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import copy
import logging
import numpy as np
import torch
import random
import cv2
from detectron2.config import configurable
from detectron2.data import detection_utils as utils
from detectron2.data import transforms as T
from detectron2.structures import BitMasks, Boxes, Instances
from pycocotools import mask as coco_mask
from pycocotools.mask import encode, decode, frPyObjects
def draw_circle(mask, center, radius):
y, x = np.ogrid[:mask.shape[0], :mask.shape[1]]
distance = np.sqrt((x - center[1]) ** 2 + (y - center[0]) ** 2)
mask[distance <= radius] = 1
def enhance_with_circles(binary_mask, radius=5):
if not isinstance(binary_mask, np.ndarray):
binary_mask = np.array(binary_mask)
binary_mask = binary_mask.astype(np.uint8)
output_mask = np.zeros_like(binary_mask, dtype=np.uint8)
points = np.argwhere(binary_mask == 1)
for point in points:
draw_circle(output_mask, (point[0], point[1]), radius)
return output_mask
def is_mask_non_empty(rle_mask):
if rle_mask is None:
return False
binary_mask = decode(rle_mask)
return binary_mask.sum() > 0
def convert_coco_poly_to_mask(segmentations, height, width):
masks = []
for polygons in segmentations:
rles = coco_mask.frPyObjects(polygons, height, width)
mask = coco_mask.decode(rles)
if len(mask.shape) < 3:
mask = mask[..., None]
mask = torch.as_tensor(mask, dtype=torch.uint8)
mask = mask.any(dim=2)
masks.append(mask)
if masks:
masks = torch.stack(masks, dim=0)
else:
masks = torch.zeros((0, height, width), dtype=torch.uint8)
return masks
def build_transform_gen(cfg):
"""
Create a list of default :class:`Augmentation` from config.
Now it includes resizing and flipping.
Returns:
list[Augmentation]
"""
image_size = cfg.INPUT.IMAGE_SIZE
min_scale = cfg.INPUT.MIN_SCALE
max_scale = cfg.INPUT.MAX_SCALE
augmentation = []
# if cfg.INPUT.RANDOM_FLIP != "none":
# augmentation.append(
# T.RandomFlip(
# horizontal=cfg.INPUT.RANDOM_FLIP == "horizontal",
# vertical=cfg.INPUT.RANDOM_FLIP == "vertical",
# )
# )
augmentation.extend([
# T.ResizeScale(
# min_scale=min_scale, max_scale=max_scale, target_height=image_size, target_width=image_size
# ),
T.ResizeShortestEdge(
short_edge_length=image_size, max_size=image_size
),
T.FixedSizeCrop(crop_size=(image_size, image_size), seg_pad_value=0),
])
return augmentation
class COCOSemanticNewBaselineDatasetMapper:
"""
A callable which takes a dataset dict in Detectron2 Dataset format,
and map it into a format used by MaskFormer.
This dataset mapper applies the same transformation as DETR for COCO panoptic segmentation.
The callable currently does the following:
1. Read the image from "file_name"
2. Applies geometric transforms to the image and annotation
3. Find and applies suitable cropping to the image and annotation
4. Prepare image and annotation to Tensors
"""
def __init__(self, cfg):
"""
NOTE: this interface is experimental.
Args:
is_train: for training or inference
augmentations: a list of augmentations or deterministic transforms to apply
tfm_gens: data augmentation
image_format: an image format supported by :func:`detection_utils.read_image`.
"""
self.tfm_gens = build_transform_gen(cfg)
self.pixel_mean = torch.Tensor([123.675, 116.28, 103.53]).view(-1, 1, 1)
self.pixel_std = torch.Tensor([58.395, 57.12, 57.375]).view(-1, 1, 1)
@classmethod
def from_config(cls, cfg, is_train=True):
# Build augmentation
tfm_gens = build_transform_gen(cfg, is_train)
ret = {
"is_train": is_train,
"tfm_gens": tfm_gens,
"image_format": cfg.INPUT.FORMAT,
}
return ret
def preprocess(self, dataset_dict, region_mask_type=None, mask_format='polygon',ignore_label=255):
"""
Args:
dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.
