yolov7如何使用softNMS

在 YOLOv7 中使用 Soft-NMS（Soft Non-Maximum Suppression）的步骤如下：

1. 在 `detect.py` 中，增加一个 `soft_nms` 的参数，用于选择是否使用 Soft-NMS。

2. 在 `utils/general.py` 中，增加一个 `soft_nms` 函数，用于执行 Soft-NMS。

3. 在 `models/yolov7.py` 中，修改 `non_max_suppression` 函数，增加 Soft-NMS 的支持。

具体实现过程可以参考以下代码示例：

1. 在 `detect.py` 中增加 `soft_nms` 参数：

parser.add_argument('--soft_nms', action='store_true', help='use Soft-NMS instead of NMS')

2. 在 `utils/general.py` 中增加 `soft_nms` 函数:

def soft_nms(boxes, scores, sigma=0.5, thresh=0.001, method='linear'):
    """
    Soft-NMS algorithm implementation.
    :param boxes: bounding boxes
    :param scores: confidence scores
    :param sigma: Gaussian function hyperparameter
    :param thresh: score threshhold for box filtering
    :param method: Soft-NMS method ('linear' or 'gaussian')
    :return: indices of boxes to keep after Soft-NMS
    """
    N = boxes.shape[0]
    indices = np.arange(N)
    for i in range(N):
        # get max score index
        max_idx = np.argmax(scores[i:])
        if i != max_idx:
            # swap i-th and max_idx boxes and scores
            boxes[[i, max_idx]] = boxes[[max_idx, i]]
            scores[[i, max_idx]] = scores[[max_idx, i]]
            indices[[i, max_idx]] = indices[[max_idx, i]]
        # apply Soft-NMS
        box_i = boxes[i]
        score_i = scores[i]
        for j in range(i+1, N):
            box_j = boxes[j]
            score_j = scores[j]
            if iou(box_i, box_j) > thresh:
                if method == 'linear':
                    decay = 1 - iou(box_i, box_j)
                elif method == 'gaussian':
                    decay = np.exp(-(iou(box_i, box_j)**2) / sigma)
                scores[j] = scores[j] * decay
    # filter boxes by score threshold
    mask = scores > thresh
    indices = indices[mask]
    return indices

3. 在 `models/yolov7.py` 中修改 `non_max_suppression` 函数：

def non_max_suppression(prediction, conf_thres=0.1, iou_thres=0.6, classes=None, agnostic=False, soft_nms=False):
    """
    Performs Non-Maximum Suppression (NMS) on inference results
    :param prediction: tensor of shape (N, 6) containing (x1, y1, x2, y2, conf, cls) where N is the number of detections
    :param conf_thres: confidence threshold
    :param iou_thres: IoU threshold for NMS
    :param classes: list of classes to perform NMS on
    :param agnostic: boolean indicating whether to perform class-agnostic NMS
    :param soft_nms: boolean indicating whether to use Soft-NMS instead of NMS
    :return: list of detections with shape (x1, y1, x2, y2, conf, cls, cls_conf)
    """
    # filter out boxes with low confidence
    conf_mask = (prediction[:, 4] > conf_thres)
    prediction = prediction[conf_mask]
    if prediction.shape[0] == 0:
        return []
    # perform NMS
    if classes is None:
        classes = prediction[:, 5].astype('int').tolist()
    unique_classes = list(set(classes))
    detections = []
    for cls in unique_classes:
        mask = (prediction[:, 5] == cls)
        class_pred = prediction[mask]
        if agnostic:
            class_mask = np.ones(class_pred.shape[0], dtype=bool)
        else:
            class_mask = (class_pred[:, 6] == cls)
        class_pred = class_pred[class_mask]
        if class_pred.shape[0] == 0:
            continue
        if soft_nms:
            boxes = class_pred[:, :4].copy()
            scores = class_pred[:, 4].copy()
            keep = soft_nms(boxes, scores)
            class_pred = class_pred[keep]
        else:
            keep = nms(class_pred[:, :4], class_pred[:, 4], iou_thres)
            class_pred = class_pred[keep]
        detections.extend(class_pred)
    return detections

这样，就可以在 YOLOv7 中使用 Soft-NMS 了，只需要在运行 `detect.py` 时增加 `--soft_nms` 参数即可。