def load_image_gt(dataset,
config,
image_id,
augment=False,
use_mini_mask=False):
image = dataset.load_image(image_id)
mask, class_ids = dataset.load_mask(image_id)
shape = image.shape
image, window, scale, padding = utils.resize_image(
image,
min_dim=config.IMAGE_MIN_DIM,
max_dim=config.IMAGE_MAX_DIM,
padding=config.IMAGE_PADDING)
mask = utils.resize_mask(mask, scale, padding)
bbox = utils.extract_bboxes(mask)
active_class_ids = np.zeros([dataset.num_classes], dtype=np.int32)
source_class_ids = dataset.source_class_ids[dataset.image_info[image_id]
["source"]]
active_class_ids[source_class_ids] = 1
# Resize masks to smaller size to reduce memory usage
if use_mini_mask:
mask = utils.minimize_mask(bbox, mask, config.MINI_MASK_SHAPE)
# Image meta data
image_meta = compose_image_meta(image_id, shape, window, active_class_ids)
return image, image_meta, class_ids, bbox, mask
def process_labels(images):
for i, img_desc in enumerate(images):
step.value += 1
print('\rSaving masked labels: {} of {}, time left: {}, predicted size: {}'
.format(step.value,
data_len,
calc_time_left(mean_time, data_len - step.value),
calc_pred_size(mean_size, data_len)),
end='', flush=True)
s = time.time()
img_id = img_desc['id']
img_w = img_desc['width']
img_h = img_desc['height']
img_annotations = collect_annotations(img_id, annotations_list)
mask = create_full_mask(img_w, img_h, img_annotations)
resized = resize_mask(mask, dst_w, dst_h)
non_zero_ids = np.count_nonzero(resized, axis=(0, 1))
non_zero_ids = np.nonzero(non_zero_ids)[0]
resized = resized[:, :, non_zero_ids]
mask_name = img_desc['file_name'].replace('jpg', 'npz')
np.savez(os.path.join(dst_dir, mask_name), resized, non_zero_ids)
e = time.time()
mean_time.value = i / (i + 1) * mean_time.value + (e - s) / (i + 1) / num_processes
mean_size.value = i / (i + 1) * mean_size.value + resized.nbytes / (i + 1)
def test_return(self):
mask: torch.Tensor = torch.randn((5, 10, 20))
height: int = 5
width: int = 15
resized_mask: torch.Tensor = utils.resize_mask(mask, height, width)
self.assertEqual(resized_mask.size(), torch.Size((5, height, width)))
def load_image_gt(dataset, config, image_id, augment=False,
use_mini_mask=False):
"""Load and return ground truth data for an image (image, mask, bounding boxes).
augment: If true, apply random image augmentation. Currently, only
horizontal flipping is offered.
use_mini_mask: If False, returns full-size masks that are the same height
and width as the original image. These can be big, for example
1024x1024x100 (for 100 instances). Mini masks are smaller, typically,
224x224 and are generated by extracting the bounding box of the
object and resizing it to MINI_MASK_SHAPE.
Returns:
image: [height, width, 3]
shape: the original shape of the image before resizing and cropping.
class_ids: [instance_count] Integer class IDs
bbox: [instance_count, (y1, x1, y2, x2)]
mask: [height, width, instance_count]. The height and width are those
of the image unless use_mini_mask is True, in which case they are
defined in MINI_MASK_SHAPE.
"""
# Load image and mask
image = dataset.load_image(image_id)
mask, class_ids = dataset.load_mask(image_id)
shape = image.shape
image, window, scale, padding = utils.resize_image(
image,
min_dim=config.IMAGE_MIN_DIM,
max_dim=config.IMAGE_MAX_DIM,
padding=config.IMAGE_PADDING)
mask = utils.resize_mask(mask, scale, padding)
# Random horizontal flips.
if augment:
if random.randint(0, 1):
image = np.fliplr(image)
mask = np.fliplr(mask)
