Github user imatiach-msft commented on a diff in the pull request:

    https://github.com/apache/spark/pull/19439#discussion_r147001869
  
    --- Diff: python/pyspark/ml/image.py ---
    @@ -0,0 +1,122 @@
    +#
    +# 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
    +}
    +
    +# DataFrame with a single column of images named "image" (nullable)
    +imageSchema = StructType(StructField("image", 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)]), True))
    +
    +
    +def toNDArray(image):
    +    """
    +    Converts an image to a one-dimensional array.
    +
    +    :param image (object): The image to be converted
    +    :rtype array: The image as a one-dimensional array
    +
    +    .. versionadded:: 2.3.0
    +    """
    +    height = image.height
    +    width = image.width
    +    nChannels = image.nChannels
    +    return np.ndarray(
    +        shape=(height, width, nChannels),
    +        dtype=np.uint8,
    +        buffer=image.data,
    +        strides=(width * nChannels, nChannels, 1))
    +
    +
    +def toImage(array, origin="", mode=ocvTypes["CV_8UC3"]):
    --- End diff --
    
    good point.  as I commented above:
    
    in the scala code we only support (in ImageSchema.scala):
    
    if (isGray) {
    (1, ocvTypes("CV_8UC1"))
    } else if (hasAlpha) {
    (4, ocvTypes("CV_8UC4"))
    } else {
    (3, ocvTypes("CV_8UC3"))
    }
    
    maybe I should just limit the ocvTypes to just those 3 for now.
    Then you won't need to worry about floating point at all.


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