import warnings import numpy as np import pytest from numpy.testing import (assert_allclose, assert_almost_equal, assert_array_almost_equal, assert_array_equal, assert_equal) from skimage import data from skimage import exposure from skimage import util from skimage.color import rgb2gray from skimage.exposure.exposure import intensity_range from skimage.util.dtype import dtype_range from skimage._shared._warnings import expected_warnings from skimage._shared.utils import _supported_float_type # Test integer histograms # ======================= @pytest.mark.parametrize('dtype', [np.int8, np.float32]) def test_wrong_source_range(dtype): im = np.array([-1, 100], dtype=dtype) with pytest.raises(ValueError, match="Incorrect value for `source_range` argument"): frequencies, bin_centers = exposure.histogram(im, source_range='foobar') def test_negative_overflow(): im = np.array([-1, 100], dtype=np.int8) frequencies, bin_centers = exposure.histogram(im) assert_array_equal(bin_centers, np.arange(-1, 101)) assert frequencies[0] == 1 assert frequencies[-1] == 1 assert_array_equal(frequencies[1:-1], 0) def test_all_negative_image(): im = np.array([-100, -1], dtype=np.int8) frequencies, bin_centers = exposure.histogram(im) assert_array_equal(bin_centers, np.arange(-100, 0)) assert frequencies[0] == 1 assert frequencies[-1] == 1 assert_array_equal(frequencies[1:-1], 0) def test_int_range_image(): im = np.array([10, 100], dtype=np.int8) frequencies, bin_centers = exposure.histogram(im) assert_equal(len(bin_centers), len(frequencies)) assert_equal(bin_centers[0], 10) assert_equal(bin_centers[-1], 100) def test_multichannel_int_range_image(): im = np.array([[10, 5], [100, 102]], dtype=np.int8) frequencies, bin_centers = exposure.histogram(im, channel_axis=-1) for ch in range(im.shape[-1]): assert_equal(len(frequencies[ch]), len(bin_centers)) assert_equal(bin_centers[0], 5) assert_equal(bin_centers[-1], 102) def test_peak_uint_range_dtype(): im = np.array([10, 100], dtype=np.uint8) frequencies, bin_centers = exposure.histogram(im, source_range='dtype') assert_array_equal(bin_centers, np.arange(0, 256)) assert_equal(frequencies[10], 1) assert_equal(frequencies[100], 1) assert_equal(frequencies[101], 0) assert_equal(frequencies.shape, (256,)) def test_peak_int_range_dtype(): im = np.array([10, 100], dtype=np.int8) frequencies, bin_centers = exposure.histogram(im, source_range='dtype') assert_array_equal(bin_centers, np.arange(-128, 128)) assert_equal(frequencies[128+10], 1) assert_equal(frequencies[128+100], 1) assert_equal(frequencies[128+101], 0) assert_equal(frequencies.shape, (256,)) def test_flat_uint_range_dtype(): im = np.linspace(0, 255, 256, dtype=np.uint8) frequencies, bin_centers = exposure.histogram(im, source_range='dtype') assert_array_equal(bin_centers, np.arange(0, 256)) assert_equal(frequencies.shape, (256,)) def test_flat_int_range_dtype(): im = np.linspace(-128, 128, 256, dtype=np.int8) frequencies, bin_centers = exposure.histogram(im, source_range='dtype') assert_array_equal(bin_centers, np.arange(-128, 128)) assert_equal(frequencies.shape, (256,)) @pytest.mark.parametrize('dtype', [np.float16, np.float32, np.float64]) def test_peak_float_out_of_range_image(dtype): im = np.array([10, 100], dtype=dtype) frequencies, bin_centers = exposure.histogram(im, nbins=90) assert bin_centers.dtype == dtype # offset values by 0.5 for float... assert_array_equal(bin_centers, np.arange(10, 100) + 