Coverage for pyspark/ml/tests/test_image.py : 95%

# 

# Licensed to the Apache Software Foundation (ASF) under one or more 

# contributor license agreements. See the NOTICE file distributed with 

# this work for additional information regarding copyright ownership. 

# The ASF licenses this file to You 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. 

# 

import unittest 

from pyspark.ml.image import ImageSchema 

from pyspark.testing.mlutils import SparkSessionTestCase 

from pyspark.sql import Row 

from pyspark.testing.utils import QuietTest 

class ImageFileFormatTest(SparkSessionTestCase): 

def test_read_images(self): 

data_path = 'data/mllib/images/origin/kittens' 

df = self.spark.read.format("image") \ 

.option("dropInvalid", True) \ 

.option("recursiveFileLookup", True) \ 

.load(data_path) 

self.assertEqual(df.count(), 4) 

first_row = df.take(1)[0][0] 

# compare `schema.simpleString()` instead of directly compare schema, 

# because the df loaded from datasource may change schema column nullability. 

self.assertEqual(df.schema.simpleString(), ImageSchema.imageSchema.simpleString()) 

self.assertEqual(df.schema["image"].dataType.simpleString(), 

ImageSchema.columnSchema.simpleString()) 

array = ImageSchema.toNDArray(first_row) 

self.assertEqual(len(array), first_row[1]) 

self.assertEqual(ImageSchema.toImage(array, origin=first_row[0]), first_row) 

expected = {'CV_8UC3': 16, 'Undefined': -1, 'CV_8U': 0, 'CV_8UC1': 0, 'CV_8UC4': 24} 

self.assertEqual(ImageSchema.ocvTypes, expected) 

expected = ['origin', 'height', 'width', 'nChannels', 'mode', 'data'] 

self.assertEqual(ImageSchema.imageFields, expected) 

self.assertEqual(ImageSchema.undefinedImageType, "Undefined") 

with QuietTest(self.sc): 

self.assertRaisesRegex( 

TypeError, 

"image argument should be pyspark.sql.types.Row; however", 

lambda: ImageSchema.toNDArray("a")) 

with QuietTest(self.sc): 

self.assertRaisesRegex( 

ValueError, 

"image argument should have attributes specified in", 

lambda: ImageSchema.toNDArray(Row(a=1))) 

with QuietTest(self.sc): 

self.assertRaisesRegex( 

TypeError, 

"array argument should be numpy.ndarray; however, it got", 

lambda: ImageSchema.toImage("a")) 

if __name__ == "__main__": 

from pyspark.ml.tests.test_image import * # noqa: F401 

try: 

import xmlrunner # type: ignore[import] 

testRunner = xmlrunner.XMLTestRunner(output='target/test-reports', verbosity=2) 

except ImportError: 

testRunner = None 

unittest.main(testRunner=testRunner, verbosity=2)