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| from __future__ import division | |
| import scipy.optimize | |
| import numpy as np | |
| def bbox_iou(boxA, boxB): | |
| # https://www.pyimagesearch.com/2016/11/07/intersection-over-union-iou-for-object-detection/ | |
| # ^^ corrected. | |
| # Determine the (x, y)-coordinates of the intersection rectangle | |
| xA = max(boxA[0], boxB[0]) | |
| yA = max(boxA[1], boxB[1]) | |
| xB = min(boxA[2], boxB[2]) | |
| yB = min(boxA[3], boxB[3]) | |
| interW = xB - xA + 1 | |
| interH = yB - yA + 1 | |
| # Correction: reject non-overlapping boxes | |
| if interW <=0 or interH <=0 : | |
| return -1.0 | |
| interArea = interW * interH | |
| boxAArea = (boxA[2] - boxA[0] + 1) * (boxA[3] - boxA[1] + 1) | |
| boxBArea = (boxB[2] - boxB[0] + 1) * (boxB[3] - boxB[1] + 1) | |
| iou = interArea / float(boxAArea + boxBArea - interArea) | |
| return iou | |
| def match_bboxes(bbox_gt, bbox_pred, IOU_THRESH=0.5): | |
| ''' | |
| Given sets of true and predicted bounding-boxes, | |
| determine the best possible match. | |
| Parameters | |
| ---------- | |
| bbox_gt, bbox_pred : N1x4 and N2x4 np array of bboxes [x1,y1,x2,y2]. | |
| The number of bboxes, N1 and N2, need not be the same. | |
| Returns | |
| ------- | |
| (idxs_true, idxs_pred, ious, labels) | |
| idxs_true, idxs_pred : indices into gt and pred for matches | |
| ious : corresponding IOU value of each match | |
| labels: vector of 0/1 values for the list of detections | |
| ''' | |
| n_true = bbox_gt.shape[0] | |
| n_pred = bbox_pred.shape[0] | |
| MAX_DIST = 1.0 | |
| MIN_IOU = 0.0 | |
| # NUM_GT x NUM_PRED | |
| iou_matrix = np.zeros((n_true, n_pred)) | |
| for i in range(n_true): | |
| for j in range(n_pred): | |
| iou_matrix[i, j] = bbox_iou(bbox_gt[i,:], bbox_pred[j,:]) | |
| if n_pred > n_true: | |
| # there are more predictions than ground-truth - add dummy rows | |
| diff = n_pred - n_true | |
| iou_matrix = np.concatenate( (iou_matrix, | |
| np.full((diff, n_pred), MIN_IOU)), | |
| axis=0) | |
| if n_true > n_pred: | |
| # more ground-truth than predictions - add dummy columns | |
| diff = n_true - n_pred | |
| iou_matrix = np.concatenate( (iou_matrix, | |
| np.full((n_true, diff), MIN_IOU)), | |
| axis=1) | |
| # call the Hungarian matching | |
| idxs_true, idxs_pred = scipy.optimize.linear_sum_assignment(1 - iou_matrix) | |
| if (not idxs_true.size) or (not idxs_pred.size): | |
| ious = np.array([]) | |
| else: | |
| ious = iou_matrix[idxs_true, idxs_pred] | |
| # remove dummy assignments | |
| sel_pred = idxs_pred<n_pred | |
| idx_pred_actual = idxs_pred[sel_pred] | |
| idx_gt_actual = idxs_true[sel_pred] | |
| ious_actual = iou_matrix[idx_gt_actual, idx_pred_actual] | |
| sel_valid = (ious_actual > IOU_THRESH) | |
| label = sel_valid.astype(int) | |
| return idx_gt_actual[sel_valid], idx_pred_actual[sel_valid], ious_actual[sel_valid], label | |
Hi, Prof. I have a question in line73:
scipy.optimize.linear_sum_assignment(1 - iou_matrix). how this line work for matching multiple bbs correctly. I am finding the method to compare iou of bounding boxes from 2 images and your method is work! however, I quite don't understand this line completly. kindly you explain/guide it. 2nd question, I am wondering on, can we compare 2 images which have different size as 1st image is the original one and 2nd is a crop from 1st image. if there are the bounding on 2 images can we compare iou matching among boxes. (not sure but it should be work by normalized value like YOLO format). Thx.
1nd question. You should read carefully a Hungarian algorithm, this algorithm to calculate min cost pair, in here they will calculate min cost predict and ground truth. And when to use scipy.optimize.linear_sum_assignment(1 - iou_matrix) it think if iou is approximate 0, they become to approximate 1 and opposite if iou is big, they become to approximate small. And this is conditional for problem to find scipy.optimize.linear_sum_assignment(1 - iou_matrix) min and deduce iou max
2nd question. I'm thinking for this
Hi, Prof. I have a question in line73:
scipy.optimize.linear_sum_assignment(1 - iou_matrix). how this line work for matching multiple bbs correctly. I am finding the method to compare iou of bounding boxes from 2 images and your method is work! however, I quite don't understand this line completly. kindly you explain/guide it. 2nd question, I am wondering on, can we compare 2 images which have different size as 1st image is the original one and 2nd is a crop from 1st image. if there are the bounding on 2 images can we compare iou matching among boxes. (not sure but it should be work by normalized value like YOLO format). Thx.1nd question. You should read carefully a Hungarian algorithm, this algorithm to calculate min cost pair, in here they will calculate min cost predict and ground truth. And when to use scipy.optimize.linear_sum_assignment(1 - iou_matrix) it think if iou is approximate 0, they become to approximate 1 and opposite if iou is big, they become to approximate small. And this is conditional for problem to find scipy.optimize.linear_sum_assignment(1 - iou_matrix) min and deduce iou max 2nd question. I'm thinking for this
So, for 1st question, min cost pair method would give probably best match results between boxes in two images?
Hows about 2nd question? I think it can not measure as exact IoU, just approximately?
images
I don't know it catch best result? When I try use in my project to evaluate match pair prediction and ground truth based on IoU, it still good but some my result it not catch boxes high iou, I dont know why they don't catch them but generally they match one-to-one so good, it can catch 90-95% to correct. If you want to know more, you can search deepsort in tracking, because they use hungarian algrithm to match one-to-one prediction and ground truth
Thanks! Didn't know about scipy.optimize.linear_sum_assignment.
Hi, Prof. I have a question in line73:
scipy.optimize.linear_sum_assignment(1 - iou_matrix). how this line work for matching multiple bbs correctly. I am finding the method to compare iou of bounding boxes from 2 images and your method is work! however, I quite don't understand this line completly. kindly you explain/guide it.2nd question, I am wondering on, can we compare 2 images which have different size as 1st image is the original one and 2nd is a crop from 1st image. if there are the bounding on 2 images can we compare iou matching among boxes.
(not sure but it should be work by normalized value like YOLO format).
Thx.