def im_detect(self, im_array, roi_array):
"""
perform detection of designated im, box, must follow minibatch.get_testbatch format
:param im_array: numpy.ndarray [b c h w]
:param roi_array: numpy.ndarray [roi_num 5]
:return: scores, pred_boxes
"""
# remove duplicate feature rois
if config.TEST.DEDUP_BOXES > 0:
roi_array = roi_array
# rank roi by v .* (b, dx, dy, dw, dh)
v = np.array([1, 1e3, 1e6, 1e9, 1e12])
# create hash and inverse index for rois
hashes = np.round(roi_array * config.TEST.DEDUP_BOXES).dot(v)
_, index, inv_index = np.unique(hashes,
return_index=True,
return_inverse=True)
roi_array = roi_array[index, :]
self.arg_params['data'] = mx.nd.array(im_array, self.ctx)
self.arg_params['rois'] = mx.nd.array(roi_array, self.ctx)
arg_shapes, out_shapes, aux_shapes = \
self.symbol.infer_shape(data=self.arg_params['data'].shape, rois=self.arg_params['rois'].shape)
arg_shapes_dict = {
name: shape
for name, shape in zip(self.symbol.list_arguments(), arg_shapes)
}
self.arg_params['cls_prob_label'] = mx.nd.zeros(
arg_shapes_dict['cls_prob_label'], self.ctx)
aux_names = self.symbol.list_auxiliary_states()
self.aux_params = {
k: mx.nd.zeros(s, self.ctx)
for k, s in zip(aux_names, aux_shapes)
}
self.executor = self.symbol.bind(self.ctx,
self.arg_params,
args_grad=None,
grad_req='null',
aux_states=self.aux_params)
output_dict = {
name: nd
for name, nd in zip(self.symbol.list_outputs(),
self.executor.outputs)
}
self.executor.forward(is_train=False)
scores = output_dict['cls_prob_output'].asnumpy()
bbox_deltas = output_dict['bbox_pred_output'].asnumpy()
pred_boxes = bbox_pred(roi_array[:, 1:], bbox_deltas)
pred_boxes = clip_boxes(pred_boxes, im_array[0].shape[-2:])
if config.TEST.DEDUP_BOXES > 0:
# map back to original
scores = scores[inv_index, :]
pred_boxes = pred_boxes[inv_index, :]
return scores, pred_boxes
def main():
color = cv2.imread(args.img) # read image in b,g,r order
img, scale = resize(color.copy(), 640, 1024)
im_info = np.array([[img.shape[0], img.shape[1], scale]],
dtype=np.float32) # (h, w, scale)
img = np.swapaxes(img, 0, 2)
img = np.swapaxes(img, 1, 2) # change to r,g,b order
img = img[np.newaxis, :] # extend to (n, c, h, w)
ctx = mx.gpu(args.gpu)
_, arg_params, aux_params = mx.model.load_checkpoint(
args.prefix, args.epoch)
arg_params, aux_params = ch_dev(arg_params, aux_params, ctx)
if 'resnet' in args.prefix:
sym = resnet_50(num_class=2,
bn_mom=0.99,
bn_global=True,
is_train=False)
else:
sym = get_vgg_test(num_classes=2)
arg_params["data"] = mx.nd.array(img, ctx)
arg_params["im_info"] = mx.nd.array(im_info, ctx)
exe = sym.bind(ctx,
arg_params,
args_grad=None,
grad_req="null",
aux_states=aux_params)
exe.forward(is_train=False)
output_dict = {
name: nd
for name, nd in zip(sym.list_outputs(), exe.outputs)
}
rois = output_dict['rpn_rois_output'].asnumpy(
)[:, 1:] # first column is index
scores = output_dict['cls_prob_reshape_output'].asnumpy()[0]
bbox_deltas = output_dict['bbox_pred_reshape_output'].asnumpy()[0]
pred_boxes = bbox_pred(rois, bbox_deltas)
pred_boxes = clip_boxes(pred_boxes, (im_info[0][0], im_info[0][1]))
cls_boxes = pred_boxes[:, 4:8]
cls_scores = scores[:, 1]
keep = np.where(cls_scores >= args.thresh)[0]
