def im_detect_bbox_aspect_ratio(model,
im,
aspect_ratio,
box_proposals=None,
hflip=False):
"""Computes bbox detections at the given width-relative aspect ratio.
Returns predictions in the original image space.
"""
# Compute predictions on the transformed image
im_ar = image_utils.aspect_ratio_rel(im, aspect_ratio)
if not cfg.MODEL.FASTER_RCNN:
box_proposals_ar = box_utils.aspect_ratio(box_proposals, aspect_ratio)
else:
box_proposals_ar = None
if hflip:
scores_ar, boxes_ar, _ = im_detect_bbox_hflip(
model,
im_ar,
cfg.TEST.SCALE,
cfg.TEST.MAX_SIZE,
box_proposals=box_proposals_ar)
else:
scores_ar, boxes_ar, _, _ = im_detect_bbox(model,
im_ar,
cfg.TEST.SCALE,
cfg.TEST.MAX_SIZE,
boxes=box_proposals_ar)
# Invert the detected boxes
boxes_inv = box_utils.aspect_ratio(boxes_ar, 1.0 / aspect_ratio)
return scores_ar, boxes_inv
def im_detect_bbox_aspect_ratio(
model, im, aspect_ratio, box_proposals=None, hflip=False
):
"""Computes bbox detections at the given width-relative aspect ratio.
Returns predictions in the original image space.
"""
# Compute predictions on the transformed image
im_ar = image_utils.aspect_ratio_rel(im, aspect_ratio)
if not cfg.MODEL.FASTER_RCNN:
box_proposals_ar = box_utils.aspect_ratio(box_proposals, aspect_ratio)
else:
box_proposals_ar = None
if hflip:
scores_ar, boxes_ar, _ = im_detect_bbox_hflip(
model,
im_ar,
cfg.TEST.SCALE,
cfg.TEST.MAX_SIZE,
box_proposals=box_proposals_ar
)
else:
scores_ar, boxes_ar, _ = im_detect_bbox(
model,
im_ar,
cfg.TEST.SCALE,
cfg.TEST.MAX_SIZE,
boxes=box_proposals_ar
)
# Invert the detected boxes
boxes_inv = box_utils.aspect_ratio(boxes_ar, 1.0 / aspect_ratio)
return scores_ar, boxes_inv
def im_detect_mask_aspect_ratio(model, im, aspect_ratio, boxes, hflip=False):
"""Computes mask detections at the given width-relative aspect ratio."""
# Perform mask detection on the transformed image
im_ar = image_utils.aspect_ratio_rel(im, aspect_ratio)
boxes_ar = box_utils.aspect_ratio(boxes, aspect_ratio)
if hflip:
masks_ar = im_detect_mask_hflip(model, im_ar, boxes_ar)
else:
im_scales = im_conv_body_only(model, im_ar)
masks_ar = im_detect_mask(model, im_scales, boxes_ar)
return masks_ar
def im_detect_mask_aspect_ratio(model, im, aspect_ratio, boxes, hflip=False):
"""Computes mask detections at the given width-relative aspect ratio."""
# Perform mask detection on the transformed image
im_ar = image_utils.aspect_ratio_rel(im, aspect_ratio)
boxes_ar = box_utils.aspect_ratio(boxes, aspect_ratio)
if hflip:
masks_ar = im_detect_mask_hflip(model, im_ar, boxes_ar)
else:
im_scales = im_conv_body_only(model, im_ar)
masks_ar = im_detect_mask(model, im_scales, boxes_ar)
return masks_ar
def im_detect_keypoints_aspect_ratio(
model, im, aspect_ratio, boxes, hflip=False):
"""Detects keypoints at the given width-relative aspect ratio."""
# Perform keypoint detectionon the transformed image
im_ar = image_utils.aspect_ratio_rel(im, aspect_ratio)
boxes_ar = box_utils.aspect_ratio(boxes, aspect_ratio)
if hflip:
heatmaps_ar = im_detect_keypoints_hflip(model, im_ar, boxes_ar)
else:
im_scales = im_conv_body_only(model, im_ar)
heatmaps_ar = im_detect_keypoints(model, im_scales, boxes_ar)
return heatmaps_ar
def im_detect_mask_aspect_ratio(model, im, aspect_ratio, boxes, hflip=False):
"""Computes mask detections at the given width-relative aspect ratio"""
# perform mask detection on the transformed image
im_ar = image_utils.aspect_ratio_rel(im, aspect_ratio)
boxes_ar = box_utils.aspect_ratio(boxes, aspect_ratio)
if hflip:
masks_ar = im_detect_mask_hflip(model, im_ar, cfg.TEST.SCALE,
cfg.TEST.MAX_SIZE, boxes_ar)
else:
blob_conv_ar, im_scale_ar = im_conv_body_only(model, im, target_scale,
target_max_size)
# _, im_scale, _ = blob_utils.get_image_blob(im, target_scale, target_max_size)
masks_ar = im_detect_mask(model, im_scale_ar, boxes_ar, blob_conv_ar)
return masks_ar
def im_detect_mask_aspect_ratio(model, im, aspect_ratio, boxes, hflip=False):
"""Computes mask detections at the given width-relative aspect ratio."""
# Perform mask detection on the transformed image
im_ar = blob_utils.pack_sequence([
image_utils.aspect_ratio_rel(x, aspect_ratio)
for x in blob_utils.unpack_sequence(im)
])
boxes_ar = box_utils.aspect_ratio(boxes, aspect_ratio)
if hflip:
masks_ar = im_detect_mask_hflip(
model, im_ar, cfg.TEST.SCALE, cfg.TEST.MAX_SIZE, boxes_ar
)
else:
blob_conv, im_scale = im_conv_body_only(
model, im_ar, cfg.TEST.SCALE, cfg.TEST.MAX_SIZE
)
masks_ar = im_detect_mask(model, im_scale, boxes_ar, blob_conv)
return masks_ar
def im_detect_keypoints_aspect_ratio(
model, im, aspect_ratio, boxes, hflip=False):
"""Detects keypoints at the given width-relative aspect ratio."""
# Perform keypoint detectionon the transformed image
im_ar = blob_utils.pack_sequence([
image_utils.aspect_ratio_rel(x, aspect_ratio)
for x in blob_utils.unpack_sequence(im)
])
boxes_ar = box_utils.aspect_ratio(boxes, aspect_ratio)
if hflip:
heatmaps_ar = im_detect_keypoints_hflip(
model, im_ar, cfg.TEST.SCALE, cfg.TEST.MAX_SIZE, boxes_ar
)
else:
blob_conv, im_scale = im_conv_body_only(
model, im_ar, cfg.TEST.SCALE, cfg.TEST.MAX_SIZE
)
heatmaps_ar = im_detect_keypoints(model, im_scale, boxes_ar, blob_conv)
return heatmaps_ar
