def test_bbox_dataset_to_prediction_roundtrip(self):
"""Simulate the process of reading a ground-truth box from a dataset,
make predictions from proposals, convert the predictions back to the
dataset format, and then use the COCO API to compute IoU overlap between
the gt box and the predictions. These should have IoU of 1.
"""
weights = (5, 5, 10, 10)
# 1/ "read" a box from a dataset in the default (x1, y1, w, h) format
gt_xywh_box = [10, 20, 100, 150]
# 2/ convert it to our internal (x1, y1, x2, y2) format
gt_xyxy_box = box_utils.xywh_to_xyxy(gt_xywh_box)
# 3/ consider nearby proposal boxes
prop_xyxy_boxes = random_boxes(gt_xyxy_box, 10, 10)
# 4/ compute proposal-to-gt transformation deltas
deltas = box_utils.bbox_transform_inv(
prop_xyxy_boxes, np.array([gt_xyxy_box]), weights=weights
)
# 5/ use deltas to transform proposals to xyxy predicted box
pred_xyxy_boxes = box_utils.bbox_transform(
prop_xyxy_boxes, deltas, weights=weights
)
# 6/ convert xyxy predicted box to xywh predicted box
pred_xywh_boxes = box_utils.xyxy_to_xywh(pred_xyxy_boxes)
# 7/ use COCO API to compute IoU
not_crowd = [int(False)] * pred_xywh_boxes.shape[0]
ious = COCOmask.iou(pred_xywh_boxes, np.array([gt_xywh_box]), not_crowd)
np.testing.assert_array_almost_equal(ious, np.ones(ious.shape))
def _compute_targets(entry):
"""Compute bounding-box regression targets for an image."""
# Indices of ground-truth ROIs
rois = entry['boxes']
overlaps = entry['max_overlaps']
labels = entry['max_classes']
gt_inds = np.where((entry['gt_classes'] > 0) & (entry['is_crowd'] == 0))[0]
# Targets has format (class, tx, ty, tw, th)
targets = np.zeros((rois.shape[0], 5), dtype=np.float32)
if len(gt_inds) == 0:
# Bail if the image has no ground-truth ROIs
return targets
# Indices of examples for which we try to make predictions
ex_inds = np.where(overlaps >= cfg.TRAIN.BBOX_THRESH)[0]
# Get IoU overlap between each ex ROI and gt ROI
ex_gt_overlaps = box_utils.bbox_overlaps(
rois[ex_inds, :].astype(dtype=np.float32, copy=False),
rois[gt_inds, :].astype(dtype=np.float32, copy=False))
# Find which gt ROI each ex ROI has max overlap with:
# this will be the ex ROI's gt target
gt_assignment = ex_gt_overlaps.argmax(axis=1)
gt_rois = rois[gt_inds[gt_assignment], :]
ex_rois = rois[ex_inds, :]
# Use class "1" for all boxes if using class_agnostic_bbox_reg
targets[ex_inds,
0] = (1 if cfg.MODEL.CLS_AGNOSTIC_BBOX_REG else labels[ex_inds])
targets[ex_inds,
1:] = box_utils.bbox_transform_inv(ex_rois, gt_rois,
cfg.MODEL.BBOX_REG_WEIGHTS)
return targets
def compute_bbox_regression_targets(entry):
"""Compute bounding-box regression targets for an image."""
# Indices of ground-truth ROIs
rois = entry['boxes']
overlaps = entry['max_overlaps']
labels = entry['max_classes']
gt_inds = np.where((entry['gt_classes'] > 0) & (entry['is_crowd'] == 0))[0]
# Targets has format (class, tx, ty, tw, th)
targets = np.zeros((rois.shape[0], 5), dtype=np.float32)
if len(gt_inds) == 0:
# Bail if the image has no ground-truth ROIs
return targets
# Indices of examples for which we try to make predictions
ex_inds = np.where(overlaps >= cfg.TRAIN.BBOX_THRESH)[0]
# Get IoU overlap between each ex ROI and gt ROI
ex_gt_overlaps = box_utils.bbox_overlaps(
rois[ex_inds, :].astype(dtype=np.float32, copy=False),
rois[gt_inds, :].astype(dtype=np.float32, copy=False))
# Find which gt ROI each ex ROI has max overlap with:
# this will be the ex ROI's gt target
gt_assignment = ex_gt_overlaps.argmax(axis=1)
gt_rois = rois[gt_inds[gt_assignment], :]
ex_rois = rois[ex_inds, :]
# Use class "1" for all boxes if using class_agnostic_bbox_reg
targets[ex_inds, 0] = (
1 if cfg.MODEL.CLS_AGNOSTIC_BBOX_REG else labels[ex_inds])
targets[ex_inds, 1:] = box_utils.bbox_transform_inv(
ex_rois, gt_rois, cfg.MODEL.BBOX_REG_WEIGHTS)
return targets
def _compute_targets(ex_rois, gt_rois, labels):
"""Compute bounding-box regression targets for an image."""
assert ex_rois.shape[0] == gt_rois.shape[0]
assert ex_rois.shape[1] == 4
assert gt_rois.shape[1] == 4
targets = box_utils.bbox_transform_inv(ex_rois, gt_rois,
cfg.MODEL.BBOX_REG_WEIGHTS)
return np.hstack((labels[:, np.newaxis], targets)).astype(np.float32,
copy=False)
def _compute_targets(ex_rois, gt_rois, labels, stage):
"""Compute bounding-box regression targets for an image."""
assert ex_rois.shape[0] == gt_rois.shape[0]
assert ex_rois.shape[1] == 4
assert gt_rois.shape[1] == 4
bbox_reg_weights = cfg.CASCADE_RCNN.BBOX_REG_WEIGHTS[stage - 1]
targets = box_utils.bbox_transform_inv(ex_rois, gt_rois, bbox_reg_weights)
return np.hstack((labels[:, np.newaxis], targets)).astype(np.float32,
copy=False)
def test_bbox_transform_and_inverse(self):
weights = (5, 5, 10, 10)
src_boxes = random_boxes([10, 10, 20, 20], 1, 10)
dst_boxes = random_boxes([10, 10, 20, 20], 1, 10)
deltas = box_utils.bbox_transform_inv(
src_boxes, dst_boxes, weights=weights
)
dst_boxes_reconstructed = box_utils.bbox_transform(
src_boxes, deltas, weights=weights
)
np.testing.assert_array_almost_equal(
dst_boxes, dst_boxes_reconstructed, decimal=5
)
def _compute_targets(ex_rois, gt_rois, labels):
"""Compute bounding-box regression targets for an image."""
assert ex_rois.shape[0] == gt_rois.shape[0]
assert ex_rois.shape[1] == 4
assert gt_rois.shape[1] == 4
targets = box_utils.bbox_transform_inv(
ex_rois, gt_rois, cfg.MODEL.BBOX_REG_WEIGHTS
)
return np.hstack((labels[:, np.newaxis], targets)).astype(
np.float32, copy=False
)
def compute_targets(ex_rois, gt_rois, weights=(1.0, 1.0, 1.0, 1.0)):
"""Compute bounding-box regression targets for an image."""
return box_utils.bbox_transform_inv(ex_rois, gt_rois, weights).astype(
np.float32, copy=False
)
def compute_targets(ex_rois, gt_rois, weights=(1.0, 1.0, 1.0, 1.0)):
"""Compute bounding-box regression targets for an image."""
return box_utils.bbox_transform_inv(ex_rois, gt_rois, weights).astype(
np.float32, copy=False
)
