scripts.utils.image_utils.dml Maven / Gradle / Ivy
Go to download
Show more of this group Show more artifacts with this name
Show all versions of systemml Show documentation
Show all versions of systemml Show documentation
Declarative Machine Learning
#-------------------------------------------------------------
#
# 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.
#
#-------------------------------------------------------------
/*
* Simple utility to crop image of shape [N, 3 * Hin * Win] into [N, 3 * Hout * Wout]
* Assumption: Hout < Hin, Wout < Win and input contains values [0, ..]
*/
crop_rgb = function(matrix[double] input, int Hin, int Win, int Hout, int Wout) return (matrix[double] out) {
start_h = ceil((Hin - Hout) / 2)
end_h = start_h + Hout - 1
start_w = ceil((Win - Wout) / 2)
end_w = start_w + Wout - 1
mask = matrix(0, rows=Hin, cols=Win)
temp_mask = matrix(1, rows=Hout, cols=Wout)
mask[start_h:end_h, start_w:end_w] = temp_mask
mask = matrix(mask, rows=1, cols=Hin*Win)
mask = cbind(cbind(mask, mask), mask)
out = removeEmpty(target=(input+1), margin="cols", select=mask) - 1
}
/*
* Simple utility to crop image of shape [N, Hin * Win] into [N, Hout * Wout]
* Assumption: Hout < Hin, Wout < Win and input contains values [0, ..]
*
* Example PySpark script:
* import matplotlib.pyplot as plt
* from sklearn import datasets
* digits = datasets.load_digits()
* image = digits.images[3,].reshape(1, -1)
* plt.imshow(image.reshape(8,8), cmap=plt.cm.gray_r)
* plt.show()
*
* script = """
* crop_grayscale = function(matrix[double] input, int Hin, int Win, int Hout, int Wout) return (matrix[double] out) {
* start_h = ceil((Hin - Hout) / 2)
* end_h = start_h + Hout - 1
* start_w = ceil((Win - Wout) / 2)
* end_w = start_w + Wout - 1
* mask = matrix(0, rows=Hin, cols=Win)
* temp_mask = matrix(1, rows=Hout, cols=Wout)
* mask[start_h:end_h, start_w:end_w] = temp_mask
* mask = matrix(mask, rows=1, cols=Hin*Win)
* out = removeEmpty(target=(input+1), margin="cols", select=mask) - 1
* }
* Y = crop_grayscale(X, 8, 8, 6, 6)
* """
* from systemml import MLContext, dml
* ml = MLContext(sc)
* script = dml(script).input(X=image).output("Y")
* out = ml.execute(script).get("Y").toNumPy()
* plt.imshow(out.reshape(6,6), cmap=plt.cm.gray_r)
* plt.show()
*
*/
crop_grayscale = function(matrix[double] input, int Hin, int Win, int Hout, int Wout) return (matrix[double] out) {
start_h = ceil((Hin - Hout) / 2)
end_h = start_h + Hout - 1
start_w = ceil((Win - Wout) / 2)
end_w = start_w + Wout - 1
mask = matrix(0, rows=Hin, cols=Win)
temp_mask = matrix(1, rows=Hout, cols=Wout)
mask[start_h:end_h, start_w:end_w] = temp_mask
mask = matrix(mask, rows=1, cols=Hin*Win)
out = removeEmpty(target=(input+1), margin="cols", select=mask) - 1
}