def validate6Dpose(self, gt_pose, instance_bb8det=None, model_id=None):
gt_pose = np.reshape(gt_pose, newshape=(4, 4))
model_objx_info = self.models_info[instance_bb8det[0] + model_id]
min_x = model_objx_info['min_x'] * self.scale_to_meters
min_y = model_objx_info['min_y'] * self.scale_to_meters
min_z = model_objx_info['min_z'] * self.scale_to_meters
size_x = model_objx_info['size_x'] * self.scale_to_meters
size_y = model_objx_info['size_y'] * self.scale_to_meters
size_z = model_objx_info['size_z'] * self.scale_to_meters
max_x = min_x + size_x
max_y = min_y + size_y
max_z = min_z + size_z
BoundingBox = np.zeros(shape=(8, 3))
BoundingBox[0, :] = np.array([min_x, min_y, min_z])
BoundingBox[1, :] = np.array([min_x, min_y, max_z])
BoundingBox[2, :] = np.array([min_x, max_y, max_z])
BoundingBox[3, :] = np.array([min_x, max_y, min_z])
BoundingBox[4, :] = np.array([max_x, min_y, min_z])
BoundingBox[5, :] = np.array([max_x, min_y, max_z])
BoundingBox[6, :] = np.array([max_x, max_y, max_z])
BoundingBox[7, :] = np.array([max_x, max_y, min_z])
Xworld = np.reshape(BoundingBox, newshape=(-1, 3, 1))
Ximg_gt_pix = np.dot(gt_pose[0:3, 0:3], BoundingBox.T) + gt_pose[0:3,
3:4]
Ximg_gt_pix /= Ximg_gt_pix[2, :]
Ximg_gt_pix = np.dot(self.cam_intrinsic[:, 0:3], Ximg_gt_pix)
Ximg_gt_pix = Ximg_gt_pix[0:2, :].T
Ximg_gt_pix = np.reshape(Ximg_gt_pix, newshape=(-1, 2, 1))
Ximg_pix = instance_bb8det[6:22]
Ximg_pix = np.reshape(Ximg_pix, newshape=(-1, 2, 1))
img_shape = np.array([[[640.], [480.]]]) # 1x2x1
Ximg_pix = Ximg_pix * img_shape
Ximg_noised_pix = Ximg_gt_pix + np.random.normal(
loc=0.0, scale=1.0, size=(8, 2, 1))
epnpSolver = EPnP()
error, Rt, Cc, Xc = epnpSolver.efficient_pnp_gauss(
Xworld, Ximg_pix, self.cam_intrinsic)
out = {"R": Rt[0:3, 0:3], "t": Rt[0:3, 3:4]}
# absolute pose error
rot_error = pose_error.re(R_est=out["R"],
R_gt=gt_pose[0:3, 0:3]) / np.pi * 180.
trans_error = pose_error.te(t_est=out["t"], t_gt=gt_pose[0:3,
3:4]) / 0.01
return out
# Maximum Corresponding Point Distance
if 'mcpd' in errs_active:
errs['mcpd'].append(
pose_error.mcpd(pose_indis_set, pose_gt_indis_set, model))
# Average Distance of Model Points for objects with no indistinguishable views
if 'add' in errs_active:
errs['add'].append(pose_error.add(pose, pose_gt, model))
# Average Distance of Model Points for objects with indistinguishable views
if 'adi' in errs_active:
errs['adi'].append(pose_error.adi(pose, pose_gt, model))
# Translational Error
if 'te' in errs_active:
errs['te'].append(pose_error.te(pose['t'], pose_gt['t']))
# Rotational Error
if 're' in errs_active:
errs['re'].append(pose_error.re(pose['R'], pose_gt['R']))
# Complement over Union
if 'cou' in errs_active:
errs['cou'].append(pose_error.cou(pose, pose_gt, model, im_size, K))
# Plot the errors
for err_name in errs_active:
plt.figure()
plt.plot(errs[err_name], c='r', lw='3')
plt.xlabel('Pose ID')
plt.ylabel(err_name)
def update(self, labels, preds):
"""
:param preds: [cls_prob, loc_loss, cls_label, bb8_loss, loc_pred, bb8_pred,
anchors, loc_label, loc_pred_masked, loc_mae, bb8_label, bb8_pred_masked, bb8_mae]
Implementation of updating metrics
"""
def iou(x, ys):
"""
Calculate intersection-over-union overlap
Params:
----------
x : numpy.array
single box [xmin, ymin ,xmax, ymax]
ys : numpy.array
multiple box [[xmin, ymin, xmax, ymax], [...], ]
Returns:
-----------
numpy.array
[iou1, iou2, ...], size == ys.shape[0]
"""
ixmin = np.maximum(ys[:, 0], x[0])
iymin = np.maximum(ys[:, 1], x[1])
ixmax = np.minimum(ys[:, 2], x[2])
iymax = np.minimum(ys[:, 3], x[3])
iw = np.maximum(ixmax - ixmin, 0.)