cls_boxes = cls_boxes[keep, :]
cls_scores = cls_scores[keep]
dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets.astype(np.float32), args.nms_thresh)
dets = dets[keep, :]
keep = nest(dets, thresh=args.nest_thresh)
dets = dets[keep, :]
for i in range(dets.shape[0]):
bbox = dets[i, :4]
cv2.rectangle(
color, (int(round(bbox[0] / scale)), int(round(bbox[1] / scale))),
(int(round(bbox[2] / scale)), int(round(bbox[3] / scale))),
(0, 255, 0), 2)
cv2.imwrite("result.jpg", color)
def im_detect(self, im_array, roi_array):
"""
perform detection of designated im, box, must follow minibatch.get_testbatch format
:param im_array: numpy.ndarray [b c h w]
:param roi_array: numpy.ndarray [roi_num 5]
:return: scores, pred_boxes
"""
# remove duplicate feature rois
if config.TEST.DEDUP_BOXES > 0:
roi_array = roi_array
# rank roi by v .* (b, dx, dy, dw, dh)
v = np.array([1, 1e3, 1e6, 1e9, 1e12])
# create hash and inverse index for rois
hashes = np.round(roi_array * config.TEST.DEDUP_BOXES).dot(v)
_, index, inv_index = np.unique(hashes, return_index=True, return_inverse=True)
roi_array = roi_array[index, :]
self.arg_params['data'] = mx.nd.array(im_array, self.ctx)
self.arg_params['rois'] = mx.nd.array(roi_array, self.ctx)
arg_shapes, out_shapes, aux_shapes = \
self.symbol.infer_shape(data=self.arg_params['data'].shape, rois=self.arg_params['rois'].shape)
arg_shapes_dict = {name: shape for name, shape in zip(self.symbol.list_arguments(), arg_shapes)}
self.arg_params['cls_prob_label'] = mx.nd.zeros(arg_shapes_dict['cls_prob_label'], self.ctx)
aux_names = self.symbol.list_auxiliary_states()
self.aux_params = {k: mx.nd.zeros(s, self.ctx) for k, s in zip(aux_names, aux_shapes)}
self.executor = self.symbol.bind(self.ctx, self.arg_params, args_grad=None,
grad_req='null', aux_states=self.aux_params)
output_dict = {name: nd for name, nd in zip(self.symbol.list_outputs(), self.executor.outputs)}
self.executor.forward(is_train=False)
scores = output_dict['cls_prob_output'].asnumpy()
bbox_deltas = output_dict['bbox_pred_output'].asnumpy()
pred_boxes = bbox_pred(roi_array[:, 1:], bbox_deltas)
pred_boxes = clip_boxes(pred_boxes, im_array[0].shape[-2:])
if config.TEST.DEDUP_BOXES > 0:
# map back to original
scores = scores[inv_index, :]
pred_boxes = pred_boxes[inv_index, :]
return scores, pred_boxes
def main():
color = cv2.imread(args.img) # read image in b,g,r order
img, scale = resize(color.copy(), 640, 1024)
im_info = np.array([[img.shape[0], img.shape[1], scale]], dtype=np.float32) # (h, w, scale)
img = np.swapaxes(img, 0, 2)
img = np.swapaxes(img, 1, 2) # change to r,g,b order
img = img[np.newaxis, :] # extend to (n, c, h, w)
ctx = mx.gpu(args.gpu)
_, arg_params, aux_params = mx.model.load_checkpoint(args.prefix, args.epoch)
arg_params, aux_params = ch_dev(arg_params, aux_params, ctx)
if 'resnet' in args.prefix:
sym = resnet_50(num_class=2, bn_mom=0.99, bn_global=True, is_train=False)
else:
sym = get_vgg_test(num_classes=2)
arg_params["data"] = mx.nd.array(img, ctx)
arg_params["im_info"] = mx.nd.array(im_info, ctx)
exe = sym.bind(ctx, arg_params, args_grad=None, grad_req="null", aux_states=aux_params)
exe.forward(is_train=False)
