def __call__(self, data, label, gt, seg_gt, seg_gt_to_compare):
#-----------------------------------------------------------------------
# Initialize the data vector and other variables
#-----------------------------------------------------------------------
if not self.initialized:
self.initialize()
vec = np.zeros((self.vheight, self.vwidth), dtype=np.float32)
#-----------------------------------------------------------------------
# For every box compute the best match and all the matches above 0.5
# Jaccard overlap
#-----------------------------------------------------------------------
overlaps = {}
for box in gt.boxes:
box_arr = box2array(box, self.img_size)
overlaps[box] = compute_overlap(box_arr, self.anchors_arr, 0.5)
#-----------------------------------------------------------------------
# Set up the training vector resolving conflicts in favor of a better
# match
#-----------------------------------------------------------------------
vec[:, self.num_classes] = 1 # background class
vec[:, self.num_classes + 1] = 0 # x offset
vec[:, self.num_classes + 2] = 0 # y offset
vec[:, self.num_classes + 3] = 0 # log width scale
vec[:, self.num_classes + 4] = 0 # log height scale
matches = {}
for box in gt.boxes:
for overlap in overlaps[box].good:
anchor = self.anchors[overlap.idx]
process_overlap(overlap, box, anchor, matches,
self.num_classes, vec)
matches = {}
for box in gt.boxes:
overlap = overlaps[box].best
if not overlap:
continue
anchor = self.anchors[overlap.idx]
process_overlap(overlap, box, anchor, matches, self.num_classes,
vec)
return data, vec, gt, seg_gt, seg_gt_to_compare
def __call__(self, data, label, gt):
if not self.initialized:
self.initialize()
vec = np.zeros((self.vheight, self.vwidth), dtype=np.float32)
overlaps = {}
for box in gt.boxes:
# box = 0~1
box_arr = box2array(
box, self.img_size
) # Convert proportional center-width bounds to absolute min-max bounds
# box_arr = (0~300, 0~300)
# gt_box xmin, xmax, ymin, ymax
overlaps[box] = compute_overlap(box_arr, self.anchors_arr, 0.4)
vec[:, self.num_classes] = 1. # background
vec[:, self.num_classes + 1] = 0. # x offset
vec[:, self.num_classes + 2] = 0. # y offset
vec[:, self.num_classes + 3] = 0. # log width scale
vec[:, self.num_classes + 4] = 0. # log height scale
matches = {}
for box in gt.boxes:
for overlap in overlaps[box].good:
anchor = self.anchors[overlap.idx]
process_overlap(overlap, box, anchor, matches,
self.num_classes, vec)
matches = {}
for box in gt.boxes:
overlap = overlaps[box].best
if not overlap:
continue
anchor = self.anchors[overlap.idx]
process_overlap(overlap, box, anchor, matches, self.num_classes,
vec)
return data, vec, gt.filename