ultralytics 目标检测 混淆矩阵 背景图像 没被记录

修改

utils/metrics.py

ConfusionMatrix

    def process_batch(self, detections, gt_bboxes, gt_cls):"""Update confusion matrix for object detection task.Args:detections (Array[N, 6] | Array[N, 7]): Detected bounding boxes and their associated information.Each row should contain (x1, y1, x2, y2, conf, class)or with an additional element `angle` when it's obb.gt_bboxes (Array[M, 4]| Array[N, 5]): Ground truth bounding boxes with xyxy/xyxyr format.gt_cls (Array[M]): The class labels."""if gt_cls.shape[0] == 0:  # Check if labels is emptyif detections is not None:detections = detections[detections[:, 4] > self.conf]if detections.shape[0] == 0:self.matrix[self.nc, self.nc] += 1  # true backgroundelse:detection_classes = detections[:, 5].int()for dc in detection_classes:self.matrix[dc, self.nc] += 1  # false positivesreturnif detections is None:gt_classes = gt_cls.int()for gc in gt_classes:self.matrix[self.nc, gc] += 1  # background FNreturndetections = detections[detections[:, 4] > self.conf]gt_classes = gt_cls.int()detection_classes = detections[:, 5].int()is_obb = detections.shape[1] == 7 and gt_bboxes.shape[1] == 5  # with additional `angle` dimensioniou = (batch_probiou(gt_bboxes, torch.cat([detections[:, :4], detections[:, -1:]], dim=-1))if is_obbelse box_iou(gt_bboxes, detections[:, :4]))x = torch.where(iou > self.iou_thres)if x[0].shape[0]:matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy()if x[0].shape[0] > 1:matches = matches[matches[:, 2].argsort()[::-1]]matches = matches[np.unique(matches[:, 1], return_index=True)[1]]matches = matches[matches[:, 2].argsort()[::-1]]matches = matches[np.unique(matches[:, 0], return_index=True)[1]]else:matches = np.zeros((0, 3))n = matches.shape[0] > 0m0, m1, _ = matches.transpose().astype(int)for i, gc in enumerate(gt_classes):j = m0 == iif n and sum(j) == 1:self.matrix[detection_classes[m1[j]], gc] += 1  # correctelse:self.matrix[self.nc, gc] += 1  # true backgroundfor i, dc in enumerate(detection_classes):if not any(m1 == i):self.matrix[dc, self.nc] += 1  # predicted background