using FluentAssertions;

using Microsoft.VisualStudio.TestTools.UnitTesting;

using NumSharp;

using System.Linq;

using Tensorflow;

using Tensorflow.UnitTest;

using static Tensorflow.Binding;

namespace TensorFlowNET.UnitTest.Basics

{

/// <summary>

/// Find more examples in https://www.programcreek.com/python/example/90444/tensorflow.read_file

/// </summary>

[TestClass]

public class ImageTest : GraphModeTestBase

{

string imgPath = "shasta-daisy.jpg";

Tensor contents;

[TestInitialize]

public void Initialize()

{

imgPath = TestHelper.GetFullPathFromDataDir(imgPath);

contents = tf.io.read_file(imgPath);

}

[TestMethod]

public void decode_image()

{

var img = tf.image.decode_image(contents);

Assert.AreEqual(img.name, "decode_image/Identity:0");

}

[TestMethod]

public void resize_image()

{

tf.enable_eager_execution();

var image = tf.constant(new int[5, 5]

{

{1, 0, 0, 0, 0 },

{0, 1, 0, 0, 0 },

{0, 0, 1, 0, 0 },

{0, 0, 0, 1, 0 },

{0, 0, 0, 0, 1 }

});

image = image[tf.newaxis, tf.ellipsis, tf.newaxis];

image = tf.image.resize(image, (3, 5));

image = image[0, tf.ellipsis, 0];

Assert.IsTrue(Enumerable.SequenceEqual(new float[] { 0.6666667f, 0.3333333f, 0, 0, 0 },

image[0].ToArray<float>()));

Assert.IsTrue(Enumerable.SequenceEqual(new float[] { 0, 0, 1, 0, 0 },

image[1].ToArray<float>()));

Assert.IsTrue(Enumerable.SequenceEqual(new float[] { 0, 0, 0, 0.3333335f, 0.6666665f },

image[2].ToArray<float>()));

tf.compat.v1.disable_eager_execution();

}

[TestMethod]

public void TestCropAndResize()

{

var graph = tf.Graph().as_default();

// 3x3 'Image' with numbered coordinates

var input = np.array(0f, 1f, 2f, 3f, 4f, 5f, 6f, 7f, 8f);

var image = tf.reshape(input, new int[] { 1, 3, 3, 1 });

// 4x4 'Image' with numbered coordinates

var input2 = np.array(0f, 1f, 2f, 3f, 4f, 5f, 6f, 7f, 8f, 9f, 10f, 11f, 12f, 13f, 14f, 15f);

var image2 = tf.reshape(input2, new int[] { 1, 4, 4, 1 });

// create one box over the full image that flips it (y1 > y2)

var box = tf.reshape(np.array(1f, 0f, 0f, 1f), new int[] { 1, 4 });

var boxInd = tf.Variable(np.array(0));

// crop first 3x3 imageto size 1x1

var cropSize1_1 = tf.Variable(np.array(1, 1));

// don't crop second 4x4 image

var cropSize2_2 = tf.Variable(np.array(4, 4));

var init = tf.global_variables_initializer();

using (Session sess = tf.Session())

{

sess.run(init);

var cropped = tf.image.crop_and_resize(image, box, boxInd, cropSize1_1);

var result = sess.run(cropped);

// check if cropped to 1x1 center was succesfull

result.size.Should().Be(1);

result[0, 0, 0, 0].Should().Be(4f);

cropped = tf.image.crop_and_resize(image2, box, boxInd, cropSize2_2);

result = sess.run(cropped);

// check if flipped and no cropping occured

result.size.Should().Be(16);

result[0, 0, 0, 0].Should().Be(12f);

}

}

}

}