output_dict = {name: nd for name, nd in zip(sym.list_outputs(), exe.outputs)}
rois = output_dict['rpn_rois_output'].asnumpy()[:, 1:] # first column is index
scores = output_dict['cls_prob_reshape_output'].asnumpy()[0]
bbox_deltas = output_dict['bbox_pred_reshape_output'].asnumpy()[0]
pred_boxes = bbox_pred(rois, bbox_deltas)
pred_boxes = clip_boxes(pred_boxes, (im_info[0][0], im_info[0][1]))
cls_boxes = pred_boxes[:, 4:8]
cls_scores = scores[:, 1]
keep = np.where(cls_scores >= args.thresh)[0]
cls_boxes = cls_boxes[keep, :]
cls_scores = cls_scores[keep]
dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets.astype(np.float32), args.nms_thresh)
dets = dets[keep, :]
keep = nest(dets, thresh=args.nest_thresh)
dets = dets[keep, :]
for i in range(dets.shape[0]):
bbox = dets[i, :4]
cv2.rectangle(color, (int(round(bbox[0]/scale)), int(round(bbox[1]/scale))),
(int(round(bbox[2]/scale)), int(round(bbox[3]/scale))), (0, 255, 0), 2)
cv2.imwrite("result.jpg", color)
def im_detect(self, im_array, im_info=None, roi_array=None):
"""
perform detection of designated im, box, must follow minibatch.get_testbatch format
:param im_array: numpy.ndarray [b c h w]
:param im_info: numpy.ndarray [b 3]
:param roi_array: numpy.ndarray [roi_num 5]
:return: scores, pred_boxes
"""
# remove duplicate feature rois
if config.TEST.DEDUP_BOXES > 0 and not config.TEST.HAS_RPN:
roi_array = roi_array
# rank roi by v .* (b, dx, dy, dw, dh)
v = np.array([1, 1e3, 1e6, 1e9, 1e12])
# create hash and inverse index for rois
hashes = np.round(roi_array * config.TEST.DEDUP_BOXES).dot(v)
_, index, inv_index = np.unique(hashes, return_index=True, return_inverse=True)
roi_array = roi_array[index, :]
if self.executor is None:
# fill in data
if config.TEST.HAS_RPN:
self.arg_params['data'] = mx.nd.array(im_array, self.ctx)
self.arg_params['im_info'] = mx.nd.array(im_info, self.ctx)
arg_shapes, out_shapes, aux_shapes = \
self.symbol.infer_shape(data=self.arg_params['data'].shape, im_info=self.arg_params['im_info'].shape)
else:
self.arg_params['data'] = mx.nd.array(im_array, self.ctx)
self.arg_params['rois'] = mx.nd.array(roi_array, self.ctx)
arg_shapes, out_shapes, aux_shapes = \
self.symbol.infer_shape(data=self.arg_params['data'].shape, rois=self.arg_params['rois'].shape)
# fill in label and aux
arg_shapes_dict = {name: shape for name, shape in zip(self.symbol.list_arguments(), arg_shapes)}
self.arg_params['cls_prob_label'] = mx.nd.zeros(arg_shapes_dict['cls_prob_label'], self.ctx)
aux_names = self.symbol.list_auxiliary_states()
self.aux_params = {k: mx.nd.zeros(s, self.ctx) for k, s in zip(aux_names, aux_shapes)}
# execute
self.executor = self.symbol.bind(self.ctx, self.arg_params, args_grad=None,
grad_req='null', aux_states=self.aux_params)
executor = self.executor
else:
if config.TEST.HAS_RPN:
# Test whether we need upsizing
if np.prod(im_array.shape) > np.prod(self.executor.arg_dict['data'].shape):
self.executor = self.executor.reshape(allow_up_sizing=True, data=im_array.shape)
executor = self.executor
else:
executor = self.executor.reshape(allow_up_sizing=False, data=im_array.shape)
# fill in data
executor.arg_dict["data"][:] = im_array
