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| # coding: utf-8 | |
| __author__ = 'ZFTurbo: https://kaggle.com/zfturbo' | |
| """ | |
| Method described in: | |
| CAD: Scale Invariant Framework for Real-Time Object Detection | |
| http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w14/Zhou_CAD_Scale_Invariant_ICCV_2017_paper.pdf | |
| """ | |
| import warnings | |
| import numpy as np | |
| from numba import jit | |
| def bb_intersection_over_union(A, B): | |
| xA = max(A[0], B[0]) | |
| yA = max(A[1], B[1]) | |
| xB = min(A[2], B[2]) | |
| yB = min(A[3], B[3]) | |
| # compute the area of intersection rectangle | |
| interArea = max(0, xB - xA) * max(0, yB - yA) | |
| if interArea == 0: | |
| return 0.0 | |
| # compute the area of both the prediction and ground-truth rectangles | |
| boxAArea = (A[2] - A[0]) * (A[3] - A[1]) | |
| boxBArea = (B[2] - B[0]) * (B[3] - B[1]) | |
| iou = interArea / float(boxAArea + boxBArea - interArea) | |
| return iou | |
| def prefilter_boxes(boxes, scores, labels, weights, thr): | |
| # Create dict with boxes stored by its label | |
| new_boxes = dict() | |
| for t in range(len(boxes)): | |
| if len(boxes[t]) != len(scores[t]): | |
| print('Error. Length of boxes arrays not equal to length of scores array: {} != {}'.format(len(boxes[t]), | |
| len(scores[t]))) | |
| exit() | |
| if len(boxes[t]) != len(labels[t]): | |
| print('Error. Length of boxes arrays not equal to length of labels array: {} != {}'.format(len(boxes[t]), | |
| len(labels[t]))) | |
| exit() | |
| for j in range(len(boxes[t])): | |
| score = scores[t][j] | |
| if score < thr: | |
| continue | |
| label = int(labels[t][j]) | |
| box_part = boxes[t][j] | |
| x1 = float(box_part[0]) | |
| y1 = float(box_part[1]) | |
| x2 = float(box_part[2]) | |
| y2 = float(box_part[3]) | |
| # Box data checks | |
| if x2 < x1: | |
| warnings.warn('X2 < X1 value in box. Swap them.') | |
| x1, x2 = x2, x1 | |
| if y2 < y1: | |
| warnings.warn('Y2 < Y1 value in box. Swap them.') | |
| y1, y2 = y2, y1 | |
| if x1 < 0: | |
| warnings.warn('X1 < 0 in box. Set it to 0.') | |
| x1 = 0 | |
| if x1 > 1: | |
| warnings.warn('X1 > 1 in box. Set it to 1. Check that you normalize boxes in [0, 1] range.') | |
| x1 = 1 | |
| if x2 < 0: | |
| warnings.warn('X2 < 0 in box. Set it to 0.') | |
| x2 = 0 | |
| if x2 > 1: | |
| warnings.warn('X2 > 1 in box. Set it to 1. Check that you normalize boxes in [0, 1] range.') | |
| x2 = 1 | |
| if y1 < 0: | |
| warnings.warn('Y1 < 0 in box. Set it to 0.') | |
| y1 = 0 | |
| if y1 > 1: | |
| warnings.warn('Y1 > 1 in box. Set it to 1. Check that you normalize boxes in [0, 1] range.') | |
| y1 = 1 | |
| if y2 < 0: | |
| warnings.warn('Y2 < 0 in box. Set it to 0.') | |
| y2 = 0 | |
| if y2 > 1: | |
| warnings.warn('Y2 > 1 in box. Set it to 1. Check that you normalize boxes in [0, 1] range.') | |
| y2 = 1 | |
| if (x2 - x1) * (y2 - y1) == 0.0: | |
| warnings.warn("Zero area box skipped: {}.".format(box_part)) | |
| continue | |
| b = [int(label), float(score) * weights[t], x1, y1, x2, y2] | |
| if label not in new_boxes: | |
| new_boxes[label] = [] | |
| new_boxes[label].append(b) | |
| # Sort each list in dict by score and transform it to numpy array | |
| for k in new_boxes: | |
| current_boxes = np.array(new_boxes[k]) | |
| new_boxes[k] = current_boxes[current_boxes[:, 1].argsort()[::-1]] | |
