model.py 文件源码

def __process_rois(self, regions, class_scores):
        """ get relevant regions, with non-maximum suppression and clipping """
        region_shape = tf.shape(regions)

        width = region_shape[2] * self.feat_stride
        height = region_shape[1] * self.feat_stride

        anchors = self.get_tiled_anchors_for_shape(width, height)

        region_boxes = self.adjust_bbox(tf.reshape(regions,
                                                                [-1, 4]), anchors)

        class_scores = tf.reshape(class_scores, [-1, 2])

        if self.is_training:
            # ignore boxes that cross the image boundary
            self.outside_anchors = tf.logical_and(tf.logical_and(anchors[:, 0] - anchors[:, 2] / 2.0 >= -self.feat_stride, anchors[:, 1] - anchors[:, 3] / 2.0 >= -self.feat_stride), tf.logical_and(
                anchors[:, 0] + anchors[:, 2] / 2.0 < tf.cast(height, tf.float32) + self.feat_stride, anchors[:, 1] + anchors[:, 3] / 2.0 < tf.cast(width, tf.float32) + self.feat_stride))
            region_boxes = tf.boolean_mask(region_boxes, self.outside_anchors)
            class_scores = tf.boolean_mask(class_scores, self.outside_anchors)

        shape = tf.shape(region_boxes)
        mask = tf.logical_or(region_boxes[:, 2] >
                                        self.min_box_size,
                                        region_boxes[:,
                                                    3] > self.min_box_size)
        region_box = tf.boolean_mask(region_boxes, mask)
        class_score = tf.boolean_mask(class_scores, mask)

        region_box = self.clip_bboxes(region_box, width, height)

        class_score = tf.nn.softmax(class_score)

        class_score = tf.slice(class_score, [0, 1], [-1, 1])
        class_score = tf.reshape(class_score, [-1])

        _, idx = tf.nn.top_k(class_score, k=tf.minimum(self.num_proposals, tf.shape(class_score)[0]))
        class_score = tf.gather(class_score, idx)
        region_box = tf.gather(region_box, idx)

        with tf.variable_scope('non_maximum_supression'):
            bboxes2 = self.bboxes_to_yxyx(
                region_box, tf.cast(height, tf.float32))
            idx = tf.image.non_max_suppression(bboxes2, class_score,
                                               self.num_proposals_after_nms,
                                               0.7)
            region_box = tf.gather(region_box, idx)

        return region_box