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How to use assert_image method in toolium

Best Python code snippet using toolium_python

test_Goulib_image.py

Source:test_Goulib_image.py

...6from skimage import data7import os8path = os.path.dirname(os.path.abspath(__file__))9results = path+'\\results\\image\\'  # path for results10def assert_image(image, name=None, convert=False):11    """ Checks if an image is present (not black, white, or low contrast12        :param image: Image to check13        :param name: str, optional file name. image is saved is specified14        :param convert: bool specifies if image is converted to RGB for saving15    """16    from skimage.exposure import is_low_contrast17    if name:18        image.save(results+name, autoconvert=convert)19    if is_low_contrast(image.array):20        logging.warning('image %s has low contrast' % name)21class TestImage:22    @classmethod23    def setup_class(self):24        self.lena = Image(path+'/data/lena.png')25        assert_equal(self.lena, self.lena)  # make sure image comparizon works26        assert_image(self.lena.grayscale('L'),27                     'grayscale.png')  # force to uint828        self.gray = Image(results+'grayscale.png')29        self.camera = Image(data.camera())30    def test_pdf(self):31        return  # for now for speed32        try:33            import pdfminer34        except:35            return  # pass36        assert_image(Image(path+'/data/lena.pdf'), 'pdf_out.png')37        assert_image(Image(path+'/data/Pantone Fan.pdf'), 'Pantone Fan.png')38        # Image(path+'/data/Pantone Fan CMYKOGB Equinox.pdf').save('Pantone Fan CMYKOGB Equinox.png')39    def test___init__(self):40        lena2 = Image(self.lena)41        assert_equal(self.lena, lena2)42    def test_generate(self):43        # from matrix44        from matplotlib import cm45        a = [[-x*x+y*y for x in range(128)] for y in range(128)]46        a = normalize(a)47        assert_image(Image(a), 'generated.png')48        assert_image(Image(a, colormap=cm.get_cmap('nipy_spectral')),49                     'gen_colormap.png', True)50    def test___hash__(self):51        h1 = hash(self.lena)52        h2 = hash(self.gray)53        diff = h1 ^ h2  # XOR54        diff = math2.digsum(diff, 2)  # number of different pixels55        assert_equal(h1, h2, msg='difference is %d pixels' % diff)56        57    def test_dist(self):58        assert_equal(self.lena.dist(self.gray),0)59                60        s=self.lena.size;61        lena2=self.lena.resize((s[0],s[1]*2))62        lena2.save(results+'lena.2.width.png')63        64        tol=4/64 # don't know why...65        66        d=self.lena.dist(lena2)67        68        assert_true(d<=tol)69        70        for method in [AVERAGE, PERCEPTUAL]:71        72            assert_true(self.lena.dist(lena2,method)<=tol)73            74            assert_true(self.lena.dist(lena2.flip(),method,symmetries=True)<=tol)75            assert_true(self.lena.dist(lena2.flip(False,True),method,symmetries=True)<=tol)76    def test___getitem__(self):77        pixel = self.gray[256, 256]78        assert_equal(pixel, 90)79        pixel = self.lena[256, 256]80        # (180,65,72))81        assert_equal(pixel, [0.70588235, 0.25490196, 0.28235294])82        left = self.lena[:, :256]83        right = self.lena[:, 256:-1]84        face = self.lena[246:374, 225:353]85        assert_image(face, 'lena.face.png')86        face = face.grayscale()87        eye = face[3:35, -35:-3]  # negative indexes are handy in some cases88        c = face.correlation(eye)89        # assert_image(c,"correlation.png")90    def test___lt__(self):91        # image = Image(data)92        # assert_equal(expected, image.__lt__(other))93        raise SkipTest94    def test___repr__(self):95        assert_true(repr(self.lena) in [96            'Image(mode=RGB shape=(512, 512, 3) type=float64)',97            # with some versions98            'Image(mode=RGB shape=(512L, 512L, 3L) type=float64)',99        ]100        )101    def test_mode(self):102        pass  # useless ?103    def test_open(self):104        lena3 = Image.open(path+'/data/lena.png')105        assert_equal(self.lena, lena3)106    def test_html(self):107        pass  # do not implement this one as it requires IPython108    def test__repr_html_(self):109        h = self.lena.convert('P')._repr_html_()110        assert_true(h)111    def test_average_hash(self):112        h1 = self.lena.average_hash()113        h2 = self.gray.average_hash()114        assert_equal(h1, h2)115    def test_perceptual_hash(self):116        h1 = self.lena.perceptual_hash()117        h2 = self.gray.perceptual_hash()118        assert_equal(h1, h2)119    def test_base64(self):120        # image = Image(data, **kwargs)121        # assert_equal(expected, image.base64(fmt))122        raise SkipTest  # implement your test here123    def test_grayscale(self):124        