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iou.py
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iou.py
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# -*- coding: utf-8 -*-
import tensorflow as tf
import math
EPS = 1e-8
def GIoU(y_pred_box, y_true_box):
"""
Calculate GIoU loss on anchor boxes
Reference Paper:
"Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression"
https://arxiv.org/abs/1902.09630
Parameters
----------
y_pred_box: tensor, shape=(batch, feat_w, feat_h, anchor_num, 4), x1y1x2y2
y_true_box: tensor, shape=(batch, feat_w, feat_h, anchor_num, 4), x1y1x2y2
Returns
-------
giou: tensor, shape=(batch, feat_w, feat_h, anchor_num, 1)
"""
y_pred_box_min = y_pred_box[..., :2]
y_pred_box_max = y_pred_box[..., 2:4]
y_pred_box_wh = y_pred_box_max - y_pred_box_min
y_true_box_min = y_true_box[..., :2]
y_true_box_max = y_true_box[..., 2:4]
y_true_box_wh = y_true_box_max - y_true_box_min
intersect_min = tf.maximum(y_true_box_min, y_pred_box_min)
intersect_max = tf.minimum(y_true_box_max, y_pred_box_max)
intersect_wh = tf.maximum(intersect_max - intersect_min, 0.)
intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1]
y_pred_box_area = y_pred_box_wh[..., 0] * y_pred_box_wh[..., 1]
y_true_box_area = y_true_box_wh[..., 0] * y_true_box_wh[..., 1]
union_area = y_pred_box_area + y_true_box_area - intersect_area
# calculate IoU, add epsilon in denominator to avoid dividing by 0
iou = intersect_area / tf.maximum(union_area, EPS)
# get enclosed area
enclose_min = tf.minimum(y_true_box_min, y_pred_box_min)
enclose_max = tf.maximum(y_true_box_max, y_pred_box_max)
enclose_wh = tf.maximum(enclose_max - enclose_min, 0.0)
enclose_area = enclose_wh[..., 0] * enclose_wh[..., 1]
# calculate GIoU, add epsilon in denominator to avoid dividing by 0
giou = iou - (enclose_area - union_area) / tf.maximum(enclose_area, EPS)
return giou
def DIoU(y_pred_box, y_true_box):
"""
Calculate DIoU loss on anchor boxes
Reference Paper:
"Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression"
https://arxiv.org/abs/1911.08287
Parameters
----------
y_pred_box: tensor, shape=(batch, feat_w, feat_h, anchor_num, 4), x1y1x2y2
y_true_box: tensor, shape=(batch, feat_w, feat_h, anchor_num, 4), x1y1x2y2
Returns
-------
diou: tensor, shape=(batch, feat_w, feat_h, anchor_num, 1)
"""
y_pred_box_min = y_pred_box[..., :2]
y_pred_box_max = y_pred_box[..., 2:4]
y_pred_box_wh = y_pred_box_max - y_pred_box_min
y_pred_box_center = (y_pred_box_min + y_pred_box_max) / 2.
y_true_box_min = y_true_box[..., :2]
y_true_box_max = y_true_box[..., 2:4]
y_true_box_wh = y_true_box_max - y_true_box_min
y_true_box_center = (y_true_box_min + y_true_box_max) / 2.
intersect_min = tf.maximum(y_pred_box_min, y_true_box_min)
intersect_max = tf.minimum(y_pred_box_max, y_true_box_max)
intersect_wh = tf.maximum(intersect_max - intersect_min, 0.)
intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1]
y_true_box_area = y_true_box_wh[..., 0] * y_true_box_wh[..., 1]
y_pred_box_area = y_pred_box_wh[..., 0] * y_pred_box_wh[..., 1]
union_area = y_true_box_area + y_pred_box_area - intersect_area
# calculate IoU, add epsilon in denominator to avoid dividing by 0
iou = intersect_area / tf.maximum(union_area, EPS)
# box center distance
center_distance = tf.reduce_sum(tf.square(y_pred_box_center - y_true_box_center), axis=-1)
# get enclosed area
enclose_min = tf.minimum(y_pred_box_min, y_true_box_min)
enclose_max = tf.maximum(y_pred_box_max, y_true_box_max)
enclose_wh = tf.maximum(enclose_max - enclose_min, 0.0)
# get enclosed diagonal distance
enclose_diagonal = tf.reduce_sum(tf.square(enclose_wh), axis=-1)
# calculate DIoU, add epsilon in denominator to avoid dividing by 0
diou = iou - center_distance / tf.maximum(enclose_diagonal, EPS)
return diou
def CIoU(y_pred_box, y_true_box):
"""
Calculate DIoU loss on anchor boxes
Reference Paper:
"Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression"
https://arxiv.org/abs/1911.08287
Parameters
----------
y_pred_box: tensor, shape=(batch, feat_w, feat_h, anchor_num, 4), x1y1x2y2
y_true_box: tensor, shape=(batch, feat_w, feat_h, anchor_num, 4), x1y1x2y2
Returns
-------
diou: tensor, shape=(batch, feat_w, feat_h, anchor_num, 1)
"""
y_pred_box_min = y_pred_box[..., :2]
y_pred_box_max = y_pred_box[..., 2:4]
y_pred_box_wh = y_pred_box_max - y_pred_box_min
y_pred_box_center = (y_pred_box_min + y_pred_box_max) / 2.
y_true_box_min = y_true_box[..., :2]
y_true_box_max = y_true_box[..., 2:4]
y_true_box_wh = y_true_box_max - y_true_box_min
y_true_box_center = (y_true_box_min + y_true_box_max) / 2.
intersect_min = tf.maximum(y_pred_box_min, y_true_box_min)
intersect_max = tf.minimum(y_pred_box_max, y_true_box_max)
intersect_wh = tf.maximum(intersect_max - intersect_min, 0.)
intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1]
y_true_box_area = y_true_box_wh[..., 0] * y_true_box_wh[..., 1]
y_pred_box_area = y_pred_box_wh[..., 0] * y_pred_box_wh[..., 1]
union_area = y_true_box_area + y_pred_box_area - intersect_area
# calculate IoU, add epsilon in denominator to avoid dividing by 0
iou = intersect_area / tf.maximum(union_area, EPS)
# box center distance
center_distance = tf.reduce_sum(tf.square(y_pred_box_center - y_true_box_center), axis=-1)
# get enclosed area
enclose_min = tf.minimum(y_pred_box_min, y_true_box_min)
enclose_max = tf.maximum(y_pred_box_max, y_true_box_max)
enclose_wh = tf.maximum(enclose_max - enclose_min, 0.0)
# get enclosed diagonal distance
enclose_diagonal = tf.reduce_sum(tf.square(enclose_wh), axis=-1)
# calculate DIoU, add epsilon in denominator to avoid dividing by 0
diou = iou - center_distance / tf.maximum(enclose_diagonal, EPS)
# calculate param v and alpha to extend to CIoU
constant = 4. / (math.pi * math.pi)
v = constant * tf.square(
tf.math.atan2(y_true_box_wh[..., 0], tf.maximum(y_true_box_wh[..., 1], EPS)) - tf.math.atan2(
y_pred_box_wh[..., 0], tf.maximum(y_pred_box_wh[..., 1], EPS)))
alpha = v / tf.maximum(1.0 - iou + v, EPS)
ciou = diou - alpha * v
return ciou