ih = np.maximum(iymax - iymin, 0.)
inters = iw * ih
uni = (x[2] - x[0]) * (x[3] - x[1]) + (ys[:, 2] - ys[:, 0]) * \
(ys[:, 3] - ys[:, 1]) - inters
ious = inters / uni
ious[uni < 1e-12] = 0 # in case bad boxes
return ious
labels = labels[0].asnumpy()
# get generated multi label from network
cls_prob = preds[0]
loc_loss = preds[1].asnumpy() # smoothL1 loss
loc_loss_in_use = loc_loss[loc_loss.nonzero()]
cls_label = preds[2].asnumpy()
bb8_loss = preds[3].asnumpy()
loc_pred = preds[4]
bb8_pred = preds[5]
anchors = preds[6]
# anchor_in_use = anchors[anchors.nonzero()]
bb8dets = BB8MultiBoxDetection(cls_prob,
loc_pred,
bb8_pred,
anchors,
nms_threshold=0.5,
force_suppress=False,
variances=(0.1, 0.1, 0.2, 0.2),
nms_topk=400)
bb8dets = bb8dets.asnumpy()
# for i in range(1,16):
# if self.classes[0] == 'obj_{:02d}'.format(i):
# model_id = i
# break
model_id = int(self.classes[0].strip('obj_'))
for nbatch in range(bb8dets.shape[0]):
self.num_inst[7] += 1
self.num_inst[8] += 1
self.num_inst[9] += 1
self.num_inst[10] += 1
self.num_inst[11] += 1
self.num_inst[12] += 1
self.num_inst[13] += 1
self.num_inst[14] += 1
self.num_inst[15] += 1
if bb8dets[nbatch, 0, 0] == -1:
continue
else:
# for LINEMOD dataset, for each image only select the first det
# self.validate6Dpose(gt_pose=labels[nbatch, 0, 24:40], instance_bb8det=bb8dets[nbatch, 0, :], model_id=model_id)
pose_est = self.calculate6Dpose(instance_bb8det=bb8dets[nbatch,
0, :],
model_id=model_id)
model_path = '/data/ZHANGXIN/DATASETS/SIXD_CHALLENGE/LINEMOD/models/' + self.classes[
int(bb8dets[nbatch, 0, 0])] + '.ply'
model_ply = inout.load_ply(model_path)
model_ply['pts'] = model_ply['pts'] * self.scale_to_meters
pose_gt_transform = np.reshape(labels[nbatch, 0, 24:40],
newshape=(4, 4))
pose_gt = {
"R": pose_gt_transform[0:3, 0:3],
"t": pose_gt_transform[0:3, 3:4]
}
# absolute pose error
rot_error = pose_error.re(R_est=pose_est["R"],
R_gt=pose_gt["R"]) / np.pi * 180.
trans_error = pose_error.te(t_est=pose_est["t"],
t_gt=pose_gt["t"]) / 0.01
# other pose metrics
if model_id in [10, 11]:
add_metric = pose_error.adi(
pose_est=pose_est, pose_gt=pose_gt, model=model_ply
) # use adi when object is eggbox or glue
else:
add_metric = pose_error.add(
pose_est=pose_est, pose_gt=pose_gt, model=model_ply
) # use adi when object is eggbox or glue
reproj_metric = pose_error.reprojectionError(
pose_est=pose_est,
pose_gt=pose_gt,
model=model_ply,
K=self.cam_intrinsic[:, 0:3])
cou_metric = pose_error.cou(pose_est=pose_est,
pose_gt=pose_gt,
model=model_ply,
im_size=(640, 480),
K=self.cam_intrinsic[:, 0:3])
# metric update
if reproj_metric <= 5: # reprojection error less than 5 pixels
self.sum_metric[7] += 1
if add_metric <= self.models_info[
bb8dets[nbatch, 0, 0] + model_id][
'diameter'] * self.scale_to_meters * 0.1: # ADD metric less than 0.1 * diameter
self.sum_metric[8] += 1
if add_metric <= self.models_info[
bb8dets[nbatch, 0, 0] + model_id][
'diameter'] * self.scale_to_meters * 0.3: # ADD metric less than 0.1 * diameter
self.sum_metric[9] += 1
if add_metric <= self.models_info[
bb8dets[nbatch, 0, 0] + model_id][
'diameter'] * self.scale_to_meters * 0.5: # ADD metric less than 0.1 * diameter
self.sum_metric[10] += 1
if rot_error < 5: # 5 degrees
self.sum_metric[11] += 1
