalg.norm(quad[1] - quad[2])) quad_w = 0.5 * (np.linalg.norm(quad[0] - quad[1]) + np.linalg.norm(quad[2] - quad[3])) # average angle of left and right line. angle = self.average_angle(quad) xy_in_poly = np.argwhere(tcl_mask == 1) for y, x in xy_in_poly: point = (x, y) line = self.theta_line_cross_point(angle, point) cross_point_upper = self.line_cross_point(up_line, line) cross_point_lower = self.line_cross_point(lower_line, line) ##FIX, offset reverse upper_offset_x, upper_offset_y = cross_point_upper - point lower_offset_x, lower_offset_y = cross_point_lower - point tbo_map[y, x, 0] = upper_offset_y tbo_map[y, x, 1] = upper_offset_x tbo_map[y, x, 2] = lower_offset_y tbo_map[y, x, 3] = lower_offset_x tbo_map[y, x, 4] = 1.0 / max(min(quad_h, quad_w), 1.0) * 2 return tbo_map def poly2quads(self, poly): """ Split poly into quads. """ quad_list = [] point_num = poly.shape[0] # point pair point_pair_list = [] for idx in range(point_num // 2): point_pair = [poly[idx], poly[point_num - 1 - idx]] point_pair_list.append(point_pair) quad_num = point_num // 2 - 1 for idx in range(quad_num): # reshape and adjust to clock-wise quad_list.append((np.array(point_pair_list)[[idx, idx + 1]] ).reshape(4, 2)[[0, 2, 3, 1]]) return np.array(quad_list) def rotate_im_poly(self, im, text_polys): """ rotate image with 90 / 180 / 270 degre """ im_w, im_h = im.shape[1], im.shape[0] dst_im = im.copy() dst_polys = [] rand_degree_ratio = np.random.rand() rand_degree_cnt = 1 if rand_degree_ratio > 0.5: rand_degree_cnt = 3 for i in range(rand_degree_cnt): dst_im = np.rot90(dst_im) rot_degree = -90 * rand_degree_cnt rot_angle = rot_degree * math.pi / 180.0 n_poly = text_polys.shape[0] cx, cy = 0.5 * im_w, 0.5 * im_h ncx, ncy = 0.5 * dst_im.shape[1], 0.5 * dst_im.shape[0] for i in range(n_poly): wordBB = text_polys[i] poly = [] for j in range(4): # 16->4 sx, sy = wordBB[j][0], wordBB[j][1] dx = math.cos(rot_angle) * (sx - cx) - math.sin(rot_angle) * ( sy - cy) + ncx dy = math.sin(rot_angle) * (sx - cx) + math.cos(rot_angle) * ( sy - cy) + ncy poly.append([dx, dy]) dst_polys.append(poly) return dst_im, np.array(dst_polys, dtype=np.float32) def __call__(self, data): input_size = 512 im = data['image'] text_polys = data['polys'] text_tags = data['ignore_tags'] text_strs = data['texts'] h, w, _ = im.shape text_polys, text_tags, hv_tags = self.check_and_validate_polys( text_polys, text_tags, (h, w)) if text_polys.shape[0] <= 0: return None # set aspect ratio and keep area fix asp_scales = np.arange(1.0, 1.55, 0.1) asp_scale = np.random.choice(asp_scales) if np.random.rand() < 0.5: asp_scale = 1.0 / asp_scale asp_scale = math.sqrt(asp_scale) asp_wx = asp_scale asp_hy = 1.0 / asp_scale im = cv2.resize(im, dsize=None, fx=asp_wx, fy=asp_hy) text_polys[:, :, 0] *= asp_wx text_polys[:, :, 1] *= asp_hy if self.use_resize is True: ori_h, ori_w, _ = im.shape if max(ori_h, ori_w) < 200: ratio = 200 / max(ori_h, ori_w) im = cv2.resize(im, (int(ori_w * ratio), int(ori_h * ratio))) text_polys[:, :, 0] *= ratio text_polys[:, :, 1] *= ratio if max(ori_h, ori_w) > 512: ratio = 512 / max(ori_h, ori_w) im = cv2.resize(im, (int(ori_w * ratio), int(ori_h * ratio))) text_polys[:, :, 0] *= ratio text_polys[:, :, 1] *= ratio elif self.use_random_crop is True: h, w, _ = im.shape if max(h, w) > 2048: rd_scale = 2048.0 / max(h, w) im = cv2.resize(im, dsize=None, fx=rd_scale, fy=rd_scale) text_polys *= rd_scale h, w, _ = im.shape if min(h, w) < 16: return None # no background im, text_polys, text_tags, hv_tags, text_strs = self.crop_area( im, text_polys, text_tags, hv_tags, text_strs, crop_background=False) if text_polys.shape[0] == 0: return None # continue for all ignore case if np.sum((text_tags * 1.0)) >= text_tags.size: return None new_h, new_w, _ = im.shape if (new_h is None) or (new_w is None): return None # resize image std_ratio = float(input_size) / max(new_w, new_h) rand_scales = np.array( [0.25, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0, 1.0, 1.0, 1.0, 1.0]) rz_scale = std_ratio * np.random.choice(rand_scales) im = cv2.resize(im, dsize=None, fx=rz_scale, fy=rz_scale) text_polys[:, :, 0] *= rz_scale text_polys[:, :, 1] *= rz_scale # add gaussian blur if np.random.rand() < 0.1 * 0.5: ks = np.random.permutation(5)[0] + 1 ks = int(ks / 2) * 2 + 1 im = cv2.GaussianBlur(im, ksize=(ks, ks), sigmaX=0, sigmaY=0) # add brighter if np.random.rand() < 0.1 * 0.5: im = im * (1.0 + np.random.rand() * 0.5) im = np.clip(im, 0.0, 255.0) # add darker if np.random.rand() < 0.1 * 0.5: im = im * (1.0 - np.random.rand() * 0.5) im = np.clip(im, 0.0, 255.0) # Padding the im to [input_size, input_size] new_h, new_w, _ = im.shape if min(new_w, new_h) < input_size * 0.5: return None im_padded = np.ones((input_size, input_size, 3), dtype=np.float32) im_padded[:, :, 2] = 0.485 * 255 im_padded[:, :, 1] = 0.456 * 255 im_padded[:, :, 0] = 0.406 * 255 # Random the start position del_h = input_size - new_h del_w = input_size - new_w sh, sw = 0, 0 if del_h > 1: sh = int(np.random.rand() * del_h) if del_w > 1: sw = int(np.random.rand() * del_w) # Padding im_padded[sh:sh + new_h, sw:sw + new_w, :] = im.copy() text_polys[:, :, 0] += sw text_polys[:, :, 1] += sh score_map, score_label_map, border_map, direction_map, training_mask, \ pos_list, pos_mask, label_list, score_label_map_text_label = self.generate_tcl_ctc_label(input_size, input_size, text_polys, text_tags, text_strs, 0.25) if len(label_list) <= 0: # eliminate negative samples return None pos_list_temp = np.zeros([64, 3]) pos_mask_temp = np.zeros([64, 1]) label_list_temp = np.zeros([self.max_text_length, 1]) + self.pad_num for i, label in enumerate(label_list): n = len(label) if n > self.max_text_length: label_list[i] = label[:self.max_text_length] continue while n < self.max_text_length: label.append([self.pad_num]) n += 1 for i in range(len(label_list)): label_list[i] = np.array(label_list[i]) if len(pos_list) <= 0 or len(pos_list) > self.max_text_nums: return None for __ in range(self.max_text_nums - len(pos_list), 0, -1): pos_list.append(pos_list_temp) pos_mask.append(pos_mask_temp) label_list.append(label_list_temp) if self.img_id == self.batch_size - 1: self.img_id = 0 else: self.img_id += 1 im_padded[:, :, 2] -= 0.485 * 255 im_padded[:, :, 1] -= 0.456 * 255 im_padded[:, :, 0] -= 0.406 * 255 im_padded[:, :, 2] /= (255.0 * 0.229) im_padded[:, :, 1] /= (255.0 * 0.224) im_padded[:, :, 0] /= (255.0 * 0.225) im_padded = im_padded.transpose((2, 0, 1)) images = im_padded[::-1, :, :] tcl_maps = score_map[np.newaxis, :, :] tcl_label_maps = score_label_map[np.newaxis, :, :] border_maps = border_map.transpose((2, 0, 1)) direction_maps = direction_map.transpose((2, 0, 1)) training_masks = training_mask[np.newaxis, :, :] pos_list = np.array(pos_list) pos_mask = np.array(pos_mask) label_list = np.array(label_list) data['images'] = images data['tcl_maps'] = tcl_maps data['tcl_label_maps'] = tcl_label_maps data['border_maps'] = border_maps data['direction_maps'] = direction_maps data['training_masks'] = training_masks data['label_list'] = label_list data['pos_list'] = pos_list data['pos_mask'] = pos_mask return data