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import cv2
import copy
import torch
import numpy as np
from maskrcnn_benchmark.layers.misc import interpolate
import pycocotools.mask as mask_utils
# transpose
FLIP_LEFT_RIGHT = 0
FLIP_TOP_BOTTOM = 1
""" ABSTRACT
Segmentations come in either:
1) Binary masks
2) Polygons
Binary masks can be represented in a contiguous array
and operations can be carried out more efficiently,
therefore BinaryMaskList handles them together.
Polygons are handled separately for each instance,
by PolygonInstance and instances are handled by
PolygonList.
SegmentationList is supposed to represent both,
therefore it wraps the functions of BinaryMaskList
and PolygonList to make it transparent.
"""
class BinaryMaskList(object):
"""
This class handles binary masks for all objects in the image
"""
def __init__(self, masks, size):
"""
Arguments:
masks: Either torch.tensor of [num_instances, H, W]
or list of torch.tensors of [H, W] with num_instances elems,
or RLE (Run Length Encoding) - interpreted as list of dicts,
or BinaryMaskList.
size: absolute image size, width first
After initialization, a hard copy will be made, to leave the
initializing source data intact.
"""
if isinstance(masks, torch.Tensor):
# The raw data representation is passed as argument
masks = masks.clone()
elif isinstance(masks, (list, tuple)):
if isinstance(masks[0], torch.Tensor):
masks = torch.stack(masks, dim=2).clone()
elif isinstance(masks[0], dict) and "count" in masks[0]:
# RLE interpretation
masks = mask_utils
else:
RuntimeError(
"Type of `masks[0]` could not be interpreted: %s" % type(masks)
)
elif isinstance(masks, BinaryMaskList):
# just hard copy the BinaryMaskList instance's underlying data
masks = masks.masks.clone()
else:
RuntimeError(
"Type of `masks` argument could not be interpreted:%s" % type(masks)
)
if len(masks.shape) == 2:
# if only a single instance mask is passed
masks = masks[None]
assert len(masks.shape) == 3
assert masks.shape[1] == size[1], "%s != %s" % (masks.shape[1], size[1])
assert masks.shape[2] == size[0], "%s != %s" % (masks.shape[2], size[0])
self.masks = masks
self.size = tuple(size)
def transpose(self, method):
dim = 1 if method == FLIP_TOP_BOTTOM else 2
flipped_masks = self.masks.flip(dim)
return BinaryMaskList(flipped_masks, self.size)
def crop(self, box):
assert isinstance(box, (list, tuple, torch.Tensor)), str(type(box))
# box is assumed to be xyxy
current_width, current_height = self.size
xmin, ymin, xmax, ymax = [round(float(b)) for b in box]
assert xmin <= xmax and ymin <= ymax, str(box)
xmin = min(max(xmin, 0), current_width - 1)
ymin = min(max(ymin, 0), current_height - 1)
xmax = min(max(xmax, 0), current_width)
ymax = min(max(ymax, 0), current_height)
xmax = max(xmax, xmin + 1)
ymax = max(ymax, ymin + 1)
width, height = xmax - xmin, ymax - ymin
cropped_masks = self.masks[:, ymin:ymax, xmin:xmax]
cropped_size = width, height
return BinaryMaskList(cropped_masks, cropped_size)
def resize(self, size):
try:
iter(size)
except TypeError:
assert isinstance(size, (int, float))
size = size, size
width, height = map(int, size)
assert width > 0
assert height > 0
# Height comes first here!
resized_masks = torch.nn.functional.interpolate(
input=self.masks[None].float(),
size=(height, width),
mode="bilinear",
align_corners=False,
)[0].type_as(self.masks)
resized_size = width, height
return BinaryMaskList(resized_masks, resized_size)
def convert_to_polygon(self):
contours = self._findContours()
return PolygonList(contours, self.size)
def to(self, *args, **kwargs):
return self
def _findContours(self):
contours = []
masks = self.masks.detach().numpy()
for mask in masks:
mask = cv2.UMat(mask)
contour, hierarchy = cv2.findContours(
mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_TC89_L1
)
reshaped_contour = []
for entity in contour:
assert len(entity.shape) == 3
assert entity.shape[1] == 1, "Hierarchical contours are not allowed"
reshaped_contour.append(entity.reshape(-1).tolist())
contours.append(reshaped_contour)
return contours
def __len__(self):
return len(self.masks)
def __getitem__(self, index):
# Probably it can cause some overhead
# but preserves consistency
masks = self.masks[index].clone()
return BinaryMaskList(masks, self.size)
def __iter__(self):
return iter(self.masks)
def __repr__(self):
s = self.__class__.__name__ + "("
s += "num_instances={}, ".format(len(self.masks))
s += "image_width={}, ".format(self.size[0])
s += "image_height={})".format(self.size[1])
return s
class PolygonInstance(object):
"""
This class holds a set of polygons that represents a single instance
of an object mask. The object can be represented as a set of
polygons
"""
def __init__(self, polygons, size):
"""
Arguments:
a list of lists of numbers.
