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119 lines
4.1 KiB
119 lines
4.1 KiB
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Operations for [N, height, width] numpy arrays representing masks.
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Example mask operations that are supported:
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* Areas: compute mask areas
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* IOU: pairwise intersection-over-union scores
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"""
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import numpy as np
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EPSILON = 1e-7
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def area(masks):
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"""Computes area of masks.
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Args:
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masks: Numpy array with shape [N, height, width] holding N masks. Masks
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values are of type np.uint8 and values are in {0,1}.
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Returns:
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a numpy array with shape [N*1] representing mask areas.
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Raises:
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ValueError: If masks.dtype is not np.uint8
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"""
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if masks.dtype != np.uint8:
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raise ValueError('Masks type should be np.uint8')
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return np.sum(masks, axis=(1, 2), dtype=np.float32)
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def intersection(masks1, masks2):
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"""Compute pairwise intersection areas between masks.
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Args:
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masks1: a numpy array with shape [N, height, width] holding N masks. Masks
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values are of type np.uint8 and values are in {0,1}.
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masks2: a numpy array with shape [M, height, width] holding M masks. Masks
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values are of type np.uint8 and values are in {0,1}.
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Returns:
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a numpy array with shape [N*M] representing pairwise intersection area.
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Raises:
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ValueError: If masks1 and masks2 are not of type np.uint8.
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"""
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if masks1.dtype != np.uint8 or masks2.dtype != np.uint8:
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raise ValueError('masks1 and masks2 should be of type np.uint8')
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n = masks1.shape[0]
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m = masks2.shape[0]
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answer = np.zeros([n, m], dtype=np.float32)
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for i in np.arange(n):
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for j in np.arange(m):
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answer[i, j] = np.sum(np.minimum(masks1[i], masks2[j]), dtype=np.float32)
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return answer
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def iou(masks1, masks2):
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"""Computes pairwise intersection-over-union between mask collections.
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Args:
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masks1: a numpy array with shape [N, height, width] holding N masks. Masks
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values are of type np.uint8 and values are in {0,1}.
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masks2: a numpy array with shape [M, height, width] holding N masks. Masks
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values are of type np.uint8 and values are in {0,1}.
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Returns:
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a numpy array with shape [N, M] representing pairwise iou scores.
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Raises:
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ValueError: If masks1 and masks2 are not of type np.uint8.
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"""
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if masks1.dtype != np.uint8 or masks2.dtype != np.uint8:
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raise ValueError('masks1 and masks2 should be of type np.uint8')
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intersect = intersection(masks1, masks2)
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area1 = area(masks1)
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area2 = area(masks2)
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union = np.expand_dims(area1, axis=1) + np.expand_dims(
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area2, axis=0) - intersect
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return intersect / np.maximum(union, EPSILON)
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def ioa(masks1, masks2):
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"""Computes pairwise intersection-over-area between box collections.
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Intersection-over-area (ioa) between two masks, mask1 and mask2 is defined as
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their intersection area over mask2's area. Note that ioa is not symmetric,
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that is, IOA(mask1, mask2) != IOA(mask2, mask1).
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Args:
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masks1: a numpy array with shape [N, height, width] holding N masks. Masks
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values are of type np.uint8 and values are in {0,1}.
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masks2: a numpy array with shape [M, height, width] holding N masks. Masks
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values are of type np.uint8 and values are in {0,1}.
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Returns:
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a numpy array with shape [N, M] representing pairwise ioa scores.
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Raises:
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ValueError: If masks1 and masks2 are not of type np.uint8.
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"""
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if masks1.dtype != np.uint8 or masks2.dtype != np.uint8:
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raise ValueError('masks1 and masks2 should be of type np.uint8')
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intersect = intersection(masks1, masks2)
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areas = np.expand_dims(area(masks2), axis=0)
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return intersect / (areas + EPSILON)
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