# Bounding boxes. Note that some boxes might be all zeros
# if the corresponding mask got cropped out.
# bbox: [num_instances, (y1, x1, y2, x2)]
bbox = utils.extract_bboxes(mask)
# Active classes
# Different datasets have different classes, so track the
# classes supported in the dataset of this image.
active_class_ids = np.zeros([dataset.num_classes], dtype=np.int32)
source_class_ids = dataset.source_class_ids[dataset.image_info[image_id]["source"]]
active_class_ids[source_class_ids] = 1
# Resize masks to smaller size to reduce memory usage
if use_mini_mask:
mask = utils.minimize_mask(bbox, mask, config.MINI_MASK_SHAPE)
# Image meta data
image_meta = compose_image_meta(image_id, shape, window, active_class_ids)
return image, image_meta, class_ids, bbox, mask
def train_generator(dataset, inputs, config, num_epoch, sess):
input_images, input_metas, input_anchors, input_rpn_gt_deltas, input_rpn_gt_matchs,\
input_gt_cls_ids, input_gt_box, input_gt_mask = inputs
for i in range(num_epoch):
scales = config.SCALES
ratios = config.RATIOS
if True:
images, gt_boxes, gt_masks, gt_class_ids, img_ids = dataset.make_batch(config.BATCH_SIZE_TRAIN, img_ids=[284282, 109441])#139, 285, 32811
else:
images, gt_boxes, gt_masks, gt_class_ids, img_ids = dataset.make_batch(config.BATCH_SIZE_TRAIN, img_ids=None)# 226111, 58636,
# batch毎にndarrayにする.
molded_images, image_metas, windows, pads, mold_scales = utils.mold_images(images, max_dim=1024, min_dim=800, config=config)
# mini_maskでないならばmaskもmoldedされる(paddingを出して)
assert not molded_images.dtype == np.dtype("O"), "image shape is not same"
molded_shape = molded_images.shape
# gt_boxをresize(scale)->シフト(window)->正規化(molded_shape)を行う.
#gt_boxes = utils.pack_gt_boxes(gt_boxes, mold_scales, windows, molded_shape)
#gt_boxes_molded = utils.mold_gt_boxes(gt_boxes, mold_scales, windows, molded_shape)
#gt_masks = utils.mold_gt_masks(gt_masks, mold_scales, pads, gt_boxes_molded, config)
#gt_masks = utils.mold_gt_masks(gt_masks, mold_scales, pads)
gt_masks = [[utils.resize_mask(m, s, p) for m in gt_mask] for gt_mask, s, p in zip(gt_masks, mold_scales, pads)]
gt_boxes = utils.make_gt_boxes_from_mask(gt_masks)
gt_boxes_molded = utils.mold_gt_boxes(gt_boxes, mold_scales, windows, molded_shape)
gt_minimasks = [utils.minimize_mask(b, np.array(m), config.MINI_MASK_SIZE) for b, m in zip(gt_boxes, gt_masks)]
#gt_minimasks = utils.pack_gt_minimasks_already_molded(gt_masks, gt_boxes_molded, config)
# gt_maskをresize->pad->crop and resize
#gt_masks = utils.pack_gt_minimasks(gt_masks, mold_scales, pads, gt_boxes_molded, config)
anchors = utils.make_anchors_nomold(scales, ratios, molded_images[0].shape, (4, 8, 16, 32, 64))
anchors_molded = utils.make_anchors(scales, ratios, molded_images[0].shape, (4, 8, 16, 32, 64))
rpn_gt_matchs, rpn_gt_deltas = zip(*[utils.make_matchs(anchors, b, config) for b in gt_boxes])
#rpn_gt_matchs_new, rpn_gt_deltas_new = zip(*[utils.build_rpn_targets(molded_shape, anchors, c, b, config) for b, c in zip(gt_boxes, gt_class_ids)])
"""
import pickle
with open("rpn.pkl", "rb") as f:
d = pickle.load(f)
with open("rpn_deltas.pkl", "rb") as f:
dd = pickle.load(f)
positive_indices_master = dd["positive_indices"][0]
negative_indices_master = dd["negative_indices"][0]
"""
#master_masks = np.transpose(np.squeeze(dd["batch_gt_masks"], axis=0), (2, 0, 1))
#utils.apply_deltas_np(anchors, rpn_gt_deltas[0]*config.RPN_BBOX_STD_DEV)
# リストをndarray(batch, anchors)(batch, anchors, 4)にbatch方向にくっつける
ix = np.where(rpn_gt_matchs[0]==1)[0]
rpn_rois = utils.apply_deltas_np(anchors[ix], rpn_gt_deltas[0]*config.RPN_BBOX_STD_DEV)
#visualize.show_boxes_demold(images[0], gt_boxes[0], windows[0], molded_shape)
#visualize.show_boxes_demold(images[0], rpn_rois, windows[0], molded_shape)
