coco_utils.py

import copy

import torch
import torch.utils.data
import torchvision
from pycocotools import mask as coco_mask
from pycocotools.coco import COCO
from tqdm import tqdm


class FilterAndRemapCocoCategories(object):
    def __init__(self, categories, remap=True):
        self.categories = categories
        self.remap = remap

    def __call__(self, image, target):
        anno = target["annotations"]
        anno = [obj for obj in anno if obj["category_id"] in self.categories]
        if not self.remap:
            target["annotations"] = anno
            return image, target
        anno = copy.deepcopy(anno)
        for obj in anno:
            obj["category_id"] = self.categories.index(obj["category_id"])
        target["annotations"] = anno
        return image, target


def convert_to_coco_api(ds):
    coco_ds = COCO()
    ann_id = 0
    dataset = {"images": [], "categories": [], "annotations": []}
    categories = set()
    for img_idx in tqdm(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"].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"]
        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


def get_coco_api_from_dataset(dataset):
    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)