if trans_error < 5: # 5 cm
self.sum_metric[12] += 1
if (rot_error < 5) and (trans_error < 5): # 5 degrees and 5 cm
self.sum_metric[13] += 1
if cou_metric < 0.5: # 2D IoU greater than 0.5
self.sum_metric[14] += 1
if cou_metric < 0.1: # 2D IoU larger than 0.9
self.sum_metric[15] += 1
loc_label = preds[7].asnumpy()
loc_label_in_use = loc_label[loc_label.nonzero()]
loc_pred_masked = preds[8].asnumpy()
loc_pred_in_use = loc_pred_masked[loc_pred_masked.nonzero()]
loc_mae = preds[9].asnumpy()
loc_mae_in_use = loc_mae[loc_mae.nonzero()]
loc_mae_pixel = np.abs((bb8dets[:, 0, 2:6] - labels[:, 0, 1:5]) *
300) # need to be refined
bb8_label = preds[10].asnumpy()
bb8_label_in_use = bb8_label[bb8_label.nonzero()]
bb8_pred = preds[11].asnumpy()
bb8_pred_in_use = bb8_pred[bb8_pred.nonzero()]
bb8_mae = preds[12].asnumpy()
bb8_mae_in_use = bb8_mae[bb8_mae.nonzero()]
bb8_mae_pixel = np.abs((labels[:, 0, 8:24] - bb8dets[:, 0, 6:22]) *
300) # need to be refined
bb8_mae_pixel_x = bb8_mae_pixel[:, [0, 2, 4, 6, 8, 10, 12, 14]]
bb8_mae_pixel_y = bb8_mae_pixel[:, [1, 3, 5, 7, 9, 11, 13, 15]]
bb8_mae_pixel = np.sqrt(
np.square(bb8_mae_pixel_x) + np.square(bb8_mae_pixel_y))
# multi objects in one image (to be done)
# loc_mae_pixel = []
# bb8_mae_pixel = []
# # independant execution for each image
# for i in range(labels.shape[0]):
# # get as numpy arrays
# label = labels[i]
# pred = bb8dets[i]
# loc_mae_pixel_per_image = []
# bb8_mae_pixel_per_image = []
# # calculate for each class
# while (pred.shape[0] > 0):
# cid = int(pred[0, 0])
# indices = np.where(pred[:, 0].astype(int) == cid)[0]
# if cid < 0:
# pred = np.delete(pred, indices, axis=0)
# continue
# dets = pred[indices]
# pred = np.delete(pred, indices, axis=0)
#
# # ground-truths
# label_indices = np.where(label[:, 0].astype(int) == cid)[0]
# gts = label[label_indices, :]
# label = np.delete(label, label_indices, axis=0)
# if gts.size > 0:
# found = [False] * gts.shape[0]
# for j in range(dets.shape[0]):
# # compute overlaps
# ious = iou(dets[j, 2:6], gts[:, 1:5])
# ovargmax = np.argmax(ious)
# ovmax = ious[ovargmax]
# if ovmax > self.ovp_thresh:
# if not found[ovargmax]:
# loc_mae_pixel_per_image.append(np.abs((dets[j, 2:6] - gts[ovargmax, 1:5]) * 300)) # tp
# bb8_mae_pixel_per_image.append(np.abs((dets[j, 6:22] - gts[ovargmax, 8:24]) * 300))
# found[ovargmax] = True
# else:
# # duplicate
# pass # fp
#
# loc_mae_pixel.append(np.mean(loc_mae_pixel_per_image, axis=1))
# bb8_mae_pixel.append(np.mean(bb8_mae_pixel_per_image, axis=1))
valid_count = np.sum(cls_label >= 0)
box_count = np.sum(cls_label > 0)
# overall accuracy & object accuracy
label = cls_label.flatten()
# in case you have a 'other' class
label[np.where(label >= cls_prob.shape[1])] = 0
mask = np.where(label >= 0)[0]
indices = np.int64(label[mask])
prob = cls_prob.transpose((0, 2, 1)).reshape(
(-1, cls_prob.shape[1])).asnumpy()
prob = prob[mask, indices]
self.sum_metric[0] += (-np.log(prob + self.eps)).sum()
self.num_inst[0] += valid_count
# loc_smoothl1loss
self.sum_metric[1] += np.sum(loc_loss)
self.num_inst[1] += box_count * 4
# loc_mae
self.sum_metric[2] += np.sum(loc_mae)
self.num_inst[2] += box_count * 4
# loc_mae_pixel
self.sum_metric[3] += np.sum(loc_mae_pixel)
self.num_inst[3] += loc_mae_pixel.size
