X-Git-Url: https://gerrit.akraino.org/r/gitweb?a=blobdiff_plain;f=example-apps%2FPDD%2Fpcb-defect-detection%2Flibs%2Fdetection_oprations%2Fanchor_target_layer_without_boxweight.py;fp=example-apps%2FPDD%2Fpcb-defect-detection%2Flibs%2Fdetection_oprations%2Fanchor_target_layer_without_boxweight.py;h=0000000000000000000000000000000000000000;hb=3ed2c61d9d7e7916481650c41bfe5604f7db22e9;hp=6179bc0290c67c45171f21bd8ba1fe4fef903235;hpb=e6d40ddb2640f434a9d7d7ed99566e5e8fa60cc1;p=ealt-edge.git

diff --git a/example-apps/PDD/pcb-defect-detection/libs/detection_oprations/anchor_target_layer_without_boxweight.py b/example-apps/PDD/pcb-defect-detection/libs/detection_oprations/anchor_target_layer_without_boxweight.py
deleted file mode 100755
index 6179bc0..0000000
--- a/example-apps/PDD/pcb-defect-detection/libs/detection_oprations/anchor_target_layer_without_boxweight.py
+++ /dev/null
@@ -1,125 +0,0 @@
-# --------------------------------------------------------
-# Faster R-CNN
-# Copyright (c) 2015 Microsoft
-# Licensed under The MIT License [see LICENSE for details]
-# Written by Ross Girshick and Xinlei Chen
-# --------------------------------------------------------
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import os
-from libs.configs import cfgs
-import numpy as np
-import numpy.random as npr
-from libs.box_utils.cython_utils.cython_bbox import bbox_overlaps
-from libs.box_utils import encode_and_decode
-
-
-def anchor_target_layer(
-        gt_boxes, img_shape, all_anchors, is_restrict_bg=False):
-    """Same as the anchor target layer in original Fast/er RCNN """
-
-    total_anchors = all_anchors.shape[0]
-    img_h, img_w = img_shape[1], img_shape[2]
-    gt_boxes = gt_boxes[:, :-1]  # remove class label
-
-
-    # allow boxes to sit over the edge by a small amount
-    _allowed_border = 0
-
-    # only keep anchors inside the image
-    if cfgs.IS_FILTER_OUTSIDE_BOXES:
-        inds_inside = np.where(
-            (all_anchors[:, 0] >= -_allowed_border) &
-            (all_anchors[:, 1] >= -_allowed_border) &
-            (all_anchors[:, 2] < img_w + _allowed_border) &  # width
-            (all_anchors[:, 3] < img_h + _allowed_border)  # height
-        )[0]
-    else:
-        inds_inside = range(all_anchors.shape[0])
-
-    anchors = all_anchors[inds_inside, :]
-
-    # label: 1 is positive, 0 is negative, -1 is dont care
-    labels = np.empty((len(inds_inside),), dtype=np.float32)
-    labels.fill(-1)
-
-    # overlaps between the anchors and the gt boxes
-    overlaps = bbox_overlaps(
-        np.ascontiguousarray(anchors, dtype=np.float),
-        np.ascontiguousarray(gt_boxes, dtype=np.float))
-
-    argmax_overlaps = overlaps.argmax(axis=1)
-    max_overlaps = overlaps[np.arange(len(inds_inside)), argmax_overlaps]
-    gt_argmax_overlaps = overlaps.argmax(axis=0)
-    gt_max_overlaps = overlaps[
-        gt_argmax_overlaps, np.arange(overlaps.shape[1])]
-    gt_argmax_overlaps = np.where(overlaps == gt_max_overlaps)[0]
-
-    if not cfgs.TRAIN_RPN_CLOOBER_POSITIVES:
-        labels[max_overlaps < cfgs.RPN_IOU_NEGATIVE_THRESHOLD] = 0
-
-    labels[gt_argmax_overlaps] = 1
-    labels[max_overlaps >= cfgs.RPN_IOU_POSITIVE_THRESHOLD] = 1
-
-    if cfgs.TRAIN_RPN_CLOOBER_POSITIVES:
-        labels[max_overlaps < cfgs.RPN_IOU_NEGATIVE_THRESHOLD] = 0
-
-    num_fg = int(cfgs.RPN_MINIBATCH_SIZE * cfgs.RPN_POSITIVE_RATE)
-    fg_inds = np.where(labels == 1)[0]
-    if len(fg_inds) > num_fg:
-        disable_inds = npr.choice(
-            fg_inds, size=(len(fg_inds) - num_fg), replace=False)
-        labels[disable_inds] = -1
-
-    num_bg = cfgs.RPN_MINIBATCH_SIZE - np.sum(labels == 1)
-    if is_restrict_bg:
-        num_bg = max(num_bg, num_fg * 1.5)
-    bg_inds = np.where(labels == 0)[0]
-    if len(bg_inds) > num_bg:
-        disable_inds = npr.choice(
-            bg_inds, size=(len(bg_inds) - num_bg), replace=False)
-        labels[disable_inds] = -1
-
-    bbox_targets = _compute_targets(anchors, gt_boxes[argmax_overlaps, :])
-
-    # map up to original set of anchors
-    labels = _unmap(labels, total_anchors, inds_inside, fill=-1)
-    bbox_targets = _unmap(bbox_targets, total_anchors, inds_inside, fill=0)
-
-    # labels = labels.reshape((1, height, width, A))
-    rpn_labels = labels.reshape((-1, 1))
-
-    # bbox_targets
-    bbox_targets = bbox_targets.reshape((-1, 4))
-    rpn_bbox_targets = bbox_targets
-
-    return rpn_labels, rpn_bbox_targets
-
-
-def _unmap(data, count, inds, fill=0):
-    """ Unmap a subset of item (data) back to the original set of items (of
-    size count) """
-    if len(data.shape) == 1:
-        ret = np.empty((count,), dtype=np.float32)
-        ret.fill(fill)
-        ret[inds] = data
-    else:
-        ret = np.empty((count,) + data.shape[1:], dtype=np.float32)
-        ret.fill(fill)
-        ret[inds, :] = data
-    return ret
-
-
-def _compute_targets(ex_rois, gt_rois):
-    """Compute bounding-box regression targets for an image."""
-    # targets = bbox_transform(ex_rois, gt_rois[:, :4]).astype(
-    #     np.float32, copy=False)
-    targets = encode_and_decode.encode_boxes(unencode_boxes=gt_rois,
-                                             reference_boxes=ex_rois,
-                                             scale_factors=cfgs.ANCHOR_SCALE_FACTORS)
-    # targets = encode_and_decode.encode_boxes(ex_rois=ex_rois,
-    #                                          gt_rois=gt_rois,
-    #                                          scale_factor=None)
-    return targets