isegm/data/datasets/sbd.py

import pickle as pkl
from pathlib import Path

import cv2
import numpy as np
from scipy.io import loadmat

from isegm.data.base import ISDataset
from isegm.data.sample import DSample
from isegm.utils.misc import get_bbox_from_mask, get_labels_with_sizes


class SBDDataset(ISDataset):
    def __init__(self, dataset_path, split="train", buggy_mask_thresh=0.08, **kwargs):
        super(SBDDataset, self).__init__(**kwargs)
        assert split in {"train", "val"}

        self.dataset_path = Path(dataset_path)
        self.dataset_split = split
        self._images_path = self.dataset_path / "img"
        self._insts_path = self.dataset_path / "inst"
        self._buggy_objects = dict()
        self._buggy_mask_thresh = buggy_mask_thresh

        with open(self.dataset_path / f"{split}.txt", "r") as f:
            self.dataset_samples = [x.strip() for x in f.readlines()]

    def get_sample(self, index):
        image_name = self.dataset_samples[index]
        image_path = str(self._images_path / f"{image_name}.jpg")
        inst_info_path = str(self._insts_path / f"{image_name}.mat")

        image = cv2.imread(image_path)
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        instances_mask = loadmat(str(inst_info_path))["GTinst"][0][0][0].astype(
            np.int32
        )
        instances_mask = self.remove_buggy_masks(index, instances_mask)
        instances_ids, _ = get_labels_with_sizes(instances_mask)

        return DSample(
            image, instances_mask, objects_ids=instances_ids, sample_id=index
        )

    def remove_buggy_masks(self, index, instances_mask):
        if self._buggy_mask_thresh > 0.0:
            buggy_image_objects = self._buggy_objects.get(index, None)
            if buggy_image_objects is None:
                buggy_image_objects = []
                instances_ids, _ = get_labels_with_sizes(instances_mask)
                for obj_id in instances_ids:
                    obj_mask = instances_mask == obj_id
                    mask_area = obj_mask.sum()
                    bbox = get_bbox_from_mask(obj_mask)
                    bbox_area = (bbox[1] - bbox[0] + 1) * (bbox[3] - bbox[2] + 1)
                    obj_area_ratio = mask_area / bbox_area
                    if obj_area_ratio < self._buggy_mask_thresh:
                        buggy_image_objects.append(obj_id)

                self._buggy_objects[index] = buggy_image_objects
            for obj_id in buggy_image_objects:
                instances_mask[instances_mask == obj_id] = 0

        return instances_mask


class SBDEvaluationDataset(ISDataset):
    def __init__(self, dataset_path, split="val", **kwargs):
        super(SBDEvaluationDataset, self).__init__(**kwargs)
        assert split in {"train", "val"}

        self.dataset_path = Path(dataset_path)
        self.dataset_split = split
        self._images_path = self.dataset_path / "img"
        self._insts_path = self.dataset_path / "inst"

        with open(self.dataset_path / f"{split}.txt", "r") as f:
            self.dataset_samples = [x.strip() for x in f.readlines()]

        self.dataset_samples = self.get_sbd_images_and_ids_list()

    def get_sample(self, index) -> DSample:
        image_name, instance_id = self.dataset_samples[index]
        image_path = str(self._images_path / f"{image_name}.jpg")
        inst_info_path = str(self._insts_path / f"{image_name}.mat")

        image = cv2.imread(image_path)
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        instances_mask = loadmat(str(inst_info_path))["GTinst"][0][0][0].astype(
            np.int32
        )
        instances_mask[instances_mask != instance_id] = 0
        instances_mask[instances_mask > 0] = 1

        return DSample(image, instances_mask, objects_ids=[1], sample_id=index)

    def get_sbd_images_and_ids_list(self):
        pkl_path = self.dataset_path / f"{self.dataset_split}_images_and_ids_list.pkl"

        if pkl_path.exists():
            with open(str(pkl_path), "rb") as fp:
                images_and_ids_list = pkl.load(fp)
        else:
            images_and_ids_list = []

            for sample in self.dataset_samples:
                inst_info_path = str(self._insts_path / f"{sample}.mat")
                instances_mask = loadmat(str(inst_info_path))["GTinst"][0][0][0].astype(
                    np.int32
                )
                instances_ids, _ = get_labels_with_sizes(instances_mask)

                for instances_id in instances_ids:
                    images_and_ids_list.append((sample, instances_id))

            with open(str(pkl_path), "wb") as fp:
                pkl.dump(images_and_ids_list, fp)

        return images_and_ids_list