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# Copyright (c) Facebook, Inc. and its affiliates.
# Modified by Bowen Cheng from https://github.com/facebookresearch/detr/blob/master/d2/detr/dataset_mapper.py
import copy
# import logging
import cv2
import numpy as np
import torch
from PIL import Image
from torchvision import transforms
from detectron2.structures import BitMasks, Boxes, Instances
from detectron2.data import MetadataCatalog, Metadata
from utils import prompt_engineering
from modeling.utils import configurable, PASCAL_CLASSES
from ..visual_sampler import build_shape_sampler
__all__ = ["PascalVOCSegDatasetMapperIX"]
# This is specifically designed for the COCO dataset.
class PascalVOCSegDatasetMapperIX:
"""
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
"""
@configurable
def __init__(
self,
is_train=True,
dataset_name='',
min_size_test=None,
max_size_test=None,
shape_sampler=None,
grounding=False,
):
"""
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.is_train = is_train
self.dataset_name = dataset_name
self.min_size_test = min_size_test
self.max_size_test = max_size_test
t = []
t.append(transforms.Resize(self.min_size_test, interpolation=Image.BICUBIC, max_size=max_size_test))
self.transform = transforms.Compose(t)
self.shape_sampler = shape_sampler
self.ignore_id = 220
if grounding:
def _setattr(self, name, value):
object.__setattr__(self, name, value)
Metadata.__setattr__ = _setattr
MetadataCatalog.get(dataset_name).evaluator_type = "interactive_grounding"
@classmethod
def from_config(cls, cfg, is_train=True, dataset_name=''):
shape_sampler = build_shape_sampler(cfg, is_train=is_train, mode=dataset_name.split('_')[-1])
ret = {
"is_train": is_train,
"dataset_name": dataset_name,
"min_size_test": cfg['INPUT']['MIN_SIZE_TEST'],
"max_size_test": cfg['INPUT']['MAX_SIZE_TEST'],
"shape_sampler": shape_sampler,
"grounding": cfg['STROKE_SAMPLER']['EVAL']['GROUNDING'],
}
return ret
def get_pascal_labels(self,):
"""Load the mapping that associates pascal classes with label colors
Returns:
np.ndarray with dimensions (21, 3)
"""
return np.asarray(
[
[0, 0, 0],
[128, 0, 0],
[0, 128, 0],
[128, 128, 0],
[0, 0, 128],
[128, 0, 128],
[0, 128, 128],
[128, 128, 128],
[64, 0, 0],
[192, 0, 0],
[64, 128, 0],
[192, 128, 0],
[64, 0, 128],
[192, 0, 128],
[64, 128, 128],
[192, 128, 128],
[0, 64, 0],
[128, 64, 0],
[0, 192, 0],
[128, 192, 0],
[0, 64, 128],
]
)
def encode_segmap(self, mask):
"""Encode segmentation label images as pascal classes
Args:
mask (np.ndarray): raw segmentation label image of dimension
(M, N, 3), in which the Pascal classes are encoded as colours.
Returns:
(np.ndarray): class map with dimensions (M,N), where the value at
a given location is the integer denoting the class index.
"""
mask = mask.astype(int)
label_mask = np.zeros((mask.shape[0], mask.shape[1]), dtype=np.int16)
for ii, label in enumerate(self.get_pascal_labels()):
label_mask[np.where(np.all(mask == label, axis=-1))[:2]] = ii
label_mask = label_mask.astype(int)
return label_mask
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
file_name = dataset_dict['file_name']
image = Image.open(file_name).convert('RGB')
dataset_dict['width'] = image.size[0]
dataset_dict['height'] = image.size[1]
if self.is_train == False:
image = self.transform(image)
image = torch.from_numpy(np.asarray(image).copy())
image = image.permute(2,0,1)
inst_name = dataset_dict['inst_name']
instances_mask = cv2.imread(inst_name)
instances_mask = cv2.cvtColor(instances_mask, cv2.COLOR_BGR2GRAY).astype(np.int32)
objects_ids = dataset_dict['objects_ids']
instances_mask_byid = [(instances_mask==idx).astype(np.int16) for idx in objects_ids]
semseg_name = dataset_dict['semseg_name']
semseg = self.encode_segmap(cv2.imread(semseg_name)[:,:,::-1])
class_names = [PASCAL_CLASSES[np.unique(semseg[instances_mask_byid[i].astype(np.bool)])[0].astype(np.int32)-1] for i in range(len(instances_mask_byid))]
_,h,w = image.shape
masks = BitMasks(torch.stack([torch.from_numpy(
cv2.resize(m.astype(np.float), (w,h), interpolation=cv2.INTER_CUBIC).astype(np.bool)
) for m in instances_mask_byid]))
instances = Instances(image.shape[-2:])
instances.gt_masks = masks
instances.gt_boxes = masks.get_bounding_boxes()
spatial_query_utils = self.shape_sampler(instances) # [n,c,h,w]
for i in range(len(instances_mask_byid)):
instances_mask_byid[i][instances_mask == self.ignore_id] = -1
gt_masks_orisize = torch.stack([torch.from_numpy(m) for m in instances_mask_byid])
dataset_dict['spatial_query'] = spatial_query_utils
dataset_dict['instances'] = instances # gt_masks, gt_boxes
dataset_dict['image'] = image # (3,h,w)
dataset_dict['gt_masks_orisize'] = gt_masks_orisize # (nm,h,w)
dataset_dict['classes'] = [prompt_engineering(x, topk=1, suffix='.') for x in class_names]
return dataset_dict |