Github user MrBago commented on a diff in the pull request:
https://github.com/apache/spark/pull/19439#discussion_r143798662
--- Diff: python/pyspark/ml/image.py ---
@@ -0,0 +1,133 @@
+#
+# 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 pyspark
+from pyspark import SparkContext
+from pyspark.sql.types import *
+from pyspark.sql.types import Row, _create_row
+from pyspark.sql import DataFrame
+from pyspark.ml.param.shared import *
+import numpy as np
+
+undefinedImageType = "Undefined"
+
+ImageFields = ["origin", "height", "width", "nChannels", "mode", "data"]
+
+ocvTypes = {
+ undefinedImageType: -1,
+ "CV_8U": 0, "CV_8UC1": 0, "CV_8UC2": 8, "CV_8UC3": 16, "CV_8UC4": 24,
+ "CV_8S": 1, "CV_8SC1": 1, "CV_8SC2": 9, "CV_8SC3": 17, "CV_8SC4": 25,
+ "CV_16U": 2, "CV_16UC1": 2, "CV_16UC2": 10, "CV_16UC3": 18,
"CV_16UC4": 26,
+ "CV_16S": 3, "CV_16SC1": 3, "CV_16SC2": 11, "CV_16SC3": 19,
"CV_16SC4": 27,
+ "CV_32S": 4, "CV_32SC1": 4, "CV_32SC2": 12, "CV_32SC3": 20,
"CV_32SC4": 28,
+ "CV_32F": 5, "CV_32FC1": 5, "CV_32FC2": 13, "CV_32FC3": 21,
"CV_32FC4": 29,
+ "CV_64F": 6, "CV_64FC1": 6, "CV_64FC2": 14, "CV_64FC3": 22,
"CV_64FC4": 30
+}
+
+ImageSchema = StructType([
+ StructField(ImageFields[0], StringType(), True),
+ StructField(ImageFields[1], IntegerType(), False),
+ StructField(ImageFields[2], IntegerType(), False),
+ StructField(ImageFields[3], IntegerType(), False),
+ # OpenCV-compatible type: CV_8UC3 in most cases
+ StructField(ImageFields[4], StringType(), False),
+ # bytes in OpenCV-compatible order: row-wise BGR in most cases
+ StructField(ImageFields[5], BinaryType(), False)])
+
+
+# TODO: generalize to other datatypes and number of channels
+def toNDArray(image):
+ """
+ Converts an image to a 1-dimensional array
+
+ Args:
+ image (object): The image to be converted
+
+ Returns:
+ array: The image as a 1-dimensional array
+
+ .. versionadded:: 2.3.0
+ """
+ height = image.height
+ width = image.width
+ return np.asarray(image.data, dtype=np.uint8) \
+ .reshape((height, width, 3))[:, :, (2, 1, 0)]
--- End diff --
This code assumes `image` is 3-channel GBR image and the user wants an RGB
image. I think we should try and support at least the 3 image types that
`readImages` (CV_8UC1, CV_8UC3, and CV_8UC4) but it would be nice to also
support 1, 3, and 4 channel float images.
The ndarray constructor is quite flexible and might be easier to work with
than calling `asarray` in this case because you want to treat the bytearray as
a buffer not as a sequence of ints.
```
np.ndarray(
shape=(height, width, nChannels),
dtype=np.uint8,
buffer=image.data,
strides=(width * nChannels, nChannels, 1))
```
Also I have mixed feelings about re-ordering the channels. I think it's
probably useful in the most common use-case, but (if my understanding is
correct) the open cv types don't require a specific channel order so we can't
just assume the input is RGB or RGBA. Maybe we should avoid re-ordering,
document the ordering we use whenever appropriate, and then leave it up to the
user to do any necessary re-order for themselves.
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