0.5) @pytest.mark.parametrize('dtype', [np.float16, np.float32, np.float64]) def test_peak_float_out_of_range_dtype(dtype): im = np.array([10, 100], dtype=dtype) nbins = 10 frequencies, bin_centers = exposure.histogram(im, nbins=nbins, source_range='dtype') assert bin_centers.dtype == dtype assert_almost_equal(np.min(bin_centers), -0.9, 3) assert_almost_equal(np.max(bin_centers), 0.9, 3) assert_equal(len(bin_centers), 10) def test_normalize(): im = np.array([0, 255, 255], dtype=np.uint8) frequencies, bin_centers = exposure.histogram(im, source_range='dtype', normalize=False) expected = np.zeros(256) expected[0] = 1 expected[-1] = 2 assert_equal(frequencies, expected) frequencies, bin_centers = exposure.histogram(im, source_range='dtype', normalize=True) expected /= 3. assert_equal(frequencies, expected) # Test multichannel histograms # ============================ @pytest.mark.parametrize('source_range', ['dtype', 'image']) @pytest.mark.parametrize('dtype', [np.uint8, np.int16, np.float64]) @pytest.mark.parametrize('channel_axis', [0, 1, -1]) def test_multichannel_hist_common_bins_uint8(dtype, source_range, channel_axis): """Check that all channels use the same binning.""" # Construct multichannel image with uniform values within each channel, # but the full range of values across channels. shape = (5, 5) channel_size = shape[0] * shape[1] imin, imax = dtype_range[dtype] im = np.stack( ( np.full(shape, imin, dtype=dtype), np.full(shape, imax, dtype=dtype), ), axis=channel_axis ) frequencies, bin_centers = exposure.histogram( im, source_range=source_range, channel_axis=channel_axis ) if np.issubdtype(dtype, np.integer): assert_array_equal(bin_centers, np.arange(imin, imax + 1)) assert frequencies[0][0] == channel_size assert frequencies[0][-1] == 0 assert frequencies[1][0] == 0 assert frequencies[1][-1] == channel_size # Test histogram equalization # =========================== np.random.seed(0) test_img_int = data.camera() # squeeze image intensities to lower image contrast test_img = util.img_as_float(test_img_int) test_img = exposure.rescale_intensity(test_img / 5. + 100) def test_equalize_uint8_approx(): """Check integer bins used for uint8 images.""" img_eq0 = exposure.equalize_hist(test_img_int) img_eq1 = exposure.equalize_hist(test_img_int, nbins=3) assert_allclose(img_eq0, img_eq1) def test_equalize_ubyte(): img = util.img_as_ubyte(test_img) img_eq = exposure.equalize_hist(img) cdf, bin_edges = exposure.cumulative_distribution(img_eq) check_cdf_slope(cdf) @pytest.mark.parametrize('dtype', [np.float16, np.float32, np.float64]) def test_equalize_float(dtype): img = util.img_as_float(test_img).astype(dtype, copy=False) img_eq = exposure.equalize_hist(img) assert img_eq.dtype == _supported_float_type(dtype) cdf, bin_edges = exposure.cumulative_distribution(img_eq) check_cdf_slope(cdf) assert bin_edges.dtype == _supported_float_type(dtype) def test_equalize_masked(): img = util.img_as_float(test_img) mask = np.zeros(test_img.shape) mask[100:400, 100:400] = 1 img_mask_eq = exposure.equalize_hist(img, mask=mask) img_eq = exposure.equalize_hist(img) cdf, bin_edges = exposure.cumulative_distribution(img_mask_eq) check_cdf_slope(cdf) assert not (img_eq == img_mask_eq).all() def check_cdf_slope(cdf): """Slope of cdf which should equal 1 for an equalized histogram.""" norm_intensity = np.linspace(0, 1, len(cdf)) slope, intercept = np.polyfit(norm_intensity, cdf, 1) assert 0.9 < slope < 1.1 # Test intensity range # ==================== @pytest.mark.parametrize("test_input,expected", [ ('image', [0, 1]), ('dtype', [0, 255]), ((10, 20), [10, 20]) ]) def test_intensity_range_uint8(test_input, expected): image = np.array([0, 1], dtype=np.uint8) out = intensity_range(image, range_values=test_input) assert_array_equal(out, expected) @pytest.mark.parametrize("test_input,expected", [ ('image', [0.1, 0.2]), ('dtype', [-1, 1]), ((0.3, 0.4), [0.3, 0.4]) ]) def test_intensity_range_float(test_input, expected): image = np.array([0.1, 0.2], dtype=np.float64) out = intensity_range(image, range_values=test_input) assert_array_equal(out, expected) def test_intensity_range_clipped_float(): image = np.array([0.1, 0.2], dtype=np.float64) out = intensity_range(image, range_values='dtype', clip_negative=True) assert_array_equal(out, (0, 1)) # Test rescale intensity # ====================== uint10_max = 2**10 - 1 uint12_max = 2**12 - 1 uint14_max = 2**14 - 1 uint16_max = 2**16 - 1 def test_rescale_stretch(): image = np.array([51, 102, 153], dtype=np.uint8) out = exposure.rescale_intensity(image) assert out.dtype == np.uint8 assert_array_almost_equal(out, [0, 127, 255]) def test_rescale_shrink(): image = np.array([51., 102., 153.]) out = exposure.rescale_intensity(image) assert_array_almost_equal(out, [0, 0.5, 1]) @pytest.mark.parametrize('dtype', [np.float16, np.float32, np.float64]) def test_rescale_in_range(dtype): image = np.array([51., 102., 153.], dtype=dtype) out = exposure.rescale_intensity(image, in_range=(0, 255)) assert_array_almost_equal(out, [0.2, 0.4, 0.6], decimal=4) # with out_range='dtype', the output has the same dtype assert out.dtype == image.dtype def test_rescale_in_range_clip(): image = np.array([51., 102., 153.]) out = exposure.rescale_intensity(image, in_range=(0, 102)) assert_array_almost_equal(out, [0.5, 1, 1]) @pytest.mark.parametrize('dtype', [np.int8, np.int32, np.float16, np.float32, np.float64]) def test_rescale_out_range(dtype): """Check that output range is correct. .. versionchanged:: 0.17 This function used to return dtype matching the input dtype. It now matches the output. .. versionchanged:: 0.19 float16 and float32 inputs now result in float32 output. Formerly they would give float64 outputs. """ image = np.array([-10, 0, 10], dtype=dtype) out = exposure.rescale_intensity(image, out_range=(0, 127)) assert out.dtype == _supported_float_type(image.dtype) assert_array_almost_equal(out, [0, 63.5, 127]) def test_rescale_named_in_range(): image = np.array([0, uint10_max, uint10_max + 100], dtype=np.uint16) out = exposure.rescale_intensity(image, in_range='uint10') assert_array_almost_equal(out, [0, uint16_max, uint16_max]) def test_rescale_named_out_range(): image = np.array([0, uint16_max], dtype=np.uint16) out = exposure.rescale_intensity(image, out_range='uint10') assert_array_almost_equal(out, [0, uint10_max]) def test_rescale_uint12_limits(): image = np.array([0, uint16_max], dtype=np.uint16) out = exposure.rescale_intensity(image, out_range='uint12') assert_array_almost_equal(out, [0, uint12_max]) def test_rescale_uint14_limits(): image = np.array([0, uint16_max], dtype=np.uint16) out = exposure.rescale_intensity(image, out_range='uint14') assert_array_almost_equal(out, [0, uint14_max]) def test_rescale_all_zeros(): image = np.zeros((2, 2), dtype=np.uint8) out = exposure.rescale_intensity(image) assert ~np.isnan(out).all() assert_array_almost_equal(out, image) def test_rescale_constant(): image = np.array([130, 