Returns:
dict: a format that builtin models in detectron2 accept
"""
ignore_label = ignore_label
dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below
image = utils.read_image(dataset_dict["file_name"], format='RGB')
utils.check_image_size(dataset_dict, image)
# TODO: get padding mask
# by feeding a "segmentation mask" to the same transforms
padding_mask = np.ones(image.shape[:2])
image, transforms = T.apply_transform_gens(self.tfm_gens, image)
# the crop transformation has default padding value 0 for segmentation
padding_mask = transforms.apply_segmentation(padding_mask)
padding_mask = ~ padding_mask.astype(bool)
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.
image = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))
dataset_dict["image"] = (image - self.pixel_mean) / self.pixel_std
dataset_dict["padding_mask"] = torch.as_tensor(np.ascontiguousarray(padding_mask))
dataset_dict['transforms'] = transforms
if "sem_seg_file_name" in dataset_dict:
# PyTorch transformation not implemented for uint16, so converting it to double first
sem_seg_gt = utils.read_image(dataset_dict["sem_seg_file_name"]).astype("double")
else:
sem_seg_gt = None
if sem_seg_gt is None:
raise ValueError(
"Cannot find 'sem_seg_file_name' for semantic segmentation dataset {}.".format(
dataset_dict["file_name"]
)
)
sem_seg_gt += 1
sem_seg_gt = transforms.apply_segmentation(sem_seg_gt)
if sem_seg_gt is not None:
sem_seg_gt = torch.as_tensor(sem_seg_gt.astype("long"))
sem_seg_gt[sem_seg_gt==0] = ignore_label + 1
sem_seg_gt -= 1
if sem_seg_gt is not None:
dataset_dict["sem_seg"] = sem_seg_gt.long()
if sem_seg_gt is not None:
sem_seg_gt = sem_seg_gt.numpy()
instances = Instances(image_shape)
classes = np.unique(sem_seg_gt)
# remove ignored region
classes = classes[classes != ignore_label]
# check class matching:
if 'segments_info' in dataset_dict:
segments_info = dataset_dict["segments_info"]
for segment_info in segments_info:
class_id = segment_info["category_id"]
if not segment_info["iscrowd"]:
if class_id not in classes:
print('Wrong samples. Can not match panoptic gt and semantic gt')
instances.gt_classes = torch.tensor(classes, dtype=torch.int64)
masks = []
for class_id in classes:
masks.append(sem_seg_gt == class_id)
if len(masks) == 0:
# Some image does not have annotation (all ignored)
instances.gt_masks = torch.zeros((0, sem_seg_gt.shape[-2], sem_seg_gt.shape[-1]))
else:
masks = BitMasks(
torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])
)
instances.gt_masks = masks.tensor
dataset_dict["instances"] = instances
return dataset_dict
def build_transform_gen_for_eval(cfg):
image_size = cfg.INPUT.IMAGE_SIZE
min_scale = cfg.INPUT.MIN_SCALE
max_scale = cfg.INPUT.MAX_SCALE
augmentation = []
# if cfg.INPUT.RANDOM_FLIP != "none":
# augmentation.append(
# T.RandomFlip(
# horizontal=cfg.INPUT.RANDOM_FLIP == "horizontal",
# vertical=cfg.INPUT.RANDOM_FLIP == "vertical",
# )
# )
augmentation.extend([
T.ResizeShortestEdge(
short_edge_length=image_size, max_size=image_size
),
T.FixedSizeCrop(crop_size=(image_size, image_size), seg_pad_value=0),
])
return augmentation
class COCOPanopticNewBaselineDatasetMapperForEval(COCOSemanticNewBaselineDatasetMapper):
def __init__(self, cfg):
super().__init__(cfg)
self.tfm_gens = build_transform_gen_for_eval(cfg)
self.pixel_mean = torch.Tensor([123.675, 116.28, 103.53]).view(-1, 1, 1)
self.pixel_std = torch.Tensor([58.395, 57.12, 57.375]).view(-1, 1, 1)
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