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450 lines
14 KiB
450 lines
14 KiB
# Copyright (c) 2020 PaddlePaddle 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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import unittest
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import os
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import tempfile
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import cv2
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import shutil
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import numpy as np
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from PIL import Image
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import paddle
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from paddle.vision import get_image_backend, set_image_backend, image_load
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from paddle.vision.datasets import DatasetFolder
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from paddle.vision.transforms import transforms
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import paddle.vision.transforms.functional as F
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class TestTransformsCV2(unittest.TestCase):
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def setUp(self):
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self.backend = self.get_backend()
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set_image_backend(self.backend)
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self.data_dir = tempfile.mkdtemp()
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for i in range(2):
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sub_dir = os.path.join(self.data_dir, 'class_' + str(i))
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if not os.path.exists(sub_dir):
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os.makedirs(sub_dir)
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for j in range(2):
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if j == 0:
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fake_img = (np.random.random(
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(280, 350, 3)) * 255).astype('uint8')
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else:
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fake_img = (np.random.random(
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(400, 300, 3)) * 255).astype('uint8')
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cv2.imwrite(os.path.join(sub_dir, str(j) + '.jpg'), fake_img)
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def get_backend(self):
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return 'cv2'
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def create_image(self, shape):
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if self.backend == 'cv2':
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return (np.random.rand(*shape) * 255).astype('uint8')
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elif self.backend == 'pil':
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return Image.fromarray((np.random.rand(*shape) * 255).astype(
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'uint8'))
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def get_shape(self, img):
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if self.backend == 'pil':
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return np.array(img).shape
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return img.shape
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def tearDown(self):
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shutil.rmtree(self.data_dir)
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def do_transform(self, trans):
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dataset_folder = DatasetFolder(self.data_dir, transform=trans)
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for _ in dataset_folder:
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pass
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def test_trans_all(self):
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normalize = transforms.Normalize(
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mean=[123.675, 116.28, 103.53],
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std=[58.395, 57.120, 57.375], )
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trans = transforms.Compose([
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transforms.RandomResizedCrop(224),
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transforms.ColorJitter(
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brightness=0.4, contrast=0.4, saturation=0.4, hue=0.4),
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transforms.RandomHorizontalFlip(),
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transforms.Transpose(),
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normalize,
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])
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self.do_transform(trans)
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def test_normalize(self):
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normalize = transforms.Normalize(mean=0.5, std=0.5)
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trans = transforms.Compose([transforms.Transpose(), normalize])
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self.do_transform(trans)
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def test_trans_resize(self):
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trans = transforms.Compose([
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transforms.Resize(300),
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transforms.RandomResizedCrop((280, 280)),
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transforms.Resize(280),
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transforms.Resize((256, 200)),
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transforms.Resize((180, 160)),
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transforms.CenterCrop(128),
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transforms.CenterCrop((128, 128)),
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])
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self.do_transform(trans)
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def test_flip(self):
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trans = transforms.Compose([
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transforms.RandomHorizontalFlip(1.0),
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transforms.RandomHorizontalFlip(0.0),
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transforms.RandomVerticalFlip(0.0),
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transforms.RandomVerticalFlip(1.0),
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])
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self.do_transform(trans)
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def test_color_jitter(self):
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trans = transforms.Compose([
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transforms.BrightnessTransform(0.0),
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transforms.HueTransform(0.0),
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transforms.SaturationTransform(0.0),
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transforms.ContrastTransform(0.0),
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])
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self.do_transform(trans)
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def test_rotate(self):
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trans = transforms.Compose([
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transforms.RandomRotation(90),
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transforms.RandomRotation([-10, 10]),
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transforms.RandomRotation(
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45, expand=True),
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transforms.RandomRotation(
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10, expand=True, center=(60, 80)),
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])
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self.do_transform(trans)
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def test_pad(self):
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trans = transforms.Compose([transforms.Pad(2)])
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self.do_transform(trans)
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fake_img = self.create_image((200, 150, 3))
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trans_pad = transforms.Pad(10)
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fake_img_padded = trans_pad(fake_img)
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np.testing.assert_equal(self.get_shape(fake_img_padded), (220, 170, 3))
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trans_pad1 = transforms.Pad([1, 2])
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trans_pad2 = transforms.Pad([1, 2, 3, 4])
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img = trans_pad1(fake_img)
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img = trans_pad2(img)
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def test_random_crop(self):
