def test_load_dpm_detections(self):
conf = dict(self.config)
conf['detectors'] = ['dpm']
policy = DatasetPolicy(self.dataset,self.train_dataset,**conf)
assert(policy.detectors == ['dpm'])
dets = policy.load_ext_detections(self.dataset,'dpm_may25',force=True)
dets = dets.with_column_omitted('time')
# load the ground truth dets, processed in Matlab
# (timely/data/test_support/concat_dets.m)
filename = os.path.join(config.test_support_dir, 'val_dets.mat')
dets_correct = Table(
scipy.io.loadmat(filename)['dets'],
['x1','y1','x2','y2','dummy','dummy','dummy','dummy','score','cls_ind','img_ind'],
'dets_correct')
dets_correct = dets_correct.subset(
['x1','y1','x2','y2','score','cls_ind','img_ind'])
dets_correct.arr[:,:4] -= 1
dets_correct.arr[:,:4] = BoundingBox.convert_arr_from_corners(
dets_correct.arr[:,:4])
dets_correct.cols = ['x','y','w','h','score','cls_ind','img_ind']
print('----mine:')
print(dets)
print('----correct:')
print(dets_correct)
assert(dets_correct == dets)
def test_load_dpm_detections(self):
conf = dict(self.config)
conf['detectors'] = ['dpm']
policy = DatasetPolicy(self.dataset, self.train_dataset, **conf)
assert (policy.detectors == ['dpm'])
dets = policy.load_ext_detections(self.dataset,
'dpm_may25',
force=True)
dets = dets.with_column_omitted('time')
# load the ground truth dets, processed in Matlab
# (timely/data/test_support/concat_dets.m)
filename = os.path.join(config.test_support_dir, 'val_dets.mat')
dets_correct = Table(
scipy.io.loadmat(filename)['dets'], [
'x1', 'y1', 'x2', 'y2', 'dummy', 'dummy', 'dummy', 'dummy',
'score', 'cls_ind', 'img_ind'
], 'dets_correct')
dets_correct = dets_correct.subset(
['x1', 'y1', 'x2', 'y2', 'score', 'cls_ind', 'img_ind'])
dets_correct.arr[:, :4] -= 1
dets_correct.arr[:, :4] = BoundingBox.convert_arr_from_corners(
dets_correct.arr[:, :4])
dets_correct.cols = ['x', 'y', 'w', 'h', 'score', 'cls_ind', 'img_ind']
print('----mine:')
print(dets)
print('----correct:')
print(dets_correct)
assert (dets_correct == dets)
def compute_det_pr_and_hard_neg(cls, dets, gt):
"""
Take Table of detections and Table of ground truth.
Ground truth can be for a single image or a whole dataset
and can contain either all classes or just one class (but the cls_ind col
must be present in either case).
Depending on these decisions, the meaning of the PR evaluation is
different.
In particular, if gt is for a single class but dets are for multiple
classes, there will be a lot of false positives!
NOTE: modifies dets in-place (sorts by score)
Return ap, recall, and precision vectors as tuple.
"""
gt = gt.copy()
# if dets or gt are empty, return 0's
nd = dets.arr.shape[0]
if nd < 1 or gt.shape[0] < 1:
ap = 0
rec = np.array([0])
prec = np.array([0])
return (ap, rec, prec)
tt = ut.TicToc().tic()
# augment gt with a column keeping track of matches
cols = list(gt.cols) + ['matched']
arr = np.zeros((gt.arr.shape[0], gt.arr.shape[1] + 1))
arr[:, :-1] = gt.arr
gt = Table(arr, cols)
# sort detections by confidence
dets.sort_by_column('score', descending=True)
# match detections to ground truth objects
npos = gt.filter_on_column('diff', 0).shape[0]
tp = np.zeros(nd)
fp = np.zeros(nd)
hard_neg = np.zeros(nd)
for d in range(nd):
if tt.qtoc() > 15:
print("... on %d/%d dets" % (d, nd))
tt.tic()
det = dets.arr[d, :]
# find ground truth for this image
if 'img_ind' in gt.cols:
img_ind = det[dets.ind('img_ind')]
inds = gt.arr[:, gt.ind('img_ind')] == img_ind
gt_for_image = gt.arr[inds, :]
else:
gt_for_image = gt.arr
if gt_for_image.shape[0] < 1:
# this can happen if we're passing ground truth for a class
# false positive due to detection in image that does not contain the class
fp[d] = 1
hard_neg[d] = 1
continue
# find the maximally overlapping ground truth element for this detection
overlaps = BoundingBox.get_overlap(gt_for_image[:, :4], det[:4])
jmax = overlaps.argmax()
ovmax = overlaps[jmax]
# assign detection as true positive/don't care/false positive
if ovmax >= cls.MIN_OVERLAP:
if not gt_for_image[jmax, gt.ind('diff')]:
is_matched = gt_for_image[jmax, gt.ind('matched')]
if is_matched == 0:
if gt_for_image[jmax, gt.ind('cls_ind')] == det[
dets.ind('cls_ind')]:
# true positive
tp[d] = 1
gt_for_image[jmax, gt.ind('matched')] = 1
else:
# false positive due to wrong class
fp[d] = 1
hard_neg[d] = 1
else:
# false positive due to multiple detection
# this is still a correct answer, so not a hard negative
fp[d] = 1
else:
None
# NOT a false positive because object is difficult!
else:
# false positive due to not matching any ground truth object
fp[d] = 1
hard_neg[d] = 1
# NOTE: this is very important: otherwise, gt.arr does not get the
# changes we make to gt_for_image
if 'img_ind' in gt.cols:
gt.arr[inds, :] = gt_for_image
ap, rec, prec = cls.compute_rec_prec_ap(tp, fp, npos)
return (ap, rec, prec, hard_neg)
def compute_det_pr_and_hard_neg(cls, dets, gt):
"""
Take Table of detections and Table of ground truth.
Ground truth can be for a single image or a whole dataset
and can contain either all classes or just one class (but the cls_ind col
must be present in either case).
Depending on these decisions, the meaning of the PR evaluation is
different.
In particular, if gt is for a single class but dets are for multiple
classes, there will be a lot of false positives!
NOTE: modifies dets in-place (sorts by score)
Return ap, recall, and precision vectors as tuple.
"""
gt = gt.copy()
# if dets or gt are empty, return 0's
nd = dets.arr.shape[0]
if nd < 1 or gt.shape[0] < 1:
ap = 0
rec = np.array([0])
prec = np.array([0])
return (ap,rec,prec)
tt = ut.TicToc().tic()
# augment gt with a column keeping track of matches
cols = list(gt.cols) + ['matched']
arr = np.zeros((gt.arr.shape[0],gt.arr.shape[1]+1))
arr[:,:-1] = gt.arr
gt = Table(arr,cols)
# sort detections by confidence
dets.sort_by_column('score',descending=True)
# match detections to ground truth objects
npos = gt.filter_on_column('diff',0).shape[0]
tp = np.zeros(nd)
fp = np.zeros(nd)
hard_neg = np.zeros(nd)
for d in range(nd):
if tt.qtoc() > 15:
print("... on %d/%d dets"%(d,nd))
tt.tic()
det = dets.arr[d,:]
# find ground truth for this image
if 'img_ind' in gt.cols:
img_ind = det[dets.ind('img_ind')]
inds = gt.arr[:,gt.ind('img_ind')] == img_ind
gt_for_image = gt.arr[inds,:]
else:
gt_for_image = gt.arr
if gt_for_image.shape[0] < 1:
# this can happen if we're passing ground truth for a class
# false positive due to detection in image that does not contain the class
fp[d] = 1
hard_neg[d] = 1
continue
# find the maximally overlapping ground truth element for this detection
overlaps = BoundingBox.get_overlap(gt_for_image[:,:4],det[:4])
jmax = overlaps.argmax()
ovmax = overlaps[jmax]
# assign detection as true positive/don't care/false positive
if ovmax >= cls.MIN_OVERLAP:
if not gt_for_image[jmax,gt.ind('diff')]:
is_matched = gt_for_image[jmax,gt.ind('matched')]
if is_matched == 0:
if gt_for_image[jmax,gt.ind('cls_ind')] == det[dets.ind('cls_ind')]:
# true positive
tp[d] = 1
gt_for_image[jmax,gt.ind('matched')] = 1
else:
# false positive due to wrong class
fp[d] = 1
hard_neg[d] = 1
else:
# false positive due to multiple detection
# this is still a correct answer, so not a hard negative
fp[d] = 1
else:
None
# NOT a false positive because object is difficult!
else:
# false positive due to not matching any ground truth object
fp[d] = 1
hard_neg[d] = 1
# NOTE: this is very important: otherwise, gt.arr does not get the
# changes we make to gt_for_image
if 'img_ind' in gt.cols:
gt.arr[inds,:] = gt_for_image
ap,rec,prec = cls.compute_rec_prec_ap(tp,fp,npos)
return (ap,rec,prec,hard_neg)
