soft-nms/py_nms.py at master · OneDirection9/soft-nms · GitHub

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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import logging

import numpy as np

logger = logging.getLogger(__name__)


def py_greedy_nms(dets, iou_thr):
    """Pure python implementation of traditional greedy NMS.

    Args:
        dets (numpy.array): Detection results with shape `(num, 5)`,
            data in second dimension are [x1, y1, x2, y2, score] respectively.
        iou_thr (float): Drop the boxes that overlap with current
            maximum > thresh.

    Returns:
        numpy.array: Retained boxes.
    """
    x1 = dets[:, 0]
    y1 = dets[:, 1]
    x2 = dets[:, 2]
    y2 = dets[:, 3]
    scores = dets[:, 4]

    areas = (x2 - x1 + 1) * (y2 - y1 + 1)
    sorted_idx = scores.argsort()[::-1]

    keep = []
    while sorted_idx.size > 0:
        i = sorted_idx[0]
        keep.append(i)

        xx1 = np.maximum(x1[i], x1[sorted_idx[1:]])
        yy1 = np.maximum(y1[i], y1[sorted_idx[1:]])
        xx2 = np.minimum(x2[i], x2[sorted_idx[1:]])
        yy2 = np.minimum(y2[i], y2[sorted_idx[1:]])

        w = np.maximum(xx2 - xx1 + 1, 0.0)
        h = np.maximum(yy2 - yy1 + 1, 0.0)
        inter = w * h
        iou = inter / (areas[i] + areas[sorted_idx[1:]] - inter)

        retained_idx = np.where(iou <= iou_thr)[0]
        sorted_idx = sorted_idx[retained_idx + 1]

    return dets[keep, :]


def py_soft_nms(dets, method='linear', iou_thr=0.3, sigma=0.5, score_thr=0.001):
    """Pure python implementation of soft NMS as described in the paper
    `Improving Object Detection With One Line of Code`_.

    Args:
        dets (numpy.array): Detection results with shape `(num, 5)`,
            data in second dimension are [x1, y1, x2, y2, score] respectively.
        method (str): Rescore method. Only can be `linear`, `gaussian`
            or 'greedy'.
        iou_thr (float): IOU threshold. Only work when method is `linear`
            or 'greedy'.
        sigma (float): Gaussian function parameter. Only work when method
            is `gaussian`.
        score_thr (float): Boxes that score less than the.

    Returns:
        numpy.array: Retained boxes.

    .. _`Improving Object Detection With One Line of Code`:
        https://arxiv.org/abs/1704.04503
    """
    if method not in ('linear', 'gaussian', 'greedy'):
        raise ValueError('method must be linear, gaussian or greedy')

    x1 = dets[:, 0]
    y1 = dets[:, 1]
    x2 = dets[:, 2]
    y2 = dets[:, 3]

    areas = (x2 - x1 + 1) * (y2 - y1 + 1)
    # expand dets with areas, and the second dimension is
    # x1, y1, x2, y2, score, area
    dets = np.concatenate((dets, areas[:, None]), axis=1)

    retained_box = []
    while dets.size > 0:
        max_idx = np.argmax(dets[:, 4], axis=0)
        dets[[0, max_idx], :] = dets[[max_idx, 0], :]
        retained_box.append(dets[0, :-1])

        xx1 = np.maximum(dets[0, 0], dets[1:, 0])
        yy1 = np.maximum(dets[0, 1], dets[1:, 1])
        xx2 = np.minimum(dets[0, 2], dets[1:, 2])
        yy2 = np.minimum(dets[0, 3], dets[1:, 3])

        w = np.maximum(xx2 - xx1 + 1, 0.0)
        h = np.maximum(yy2 - yy1 + 1, 0.0)
        inter = w * h
        iou = inter / (dets[0, 5] + dets[1:, 5] - inter)

        if method == 'linear':
            weight = np.ones_like(iou)
            weight[iou > iou_thr] -= iou[iou > iou_thr]
        elif method == 'gaussian':
            weight = np.exp(-(iou * iou) / sigma)
        else:  # traditional nms
            weight = np.ones_like(iou)
            weight[iou > iou_thr] = 0

        dets[1:, 4] *= weight
        retained_idx = np.where(dets[1:, 4] >= score_thr)[0]
        dets = dets[retained_idx + 1, :]

    return np.vstack(retained_box)


if __name__ == '__main__':
    boxes = np.array([[100, 100, 210, 210, 0.72],
                      [250, 250, 420, 420, 0.8],
                      [220, 220, 320, 330, 0.92],
                      [100, 100, 210, 210, 0.72],
                      [230, 240, 325, 330, 0.81],
                      [220, 230, 315, 340, 0.9]], dtype=np.float32)
    print('greedy result:')
    print(py_greedy_nms(boxes, 0.7))
    print('soft nms result:')
    print(py_soft_nms(boxes, method='gaussian'))