pass125    def test_invert(self):126        # image = Image(data, **kwargs)127        # assert_equal(expected, image.invert())128        raise SkipTest  # implement your test here129    def test_ndarray(self):130        pass131    def test_render(self):132        # same as h(self.lena) but without IPython133        h = self.lena.render()134        assert_true(h)135    """136    def test_convert(self):137        for mode in modes:138            im = self.lena.convert(mode)139            assert_image(im, 'convert_%s.png' % mode)140            try:141                im2 = im.convert('RGB')142                assert_image(im2, 'convert_%s_round_trip.png' % mode)143            except Exception as e:144                logging.error(145                    '%s round trip conversion failed with %s' % (mode, e))146                im2 = im.convert('RGB')147    """148    def test_split(self):149        rgb = self.lena.split()150        for im, c in zip(rgb, 'RGB'):151            assert_image(im, 'split_%s.png' % c)152        assert_image(Image(rgb), 'RGB_merge.png')153        colors = ['Cyan', 'Magenta', 'Yellow', 'blacK']154        cmyk = self.lena.split('CMYK')155        cmyk2 = [im.colorize(col) for im, col in zip(cmyk, colors)]156        for im, c in zip(cmyk2, 'CMYK'):157            assert_image(im, 'split_%s.png' % c)158        assert_image(Image(cmyk, mode='CMYK'), 'CMYK_merge.png')159        lab = self.lena.split('Lab')160        for im, c in zip(lab, 'LAB'):161            assert_image(im, 'split_%s.png' % c)162        lab = Image(lab, mode='LAB')163        assert_image(lab, 'Lab.png')164    def test_filter(self):165        from PIL.ImageFilter import BLUR, CONTOUR, DETAIL, EDGE_ENHANCE, EDGE_ENHANCE_MORE, EMBOSS, FIND_EDGES, SMOOTH, SMOOTH_MORE, SHARPEN166        for f in [BLUR, CONTOUR, DETAIL, EDGE_ENHANCE, EDGE_ENHANCE_MORE, EMBOSS, FIND_EDGES, SMOOTH, SMOOTH_MORE, SHARPEN]:167            r = self.lena.filter(f)168            assert_image(r, 'filter_pil_%s.png' % f.__name__)169        from skimage.filters import sobel, prewitt, scharr, roberts170        from skimage.restoration import denoise_bilateral171        for f in [sobel, prewitt, scharr, roberts, denoise_bilateral, ]:172            try:173                assert_image(self.lena.filter(174                    f), 'filter_sk_%s.png' % f.__name__)175            except:176                pass177    def test_dither(self):178        for k in dithering:179            im = self.gray.dither(k)*255180            assert_image(im, 'dither_%s.png' % dithering[k])181        assert_image(self.lena.dither(), 'dither_color_2.png')182        assert_image(self.lena.dither(n=4), 'dither_color_4.png')183    def test_resize(self):184        size = 128185        im = self.lena.resize((size, size))186        assert_image(im, 'resize_%d.png' % size)187        im = self.camera.resize((size, size))188        assert_image(im, 'camera_resize_%d.png' % size)189    def test_expand(self):190        size = 128191        im = self.lena.resize((size, size))192        # can you see that half pixel border ?193        im = im.expand((size+1, size+1), .5, .5)194        assert_image(im, 'expand_0.5 border.png')195    def test_add(self):196        dot = disk(20)197        im = Image()198        for i in range(3):199            for j in range(3):200                im = im.add(dot, (j*38.5, i*41.5), 0.5)201        assert_image(im, 'image_add.png')202    def test_colorize(self):203        cmyk = self.lena.split('CMYK')204        colors = ['Cyan', 'Magenta', 'Yellow', 'blacK']205        cmyk2 = [im.colorize(col) for im, col in zip(cmyk, colors)]206        # what a strange syntax ...207        back = cmyk2[0]-(-cmyk2[1])-(-cmyk2[2])-(-cmyk2[3])208        assert_image(back, 'image_add_sum_cmyk.png')209        assert_equal(self.lena.dist(back), 0)210    def test_mul(self):211        mask = disk(self.lena.size[0]/2)212        res = self.lena*mask213        assert_image(res, 'disk_mul.png')214    def test_shift(self):215        left = self.lena[:, 0:256]216        right = self.lena[:, 256:]217        blank = Image(size=(513, 513), mode='RGB', color='white')218        blank.paste(left, (0, 0))219        blank.paste(right.shift(1, 1), (256, 0))220        assert_image(blank, 'image_stitched.png')221    def test___abs__(self):222        # image = Image(data, mode, **kwargs)223        # assert_equal(expected, image.__abs__())224        raise SkipTest  # implement your test here225    def test___add__(self):226        # image = Image(data, mode, **kwargs)227        # assert_equal(expected, image.__add__(other))228        raise SkipTest  # implement your test here229    def test___div__(self):230        # image = Image(data, mode, **kwargs)231        # assert_equal(expected, image.__div__(f))232        raise SkipTest  # implement your test here233    def test___mul__(self):234        # image = Image(data, mode, **kwargs)...

