import torch
import torch.utils.data
import torchvision
from pycocotools import mask as coco_mask
from pycocotools.coco import COCO
[docs]
def convert_to_coco_api(ds):
coco_ds = COCO()
# annotation IDs need to start at 1, not 0, see torchvision issue #1530
ann_id = 1
dataset = {"images": [], "categories": [], "annotations": [], "info": ""}
categories = set()
for img_idx in range(len(ds)):
# find better way to get target
# targets = ds.get_annotations(img_idx)
img, targets = ds[img_idx]
image_id = targets["image_id"]
if isinstance(image_id, torch.Tensor):
image_id = image_id.item()
img_dict = {}
img_dict["id"] = image_id
img_dict["height"] = img.shape[-2]
img_dict["width"] = img.shape[-1]
dataset["images"].append(img_dict)
bboxes = targets["boxes"].clone()
bboxes[:, 2:] -= bboxes[:, :2]
bboxes = bboxes.tolist()
labels = targets["labels"].tolist()
areas = targets["area"].tolist()
iscrowd = targets["iscrowd"].tolist()
if "masks" in targets:
masks = targets["masks"]
# make masks Fortran contiguous for coco_mask
masks = masks.permute(0, 2, 1).contiguous().permute(0, 2, 1)
if "keypoints" in targets:
keypoints = targets["keypoints"]
keypoints = keypoints.reshape(keypoints.shape[0], -1).tolist()
num_objs = len(bboxes)
for i in range(num_objs):
ann = {}
ann["image_id"] = image_id
ann["bbox"] = bboxes[i]
ann["category_id"] = labels[i]
categories.add(labels[i])
ann["area"] = areas[i]
ann["iscrowd"] = iscrowd[i]
ann["id"] = ann_id
if "masks" in targets:
ann["segmentation"] = coco_mask.encode(masks[i].numpy())
if "keypoints" in targets:
ann["keypoints"] = keypoints[i]
ann["num_keypoints"] = sum(k != 0 for k in keypoints[i][2::3])
dataset["annotations"].append(ann)
ann_id += 1
dataset["categories"] = [{"id": i} for i in sorted(categories)]
coco_ds.dataset = dataset
coco_ds.createIndex()
return coco_ds
[docs]
def get_coco_api_from_dataset(dataset):
# FIXME: This is... awful?
for _ in range(10):
if isinstance(dataset, torchvision.datasets.CocoDetection):
break
if isinstance(dataset, torch.utils.data.Subset):
dataset = dataset.dataset
if isinstance(dataset, torchvision.datasets.CocoDetection):
return dataset.coco
return convert_to_coco_api(dataset)