def get_neg_windows(self, num, cls=None, window_params=None, max_overlap=0, max_num_images=250):
"""
Return array of num windows that can be generated with window_params
that do not overlap with ground truth by more than max_overlap.
* If cls is not given, returns ground truth for all classes.
* If max_num_images is given, samples from at most that many images.
"""
sw = SlidingWindows(self, self)
if not window_params:
window_params = sw.get_default_window_params(cls)
all_windows = []
image_inds = self.get_pos_samples_for_class(cls)
max_num = len(image_inds)
inds = image_inds
if max_num_images:
inds = ut.random_subset(image_inds, max_num_images)
num_per_image = round(1.0 * num / max_num)
for ind in inds:
image = self.images[ind]
windows = image.get_windows(window_params)
gts = image.get_ground_truth(cls)
for gt in gts.arr:
overlaps = BoundingBox.get_overlap(windows[:, :4], gt[:4])
windows = windows[overlaps <= max_overlap, :]
if windows.shape[0] == 0:
continue
ind_to_take = ut.random_subset_up_to_N(windows.shape[0], num_per_image)
all_windows.append(np.hstack((windows[ind_to_take, :], np.tile(ind, (ind_to_take.shape[0], 1)))))
all_windows = np.concatenate(all_windows, 0)
return all_windows[:num, :]
def get_pos_windows(self, cls=None, window_params=None, min_overlap=0.7):
"""
Return array of all ground truth windows for the class, plus windows
that can be generated with window_params that overlap with it by more
than min_overlap.
* If cls not given, return positive windows for all classes.
* If window_params not given, use default for the class.
* Adjust min_overlap to fetch fewer windows.
"""
sw = SlidingWindows(self, self)
if not window_params:
window_params = sw.get_default_window_params(cls)
overlapping_windows = []
image_inds = self.get_pos_samples_for_class(cls)
times = []
window_nums = []
for i in image_inds:
image = self.images[i]
gts = image.get_ground_truth(cls)
if gts.arr.shape[0] > 0:
overlap_wins = gts.arr[:, :4]
overlap_wins = np.hstack(
(overlap_wins, np.tile(i, (overlap_wins.shape[0], 1))))
overlapping_windows.append(overlap_wins.astype(int))
windows, time_elapsed = image.get_windows(window_params,
with_time=True)
window_nums.append(windows.shape[0])
times.append(time_elapsed)
for gt in gts.arr:
overlaps = BoundingBox.get_overlap(windows[:, :4], gt[:4])
overlap_wins = windows[overlaps >= min_overlap, :]
overlap_wins = np.hstack(
(overlap_wins, np.tile(i, (overlap_wins.shape[0], 1))))
overlapping_windows.append(overlap_wins.astype(int))
windows = windows[overlaps < min_overlap, :]
overlapping_windows = np.concatenate(overlapping_windows, 0)
print(
"Windows generated per image: %d +/- %.3f, in %.3f +/- %.3f sec" %
(np.mean(window_nums), np.std(window_nums), np.mean(times),
np.std(times)))
return overlapping_windows
def get_neg_windows(self,
num,
cls=None,
window_params=None,
max_overlap=0,
max_num_images=250):
"""
Return array of num windows that can be generated with window_params
that do not overlap with ground truth by more than max_overlap.
* If cls is not given, returns ground truth for all classes.
* If max_num_images is given, samples from at most that many images.
"""
sw = SlidingWindows(self, self)
if not window_params:
window_params = sw.get_default_window_params(cls)
all_windows = []
image_inds = self.get_pos_samples_for_class(cls)
max_num = len(image_inds)
inds = image_inds
if max_num_images:
inds = ut.random_subset(image_inds, max_num_images)
num_per_image = round(1. * num / max_num)
for ind in inds:
image = self.images[ind]
windows = image.get_windows(window_params)
gts = image.get_ground_truth(cls)
for gt in gts.arr:
overlaps = BoundingBox.get_overlap(windows[:, :4], gt[:4])
windows = windows[overlaps <= max_overlap, :]
if windows.shape[0] == 0:
continue
ind_to_take = ut.random_subset_up_to_N(windows.shape[0],
num_per_image)
all_windows.append(
np.hstack((windows[ind_to_take, :],
np.tile(ind, (ind_to_take.shape[0], 1)))))
all_windows = np.concatenate(all_windows, 0)
return all_windows[:num, :]
def get_pos_windows(self, cls=None, window_params=None, min_overlap=0.7):
"""
Return array of all ground truth windows for the class, plus windows
that can be generated with window_params that overlap with it by more
than min_overlap.
* If cls not given, return positive windows for all classes.
* If window_params not given, use default for the class.
* Adjust min_overlap to fetch fewer windows.
"""
sw = SlidingWindows(self, self)
if not window_params:
window_params = sw.get_default_window_params(cls)
overlapping_windows = []
image_inds = self.get_pos_samples_for_class(cls)
times = []
window_nums = []
for i in image_inds:
image = self.images[i]
gts = image.get_ground_truth(cls)
if gts.arr.shape[0] > 0:
overlap_wins = gts.arr[:, :4]
overlap_wins = np.hstack((overlap_wins, np.tile(i, (overlap_wins.shape[0], 1))))
overlapping_windows.append(overlap_wins.astype(int))
windows, time_elapsed = image.get_windows(window_params, with_time=True)
window_nums.append(windows.shape[0])
times.append(time_elapsed)
for gt in gts.arr:
overlaps = BoundingBox.get_overlap(windows[:, :4], gt[:4])
overlap_wins = windows[overlaps >= min_overlap, :]
overlap_wins = np.hstack((overlap_wins, np.tile(i, (overlap_wins.shape[0], 1))))
overlapping_windows.append(overlap_wins.astype(int))
windows = windows[overlaps < min_overlap, :]
overlapping_windows = np.concatenate(overlapping_windows, 0)
print(
"Windows generated per image: %d +/- %.3f, in %.3f +/- %.3f sec"
% (np.mean(window_nums), np.std(window_nums), np.mean(times), np.std(times))
)
return overlapping_windows
