def __call__(self, dataset_dict):
"""
Args:
dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.
Returns:
dict: a format that builtin models in detectron2 accept
"""
dataset_dict = copy.deepcopy(
dataset_dict) # it will be modified by code below
# USER: Write your own image loading if it's not from a file
image = utils.read_image(dataset_dict["file_name"],
format=self.img_format)
utils.check_image_size(dataset_dict, image)
if "annotations" not in dataset_dict:
image, transforms = T.apply_transform_gens(
([self.crop_gen] if self.crop_gen else []) + self.tfm_gens,
image)
else:
# Crop around an instance if there are instances in the image.
# USER: Remove if you don't use cropping
if self.crop_gen:
crop_tfm = utils.gen_crop_transform_with_instance(
self.crop_gen.get_crop_size(image.shape[:2]),
image.shape[:2],
np.random.choice(dataset_dict["annotations"]),
)
image = crop_tfm.apply_image(image)
image, transforms = T.apply_transform_gens(self.tfm_gens, image)
if self.crop_gen:
transforms = crop_tfm + transforms
image_shape = image.shape[:2] # h, w
# Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,
# but not efficient on large generic data structures due to the use of pickle & mp.Queue.
# Therefore it's important to use torch.Tensor.
dataset_dict["image"] = torch.as_tensor(
np.ascontiguousarray(image.transpose(2, 0, 1)))
if not self.is_train:
# USER: Modify this if you want to keep them for some reason.
dataset_dict.pop("annotations", None)
return dataset_dict
if "annotations" in dataset_dict:
# USER: Implement additional transformations if you have other types of data
annos = [
utils.transform_instance_annotations(
obj,
transforms,
image_shape,
) for obj in dataset_dict.pop("annotations")
if obj.get("iscrowd", 0) == 0
]
instances = annotations_to_instances(annos, image_shape)
dataset_dict["instances"] = utils.filter_empty_instances(instances)
return dataset_dict
def __call__(self, dataset_dict):
dataset_dict = copy.deepcopy(
dataset_dict) # it will be modified by code below
image = utils.read_image(dataset_dict["file_name"], format="BGR")
aug_input = T.AugInput(image)
transforms = self.augmentations(aug_input)
image = aug_input.image
# if not self.is_train:
# # USER: Modify this if you want to keep them for some reason.
# dataset_dict.pop("annotations", None)
# dataset_dict.pop("sem_seg_file_name", None)
# return dataset_dict
image_shape = image.shape[:2] # h, w
dataset_dict["image"] = torch.as_tensor(
image.transpose(2, 0, 1).astype("float32"))
annos = [
utils.transform_instance_annotations(obj, transforms, image_shape)
for obj in dataset_dict.pop("annotations")
if obj.get("iscrowd", 0) == 0
]
instances = utils.annotations_to_instances(annos, image_shape)
dataset_dict["instances"] = utils.filter_empty_instances(instances)
return dataset_dict
def mapper(dataset_dict):
# 自定义mapper
dataset_dict = copy.deepcopy(dataset_dict) # 后面要改变这个dict，所以先复制
image = utils.read_image(dataset_dict["file_name"], format="BGR") # 读取图片，numpy array
# image, transforms = T.apply_transform_gens(
# [T.Resize((800, 800)), T.RandomContrast(0.1, 3), T.RandomSaturation(0.1, 2), T.RandomRotation(angle=[0, 180]),
# T.RandomFlip(prob=0.4, horizontal=False, vertical=True), T.RandomCrop('relative_range', (0.4, 0.6))], image) # 数组增强
# image, transforms = T.apply_transform_gens(
# [T.Resize((800, 800)), T.RandomContrast(0.1, 3), T.RandomSaturation(0.1, 2),
# T.RandomFlip(prob=0.4, horizontal=True, vertical=False), T.RandomCrop('relative_range', (0.4, 0.6))], image)
image, transforms = T.apply_transform_gens(
[T.Resize((800, 800)), T.RandomContrast(0.1, 3), T.RandomSaturation(0.1, 2),
T.RandomFlip(prob=0.4, horizontal=True, vertical=False)], image)
# 数组增强
dataset_dict["image"] = torch.as_tensor(image.transpose(2, 0, 1).astype("float32")) # 转成Tensor
annos = [
utils.transform_instance_annotations(obj, transforms, image.shape[:2])
for obj in dataset_dict.pop("annotations")
if obj.get("iscrowd", 0) == 0
] # 数据增强要同步标注
instances = utils.annotations_to_instances(annos, image.shape[:2]) # 将标注转成Instance（Tensor）
dataset_dict["instances"] = utils.filter_empty_instances(instances) # 去除空的
return dataset_dict
def mapper(dataset_dict):
