fasterrcnn.py 文件源码

def _generate_anchors(self, feature_map_shape):
        """Generate anchor for an image.

        Using the feature map, the output of the pretrained network for an
        image, and the anchor_reference generated using the anchor config
        values. We generate a list of anchors.

        Anchors are just fixed bounding boxes of different ratios and sizes
        that are uniformly generated throught the image.

        Args:
            feature_map_shape: Shape of the convolutional feature map used as
                input for the RPN. Should be (batch, height, width, depth).

        Returns:
            all_anchors: A flattened Tensor with all the anchors of shape
                `(num_anchors_per_points * feature_width * feature_height, 4)`
                using the (x1, y1, x2, y2) convention.
        """
        with tf.variable_scope('generate_anchors'):
            grid_width = feature_map_shape[2]  # width
            grid_height = feature_map_shape[1]  # height
            shift_x = tf.range(grid_width) * self._anchor_stride
            shift_y = tf.range(grid_height) * self._anchor_stride
            shift_x, shift_y = tf.meshgrid(shift_x, shift_y)

            shift_x = tf.reshape(shift_x, [-1])
            shift_y = tf.reshape(shift_y, [-1])

            shifts = tf.stack(
                [shift_x, shift_y, shift_x, shift_y],
                axis=0
            )

            shifts = tf.transpose(shifts)
            # Shifts now is a (H x W, 4) Tensor

            # Expand dims to use broadcasting sum.
            all_anchors = (
                np.expand_dims(self._anchor_reference, axis=0) +
                tf.expand_dims(shifts, axis=1)
            )

            # Flatten
            all_anchors = tf.reshape(
                all_anchors, (-1, 4)
            )
            return all_anchors