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| ''' | |
| M-LSD | |
| Copyright 2021-present NAVER Corp. | |
| Apache License v2.0 | |
| ''' | |
| import os | |
| import numpy as np | |
| import cv2 | |
| import tensorflow as tf | |
| def pred_lines(image, interpreter, input_details, output_details, input_shape=[512, 512], score_thr=0.10, dist_thr=20.0): | |
| h, w, _ = image.shape | |
| h_ratio, w_ratio = [h / input_shape[0], w / input_shape[1]] | |
| resized_image = np.concatenate([cv2.resize(image, (input_shape[0], input_shape[1]), interpolation=cv2.INTER_AREA), np.ones([input_shape[0], input_shape[1], 1])], axis=-1) | |
| batch_image = np.expand_dims(resized_image, axis=0).astype('float32') | |
| interpreter.set_tensor(input_details[0]['index'], batch_image) | |
| interpreter.invoke() | |
| pts = interpreter.get_tensor(output_details[0]['index'])[0] | |
| pts_score = interpreter.get_tensor(output_details[1]['index'])[0] | |
| vmap = interpreter.get_tensor(output_details[2]['index'])[0] | |
| start = vmap[:,:,:2] | |
| end = vmap[:,:,2:] | |
| dist_map = np.sqrt(np.sum((start - end) ** 2, axis=-1)) | |
| segments_list = [] | |
| for center, score in zip(pts, pts_score): | |
| y, x = center | |
| distance = dist_map[y, x] | |
| if score > score_thr and distance > dist_thr: | |
| disp_x_start, disp_y_start, disp_x_end, disp_y_end = vmap[y, x, :] | |
| x_start = x + disp_x_start | |
| y_start = y + disp_y_start | |
| x_end = x + disp_x_end | |
| y_end = y + disp_y_end | |
| segments_list.append([x_start, y_start, x_end, y_end]) | |
| lines = 2 * np.array(segments_list) # 256 > 512 | |
| lines[:,0] = lines[:,0] * w_ratio | |
| lines[:,1] = lines[:,1] * h_ratio | |
| lines[:,2] = lines[:,2] * w_ratio | |
| lines[:,3] = lines[:,3] * h_ratio | |
| return lines | |
| def pred_squares(image, | |
| interpreter, | |
| input_details, | |
| output_details, | |
| input_shape=[512, 512], | |
| params={'score': 0.06, | |
| 'outside_ratio': 0.28, | |
| 'inside_ratio': 0.45, | |
| 'w_overlap': 0.0, | |
| 'w_degree': 1.95, | |
| 'w_length': 0.0, | |
| 'w_area': 1.86, | |
| 'w_center': 0.14}): | |
| h, w, _ = image.shape | |
| original_shape = [h, w] | |
| resized_image = np.concatenate([cv2.resize(image, (input_shape[0], input_shape[1]), interpolation=cv2.INTER_AREA), np.ones([input_shape[0], input_shape[1], 1])], axis=-1) | |
| batch_image = np.expand_dims(resized_image, axis=0).astype('float32') | |
| interpreter.set_tensor(input_details[0]['index'], batch_image) | |
| interpreter.invoke() | |
| pts = interpreter.get_tensor(output_details[0]['index'])[0] | |
| pts_score = interpreter.get_tensor(output_details[1]['index'])[0] | |
| vmap = interpreter.get_tensor(output_details[2]['index'])[0] | |
| start = vmap[:,:,:2] # (x, y) | |
| end = vmap[:,:,2:] # (x, y) | |
| dist_map = np.sqrt(np.sum((start - end) ** 2, axis=-1)) | |
| junc_list = [] | |
| segments_list = [] | |
| for junc, score in zip(pts, pts_score): | |
| y, x = junc | |
| distance = dist_map[y, x] | |
| if score > params['score'] and distance > 20.0: | |
| junc_list.append([x, y]) | |
| disp_x_start, disp_y_start, disp_x_end, disp_y_end = vmap[y, x, :] | |
| d_arrow = 1.0 | |
| x_start = x + d_arrow * disp_x_start | |