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trans = transforms.Compose([
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transforms.RandomCrop(200),
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transforms.RandomCrop((140, 160)),
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])
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self.do_transform(trans)
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trans_random_crop1 = transforms.RandomCrop(224)
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trans_random_crop2 = transforms.RandomCrop((140, 160))
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fake_img = self.create_image((500, 400, 3))
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fake_img_crop1 = trans_random_crop1(fake_img)
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fake_img_crop2 = trans_random_crop2(fake_img_crop1)
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np.testing.assert_equal(self.get_shape(fake_img_crop1), (224, 224, 3))
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np.testing.assert_equal(self.get_shape(fake_img_crop2), (140, 160, 3))
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trans_random_crop_same = transforms.RandomCrop((140, 160))
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img = trans_random_crop_same(fake_img_crop2)
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trans_random_crop_bigger = transforms.RandomCrop(
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(180, 200), pad_if_needed=True)
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img = trans_random_crop_bigger(img)
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trans_random_crop_pad = transforms.RandomCrop((224, 256), 2, True)
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img = trans_random_crop_pad(img)
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def test_grayscale(self):
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trans = transforms.Compose([transforms.Grayscale()])
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self.do_transform(trans)
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trans_gray = transforms.Grayscale()
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fake_img = self.create_image((500, 400, 3))
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fake_img_gray = trans_gray(fake_img)
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np.testing.assert_equal(self.get_shape(fake_img_gray)[0], 500)
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np.testing.assert_equal(self.get_shape(fake_img_gray)[1], 400)
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trans_gray3 = transforms.Grayscale(3)
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fake_img = self.create_image((500, 400, 3))
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fake_img_gray = trans_gray3(fake_img)
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def test_tranpose(self):
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trans = transforms.Compose([transforms.Transpose()])
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self.do_transform(trans)
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fake_img = self.create_image((50, 100, 3))
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converted_img = trans(fake_img)
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np.testing.assert_equal(self.get_shape(converted_img), (3, 50, 100))
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def test_to_tensor(self):
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trans = transforms.Compose([transforms.ToTensor()])
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fake_img = self.create_image((50, 100, 3))
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tensor = trans(fake_img)
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assert isinstance(tensor, paddle.Tensor)
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np.testing.assert_equal(tensor.shape, (3, 50, 100))
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def test_keys(self):
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fake_img1 = self.create_image((200, 150, 3))
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fake_img2 = self.create_image((200, 150, 3))
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trans_pad = transforms.Pad(10, keys=("image", ))
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fake_img_padded = trans_pad((fake_img1, fake_img2))
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def test_exception(self):
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trans = transforms.Compose([transforms.Resize(-1)])
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trans_batch = transforms.Compose([transforms.Resize(-1)])
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with self.assertRaises(Exception):
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self.do_transform(trans)
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with self.assertRaises(Exception):
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self.do_transform(trans_batch)
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with self.assertRaises(ValueError):
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transforms.ContrastTransform(-1.0)
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with self.assertRaises(ValueError):
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transforms.SaturationTransform(-1.0),
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with self.assertRaises(ValueError):
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transforms.HueTransform(-1.0)
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with self.assertRaises(ValueError):
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transforms.BrightnessTransform(-1.0)
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with self.assertRaises(ValueError):
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transforms.Pad([1.0, 2.0, 3.0])
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with self.assertRaises(TypeError):
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fake_img = self.create_image((100, 120, 3))
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F.pad(fake_img, '1')
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with self.assertRaises(TypeError):
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fake_img = self.create_image((100, 120, 3))
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F.pad(fake_img, 1, {})
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with self.assertRaises(TypeError):
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fake_img = self.create_image((100, 120, 3))
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F.pad(fake_img, 1, padding_mode=-1)
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with self.assertRaises(ValueError):
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fake_img = self.create_image((100, 120, 3))
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F.pad(fake_img, [1.0, 2.0, 3.0])
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with self.assertRaises(ValueError):
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transforms.RandomRotation(-2)
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with self.assertRaises(ValueError):
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transforms.RandomRotation([1, 2, 3])
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with self.assertRaises(ValueError):
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trans_gray = transforms.Grayscale(5)
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fake_img = self.create_image((100, 120, 3))
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trans_gray(fake_img)
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with self.assertRaises(TypeError):
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transform = transforms.RandomResizedCrop(64)
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transform(1)
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with self.assertRaises(ValueError):
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transform = transforms.BrightnessTransform([-0.1, -0.2])
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with self.assertRaises(TypeError):
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transform = transforms.BrightnessTransform('0.1')
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with self.assertRaises(ValueError):
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transform = transforms.BrightnessTransform('0.1', keys=1)
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with self.assertRaises(NotImplementedError):
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transform = transforms.BrightnessTransform('0.1', keys='a')
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def test_info(self):
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str(transforms.Compose([transforms.Resize((224, 224))]))
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str(transforms.Compose([transforms.Resize((224, 224))]))
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class TestTransformsPIL(TestTransformsCV2):
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def get_backend(self):
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return 'pil'
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class TestFunctional(unittest.TestCase):
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def test_errors(self):
