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# coding=utf-8
# Copyright 2024 The Google Research Authors.
#
# 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
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""This file contains the unit tests for the utils.py file."""
import numpy as np
from PIL import Image
import torch
# pylint: disable=g-bad-import-order
from modeling.model import utils
def test_scoremap2bbox():
"""Test the scoremap2bbox function."""
scoremap = np.zeros((10, 10))
scoremap[1:5, 1:5] = 1
scoremap[5:9, 5:9] = 2
scoremap[5:9, 1:5] = 3
scoremap[1:5, 5:9] = 4
bbox, len_bboxes = utils.scoremap2bbox(scoremap, 0.5)
assert len_bboxes == 1
assert bbox[0, 0] == 1
assert bbox[0, 1] == 1
assert bbox[0, 2] == 9
assert bbox[0, 3] == 9
def test_mask2chw():
"""Test the mask2chw function."""
mask = np.zeros((10, 10))
mask[1:5, 1:5] = 1
mask[5:9, 5:9] = 2
mask[5:9, 1:5] = 3
mask[1:5, 5:9] = 4
mask = torch.tensor(mask)
mask_center, mask_height, mask_width = utils.mask2chw(mask)
assert len(mask_center) == 2
assert mask_center[0] == 2
assert mask_center[1] == 2
assert mask_height == 4
assert mask_width == 4
def test_unpad():
"""Test the unpad function."""
image = np.zeros((10, 10, 1))
image[1:5, 1:5] = 1
image[5:9, 5:9] = 2
image[5:9, 1:5] = 3
image[1:5, 5:9] = 4
unpad_image = utils.unpad(image, pad=(1, 1, 8, 8))
assert len(unpad_image[0]) == 8, 'The width of the image is not 8.'
assert len(unpad_image[1]) == 8, 'The height of the image is not 8.'
unpad_image = utils.unpad(image, None)
assert (unpad_image == image).sum() == 100
def test_apply_visual_prompts():
"""Test the apply_visual_prompts function."""
image = np.ones((5, 5))
mask = np.array([
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 1.0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
])
target = np.array([
[1, 1, 255, 1, 1],
[1, 255, 1, 255, 1],
[255, 1, 1, 1, 255],
[1, 255, 1, 255, 1],
[1, 1, 255, 1, 1],
])
mask[1:5, 1:5] = 1
prompted_image = utils.apply_visual_prompts(
image, mask, visual_prompt_type='circle', thickness=1
)
prompted_array = np.array(prompted_image)
assert (prompted_array == target).sum() == 25
def test_reshape_transform():
"""Test the reshape_transform function."""
image = torch.zeros((101, 10, 32))
image = utils.reshape_transform(image, height=10, width=10)
b, c, h, w = image.shape
assert b == 10
assert c == 32
assert h == 10
assert w == 10
def test_img_ms_and_flip():
"""Test the img_ms_and_flip function."""
image = np.zeros((120, 150))
image[1:5, 1:5] = 1
image[5:9, 5:9] = 2
image[5:9, 1:5] = 3
image[1:5, 5:9] = 4
image = Image.fromarray(image)
image = utils.img_ms_and_flip(image, 120, 150, scales=[1.2], patch_size=16)
image = image[0]
h, w = image.shape[-2:]
assert h == int(np.ceil(1.2 * 120 / 16) * 16)
assert w == int(np.ceil(1.2 * 150 / 16) * 16)
if __name__ == '__main__':
test_scoremap2bbox()
test_mask2chw()
test_unpad()
test_apply_visual_prompts()
test_reshape_transform()
test_img_ms_and_flip()
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