# Implement a mapper, similar to the default DatasetMapper, but with your own customizations
dataset_dict = copy.deepcopy(
dataset_dict) # it will be modified by code below
image = utils.read_image(dataset_dict["file_name"], format="BGR")
image, transforms = T.apply_transform_gens([
T.RandomFlip(prob=0.50, horizontal=True, vertical=False),
T.RandomApply(tfm_or_aug=T.RandomBrightness(intensity_min=0.7,
intensity_max=1.1),
prob=0.40),
T.RandomApply(tfm_or_aug=T.RandomSaturation(intensity_min=0.7,
intensity_max=1.1),
prob=0.40)
], image)
dataset_dict["image"] = torch.as_tensor(
image.transpose(2, 0, 1).astype("float32"))
annos = [
utils.transform_instance_annotations(obj, transforms, image.shape[:2])
for obj in dataset_dict.pop("annotations")
if obj.get("iscrowd", 0) == 0
]
instances = utils.annotations_to_instances(annos, image.shape[:2])
dataset_dict["instances"] = utils.filter_empty_instances(instances)
return dataset_dict
def augment(record):
record = deepcopy(record)
image = plt.imread(record["filepath"])
annotations = record["annotations"]
boxes = [annotation["bbox"] for annotation in annotations]
classes = [annotation["category_id"] for annotation in annotations]
boxes = BoundingBoxesOnImage(
[
BoundingBox(*box, label=class_)
for box, class_ in zip(boxes, classes)
],
shape=image.shape,
)
image, boxes = AUGMENTER(image=image, bounding_boxes=boxes)
classes = [bbox.label for bbox in boxes.bounding_boxes]
boxes = np.array([[box.x1, box.y1, box.x2, box.y2] for box in boxes.items])
image = image[..., [2, 1]]
image = image.transpose(2, 0, 1).astype(np.float32)
annotations = [{
"bbox": box,
"bbox_mode": BoxMode.XYXY_ABS,
"category_id": class_
} for box, class_ in zip(boxes, classes)]
record["image"] = torch.as_tensor(image)
instances = utils.annotations_to_instances(annotations, image.shape[1:])
record["instances"] = utils.filter_empty_instances(instances)
return record
def custom_mapper(dataset_dict):
# it will be modified by code below
dataset_dict = copy.deepcopy(dataset_dict)
image = utils.read_image(dataset_dict["file_name"], format="BGR")
transform_list = [
T.Resize((512, 512)),
T.RandomBrightness(0.8, 1.8),
T.RandomContrast(0.6, 1.3),
T.RandomSaturation(0.8, 1.4),
T.RandomRotation(angle=[30, 30]),
T.RandomLighting(0.7),
T.RandomFlip(prob=0.4, horizontal=False, vertical=True),
]
image, transforms = T.apply_transform_gens(transform_list, image)
dataset_dict["image"] = torch.as_tensor(
image.transpose(2, 0, 1).astype("float32"))
annos = [
utils.transform_instance_annotations(obj, transforms, image.shape[:2])
for obj in dataset_dict.pop("annotations")
if obj.get("iscrowd", 0) == 0
]
instances = utils.annotations_to_instances(annos, image.shape[:2])
dataset_dict["instances"] = utils.filter_empty_instances(instances)
return dataset_dict
def custom_mapper(dataset_dict):
# Implement a mapper, similar to the default DatasetMapper, but with your own customizations
dataset_dict = copy.deepcopy(
dataset_dict) # it will be modified by code below
image = utils.read_image(dataset_dict["file_name"], format="BGR")
# transform_list = [T.Resize(800,600),
# T.RandomFlip(prob=0.5, horizontal=True, vertical=True),
# T.RandomContrast(0.8, 3),
# T.RandomBrightness(0.8, 1.6),
# ]
transform_list = [ #T.Resize((800, 800)),
T.RandomContrast(0.8, 3),
T.RandomBrightness(0.8, 1.6),
T.RandomFlip(prob=0.5, horizontal=False, vertical=True),
T.RandomFlip(prob=0.5, horizontal=True, vertical=False)
] ### 数据增强方式
image, transforms = T.apply_transform_gens(transform_list,
image) ## # 数组增强
dataset_dict["image"] = torch.as_tensor(
image.transpose(2, 0, 1).astype("float32")) ##转成Tensor
annos = [
utils.transform_instance_annotations(obj, transforms, image.shape[:2])
for obj in dataset_dict.pop("annotations")
if obj.get("iscrowd", 0) == 0
]
instances = utils.annotations_to_instances(
annos, image.shape[:2]) # 将标注转成Instance（Tensor）
dataset_dict["instances"] = utils.filter_empty_instances(
instances) ## 去除空的
return dataset_dict
def custom_mapper(dataset_dict, size, flip_prob, min_brightness, max_brightness, \
min_contrast, max_contrast, min_saturation, max_saturation):
# Implement a mapper, similar to the default DatasetMapper, but with your own customizations
dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below
