TensorFlow.NET/test/TensorFlowNET.UnitTest/img_test/TestCrop.cs

using FluentAssertions;
using Microsoft.VisualStudio.TestTools.UnitTesting;
using NumSharp;
using Tensorflow;
using static Tensorflow.Binding;

namespace TensorFlowNET.UnitTest.img_test
{
    [TestClass]
    public class TestCrop
    {

        [TestMethod]
        public void TestCropAndResize()
        {
            // 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);

            }

        }

    }
}