| y_start = y + d_arrow * disp_y_start | |
| x_end = x + d_arrow * disp_x_end | |
| y_end = y + d_arrow * disp_y_end | |
| segments_list.append([x_start, y_start, x_end, y_end]) | |
| segments = np.array(segments_list) | |
| ####### post processing for squares | |
| # 1. get unique lines | |
| point = np.array([[0, 0]]) | |
| point = point[0] | |
| start = segments[:,:2] | |
| end = segments[:,2:] | |
| diff = start - end | |
| a = diff[:, 1] | |
| b = -diff[:, 0] | |
| c = a * start[:,0] + b * start[:,1] | |
| d = np.abs(a * point[0] + b * point[1] - c) / np.sqrt(a ** 2 + b ** 2 + 1e-10) | |
| theta = np.arctan2(diff[:,0], diff[:,1]) * 180 / np.pi | |
| theta[theta < 0.0] += 180 | |
| hough = np.concatenate([d[:,None], theta[:,None]], axis=-1) | |
| d_quant = 1 | |
| theta_quant = 2 | |
| hough[:,0] //= d_quant | |
| hough[:,1] //= theta_quant | |
| _, indices, counts = np.unique(hough, axis=0, return_index=True, return_counts=True) | |
| acc_map = np.zeros([512 // d_quant + 1, 360 // theta_quant + 1], dtype='float32') | |
| idx_map = np.zeros([512 // d_quant + 1, 360 // theta_quant + 1], dtype='int32') - 1 | |
| yx_indices = hough[indices,:].astype('int32') | |
| acc_map[yx_indices[:,0], yx_indices[:,1]] = counts | |
| idx_map[yx_indices[:,0], yx_indices[:,1]] = indices | |
| acc_map_np = acc_map | |
| acc_map = acc_map[None,:,:,None] | |
| ### fast suppression using tensorflow op | |
| acc_map = tf.constant(acc_map, dtype=tf.float32) | |
| max_acc_map = tf.keras.layers.MaxPool2D(pool_size=(5,5), strides=1, padding='same')(acc_map) | |
| acc_map = acc_map * tf.cast(tf.math.equal(acc_map, max_acc_map), tf.float32) | |
| flatten_acc_map = tf.reshape(acc_map, [1, -1]) | |
| topk_values, topk_indices = tf.math.top_k(flatten_acc_map, k=len(pts)) | |
| _, h, w, _ = acc_map.shape | |
| y = tf.expand_dims(topk_indices // w, axis=-1) | |
| x = tf.expand_dims(topk_indices % w, axis=-1) | |
| yx = tf.concat([y, x], axis=-1) | |
| ### | |
| yx = yx[0].numpy() | |
| indices = idx_map[yx[:,0], yx[:,1]] | |
| topk_values = topk_values.numpy()[0] | |
| basis = 5 // 2 | |
| merged_segments = [] | |
| for yx_pt, max_indice, value in zip(yx, indices, topk_values): | |
| y, x = yx_pt | |
| if max_indice == -1 or value == 0: | |
| continue | |
| segment_list = [] | |
| for y_offset in range(-basis, basis+1): | |
| for x_offset in range(-basis, basis+1): | |
| indice = idx_map[y+y_offset,x+x_offset] | |
| cnt = int(acc_map_np[y+y_offset,x+x_offset]) | |
| if indice != -1: | |
| segment_list.append(segments[indice]) | |
| if cnt > 1: | |
| check_cnt = 1 | |
| current_hough = hough[indice] | |
| for new_indice, new_hough in enumerate(hough): | |
| if (current_hough == new_hough).all() and indice != new_indice: | |
| segment_list.append(segments[new_indice]) | |
| check_cnt += 1 | |
| if check_cnt == cnt: | |
| break | |
| group_segments = np.array(segment_list).reshape([-1, 2]) | |
| sorted_group_segments = np.sort(group_segments, axis=0) | |
| x_min, y_min = sorted_group_segments[0,:] | |
| x_max, y_max = sorted_group_segments[-1,:] | |
| deg = theta[max_indice] | |
| if deg >= 90: | |
| merged_segments.append([x_min, y_max, x_max, y_min]) | |
| else: | |
| merged_segments.append([x_min, y_min, x_max, y_max]) | |
