X-Git-Url: https://gerrit.akraino.org/r/gitweb?a=blobdiff_plain;f=example-apps%2FPDD%2Fpcb-defect-detection%2Flibs%2Fdetection_oprations%2Fproposal_opr.py;fp=example-apps%2FPDD%2Fpcb-defect-detection%2Flibs%2Fdetection_oprations%2Fproposal_opr.py;h=0000000000000000000000000000000000000000;hb=3ed2c61d9d7e7916481650c41bfe5604f7db22e9;hp=54cf9b80668d6714d83270eb5cdd627e5fca1e05;hpb=e6d40ddb2640f434a9d7d7ed99566e5e8fa60cc1;p=ealt-edge.git

diff --git a/example-apps/PDD/pcb-defect-detection/libs/detection_oprations/proposal_opr.py b/example-apps/PDD/pcb-defect-detection/libs/detection_oprations/proposal_opr.py
deleted file mode 100755
index 54cf9b8..0000000
--- a/example-apps/PDD/pcb-defect-detection/libs/detection_oprations/proposal_opr.py
+++ /dev/null
@@ -1,66 +0,0 @@
-# encoding: utf-8
-"""
-@author: zeming li
-@contact: zengarden2009@gmail.com
-"""
-
-from libs.configs import cfgs
-from libs.box_utils import encode_and_decode
-from libs.box_utils import boxes_utils
-import tensorflow as tf
-import numpy as np
-
-
-def postprocess_rpn_proposals(rpn_bbox_pred, rpn_cls_prob, img_shape, anchors, is_training):
-    '''
-
-    :param rpn_bbox_pred: [-1, 4]
-    :param rpn_cls_prob: [-1, 2]
-    :param img_shape:
-    :param anchors:[-1, 4]
-    :param is_training:
-    :return:
-    '''
-
-    if is_training:
-        pre_nms_topN = cfgs.RPN_TOP_K_NMS_TRAIN
-        post_nms_topN = cfgs.RPN_MAXIMUM_PROPOSAL_TARIN
-        # pre_nms_topN = cfgs.FPN_TOP_K_PER_LEVEL_TRAIN
-        # post_nms_topN = pre_nms_topN
-    else:
-        pre_nms_topN = cfgs.RPN_TOP_K_NMS_TEST
-        post_nms_topN = cfgs.RPN_MAXIMUM_PROPOSAL_TEST
-        # pre_nms_topN = cfgs.FPN_TOP_K_PER_LEVEL_TEST
-        # post_nms_topN = pre_nms_topN
-
-    nms_thresh = cfgs.RPN_NMS_IOU_THRESHOLD
-
-    cls_prob = rpn_cls_prob[:, 1]
-
-    # 1. decode boxes
-    decode_boxes = encode_and_decode.decode_boxes(encoded_boxes=rpn_bbox_pred,
-                                                  reference_boxes=anchors,
-                                                  scale_factors=cfgs.ANCHOR_SCALE_FACTORS)
-
-    # 2. clip to img boundaries
-    decode_boxes = boxes_utils.clip_boxes_to_img_boundaries(decode_boxes=decode_boxes,
-                                                            img_shape=img_shape)
-
-    # 3. get top N to NMS
-    if pre_nms_topN > 0:
-        pre_nms_topN = tf.minimum(pre_nms_topN, tf.shape(decode_boxes)[0], name='avoid_unenough_boxes')
-        cls_prob, top_k_indices = tf.nn.top_k(cls_prob, k=pre_nms_topN)
-        decode_boxes = tf.gather(decode_boxes, top_k_indices)
-
-    # 4. NMS
-    keep = tf.image.non_max_suppression(
-        boxes=decode_boxes,
-        scores=cls_prob,
-        max_output_size=post_nms_topN,
-        iou_threshold=nms_thresh)
-
-    final_boxes = tf.gather(decode_boxes, keep)
-    final_probs = tf.gather(cls_prob, keep)
-
-    return final_boxes, final_probs
-