LegoDataset.py

import os
import torch
import numpy as np
from PIL import Image
from pycocotools.coco import COCO
import torchvision.transforms as T


class LegoDataset(torch.utils.data.Dataset):
    def __init__(self, root, annFile, transforms=None):
        self.root = root
        self.coco = COCO(annFile)
        self.ids = list(self.coco.imgs.keys())
        self.transforms = transforms or T.Compose([T.ToTensor()])

    def __getitem__(self, index):
        img_id = self.ids[index]
        img_info = self.coco.loadImgs(img_id)[0]
        path = img_info["file_name"]
        img = Image.open(os.path.join(self.root, path)).convert("RGB")

        ann_ids = self.coco.getAnnIds(imgIds=img_id)
        annotations = self.coco.loadAnns(ann_ids)

        boxes = []
        labels = []
        masks = []  # Dummy masks

        for ann in annotations:
            xmin, ymin, width, height = ann["bbox"]
            boxes.append([xmin, ymin, xmin + width, ymin + height])
            labels.append(1)  # 'lego' is the only class, labeled as 1

            # Dummy mask for Mask R-CNN, filled with zeros
            dummy_mask = np.zeros(
                (img_info["height"], img_info["width"]), dtype=np.uint8
            )
            masks.append(dummy_mask)

        boxes = torch.as_tensor(boxes, dtype=torch.float32)
        labels = torch.as_tensor(labels, dtype=torch.int64)
        masks = torch.as_tensor(np.array(masks), dtype=torch.uint8)

        target = {
            "boxes": boxes,
            "labels": labels,
            "masks": masks,
            "image_id": torch.tensor([img_id]),
        }

        if self.transforms:
            img = self.transforms(img)

        return img, target

    def __len__(self):
        return len(self.ids)