image = detection_utils.read_image(dataset_dict["file_name"], format="BGR")
transform_list = [
T.Resize(size),
T.RandomBrightness(min_brightness, max_brightness),
T.RandomContrast(min_contrast, max_contrast),
T.RandomSaturation(min_saturation, max_saturation),
T.RandomFlip(prob=flip_prob, horizontal=False, vertical=True),
T.RandomFlip(prob=flip_prob, horizontal=True, vertical=False),
]
image, transforms = T.apply_transform_gens(transform_list, image)
dataset_dict["image"] = torch.as_tensor(image.transpose(2, 0, 1).astype("float32"))
annos = [
detection_utils.transform_instance_annotations(obj, transforms, image.shape[:2])
for obj in dataset_dict.pop("annotations")
if obj.get("iscrowd", 0) == 0
]
instances = detection_utils.annotations_to_instances(annos, image.shape[:2])
dataset_dict["instances"] = detection_utils.filter_empty_instances(instances)
return dataset_dict
def __call__(self, dataset_dict):
dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below
image = utils.read_image(dataset_dict["file_name"], format="BGR")
# aug_input = T.AugInput(image)
# transforms = self.augmentations(aug_input)
# image = aug_input.image
prev_anno = dataset_dict["annotations"]
bboxes = np.array([obj["bbox"] for obj in prev_anno], dtype=np.float32)
# category_id = np.array([obj["category_id"] for obj in dataset_dict["annotations"]], dtype=np.int64)
category_id = np.arange(len(dataset_dict["annotations"]))
transformed = self.transform(image=image, bboxes=bboxes, category_ids=category_id)
image = transformed["image"]
annos = []
for i, j in enumerate(transformed["category_ids"]):
d = prev_anno[j]
d["bbox"] = transformed["bboxes"][i]
annos.append(d)
dataset_dict.pop("annotations", None) # Remove unnecessary field.
# if not self.is_train:
# # USER: Modify this if you want to keep them for some reason.
# dataset_dict.pop("annotations", None)
# dataset_dict.pop("sem_seg_file_name", None)
# return dataset_dict
image_shape = image.shape[:2] # h, w
dataset_dict["image"] = torch.as_tensor(image.transpose(2, 0, 1).astype("float32"))
instances = utils.annotations_to_instances(annos, image_shape)
dataset_dict["instances"] = utils.filter_empty_instances(instances)
return dataset_dict
def __call__(self, dataset_dict):
self.tfm_gens = []
dataset_dict = deepcopy(dataset_dict)
image = utils.read_image(dataset_dict["file_name"],
format=self.img_format)
utils.check_image_size(dataset_dict, image)
if self.is_train:
# Crop
if 'crop' in self.da.keys():
crop_gen = T.RandomCrop(self.da['crop']['type'],
self.da['crop']['size'])
self.tfm_gens.append(crop_gen)
# Horizontal flip
if 'flip' in self.da.keys():
flip_gen = T.RandomFlip(
prob=self.da['flip']['prob'],
horizontal=self.da['flip']['horizontal'],
vertical=self.da['flip']['vertical'])
self.tfm_gens.append(flip_gen)
image, transforms = T.apply_transform_gens(self.tfm_gens, image)
image_shape = image.shape[:2] # h, w
dataset_dict["image"] = torch.as_tensor(
np.ascontiguousarray(image.transpose(2, 0, 1)))
if not self.is_train:
dataset_dict.pop("annotations", None)
dataset_dict.pop("sem_seg_file_name", None)
return dataset_dict
if "annotations" in dataset_dict:
for anno in dataset_dict["annotations"]:
if not self.mask_on:
anno.pop("segmentation", None)
if not self.keypoint_on:
anno.pop("keypoints", None)
annos = [
utils.transform_instance_annotations(
obj,
transforms,
image_shape,
keypoint_hflip_indices=self.keypoint_hflip_indices)
for obj in dataset_dict.pop("annotations")
if obj.get("iscrowd", 0) == 0
]
instances = utils.annotations_to_instances(
annos, image_shape, mask_format=self.mask_format)
if self.crop_gen and instances.has("gt_masks"):
instances.gt_boxes = instances.gt_masks.get_bounding_boxes()
dataset_dict["instances"] = utils.filter_empty_instances(instances)
return dataset_dict
def apply_image_augmentations(image, dataset_dict, sem_seg_gt,
augmentations):
"""Applies given augmentation to the given image and its attributes (segm, instances, etc).
Almost no changes from D2's original code (apart from erasing non-relevant portions, e.g. for
keypoints), just wrapped it in a function to avoid duplicate code."""
