removed exmple apps code

[ealt-edge.git] / example-apps / PDD / pcb-defect-detection / libs / box_utils / anchor_utils.py

diff --git a/example-apps/PDD/pcb-defect-detection/libs/box_utils/anchor_utils.py b/example-apps/PDD/pcb-defect-detection/libs/box_utils/anchor_utils.py
deleted file mode 100755 (executable)
index d01c54b..0000000
--- a/example-apps/PDD/pcb-defect-detection/libs/box_utils/anchor_utils.py
+++ /dev/null
@@ -1,92 +0,0 @@
-# -*- coding: utf-8 -*-
-from __future__ import absolute_import, print_function, division
-
-import tensorflow as tf
-from libs.configs import cfgs
-
-
-def make_anchors(base_anchor_size, anchor_scales, anchor_ratios,
-                 featuremap_height, featuremap_width,
-                 stride, name='make_anchors'):
-    '''
-    :param base_anchor_size:256
-    :param anchor_scales:
-    :param anchor_ratios:
-    :param featuremap_height:
-    :param featuremap_width:
-    :param stride:
-    :return:
-    '''
-    with tf.variable_scope(name):
-        base_anchor = tf.constant([0, 0, base_anchor_size, base_anchor_size], tf.float32)  # [x_center, y_center, w, h]
-
-        ws, hs = enum_ratios(enum_scales(base_anchor, anchor_scales),
-                             anchor_ratios)  # per locations ws and hs
-
-        # featuremap_height = tf.Print(featuremap_height,
-        #                              [featuremap_height, featuremap_width], summarize=10,
-        #                              message=name+"_SHAPE***")
-
-        x_centers = tf.range(featuremap_width, dtype=tf.float32) * stride
-        y_centers = tf.range(featuremap_height, dtype=tf.float32) * stride
-
-        if cfgs.USE_CENTER_OFFSET:
-            x_centers = x_centers + stride/2.
-            y_centers = y_centers + stride/2.
-
-        x_centers, y_centers = tf.meshgrid(x_centers, y_centers)
-
-        ws, x_centers = tf.meshgrid(ws, x_centers)
-        hs, y_centers = tf.meshgrid(hs, y_centers)
-
-        anchor_centers = tf.stack([x_centers, y_centers], 2)
-        anchor_centers = tf.reshape(anchor_centers, [-1, 2])
-
-        box_sizes = tf.stack([ws, hs], axis=2)
-        box_sizes = tf.reshape(box_sizes, [-1, 2])
-        # anchors = tf.concat([anchor_centers, box_sizes], axis=1)
-        anchors = tf.concat([anchor_centers - 0.5*box_sizes,
-                             anchor_centers + 0.5*box_sizes], axis=1)
-        return anchors
-
-
-def enum_scales(base_anchor, anchor_scales):
-
-    anchor_scales = base_anchor * tf.constant(anchor_scales, dtype=tf.float32, shape=(len(anchor_scales), 1))
-
-    return anchor_scales
-
-
-def enum_ratios(anchors, anchor_ratios):
-    '''
-    ratio = h /w
-    :param anchors:
-    :param anchor_ratios:
-    :return:
-    '''
-    ws = anchors[:, 2]  # for base anchor: w == h
-    hs = anchors[:, 3]
-    sqrt_ratios = tf.sqrt(tf.constant(anchor_ratios))
-
-    ws = tf.reshape(ws / sqrt_ratios[:, tf.newaxis], [-1, 1])
-    hs = tf.reshape(hs * sqrt_ratios[:, tf.newaxis], [-1, 1])
-
-    return hs, ws
-
-
-if __name__ == '__main__':
-    import os
-    os.environ["CUDA_VISIBLE_DEVICES"] = '0'
-    base_anchor_size = 256
-    anchor_scales = [1.0]
-    anchor_ratios = [0.5, 2.0, 1.0]
-    anchors = make_anchors(base_anchor_size=base_anchor_size, anchor_ratios=anchor_ratios,
-                           anchor_scales=anchor_scales,
-                           featuremap_width=32,
-                           featuremap_height=63,
-                           stride=16)
-    init = tf.global_variables_initializer()
-    with tf.Session() as sess:
-        sess.run(init)
-        anchor_result = sess.run(anchors)
-        print (anchor_result.shape)