def __iter__(self):
"""
Yields:
Batched data by stacking each component on an extra 0th dimension.
"""
##### pick a scale and shape aligment
holder = []
for data in self.ds:
image, boxes_, klass_ = data[0], data[1], data[2]
###cove the small faces
boxes_clean = []
for i in range(boxes_.shape[0]):
box = boxes_[i]
if (box[3] - box[1]) < cfg.DATA.cover_small_face or (
box[2] - box[0]) < cfg.DATA.cover_small_face:
image[int(box[1]):int(box[3]),
int(box[0]):int(box[2]), :] = np.array(
cfg.DATA.PIXEL_MEAN, dtype=image.dtype)
continue
else:
boxes_clean.append(box)
boxes_ = np.array(boxes_)
data = [image, boxes_, klass_]
holder.append(data)
if len(holder) == self.batch_size:
target = self.produce_target(holder)
yield BatchData._aggregate_batch(target, self.use_list)
del holder[:]
if self.remainder and len(holder) > 0:
yield BatchData._aggregate_batch(holder, self.use_list)
def __iter__(self):
"""
Yields:
Batched data by stacking each component on an extra 0th dimension.
"""
##### pick a scale and shape aligment
holder = []
for data in self.ds:
image, boxes_, klass_ = data[0], data[1], data[2]
data = [image, boxes_, klass_]
holder.append(data)
### do crazy crop
if random.uniform(0, 1) < cfg.DATA.cracy_crop and self.traing_flag:
if len(holder) == self.batch_size:
crazy_holder = []
for i in range(0, len(holder), 4):
crazy_iamge = np.zeros(shape=(2 * cfg.DATA.hin,
2 * cfg.DATA.win, 3),
dtype=holder[i][0].dtype)
crazy_iamge[:cfg.DATA.hin, :cfg.DATA.
win, :] = holder[i][0]
crazy_iamge[:cfg.DATA.hin,
cfg.DATA.win:, :] = holder[i + 1][0]
crazy_iamge[cfg.DATA.hin:, :cfg.DATA.win, :] = holder[
i + 2][0]
crazy_iamge[cfg.DATA.hin:,
cfg.DATA.win:, :] = holder[i + 3][0]
holder[i + 1][1][:, [0, 2]] = holder[
i + 1][1][:, [0, 2]] + cfg.DATA.win
holder[i + 2][1][:, [1, 3]] = holder[
i + 2][1][:, [1, 3]] + cfg.DATA.hin
holder[i + 3][1][:, [0, 2]] = holder[
i + 3][1][:, [0, 2]] + cfg.DATA.win
holder[i + 3][1][:, [1, 3]] = holder[
i + 3][1][:, [1, 3]] + cfg.DATA.hin
tmp_bbox = np.concatenate(
(holder[i][1], holder[i + 1][1], holder[i + 2][1],
holder[i + 3][1]),
axis=0)
tmp_klass = np.concatenate(
(holder[i][2], holder[i + 1][2], holder[i + 2][2],
holder[i + 3][2]),
axis=0)
### do random crop 4 times:
for j in range(4):
curboxes = tmp_bbox.copy()
cur_klasses = tmp_klass.copy()
start_h = random.randint(0, cfg.DATA.hin)
start_w = random.randint(0, cfg.DATA.win)
cur_img_block = np.array(
crazy_iamge[start_h:start_h + cfg.DATA.hin,
start_w:start_w + cfg.DATA.win, :])
for k in range(len(curboxes)):
curboxes[k][0] = curboxes[k][0] - start_w
curboxes[k][1] = curboxes[k][1] - start_h
curboxes[k][2] = curboxes[k][2] - start_w
curboxes[k][3] = curboxes[k][3] - start_h
curboxes[:,
[0, 2]] = np.clip(curboxes[:, [0, 2]], 0,
cfg.DATA.win - 1)
curboxes[:,
[1, 3]] = np.clip(curboxes[:, [1, 3]], 0,
cfg.DATA.hin - 1)
###cove the small faces
boxes_clean = []
klsses_clean = []
for k in range(curboxes.shape[0]):
box = curboxes[k]
if not (
(box[3] - box[1]) < cfg.DATA.cover_obj or
(box[2] - box[0]) < cfg.DATA.cover_obj):
boxes_clean.append(curboxes[k])
klsses_clean.append(cur_klasses[k])
boxes_clean = np.array(boxes_clean)
klsses_clean = np.array(klsses_clean)
crazy_holder.append(
[cur_img_block, boxes_clean, klsses_clean])
del holder
holder = crazy_holder
if len(holder) == self.batch_size:
target = self.produce_target(holder)
yield BatchData.aggregate_batch(target, self.use_list)
del holder[:]
if self.remainder and len(holder) > 0:
yield BatchData._aggregate_batch(holder, self.use_list)
