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T4
| # -------------------------------------------------------- | |
| # SiamMask | |
| # Licensed under The MIT License | |
| # Written by Qiang Wang (wangqiang2015 at ia.ac.cn) | |
| # -------------------------------------------------------- | |
| import numpy as np | |
| import math | |
| from SiamMask.utils.bbox_helper import center2corner, corner2center | |
| class Anchors: | |
| def __init__(self, cfg): | |
| self.stride = 8 | |
| self.ratios = [0.33, 0.5, 1, 2, 3] | |
| self.scales = [8] | |
| self.round_dight = 0 | |
| self.image_center = 0 | |
| self.size = 0 | |
| self.anchor_density = 1 | |
| self.__dict__.update(cfg) | |
| self.anchor_num = len(self.scales) * len(self.ratios) * (self.anchor_density**2) | |
| self.anchors = None # in single position (anchor_num*4) | |
| self.all_anchors = None # in all position 2*(4*anchor_num*h*w) | |
| self.generate_anchors() | |
| def generate_anchors(self): | |
| self.anchors = np.zeros((self.anchor_num, 4), dtype=np.float32) | |
| size = self.stride * self.stride | |
| count = 0 | |
| anchors_offset = self.stride / self.anchor_density | |
| anchors_offset = np.arange(self.anchor_density)*anchors_offset | |
| anchors_offset = anchors_offset - np.mean(anchors_offset) | |
| x_offsets, y_offsets = np.meshgrid(anchors_offset, anchors_offset) | |
| for x_offset, y_offset in zip(x_offsets.flatten(), y_offsets.flatten()): | |
| for r in self.ratios: | |
| if self.round_dight > 0: | |
| ws = round(math.sqrt(size*1. / r), self.round_dight) | |
| hs = round(ws * r, self.round_dight) | |
| else: | |
| ws = int(math.sqrt(size*1. / r)) | |
| hs = int(ws * r) | |
| for s in self.scales: | |
| w = ws * s | |
| h = hs * s | |
| self.anchors[count][:] = [-w*0.5+x_offset, -h*0.5+y_offset, w*0.5+x_offset, h*0.5+y_offset][:] | |
| count += 1 | |
| def generate_all_anchors(self, im_c, size): | |
| if self.image_center == im_c and self.size == size: | |
| return False | |
| self.image_center = im_c | |
| self.size = size | |
| a0x = im_c - size // 2 * self.stride | |
| ori = np.array([a0x] * 4, dtype=np.float32) | |
| zero_anchors = self.anchors + ori | |
| x1 = zero_anchors[:, 0] | |
| y1 = zero_anchors[:, 1] | |
| x2 = zero_anchors[:, 2] | |
| y2 = zero_anchors[:, 3] | |
| x1, y1, x2, y2 = map(lambda x: x.reshape(self.anchor_num, 1, 1), [x1, y1, x2, y2]) | |
| cx, cy, w, h = corner2center([x1, y1, x2, y2]) | |
| disp_x = np.arange(0, size).reshape(1, 1, -1) * self.stride | |
| disp_y = np.arange(0, size).reshape(1, -1, 1) * self.stride | |
| cx = cx + disp_x | |
| cy = cy + disp_y | |
| # broadcast | |
| zero = np.zeros((self.anchor_num, size, size), dtype=np.float32) | |
| cx, cy, w, h = map(lambda x: x + zero, [cx, cy, w, h]) | |
| x1, y1, x2, y2 = center2corner([cx, cy, w, h]) | |
| self.all_anchors = np.stack([x1, y1, x2, y2]), np.stack([cx, cy, w, h]) | |
| return True | |
| # if __name__ == '__main__': | |
| # anchors = Anchors(cfg={'stride':16, 'anchor_density': 2}) | |
| # anchors.generate_all_anchors(im_c=255//2, size=(255-127)//16+1+8) | |
| # a = 1 | |