aug_input = T.AugInput(image, sem_seg=sem_seg_gt)
transforms = augmentations(aug_input)
image, sem_seg_gt = aug_input.image, aug_input.sem_seg
image_shape = image.shape[:2] # h, w
# Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,
# but not efficient on large generic data structures due to the use of pickle & mp.Queue.
# Therefore it's important to use torch.Tensor.
dataset_dict["image"] = torch.as_tensor(
np.ascontiguousarray(image.transpose(2, 0, 1)))
if sem_seg_gt is not None:
dataset_dict["sem_seg"] = torch.as_tensor(
sem_seg_gt.astype("long"))
if not self.is_train:
dataset_dict.pop("annotations", None)
dataset_dict.pop("sem_seg_file_name", None)
return dataset_dict
if "annotations" in dataset_dict:
for anno in dataset_dict["annotations"]:
if not self.use_instance_mask:
anno.pop("segmentation", None)
if not self.use_keypoint:
anno.pop("keypoints", None)
annos = [
utils.transform_instance_annotations(
obj,
transforms,
image_shape,
keypoint_hflip_indices=self.keypoint_hflip_indices,
) for obj in dataset_dict.pop("annotations")
if obj.get("iscrowd", 0) == 0
]
instances = utils.annotations_to_instances(
annos, image_shape, mask_format=self.instance_mask_format)
# After transforms such as cropping are applied, the bounding box may no longer
# tightly bound the object. As an example, imagine a triangle object
# [(0,0), (2,0), (0,2)] cropped by a box [(1,0),(2,2)] (XYXY format). The tight
# bounding box of the cropped triangle should be [(1,0),(2,1)], which is not equal to
# the intersection of original bounding box and the cropping box.
if self.recompute_boxes:
instances.gt_boxes = instances.gt_masks.get_bounding_boxes(
)
dataset_dict["instances"] = utils.filter_empty_instances(
instances)
return dataset_dict, transforms
def test_random_instance_crop(self):
from detectron2.data import detection_utils as du
from detectron2.data.transforms.augmentation import (
AugInput,
AugmentationList,
)
from detectron2.structures import BoxMode
aug = tf_crop.RandomInstanceCrop([1.0, 1.0])
img_w, img_h = 10, 7
annotations = [
{
"category_id": 0,
"bbox": [1, 1, 4, 3],
"bbox_mode": BoxMode.XYWH_ABS,
},
{
"category_id": 0,
"bbox": [2, 2, 4, 3],
"bbox_mode": BoxMode.XYWH_ABS,
},
{
"category_id": 0,
"bbox": [6, 5, 3, 2],
"bbox_mode": BoxMode.XYWH_ABS,
},
]
img = np.ones([img_h, img_w, 3]) * 3
inputs = AugInput(image=img)
# pass additional arguments
inputs.annotations = annotations
transforms = AugmentationList([aug])(inputs)
self.assertIn(inputs.image.shape,
[torch.Size([3, 4, 3]),
torch.Size([2, 3, 3])])
# from dataset mapper unused annotations will be filtered out due to the
# iscrowd flag
image_shape = inputs.image.shape[:2]
annos = [
du.transform_instance_annotations(
obj,
transforms,
image_shape,
) for obj in annotations if obj.get("iscrowd", 0) == 0
]
instances = du.annotations_to_instances(annos, image_shape)
filtered_instances = du.filter_empty_instances(instances)
self.assertEqual(len(filtered_instances), 1)
self.assertArrayEqual(
filtered_instances.gt_boxes.tensor.tolist(),
[[0, 0, image_shape[1], image_shape[0]]],
)
def __call__(self, dataset_dict):
"""
Args:
dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.
Returns:
dict: a format that builtin models in detectron2 accept
"""
dataset_dict = copy.deepcopy(
dataset_dict) # it will be modified by code below
image = utils.read_image(dataset_dict["file_name"],
format=self.img_format)
utils.check_image_size(dataset_dict, image)
if self.crop_gen is None:
image, transforms = T.apply_transform_gens(self.tfm_gens, image)
else:
if np.random.rand() > 0.5:
image, transforms = T.apply_transform_gens(
self.tfm_gens, image)
else:
image, transforms = T.apply_transform_gens(
self.tfm_gens[:-1] + self.crop_gen + self.tfm_gens[-1:],
image)
image_shape = image.shape[:2] # h, w
# Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,
# but not efficient on large generic data structures due to the use of pickle & mp.Queue.
# Therefore it's important to use torch.Tensor.
dataset_dict["image"] = torch.as_tensor(
np.ascontiguousarray(image.transpose(2, 0, 1)))
if not self.is_train:
# USER: Modify this if you want to keep them for some reason.
dataset_dict.pop("annotations", None)
return dataset_dict
if "annotations" in dataset_dict:
