import math import cv2 import numpy as np __all__ = ["SASTProcessTrain"] class SASTProcessTrain(object): def __init__( self, image_shape=[512, 512], min_crop_size=24, min_crop_side_ratio=0.3, min_text_size=10, max_text_size=512, **kwargs ): self.input_size = image_shape[1] self.min_crop_size = min_crop_size self.min_crop_side_ratio = min_crop_side_ratio self.min_text_size = min_text_size self.max_text_size = max_text_size 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) / 2.0 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) if True: 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 def check_and_validate_polys(self, polys, tags, xxx_todo_changeme): """ check so that the text poly is in the same direction, and also filter some invalid polygons :param polys: :param tags: :return: """ (h, w) = xxx_todo_changeme 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, crop_background=False, max_tries=25): """ make random crop from the input image :param im: :param polys: :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 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 < ARGS.min_crop_side_ratio * w or \ # ymax - ymin < ARGS.min_crop_side_ratio * h: if xmax - xmin < self.min_crop_size or ymax - ymin < self.min_crop_size: # area too small 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: return ( im[ymin : ymax + 1, xmin : xmax + 1, :], polys[selected_polys], tags[selected_polys], hv_tags[selected_polys], ) else: continue im = im[ymin : ymax + 1, xmin : xmax + 1, :] polys = polys[selected_polys] tags = tags[selected_polys] hv_tags = hv_tags[selected_polys] polys[:, :, 0] -= xmin polys[:, :, 1] -= ymin return im, polys, tags, hv_tags return im, polys, tags, hv_tags def generate_direction_map(self, poly_quads, 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) / (len(width_list) + 1e-6), 1.0) average_height = max(sum(height_list) / (len(height_list) + 1e-6), 1.0) 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 + 1e-6)], ) ) cv2.fillPoly( direction_map, quad.round().astype(np.int32)[np.newaxis, :, :], direction_label, ) 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_label( self, hw, polys, tags, ds_ratio, tcl_ratio=0.3, shrink_ratio_of_width=0.15 ): """ Generate polygon. """ h, w = hw h, w = int(h * ds_ratio), int(w * ds_ratio) polys = polys * ds_ratio score_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) 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: # continue cv2.fillPoly( training_mask, poly.astype(np.int32)[np.newaxis, :, :], 0.15 ) else: tcl_poly = self.poly2tcl(poly, tcl_ratio) tcl_quads = self.poly2quads(tcl_poly) poly_quads = self.poly2quads(poly) # stcl map 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, ) # generate tcl map cv2.fillPoly(score_map, np.round(stcl_quads).astype(np.int32), 1.0) # generate tbo map 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 ) return score_map, tbo_map, training_mask def generate_tvo_and_tco(self, hw, polys, tags, tcl_ratio=0.3, ds_ratio=0.25): """ Generate tcl map, tvo map and tbo map. """ h, w = hw h, w = int(h * ds_ratio), int(w * ds_ratio) polys = polys * ds_ratio poly_mask = np.zeros((h, w), dtype=np.float32) tvo_map = np.ones((9, h, w), dtype=np.float32) tvo_map[0:-1:2] = np.tile(np.arange(0, w), (h, 1)) tvo_map[1:-1:2] = np.tile(np.arange(0, w), (h, 1)).T poly_tv_xy_map = np.zeros((8, h, w), dtype=np.float32) # tco map tco_map = np.ones((3, h, w), dtype=np.float32) tco_map[0] = np.tile(np.arange(0, w), (h, 1)) tco_map[1] = np.tile(np.arange(0, w), (h, 1)).T poly_tc_xy_map = np.zeros((2, h, w), dtype=np.float32) poly_short_edge_map = np.ones((h, w), dtype=np.float32) for poly, poly_tag in zip(polys, tags): if poly_tag == True: continue # adjust point order for vertical poly poly = self.adjust_point(poly) # 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]) ) # generate tcl map and text, 128 * 128 tcl_poly = self.poly2tcl(poly, tcl_ratio) # generate poly_tv_xy_map for idx in range(4): cv2.fillPoly( poly_tv_xy_map[2 * idx], np.round(tcl_poly[np.newaxis, :, :]).astype(np.int32), float(min(max(min_area_quad[idx, 0], 0), w)), ) cv2.fillPoly( poly_tv_xy_map[2 * idx + 1], np.round(tcl_poly[np.newaxis, :, :]).astype(np.int32), float(min(max(min_area_quad[idx, 1], 0), h)), ) # generate poly_tc_xy_map for idx in range(2): cv2.fillPoly( poly_tc_xy_map[idx], np.round(tcl_poly[np.newaxis, :, :]).astype(np.int32), float(center_point[idx]), ) # generate poly_short_edge_map cv2.fillPoly( poly_short_edge_map, np.round(tcl_poly[np.newaxis, :, :]).astype(np.int32), float(max(min(min_area_quad_h, min_area_quad_w), 1.0)), ) # generate poly_mask and training_mask cv2.fillPoly( poly_mask, np.round(tcl_poly[np.newaxis, :, :]).astype(np.int32), 1 ) tvo_map *= poly_mask tvo_map[:8] -= poly_tv_xy_map tvo_map[-1] /= poly_short_edge_map tvo_map = tvo_map.transpose((1, 2, 0)) tco_map *= poly_mask tco_map[:2] -= poly_tc_xy_map tco_map[-1] /= poly_short_edge_map tco_map = tco_map.transpose((1, 2, 0)) return tvo_map, tco_map 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=0.0, end_width_ratio=1.0): """ 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 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("line1", line1) # print("line2", line2) 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 __call__(self, data): im = data["image"] text_polys = data["polys"] text_tags = data["ignore_tags"] if im is None: return None if text_polys.shape[0] == 0: return None 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 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 = self.crop_area( im, text_polys, text_tags, hv_tags, 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(self.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) < self.input_size * 0.5: return None im_padded = np.ones((self.input_size, self.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 = self.input_size - new_h del_w = self.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, border_map, training_mask = self.generate_tcl_label( (self.input_size, self.input_size), text_polys, text_tags, 0.25 ) # SAST head tvo_map, tco_map = self.generate_tvo_and_tco( (self.input_size, self.input_size), text_polys, text_tags, tcl_ratio=0.3, ds_ratio=0.25, ) # print("test--------tvo_map shape:", tvo_map.shape) 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)) data["image"] = im_padded[::-1, :, :] data["score_map"] = score_map[np.newaxis, :, :] data["border_map"] = border_map.transpose((2, 0, 1)) data["training_mask"] = training_mask[np.newaxis, :, :] data["tvo_map"] = tvo_map.transpose((2, 0, 1)) data["tco_map"] = tco_map.transpose((2, 0, 1)) return data