# bb8_smoothl1loss
self.sum_metric[4] += np.sum(bb8_loss)
self.num_inst[4] += box_count * 16
# bb8_mae
self.sum_metric[5] += np.sum(bb8_mae)
self.num_inst[5] += box_count * 16
# bb8_mae_pixel
self.sum_metric[6] += np.sum(bb8_mae_pixel)
self.num_inst[6] += bb8_mae_pixel.size
def update(self, labels, preds):
"""
:param preds: [cls_prob, loc_loss, cls_label, bb8_loss, loc_pred, bb8_pred,
anchors, loc_label, loc_pred_masked, loc_mae, bb8_label, bb8_pred_masked, bb8_mae]
Implementation of updating metrics
"""
labels = labels[0].asnumpy() # batchsize x 8 x 40
# get generated multi label from network
cls_prob = preds[0] # batchsize x num_cls x num_anchors
loc_loss = preds[1].asnumpy() # smoothL1 loss
# loc_loss_in_use = loc_loss[loc_loss.nonzero()]
cls_target = preds[2].asnumpy() # batchsize x num_anchors
bb8_loss = preds[3].asnumpy()
loc_pred = preds[4]
bb8_pred = preds[5]
anchors = preds[6]
# anchor_in_use = anchors[anchors.nonzero()]
# monitor results
# loc_target = preds[7].asnumpy()
# loc_target_in_use = loc_target[loc_target.nonzero()]
# loc_pred_masked = preds[8].asnumpy()
# loc_pred_in_use = loc_pred_masked[loc_pred_masked.nonzero()]
loc_mae = preds[9].asnumpy()
# loc_mae_in_use = loc_mae[loc_mae.nonzero()]
# bb8_target = preds[10].asnumpy()
# bb8_target_in_use = bb8_target[bb8_target.nonzero()]
# bb8_pred_masked = preds[11].asnumpy()
# bb8_pred_in_use = bb8_pred_masked[bb8_pred_masked.nonzero()]
bb8_mae = preds[12].asnumpy()
# bb8_mae_in_use = bb8_mae[bb8_mae.nonzero()]
# basic evaluation
valid_count = np.sum(cls_target >= 0)
box_count = np.sum(cls_target > 0)
# overall accuracy & object accuracy
label = cls_target.flatten()
# in case you have a 'other' class
label[np.where(label >= cls_prob.shape[1])] = 0
mask = np.where(label >= 0)[0]
indices = np.int64(label[mask])
prob = cls_prob.transpose((0, 2, 1)).reshape(
(-1, cls_prob.shape[1])).asnumpy()
prob = prob[mask, indices]
self.sum_metric[0] += (-np.log(prob + self.eps)).sum()
self.num_inst[0] += valid_count
# loc_smoothl1loss
self.sum_metric[1] += np.sum(loc_loss)
self.num_inst[1] += box_count * 4
# loc_mae
self.sum_metric[2] += np.sum(loc_mae)
self.num_inst[2] += box_count * 4
# bb8_smoothl1loss
self.sum_metric[4] += np.sum(bb8_loss)
self.num_inst[4] += box_count * 16
# bb8_mae
self.sum_metric[5] += np.sum(bb8_mae)
self.num_inst[5] += box_count * 16
bb8dets = IndirectBB8MultiBoxDetection(cls_prob,
loc_pred,
bb8_pred,
anchors,
nms_threshold=0.5,
force_suppress=False,
variances=(0.1, 0.1, 0.2, 0.2),
nms_topk=400)
bb8dets = bb8dets.asnumpy()
loc_mae_pixel = []
bb8_mae_pixel = []
# pose metrics, adapt to multi-class case
for sampleDet, sampleLabel in zip(bb8dets, labels):
# calculate for each class
for instanceLabel in sampleLabel:
if instanceLabel[0] < 0:
continue
else:
cid = instanceLabel[0].astype(np.int16)
model_id = int(self.classes[cid].strip("obj_"))
indices = np.where(sampleDet[:, 0] == cid)[0]
if cid in self.counts:
self.counts[cid] += 1
else:
self.counts[cid] = 1
if indices.size > 0:
instanceDet = sampleDet[indices[
0]] # only consider the most confident instance
loc_mae_pixel.append(
np.abs((instanceDet[2:6] - instanceLabel[1:5]) *
300.))
bb8_mae_pixel.append(
np.abs((instanceDet[6:22] - instanceLabel[8:24]) *
300.))