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with self.assertRaises(TypeError):
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F.to_tensor(1)
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with self.assertRaises(ValueError):
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fake_img = Image.fromarray((np.random.rand(28, 28, 3) * 255).astype(
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'uint8'))
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F.to_tensor(fake_img, data_format=1)
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with self.assertRaises(TypeError):
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fake_img = Image.fromarray((np.random.rand(28, 28, 3) * 255).astype(
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'uint8'))
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F.resize(fake_img, '1')
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with self.assertRaises(TypeError):
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F.resize(1, 1)
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with self.assertRaises(TypeError):
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F.pad(1, 1)
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with self.assertRaises(TypeError):
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F.crop(1, 1, 1, 1, 1)
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with self.assertRaises(TypeError):
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F.hflip(1)
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with self.assertRaises(TypeError):
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F.vflip(1)
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with self.assertRaises(TypeError):
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F.adjust_brightness(1, 0.1)
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with self.assertRaises(TypeError):
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F.adjust_contrast(1, 0.1)
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with self.assertRaises(TypeError):
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F.adjust_hue(1, 0.1)
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with self.assertRaises(TypeError):
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F.adjust_saturation(1, 0.1)
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with self.assertRaises(TypeError):
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F.rotate(1, 0.1)
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with self.assertRaises(TypeError):
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F.to_grayscale(1)
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with self.assertRaises(ValueError):
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set_image_backend(1)
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with self.assertRaises(ValueError):
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image_load('tmp.jpg', backend=1)
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def test_normalize(self):
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np_img = (np.random.rand(28, 24, 3)).astype('uint8')
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pil_img = Image.fromarray(np_img)
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tensor_img = F.to_tensor(pil_img)
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tensor_img_hwc = F.to_tensor(pil_img, data_format='HWC')
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mean = [0.5, 0.5, 0.5]
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std = [0.5, 0.5, 0.5]
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normalized_img = F.normalize(tensor_img, mean, std)
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normalized_img = F.normalize(
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tensor_img_hwc, mean, std, data_format='HWC')
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normalized_img = F.normalize(pil_img, mean, std, data_format='HWC')
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normalized_img = F.normalize(
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np_img, mean, std, data_format='HWC', to_rgb=True)
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def test_center_crop(self):
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np_img = (np.random.rand(28, 24, 3)).astype('uint8')
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pil_img = Image.fromarray(np_img)
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np_cropped_img = F.center_crop(np_img, 4)
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pil_cropped_img = F.center_crop(pil_img, 4)
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np.testing.assert_almost_equal(np_cropped_img,
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np.array(pil_cropped_img))
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def test_pad(self):
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np_img = (np.random.rand(28, 24, 3)).astype('uint8')
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pil_img = Image.fromarray(np_img)
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np_padded_img = F.pad(np_img, [1, 2], padding_mode='reflect')
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pil_padded_img = F.pad(pil_img, [1, 2], padding_mode='reflect')
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np.testing.assert_almost_equal(np_padded_img, np.array(pil_padded_img))
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pil_p_img = pil_img.convert('P')
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pil_padded_img = F.pad(pil_p_img, [1, 2])
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pil_padded_img = F.pad(pil_p_img, [1, 2], padding_mode='reflect')
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def test_resize(self):
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np_img = (np.zeros([28, 24, 3])).astype('uint8')
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pil_img = Image.fromarray(np_img)
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np_reseized_img = F.resize(np_img, 40)
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pil_reseized_img = F.resize(pil_img, 40)
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np.testing.assert_almost_equal(np_reseized_img,
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np.array(pil_reseized_img))
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gray_img = (np.zeros([28, 32])).astype('uint8')
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gray_resize_img = F.resize(gray_img, 40)
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def test_to_tensor(self):
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np_img = (np.random.rand(28, 28) * 255).astype('uint8')
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pil_img = Image.fromarray(np_img)
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np_tensor = F.to_tensor(np_img, data_format='HWC')
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pil_tensor = F.to_tensor(pil_img, data_format='HWC')
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np.testing.assert_allclose(np_tensor.numpy(), pil_tensor.numpy())
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# test float dtype
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float_img = np.random.rand(28, 28)
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float_tensor = F.to_tensor(float_img)
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pil_img = Image.fromarray(np_img).convert('I')
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pil_tensor = F.to_tensor(pil_img)
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pil_img = Image.fromarray(np_img).convert('I;16')
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pil_tensor = F.to_tensor(pil_img)
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pil_img = Image.fromarray(np_img).convert('F')
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pil_tensor = F.to_tensor(pil_img)
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pil_img = Image.fromarray(np_img).convert('1')
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pil_tensor = F.to_tensor(pil_img)
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pil_img = Image.fromarray(np_img).convert('YCbCr')
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pil_tensor = F.to_tensor(pil_img)
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def test_image_load(self):
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fake_img = Image.fromarray((np.random.random((32, 32, 3)) * 255).astype(
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'uint8'))
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path = 'temp.jpg'
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fake_img.save(path)
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set_image_backend('pil')
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pil_img = image_load(path).convert('RGB')
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print(type(pil_img))
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set_image_backend('cv2')
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np_img = image_load(path)
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os.remove(path)
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if __name__ == '__main__':
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unittest.main()
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