evaluator/ava_evaluation/np_mask_ops.py

# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
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# distributed under the License is distributed on an "AS IS" BASIS,
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# ==============================================================================

"""Operations for [N, height, width] numpy arrays representing masks.

Example mask operations that are supported:
  * Areas: compute mask areas
  * IOU: pairwise intersection-over-union scores
"""
from __future__ import (
    absolute_import,
    division,
    print_function,
    unicode_literals,
)
import numpy as np

EPSILON = 1e-7


def area(masks):
    """Computes area of masks.

  Args:
    masks: Numpy array with shape [N, height, width] holding N masks. Masks
      values are of type np.uint8 and values are in {0,1}.

  Returns:
    a numpy array with shape [N*1] representing mask areas.

  Raises:
    ValueError: If masks.dtype is not np.uint8
  """
    if masks.dtype != np.uint8:
        raise ValueError("Masks type should be np.uint8")
    return np.sum(masks, axis=(1, 2), dtype=np.float32)


def intersection(masks1, masks2):
    """Compute pairwise intersection areas between masks.

  Args:
    masks1: a numpy array with shape [N, height, width] holding N masks. Masks
      values are of type np.uint8 and values are in {0,1}.
    masks2: a numpy array with shape [M, height, width] holding M masks. Masks
      values are of type np.uint8 and values are in {0,1}.

  Returns:
    a numpy array with shape [N*M] representing pairwise intersection area.

  Raises:
    ValueError: If masks1 and masks2 are not of type np.uint8.
  """
    if masks1.dtype != np.uint8 or masks2.dtype != np.uint8:
        raise ValueError("masks1 and masks2 should be of type np.uint8")
    n = masks1.shape[0]
    m = masks2.shape[0]
    answer = np.zeros([n, m], dtype=np.float32)
    for i in np.arange(n):
        for j in np.arange(m):
            answer[i, j] = np.sum(
                np.minimum(masks1[i], masks2[j]), dtype=np.float32
            )
    return answer


def iou(masks1, masks2):
    """Computes pairwise intersection-over-union between mask collections.

  Args:
    masks1: a numpy array with shape [N, height, width] holding N masks. Masks
      values are of type np.uint8 and values are in {0,1}.
    masks2: a numpy array with shape [M, height, width] holding N masks. Masks
      values are of type np.uint8 and values are in {0,1}.

  Returns:
    a numpy array with shape [N, M] representing pairwise iou scores.

  Raises:
    ValueError: If masks1 and masks2 are not of type np.uint8.
  """
    if masks1.dtype != np.uint8 or masks2.dtype != np.uint8:
        raise ValueError("masks1 and masks2 should be of type np.uint8")
    intersect = intersection(masks1, masks2)
    area1 = area(masks1)
    area2 = area(masks2)
    union = (
        np.expand_dims(area1, axis=1)
        + np.expand_dims(area2, axis=0)
        - intersect
    )
    return intersect / np.maximum(union, EPSILON)


def ioa(masks1, masks2):
    """Computes pairwise intersection-over-area between box collections.

  Intersection-over-area (ioa) between two masks, mask1 and mask2 is defined as
  their intersection area over mask2's area. Note that ioa is not symmetric,
  that is, IOA(mask1, mask2) != IOA(mask2, mask1).

  Args:
    masks1: a numpy array with shape [N, height, width] holding N masks. Masks
      values are of type np.uint8 and values are in {0,1}.
    masks2: a numpy array with shape [M, height, width] holding N masks. Masks
      values are of type np.uint8 and values are in {0,1}.

  Returns:
    a numpy array with shape [N, M] representing pairwise ioa scores.

  Raises:
    ValueError: If masks1 and masks2 are not of type np.uint8.
  """
    if masks1.dtype != np.uint8 or masks2.dtype != np.uint8:
        raise ValueError("masks1 and masks2 should be of type np.uint8")
    intersect = intersection(masks1, masks2)
    areas = np.expand_dims(area(masks2), axis=0)
    return intersect / (areas + EPSILON)