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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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