executor.arg_dict["im_info"][:] = im_info
else:
if np.prod(im_array.shape) > np.prod(self.executor.arg_dict['data'].shape) or\
np.prod(roi_array.shape) > np.prod(self.executor.arg_dict['rois'].shape):
self.executor = self.executor.reshape(partial_shaping=True,
allow_up_sizing=True,
data=im_array.shape,
rois=roi_array.shape)
executor = self.executor
else:
executor = self.executor.reshape(partial_shaping=True,
allow_up_sizing=False,
data=im_array.shape,
rois=roi_array.shape)
# fill in data
executor.arg_dict["data"][:] = im_array
executor.arg_dict["rois"][:] = roi_array
executor.forward(is_train=False)
# save output
scores = executor.output_dict['cls_prob_reshape_output'].asnumpy()[0]
bbox_deltas = executor.output_dict['bbox_pred_reshape_output'].asnumpy()[0]
if config.TEST.HAS_RPN:
rois = executor.output_dict['rois_output'].asnumpy()
rois = rois[:, 1:].copy() # scale back
else:
rois = roi_array[:, 1:]
# post processing
pred_boxes = bbox_pred(rois, bbox_deltas)
pred_boxes = clip_boxes(pred_boxes, im_array[0].shape[-2:])
if config.TEST.DEDUP_BOXES > 0 and not config.TEST.HAS_RPN:
# map back to original
scores = scores[inv_index, :]
pred_boxes = pred_boxes[inv_index, :]
return scores, pred_boxes
def forward(self, is_train, req, in_data, out_data, aux):
# for each (H, W) location i
# generate A anchor boxes centered on cell i
# apply predicted bbox deltas at cell i to each of the A anchors
# clip predicted boxes to image
# remove predicted boxes with either height or width < threshold
# sort all (proposal, score) pairs by score from highest to lowest
# take top pre_nms_topN proposals before NMS
# apply NMS with threshold 0.7 to remaining proposals
# take after_nms_topN proposals after NMS
# return the top proposals (-> RoIs top, scores top)
pre_nms_topN = config[self.cfg_key].RPN_PRE_NMS_TOP_N
post_nms_topN = config[self.cfg_key].RPN_POST_NMS_TOP_N
nms_thresh = config[self.cfg_key].RPN_NMS_THRESH
min_size = config[self.cfg_key].RPN_MIN_SIZE
# the first set of anchors are background probabilities
# keep the second part
scores = in_data[0].asnumpy()[:, self._num_anchors:, :, :]
bbox_deltas = in_data[1].asnumpy()
im_info = in_data[2].asnumpy()[0, :]
if DEBUG:
print 'im_size: ({}, {})'.format(im_info[0], im_info[1])
print 'scale: {}'.format(im_info[2])
# 1. Generate proposals from bbox_deltas and shifted anchors
height, width = scores.shape[-2:]
if DEBUG:
print 'score map size: {}'.format(scores.shape)
# Enumerate all shifts
shift_x = np.arange(0, width) * self._feat_stride
shift_y = np.arange(0, height) * self._feat_stride
shift_x, shift_y = np.meshgrid(shift_x, shift_y)
shifts = np.vstack((shift_x.ravel(), shift_y.ravel(), shift_x.ravel(),
shift_y.ravel())).transpose()
# Enumerate all shifted anchors:
#
# add A anchors (1, A, 4) to
# cell K shifts (K, 1, 4) to get
# shift anchors (K, A, 4)
# reshape to (K*A, 4) shifted anchors
A = self._num_anchors
K = shifts.shape[0]
anchors = self._anchors.reshape((1, A, 4)) + shifts.reshape(
(1, K, 4)).transpose((1, 0, 2))
anchors = anchors.reshape((K * A, 4))