The first level refers to all the polygons that compose the
object, and the second level to the polygon coordinates.
"""
if isinstance(polygons, (list, tuple)):
valid_polygons = []
for p in polygons:
p = torch.as_tensor(p, dtype=torch.float32)
if len(p) >= 6: # 3 * 2 coordinates
valid_polygons.append(p)
polygons = valid_polygons
elif isinstance(polygons, PolygonInstance):
polygons = copy.copy(polygons.polygons)
else:
RuntimeError(
"Type of argument `polygons` is not allowed:%s" % (type(polygons))
)
""" This crashes the training way too many times...
for p in polygons:
assert p[::2].min() >= 0
assert p[::2].max() < size[0]
assert p[1::2].min() >= 0
assert p[1::2].max() , size[1]
"""
self.polygons = polygons
self.size = tuple(size)
def transpose(self, method):
if method not in (FLIP_LEFT_RIGHT, FLIP_TOP_BOTTOM):
raise NotImplementedError(
"Only FLIP_LEFT_RIGHT and FLIP_TOP_BOTTOM implemented"
)
flipped_polygons = []
width, height = self.size
if method == FLIP_LEFT_RIGHT:
dim = width
idx = 0
elif method == FLIP_TOP_BOTTOM:
dim = height
idx = 1
for poly in self.polygons:
p = poly.clone()
TO_REMOVE = 1
p[idx::2] = dim - poly[idx::2] - TO_REMOVE
flipped_polygons.append(p)
return PolygonInstance(flipped_polygons, size=self.size)
def crop(self, box):
assert isinstance(box, (list, tuple, torch.Tensor)), str(type(box))
# box is assumed to be xyxy
current_width, current_height = self.size
xmin, ymin, xmax, ymax = map(float, box)
assert xmin <= xmax and ymin <= ymax, str(box)
xmin = min(max(xmin, 0), current_width - 1)
ymin = min(max(ymin, 0), current_height - 1)
xmax = min(max(xmax, 0), current_width)
ymax = min(max(ymax, 0), current_height)
xmax = max(xmax, xmin + 1)
ymax = max(ymax, ymin + 1)
w, h = xmax - xmin, ymax - ymin
cropped_polygons = []
for poly in self.polygons:
p = poly.clone()
p[0::2] = p[0::2] - xmin # .clamp(min=0, max=w)
p[1::2] = p[1::2] - ymin # .clamp(min=0, max=h)
cropped_polygons.append(p)
return PolygonInstance(cropped_polygons, size=(w, h))
def resize(self, size):
try:
iter(size)
except TypeError:
assert isinstance(size, (int, float))
size = size, size
ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(size, self.size))
if ratios[0] == ratios[1]:
ratio = ratios[0]
scaled_polys = [p * ratio for p in self.polygons]
return PolygonInstance(scaled_polys, size)
ratio_w, ratio_h = ratios
scaled_polygons = []
for poly in self.polygons:
p = poly.clone()
p[0::2] *= ratio_w
p[1::2] *= ratio_h
scaled_polygons.append(p)
return PolygonInstance(scaled_polygons, size=size)
def convert_to_binarymask(self):
width, height = self.size
# formatting for COCO PythonAPI
polygons = [p.numpy() for p in self.polygons]
rles = mask_utils.frPyObjects(polygons, height, width)
rle = mask_utils.merge(rles)
mask = mask_utils.decode(rle)
mask = torch.from_numpy(mask)
return mask
def __len__(self):
return len(self.polygons)
def __repr__(self):
s = self.__class__.__name__ + "("
s += "num_groups={}, ".format(len(self.polygons))
s += "image_width={}, ".format(self.size[0])
s += "image_height={}, ".format(self.size[1])
return s
class PolygonList(object):
"""
This class handles PolygonInstances for all objects in the image
"""
def __init__(self, polygons, size):
"""
Arguments:
polygons:
a list of list of lists of numbers. The first
level of the list correspond to individual instances,
the second level to all the polygons that compose the
object, and the third level to the polygon coordinates.
OR
a list of PolygonInstances.
