2021 the huggingface team licensed 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 orglicenseslicense2 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 rgba la l 2021 the huggingface team licensed 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 rgba la l
import unittest

from transformers import (
    MODEL_FOR_OBJECT_DETECTION_MAPPING,
    AutoFeatureExtractor,
    AutoModelForObjectDetection,
    ObjectDetectionPipeline,
    is_vision_available,
    pipeline,
)
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_pytesseract,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
    from PIL import Image
else:

    class Image:
        @staticmethod
        def open(*args, **kwargs):
            pass


@is_pipeline_test
@require_vision
@require_timm
@require_torch
class ObjectDetectionPipelineTests(unittest.TestCase):
    model_mapping = MODEL_FOR_OBJECT_DETECTION_MAPPING

    def get_test_pipeline(self, model, tokenizer, processor):
        object_detector = ObjectDetectionPipeline(model=model, image_processor=processor)
        return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]

    def run_pipeline_test(self, object_detector, examples):
        outputs = object_detector("./tests/fixtures/tests_samples/COCO/000000039769.png", threshold=0.0)

        self.assertGreater(len(outputs), 0)
        for detected_object in outputs:
            self.assertEqual(
                detected_object,
                {
                    "score": ANY(float),
                    "label": ANY(str),
                    "box": {"xmin": ANY(int), "ymin": ANY(int), "xmax": ANY(int), "ymax": ANY(int)},
                },
            )

        import datasets

        dataset = datasets.load_dataset("hf-internal-testing/fixtures_image_utils", "image", split="test")

        batch = [
            Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
            "http://images.cocodataset.org/val2017/000000039769.jpg",
            
            dataset[0]["file"],
            
            dataset[1]["file"],
            
            dataset[2]["file"],
        ]
        batch_outputs = object_detector(batch, threshold=0.0)

        self.assertEqual(len(batch), len(batch_outputs))
        for outputs in batch_outputs:
            self.assertGreater(len(outputs), 0)
            for detected_object in outputs:
                self.assertEqual(
                    detected_object,
                    {
                        "score": ANY(float),
                        "label": ANY(str),
                        "box": {"xmin": ANY(int), "ymin": ANY(int), "xmax": ANY(int), "ymax": ANY(int)},
                    },
                )

    @require_tf
    @unittest.skip("Object detection not implemented in TF")
    def test_small_model_tf(self):
        pass

    @require_torch
    def test_small_model_pt(self):
        model_id = "hf-internal-testing/tiny-detr-mobilenetsv3"

        model = AutoModelForObjectDetection.from_pretrained(model_id)
        feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
        object_detector = ObjectDetectionPipeline(model=model, feature_extractor=feature_extractor)

        outputs = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg", threshold=0.0)

        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [
                {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
                {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
            ],
        )

        outputs = object_detector(
            [
                "http://images.cocodataset.org/val2017/000000039769.jpg",
                "http://images.cocodataset.org/val2017/000000039769.jpg",
            ],
            threshold=0.0,
        )

        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [
                [
                    {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
                    {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
                ],
                [
                    {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
                    {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
                ],
            ],
        )

    @require_torch
    @slow
    def test_large_model_pt(self):
        model_id = "facebook/detr-resnet-50"

        model = AutoModelForObjectDetection.from_pretrained(model_id)
        feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
        object_detector = ObjectDetectionPipeline(model=model, feature_extractor=feature_extractor)

        outputs = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg")
        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [
                {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
                {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
                {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
                {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
                {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
            ],
        )

        outputs = object_detector(
            [
                "http://images.cocodataset.org/val2017/000000039769.jpg",
                "http://images.cocodataset.org/val2017/000000039769.jpg",
            ]
        )
        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [
                [
                    {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
                    {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
                    {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
                    {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
                    {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
                ],
                [
                    {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
                    {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
                    {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
                    {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
                    {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
                ],
            ],
        )

    @require_torch
    @slow
    def test_integration_torch_object_detection(self):
        model_id = "facebook/detr-resnet-50"

        object_detector = pipeline("object-detection", model=model_id)

        outputs = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg")
        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [
                {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
                {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
                {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
                {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
                {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
            ],
        )

        outputs = object_detector(
            [
                "http://images.cocodataset.org/val2017/000000039769.jpg",
                "http://images.cocodataset.org/val2017/000000039769.jpg",
            ]
        )
        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [
                [
                    {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
                    {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
                    {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
                    {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
                    {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
                ],
                [
                    {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
                    {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
                    {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
                    {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
                    {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
                ],
            ],
        )

    @require_torch
    @slow
    def test_threshold(self):
        threshold = 0.9985
        model_id = "facebook/detr-resnet-50"

        object_detector = pipeline("object-detection", model=model_id)

        outputs = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg", threshold=threshold)
        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [
                {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
                {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
            ],
        )

    @require_torch
    @require_pytesseract
    @slow
    def test_layoutlm(self):
        model_id = "Narsil/layoutlmv3-finetuned-funsd"
        threshold = 0.9993

        object_detector = pipeline("object-detection", model=model_id, threshold=threshold)

        outputs = object_detector(
            "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"
        )
        self.assertEqual(
            nested_simplify(outputs, decimals=4),
            [
                {"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
                {"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
            ],
        )