# USER: Modify this if you want to keep them for some reason.
for anno in dataset_dict["annotations"]:
if not self.mask_on:
anno.pop("segmentation", None)
anno.pop("keypoints", None)
# USER: Implement additional transformations if you have other types of data
annos = [
utils.transform_instance_annotations(obj, transforms,
image_shape)
for obj in dataset_dict.pop("annotations")
if obj.get("iscrowd", 0) == 0
]
instances = utils.annotations_to_instances(annos, image_shape)
dataset_dict["instances"] = utils.filter_empty_instances(instances)
return dataset_dict
def __call__(self, dataset_dict):
"""
:dataset_dict: this contains a single record(as per dataset) which the dataloader requested
:return: Apply augmentation and convert record(as per dataset) into format required by
model (https://detectron2.readthedocs.io/en/v0.2.1/tutorials/models.html#model-input-format)
"""
dataset_dict = copy.deepcopy(dataset_dict)
# detectron utils reads images in batch, thus shape of image = (Height x Width x Channel)
image = utils.read_image(dataset_dict["file_name"], format="BGR")
# convert record into model input format
prev_anno = dataset_dict["annotations"]
bboxes = np.array([obj["bbox"] for obj in prev_anno], dtype=np.float32)
# make dummy category ids for indexing purposes
category_id = np.arange(len(dataset_dict["annotations"]))
# transform the input image
print(dataset_dict["file_name"])
transformed = self.transform(image=image, bboxes=bboxes, category_ids=category_id)
# extract transformed image
image = transformed["image"]
# prep annotations for model input
annos = []
for i, j in enumerate(transformed["category_ids"]):
# fetch old annotation using dummy category_id
temp = prev_anno[j]
# updating the bboxes of old annotation
temp["bbox"] = transformed["bboxes"][i]
annos.append(temp)
# delete annotations as model will use "instances" instead of annotations
dataset_dict.pop("annotations", None)
# image.shape returns H x W x C
# image.shape[:2] returns H x W
image_shape = image.shape[:2]
# transpose operation converts H x W x C --> C x H x W
dataset_dict["image"] = torch.as_tensor(image.transpose(2, 0, 1).astype("float32"))
# convert annotations to instances format
instances = utils.annotations_to_instances(annos, image_shape)
# delete any images which do not contain annotations
dataset_dict["instances"] = utils.filter_empty_instances(instances)
return dataset_dict
def __call__(self, dataset_dict):
"""
Args:
dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.
Returns:
dict: a format that builtin models in detectron2 accept
"""
dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below
image = utils.read_image(dataset_dict["file_name"], format=self.image_format)
utils.check_image_size(dataset_dict, image)
aug_input = T.AugInput(image)
transforms = self.augmentations(aug_input)
image = aug_input.image
image_shape = image.shape[:2] # h, w
# Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,
# but not efficient on large generic data structures due to the use of pickle & mp.Queue.
# Therefore it's important to use torch.Tensor.
dataset_dict["image"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))
if not self.is_train:
dataset_dict.pop("annotations", None)
return dataset_dict
if "annotations" in dataset_dict:
# Maps points from the closed interval [0, image_size - 1] on discrete
# image coordinates to the half-open interval [x1, x2) on continuous image
# coordinates. We use the continuous-discrete conversion from Heckbert
# 1990 ("What is the coordinate of a pixel?"): d = floor(c) and c = d + 0.5,
# where d is a discrete coordinate and c is a continuous coordinate.
for ann in dataset_dict["annotations"]:
point_coords_wrt_image = np.array(ann["point_coords"]).astype(np.float)
point_coords_wrt_image = point_coords_wrt_image + 0.5
ann["point_coords"] = point_coords_wrt_image
annos = [
# also need to transform point coordinates
transform_instance_annotations(
obj,
transforms,
image_shape,
)
for obj in dataset_dict.pop("annotations")
if obj.get("iscrowd", 0) == 0
]
instances = annotations_to_instances(
annos,
image_shape,
sample_points=self.sample_points,
)
dataset_dict["instances"] = utils.filter_empty_instances(instances)
return dataset_dict
def __call__(self, dataset_dict):
self.tfm_gens = []
dataset_dict = deepcopy(dataset_dict)
image = utils.read_image(dataset_dict["file_name"],
format=self.img_format)
utils.check_image_size(dataset_dict, image)
if self.is_train:
# Crop
'''print("Augmentation: ", "T.RandomCrop('relative', [0.8, 0.4])")
crop_gen = T.RandomCrop('relative', [0.8, 0.4])
self.tfm_gens.append(crop_gen)'''
# Horizontal flip
print("Augmentation: ",
"T.RandomFlip(prob=0.5, horizontal=True, vertical=False)")
flip_gen = T.RandomFlip(prob=0.5, horizontal=True, vertical=False)
self.tfm_gens.append(flip_gen)
image, transforms = T.apply_transform_gens(self.tfm_gens, image)
image_shape = image.shape[:2] # h, w
dataset_dict["image"] = torch.as_tensor(
np.ascontiguousarray(image.transpose(2, 0, 1)))
if not self.is_train:
dataset_dict.pop("annotations", None)
return dataset_dict
if "annotations" in dataset_dict:
for anno in dataset_dict["annotations"]:
if not self.mask_on:
anno.pop("segmentation", None)
annos = [
utils.transform_instance_annotations(
obj,
transforms,
image_shape,
keypoint_hflip_indices=self.keypoint_hflip_indices)
for obj in dataset_dict.pop("annotations")
if obj.get("iscrowd", 0) == 0
]
instances = utils.annotations_to_instances(
annos, image_shape, mask_format=self.mask_format)
if self.crop_gen and instances.has("gt_masks"):
instances.gt_boxes = instances.gt_masks.get_bounding_boxes()
dataset_dict["instances"] = utils.filter_empty_instances(instances)
return dataset_dict
def mapper(dataset_dict):