130], dtype=np.uint16) out = exposure.rescale_intensity(image, out_range=(0, 127)) assert_array_almost_equal(out, [127, 127]) def test_rescale_same_values(): image = np.ones((2, 2)) out = exposure.rescale_intensity(image) assert ~np.isnan(out).all() assert_array_almost_equal(out, image) @pytest.mark.parametrize( "in_range,out_range", [("image", "dtype"), ("dtype", "image")] ) def test_rescale_nan_warning(in_range, out_range): image = np.arange(12, dtype=float).reshape(3, 4) image[1, 1] = np.nan msg = ( r"One or more intensity levels are NaN\." r" Rescaling will broadcast NaN to the full image\." ) # 2019/11/10 Passing NaN to np.clip raises a DeprecationWarning for # versions above 1.17 # TODO: Remove once NumPy removes this DeprecationWarning numpy_warning_1_17_plus = ( "Passing `np.nan` to mean no clipping in np.clip" ) if in_range == "image": exp_warn = [msg, numpy_warning_1_17_plus] else: exp_warn = [msg] with expected_warnings(exp_warn): exposure.rescale_intensity(image, in_range, out_range) @pytest.mark.parametrize( "out_range, out_dtype", [ ('uint8', np.uint8), ('uint10', np.uint16), ('uint12', np.uint16), ('uint16', np.uint16), ('float', float), ] ) def test_rescale_output_dtype(out_range, out_dtype): image = np.array([-128, 0, 127], dtype=np.int8) output_image = exposure.rescale_intensity(image, out_range=out_range) assert output_image.dtype == out_dtype def test_rescale_no_overflow(): image = np.array([-128, 0, 127], dtype=np.int8) output_image = exposure.rescale_intensity(image, out_range=np.uint8) assert_array_equal(output_image, [0, 128, 255]) assert output_image.dtype == np.uint8 def test_rescale_float_output(): image = np.array([-128, 0, 127], dtype=np.int8) output_image = exposure.rescale_intensity(image, out_range=(0, 255)) assert_array_equal(output_image, [0, 128, 255]) assert output_image.dtype == float def test_rescale_raises_on_incorrect_out_range(): image = np.array([-128, 0, 127], dtype=np.int8) with pytest.raises(ValueError): _ = exposure.rescale_intensity(image, out_range='flat') # Test adaptive histogram equalization # ==================================== @pytest.mark.parametrize('dtype', [np.float16, np.float32, np.float64]) def test_adapthist_grayscale(dtype): """Test a grayscale float image """ img = util.img_as_float(data.astronaut()).astype(dtype, copy=False) img = rgb2gray(img) img = np.dstack((img, img, img)) adapted = exposure.equalize_adapthist(img, kernel_size=(57, 51), clip_limit=0.01, nbins=128) assert img.shape == adapted.shape assert adapted.dtype == _supported_float_type(dtype) snr_decimal = 3 if dtype != np.float16 else 2 assert_almost_equal(peak_snr(img, adapted), 100.140, snr_decimal) assert_almost_equal(norm_brightness_err(img, adapted), 0.0529, 3) def test_adapthist_color(): """Test an RGB color uint16 image """ img = util.img_as_uint(data.astronaut()) with warnings.catch_warnings(record=True) as w: warnings.simplefilter('always') hist, bin_centers = exposure.histogram(img) assert len(w) > 0 adapted = exposure.equalize_adapthist(img, clip_limit=0.01) assert adapted.min() == 0 assert adapted.max() == 1.0 assert img.shape == adapted.shape full_scale = exposure.rescale_intensity(img) assert_almost_equal(peak_snr(full_scale, adapted), 109.393, 1) assert_almost_equal(norm_brightness_err(full_scale, adapted), 0.02, 2) def test_adapthist_alpha(): """Test an RGBA color image """ img = util.img_as_float(data.astronaut()) alpha = np.ones((img.shape[0], img.shape[1]), dtype=float) img = np.dstack((img, alpha)) adapted = exposure.equalize_adapthist(img) assert adapted.shape != img.shape img = img[:, :, :3] full_scale = exposure.rescale_intensity(img) assert img.shape == adapted.shape assert_almost_equal(peak_snr(full_scale, adapted), 109.393, 2) assert_almost_equal(norm_brightness_err(full_scale, adapted), 0.0248, 3) def test_adapthist_grayscale_Nd(): """ Test for n-dimensional consistency with float images Note: Currently if img.ndim == 3, img.shape[2] > 4 must hold for the image not to be interpreted as a color image by @adapt_rgb """ # take 2d image, subsample and stack it img = util.img_as_float(data.astronaut()) img = rgb2gray(img) a = 15 img2d = util.img_as_float(img[0:-1:a, 0:-1:a]) img3d = np.array([img2d] * (img.shape[0] // a)) # apply CLAHE adapted2d = exposure.equalize_adapthist(img2d, kernel_size=5, clip_limit=0.05) adapted3d = exposure.equalize_adapthist(img3d, kernel_size=5, clip_limit=0.05) # check that dimensions of input and output match assert img2d.shape == adapted2d.shape assert img3d.shape == adapted3d.shape # check that the result from the stack of 2d images is similar # to the underlying 2d image assert np.mean(np.abs(adapted2d - adapted3d[adapted3d.shape[0] // 2])) < 0.02 def test_adapthist_constant(): """Test constant image, float and uint """ img = np.zeros((8, 8)) img += 2 img = img.astype(np.uint16) adapted = exposure.equalize_adapthist(img, 3) assert np.min(adapted) == np.max(adapted) img = np.zeros((8, 8)) img += 0.1 img = img.astype(np.float64) adapted = exposure.equalize_adapthist(img, 3) assert np.min(adapted) == np.max(adapted) def test_adapthist_borders(): """Test border processing """ img = rgb2gray(util.img_as_float(data.astronaut())) # maximize difference between orig and processed img img /= 100. img[img.shape[0] // 2, img.shape[1] // 2] = 1. # check borders are processed for different kernel sizes border_index = -1 for kernel_size in range(51, 71, 2): adapted = exposure.equalize_adapthist(img, kernel_size, clip_limit=0.5) # Check last columns are processed assert norm_brightness_err(adapted[:, border_index], img[:, border_index]) > 0.1 # Check last rows are processed assert norm_brightness_err(adapted[border_index, :], img[border_index, :]) > 0.1 def test_adapthist_clip_limit(): img_u = data.moon() img_f = util.img_as_float(img_u) # uint8 input img_clahe0 = exposure.equalize_adapthist(img_u, clip_limit=0) img_clahe1 = exposure.equalize_adapthist(img_u, clip_limit=1) assert_array_equal(img_clahe0, img_clahe1) # float64 input img_clahe0 = exposure.equalize_adapthist(img_f, clip_limit=0) img_clahe1 = exposure.equalize_adapthist(img_f, clip_limit=1) assert_array_equal(img_clahe0, img_clahe1) def peak_snr(img1, img2): """Peak signal to noise ratio of two images Parameters ---------- img1 : array-like img2 : array-like Returns ------- peak_snr : float Peak signal to noise ratio """ if img1.ndim == 3: img1, img2 = rgb2gray(img1.copy()), rgb2gray(img2.copy()) img1 = util.img_as_float(img1) img2 = util.img_as_float(img2) mse = 1. / img1.size * np.square(img1 - img2).sum() _, max_ = dtype_range[img1.dtype.type] return 20 * np.log(max_ / mse) def norm_brightness_err(img1, img2): """Normalized Absolute Mean Brightness Error between two images Parameters ---------- img1 : array-like img2 : array-like Returns ------- norm_brightness_error : float Normalized absolute mean brightness error """ if img1.ndim == 3: img1, img2 = rgb2gray(img1), rgb2gray(img2) ambe = np.abs(img1.mean() - img2.mean()) nbe = ambe / dtype_range[img1.dtype.type][1] return nbe def test_adapthist_incorrect_kernel_size(): img = np.ones((8, 8), dtype=float) with pytest.raises(ValueError, match="Incorrect value of `kernel_size`"): exposure.equalize_adapthist(img, (3, 3, 3)) # Test Gamma Correction # ===================== def test_adjust_gamma_1x1_shape(): """Check that the shape is maintained""" img = np.ones([1, 1]) result = exposure.adjust_gamma(img, 1.5) assert img.shape == result.shape def test_adjust_gamma_one(): """Same image should be returned for gamma equal to one""" image = np.arange(0, 256, dtype=np.uint8).reshape((16, 16)) result = exposure.adjust_gamma(image, 1) assert_array_equal(result, image) @pytest.mark.parametrize('dtype', [np.float16, np.float32, np.float64]) def test_adjust_gamma_zero(dtype): """White image should be returned for gamma equal to zero""" image = np.random.uniform(0, 255, (8, 8)).astype(dtype, copy=False) result = exposure.adjust_gamma(image, 0) dtype = image.dtype.type assert_array_equal(result, dtype_range[dtype][1]) assert result.dtype == image.dtype def test_adjust_gamma_less_one(): """Verifying the output with expected results for gamma correction with gamma equal to half""" image = np.arange(0, 256, dtype=np.uint8).reshape((16, 16)) expected = np.array([0, 16, 23, 28, 32, 36, 39, 42, 45, 48, 50, 53, 55, 58, 60, 62, 64, 66, 68, 70, 71, 73, 75, 77, 78, 80, 81, 83, 84, 86, 87, 89, 90, 92, 93, 94, 96, 97, 98, 100, 101, 102, 103, 105, 106, 107, 108, 109, 111, 112, 113, 114, 115, 116, 117, 118, 119, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 145, 146, 147, 148, 149, 150, 151, 151, 152, 153, 154, 155, 156, 156, 157, 158, 159, 160, 160, 161, 162, 163, 164, 164, 165, 166, 167, 167, 168, 169, 170, 170, 171, 172, 173, 173, 174, 175, 176, 176, 177, 178, 179, 179, 180, 181, 181, 182, 183, 183, 184, 185, 186, 186, 187, 188, 188, 189, 190, 190, 191, 192, 192, 193, 194, 194, 195, 196, 196, 197, 198, 198, 199, 199, 200, 201, 201, 202, 203, 203, 204, 204, 205, 206, 206, 207, 208, 208, 209, 209, 210, 211, 211, 212, 212, 213, 214, 214, 215, 215, 216, 217, 217, 218, 218, 219, 220, 220, 221, 221, 222, 222, 223, 224, 224, 225, 225, 226, 226, 227, 228, 228, 229, 229, 230, 230, 231, 231, 232, 233, 233, 234, 234, 235, 235, 236, 236, 237, 237, 238, 238, 239, 240, 240, 241, 241, 242, 242, 243, 243, 244, 244, 245, 245, 246, 246, 247, 247, 248, 248, 249, 249, 250, 250, 251, 251, 252, 252, 253, 253, 254, 254, 255], dtype=np.uint8).reshape((16, 16)) result = exposure.adjust_gamma(image, 0.5) assert_array_equal(result, expected) def test_adjust_gamma_greater_one(): """Verifying the output with expected results for gamma correction with gamma equal to two""" image = np.arange(0, 256, dtype=np.uint8).reshape((16, 16)) expected = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 7, 7, 7, 8, 8, 8, 9, 9, 9, 10, 10, 11, 11, 11, 12, 12, 13, 13, 14, 14, 15, 15, 16, 16, 17, 17, 18, 18, 19, 19, 20, 20, 21, 21, 22, 23, 23, 24, 24, 25, 26, 26, 27, 28, 28, 29, 30, 30, 31, 32, 32, 33, 34, 35, 35, 36, 37, 38, 38, 39, 40, 41, 42, 42, 43, 44, 45, 46, 47, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 91, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 111, 112, 113, 115, 116, 117, 119, 120, 121, 123, 124, 126, 127, 128, 130, 131, 133, 134, 136, 137, 139, 140, 142, 143, 145, 146, 148, 149, 151, 152, 154, 155, 157, 158, 160, 162, 163, 165, 166, 168, 170, 171, 173, 175, 176, 178, 