OR
a PolygonList
size: absolute image size
"""
if isinstance(polygons, (list, tuple)):
if len(polygons) == 0:
polygons = [[[]]]
if isinstance(polygons[0], (list, tuple)):
assert isinstance(polygons[0][0], (list, tuple)), str(
type(polygons[0][0])
)
else:
assert isinstance(polygons[0], PolygonInstance), str(type(polygons[0]))
elif isinstance(polygons, PolygonList):
size = polygons.size
polygons = polygons.polygons
else:
RuntimeError(
"Type of argument `polygons` is not allowed:%s" % (type(polygons))
)
assert isinstance(size, (list, tuple)), str(type(size))
self.polygons = []
for p in polygons:
p = PolygonInstance(p, size)
if len(p) > 0:
self.polygons.append(p)
self.size = tuple(size)
def transpose(self, method):
if method not in (FLIP_LEFT_RIGHT, FLIP_TOP_BOTTOM):
raise NotImplementedError(
"Only FLIP_LEFT_RIGHT and FLIP_TOP_BOTTOM implemented"
)
flipped_polygons = []
for polygon in self.polygons:
flipped_polygons.append(polygon.transpose(method))
return PolygonList(flipped_polygons, size=self.size)
def crop(self, box):
w, h = box[2] - box[0], box[3] - box[1]
cropped_polygons = []
for polygon in self.polygons:
cropped_polygons.append(polygon.crop(box))
cropped_size = w, h
return PolygonList(cropped_polygons, cropped_size)
def resize(self, size):
resized_polygons = []
for polygon in self.polygons:
resized_polygons.append(polygon.resize(size))
resized_size = size
return PolygonList(resized_polygons, resized_size)
def to(self, *args, **kwargs):
return self
def convert_to_binarymask(self):
if len(self) > 0:
masks = torch.stack([p.convert_to_binarymask() for p in self.polygons])
else:
size = self.size
masks = torch.empty([0, size[1], size[0]], dtype=torch.uint8)
return BinaryMaskList(masks, size=self.size)
def __len__(self):
return len(self.polygons)
def __getitem__(self, item):
if isinstance(item, int):
selected_polygons = [self.polygons[item]]
elif isinstance(item, slice):
selected_polygons = self.polygons[item]
else:
# advanced indexing on a single dimension
selected_polygons = []
if isinstance(item, torch.Tensor) and item.dtype == torch.uint8:
item = item.nonzero()
item = item.squeeze(1) if item.numel() > 0 else item
item = item.tolist()
for i in item:
selected_polygons.append(self.polygons[i])
return PolygonList(selected_polygons, size=self.size)
def __iter__(self):
return iter(self.polygons)
def __repr__(self):
s = self.__class__.__name__ + "("
s += "num_instances={}, ".format(len(self.polygons))
s += "image_width={}, ".format(self.size[0])
s += "image_height={})".format(self.size[1])
return s
class SegmentationMask(object):
"""
This class stores the segmentations for all objects in the image.
It wraps BinaryMaskList and PolygonList conveniently.
"""
def __init__(self, instances, size, mode="poly"):
"""
Arguments:
instances: two types
(1) polygon
(2) binary mask
size: (width, height)
mode: 'poly', 'mask'. if mode is 'mask', convert mask of any format to binary mask
"""
assert isinstance(size, (list, tuple))
assert len(size) == 2
if isinstance(size[0], torch.Tensor):
assert isinstance(size[1], torch.Tensor)
size = size[0].item(), size[1].item()
assert isinstance(size[0], (int, float))
assert isinstance(size[1], (int, float))
if mode == "poly":
self.instances = PolygonList(instances, size)
elif mode == "mask":
self.instances = BinaryMaskList(instances, size)
else:
raise NotImplementedError("Unknown mode: %s" % str(mode))
self.mode = mode
self.size = tuple(size)
def transpose(self, method):
flipped_instances = self.instances.transpose(method)
return SegmentationMask(flipped_instances, self.size, self.mode)
def crop(self, box):
cropped_instances = self.instances.crop(box)
cropped_size = cropped_instances.size
return SegmentationMask(cropped_instances, cropped_size, self.mode)
def resize(self, size, *args, **kwargs):
resized_instances = self.instances.resize(size)
resized_size = size
return SegmentationMask(resized_instances, resized_size, self.mode)
def to(self, *args, **kwargs):
return self
def convert(self, mode):
if mode == self.mode:
return self
if mode == "poly":
converted_instances = self.instances.convert_to_polygon()
elif mode == "mask":
converted_instances = self.instances.convert_to_binarymask()
else:
raise NotImplementedError("Unknown mode: %s" % str(mode))
return SegmentationMask(converted_instances, self.size, mode)
def get_mask_tensor(self):
instances = self.instances
if self.mode == "poly":
instances = instances.convert_to_binarymask()
# If there is only 1 instance
return instances.masks.squeeze(0)
def __len__(self):
return len(self.instances)
def __getitem__(self, item):
selected_instances = self.instances.__getitem__(item)
return SegmentationMask(selected_instances, self.size, self.mode)
def __iter__(self):
self.iter_idx = 0
return self
def __next__(self):
if self.iter_idx < self.__len__():
next_segmentation = self.__getitem__(self.iter_idx)
self.iter_idx += 1
return next_segmentation
raise StopIteration()
next = __next__ # Python 2 compatibility
def __repr__(self):
s = self.__class__.__name__ + "("
s += "num_instances={}, ".format(len(self.instances))
s += "image_width={}, ".format(self.size[0])
s += "image_height={}, ".format(self.size[1])
s += "mode={})".format(self.mode)
return s
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