# Implement a mapper, similar to the default DatasetMapper, but with your own customizations
dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below
image = utils.read_image(dataset_dict["file_name"], format="BGR")
image, transforms = T.apply_transform_gens([T.Resize((800, 800))], image)
dataset_dict["image"] = torch.as_tensor(image.transpose(2, 0, 1).astype("float32"))
annos = [
utils.transform_instance_annotations(obj, transforms, image.shape[:2])
for obj in dataset_dict.pop("annotations")
if obj.get("iscrowd", 0) == 0
]
instances = utils.annotations_to_instances(annos, image.shape[:2])
dataset_dict["instances"] = utils.filter_empty_instances(instances)
return dataset_dict
def mapper(dataset_dict):
dataset_dict = copy.deepcopy(dataset_dict)
image = utils.read_image(dataset_dict["file_name"], format="BGR")
image, transforms = T.apply_transform_gens([T.Resize((1152, 1152))], image)
dataset_dict["image"] = torch.as_tensor(
image.transpose(2, 0, 1).astype("float32"))
annos = [
transform_instance_annotations(obj, transforms, image.shape[:2])
for obj in dataset_dict.pop("annotations")
if obj.get("iscrowd", 0) == 0
]
instances = utils.annotations_to_instances_rotated(annos, image.shape[:2])
dataset_dict["instances"] = utils.filter_empty_instances(instances)
return dataset_dict
def custom_mapper(dataset_dict, transform_list):
dataset_dict = copy.deepcopy(
dataset_dict) # it will be modified by code below
image = utils.read_image(dataset_dict["file_name"], format="BGR")
image, transforms = T.apply_transform_gens(transform_list, image)
dataset_dict["image"] = torch.as_tensor(
image.transpose(2, 0, 1).astype("float32"))
annos = [
utils.transform_instance_annotations(obj, transforms, image.shape[:2])
for obj in dataset_dict.pop("annotations")
]
instances = utils.annotations_to_instances(annos, image.shape[:2])
dataset_dict["instances"] = utils.filter_empty_instances(instances)
return dataset_dict
def __call__(self, dataset_dict):
"""
Args:
dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.
Returns:
dict: a format that builtin models in detectron2 accept
"""
dataset_dict = copy.deepcopy(
dataset_dict) # it will be modified by code below
# USER: Write your own image loading if it's not from a file
image = utils.read_image(dataset_dict["file_name"],
format=self.image_format)
utils.check_image_size(dataset_dict, image)
aug_input = T.AugInput(image)
transforms = self.augmentations(aug_input)
image = aug_input.image
image_shape = image.shape[:2] # h, w
# Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,
# but not efficient on large generic data structures due to the use of pickle & mp.Queue.
# Therefore it's important to use torch.Tensor.
dataset_dict["image"] = torch.as_tensor(
np.ascontiguousarray(image.transpose(2, 0, 1)))
if "annotations" in dataset_dict:
# USER: Implement additional transformations if you have other types of data
annos = [
utils.transform_instance_annotations(
obj,
transforms,
image_shape,
keypoint_hflip_indices=self.keypoint_hflip_indices)
for obj in dataset_dict.pop("annotations")
if obj.get("iscrowd", 0) == 0
]
instances = utils.annotations_to_instances(
annos, image_shape, mask_format=self.instance_mask_format)
# After transforms such as cropping are applied, the bounding box may no longer
# tightly bound the object. As an example, imagine a triangle object
# [(0,0), (2,0), (0,2)] cropped by a box [(1,0),(2,2)] (XYXY format). The tight
# bounding box of the cropped triangle should be [(1,0),(2,1)], which is not equal to
# the intersection of original bounding box and the cropping box.
dataset_dict["instances"] = utils.filter_empty_instances(instances)
return dataset_dict
def __call__(self, data_dict):
data_dict = copy.deepcopy(data_dict)
image = cv2.imread(data_dict["file_name"], cv2.IMREAD_UNCHANGED)
image = image[:, :, :self.nb_channels]
# if image.shape[2] == 4:
# conversion = cv2.COLOR_BGRA2RGBA
# else:
# conversion = cv2.COLOR_BGR2RGB
# image = cv2.cvtColor(image, conversion)
# data_dict["image"] = torch.as_tensor(image.transpose(2, 0, 1)
# .astype("float32"))
if self.augmenter:
rgb_image = image[:, :, :3]
ndvi_image = None
if image.shape[2] == 4:
ndvi_image = image[:, :, 3]
data_dict["original_annotations"] = data_dict["annotations"]
data_dict["original_image"] = image
augmented = self.augmenter(rgb_image, data_dict["annotations"],
ndvi_image)
data_dict["annotations"] = augmented["annotations"]
if augmented['ndvi'] is not None:
image = np.dstack([augmented["rgb_image"], augmented["ndvi"]])
else:
image = augmented["rgb_image"]
if self.cfg.INPUT.FORMAT == "BGR":
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
elif self.cfg.INPUT.FORMAT == "BGRN":
image = cv2.cvtColor(image, cv2.COLOR_RGBA2BGRA)
data_dict["image"] = torch.as_tensor(
image.transpose(2, 0, 1).astype("float32"))
instances = det_utils.annotations_to_instances(
data_dict["annotations"], image.shape[:2])
data_dict["instances"] = det_utils.filter_empty_instances(instances)
return data_dict
def __call__(self, dataset_dict):
dataset_dict = copy.deepcopy(dataset_dict)
image = utils.read_image(dataset_dict["file_name"], format="BGR")
aug_input = T.AugInput(image)
transforms = self.augmentations(aug_input)
image = aug_input.image
image_shape = image.shape[:2] #h, w
dataset_dict["image"] = torch.as_tensor(
image.transpose(2, 0, 1).astype("float32"))
annos = [
utils.transform_instance_annotations(obj, transforms, image_shape)
for obj in dataset_dict.pop("annotations")
if obj.get("iscrowd", 0) == 0
]
instances = utils.annotations_to_instances(annos, image_shape)
dataset_dict["instances"] = utils.filter_empty_instances(instances)
return dataset_dict
def __call__(self, dataset_dict):
"""
Args:
dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.
