forked from mindspore-Ecosystem/mindspore
242 lines
10 KiB
Python
242 lines
10 KiB
Python
# Copyright 2022 Huawei Technologies Co., Ltd
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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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"""
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Testing Perspective op in DE
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"""
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import cv2
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import numpy as np
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from numpy import random
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from numpy.testing import assert_allclose
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import PIL
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import mindspore.dataset as ds
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import mindspore.dataset.transforms.transforms
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import mindspore.dataset.vision.transforms as vision
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from mindspore.dataset.vision.utils import Inter
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from mindspore import log as logger
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DATA_DIR = "../data/dataset/testImageNetData/train/"
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MNIST_DATA_DIR = "../data/dataset/testMnistData"
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DATA_DIR_2 = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]
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SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json"
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IMAGE_FILE = "../data/dataset/apple.jpg"
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def generate_numpy_random_rgb(shape):
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"""
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Only generate floating points that are fractions like n / 256, since they
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are RGB pixels. Some low-precision floating point types in this test can't
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handle arbitrary precision floating points well.
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"""
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return np.random.randint(0, 256, shape) / 255.
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def test_perspective_eager():
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"""
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Feature: Perspective
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Description: Test eager support for Perspective Cpp implementation
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Expectation: Receive correct output image from op
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"""
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# Eager 3-channel
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rgb_flat = generate_numpy_random_rgb((40000, 3)).astype(np.float32)
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img_in1 = rgb_flat.reshape((200, 200, 3))
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img_in1_cv = PIL.Image.fromarray(np.uint8(img_in1))
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img_width, img_height = 200, 200
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top_left = [random.randint(0, img_width - 1),
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random.randint(0, img_height - 1)]
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top_right = [random.randint(0, img_width - 1),
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random.randint(0, img_height - 1)]
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bottom_right = [random.randint(0, img_width - 1),
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random.randint(0, img_height - 1)]
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bottom_left = [random.randint(0, img_width-1),
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random.randint(0, img_height - 1)]
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src = [[0, 0], [img_width - 1, 0], [img_width - 1, img_height - 1], [0, img_height - 1]]
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dst = [top_left, top_right, bottom_right, bottom_left]
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src_points = np.array(src, dtype="float32")
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dst_points = np.array(dst, dtype="float32")
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y = cv2.getPerspectiveTransform(src_points, dst_points)
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img_cv1 = cv2.warpPerspective(np.array(img_in1), y, img_in1_cv.size, cv2.INTER_LINEAR)
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perspective_op = vision.Perspective(src, dst, Inter.BILINEAR)
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img_ms1 = perspective_op(img_in1)
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assert_allclose(img_ms1.flatten(),
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img_cv1.flatten(),
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rtol=1e-3,
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atol=0)
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def test_perspective_invalid_param():
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"""
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Feature: Perspective
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Description: Test Perspective implementation with invalid ignore parameter
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Expectation: Correct error is raised as expected
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"""
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logger.info("Test Perspective implementation with invalid ignore parameter")
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try:
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data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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data_set = data_set.map(
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operations=[vision.Decode(), vision.Resize((224, 224)), lambda img: np.array(img[:, :, 0])],
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input_columns=["image"])
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# invalid list
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src = 1.0
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dst = 2.0
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data_set = data_set.map(operations=vision.Perspective(src, dst, Inter.BILINEAR),
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input_columns="image")
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except TypeError as error:
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logger.info("Got an exception in Perspective: {}".format(str(error)))
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assert "is not of type [<class 'list'>, <class 'tuple'>], but got" in str(error)
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try:
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data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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data_set = data_set.map(
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operations=[vision.Decode(), vision.Resize((224, 224)), lambda img: np.array(img[:, :, 0])],
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input_columns=["image"])
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# invalid list
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src = [2, 1, 3]
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dst = [1, 2, 3]
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data_set = data_set.map(operations=vision.Perspective(src, dst, Inter.BILINEAR),
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input_columns="image")
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except TypeError as error:
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logger.info("Got an exception in Perspective: {}".format(str(error)))
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assert "Argument start_points[0] with value 2 is not of type [<class 'list'>, <class 'tuple'>]," in str(error)
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try:
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data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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data_set = data_set.map(
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operations=[vision.Decode(), vision.Resize((224, 224)), lambda img: np.array(img[:, :, 0])],
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input_columns=["image"])
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# invalid list
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src = [[2], [1], [3]]
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dst = [[2], [1], [3]]
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data_set = data_set.map(operations=vision.Perspective(src, dst, Inter.BILINEAR),