pose_est = self.calculate6Dpose(
instance_bb8det=instanceDet, model_id=model_id)
model_path = os.path.join(
self.LINEMOD_path, 'models',
'{}.ply'.format(self.classes[cid]))
model_ply = inout.load_ply(model_path)
model_ply[
'pts'] = model_ply['pts'] * self.scale_to_meters
pose_gt_transform = np.reshape(instanceLabel[24:40],
newshape=(4, 4))
pose_gt = {
"R": pose_gt_transform[0:3, 0:3],
"t": pose_gt_transform[0:3, 3:4]
}
# absolute pose error
rot_error = pose_error.re(
R_est=pose_est["R"],
R_gt=pose_gt["R"]) / np.pi * 180.
trans_error = pose_error.te(t_est=pose_est["t"],
t_gt=pose_gt["t"]) / 0.01
# other pose metrics
if model_id in [10, 11]:
add_metric = pose_error.adi(
pose_est=pose_est,
pose_gt=pose_gt,
model=model_ply
) # use adi when object is eggbox or glue
else:
add_metric = pose_error.add(
pose_est=pose_est,
pose_gt=pose_gt,
model=model_ply) # use add otherwise
reproj_metric = pose_error.reprojectionError(
pose_est=pose_est,
pose_gt=pose_gt,
model=model_ply,
K=self.cam_intrinsic[:, 0:3])
cou_metric = pose_error.cou(pose_est=pose_est,
pose_gt=pose_gt,
model=model_ply,
im_size=(640, 480),
K=self.cam_intrinsic[:,
0:3])
# record all the Reproj. error to plot curve
if cid not in self.Reproj:
self.Reproj[cid] = [reproj_metric]
else:
assert cid in self.counts
self.Reproj[cid] += [reproj_metric]
# metric update
if reproj_metric <= 5: # reprojection error less than 5 pixels
if cid not in self.Reproj5px:
self.Reproj5px[cid] = 1
else:
assert cid in self.counts
self.Reproj5px[cid] += 1
if add_metric <= self.models_info[model_id][
'diameter'] * self.scale_to_meters * 0.1: # ADD metric less than 0.1 * diameter
if cid not in self.ADD0_1:
self.ADD0_1[cid] = 1
else:
assert cid in self.counts
self.ADD0_1[cid] += 1
if add_metric <= self.models_info[model_id][
'diameter'] * self.scale_to_meters * 0.3: # ADD metric less than 0.3 * diameter
if cid not in self.ADD0_3:
self.ADD0_3[cid] = 1
else:
assert cid in self.counts
self.ADD0_3[cid] += 1
if add_metric <= self.models_info[model_id][
'diameter'] * self.scale_to_meters * 0.5: # ADD metric less than 0.5 * diameter
if cid not in self.ADD0_5:
self.ADD0_5[cid] = 1
else:
assert cid in self.counts
self.ADD0_5[cid] += 1
if rot_error < 5: # 5 degrees
if cid not in self.re:
self.re[cid] = 1
else:
assert cid in self.counts
self.re[cid] += 1
if trans_error < 5: # 5 cm
if cid not in self.te:
self.te[cid] = 1
else:
assert cid in self.counts
self.te[cid] += 1
if (rot_error < 5) and (trans_error <
5): # 5 degrees and 5 cm
if cid not in self.re_te:
self.re_te[cid] = 1
else:
assert cid in self.counts
self.re_te[cid] += 1
if cou_metric < 0.5: # 2D IoU greater than 0.5
if cid not in self.IoU2D0_5:
self.IoU2D0_5[cid] = 1
else:
assert cid in self.counts
self.IoU2D0_5[cid] += 1
if cou_metric < 0.4: # 2D IoU greater than 0.6
if cid not in self.IoU2D0_6:
self.IoU2D0_6[cid] = 1
else:
assert cid in self.counts
self.IoU2D0_6[cid] += 1
if cou_metric < 0.3: # 2D IoU greater than 0.7
if cid not in self.IoU2D0_7:
self.IoU2D0_7[cid] = 1
else:
assert cid in self.counts
self.IoU2D0_7[cid] += 1
if cou_metric < 0.2: # 2D IoU greater than 0.8
if cid not in self.IoU2D0_8:
self.IoU2D0_8[cid] = 1
else:
assert cid in self.counts
self.IoU2D0_8[cid] += 1
if cou_metric < 0.1: # 2D IoU larger than 0.9
if cid not in self.IoU2D0_9:
self.IoU2D0_9[cid] = 1
else:
assert cid in self.counts
self.IoU2D0_9[cid] += 1
# else:
# loc_mae_pixel.append(np.ones((4, )) * 300.)
# bb8_mae_pixel.append(np.ones((16, )) * 300.)
loc_mae_pixel = np.array(loc_mae_pixel)
loc_mae_pixel_x = loc_mae_pixel[:, [0, 2]]
loc_mae_pixel_y = loc_mae_pixel[:, [1, 3]]
loc_mae_pixel = np.sqrt(
np.square(loc_mae_pixel_x) + np.square(loc_mae_pixel_y))
bb8_mae_pixel = np.array(bb8_mae_pixel)
bb8_mae_pixel_x = bb8_mae_pixel[:, [0, 2, 4, 6, 8, 10, 12, 14]]
bb8_mae_pixel_y = bb8_mae_pixel[:, [1, 3, 5, 7, 9, 11, 13, 15]]
bb8_mae_pixel = np.sqrt(
np.square(bb8_mae_pixel_x) + np.square(bb8_mae_pixel_y))
# loc_mae_pixel
self.sum_metric[3] += np.sum(loc_mae_pixel)
self.num_inst[3] += loc_mae_pixel.size
# bb8_mae_pixel
self.sum_metric[6] += np.sum(bb8_mae_pixel)
self.num_inst[6] += bb8_mae_pixel.size
def update(self, labels, preds):
"""
:param preds: [cls_prob, loc_loss, cls_label, bb8_loss, loc_pred, bb8_pred,
anchors, loc_label, loc_pred_masked, loc_mae, bb8_label, bb8_pred_masked, bb8_mae]
Implementation of updating metrics
"""
def iou(x, ys):
"""
Calculate intersection-over-union overlap
Params:
----------
x : numpy.array
single box [xmin, ymin ,xmax, ymax]
ys : numpy.array
multiple box [[xmin, ymin, xmax, ymax], [...], ]
Returns:
-----------
numpy.array
[iou1, iou2, ...], size == ys.shape[0]
"""
ixmin = np.maximum(ys[:, 0], x[0])
iymin = np.maximum(ys[:, 1], x[1])
ixmax = np.minimum(ys[:, 2], x[2])
iymax = np.minimum(ys[:, 3], x[3])
iw = np.maximum(ixmax - ixmin, 0.)
ih = np.maximum(iymax - iymin, 0.)
inters = iw * ih
uni = (x[2] - x[0]) * (x[3] - x[1]) + (ys[:, 2] - ys[:, 0]) * \
(ys[:, 3] - ys[:, 1]) - inters
ious = inters / uni
ious[uni < 1e-12] = 0 # in case bad boxes
return ious
labels = labels[0].asnumpy() # batchsize x 8 x 40
# get generated multi label from network
cls_prob = preds[0] # batchsize x num_cls x num_anchors
loc_loss = preds[1].asnumpy() # smoothL1 loss
loc_loss_in_use = loc_loss[loc_loss.nonzero()]
cls_label = preds[2].asnumpy() # batchsize x num_anchors
bb8_loss = preds[3].asnumpy()
loc_pred = preds[4]
bb8_pred = preds[5]
anchors = preds[6]
# anchor_in_use = anchors[anchors.nonzero()]
# basic evaluation, adapt to multi-class
loc_label = preds[7].asnumpy()
loc_label_in_use = loc_label[loc_label.nonzero()]
loc_pred_masked = preds[8].asnumpy()
loc_pred_in_use = loc_pred_masked[loc_pred_masked.nonzero()]
loc_mae = preds[9].asnumpy()
loc_mae_in_use = loc_mae[loc_mae.nonzero()]
bb8_label = preds[10].asnumpy()
bb8_label_in_use = bb8_label[bb8_label.nonzero()]
bb8_pred_masked = preds[11].asnumpy()
bb8_pred_in_use = bb8_pred_masked[bb8_pred_masked.nonzero()]
bb8_mae = preds[12].asnumpy()
bb8_mae_in_use = bb8_mae[bb8_mae.nonzero()]
valid_count = np.sum(cls_label >= 0)
box_count = np.sum(cls_label > 0)
# overall accuracy & object accuracy
label = cls_label.flatten()
# in case you have a 'other' class
label[np.where(label >= cls_prob.shape[1])] = 0
mask = np.where(label >= 0)[0]
indices = np.int64(label[mask])
prob = cls_prob.transpose((0, 2, 1)).reshape(
(-1, cls_prob.shape[1])).asnumpy()
prob = prob[mask, indices]
self.sum_metric[0] += (-np.log(prob + self.eps)).sum()
self.num_inst[0] += valid_count
# loc_smoothl1loss
self.sum_metric[1] += np.sum(loc_loss)
self.num_inst[1] += box_count * 4
# loc_mae
self.sum_metric[2] += np.sum(loc_mae)
self.num_inst[2] += box_count * 4
# bb8_smoothl1loss
self.sum_metric[4] += np.sum(bb8_loss)
self.num_inst[4] += box_count * 16
# bb8_mae
self.sum_metric[5] += np.sum(bb8_mae)
self.num_inst[5] += box_count * 16
bb8dets = BB8MultiBoxDetection(cls_prob,
loc_pred,
bb8_pred,
anchors,
nms_threshold=0.5,
force_suppress=False,
variances=(0.1, 0.1, 0.2, 0.2),
nms_topk=400)
bb8dets = bb8dets.asnumpy()
