--- /dev/null
+# -*- coding: utf-8 -*-
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+def ious_calu(boxes_1, boxes_2):
+ '''
+
+ :param boxes_1: [N, 4] [xmin, ymin, xmax, ymax]
+ :param boxes_2: [M, 4] [xmin, ymin. xmax, ymax]
+ :return:
+ '''
+ boxes_1 = tf.cast(boxes_1, tf.float32)
+ boxes_2 = tf.cast(boxes_2, tf.float32)
+ xmin_1, ymin_1, xmax_1, ymax_1 = tf.split(boxes_1, 4, axis=1) # xmin_1 shape is [N, 1]..
+ xmin_2, ymin_2, xmax_2, ymax_2 = tf.unstack(boxes_2, axis=1) # xmin_2 shape is [M, ]..
+
+ max_xmin = tf.maximum(xmin_1, xmin_2)
+ min_xmax = tf.minimum(xmax_1, xmax_2)
+
+ max_ymin = tf.maximum(ymin_1, ymin_2)
+ min_ymax = tf.minimum(ymax_1, ymax_2)
+
+ overlap_h = tf.maximum(0., min_ymax - max_ymin) # avoid h < 0
+ overlap_w = tf.maximum(0., min_xmax - max_xmin)
+
+ overlaps = overlap_h * overlap_w
+
+ area_1 = (xmax_1 - xmin_1) * (ymax_1 - ymin_1) # [N, 1]
+ area_2 = (xmax_2 - xmin_2) * (ymax_2 - ymin_2) # [M, ]
+
+ ious = overlaps / (area_1 + area_2 - overlaps)
+
+ return ious
+
+
+def clip_boxes_to_img_boundaries(decode_boxes, img_shape):
+ '''
+
+ :param decode_boxes:
+ :return: decode boxes, and already clip to boundaries
+ '''
+
+ with tf.name_scope('clip_boxes_to_img_boundaries'):
+
+ # xmin, ymin, xmax, ymax = tf.unstack(decode_boxes, axis=1)
+ xmin = decode_boxes[:, 0]
+ ymin = decode_boxes[:, 1]
+ xmax = decode_boxes[:, 2]
+ ymax = decode_boxes[:, 3]
+ img_h, img_w = img_shape[1], img_shape[2]
+
+ img_h, img_w = tf.cast(img_h, tf.float32), tf.cast(img_w, tf.float32)
+
+ xmin = tf.maximum(tf.minimum(xmin, img_w-1.), 0.)
+ ymin = tf.maximum(tf.minimum(ymin, img_h-1.), 0.)
+
+ xmax = tf.maximum(tf.minimum(xmax, img_w-1.), 0.)
+ ymax = tf.maximum(tf.minimum(ymax, img_h-1.), 0.)
+
+ return tf.transpose(tf.stack([xmin, ymin, xmax, ymax]))
+
+
+def filter_outside_boxes(boxes, img_h, img_w):
+ '''
+ :param anchors:boxes with format [xmin, ymin, xmax, ymax]
+ :param img_h: height of image
+ :param img_w: width of image
+ :return: indices of anchors that inside the image boundary
+ '''
+
+ with tf.name_scope('filter_outside_boxes'):
+ xmin, ymin, xmax, ymax = tf.unstack(boxes, axis=1)
+
+ xmin_index = tf.greater_equal(xmin, 0)
+ ymin_index = tf.greater_equal(ymin, 0)
+ xmax_index = tf.less_equal(xmax, tf.cast(img_w, tf.float32))
+ ymax_index = tf.less_equal(ymax, tf.cast(img_h, tf.float32))
+
+ indices = tf.transpose(tf.stack([xmin_index, ymin_index, xmax_index, ymax_index]))
+ indices = tf.cast(indices, dtype=tf.int32)
+ indices = tf.reduce_sum(indices, axis=1)
+ indices = tf.where(tf.equal(indices, 4))
+ # indices = tf.equal(indices, 4)
+ return tf.reshape(indices, [-1])
+
+
+def padd_boxes_with_zeros(boxes, scores, max_num_of_boxes):
+
+ '''
+ num of boxes less than max num of boxes, so it need to pad with zeros[0, 0, 0, 0]
+ :param boxes:
+ :param scores: [-1]
+ :param max_num_of_boxes:
+ :return:
+ '''
+
+ pad_num = tf.cast(max_num_of_boxes, tf.int32) - tf.shape(boxes)[0]
+
+ zero_boxes = tf.zeros(shape=[pad_num, 4], dtype=boxes.dtype)
+ zero_scores = tf.zeros(shape=[pad_num], dtype=scores.dtype)
+
+ final_boxes = tf.concat([boxes, zero_boxes], axis=0)
+
+ final_scores = tf.concat([scores, zero_scores], axis=0)
+
+ return final_boxes, final_scores
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