180, 181, 183, 185, 186, 188, 190, 192, 193, 195, 197, 199, 200, 202, 204, 206, 207, 209, 211, 213, 215, 217, 218, 220, 222, 224, 226, 228, 230, 232, 233, 235, 237, 239, 241, 243, 245, 247, 249, 251, 253, 255] , dtype=np.uint8).reshape((16, 16)) result = exposure.adjust_gamma(image, 2) assert_array_equal(result, expected) def test_adjust_gamma_negative(): image = np.arange(0, 255, 4, np.uint8).reshape((8, 8)) with pytest.raises(ValueError): exposure.adjust_gamma(image, -1) def test_adjust_gamma_u8_overflow(): img = 255 * np.ones((2, 2), dtype=np.uint8) assert np.all(exposure.adjust_gamma(img, gamma=1, gain=1.1) == 255) # Test Logarithmic Correction # =========================== @pytest.mark.parametrize('dtype', [np.float16, np.float32, np.float64]) def test_adjust_log_1x1_shape(dtype): """Check that the shape is maintained""" img = np.ones([1, 1], dtype=dtype) result = exposure.adjust_log(img, 1) assert img.shape == result.shape assert result.dtype == dtype def test_adjust_log(): """Verifying the output with expected results for logarithmic correction with multiplier constant multiplier equal to unity""" image = np.arange(0, 255, 4, np.uint8).reshape((8, 8)) expected = np.array([ [ 0, 5, 11, 16, 22, 27, 33, 38], [ 43, 48, 53, 58, 63, 68, 73, 77], [ 82, 86, 91, 95, 100, 104, 109, 113], [117, 121, 125, 129, 133, 137, 141, 145], [149, 153, 157, 160, 164, 168, 172, 175], [179, 182, 186, 189, 193, 196, 199, 203], [206, 209, 213, 216, 219, 222, 225, 228], [231, 234, 238, 241, 244, 246, 249, 252]], dtype=np.uint8) result = exposure.adjust_log(image, 1) assert_array_equal(result, expected) def test_adjust_inv_log(): """Verifying the output with expected results for inverse logarithmic correction with multiplier constant multiplier equal to unity""" image = np.arange(0, 255, 4, np.uint8).reshape((8, 8)) expected = np.array([ [ 0, 2, 5, 8, 11, 14, 17, 20], [ 23, 26, 29, 32, 35, 38, 41, 45], [ 48, 51, 55, 58, 61, 65, 68, 72], [ 76, 79, 83, 87, 90, 94, 98, 102], [106, 110, 114, 118, 122, 126, 130, 134], [138, 143, 147, 151, 156, 160, 165, 170], [174, 179, 184, 188, 193, 198, 203, 208], [213, 218, 224, 229, 234, 239, 245, 250]], dtype=np.uint8) result = exposure.adjust_log(image, 1, True) assert_array_equal(result, expected) # Test Sigmoid Correction # ======================= @pytest.mark.parametrize('dtype', [np.float16, np.float32, np.float64]) def test_adjust_sigmoid_1x1_shape(dtype): """Check that the shape is maintained""" img = np.ones([1, 1], dtype=dtype) result = exposure.adjust_sigmoid(img, 1, 5) assert img.shape == result.shape assert result.dtype == dtype def test_adjust_sigmoid_cutoff_one(): """Verifying the output with expected results for sigmoid correction with cutoff equal to one and gain of 5""" image = np.arange(0, 255, 4, np.uint8).reshape((8, 8)) expected = np.array([ [ 1, 1, 1, 2, 2, 2, 2, 2], [ 3, 3, 3, 4, 4, 4, 5, 5], [ 5, 6, 6, 7, 7, 8, 9, 10], [ 10, 11, 12, 13, 14, 15, 16, 18], [ 19, 20, 22, 24, 25, 27, 29, 32], [ 34, 36, 39, 41, 44, 47, 50, 54], [ 57, 61, 64, 68, 72, 76, 80, 85], [ 89, 94, 99, 104, 108, 113, 118, 123]], dtype=np.uint8) result = exposure.adjust_sigmoid(image, 1, 5) assert_array_equal(result, expected) def test_adjust_sigmoid_cutoff_zero(): """Verifying the output with expected results for sigmoid correction with cutoff equal to zero and gain of 10""" image = np.arange(0, 255, 4, np.uint8).reshape((8, 8)) expected = np.array([ [127, 137, 147, 156, 166, 175, 183, 191], [198, 205, 