faster-rcnn-pytorch-master/utils/anchors.py

import numpy as np

#--------------------------------------------#
#   生成基础的先验框
#--------------------------------------------#
def generate_anchor_base(base_size=16, ratios=[0.5, 1, 2], anchor_scales=[8, 16, 32]):
    anchor_base = np.zeros((len(ratios) * len(anchor_scales), 4), dtype=np.float32)
    for i in range(len(ratios)):
        for j in range(len(anchor_scales)):
            h = base_size * anchor_scales[j] * np.sqrt(ratios[i])
            w = base_size * anchor_scales[j] * np.sqrt(1. / ratios[i])

            index = i * len(anchor_scales) + j
            anchor_base[index, 0] = - h / 2.
            anchor_base[index, 1] = - w / 2.
            anchor_base[index, 2] = h / 2.
            anchor_base[index, 3] = w / 2.
    return anchor_base

#--------------------------------------------#
#   对基础先验框进行拓展对应到所有特征点上
#--------------------------------------------#
def _enumerate_shifted_anchor(anchor_base, feat_stride, height, width):
    #---------------------------------#
    #   计算网格中心点
    #---------------------------------#
    shift_x             = np.arange(0, width * feat_stride, feat_stride)
    shift_y             = np.arange(0, height * feat_stride, feat_stride)
    shift_x, shift_y    = np.meshgrid(shift_x, shift_y)
    shift               = np.stack((shift_x.ravel(), shift_y.ravel(), shift_x.ravel(), shift_y.ravel(),), axis=1)

    #---------------------------------#
    #   每个网格点上的9个先验框
    #---------------------------------#
    A       = anchor_base.shape[0]
    K       = shift.shape[0]
    anchor  = anchor_base.reshape((1, A, 4)) + shift.reshape((K, 1, 4))
    #---------------------------------#
    #   所有的先验框
    #---------------------------------#
    anchor  = anchor.reshape((K * A, 4)).astype(np.float32)
    return anchor
    
if __name__ == "__main__":
    import matplotlib.pyplot as plt
    nine_anchors = generate_anchor_base()
    print(nine_anchors)

    height, width, feat_stride  = 38,38,16
    anchors_all                 = _enumerate_shifted_anchor(nine_anchors, feat_stride, height, width)
    print(np.shape(anchors_all))
    
    fig     = plt.figure()
    ax      = fig.add_subplot(111)
    plt.ylim(-300,900)
    plt.xlim(-300,900)
    shift_x = np.arange(0, width * feat_stride, feat_stride)
    shift_y = np.arange(0, height * feat_stride, feat_stride)
    shift_x, shift_y = np.meshgrid(shift_x, shift_y)
    plt.scatter(shift_x,shift_y)
    box_widths  = anchors_all[:,2]-anchors_all[:,0]
    box_heights = anchors_all[:,3]-anchors_all[:,1]
    
    for i in [108, 109, 110, 111, 112, 113, 114, 115, 116]:
        rect = plt.Rectangle([anchors_all[i, 0],anchors_all[i, 1]],box_widths[i],box_heights[i],color="r",fill=False)
        ax.add_patch(rect)
    plt.show()