Returns:
dict: a format that builtin models in detectron2 accept
"""
image_shape = dataset_dict['height'], dataset_dict['width']
annos = dataset_dict["annotations"]
mislabeled = [obj["mislabeled"] for obj in annos]
instances = utils.annotations_to_instances(
annos, image_shape, mask_format=self.instance_mask_format)
instances.gt_mislabeled = torch.tensor(mislabeled, dtype=torch.bool)
dataset_dict = super(CustomDatasetMapper, self).__call__(dataset_dict)
dataset_dict["instances"] = utils.filter_empty_instances(instances)
return dataset_dict
def customMapper(dataset_dict):
dataset_dict = copy.deepcopy(dataset_dict)
image = utils.read_image(dataset_dict["file_name"], format="BGR")
transform_list = [
T.Resize((600, 800)),
T.RandomFlip(prob=0.6, horizontal=True, vertical=False),
T.RandomFlip(prob=0.6, horizontal=False, vertical=True),
]
image, transforms = T.apply_transform_gens(transform_list, image)
dataset_dict["image"] = torch.as_tensor(image.transpose(2, 0, 1).astype("float32"))
annos = [
utils.transform_instance_annotations(obj, transforms, image.shape[:2])
for obj in dataset_dict.pop("annotations")
if obj.get("iscrowd", 0) == 0
]
instances = utils.annotations_to_instances(annos, image.shape[:2])
dataset_dict["instances"] = utils.filter_empty_instances(instances)
return dataset_dict
def __call__(self, dataset_dict):
"""
Args:
dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.
Returns:
dict: a format that builtin models in detectron2 accept
"""
# Implement a mapper, similar to the default DatasetMapper, but with own customizations
dataset_dict = copy.deepcopy(
dataset_dict) # it will be modified by code below
image = utils.read_image(dataset_dict["file_name"], format="BGR")
# Custom augs to be used while training
# Only HFlip and Resize are supported for rotated_boxes
augs = [T.RandomFlip(0.4, horizontal=True,
vertical=False)] #[T.RandomRotation([0,90])]
if self.is_train:
tfm_gens = self.tfm_gens + augs
else:
tfm_gens = self.tfm_gens
logging.getLogger(__name__).info("Original Augmentation: " +
str(self.tfm_gens))
logging.getLogger(__name__).info("Updated Augmentation List: " +
str(tfm_gens))
image, transforms = T.apply_transform_gens(tfm_gens, image)
dataset_dict["image"] = torch.as_tensor(
image.transpose(2, 0, 1).astype("float32"))
for a in dataset_dict['annotations']:
a['bbox'] = transforms.apply_rotated_box(np.asarray(
[a['bbox']]))[0].tolist()
annos = dataset_dict['annotations']
instances = utils.annotations_to_instances_rotated(
annos, image.shape[:2])
dataset_dict["instances"] = utils.filter_empty_instances(instances)
return dataset_dict
def custom_mapper(input_dict):
dataset_dict = copy.deepcopy(
input_dict) # it will be modified by code below
image = utils.read_image(dataset_dict["file_name"], format="BGR")
transform_list = [
T.Resize((1200, 1200)),
T.RandomFlip(prob=0.6, horizontal=True, vertical=False),
T.RandomFlip(prob=0.6, horizontal=False, vertical=True),
T.RandomContrast(0.7, 3.2),
T.RandomBrightness(0.6, 1.8),
]
image, transforms = T.apply_transform_gens(transform_list, image)
dataset_dict["image"] = torch.as_tensor(
image.transpose(2, 0, 1).astype("float32"))
annos = [
utils.transform_instance_annotations(obj, transforms, image.shape[:2])
for obj in dataset_dict.pop("annotations")
]
instances = utils.annotations_to_instances(annos, image.shape[:2])
dataset_dict["instances"] = utils.filter_empty_instances(instances)
return dataset_dict
def custom_mapper(dataset_dict):
dataset_dict = copy.deepcopy(
dataset_dict) # it will be modified by code below
image = utils.read_image(dataset_dict["file_name"], format="BGR")
transform_list = [
InvertColors(),
T.RandomBrightness(0.8, 1.8),
T.RandomContrast(0.6, 1.3),
T.RandomSaturation(0.8, 1.4),
T.RandomLighting(0.7),
]
image, transforms = T.apply_transform_gens(transform_list, image)
dataset_dict["image"] = torch.as_tensor(
