X-Git-Url: https://gerrit.akraino.org/r/gitweb?a=blobdiff_plain;f=example-apps%2FPDD%2Fpcb-defect-detection%2Flibs%2Fbox_utils%2Fboxes_utils_backup.py;fp=example-apps%2FPDD%2Fpcb-defect-detection%2Flibs%2Fbox_utils%2Fboxes_utils_backup.py;h=0000000000000000000000000000000000000000;hb=3ed2c61d9d7e7916481650c41bfe5604f7db22e9;hp=e9b188533a3b922678961d32ce2e539082f44a4f;hpb=e6d40ddb2640f434a9d7d7ed99566e5e8fa60cc1;p=ealt-edge.git

diff --git a/example-apps/PDD/pcb-defect-detection/libs/box_utils/boxes_utils_backup.py b/example-apps/PDD/pcb-defect-detection/libs/box_utils/boxes_utils_backup.py
deleted file mode 100755
index e9b1885..0000000
--- a/example-apps/PDD/pcb-defect-detection/libs/box_utils/boxes_utils_backup.py
+++ /dev/null
@@ -1,110 +0,0 @@
-# -*- 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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