| # 2. get intersections | |
| new_segments = np.array(merged_segments) # (x1, y1, x2, y2) | |
| start = new_segments[:,:2] # (x1, y1) | |
| end = new_segments[:,2:] # (x2, y2) | |
| new_centers = (start + end) / 2.0 | |
| diff = start - end | |
| dist_segments = np.sqrt(np.sum(diff ** 2, axis=-1)) | |
| # ax + by = c | |
| a = diff[:,1] | |
| b = -diff[:,0] | |
| c = a * start[:,0] + b * start[:,1] | |
| pre_det = a[:,None] * b[None,:] | |
| det = pre_det - np.transpose(pre_det) | |
| pre_inter_y = a[:,None] * c[None,:] | |
| inter_y = (pre_inter_y - np.transpose(pre_inter_y)) / (det + 1e-10) | |
| pre_inter_x = c[:,None] * b[None,:] | |
| inter_x = (pre_inter_x - np.transpose(pre_inter_x)) / (det + 1e-10) | |
| inter_pts = np.concatenate([inter_x[:,:,None], inter_y[:,:,None]], axis=-1).astype('int32') | |
| # 3. get corner information | |
| # 3.1 get distance | |
| ''' | |
| dist_segments: | |
| | dist(0), dist(1), dist(2), ...| | |
| dist_inter_to_segment1: | |
| | dist(inter,0), dist(inter,0), dist(inter,0), ... | | |
| | dist(inter,1), dist(inter,1), dist(inter,1), ... | | |
| ... | |
| dist_inter_to_semgnet2: | |
| | dist(inter,0), dist(inter,1), dist(inter,2), ... | | |
| | dist(inter,0), dist(inter,1), dist(inter,2), ... | | |
| ... | |
| ''' | |
| dist_inter_to_segment1_start = np.sqrt(np.sum(((inter_pts - start[:,None,:]) ** 2), axis=-1, keepdims=True)) # [n_batch, n_batch, 1] | |
| dist_inter_to_segment1_end = np.sqrt(np.sum(((inter_pts - end[:,None,:]) ** 2), axis=-1, keepdims=True)) # [n_batch, n_batch, 1] | |
| dist_inter_to_segment2_start = np.sqrt(np.sum(((inter_pts - start[None,:,:]) ** 2), axis=-1, keepdims=True)) # [n_batch, n_batch, 1] | |
| dist_inter_to_segment2_end = np.sqrt(np.sum(((inter_pts - end[None,:,:]) ** 2), axis=-1, keepdims=True)) # [n_batch, n_batch, 1] | |
| # sort ascending | |
| dist_inter_to_segment1 = np.sort(np.concatenate([dist_inter_to_segment1_start, dist_inter_to_segment1_end], axis=-1), axis=-1) # [n_batch, n_batch, 2] | |
| dist_inter_to_segment2 = np.sort(np.concatenate([dist_inter_to_segment2_start, dist_inter_to_segment2_end], axis=-1), axis=-1) # [n_batch, n_batch, 2] | |
| # 3.2 get degree | |
| inter_to_start = new_centers[:,None,:] - inter_pts | |
| deg_inter_to_start = np.arctan2(inter_to_start[:,:,1], inter_to_start[:,:,0]) * 180 / np.pi | |
| deg_inter_to_start[deg_inter_to_start < 0.0] += 360 | |
| inter_to_end = new_centers[None,:,:] - inter_pts | |
| deg_inter_to_end = np.arctan2(inter_to_end[:,:,1], inter_to_end[:,:,0]) * 180 / np.pi | |
| deg_inter_to_end[deg_inter_to_end < 0.0] += 360 | |
| ''' | |
| 0 -- 1 | |
| | | | |
| 3 -- 2 | |
| ''' | |
| # rename variables | |
| deg1_map, deg2_map = deg_inter_to_start, deg_inter_to_end | |
| # sort deg ascending | |
| deg_sort = np.sort(np.concatenate([deg1_map[:,:,None], deg2_map[:,:,None]], axis=-1), axis=-1) | |
| deg_diff_map = np.abs(deg1_map - deg2_map) | |
| # we only consider the smallest degree of intersect | |
| deg_diff_map[deg_diff_map > 180] = 360 - deg_diff_map[deg_diff_map > 180] | |
| # define available degree range | |
| deg_range = [60, 120] | |
| corner_dict = {corner_info: [] for corner_info in range(4)} | |
| inter_points = [] | |
