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| import numpy as np | |
| import time | |
| def IoU(box, boxes): | |
| """Compute IoU between detect box and gt boxes | |
| Parameters: | |
| ---------- | |
| box: numpy array , shape (5, ): x1, y1, x2, y2, score | |
| input box | |
| boxes: numpy array, shape (n, 4): x1, y1, x2, y2 | |
| input ground truth boxes | |
| Returns: | |
| ------- | |
| ovr: numpy.array, shape (n, ) | |
| IoU | |
| """ | |
| box_area = (box[2] - box[0] + 1) * (box[3] - box[1] + 1) | |
| area = (boxes[:, 2] - boxes[:, 0] + 1) * (boxes[:, 3] - boxes[:, 1] + 1) | |
| xx1 = np.maximum(box[0], boxes[:, 0]) | |
| yy1 = np.maximum(box[1], boxes[:, 1]) | |
| xx2 = np.minimum(box[2], boxes[:, 2]) | |
| yy2 = np.minimum(box[3], boxes[:, 3]) | |
| # compute the width and height of the bounding box | |
| w = np.maximum(0, xx2 - xx1 + 1) | |
| h = np.maximum(0, yy2 - yy1 + 1) | |
| inter = w * h | |
| ovr = np.true_divide(inter,(box_area + area - inter)) | |
| #ovr = inter / (box_area + area - inter) | |
| return ovr | |
| def convert_to_square(bbox): | |
| """Convert bbox to square | |
| Parameters: | |
| ---------- | |
| bbox: numpy array , shape n x 5 | |
| input bbox | |
| Returns: | |
| ------- | |
| square bbox | |
| """ | |
| square_bbox = bbox.copy() | |
| h = bbox[:, 3] - bbox[:, 1] + 1 | |
| w = bbox[:, 2] - bbox[:, 0] + 1 | |
| max_side = np.maximum(h,w) | |
| square_bbox[:, 0] = bbox[:, 0] + w*0.5 - max_side*0.5 | |
| square_bbox[:, 1] = bbox[:, 1] + h*0.5 - max_side*0.5 | |
| square_bbox[:, 2] = square_bbox[:, 0] + max_side - 1 | |
| square_bbox[:, 3] = square_bbox[:, 1] + max_side - 1 | |
| return square_bbox | |
| # non-maximum suppression: eleminates the box which have large interception with the box which have the largest score | |
| def nms(dets, thresh, mode="Union"): | |
| """ | |
| greedily select boxes with high confidence | |
| keep boxes overlap <= thresh | |
| rule out overlap > thresh | |
| :param dets: [[x1, y1, x2, y2 score]] | |
| :param thresh: retain overlap <= thresh | |
| :return: indexes to keep | |
| """ | |
| x1 = dets[:, 0] | |
| y1 = dets[:, 1] | |
| x2 = dets[:, 2] | |
| y2 = dets[:, 3] | |
| scores = dets[:, 4] | |
| # shape of x1 = (454,), shape of scores = (454,) | |
| # print("shape of x1 = {0}, shape of scores = {1}".format(x1.shape, scores.shape)) | |
| # time.sleep(5) | |
| areas = (x2 - x1 + 1) * (y2 - y1 + 1) | |
| order = scores.argsort()[::-1] # argsort: ascending order then [::-1] reverse the order --> descending order | |
| # print("shape of order {0}".format(order.size)) # (454,) | |
| # time.sleep(5) | |
| # eleminates the box which have large interception with the box which have the largest score in order | |
| # matain the box with largest score and boxes don't have large interception with it | |
| keep = [] | |
| while order.size > 0: | |
| i = order[0] | |
| keep.append(i) | |
| xx1 = np.maximum(x1[i], x1[order[1:]]) | |
| yy1 = np.maximum(y1[i], y1[order[1:]]) | |
| xx2 = np.minimum(x2[i], x2[order[1:]]) | |
| yy2 = np.minimum(y2[i], y2[order[1:]]) | |
| w = np.maximum(0.0, xx2 - xx1 + 1) | |
| h = np.maximum(0.0, yy2 - yy1 + 1) | |
| inter = w * h | |
| # cacaulate the IOU between box which have largest score with other boxes | |
| if mode == "Union": | |
| # area[i]: the area of largest score | |
| ovr = inter / (areas[i] + areas[order[1:]] - inter) | |
| elif mode == "Minimum": | |
| ovr = inter / np.minimum(areas[i], areas[order[1:]]) | |
| inds = np.where(ovr <= thresh)[0] | |
| order = order[inds + 1] # +1: eliminates the first element in order | |
| # print(inds) | |
| # print("shape of order {0}".format(order.shape)) # (454,) | |
| # time.sleep(2) | |
| return keep | |