| return new_boxes | |
| def get_weighted_box(boxes): | |
| """ | |
| Create weighted box for set of boxes | |
| :param boxes: set of boxes to fuse | |
| :return: weighted box | |
| """ | |
| box = np.zeros(6, dtype=np.float32) | |
| best_box = boxes[0] | |
| conf = 0 | |
| for b in boxes: | |
| iou = bb_intersection_over_union(b[2:], best_box[2:]) | |
| weight = b[1] * iou | |
| box[2:] += (weight * b[2:]) | |
| conf += weight | |
| box[0] = best_box[0] | |
| box[1] = best_box[1] | |
| box[2:] /= conf | |
| return box | |
| def find_matching_box(boxes_list, new_box, match_iou): | |
| best_iou = match_iou | |
| best_index = -1 | |
| for i in range(len(boxes_list)): | |
| box = boxes_list[i] | |
| if box[0] != new_box[0]: | |
| continue | |
| iou = bb_intersection_over_union(box[2:], new_box[2:]) | |
| if iou > best_iou: | |
| best_index = i | |
| best_iou = iou | |
| return best_index, best_iou | |
| def non_maximum_weighted(boxes_list, scores_list, labels_list, weights=None, iou_thr=0.55, skip_box_thr=0.0): | |
| ''' | |
| :param boxes_list: list of boxes predictions from each model, each box is 4 numbers. | |
| It has 3 dimensions (models_number, model_preds, 4) | |
| Order of boxes: x1, y1, x2, y2. We expect float normalized coordinates [0; 1] | |
| :param scores_list: list of scores for each model | |
| :param labels_list: list of labels for each model | |
| :param weights: list of weights for each model. Default: None, which means weight == 1 for each model | |
| :param iou_thr: IoU value for boxes to be a match | |
| :param skip_box_thr: exclude boxes with score lower than this variable | |
| :return: boxes: boxes coordinates (Order of boxes: x1, y1, x2, y2). | |
| :return: scores: confidence scores | |
| :return: labels: boxes labels | |
| ''' | |
| if weights is None: | |
| weights = np.ones(len(boxes_list)) | |
| if len(weights) != len(boxes_list): | |
| print('Warning: incorrect number of weights {}. Must be: {}. Set weights equal to 1.'.format(len(weights), len(boxes_list))) | |
| weights = np.ones(len(boxes_list)) | |
| weights = np.array(weights) / max(weights) | |
| # for i in range(len(weights)): | |
| # scores_list[i] = (np.array(scores_list[i]) * weights[i]) | |
| filtered_boxes = prefilter_boxes(boxes_list, scores_list, labels_list, weights, skip_box_thr) | |
| if len(filtered_boxes) == 0: | |
| return np.zeros((0, 4)), np.zeros((0,)), np.zeros((0,)) | |
| overall_boxes = [] | |
| for label in filtered_boxes: | |
| boxes = filtered_boxes[label] | |
| new_boxes = [] | |
| main_boxes = [] | |
| # Clusterize boxes | |
| for j in range(0, len(boxes)): | |
| index, best_iou = find_matching_box(main_boxes, boxes[j], iou_thr) | |
| if index != -1: | |
| new_boxes[index].append(boxes[j].copy()) | |
| else: | |
| new_boxes.append([boxes[j].copy()]) | |
| main_boxes.append(boxes[j].copy()) | |
| weighted_boxes = [] | |
| for j in range(0, len(new_boxes)): | |
| box = get_weighted_box(new_boxes[j]) | |
| weighted_boxes.append(box.copy()) | |
| overall_boxes.append(np.array(weighted_boxes)) | |
| overall_boxes = np.concatenate(overall_boxes, axis=0) | |
| overall_boxes = overall_boxes[overall_boxes[:, 1].argsort()[::-1]] | |
| boxes = overall_boxes[:, 2:] | |
| scores = overall_boxes[:, 1] | |
| labels = overall_boxes[:, 0] | |
| return boxes, scores, labels | |