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input_columns="image")
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except TypeError as error:
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logger.info("Got an exception in Perspective: {}".format(str(error)))
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assert "start_points should be a list or tuple of length 4" in str(error)
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try:
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data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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data_set = data_set.map(
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operations=[vision.Decode(), vision.Resize((224, 224)), lambda img: np.array(img[:, :, 0])],
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input_columns=["image"])
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# invalid list
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src = [[2], [1], [3], [4]]
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dst = [[2], [1], [3], [4]]
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data_set = data_set.map(operations=vision.Perspective(src, dst, Inter.BILINEAR),
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input_columns="image")
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except TypeError as error:
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logger.info("Got an exception in Perspective: {}".format(str(error)))
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assert "start_points[0] should be a list or tuple of length 2" in str(error)
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try:
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data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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data_set = data_set.map(
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operations=[vision.Decode(), vision.Resize((224, 224)), lambda img: np.array(img[:, :, 0])],
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input_columns=["image"])
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# invalid list
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src = [[2, 2], [1, 1], [3, 3], [4, 4]]
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dst = [[2, 2], [1, 1], [3, 3], [4, 2147483648]]
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data_set = data_set.map(operations=vision.Perspective(src, dst, Inter.BILINEAR),
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input_columns="image")
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except ValueError as error:
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logger.info("Got an exception in Perspective: {}".format(str(error)))
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assert "Input end_points[3][1] is not within the required interval of [-2147483648, 2147483647]" in str(error)
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try:
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data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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data_set = data_set.map(
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operations=[vision.Decode(), vision.Resize((224, 224)), lambda img: np.array(img[:, :, 0])],
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input_columns=["image"])
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# invalid list
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src = [[2, 2], [1, 1], [3, 3], [4, 2147483648]]
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dst = [[2, 2], [1, 1], [3, 3], [4, 4]]
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data_set = data_set.map(operations=vision.Perspective(src, dst, Inter.BILINEAR),
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input_columns="image")
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except ValueError as error:
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logger.info("Got an exception in Perspective: {}".format(str(error)))
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assert "Input start_points[3][1] is not within the required interval of [-2147483648, 2147483647]" in str(error)
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def test_perspective_invalid_interpolation():
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"""
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Feature: Perspective
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Description: test Perspective with invalid interpolation
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Expectation: throw TypeError
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"""
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logger.info("test_perspective_invalid_interpolation")
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dataset = ds.ImageFolderDataset(DATA_DIR, 1, shuffle=False, decode=True)
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try:
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src = [[0, 63], [63, 63], [63, 0], [0, 0]]
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dst = [[0, 63], [63, 63], [63, 0], [0, 0]]
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perspective_op = vision.Perspective(src, dst, interpolation="invalid")
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dataset.map(operations=perspective_op, input_columns=['image'])
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except TypeError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert "Argument interpolation with value invalid is not of type [<enum 'Inter'>]" in str(e)
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def test_perspective_pipeline():
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"""
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Feature: Perspective
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Description: Test Perspective C implementation Pipeline
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Expectation: Runs successfully
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"""
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src = [[0, 63], [63, 63], [63, 0], [0, 0]]
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dst = [[0, 63], [63, 63], [63, 0], [0, 0]]
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# First dataset
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transforms1 = [vision.Decode(), vision.Resize([64, 64])]
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transforms1 = mindspore.dataset.transforms.transforms.Compose(
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transforms1)
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ds1 = ds.TFRecordDataset(DATA_DIR_2,
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SCHEMA_DIR,
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columns_list=["image"],
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shuffle=False)
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ds1 = ds1.map(operations=transforms1, input_columns=["image"])
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# Second dataset
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transforms2 = [
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vision.Decode(),
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vision.Resize([64, 64]),
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vision.Perspective(src, dst, Inter.BILINEAR)
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]
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transform2 = mindspore.dataset.transforms.transforms.Compose(
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transforms2)
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ds2 = ds.TFRecordDataset(DATA_DIR_2,
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SCHEMA_DIR,
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columns_list=["image"],
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shuffle=False)
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ds2 = ds2.map(operations=transform2, input_columns=["image"])
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for data1, data2 in zip(ds1.create_dict_iterator(num_epochs=1),
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ds2.create_dict_iterator(num_epochs=1)):
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ori_img = data1["image"].asnumpy()
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cvt_img = data2["image"].asnumpy()
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assert_allclose(ori_img.flatten(),
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cvt_img.flatten(),
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rtol=1e-5,
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atol=0)
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assert ori_img.shape == cvt_img.shape
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if __name__ == "__main__":
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test_perspective_eager()
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test_perspective_invalid_param()
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test_perspective_invalid_interpolation()
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test_perspective_pipeline()
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