isegm/data/transforms.py

import cv2
import random
import numpy as np

from albumentations.core.serialization import SERIALIZABLE_REGISTRY
from albumentations import ImageOnlyTransform, DualTransform
from albumentations.core.transforms_interface import to_tuple
from albumentations.augmentations import functional as F
from isegm.utils.misc import get_bbox_from_mask, expand_bbox, clamp_bbox, get_labels_with_sizes


class UniformRandomResize(DualTransform):
    def __init__(self, scale_range=(0.9, 1.1), interpolation=cv2.INTER_LINEAR, always_apply=False, p=1):
        super().__init__(always_apply, p)
        self.scale_range = scale_range
        self.interpolation = interpolation

    def get_params_dependent_on_targets(self, params):
        scale = random.uniform(*self.scale_range)
        height = int(round(params['image'].shape[0] * scale))
        width = int(round(params['image'].shape[1] * scale))
        return {'new_height': height, 'new_width': width}

    def apply(self, img, new_height=0, new_width=0, interpolation=cv2.INTER_LINEAR, **params):
        return F.resize(img, height=new_height, width=new_width, interpolation=interpolation)

    def apply_to_keypoint(self, keypoint, new_height=0, new_width=0, **params):
        scale_x = new_width / params["cols"]
        scale_y = new_height / params["rows"]
        return F.keypoint_scale(keypoint, scale_x, scale_y)

    def get_transform_init_args_names(self):
        return "scale_range", "interpolation"

    @property
    def targets_as_params(self):
        return ["image"]


class ZoomIn(DualTransform):
    def __init__(
            self,
            height,
            width,
            bbox_jitter=0.1,
            expansion_ratio=1.4,
            min_crop_size=200,
            min_area=100,
            always_resize=False,
            always_apply=False,
            p=0.5,
    ):
        super(ZoomIn, self).__init__(always_apply, p)
        self.height = height
        self.width = width
        self.bbox_jitter = to_tuple(bbox_jitter)
        self.expansion_ratio = expansion_ratio
        self.min_crop_size = min_crop_size
        self.min_area = min_area
        self.always_resize = always_resize

    def apply(self, img, selected_object, bbox, **params):
        if selected_object is None:
            if self.always_resize:
                img = F.resize(img, height=self.height, width=self.width)
            return img

        rmin, rmax, cmin, cmax = bbox
        img = img[rmin:rmax + 1, cmin:cmax + 1]
        img = F.resize(img, height=self.height, width=self.width)

        return img

    def apply_to_mask(self, mask, selected_object, bbox, **params):
        if selected_object is None:
            if self.always_resize:
                mask = F.resize(mask, height=self.height, width=self.width,
                                interpolation=cv2.INTER_NEAREST)
            return mask

        rmin, rmax, cmin, cmax = bbox
        mask = mask[rmin:rmax + 1, cmin:cmax + 1]
        if isinstance(selected_object, tuple):
            layer_indx, mask_id = selected_object
            obj_mask = mask[:, :, layer_indx] == mask_id
            new_mask = np.zeros_like(mask)
            new_mask[:, :, layer_indx][obj_mask] = mask_id
        else:
            obj_mask = mask == selected_object
            new_mask = mask.copy()
            new_mask[np.logical_not(obj_mask)] = 0

        new_mask = F.resize(new_mask, height=self.height, width=self.width,
                            interpolation=cv2.INTER_NEAREST)
        return new_mask

    def get_params_dependent_on_targets(self, params):
        instances = params['mask']

        is_mask_layer = len(instances.shape) > 2
        candidates = []
        if is_mask_layer:
            for layer_indx in range(instances.shape[2]):
                labels, areas = get_labels_with_sizes(instances[:, :, layer_indx])
                candidates.extend([(layer_indx, obj_id)
                                   for obj_id, area in zip(labels, areas)
                                   if area > self.min_area])
        else:
            labels, areas = get_labels_with_sizes(instances)
            candidates = [obj_id for obj_id, area in zip(labels, areas)
                          if area > self.min_area]

        selected_object = None
        bbox = None
        if candidates:
            selected_object = random.choice(candidates)
            if is_mask_layer:
                layer_indx, mask_id = selected_object
                obj_mask = instances[:, :, layer_indx] == mask_id
            else:
                obj_mask = instances == selected_object

            bbox = get_bbox_from_mask(obj_mask)

            if isinstance(self.expansion_ratio, tuple):
                expansion_ratio = random.uniform(*self.expansion_ratio)
            else:
                expansion_ratio = self.expansion_ratio

            bbox = expand_bbox(bbox, expansion_ratio, self.min_crop_size)
            bbox = self._jitter_bbox(bbox)
            bbox = clamp_bbox(bbox, 0, obj_mask.shape[0] - 1, 0, obj_mask.shape[1] - 1)

        return {
            'selected_object': selected_object,
            'bbox': bbox
        }

    def _jitter_bbox(self, bbox):
        rmin, rmax, cmin, cmax = bbox
        height = rmax - rmin + 1
        width = cmax - cmin + 1
        rmin = int(rmin + random.uniform(*self.bbox_jitter) * height)
        rmax = int(rmax + random.uniform(*self.bbox_jitter) * height)
        cmin = int(cmin + random.uniform(*self.bbox_jitter) * width)
        cmax = int(cmax + random.uniform(*self.bbox_jitter) * width)

        return rmin, rmax, cmin, cmax

    def apply_to_bbox(self, bbox, **params):
        raise NotImplementedError

    def apply_to_keypoint(self, keypoint, **params):
        raise NotImplementedError

    @property
    def targets_as_params(self):
        return ["mask"]

    def get_transform_init_args_names(self):
        return ("height", "width", "bbox_jitter",
                "expansion_ratio", "min_crop_size", "min_area", "always_resize")


def remove_image_only_transforms(sdict):
    if not 'transforms' in sdict:
        return sdict

    keep_transforms = []
    for tdict in sdict['transforms']:
        cls = SERIALIZABLE_REGISTRY[tdict['__class_fullname__']]
        if 'transforms' in tdict:
            keep_transforms.append(remove_image_only_transforms(tdict))
        elif not issubclass(cls, ImageOnlyTransform):
            keep_transforms.append(tdict)
    sdict['transforms'] = keep_transforms

    return sdict