| for i in range(inter_pts.shape[0]): | |
| for j in range(i + 1, inter_pts.shape[1]): | |
| # i, j > line index, always i < j | |
| x, y = inter_pts[i, j, :] | |
| deg1, deg2 = deg_sort[i, j, :] | |
| deg_diff = deg_diff_map[i, j] | |
| check_degree = deg_diff > deg_range[0] and deg_diff < deg_range[1] | |
| outside_ratio = params['outside_ratio'] # over ratio >>> drop it! | |
| inside_ratio = params['inside_ratio'] # over ratio >>> drop it! | |
| check_distance = ((dist_inter_to_segment1[i,j,1] >= dist_segments[i] and \ | |
| dist_inter_to_segment1[i,j,0] <= dist_segments[i] * outside_ratio) or \ | |
| (dist_inter_to_segment1[i,j,1] <= dist_segments[i] and \ | |
| dist_inter_to_segment1[i,j,0] <= dist_segments[i] * inside_ratio)) and \ | |
| ((dist_inter_to_segment2[i,j,1] >= dist_segments[j] and \ | |
| dist_inter_to_segment2[i,j,0] <= dist_segments[j] * outside_ratio) or \ | |
| (dist_inter_to_segment2[i,j,1] <= dist_segments[j] and \ | |
| dist_inter_to_segment2[i,j,0] <= dist_segments[j] * inside_ratio)) | |
| if check_degree and check_distance: | |
| corner_info = None | |
| if (deg1 >= 0 and deg1 <= 45 and deg2 >=45 and deg2 <= 120) or \ | |
| (deg2 >= 315 and deg1 >= 45 and deg1 <= 120): | |
| corner_info, color_info = 0, 'blue' | |
| elif (deg1 >= 45 and deg1 <= 125 and deg2 >= 125 and deg2 <= 225): | |
| corner_info, color_info = 1, 'green' | |
| elif (deg1 >= 125 and deg1 <= 225 and deg2 >= 225 and deg2 <= 315): | |
| corner_info, color_info = 2, 'black' | |
| elif (deg1 >= 0 and deg1 <= 45 and deg2 >= 225 and deg2 <= 315) or \ | |
| (deg2 >= 315 and deg1 >= 225 and deg1 <= 315): | |
| corner_info, color_info = 3, 'cyan' | |
| else: | |
| corner_info, color_info = 4, 'red' # we don't use it | |
| continue | |
| corner_dict[corner_info].append([x, y, i, j]) | |
| inter_points.append([x, y]) | |
| square_list = [] | |
| connect_list = [] | |
| segments_list = [] | |
| for corner0 in corner_dict[0]: | |
| for corner1 in corner_dict[1]: | |
| connect01 = False | |
| for corner0_line in corner0[2:]: | |
| if corner0_line in corner1[2:]: | |
| connect01 = True | |
| break | |
| if connect01: | |
| for corner2 in corner_dict[2]: | |
| connect12 = False | |
| for corner1_line in corner1[2:]: | |
| if corner1_line in corner2[2:]: | |
| connect12 = True | |
| break | |
| if connect12: | |
| for corner3 in corner_dict[3]: | |
| connect23 = False | |
| for corner2_line in corner2[2:]: | |
| if corner2_line in corner3[2:]: | |
| connect23 = True | |
| break | |
| if connect23: | |
| for corner3_line in corner3[2:]: | |
| if corner3_line in corner0[2:]: | |
| # SQUARE!!! | |
| ''' | |
| 0 -- 1 | |
| | | | |
| 3 -- 2 | |
| square_list: | |
| order: 0 > 1 > 2 > 3 | |
| | x0, y0, x1, y1, x2, y2, x3, y3 | | |
| | x0, y0, x1, y1, x2, y2, x3, y3 | | |
| ... | |
| connect_list: | |
| order: 01 > 12 > 23 > 30 | |
| | line_idx01, line_idx12, line_idx23, line_idx30 | | |
| | line_idx01, line_idx12, line_idx23, line_idx30 | | |
| ... | |
| segments_list: | |
| order: 0 > 1 > 2 > 3 | |
| | line_idx0_i, line_idx0_j, line_idx1_i, line_idx1_j, line_idx2_i, line_idx2_j, line_idx3_i, line_idx3_j | | |
| | line_idx0_i, line_idx0_j, line_idx1_i, line_idx1_j, line_idx2_i, line_idx2_j, line_idx3_i, line_idx3_j | | |
| ... | |