# Transpose and reshape predicted bbox transformations to get them
# into the same order as the anchors:
#
# bbox deltas will be (1, 4 * A, H, W) format
# transpose to (1, H, W, 4 * A)
# reshape to (1 * H * W * A, 4) where rows are ordered by (h, w, a)
# in slowest to fastest order
bbox_deltas = bbox_deltas.transpose((0, 2, 3, 1)).reshape((-1, 4))
# Same story for the scores:
#
# scores are (1, A, H, W) format
# transpose to (1, H, W, A)
# reshape to (1 * H * W * A, 1) where rows are ordered by (h, w, a)
scores = scores.transpose((0, 2, 3, 1)).reshape((-1, 1))
# Convert anchors into proposals via bbox transformations
proposals = bbox_pred(anchors, bbox_deltas)
# 2. clip predicted boxes to image
proposals = clip_boxes(proposals, im_info[:2])
# 3. remove predicted boxes with either height or width < threshold
# (NOTE: convert min_size to input image scale stored in im_info[2])
keep = ProposalOperator._filter_boxes(proposals, min_size * im_info[2])
proposals = proposals[keep, :]
scores = scores[keep]
# 4. sort all (proposal, score) pairs by score from highest to lowest
# 5. take top pre_nms_topN (e.g. 6000)
order = scores.ravel().argsort()[::-1]
if pre_nms_topN > 0:
order = order[:pre_nms_topN]
proposals = proposals[order, :]
scores = scores[order]
# 6. apply nms (e.g. threshold = 0.7)
# 7. take after_nms_topN (e.g. 300)
# 8. return the top proposals (-> RoIs top)
keep = nms(np.hstack((proposals, scores)), nms_thresh)
if post_nms_topN > 0:
keep = keep[:post_nms_topN]
# pad to ensure output size remains unchanged
if len(keep) < post_nms_topN:
pad = npr.choice(keep, size=post_nms_topN - len(keep))
keep = np.hstack((keep, pad))
proposals = proposals[keep, :]
scores = scores[keep]
# Output rois array
# Our RPN implementation only supports a single input image, so all
# batch inds are 0
batch_inds = np.zeros((proposals.shape[0], 1), dtype=np.float32)
blob = np.hstack((batch_inds, proposals.astype(np.float32,
copy=False)))
self.assign(out_data[0], req[0], blob)
if self._output_score:
self.assign(out_data[1], req[1],
scores.astype(np.float32, copy=False))
def forward(self, is_train, req, in_data, out_data, aux):
# for each (H, W) location i
# generate A anchor boxes centered on cell i
# apply predicted bbox deltas at cell i to each of the A anchors
# clip predicted boxes to image
# remove predicted boxes with either height or width < threshold
# sort all (proposal, score) pairs by score from highest to lowest
# take top pre_nms_topN proposals before NMS
# apply NMS with threshold 0.7 to remaining proposals
# take after_nms_topN proposals after NMS
# return the top proposals (-> RoIs top, scores top)
pre_nms_topN = config[self.cfg_key].RPN_PRE_NMS_TOP_N
post_nms_topN = config[self.cfg_key].RPN_POST_NMS_TOP_N
nms_thresh = config[self.cfg_key].RPN_NMS_THRESH
min_size = config[self.cfg_key].RPN_MIN_SIZE
# the first set of anchors are background probabilities
# keep the second part
scores = in_data[0].asnumpy()[:, self._num_anchors:, :, :]
if np.isnan(scores).any():
raise ValueError("there is nan in input scores")
bbox_deltas = in_data[1].asnumpy()
if np.isnan(bbox_deltas).any():
raise ValueError("there is nan in input bbox_deltas")
im_info = in_data[2].asnumpy()[0, :]
if DEBUG:
print 'im_size: ({}, {})'.format(im_info[0], im_info[1])
print 'scale: {}'.format(im_info[2])