loaders_test.py

Source:loaders_test.py

...8import numpy as np9from PIL import Image10from tifffile import TiffFile, TiffWriter, imwrite11from deepcell_label.loaders import Loader12def assert_image(archive, expected):13    """Assert that image is as expected."""14    # TZCYXS dimension order with squeeze=False15    image = TiffFile(archive.open('X.ome.tiff')).asarray(squeeze=False)16    # Drop the T and S dimension and move C to last dimension17    image = image[0, :, :, :, :, 0]18    image = np.moveaxis(image, 1, -1)19    np.testing.assert_array_equal(image, expected)20def assert_segmentation(archive, expected):21    """Assert that segmentation is as expected."""22    segmentation = TiffFile(archive.open('y.ome.tiff')).asarray(squeeze=False)23    # Drop the T and S dimension and move C to last dimension24    segmentation = segmentation[0, :, :, :, :, 0]25    segmentation = np.moveaxis(segmentation, 1, -1)26    np.testing.assert_array_equal(segmentation, expected)27    assert json.loads(archive.open('cells.json').read()) is not None28def test_load_npz():29    """Load npz with image data."""30    expected = np.zeros((1, 1, 1, 1))31    with tempfile.NamedTemporaryFile() as images:32        np.savez(images, X=expected)33        images.seek(0)34        loader = Loader(images)35    loaded_zip = zipfile.ZipFile(io.BytesIO(loader.data))36    assert_image(loaded_zip, expected)37def test_load_two_channel_npz():38    """Load npz with image data with two channels."""39    expected = np.zeros((1, 100, 100, 2))40    with tempfile.NamedTemporaryFile() as images:41        np.savez(images, X=expected)42        images.seek(0)43        loader = Loader(images)44    loaded_zip = zipfile.ZipFile(io.BytesIO(loader.data))45    assert_image(loaded_zip, expected)46def test_load_npz_with_segmentation():47    """Loads npz with image and segmentation."""48    expected_image = np.zeros((1, 1, 1, 1))49    expected_segmentation = np.ones((1, 1, 1, 1))50    with tempfile.NamedTemporaryFile() as images:51        np.savez(images, X=expected_image, y=expected_segmentation)52        images.seek(0)53        loader = Loader(images)54    loaded_zip = zipfile.ZipFile(io.BytesIO(loader.data))55    assert_image(loaded_zip, expected_image)56    assert_segmentation(loaded_zip, expected_segmentation)57def test_load_separate_npz():58    """Loads image and segmentation from separate npz."""59    expected_image = np.zeros((1, 1, 1, 1))60    expected_segmentation = np.ones((1, 1, 1, 1))61    with tempfile.NamedTemporaryFile() as images, tempfile.NamedTemporaryFile() as labels:62        np.savez(images, X=expected_image)63        np.savez(labels, y=expected_segmentation)64        loader = Loader(images, labels)65    loaded_zip = zipfile.ZipFile(io.BytesIO(loader.data))66    assert_image(loaded_zip, expected_image)67    assert_segmentation(loaded_zip, expected_segmentation)68def test_load_image_tiff():69    """Load image from tiff file."""70    expected = np.zeros((1, 1, 1, 1))71    with tempfile.NamedTemporaryFile() as images:72        with TiffWriter(images) as writer:73            writer.write(expected)74            images.seek(0)75        loader = Loader(images)76    loaded_zip = zipfile.ZipFile(io.BytesIO(loader.data))77    assert_image(loaded_zip, expected)78def test_load_image_and_segmentation_tiff():79    """Load image from a tiff and labeled array a zipped tiff."""80    expected_image = np.zeros((1, 1, 1, 1))81    expected_segmentation = np.ones((1, 1, 1, 1))82    with tempfile.NamedTemporaryFile() as images, tempfile.NamedTemporaryFile() as labels:83        with TiffWriter(images) as writer:84            writer.write(expected_image)85            images.seek(0)86        with zipfile.ZipFile(labels, mode='w') as zf:87            tiff = io.BytesIO()88            with TiffWriter(tiff) as writer:89                writer.write(np.array([[1]]))90                tiff.seek(0)91            zf.writestr('feature0.tiff', tiff.read())92        loader = Loader(images, labels)93    loaded_zip = zipfile.ZipFile(io.BytesIO(loader.data))94    