| ''' | |
| square_list.append(corner0[:2] + corner1[:2] + corner2[:2] + corner3[:2]) | |
| connect_list.append([corner0_line, corner1_line, corner2_line, corner3_line]) | |
| segments_list.append(corner0[2:] + corner1[2:] + corner2[2:] + corner3[2:]) | |
| def check_outside_inside(segments_info, connect_idx): | |
| # return 'outside or inside', min distance, cover_param, peri_param | |
| if connect_idx == segments_info[0]: | |
| check_dist_mat = dist_inter_to_segment1 | |
| else: | |
| check_dist_mat = dist_inter_to_segment2 | |
| i, j = segments_info | |
| min_dist, max_dist = check_dist_mat[i, j, :] | |
| connect_dist = dist_segments[connect_idx] | |
| if max_dist > connect_dist: | |
| return 'outside', min_dist, 0, 1 | |
| else: | |
| return 'inside', min_dist, -1, -1 | |
| top_square = None | |
| try: | |
| map_size = input_shape[0] / 2 | |
| squares = np.array(square_list).reshape([-1,4,2]) | |
| score_array = [] | |
| connect_array = np.array(connect_list) | |
| segments_array = np.array(segments_list).reshape([-1,4,2]) | |
| # get degree of corners: | |
| squares_rollup = np.roll(squares, 1, axis=1) | |
| squares_rolldown = np.roll(squares, -1, axis=1) | |
| vec1 = squares_rollup - squares | |
| normalized_vec1 = vec1 / (np.linalg.norm(vec1, axis=-1, keepdims=True) + 1e-10) | |
| vec2 = squares_rolldown - squares | |
| normalized_vec2 = vec2 / (np.linalg.norm(vec2, axis=-1, keepdims=True) + 1e-10) | |
| inner_products = np.sum(normalized_vec1 * normalized_vec2, axis=-1) # [n_squares, 4] | |
| squares_degree = np.arccos(inner_products) * 180 / np.pi # [n_squares, 4] | |
| # get square score | |
| overlap_scores = [] | |
| degree_scores = [] | |
| length_scores = [] | |
| for connects, segments, square, degree in zip(connect_array, segments_array, squares, squares_degree): | |
| ''' | |
| 0 -- 1 | |
| | | | |
| 3 -- 2 | |
| # segments: [4, 2] | |
| # connects: [4] | |
| ''' | |
| ###################################### OVERLAP SCORES | |
| cover = 0 | |
| perimeter = 0 | |
| # check 0 > 1 > 2 > 3 | |
| square_length = [] | |
| for start_idx in range(4): | |
| end_idx = (start_idx + 1) % 4 | |
| connect_idx = connects[start_idx] # segment idx of segment01 | |
| start_segments = segments[start_idx] | |
| end_segments = segments[end_idx] | |
| start_point = square[start_idx] | |
| end_point = square[end_idx] | |
| # check whether outside or inside | |
| start_position, start_min, start_cover_param, start_peri_param = check_outside_inside(start_segments, connect_idx) | |
| end_position, end_min, end_cover_param, end_peri_param = check_outside_inside(end_segments, connect_idx) | |
| cover += dist_segments[connect_idx] + start_cover_param * start_min + end_cover_param * end_min | |
| perimeter += dist_segments[connect_idx] + start_peri_param * start_min + end_peri_param * end_min | |
| square_length.append(dist_segments[connect_idx] + start_peri_param * start_min + end_peri_param * end_min) | |
| overlap_scores.append(cover / perimeter) | |
| ###################################### | |
| ###################################### DEGREE SCORES | |
| ''' | |
| deg0 vs deg2 | |
| deg1 vs deg3 | |
| ''' | |
| deg0, deg1, deg2, deg3 = degree | |
| deg_ratio1 = deg0 / deg2 | |