rpn_gt_matchs, = [np.stack(gts, axis=0) for gts in [rpn_gt_matchs]]
gt_boxes_molded, gt_class_ids, gt_minimasks, rpn_gt_deltas =\
[utils.pack_on(m) for m in (gt_boxes_molded, gt_class_ids, gt_minimasks, rpn_gt_deltas)]
feed_dict = {
input_images: molded_images,
input_metas: image_metas,
input_anchors: anchors_molded,
input_rpn_gt_matchs: rpn_gt_matchs,
input_rpn_gt_deltas: rpn_gt_deltas,
input_gt_cls_ids: gt_class_ids,
input_gt_box: gt_boxes_molded,
input_gt_mask: gt_minimasks,
#input_positive_indices_master: positive_indices_master,
#input_negative_indices_master: negative_indices_master,
}
yield i, feed_dict, images, windows, molded_shape, anchors_molded[ix], img_ids
dataset_train.prepare()
image_ids = np.random.choice(dataset_train.image_ids, 4)
for image_id in image_ids:
image = dataset_train.load_image(image_id)
mask, class_ids = dataset_train.load_mask(image_id)
visualize.display_top_masks(image, mask, class_ids,
dataset_train.class_names)
original_shape = image.shape
# Resize
image, window, scale, padding, _ = utils.resize_image(
image,
min_dim=config.IMAGE_MIN_DIM,
max_dim=config.IMAGE_MAX_DIM,
mode=config.IMAGE_RESIZE_MODE)
mask = utils.resize_mask(mask, scale, padding)
# Compute Bounding box
bbox = utils.extract_bboxes(mask)
# Display image and additional stats
print("image_id: ", image_id, dataset_train.image_reference(image_id))
print("Original shape: ", original_shape)
log("image", image)
log("mask", mask)
log("class_ids", class_ids)
log("bbox", bbox)
# Display image and instances
visualize.display_instances(image, bbox, mask, class_ids,
dataset_train.class_names)
# Validation dataset
def load_image_gt(dataset,
image_id,
augment=False,
augmentation=None,
use_mini_mask=False):
image = dataset.load_image(image_id)
mask, class_ids = dataset.load_mask(image_id)
origin_shape = image.shape
image, window, scale, padding, crop = utils.resize_image(
image,
min_dim=hyper_parameters.FLAGS.IMAGE_MIN_DIM,
min_scale=hyper_parameters.FLAGS.IMAGE_MIN_SCALE,
max_dim=hyper_parameters.FLAGS.IMAGE_MAX_DIM,
mode=hyper_parameters.FLAGS.IMAGE_RESIZE_MODE)
mask = utils.resize_mask(mask, scale, padding, crop)
if augment:
logging.warning("'augment' is deprecated. Use 'augmentation' instead.")
if random.randint(0, 1):
image = np.fliplr(image)
mask = np.fliplr(mask)
if augmentation:
import imgaug
mask_augmenters = [
"Sequential", "SomeOf", "OneOf", "Sometimes", "Fliplr", "Flipud",
"CropAndPad", "Affine", "PiecewiseAffine"
]
def hook(images, augmenter, parents, default):
return augmenter.__class__.__name__ in mask_augmenters
image_shape = image.shape
mask_shape = mask.shape
det = augmentation.to_deterministic()
image = det.augment_image(image)
mask = det.augment_image(mask.astype(np.uint8),
hooks=imgaug.HooksImages(activator=hook))
assert image.shape == image_shape, "Augmentation shouldn't change image size"
assert mask.shape == mask_shape, "Augmentation shouldn't change mask size"
# Change mask back to bool
mask = mask.astype(np.bool)
_idx = np.sum(mask, axis=(0, 1)) > 0
mask = mask[:, :, _idx]
class_ids = class_ids[_idx]
bbox = utils.extract_bboxes(mask)
active_class_ids = np.zeros([dataset.num_classes], dtype=np.int32)
source_class_ids = dataset.source_class_ids[dataset.image_info[image_id]
["source"]]
active_class_ids[source_class_ids] = 1
if use_mini_mask:
mask = utils.minimize_mask(
bbox, mask, tuple(hyper_parameters.FLAGS.MINI_MASK_SHAPE))
image_meta = utils.compose_image_meta(image_id, origin_shape, image.shape,
window, scale, active_class_ids)
return image, image_meta, class_ids, bbox, mask
def load_image_gt(config,
image_id,
image,
depth,
mask,
class_ids,
parameters,
augment=False,
use_mini_mask=True):
"""Load and return ground truth data for an image (image, mask, bounding boxes).
