# Copyright (c) Alibaba, Inc. and its affiliates.
import os.path as osp
import shutil
import tempfile
import unittest

import cv2

from modelscope.fileio import File
from modelscope.pipelines import pipeline
from modelscope.pydatasets import PyDataset
from modelscope.utils.constant import ModelFile, Tasks
from modelscope.utils.hub import get_model_cache_dir
from modelscope.utils.test_utils import test_level


class ImageMattingTest(unittest.TestCase):

    def setUp(self) -> None:
        self.model_id = 'damo/cv_unet_image-matting_damo'
        # switch to False if downloading everytime is not desired
        purge_cache = True
        if purge_cache:
            shutil.rmtree(
                get_model_cache_dir(self.model_id), ignore_errors=True)

    @unittest.skip('deprecated, download model from model hub instead')
    def test_run_with_direct_file_download(self):
        model_path = 'http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs' \
                     '.com/data/test/maas/image_matting/matting_person.pb'
        with tempfile.TemporaryDirectory() as tmp_dir:
            model_file = osp.join(tmp_dir, ModelFile.TF_GRAPH_FILE)
            with open(model_file, 'wb') as ofile:
                ofile.write(File.read(model_path))
            img_matting = pipeline(Tasks.image_matting, model=tmp_dir)

            result = img_matting(
                'http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/data/test/maas/image_matting/test.png'
            )
            cv2.imwrite('result.png', result['output_png'])

    @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
    def test_run_with_dataset(self):
        input_location = [
            'http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/data/test/maas/image_matting/test.png'
        ]
        # alternatively:
        # input_location = '/dir/to/images'

        dataset = PyDataset.load(input_location, target='image')
        img_matting = pipeline(Tasks.image_matting, model=self.model_id)
        # note that for dataset output, the inference-output is a Generator that can be iterated.
        result = img_matting(dataset)
        cv2.imwrite('result.png', next(result)['output_png'])
        print(f'Output written to {osp.abspath("result.png")}')

    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
    def test_run_modelhub(self):
        img_matting = pipeline(Tasks.image_matting, model=self.model_id)

        result = img_matting(
            'http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/data/test/maas/image_matting/test.png'
        )
        cv2.imwrite('result.png', result['output_png'])
        print(f'Output written to {osp.abspath("result.png")}')

    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
    def test_run_modelhub_default_model(self):
        img_matting = pipeline(Tasks.image_matting)

        result = img_matting(
            'http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/data/test/maas/image_matting/test.png'
        )
        cv2.imwrite('result.png', result['output_png'])
        print(f'Output written to {osp.abspath("result.png")}')


if __name__ == '__main__':
    unittest.main()