# model_id = int(self.classes[0].strip("obj_"))
#
# for nbatch in range(bb8dets.shape[0]):
#
# self.num_inst[7] += 1
# self.num_inst[8] += 1
# self.num_inst[9] += 1
# self.num_inst[10] += 1
# self.num_inst[11] += 1
# self.num_inst[12] += 1
# self.num_inst[13] += 1
# self.num_inst[14] += 1
# self.num_inst[15] += 1
#
# if bb8dets[nbatch, 0, 0] == -1:
# continue
# else:
# # for LINEMOD dataset, for each image only select the first det
# # self.validate6Dpose(gt_pose=labels[nbatch, 0, 24:40], instance_bb8det=bb8dets[nbatch, 0, :], model_id=model_id)
# pose_est = self.calculate6Dpose(instance_bb8det=bb8dets[nbatch, 0, :], model_id=model_id)
# model_path = '/data/ZHANGXIN/DATASETS/SIXD_CHALLENGE/LINEMOD/models/' + self.classes[int(bb8dets[nbatch, 0, 0])] + '.ply'
# model_ply = inout.load_ply(model_path)
# model_ply['pts'] = model_ply['pts'] * self.scale_to_meters
# pose_gt_transform = np.reshape(labels[nbatch, 0, 24:40], newshape=(4, 4))
# pose_gt = {"R": pose_gt_transform[0:3, 0:3],
# "t": pose_gt_transform[0:3, 3:4]}
#
# # absolute pose error
# rot_error = pose_error.re(R_est=pose_est["R"], R_gt=pose_gt["R"]) / np.pi * 180.
# trans_error = pose_error.te(t_est=pose_est["t"], t_gt=pose_gt["t"]) / 0.01
#
# # other pose metrics
# add_metric = pose_error.add(pose_est=pose_est, pose_gt=pose_gt, model=model_ply) # use adi when object is eggbox or glue
# reproj_metric = pose_error.reprojectionError(pose_est=pose_est, pose_gt=pose_gt,
# model=model_ply, K=self.cam_intrinsic[:, 0:3])
# cou_metric = pose_error.cou(pose_est=pose_est, pose_gt=pose_gt,
# model=model_ply, im_size=(640, 480), K=self.cam_intrinsic[:, 0:3])
#
# # metric update
# if reproj_metric <= 5: # reprojection error less than 5 pixels
# self.sum_metric[7] += 1
#
# if add_metric <= self.models_info[bb8dets[nbatch, 0, 0] + model_id]['diameter'] * self.scale_to_meters * 0.1: # ADD metric less than 0.1 * diameter
# self.sum_metric[8] += 1
#
# if add_metric <= self.models_info[bb8dets[nbatch, 0, 0] + model_id]['diameter'] * self.scale_to_meters * 0.3: # ADD metric less than 0.1 * diameter
# self.sum_metric[9] += 1
#
# if add_metric <= self.models_info[bb8dets[nbatch, 0, 0] + model_id]['diameter'] * self.scale_to_meters * 0.5: # ADD metric less than 0.1 * diameter
# self.sum_metric[10] += 1
#
# if rot_error < 5: # 5 degrees
# self.sum_metric[11] += 1
#
# if trans_error < 5: # 5 cm
# self.sum_metric[12] += 1
#
# if (rot_error < 5) and (trans_error < 5): # 5 degrees and 5 cm
# self.sum_metric[13] += 1
#
# if cou_metric < 0.5: # 2D IoU greater than 0.5
# self.sum_metric[14] += 1
#
# if cou_metric < 0.1: # 2D IoU larger than 0.9
# self.sum_metric[15] += 1
#
# for each class, only consider the most confident instance
loc_mae_pixel = []
bb8_mae_pixel = []
# pose metrics, adapt to multi-class case
for sampleDet, sampleLabel in zip(bb8dets, labels):
# calculate for each class
for instanceLabel in sampleLabel:
if instanceLabel[0] < 0:
continue
else:
cid = instanceLabel[0].astype(np.int16)
model_id = int(self.classes[cid].strip("obj_"))
indices = np.where(sampleDet[:, 0] == cid)[0]
if cid in self.counts:
self.counts[cid] += 1
else:
self.counts[cid] = 1
if indices.size > 0:
instanceDet = sampleDet[indices[
0]] # only consider the most confident instance
loc_mae_pixel.append(
np.abs(
(instanceDet[2:6] - instanceLabel[1:5]) * 300))
bb8_mae_pixel.append(
np.abs((instanceDet[6:22] - instanceLabel[8:24]) *
300))
pose_est = self.calculate6Dpose(
instance_bb8det=instanceDet, model_id=model_id)
model_path = os.path.join(
self.LINEMOD_path, 'models',
'{}.ply'.format(self.classes[cid]))
model_ply = inout.load_ply(model_path)
model_ply[
'pts'] = model_ply['pts'] * self.scale_to_meters
pose_gt_transform = np.reshape(instanceLabel[24:40],
newshape=(4, 4))
pose_gt = {
"R": pose_gt_transform[0:3, 0:3],
"t": pose_gt_transform[0:3, 3:4]
}
# absolute pose error
rot_error = pose_error.re(
R_est=pose_est["R"],
R_gt=pose_gt["R"]) / np.pi * 180.