image.transpose(2, 0, 1).astype("float32"))
annos = [
utils.transform_instance_annotations(obj, transforms, image.shape[:2])
for obj in dataset_dict.pop("annotations")
]
instances = utils.annotations_to_instances(annos, image.shape[:2])
dataset_dict["instances"] = utils.filter_empty_instances(instances)
return dataset_dict
def mapper(dataset_dict):
# Implement a mapper, similar to the default DatasetMapper, but with your own customizations
dataset_dict = copy.deepcopy(
dataset_dict) # it will be modified by code below
image = utils.read_image(dataset_dict["file_name"], format="BGR")
handler = load_augmentation_settings(
handler_save_path='augmentation_settings.json')
image, dataset_dict = handler(image=image,
dataset_dict_detectron=dataset_dict)
annots = [
obj for obj in dataset_dict.pop("annotations")
if obj.get("iscrowd", 0) == 0
]
dataset_dict["image"] = torch.as_tensor(
image.transpose(2, 0, 1).astype("float32"))
instances = utils.annotations_to_instances(annots, image.shape[:2])
dataset_dict["instances"] = utils.filter_empty_instances(instances)
return dataset_dict
def mapper(dataset_dict):
# Implement a mapper, similar to the default DatasetMapper, but with your own customizations
dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below
image = utils.read_image(dataset_dict["file_name"], format="BGR")
#print(dataset_dict["file_name"])
#cv2.imwrite(os.path.join(aug_save_path, f'{str(count.count)}_asd.jpg'),image)
handler = load_augmentation_settings(handler_save_path = 'test_handler.json')
for i in range(len(dataset_dict["annotations"])):
dataset_dict["annotations"][i]["segmentation"] = []
image, dataset_dict = handler(image=image, dataset_dict_detectron=dataset_dict)
annots = []
for item in dataset_dict["annotations"]:
annots.append(item)
dataset_dict["image"] = torch.as_tensor(image.transpose(2, 0, 1).astype("float32"))
instances = utils.annotations_to_instances(annots, image.shape[:2])
dataset_dict["instances"] = utils.filter_empty_instances(instances)
if True:
vis_img = image.copy()
bbox_list = [BBox.from_list(vals) for vals in dataset_dict["instances"].gt_boxes.tensor.numpy().tolist()]
# seg_list = [Segmentation([Polygon.from_list(poly.tolist(), demarcation=False) for poly in seg_polys]) for seg_polys in dataset_dict["instances"].gt_masks.polygons]
for bbox in (bbox_list):
# if len(seg) > 0 and False:
# vis_img = draw_segmentation(img=vis_img, segmentation=seg, transparent=True)
vis_img = draw_bbox(img=vis_img, bbox=bbox)
height,width,channels = vis_img.shape
print(height,width)
if count.count<100:
cv2.imwrite(os.path.join(aug_save_path, f'{str(count.count)}.jpg'),vis_img)
count.count_add()
aug_vis.step(vis_img)
return dataset_dict
def mapper(dataset_dict):
# Implement a mapper, similar to the default DatasetMapper, but with your own customizations
dataset_dict = copy.deepcopy(
dataset_dict) # it will be modified by code below
image = utils.read_image(dataset_dict["file_name"], format="BGR")
masks = anno_to_mask(image, dataset_dict['annotations'])
#plt.figure(1);plt.subplot(211);plt.imshow(image);plt.subplot(212);plt.imshow(masks.sum(axis=2))
# Crop and augmentation
image, masks = random_crop(image, masks)
masks = masks[:, :, np.where(masks.sum(axis=(0, 1)) > 50)[0]]
augmentation = create_augmentation()
image, masks = imgaug_augmentation(image, masks, augmentation)
masks = masks[:, :, np.where(masks.sum(axis=(0, 1)) > 50)[0]]
masks = masks.transpose([2, 0, 1])
annotations = masks_to_anno(masks)
#plt.figure(2);plt.subplot(211);plt.imshow(image);plt.subplot(212);plt.imshow(masks.sum(axis=0))
# Save into dataset_dict
dataset_dict["image"] = torch.as_tensor(
image.transpose(2, 0, 1).astype("float32"))
instances = utils.annotations_to_instances(annotations, image.shape[:2])
dataset_dict["instances"] = utils.filter_empty_instances(instances)
return dataset_dict
