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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import os
import os.path
import math
from PIL import Image, ImageDraw
import random
import numpy as np
import torch
import torchvision
import torch.utils.data as data
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.structures.segmentation_mask import SegmentationMask
from maskrcnn_benchmark.structures.keypoint import PersonKeypoints
from maskrcnn_benchmark.config import cfg
import pdb
def _count_visible_keypoints(anno):
return sum(sum(1 for v in ann["keypoints"][2::3] if v > 0) for ann in anno)
def _has_only_empty_bbox(anno):
return all(any(o <= 1 for o in obj["bbox"][2:]) for obj in anno)
def has_valid_annotation(anno):
# if it's empty, there is no annotation
if len(anno) == 0:
return False
# if all boxes have close to zero area, there is no annotation
if _has_only_empty_bbox(anno):
return False
# keypoints task have a slight different critera for considering
# if an annotation is valid
if "keypoints" not in anno[0]:
return True
# for keypoint detection tasks, only consider valid images those
# containing at least min_keypoints_per_image
if _count_visible_keypoints(anno) >= cfg.DATALOADER.MIN_KPS_PER_IMS:
return True
return False
def pil_loader(path, retry=5):
# open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835)
ri = 0
while ri < retry:
try:
with open(path, "rb") as f:
img = Image.open(f)
return img.convert("RGB")
except:
ri += 1
def rgb2id(color):
if isinstance(color, np.ndarray) and len(color.shape) == 3:
if color.dtype == np.uint8:
color = color.astype(np.int32)
return color[:, :, 0] + 256 * color[:, :, 1] + 256 * 256 * color[:, :, 2]
return int(color[0] + 256 * color[1] + 256 * 256 * color[2])
class CocoDetection(data.Dataset):
"""`MS Coco Detection <http://mscoco.org/dataset/#detections-challenge2016>`_ Dataset.
Args:
root (string): Root directory where images are downloaded to.
annFile (string): Path to json annotation file.
transform (callable, optional): A function/transform that takes in an PIL image
and returns a transformed version. E.g, ``transforms.ToTensor``
target_transform (callable, optional): A function/transform that takes in the
target and transforms it.
"""
def __init__(self, root, annFile, transform=None, target_transform=None):
from pycocotools.coco import COCO
self.root = root
self.coco = COCO(annFile)
self.ids = list(self.coco.imgs.keys())
self.transform = transform
self.target_transform = target_transform
def __getitem__(self, index, return_meta=False):
"""
Args:
index (int): Index
Returns:
tuple: Tuple (image, target). target is the object returned by ``coco.loadAnns``.
"""
coco = self.coco
img_id = self.ids[index]
if isinstance(img_id, str):
img_id = [img_id]
ann_ids = coco.getAnnIds(imgIds=img_id)
target = coco.loadAnns(ann_ids)
meta = coco.loadImgs(img_id)[0]
path = meta["file_name"]
img = pil_loader(os.path.join(self.root, path))
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
target = self.target_transform(target)
if return_meta:
return img, target, meta
else:
return img, target
def __len__(self):
return len(self.ids)
def __repr__(self):
fmt_str = "Dataset " + self.__class__.__name__ + "\n"
fmt_str += " Number of datapoints: {}\n".format(self.__len__())
fmt_str += " Root Location: {}\n".format(self.root)
tmp = " Transforms (if any): "
fmt_str += "{0}{1}\n".format(tmp, self.transform.__repr__().replace("\n", "\n" + " " * len(tmp)))
tmp = " Target Transforms (if any): "
fmt_str += "{0}{1}".format(tmp, self.target_transform.__repr__().replace("\n", "\n" + " " * len(tmp)))
return fmt_str
class COCODataset(CocoDetection):
def __init__(
self,
ann_file,
root,
remove_images_without_annotations,
transforms=None,
ignore_crowd=True,
max_box=-1,
few_shot=0,
one_hot=False,
override_category=None,
**kwargs
):
super(COCODataset, self).__init__(root, ann_file)
# sort indices for reproducible results
self.ids = sorted(self.ids)
# filter images without detection annotations
if remove_images_without_annotations:
ids = []
for img_id in self.ids:
if isinstance(img_id, str):