augment: If true, apply random image augmentation. Currently, only
horizontal flipping is offered.
use_mini_mask: If False, returns full-size masks that are the same height
and width as the original image. These can be big, for example
1024x1024x100 (for 100 instances). Mini masks are smaller, typically,
224x224 and are generated by extracting the bounding box of the
object and resizing it to MINI_MASK_SHAPE.
Returns:
image: [height, width, 3]
shape: the original shape of the image before resizing and cropping.
class_ids: [instance_count] Integer class IDs
bbox: [instance_count, (y1, x1, y2, x2)]
mask: [height, width, instance_count]. The height and width are those
of the image unless use_mini_mask is True, in which case they are
defined in MINI_MASK_SHAPE.
"""
## Load image and mask
shape = image.shape
image, window, scale, padding = utils.resize_image(
image,
min_dim=config.IMAGE_MAX_DIM,
max_dim=config.IMAGE_MAX_DIM,
padding=config.IMAGE_PADDING)
mask = utils.resize_mask(mask, scale, padding)
## Random horizontal flips.
if augment and False:
if np.random.randint(0, 1):
image = np.fliplr(image)
mask = np.fliplr(mask)
depth = np.fliplr(depth)
pass
pass
## Bounding boxes. Note that some boxes might be all zeros
## if the corresponding mask got cropped out.
## bbox: [num_instances, (y1, x1, y2, x2)]
bbox = utils.extract_bboxes(mask)
## Resize masks to smaller size to reduce memory usage
if use_mini_mask:
mask = utils.minimize_mask(bbox, mask, config.MINI_MASK_SHAPE)
pass
active_class_ids = np.ones(config.NUM_CLASSES, dtype=np.int32)
## Image meta data
image_meta = utils.compose_image_meta(image_id, shape, window,
active_class_ids)
if config.NUM_PARAMETER_CHANNELS > 0:
if config.OCCLUSION:
depth = utils.resize_mask(depth, scale, padding)
mask_visible = utils.minimize_mask(bbox, depth,
config.MINI_MASK_SHAPE)
mask = np.stack([mask, mask_visible], axis=-1)
else:
depth = np.expand_dims(depth, -1)
depth = utils.resize_mask(depth, scale, padding).squeeze(-1)
depth = utils.minimize_depth(bbox, depth, config.MINI_MASK_SHAPE)
mask = np.stack([mask, depth], axis=-1)
pass
pass
return image, image_meta, class_ids, bbox, mask, parameters
def load_image_gt(dataset, config, image_id, augment=False, augmentation=None, use_mini_mask=False):
"""Load and return ground truth data for an image (image, mask, bounding boxes).
augment: (deprecated. Use augmentation instead). If true, apply random
image augmentation. Currently, only horizontal flipping is offered.
augmentation: Optional. An imgaug (https://github.com/aleju/imgaug) augmentation.
For example, passing imgaug.augmenters.Fliplr(0.5) flips images
right/left 50% of the time.
use_mini_mask: If False, returns full-size masks that are the same height
and width as the original image. These can be big, for example
1024x1024x100 (for 100 instances). Mini masks are smaller, typically,
224x224 and are generated by extracting the bounding box of the
object and resizing it to MINI_MASK_SHAPE.
Returns:
image: [height, width, 3]
shape: the original shape of the image before resizing and cropping.
class_ids: [instance_count] Integer class IDs
bbox: [instance_count, (y1, x1, y2, x2)]
mask: [height, width, instance_count]. The height and width are those
of the image unless use_mini_mask is True, in which case they are
defined in MINI_MASK_SHAPE.