# 1. Generate proposals from bbox_deltas and shifted anchors
height, width = scores.shape[-2:]
if self.cfg_key == 'TRAIN':
height, width = int(im_info[0] / self._feat_stride), int(im_info[1] / self._feat_stride)
if DEBUG:
print 'score map size: {}'.format(scores.shape)
print "resudial = ", scores.shape[2] - height, scores.shape[3] - width
# Enumerate all shifts
shift_x = np.arange(0, width) * self._feat_stride
shift_y = np.arange(0, height) * self._feat_stride
shift_x, shift_y = np.meshgrid(shift_x, shift_y)
shifts = np.vstack((shift_x.ravel(), shift_y.ravel(), shift_x.ravel(), shift_y.ravel())).transpose()
# Enumerate all shifted anchors:
#
# add A anchors (1, A, 4) to
# cell K shifts (K, 1, 4) to get
# shift anchors (K, A, 4)
# reshape to (K*A, 4) shifted anchors
A = self._num_anchors
K = shifts.shape[0]
anchors = self._anchors.reshape((1, A, 4)) + shifts.reshape((1, K, 4)).transpose((1, 0, 2))
anchors = anchors.reshape((K * A, 4))
# Transpose and reshape predicted bbox transformations to get them
# into the same order as the anchors:
#
# bbox deltas will be (1, 4 * A, H, W) format
# transpose to (1, H, W, 4 * A)
# reshape to (1 * H * W * A, 4) where rows are ordered by (h, w, a)
# in slowest to fastest order
bbox_deltas = clip_pad(bbox_deltas, (height, width))
bbox_deltas = bbox_deltas.transpose((0, 2, 3, 1)).reshape((-1, 4))
# Same story for the scores:
#
# scores are (1, A, H, W) format
# transpose to (1, H, W, A)
# reshape to (1 * H * W * A, 1) where rows are ordered by (h, w, a)
scores = scores.transpose((0, 2, 3, 1)).reshape((-1, 1))
# Convert anchors into proposals via bbox transformations
proposals = bbox_pred(anchors, bbox_deltas)
# 2. clip predicted boxes to image
proposals = clip_boxes(proposals, im_info[:2])
# 3. remove predicted boxes with either height or width < threshold
# (NOTE: convert min_size to input image scale stored in im_info[2])
keep = ProposalOperator._filter_boxes(proposals, min_size * im_info[2])
proposals = proposals[keep, :]
scores = scores[keep]
# 4. sort all (proposal, score) pairs by score from highest to lowest
# 5. take top pre_nms_topN (e.g. 6000)
order = scores.ravel().argsort()[::-1]
if pre_nms_topN > 0:
order = order[:pre_nms_topN]
proposals = proposals[order, :]
scores = scores[order]
# 6. apply nms (e.g. threshold = 0.7)
# 7. take after_nms_topN (e.g. 300)
# 8. return the top proposals (-> RoIs top)
keep = nms(np.hstack((proposals, scores)), nms_thresh)
if post_nms_topN > 0:
keep = keep[:post_nms_topN]
# pad to ensure output size remains unchanged
if len(keep) < post_nms_topN:
if len(keep) == 0:
logging.log(logging.ERROR, "currently len(keep) is zero")
pad = npr.choice(keep, size=post_nms_topN - len(keep))
keep = np.hstack((keep, pad))
proposals = proposals[keep, :]
scores = scores[keep]
# Output rois array
# Our RPN implementation only supports a single input image, so all
# batch inds are 0
batch_inds = np.zeros((proposals.shape[0], 1), dtype=np.float32)
blob = np.hstack((batch_inds, proposals.astype(np.float32, copy=False)))
self.assign(out_data[0], req[0], blob)
if self._output_score:
self.assign(out_data[1], req[1], scores.astype(np.float32, copy=False))