trans_error = pose_error.te(t_est=pose_est["t"],
t_gt=pose_gt["t"]) / 0.01
# other pose metrics
if model_id in [10, 11]:
add_metric = pose_error.adi(
pose_est=pose_est,
pose_gt=pose_gt,
model=model_ply
) # use adi when object is eggbox or glue
else:
add_metric = pose_error.add(
pose_est=pose_est,
pose_gt=pose_gt,
model=model_ply
) # use adi when object is eggbox or glue
reproj_metric = pose_error.reprojectionError(
pose_est=pose_est,
pose_gt=pose_gt,
model=model_ply,
K=self.cam_intrinsic[:, 0:3])
cou_metric = pose_error.cou(pose_est=pose_est,
pose_gt=pose_gt,
model=model_ply,
im_size=(640, 480),
K=self.cam_intrinsic[:,
0:3])
if cid not in self.Reproj:
self.Reproj[cid] = [reproj_metric]
else:
assert cid in self.counts
self.Reproj[cid] += [reproj_metric]
# metric update
if reproj_metric <= 5: # reprojection error less than 5 pixels
if cid not in self.Reproj5px:
self.Reproj5px[cid] = 1
else:
assert cid in self.counts
self.Reproj5px[cid] += 1
if add_metric <= self.models_info[model_id][
'diameter'] * self.scale_to_meters * 0.1: # ADD metric less than 0.1 * diameter
if cid not in self.ADD0_1:
self.ADD0_1[cid] = 1
else:
assert cid in self.counts
self.ADD0_1[cid] += 1
if add_metric <= self.models_info[model_id][
'diameter'] * self.scale_to_meters * 0.3: # ADD metric less than 0.3 * diameter
if cid not in self.ADD0_3:
self.ADD0_3[cid] = 1
else:
assert cid in self.counts
self.ADD0_3[cid] += 1
if add_metric <= self.models_info[model_id][
'diameter'] * self.scale_to_meters * 0.5: # ADD metric less than 0.5 * diameter
if cid not in self.ADD0_5:
self.ADD0_5[cid] = 1
else:
assert cid in self.counts
self.ADD0_5[cid] += 1
if rot_error < 5: # 5 degrees
if cid not in self.re:
self.re[cid] = 1
else:
assert cid in self.counts
self.re[cid] += 1
if trans_error < 5: # 5 cm
if cid not in self.te:
self.te[cid] = 1
else:
assert cid in self.counts
self.te[cid] += 1
if (rot_error < 5) and (trans_error <
5): # 5 degrees and 5 cm
if cid not in self.re_te:
self.re_te[cid] = 1
else:
assert cid in self.counts
self.re_te[cid] += 1
if cou_metric < 0.5: # 2D IoU greater than 0.5
if cid not in self.IoU2D0_5:
self.IoU2D0_5[cid] = 1
else:
assert cid in self.counts
self.IoU2D0_5[cid] += 1
if cou_metric < 0.1: # 2D IoU larger than 0.9
if cid not in self.IoU2D0_9:
self.IoU2D0_9[cid] = 1
else:
assert cid in self.counts
self.IoU2D0_9[cid] += 1
loc_mae_pixel = np.array(loc_mae_pixel)
bb8_mae_pixel = np.array(bb8_mae_pixel)
bb8_mae_pixel_x = bb8_mae_pixel[:, [0, 2, 4, 6, 8, 10, 12, 14]]
bb8_mae_pixel_y = bb8_mae_pixel[:, [1, 3, 5, 7, 9, 11, 13, 15]]
bb8_mae_pixel = np.sqrt(
np.square(bb8_mae_pixel_x) + np.square(bb8_mae_pixel_y))
# loc_mae_pixel
self.sum_metric[3] += np.sum(loc_mae_pixel)
self.num_inst[3] += loc_mae_pixel.size
# bb8_mae_pixel
self.sum_metric[6] += np.sum(bb8_mae_pixel)
self.num_inst[6] += bb8_mae_pixel.size