"""
# Load image and mask
image = dataset.load_image(image_id)
mask, class_ids = dataset.load_mask(image_id)
original_shape = image.shape
image, window, scale, padding, crop = utils.resize_image(
image,
min_dim=config.IMAGE_MIN_DIM,
min_scale=config.IMAGE_MIN_SCALE,
max_dim=config.IMAGE_MAX_DIM,
mode=config.IMAGE_RESIZE_MODE
)
mask = utils.resize_mask(mask, scale, padding, crop)
# Random horizontal flips.
# TODO: will be removed in a future update in favor of augmentation
if augment:
logging.warning("'augment' id deprecated. Use 'augmentation' instead.")
if random.randint(0, 1):
image = np.fliplr(image)
mask = np.fliplr(mask)
# Augmentation
# This requires the imgaug lib (https://github.com/aleju/imgaug)
if augmentation:
import imgaug
# Augmenters that are safe to apply to masks
# Some, such as Affine, have settings that make them unsafe, so always
# test your augmentation on masks
MASK_AUGMENTS = ["Sequential", "SomeOf", "OneOf", "Sometimes",
"Fliplr", 'Flipud', 'CropAndPad', "Affine", "PiecewiseAffine"]
def hook(images, augmenter, parents, default):
"""Determines which augmenters to apply to masks."""
return augmenter.__class__.__name__ in MASK_AUGMENTS
# Store shapes before augmentation to compare
image_shape = image.shape
mask_shape = mask.shape
# Make augmenters deterministic to apply similarly to images and masks
det = augmentation.to_deterministic()
image = det.augment_image(image)
# Change mask to np.uint because imgaug does not support np.bool
mask = det.augment_image(mask.astype(np.uint8),
hooks=imgaug.HooksImage(activator=hook))
# Verify that shapes didn't change
det = augmentation.to_deterministic()
image = det.augment_image(image)
# Change mask to np.uint8 because imgaug doesn't support np.bool
mask = det.augment_image(mask.astype(np.uint8),
hooks=imgaug.HooksImage(activator=hook))
# Verify that shapes didn't change
assert image.shape == image_shape, "Augmentation shouldn't change image size"
assert mask.shape == mask_shape, "Augmentation shouldn;t change mask size"
# Change mask back to bool
mask = mask.astype(np.bool)
# Note that some boxes might be all zeros if the corresponding mask got cropped out.
# and here is to filter them out
_idx = np.sum(mask, axis=(0, 1)) > 0
mask = mask[:, :, _idx]
class_ids = class_ids[_idx]
# Bounding boxes. Note that some boxes might be all zeros
# if the corresponding mask got cropped out.
# bbox: [num_instances, (y1, x1, y2, x2)]
bbox = utils.extract_bboxes(mask)
# Active classes
# Different datasets have different classes, so track the
# classes supported in the dataset of this image.
active_class_ids = np.zeros([dataset.num_classes], dtype='np.int32')
source_class_ids = dataset.source_class_ids(dataset.image_info[image_id]["source"])
active_class_ids[source_class_ids] = 1
# Resize masks to smaller size to reduce memory usage
if use_mini_mask:
mask = utils.minimize_mask(bbox, mask, config.MNI_MASK_SHAPE)
# Image meta data
image_meta = utils.compose_image_meta(image_id, original_shape, image.shape,
window, scale, active_class_ids)
return image, image_meta, class_ids, bbox, mask
def data_generator(config, shuffle=True, augmentation=None,batch_size=1):
"""
A generator that returns images and corresponding target class ids,
bounding box deltas, and masks.
Returns a Python generator. Upon calling next() on it, the
generator returns two lists, inputs and outputs. The contents
of the lists differs depending on the received arguments:
inputs list:
- images: [batch, H, W, C]
- image_meta: [batch, (meta data)] Image details. See compose_image_meta()
- rpn_match: [batch, N] Integer (1=positive anchor, -1=negative, 0=neutral)
- rpn_bbox: [batch, N, (dy, dx, log(dh), log(dw))] Anchor bbox deltas.
- gt_class_ids: [batch, MAX_GT_INSTANCES] Integer class IDs
- gt_boxes: [batch, MAX_GT_INSTANCES, (y1, x1, y2, x2)]
- gt_masks: [batch, height, width, MAX_GT_INSTANCES]. The height and width
are those of the image unless use_mini_mask is True, in which
case they are defined in MINI_MASK_SHAPE.
outputs list: Usually empty in regular training. But if detection_targets
is True then the outputs list contains target class_ids, bbox deltas,
and masks.