211, 216, 221, 225, 229, 232], [235, 238, 240, 242, 244, 245, 247, 248], [249, 250, 250, 251, 251, 252, 252, 253], [253, 253, 253, 253, 254, 254, 254, 254], [254, 254, 254, 254, 254, 254, 254, 254], [254, 254, 254, 254, 254, 254, 254, 254], [254, 254, 254, 254, 254, 254, 254, 254]], dtype=np.uint8) result = exposure.adjust_sigmoid(image, 0, 10) assert_array_equal(result, expected) def test_adjust_sigmoid_cutoff_half(): """Verifying the output with expected results for sigmoid correction with cutoff equal to half and gain of 10""" image = np.arange(0, 255, 4, np.uint8).reshape((8, 8)) expected = np.array([ [ 1, 1, 2, 2, 3, 3, 4, 5], [ 5, 6, 7, 9, 10, 12, 14, 16], [ 19, 22, 25, 29, 34, 39, 44, 50], [ 57, 64, 72, 80, 89, 99, 108, 118], [128, 138, 148, 158, 167, 176, 184, 192], [199, 205, 211, 217, 221, 226, 229, 233], [236, 238, 240, 242, 244, 246, 247, 248], [249, 250, 250, 251, 251, 252, 252, 253]], dtype=np.uint8) result = exposure.adjust_sigmoid(image, 0.5, 10) assert_array_equal(result, expected) def test_adjust_inv_sigmoid_cutoff_half(): """Verifying the output with expected results for inverse sigmoid correction with cutoff equal to half and gain of 10""" image = np.arange(0, 255, 4, np.uint8).reshape((8, 8)) expected = np.array([ [253, 253, 252, 252, 251, 251, 250, 249], [249, 248, 247, 245, 244, 242, 240, 238], [235, 232, 229, 225, 220, 215, 210, 204], [197, 190, 182, 174, 165, 155, 146, 136], [126, 116, 106, 96, 87, 78, 70, 62], [ 55, 49, 43, 37, 33, 28, 25, 21], [ 18, 16, 14, 12, 10, 8, 7, 6], [ 5, 4, 4, 3, 3, 2, 2, 1]], dtype=np.uint8) result = exposure.adjust_sigmoid(image, 0.5, 10, True) assert_array_equal(result, expected) def test_is_low_contrast(): image = np.linspace(0, 0.04, 100) assert exposure.is_low_contrast(image) image[-1] = 1 assert exposure.is_low_contrast(image) assert not exposure.is_low_contrast(image, upper_percentile=100) image = (image * 255).astype(np.uint8) assert exposure.is_low_contrast(image) assert not exposure.is_low_contrast(image, upper_percentile=100) image = (image.astype(np.uint16)) * 2**8 assert exposure.is_low_contrast(image) assert not exposure.is_low_contrast(image, upper_percentile=100) def test_is_low_contrast_boolean(): image = np.zeros((8, 8), dtype=bool) assert exposure.is_low_contrast(image) image[:5] = 1 assert not exposure.is_low_contrast(image) # Test negative input ##################### @pytest.mark.parametrize("exposure_func", [exposure.adjust_gamma, exposure.adjust_log, exposure.adjust_sigmoid]) def test_negative_input(exposure_func): image = np.arange(-10, 245, 4).reshape((8, 8)).astype(np.float64) with pytest.raises(ValueError): exposure_func(image) # Test Dask Compatibility # ======================= def test_dask_histogram(): pytest.importorskip('dask', reason="dask python library is not installed") import dask.array as da dask_array = da.from_array(np.array([[0, 1], [1, 2]]), chunks=(1, 2)) output_hist, output_bins = exposure.histogram(dask_array) expected_bins = [0, 1, 2] expected_hist = [1, 2, 1] assert np.allclose(expected_bins, output_bins) assert np.allclose(expected_hist, output_hist) assert isinstance(output_hist, da.Array) def test_dask_rescale(): pytest.importorskip('dask', reason="dask python library is not installed") import dask.array as da image = da.array([51, 102, 153], dtype=np.uint8) out = exposure.rescale_intensity(image) assert out.dtype == np.uint8 assert_array_almost_equal(out, [0, 127, 255]) assert isinstance(out, da.Array)