chore: import upstream snapshot with attribution

test/python/testpipeline/testimage/testcaption.py

@@ -0,0 +1,36 @@
+"""
+Caption module tests
+"""
+
+import unittest
+
+from PIL import Image
+
+from transformers import AutoModelForImageTextToText, AutoImageProcessor, AutoTokenizer
+from txtai.pipeline import Caption
+
+# pylint: disable=C0411
+from utils import Utils
+
+
+class TestCaption(unittest.TestCase):
+    """
+    Caption tests.
+    """
+
+    def testCaption(self):
+        """
+        Test captions
+        """
+
+        caption = Caption()
+        self.assertEqual(caption(Image.open(Utils.PATH + "/books.jpg")), "a book shelf filled with books and a stack of books")
+
+        # Load passing models directly
+        path = "ydshieh/vit-gpt2-coco-en"
+        model = AutoModelForImageTextToText.from_pretrained(path)
+        tokenizer = AutoTokenizer.from_pretrained(path)
+        processor = AutoImageProcessor.from_pretrained(path)
+
+        caption = Caption((model, tokenizer, processor))
+        self.assertEqual(caption(Image.open(Utils.PATH + "/books.jpg")), "a book shelf filled with books and a stack of books")

test/python/testpipeline/testimage/testimagehash.py

@@ -0,0 +1,74 @@
+"""
+ImageHash module tests
+"""
+
+import unittest
+
+from PIL import Image
+
+from txtai.pipeline import ImageHash
+
+# pylint: disable=C0411
+from utils import Utils
+
+
+class TestImageHash(unittest.TestCase):
+    """
+    ImageHash tests.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        """
+        Caches an image to hash
+        """
+
+        cls.image = Image.open(Utils.PATH + "/books.jpg")
+
+    def testArray(self):
+        """
+        Test numpy return type
+        """
+
+        ihash = ImageHash(strings=False)
+        self.assertEqual(ihash(self.image).shape, (64,))
+
+    def testAverage(self):
+        """
+        Test average hash
+        """
+
+        ihash = ImageHash("average")
+        self.assertIn(ihash(self.image), ["0859dd04bfbfbf00", "0859dd04ffbfbf00"])
+
+    def testColor(self):
+        """
+        Test color hash
+        """
+
+        ihash = ImageHash("color")
+        self.assertIn(ihash(self.image), ["1ffffe02000e000c0e0000070000", "1ff8fe03000e00070e0000070000"])
+
+    def testDifference(self):
+        """
+        Test difference hash
+        """
+
+        ihash = ImageHash("difference")
+        self.assertEqual(ihash(self.image), "d291996d6969686a")
+
+    def testPerceptual(self):
+        """
+        Test perceptual hash
+        """
+
+        ihash = ImageHash("perceptual")
+        self.assertEqual(ihash(self.image), "8be8418577b331b9")
+
+    def testWavelet(self):
+        """
+        Test wavelet hash
+        """
+
+        ihash = ImageHash("wavelet")
+        self.assertEqual(ihash(Utils.PATH + "/books.jpg"), "68015d85bfbf3f00")

test/python/testpipeline/testimage/testobjects.py

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+"""
+Objects module tests
+"""
+
+import unittest
+
+from txtai.pipeline import Objects
+
+# pylint: disable=C0411
+from utils import Utils
+
+
+class TestObjects(unittest.TestCase):
+    """
+    Object detection tests.
+    """
+
+    def testClassification(self):
+        """
+        Test object detection using an image classification model
+        """
+
+        objects = Objects(classification=True, threshold=0.3)
+        self.assertEqual(objects(Utils.PATH + "/books.jpg")[0][0], "library")
+
+    def testDetection(self):
+        """
+        Test object detection using an object detection model
+        """
+
+        objects = Objects()
+        self.assertEqual(objects(Utils.PATH + "/books.jpg")[0][0], "book")
+
+    def testFlatten(self):
+        """
+        Test object detection using an object detection model, flatten to return only objects
+        """
+
+        objects = Objects()
+        self.assertEqual(objects(Utils.PATH + "/books.jpg", flatten=True)[0], "book")