assert_image(loaded_zip, expected_image)95    assert_segmentation(loaded_zip, expected_segmentation)96def test_load_image_png():97    """Load image array from png file."""98    expected = np.zeros((1, 1, 1, 1))99    with tempfile.NamedTemporaryFile() as images:100        img = Image.fromarray(np.zeros((1, 1)), mode='L')101        img.save(images, format='png')102        images.seek(0)103        loader = Loader(images)104    loaded_zip = zipfile.ZipFile(io.BytesIO(loader.data))105    assert_image(loaded_zip, expected)106def test_load_image_zip():107    """Load image array from zip of tiff files."""108    expected_image = np.zeros((1, 100, 100, 2))109    with tempfile.NamedTemporaryFile() as images:110        def make_tiff(array):111            tiff = io.BytesIO()112            with TiffWriter(tiff) as writer:113                writer.write(array)114                tiff.seek(0)115            return tiff.read()116        with zipfile.ZipFile(images, mode='w') as zf:117            with zf.open('channel0.tiff', 'w') as tiff:118                channel_0 = make_tiff(np.zeros((1, 100, 100)))119                tiff.write(channel_0)120            with zf.open('channel1.tiff', 'w') as tiff:121                channel_1 = make_tiff(np.zeros((1, 100, 100)))122                tiff.write(channel_1)123        images.seek(0)124        loader = Loader(images)125    loaded_zip = zipfile.ZipFile(io.BytesIO(loader.data))126    assert_image(loaded_zip, expected_image)127def test_load_segmentation_zip():128    """Load labeled array from zip of tiff files."""129    expected_image = np.zeros((1, 100, 100, 2))130    expected_segmentation = np.zeros((1, 100, 100, 2))131    with tempfile.NamedTemporaryFile() as images, tempfile.NamedTemporaryFile() as labels:132        def make_tiff(array):133            tiff = io.BytesIO()134            with TiffWriter(tiff) as writer:135                writer.write(array)136                tiff.seek(0)137            return tiff.read()138        with zipfile.ZipFile(images, mode='w') as zf:139            with zf.open('channel_0.tiff', 'w') as tiff:140                channel_0 = make_tiff(np.zeros((1, 100, 100)))141                tiff.write(channel_0)142            with zf.open('channel_1.tiff', 'w') as tiff:143                channel_1 = make_tiff(np.zeros((1, 100, 100)))144                tiff.write(channel_1)145        images.seek(0)146        with zipfile.ZipFile(labels, mode='w') as zf:147            with zf.open('feature_0.tiff', 'w') as tiff:148                feature_0 = make_tiff(np.zeros((1, 100, 100)))149                tiff.write(feature_0)150            with zf.open('feature_1.tiff', 'w') as tiff:151                feature_1 = make_tiff(np.zeros((1, 100, 100)))152                tiff.write(feature_1)153        labels.seek(0)154        loader = Loader(images, labels)155    loaded_zip = zipfile.ZipFile(io.BytesIO(loader.data))156    assert_image(loaded_zip, expected_image)157    assert_segmentation(loaded_zip, expected_segmentation)158def test_load_batches():159    """Load labeled array from zip of tiff files with multiple batches."""160    expected_image = np.zeros((2, 100, 100, 1))161    expected_segmentation = np.zeros((2, 100, 100, 1))162    expected_segmentation[1, :, :, :] = 1163    with tempfile.NamedTemporaryFile() as images, tempfile.NamedTemporaryFile() as labels:164        def make_tiff(array):165            tiff = io.BytesIO()166            imwrite(tiff, array)167            tiff.seek(0)168            return tiff.read()169        images.write(make_tiff(np.zeros((2, 100, 100, 1))))170        images.seek(0)171        with zipfile.ZipFile(labels, mode='w') as zf:172            with zf.open('batch_0_feature_0.tiff', 'w') as tiff:173                tiff.write(make_tiff(np.zeros((100, 100))))174            with zf.open('batch_1_feature_0.tiff', 'w') as tiff:175                tiff.write(make_tiff(np.zeros((100, 100)) + 1))176        labels.seek(0)177        loader = Loader(images, labels, 'BYXC')178    loaded_zip = zipfile.ZipFile(io.BytesIO(loader.data))179    assert_image(loaded_zip, expected_image)...