| if deg_ratio1 > 1.0: | |
| deg_ratio1 = 1 / deg_ratio1 | |
| deg_ratio2 = deg1 / deg3 | |
| if deg_ratio2 > 1.0: | |
| deg_ratio2 = 1 / deg_ratio2 | |
| degree_scores.append((deg_ratio1 + deg_ratio2) / 2) | |
| ###################################### | |
| ###################################### LENGTH SCORES | |
| ''' | |
| len0 vs len2 | |
| len1 vs len3 | |
| ''' | |
| len0, len1, len2, len3 = square_length | |
| len_ratio1 = len0 / len2 if len2 > len0 else len2 / len0 | |
| len_ratio2 = len1 / len3 if len3 > len1 else len3 / len1 | |
| length_scores.append((len_ratio1 + len_ratio2) / 2) | |
| ###################################### | |
| overlap_scores = np.array(overlap_scores) | |
| overlap_scores /= np.max(overlap_scores) | |
| degree_scores = np.array(degree_scores) | |
| #degree_scores /= np.max(degree_scores) | |
| length_scores = np.array(length_scores) | |
| ###################################### AREA SCORES | |
| area_scores = np.reshape(squares, [-1, 4, 2]) | |
| area_x = area_scores[:,:,0] | |
| area_y = area_scores[:,:,1] | |
| correction = area_x[:,-1] * area_y[:,0] - area_y[:,-1] * area_x[:,0] | |
| area_scores = np.sum(area_x[:,:-1] * area_y[:,1:], axis=-1) - np.sum(area_y[:,:-1] * area_x[:,1:], axis=-1) | |
| area_scores = 0.5 * np.abs(area_scores + correction) | |
| area_scores /= (map_size * map_size) #np.max(area_scores) | |
| ###################################### | |
| ###################################### CENTER SCORES | |
| centers = np.array([[256 // 2, 256 // 2]], dtype='float32') # [1, 2] | |
| # squares: [n, 4, 2] | |
| square_centers = np.mean(squares, axis=1) # [n, 2] | |
| center2center = np.sqrt(np.sum((centers - square_centers) ** 2)) | |
| center_scores = center2center / (map_size / np.sqrt(2.0)) | |
| ''' | |
| score_w = [overlap, degree, area, center, length] | |
| ''' | |
| score_w = [0.0, 1.0, 10.0, 0.5, 1.0] | |
| score_array = params['w_overlap'] * overlap_scores \ | |
| + params['w_degree'] * degree_scores \ | |
| + params['w_area'] * area_scores \ | |
| - params['w_center'] * center_scores \ | |
| + params['w_length'] * length_scores | |
| best_square = [] | |
| sorted_idx = np.argsort(score_array)[::-1] | |
| score_array = score_array[sorted_idx] | |
| squares = squares[sorted_idx] | |
| except Exception as e: | |
| pass | |
| try: | |
| new_segments[:,0] = new_segments[:,0] * 2 / input_shape[1] * original_shape[1] | |
| new_segments[:,1] = new_segments[:,1] * 2 / input_shape[0] * original_shape[0] | |
| new_segments[:,2] = new_segments[:,2] * 2 / input_shape[1] * original_shape[1] | |
| new_segments[:,3] = new_segments[:,3] * 2 / input_shape[0] * original_shape[0] | |
| except: | |
| new_segments = [] | |
| try: | |
| squares[:,:,0] = squares[:,:,0] * 2 / input_shape[1] * original_shape[1] | |
| squares[:,:,1] = squares[:,:,1] * 2 / input_shape[0] * original_shape[0] | |
| except: | |
| squares = [] | |
| score_array = [] | |
| try: | |
| inter_points = np.array(inter_points) | |
| inter_points[:,0] = inter_points[:,0] * 2 / input_shape[1] * original_shape[1] | |
| inter_points[:,1] = inter_points[:,1] * 2 / input_shape[0] * original_shape[0] | |
| except: | |
| inter_points = [] | |
| return new_segments, squares, score_array, inter_points | |