preprocessing/midas/transforms.py

# -*- coding: utf-8 -*-
# Copyright (c) Alibaba, Inc. and its affiliates.
import math

import cv2
import numpy as np


def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AREA):
    """Rezise the sample to ensure the given size. Keeps aspect ratio.

    Args:
        sample (dict): sample
        size (tuple): image size

    Returns:
        tuple: new size
    """
    shape = list(sample['disparity'].shape)

    if shape[0] >= size[0] and shape[1] >= size[1]:
        return sample

    scale = [0, 0]
    scale[0] = size[0] / shape[0]
    scale[1] = size[1] / shape[1]

    scale = max(scale)

    shape[0] = math.ceil(scale * shape[0])
    shape[1] = math.ceil(scale * shape[1])

    # resize
    sample['image'] = cv2.resize(sample['image'],
                                 tuple(shape[::-1]),
                                 interpolation=image_interpolation_method)

    sample['disparity'] = cv2.resize(sample['disparity'],
                                     tuple(shape[::-1]),
                                     interpolation=cv2.INTER_NEAREST)
    sample['mask'] = cv2.resize(
        sample['mask'].astype(np.float32),
        tuple(shape[::-1]),
        interpolation=cv2.INTER_NEAREST,
    )
    sample['mask'] = sample['mask'].astype(bool)

    return tuple(shape)


class Resize(object):
    """Resize sample to given size (width, height).
    """
    def __init__(
        self,
        width,
        height,
        resize_target=True,
        keep_aspect_ratio=False,
        ensure_multiple_of=1,
        resize_method='lower_bound',
        image_interpolation_method=cv2.INTER_AREA,
    ):
        """Init.

        Args:
            width (int): desired output width
            height (int): desired output height
            resize_target (bool, optional):
                True: Resize the full sample (image, mask, target).
                False: Resize image only.
                Defaults to True.
            keep_aspect_ratio (bool, optional):
                True: Keep the aspect ratio of the input sample.
                Output sample might not have the given width and height, and
                resize behaviour depends on the parameter 'resize_method'.
                Defaults to False.
            ensure_multiple_of (int, optional):
                Output width and height is constrained to be multiple of this parameter.
                Defaults to 1.
            resize_method (str, optional):
                "lower_bound": Output will be at least as large as the given size.
                "upper_bound": Output will be at max as large as the given size. "
                "(Output size might be smaller than given size.)"
                "minimal": Scale as least as possible.  (Output size might be smaller than given size.)
                Defaults to "lower_bound".
        """
        self.__width = width
        self.__height = height

        self.__resize_target = resize_target
        self.__keep_aspect_ratio = keep_aspect_ratio
        self.__multiple_of = ensure_multiple_of
        self.__resize_method = resize_method
        self.__image_interpolation_method = image_interpolation_method

    def constrain_to_multiple_of(self, x, min_val=0, max_val=None):
        y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int)

        if max_val is not None and y > max_val:
            y = (np.floor(x / self.__multiple_of) *
                 self.__multiple_of).astype(int)

        if y < min_val:
            y = (np.ceil(x / self.__multiple_of) *
                 self.__multiple_of).astype(int)

        return y

    def get_size(self, width, height):
        # determine new height and width
        scale_height = self.__height / height
        scale_width = self.__width / width

        if self.__keep_aspect_ratio:
            if self.__resize_method == 'lower_bound':
                # scale such that output size is lower bound
                if scale_width > scale_height:
                    # fit width
                    scale_height = scale_width
                else:
                    # fit height
                    scale_width = scale_height
            elif self.__resize_method == 'upper_bound':
                # scale such that output size is upper bound
                if scale_width < scale_height:
                    # fit width
                    scale_height = scale_width
                else:
                    # fit height
                    scale_width = scale_height
            elif self.__resize_method == 'minimal':
                # scale as least as possbile
                if abs(1 - scale_width) < abs(1 - scale_height):
                    # fit width
                    scale_height = scale_width
                else:
                    # fit height
                    scale_width = scale_height
            else:
                raise ValueError(
                    f'resize_method {self.__resize_method} not implemented')

        if self.__resize_method == 'lower_bound':
            new_height = self.constrain_to_multiple_of(scale_height * height,
                                                       min_val=self.__height)
            new_width = self.constrain_to_multiple_of(scale_width * width,
                                                      min_val=self.__width)
        elif self.__resize_method == 'upper_bound':
            new_height = self.constrain_to_multiple_of(scale_height * height,
                                                       max_val=self.__height)
            new_width = self.constrain_to_multiple_of(scale_width * width,
                                                      max_val=self.__width)
        elif self.__resize_method == 'minimal':
            new_height = self.constrain_to_multiple_of(scale_height * height)
            new_width = self.constrain_to_multiple_of(scale_width * width)
        else:
            raise ValueError(
                f'resize_method {self.__resize_method} not implemented')

        return (new_width, new_height)

    def __call__(self, sample):
        width, height = self.get_size(sample['image'].shape[1],
                                      sample['image'].shape[0])

        # resize sample
        sample['image'] = cv2.resize(
            sample['image'],
            (width, height),
            interpolation=self.__image_interpolation_method,
        )

        if self.__resize_target:
            if 'disparity' in sample:
                sample['disparity'] = cv2.resize(
                    sample['disparity'],
                    (width, height),
                    interpolation=cv2.INTER_NEAREST,
                )

            if 'depth' in sample:
                sample['depth'] = cv2.resize(sample['depth'], (width, height),
                                             interpolation=cv2.INTER_NEAREST)

            sample['mask'] = cv2.resize(
                sample['mask'].astype(np.float32),
                (width, height),
                interpolation=cv2.INTER_NEAREST,
            )
            sample['mask'] = sample['mask'].astype(bool)

        return sample


class NormalizeImage(object):
    """Normlize image by given mean and std.
    """
    def __init__(self, mean, std):
        self.__mean = mean
        self.__std = std

    def __call__(self, sample):
        sample['image'] = (sample['image'] - self.__mean) / self.__std

        return sample


class PrepareForNet(object):
    """Prepare sample for usage as network input.
    """
    def __init__(self):
        pass

    def __call__(self, sample):
        image = np.transpose(sample['image'], (2, 0, 1))
        sample['image'] = np.ascontiguousarray(image).astype(np.float32)

        if 'mask' in sample:
            sample['mask'] = sample['mask'].astype(np.float32)
            sample['mask'] = np.ascontiguousarray(sample['mask'])

        if 'disparity' in sample:
            disparity = sample['disparity'].astype(np.float32)
            sample['disparity'] = np.ascontiguousarray(disparity)

        if 'depth' in sample:
            depth = sample['depth'].astype(np.float32)
            sample['depth'] = np.ascontiguousarray(depth)

        return sample