"""
b = 0
ix = 0
image_files = glob.glob("./data/train/*.jpg")
# Anchors
# [anchor_count, (y1, x1, y2, x2)]
backbone_shapes = compute_backbone_shapes(config, config.IMAGE_SHAPE)
anchors = utils.generate_pyramid_anchors(config.RPN_ANCHOR_SCALES,
config.RPN_ANCHOR_RATIOS,
backbone_shapes,
config.BACKBONE_STRIDES,
config.RPN_ANCHOR_STRIDE)
while True:
if shuffle and ix == 0:
np.random.shuffle(image_files)
image_path = image_files[ix]
json_path = image_files[ix].replace("jpg", "json")
image = load_image(image_path)
original_shape = image.shape
mask, class_ids = load_mask(json_path)
image, window, scale, padding, crop = utils.resize_image(
image,
min_dim=config.IMAGE_MIN_DIM,
min_scale=config.IMAGE_MIN_SCALE,
max_dim=config.IMAGE_MAX_DIM,
mode=config.IMAGE_RESIZE_MODE)
mask = utils.resize_mask(mask, scale, padding, crop)
# Augmentation
# This requires the imgaug lib (https://github.com/aleju/imgaug)
if augmentation:
import imgaug
# Augmenters that are safe to apply to masks
# Some, such as Affine, have settings that make them unsafe, so always
# test your augmentation on masks
MASK_AUGMENTERS = ["Sequential", "SomeOf", "OneOf", "Sometimes",
"Fliplr", "Flipud", "CropAndPad",
"Affine", "PiecewiseAffine"]
def hook(images, augmenter, parents, default):
"""Determines which augmenters to apply to masks."""
return augmenter.__class__.__name__ in MASK_AUGMENTERS
# Store shapes before augmentation to compare
image_shape = image.shape
mask_shape = mask.shape
# Make augmenters deterministic to apply similarly to images and masks
det = augmentation.to_deterministic()
image = det.augment_image(image)
# Change mask to np.uint8 because imgaug doesn't support np.bool
mask = det.augment_image(mask.astype(np.uint8),
hooks=imgaug.HooksImages(activator=hook))
# Verify that shapes didn't change
assert image.shape == image_shape, "Augmentation shouldn't change image size"
assert mask.shape == mask_shape, "Augmentation shouldn't change mask size"
# Change mask back to bool
mask = mask.astype(np.bool)
bbox = utils.extract_bboxes(mask)
use_mini_mask = True
if use_mini_mask:
mask = utils.minimize_mask(bbox, mask, config.MINI_MASK_SHAPE)
# image_meta is for debug
image_meta = compose_image_meta(0, original_shape, image.shape,
window, scale, np.ones(len(class_name2idx)))
# RPN Targets
rpn_match, rpn_bbox = build_rpn_targets(image.shape, anchors,
class_ids, bbox, config)
if b == 0:
batch_image_meta = np.zeros(
(batch_size,) + image_meta.shape, dtype=image_meta.dtype)
batch_rpn_match = np.zeros(
[batch_size, anchors.shape[0], 1], dtype=rpn_match.dtype)
batch_rpn_bbox = np.zeros(
[batch_size, config.RPN_TRAIN_ANCHORS_PER_IMAGE, 4], dtype=rpn_bbox.dtype)
batch_images = np.zeros(
(batch_size,) + image.shape, dtype=np.float32)
batch_gt_class_ids = np.zeros(
(batch_size, config.MAX_GT_INSTANCES), dtype=np.int32)
batch_gt_boxes = np.zeros(
(batch_size, config.MAX_GT_INSTANCES, 4), dtype=np.int32)
batch_gt_masks = np.zeros(
(batch_size, mask.shape[0], mask.shape[1],
config.MAX_GT_INSTANCES), dtype=mask.dtype)
# Add to batch
batch_image_meta[b] = image_meta
batch_rpn_match[b] = rpn_match[:, np.newaxis]
batch_rpn_bbox[b] = rpn_bbox
batch_images[b] = mold_image(image.astype(np.float32), config)
batch_gt_class_ids[b, :class_ids.shape[0]] = class_ids
batch_gt_boxes[b, :bbox.shape[0]] = bbox
batch_gt_masks[b, :, :, :mask.shape[-1]] = mask
b += 1
ix = (ix + 1) % len(image_files)
if b >= batch_size:
inputs = [batch_images, batch_image_meta, batch_rpn_match, batch_rpn_bbox,
batch_gt_class_ids, batch_gt_boxes, batch_gt_masks]
outputs = []
yield inputs,outputs
b = 0
