From 92f0b26e983fad395abb75a1c3736f9d38f82d14 Mon Sep 17 00:00:00 2001 From: triplemu Date: Mon, 6 May 2024 16:12:29 +0800 Subject: [PATCH] add nmsboxes --- models/utils.py | 160 ++++++++++++++---------------------------------- 1 file changed, 46 insertions(+), 114 deletions(-) diff --git a/models/utils.py b/models/utils.py index 50b5a70..44226d0 100644 --- a/models/utils.py +++ b/models/utils.py @@ -61,118 +61,6 @@ def sigmoid(x: ndarray) -> ndarray: return 1. / (1. + np.exp(-x)) -def bbox_iou(boxes1: ndarray, boxes2: ndarray) -> ndarray: - boxes1_area = (boxes1[..., 2] - boxes1[..., 0]) * \ - (boxes1[..., 3] - boxes1[..., 1]) - boxes2_area = (boxes2[..., 2] - boxes2[..., 0]) * \ - (boxes2[..., 3] - boxes2[..., 1]) - left_up = np.maximum(boxes1[..., :2], boxes2[..., :2]) - right_down = np.minimum(boxes1[..., 2:], boxes2[..., 2:]) - inter_section = np.maximum(right_down - left_up, 0.0) - inter_area = inter_section[..., 0] * inter_section[..., 1] - union_area = boxes1_area + boxes2_area - inter_area - ious = np.maximum(1.0 * inter_area / union_area, np.finfo(np.float32).eps) - - return ious - - -def batched_nms(boxes: ndarray, - scores: ndarray, - iou_thres: float = 0.65, - conf_thres: float = 0.25): - labels = np.argmax(scores, axis=-1) - scores = np.max(scores, axis=-1) - - cand = scores > conf_thres - boxes = boxes[cand] - scores = scores[cand] - labels = labels[cand] - - keep_boxes = [] - keep_scores = [] - keep_labels = [] - - for cls in np.unique(labels): - cls_mask = labels == cls - cls_boxes = boxes[cls_mask] - cls_scores = scores[cls_mask] - - while cls_boxes.shape[0] > 0: - max_idx = np.argmax(cls_scores) - max_box = cls_boxes[max_idx:max_idx + 1] - max_score = cls_scores[max_idx:max_idx + 1] - max_label = np.array([cls], dtype=np.int32) - keep_boxes.append(max_box) - keep_scores.append(max_score) - keep_labels.append(max_label) - other_boxes = np.delete(cls_boxes, max_idx, axis=0) - other_scores = np.delete(cls_scores, max_idx, axis=0) - ious = bbox_iou(max_box, other_boxes) - iou_mask = ious < iou_thres - if not iou_mask.any(): - break - cls_boxes = other_boxes[iou_mask] - cls_scores = other_scores[iou_mask] - - if len(keep_boxes) == 0: - keep_boxes = np.empty((0, 4), dtype=np.float32) - keep_scores = np.empty((0, ), dtype=np.float32) - keep_labels = np.empty((0, ), dtype=np.float32) - - else: - keep_boxes = np.concatenate(keep_boxes, axis=0) - keep_scores = np.concatenate(keep_scores, axis=0) - keep_labels = np.concatenate(keep_labels, axis=0) - - return keep_boxes, keep_scores, keep_labels - - -def nms(boxes: ndarray, - scores: ndarray, - iou_thres: float = 0.65, - conf_thres: float = 0.25): - labels = np.argmax(scores, axis=-1) - scores = np.max(scores, axis=-1) - - cand = scores > conf_thres - boxes = boxes[cand] - scores = scores[cand] - labels = labels[cand] - - keep_boxes = [] - keep_scores = [] - keep_labels = [] - - idxs = scores.argsort() - while idxs.size > 0: - max_score_index = idxs[-1] - max_box = boxes[max_score_index:max_score_index + 1] - max_score = scores[max_score_index:max_score_index + 1] - max_label = np.array([labels[max_score_index]], dtype=np.int32) - keep_boxes.append(max_box) - keep_scores.append(max_score) - keep_labels.append(max_label) - if idxs.size == 1: - break - idxs = idxs[:-1] - other_boxes = boxes[idxs] - ious = bbox_iou(max_box, other_boxes) - iou_mask = ious < iou_thres - idxs = idxs[iou_mask] - - if len(keep_boxes) == 0: - keep_boxes = np.empty((0, 4), dtype=np.float32) - keep_scores = np.empty((0, ), dtype=np.float32) - keep_labels = np.empty((0, ), dtype=np.float32) - - else: - keep_boxes = np.concatenate(keep_boxes, axis=0) - keep_scores = np.concatenate(keep_scores, axis=0) - keep_labels = np.concatenate(keep_labels, axis=0) - - return keep_boxes, keep_scores, keep_labels - - def path_to_list(images_path: Union[str, Path]) -> List: if isinstance(images_path, str): images_path = Path(images_path) @@ -197,13 +85,57 @@ def crop_mask(masks: ndarray, bboxes: ndarray) -> ndarray: return masks * ((r >= x1) * (r < x2) * (c >= y1) * (c < y2)) +def box_iou(box1: ndarray, box2: ndarray) -> float: + x11, y11, x21, y21 = box1 + x12, y12, x22, y22 = box2 + x1 = max(x11, x12) + y1 = max(y11, y12) + x2 = min(x21, x22) + y2 = min(y21, y22) + inter_area = max(0, x2 - x1) * max(0, y2 - y1) + union_area = (x21 - x11) * (y21 - y11) + (x22 - x12) * (y22 - y12) - inter_area + return max(0, inter_area / union_area) + + +def NMSBoxes( + boxes: ndarray, + scores: ndarray, + labels: ndarray, + iou_thres: float, + agnostic: bool = False +): + num_boxes = boxes.shape[0] + order = np.argsort(scores)[::-1] + boxes = boxes[order] + labels = labels[order] + + indices = [] + + for i in range(num_boxes): + box_a = boxes[i] + label_a = labels[i] + keep = True + for j in indices: + box_b = boxes[j] + label_b = labels[j] + if not agnostic and label_a != label_b: + continue + if box_iou(box_a, box_b) > iou_thres: + keep = False + if keep: + indices.append(i) + + indices = np.array(indices, dtype=np.int32) + return order[indices] + + def det_postprocess(data: Tuple[ndarray, ndarray, ndarray, ndarray]): assert len(data) == 4 num_dets, bboxes, scores, labels = (i[0] for i in data) nums = num_dets.item() if nums == 0: return np.empty((0, 4), dtype=np.float32), np.empty( - (0, ), dtype=np.float32), np.empty((0, ), dtype=np.int32) + (0,), dtype=np.float32), np.empty((0,), dtype=np.int32) # check score negative scores[scores < 0] = 1 + scores[scores < 0] bboxes = bboxes[:nums] @@ -268,7 +200,7 @@ def pose_postprocess( idx = scores > conf_thres if not idx.any(): # no bounding boxes or seg were created return np.empty((0, 4), dtype=np.float32), np.empty( - (0, ), dtype=np.float32), np.empty((0, 0, 0), dtype=np.float32) + (0,), dtype=np.float32), np.empty((0, 0, 0), dtype=np.float32) bboxes, scores, kpts = bboxes[idx], scores[idx], kpts[idx] xycenter, wh = np.split(bboxes, [ 2,