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import torch
import numpy as np
import math
import base64
import collections
import pycocotools.mask as mask_utils
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.structures.segmentation_mask import SegmentationMask
class LabelLoader(object):
def __init__(
self,
labelmap,
extra_fields=(),
filter_duplicate_relations=False,
ignore_attr=None,
ignore_rel=None,
mask_mode="poly",
):
self.labelmap = labelmap
self.extra_fields = extra_fields
self.supported_fields = ["class", "conf", "attributes", "scores_all", "boxes_all", "feature", "mask"]
self.filter_duplicate_relations = filter_duplicate_relations
self.ignore_attr = set(ignore_attr) if ignore_attr != None else set()
self.ignore_rel = set(ignore_rel) if ignore_rel != None else set()
assert mask_mode == "poly" or mask_mode == "mask"
self.mask_mode = mask_mode
def __call__(self, annotations, img_size, remove_empty=False, load_fields=None):
boxes = [obj["rect"] for obj in annotations]
boxes = torch.as_tensor(boxes).reshape(-1, 4)
target = BoxList(boxes, img_size, mode="xyxy")
if load_fields is None:
load_fields = self.extra_fields
for field in load_fields:
assert field in self.supported_fields, "Unsupported field {}".format(field)
if field == "class":
classes = self.add_classes(annotations)
target.add_field("labels", classes)
elif field == "conf":
confidences = self.add_confidences(annotations)
target.add_field("scores", confidences)
elif field == "attributes":
attributes = self.add_attributes(annotations)
target.add_field("attributes", attributes)
elif field == "scores_all":
scores_all = self.add_scores_all(annotations)
target.add_field("scores_all", scores_all)
elif field == "boxes_all":
boxes_all = self.add_boxes_all(annotations)
target.add_field("boxes_all", boxes_all)
elif field == "feature":
features = self.add_features(annotations)
target.add_field("box_features", features)
elif field == "mask":
masks, is_box_mask = self.add_masks(annotations, img_size)
target.add_field("masks", masks)
target.add_field("is_box_mask", is_box_mask)
target = target.clip_to_image(remove_empty=remove_empty)
return target
def get_box_mask(self, rect, img_size):
x1, y1, x2, y2 = rect[0], rect[1], rect[2], rect[3]
if self.mask_mode == "poly":
return [[x1, y1, x1, y2, x2, y2, x2, y1]]
elif self.mask_mode == "mask":
# note the order of height/width order in mask is opposite to image
mask = np.zeros([img_size[1], img_size[0]], dtype=np.uint8)
mask[math.floor(y1) : math.ceil(y2), math.floor(x1) : math.ceil(x2)] = 255
encoded_mask = mask_utils.encode(np.asfortranarray(mask))
encoded_mask["counts"] = encoded_mask["counts"].decode("utf-8")
return encoded_mask
def add_masks(self, annotations, img_size):
masks = []
is_box_mask = []
for obj in annotations:
if "mask" in obj:
masks.append(obj["mask"])
is_box_mask.append(0)
else:
masks.append(self.get_box_mask(obj["rect"], img_size))
is_box_mask.append(1)
masks = SegmentationMask(masks, img_size, mode=self.mask_mode)
is_box_mask = torch.tensor(is_box_mask)
return masks, is_box_mask
def add_classes(self, annotations):
class_names = [obj["class"] for obj in annotations]
classes = [None] * len(class_names)
for i in range(len(class_names)):
classes[i] = self.labelmap["class_to_ind"][class_names[i]]
return torch.tensor(classes)
def add_confidences(self, annotations):
confidences = []
for obj in annotations:
if "conf" in obj:
confidences.append(obj["conf"])
else:
confidences.append(1.0)
return torch.tensor(confidences)
def add_attributes(self, annotations):
# the maximal number of attributes per object is 16
attributes = [[0] * 16 for _ in range(len(annotations))]
for i, obj in enumerate(annotations):
for j, attr in enumerate(obj["attributes"]):
attributes[i][j] = self.labelmap["attribute_to_ind"][attr]
return torch.tensor(attributes)
def add_features(self, annotations):
features = []
for obj in annotations:
features.append(np.frombuffer(base64.b64decode(obj["feature"]), np.float32))
return torch.tensor(features)
def add_scores_all(self, annotations):
scores_all = []
for obj in annotations:
scores_all.append(np.frombuffer(base64.b64decode(obj["scores_all"]), np.float32))
return torch.tensor(scores_all)
def add_boxes_all(self, annotations):
boxes_all = []
for obj in annotations:
boxes_all.append(np.frombuffer(base64.b64decode(obj["boxes_all"]), np.float32).reshape(-1, 4))
return torch.tensor(boxes_all)
def relation_loader(self, relation_annos, target):
if self.filter_duplicate_relations:
# Filter out dupes!
all_rel_sets = collections.defaultdict(list)
for triplet in relation_annos:
all_rel_sets[(triplet["subj_id"], triplet["obj_id"])].append(triplet)
relation_annos = [np.random.choice(v) for v in all_rel_sets.values()]
# get M*M pred_labels
relation_triplets = []
relations = torch.zeros([len(target), len(target)], dtype=torch.int64)
for i in range(len(relation_annos)):
if len(self.ignore_rel) != 0 and relation_annos[i]["class"] in self.ignore_rel:
continue
subj_id = relation_annos[i]["subj_id"]
obj_id = relation_annos[i]["obj_id"]
predicate = self.labelmap["relation_to_ind"][relation_annos[i]["class"]]
relations[subj_id, obj_id] = predicate
relation_triplets.append([subj_id, obj_id, predicate])
relation_triplets = torch.tensor(relation_triplets)
target.add_field("relation_labels", relation_triplets)
target.add_field("pred_labels", relations)
return target
class BoxLabelLoader(object):
def __init__(self, labelmap, extra_fields=(), ignore_attrs=(), mask_mode="poly"):
self.labelmap = labelmap
self.extra_fields = extra_fields
self.ignore_attrs = ignore_attrs
assert mask_mode == "poly" or mask_mode == "mask"
self.mask_mode = mask_mode
self.all_fields = ["class", "mask", "confidence", "attributes_encode", "IsGroupOf", "IsProposal"]
def __call__(self, annotations, img_size, remove_empty=True):
boxes = [obj["rect"] for obj in annotations]
boxes = torch.as_tensor(boxes).reshape(-1, 4)
target = BoxList(boxes, img_size, mode="xyxy")
for field in self.extra_fields:
assert field in self.all_fields, "Unsupported field {}".format(field)
if field == "class":
classes = self.add_classes_with_ignore(annotations)
target.add_field("labels", classes)
elif field == "mask":
masks, is_box_mask = self.add_masks(annotations, img_size)
target.add_field("masks", masks)
target.add_field("is_box_mask", is_box_mask)
elif field == "confidence":
confidences = self.add_confidences(annotations)
target.add_field("confidences", confidences)
elif field == "attributes_encode":
attributes = self.add_attributes(annotations)
target.add_field("attributes", attributes)
elif field == "IsGroupOf":
is_group = [1 if "IsGroupOf" in obj and obj["IsGroupOf"] == 1 else 0 for obj in annotations]
target.add_field("IsGroupOf", torch.tensor(is_group))
elif field == "IsProposal":
is_proposal = [1 if "IsProposal" in obj and obj["IsProposal"] == 1 else 0 for obj in annotations]
target.add_field("IsProposal", torch.tensor(is_proposal))
target = target.clip_to_image(remove_empty=remove_empty)
return target
def add_classes_with_ignore(self, annotations):
class_names = [obj["class"] for obj in annotations]
classes = [None] * len(class_names)
if self.ignore_attrs:
for i, obj in enumerate(annotations):
if any([obj[attr] for attr in self.ignore_attrs if attr in obj]):
classes[i] = -1
for i, cls in enumerate(classes):
if cls != -1:
classes[i] = self.labelmap[class_names[i]] + 1 # 0 is saved for background
return torch.tensor(classes)
def add_masks(self, annotations, img_size):
masks = []
is_box_mask = []
for obj in annotations:
if "mask" in obj:
masks.append(obj["mask"])
is_box_mask.append(0)
else:
masks.append(self.get_box_mask(obj["rect"], img_size))
is_box_mask.append(1)
masks = SegmentationMask(masks, img_size, mode=self.mask_mode)
is_box_mask = torch.tensor(is_box_mask)
return masks, is_box_mask
def get_box_mask(self, rect, img_size):
x1, y1, x2, y2 = rect[0], rect[1], rect[2], rect[3]
if self.mask_mode == "poly":
return [[x1, y1, x1, y2, x2, y2, x2, y1]]
elif self.mask_mode == "mask":
# note the order of height/width order in mask is opposite to image
mask = np.zeros([img_size[1], img_size[0]], dtype=np.uint8)
mask[math.floor(y1) : math.ceil(y2), math.floor(x1) : math.ceil(x2)] = 255
encoded_mask = mask_utils.encode(np.asfortranarray(mask))
encoded_mask["counts"] = encoded_mask["counts"].decode("utf-8")
return encoded_mask
def add_confidences(self, annotations):
confidences = []
for obj in annotations:
if "confidence" in obj:
confidences.append(obj["confidence"])
elif "conf" in obj:
confidences.append(obj["conf"])
else:
confidences.append(1.0)
return torch.tensor(confidences)
def add_attributes(self, annotations):
# we know that the maximal number of attributes per object is 16
attributes = [[0] * 16 for _ in range(len(annotations))]
for i, obj in enumerate(annotations):
attributes[i][: len(obj["attributes_encode"])] = obj["attributes_encode"]
return torch.tensor(attributes)
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