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- # Copyright 2021 Huawei Technologies Co., Ltd
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- # ============================================================================
-
- """Anchor Generator"""
-
- import numpy as np
-
-
- class GridAnchorGenerator:
- """
- Anchor Generator
- """
- def __init__(self, image_shape, scale, scales_per_octave, aspect_ratios):
- super(GridAnchorGenerator, self).__init__()
- self.scale = scale
- self.scales_per_octave = scales_per_octave
- self.aspect_ratios = aspect_ratios
- self.image_shape = image_shape
-
-
- def generate(self, step):
- """generate bbox"""
- scales = np.array([2**(float(scale) / self.scales_per_octave)
- for scale in range(self.scales_per_octave)]).astype(np.float32)
- aspects = np.array(list(self.aspect_ratios)).astype(np.float32)
-
- scales_grid, aspect_ratios_grid = np.meshgrid(scales, aspects)
- scales_grid = scales_grid.reshape([-1])
- aspect_ratios_grid = aspect_ratios_grid.reshape([-1])
-
- feature_size = [self.image_shape[0] / step, self.image_shape[1] / step]
- grid_height, grid_width = feature_size
-
- base_size = np.array([self.scale * step, self.scale * step]).astype(np.float32)
- anchor_offset = step / 2.0
-
- ratio_sqrt = np.sqrt(aspect_ratios_grid)
- heights = scales_grid / ratio_sqrt * base_size[0]
- widths = scales_grid * ratio_sqrt * base_size[1]
-
- y_centers = np.arange(grid_height).astype(np.float32)
- y_centers = y_centers * step + anchor_offset
- x_centers = np.arange(grid_width).astype(np.float32)
- x_centers = x_centers * step + anchor_offset
- x_centers, y_centers = np.meshgrid(x_centers, y_centers)
-
- x_centers_shape = x_centers.shape
- y_centers_shape = y_centers.shape
-
- widths_grid, x_centers_grid = np.meshgrid(widths, x_centers.reshape([-1]))
- heights_grid, y_centers_grid = np.meshgrid(heights, y_centers.reshape([-1]))
-
- x_centers_grid = x_centers_grid.reshape(*x_centers_shape, -1)
- y_centers_grid = y_centers_grid.reshape(*y_centers_shape, -1)
- widths_grid = widths_grid.reshape(-1, *x_centers_shape)
- heights_grid = heights_grid.reshape(-1, *y_centers_shape)
-
-
- bbox_centers = np.stack([y_centers_grid, x_centers_grid], axis=3)
- bbox_sizes = np.stack([heights_grid, widths_grid], axis=3)
- bbox_centers = bbox_centers.reshape([-1, 2])
- bbox_sizes = bbox_sizes.reshape([-1, 2])
- bbox_corners = np.concatenate([bbox_centers - 0.5 * bbox_sizes, bbox_centers + 0.5 * bbox_sizes], axis=1)
- self.bbox_corners = bbox_corners / np.array([*self.image_shape, *self.image_shape]).astype(np.float32)
- self.bbox_centers = np.concatenate([bbox_centers, bbox_sizes], axis=1)
- self.bbox_centers = self.bbox_centers / np.array([*self.image_shape, *self.image_shape]).astype(np.float32)
-
- print(self.bbox_centers.shape)
- return self.bbox_centers, self.bbox_corners
-
- def generate_multi_levels(self, steps):
- """generate multi_levels"""
- bbox_centers_list = []
- bbox_corners_list = []
- for step in steps:
- bbox_centers, bbox_corners = self.generate(step)
- bbox_centers_list.append(bbox_centers)
- bbox_corners_list.append(bbox_corners)
-
- self.bbox_centers = np.concatenate(bbox_centers_list, axis=0)
- self.bbox_corners = np.concatenate(bbox_corners_list, axis=0)
- return self.bbox_centers, self.bbox_corners
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