test_numpy.py

Source:test_numpy.py

...24            if list(i.getchannel(0).getdata()) != list(range(100)):25                print("data mismatch for", dtype)26        return i27    # Check supported 1-bit integer formats28    assert_image(to_image(bool, 1, 1), "1", TEST_IMAGE_SIZE)29    assert_image(to_image(numpy.bool8, 1, 1), "1", TEST_IMAGE_SIZE)30    # Check supported 8-bit integer formats31    assert_image(to_image(numpy.uint8), "L", TEST_IMAGE_SIZE)32    assert_image(to_image(numpy.uint8, 3), "RGB", TEST_IMAGE_SIZE)33    assert_image(to_image(numpy.uint8, 4), "RGBA", TEST_IMAGE_SIZE)34    assert_image(to_image(numpy.int8), "I", TEST_IMAGE_SIZE)35    # Check non-fixed-size integer types36    # These may fail, depending on the platform, since we have no native37    # 64-bit int image types.38    # assert_image(to_image(numpy.uint), "I", TEST_IMAGE_SIZE)39    # assert_image(to_image(numpy.int), "I", TEST_IMAGE_SIZE)40    # Check 16-bit integer formats41    if Image._ENDIAN == "<":42        assert_image(to_image(numpy.uint16), "I;16", TEST_IMAGE_SIZE)43    else:44        assert_image(to_image(numpy.uint16), "I;16B", TEST_IMAGE_SIZE)45    assert_image(to_image(numpy.int16), "I", TEST_IMAGE_SIZE)46    # Check 32-bit integer formats47    assert_image(to_image(numpy.uint32), "I", TEST_IMAGE_SIZE)48    assert_image(to_image(numpy.int32), "I", TEST_IMAGE_SIZE)49    # Check 64-bit integer formats50    with pytest.raises(TypeError):51        to_image(numpy.uint64)52    with pytest.raises(TypeError):53        to_image(numpy.int64)54    # Check floating-point formats55    assert_image(to_image(float), "F", TEST_IMAGE_SIZE)56    with pytest.raises(TypeError):57        to_image(numpy.float16)58    assert_image(to_image(numpy.float32), "F", TEST_IMAGE_SIZE)59    assert_image(to_image(numpy.float64), "F", TEST_IMAGE_SIZE)60    assert_image(to_image(numpy.uint8, 2), "LA", (10, 10))61    assert_image(to_image(numpy.uint8, 3), "RGB", (10, 10))62    assert_image(to_image(numpy.uint8, 4), "RGBA", (10, 10))63# Based on an erring example at64# https://stackoverflow.com/questions/10854903/what-is-causing-dimension-dependent-attributeerror-in-pil-fromarray-function65def test_3d_array():66    size = (5, TEST_IMAGE_SIZE[0], TEST_IMAGE_SIZE[1])67    a = numpy.ones(size, dtype=numpy.uint8)68    assert_image(Image.fromarray(a[1, :, :]), "L", TEST_IMAGE_SIZE)69    size = (TEST_IMAGE_SIZE[0], 5, TEST_IMAGE_SIZE[1])70    a = numpy.ones(size, dtype=numpy.uint8)71    assert_image(Image.fromarray(a[:, 1, :]), "L", TEST_IMAGE_SIZE)72    size = (TEST_IMAGE_SIZE[0], TEST_IMAGE_SIZE[1], 5)73    a = numpy.ones(size, dtype=numpy.uint8)74    assert_image(Image.fromarray(a[:, :, 1]), "L", TEST_IMAGE_SIZE)75def test_1d_array():76    a = numpy.ones(5, dtype=numpy.uint8)77    assert_image(Image.fromarray(a), "L", (1, 5))78def _test_img_equals_nparray(img, np):79    assert len(np.shape) >= 280    np_size = np.shape[1], np.shape[0]81    assert img.size == np_size82    px = img.load()83    for x in range(0, img.size[0], int(img.size[0] / 10)):84        for y in range(0, img.size[1], int(img.size[1] / 10)):85            assert_deep_equal(px[x, y], np[y, x])86def test_16bit():87    with Image.open("Tests/images/16bit.cropped.tif") as img:88        np_img = numpy.array(img)89        _test_img_equals_nparray(img, np_img)90    assert np_img.dtype == numpy.dtype("<u2")91def test_1bit():...

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