ann_ids = self.coco.getAnnIds(imgIds=[img_id], iscrowd=None)
else:
ann_ids = self.coco.getAnnIds(imgIds=img_id, iscrowd=None)
anno = self.coco.loadAnns(ann_ids)
if has_valid_annotation(anno):
ids.append(img_id)
self.ids = ids
if few_shot:
ids = []
cats_freq = [few_shot] * len(self.coco.cats.keys())
if "shuffle_seed" in kwargs and kwargs["shuffle_seed"] != 0:
import random
random.Random(kwargs["shuffle_seed"]).shuffle(self.ids)
print("Shuffle the dataset with random seed: ", kwargs["shuffle_seed"])
for img_id in self.ids:
if isinstance(img_id, str):
ann_ids = self.coco.getAnnIds(imgIds=[img_id], iscrowd=None)
else:
ann_ids = self.coco.getAnnIds(imgIds=img_id, iscrowd=None)
anno = self.coco.loadAnns(ann_ids)
cat = set([ann["category_id"] for ann in anno]) # set/tuple corresponde to instance/image level
is_needed = sum([cats_freq[c - 1] > 0 for c in cat])
if is_needed:
ids.append(img_id)
for c in cat:
cats_freq[c - 1] -= 1
# print(cat, cats_freq)
self.ids = ids
if override_category is not None:
self.coco.dataset["categories"] = override_category
print("Override category: ", override_category)
self.json_category_id_to_contiguous_id = {v: i + 1 for i, v in enumerate(self.coco.getCatIds())}
self.contiguous_category_id_to_json_id = {v: k for k, v in self.json_category_id_to_contiguous_id.items()}
self.id_to_img_map = {k: v for k, v in enumerate(self.ids)}
self.transforms = transforms
self.ignore_crowd = ignore_crowd
self.max_box = max_box
self.one_hot = one_hot
def categories(self, no_background=True):
categories = self.coco.dataset["categories"]
label_list = {}
for index, i in enumerate(categories):
if not no_background or (i["name"] != "__background__" and i["id"] != 0):
label_list[self.json_category_id_to_contiguous_id[i["id"]]] = i["name"]
return label_list
def __getitem__(self, idx):
img, anno = super(COCODataset, self).__getitem__(idx)
# filter crowd annotations
if self.ignore_crowd:
anno = [obj for obj in anno if obj["iscrowd"] == 0]
boxes = [obj["bbox"] for obj in anno]
boxes = torch.as_tensor(boxes).reshape(-1, 4) # guard against no boxes
if self.max_box > 0 and len(boxes) > self.max_box:
rand_idx = torch.randperm(self.max_box)
boxes = boxes[rand_idx, :]
else:
rand_idx = None
target = BoxList(boxes, img.size, mode="xywh").convert("xyxy")
classes = [obj["category_id"] for obj in anno]
classes = [self.json_category_id_to_contiguous_id[c] for c in classes]
classes = torch.tensor(classes)
if rand_idx is not None:
classes = classes[rand_idx]
if cfg.DATASETS.CLASS_AGNOSTIC:
classes = torch.ones_like(classes)
target.add_field("labels", classes)
if anno and "segmentation" in anno[0]:
masks = [obj["segmentation"] for obj in anno]
masks = SegmentationMask(masks, img.size, mode="poly")
target.add_field("masks", masks)
if anno and "cbox" in anno[0]:
cboxes = [obj["cbox"] for obj in anno]
cboxes = torch.as_tensor(cboxes).reshape(-1, 4) # guard against no boxes
cboxes = BoxList(cboxes, img.size, mode="xywh").convert("xyxy")
target.add_field("cbox", cboxes)
if anno and "keypoints" in anno[0]:
keypoints = []
gt_keypoint = self.coco.cats[1]["keypoints"] # <TODO> a better way to get keypoint description
use_keypoint = cfg.MODEL.ROI_KEYPOINT_HEAD.KEYPOINT_NAME
for obj in anno:
if len(use_keypoint) > 0:
kps = []
for name in use_keypoint:
kp_idx = slice(3 * gt_keypoint.index(name), 3 * gt_keypoint.index(name) + 3)
kps += obj["keypoints"][kp_idx]
keypoints.append(kps)
else:
keypoints.append(obj["keypoints"])
keypoints = PersonKeypoints(keypoints, img.size)
target.add_field("keypoints", keypoints)
target = target.clip_to_image(remove_empty=True)
if self.transforms is not None:
img, target = self.transforms(img, target)
if cfg.DATASETS.SAMPLE_RATIO != 0.0:
ratio = cfg.DATASETS.SAMPLE_RATIO
num_sample_target = math.ceil(len(target) * ratio) if ratio > 0 else math.ceil(-ratio)
sample_idx = torch.randperm(len(target))[:num_sample_target]
target = target[sample_idx]
return img, target, idx
def get_img_info(self, index):
img_id = self.id_to_img_map[index]
img_data = self.coco.imgs[img_id]
return img_data
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