rapidsai · rapids-bot · Nov 17, 2022 · Oct 30, 2022 · Oct 30, 2022 · Oct 30, 2022
@@ -1,6 +1,6 @@
 from .version_requirements import is_installed
 
-has_mpl = is_installed("matplotlib", ">=3.0.3")
+has_mpl = is_installed("matplotlib", ">=3.3")
 if has_mpl:
     try:
         # will fail with

@@ -46,7 +46,7 @@ def all_warnings():
     import inspect
 
     # Whenever a warning is triggered, Python adds a __warningregistry__
-    # member to the *calling* module.  The exercize here is to find
+    # member to the *calling* module.  The exercise here is to find
     # and eradicate all those breadcrumbs that were left lying around.
     #
     # We proceed by first searching all parent calling frames and explicitly
@@ -140,7 +140,7 @@ def expected_warnings(matching):
                     if match in remaining:
                         remaining.remove(match)
             if strict_warnings and not found:
-                raise ValueError('Unexpected warning: %s' % str(warn.message))
+                raise ValueError(f'Unexpected warning: {str(warn.message)}')
         if strict_warnings and (len(remaining) > 0):
-            msg = 'No warning raised matching:\n%s' % '\n'.join(remaining)
+            msg = f"No warning raised matching:\n{{'\n'.join(remaining)}}"
             raise ValueError(msg)
@@ -345,7 +345,7 @@ def fixed_func(*args, **kwargs):
                 channel_axis = (channel_axis,)
             if len(channel_axis) > 1:
                 raise ValueError(
-                    "only a single channel axis is currently suported")
+                    "only a single channel axis is currently supported")
 
             if channel_axis == (-1,) or channel_axis == -1:
                 return func(*args, **kwargs)
@@ -402,14 +402,12 @@ def __call__(self, func):
 
         alt_msg = ''
         if self.alt_func is not None:
-            alt_msg = ' Use ``%s`` instead.' % self.alt_func
+            alt_msg = f' Use ``{self.alt_func}`` instead.'
         rmv_msg = ''
         if self.removed_version is not None:
-            rmv_msg = (' and will be removed in version %s' %
-                       self.removed_version)
+            rmv_msg = f' and will be removed in version {self.removed_version}'
 
-        msg = ('Function ``%s`` is deprecated' % func.__name__
-               + rmv_msg + '.' + alt_msg)
+        msg = f'Function ``{func.__name__}`` is deprecated{rmv_msg}.{alt_msg}'
 
         @functools.wraps(func)
         def wrapped(*args, **kwargs):
@@ -695,7 +693,7 @@ def _validate_interpolation_order(image_dtype, order):
     if image_dtype == bool and order != 0:
         raise ValueError(
             "Input image dtype is bool. Interpolation is not defined "
-            "with bool data type. Please set order to 0 or explicitely "
+            "with bool data type. Please set order to 0 or explicitly "
             "cast input image to another data type.")
 
     return order
@@ -717,10 +715,10 @@ def _to_ndimage_mode(mode):
                                  wrap='wrap')
     if mode not in mode_translation_dict:
         raise ValueError(
-            (f"Unknown mode: '{mode}', or cannot translate mode. The "
-             f"mode should be one of 'constant', 'edge', 'symmetric', "
-             f"'reflect', or 'wrap'. See the documentation of numpy.pad for "
-             f"more info."))
+            f"Unknown mode: '{mode}', or cannot translate mode. The "
+            f"mode should be one of 'constant', 'edge', 'symmetric', "
+            f"'reflect', or 'wrap'. See the documentation of numpy.pad for "
+            f"more info.")
     return _fix_ndimage_mode(mode_translation_dict[mode])
 
 

@@ -3,31 +3,6 @@
 from packaging import version as _version
 
 
-def ensure_python_version(min_version):
-    if not isinstance(min_version, tuple):
-        min_version = (min_version, )
-    if sys.version_info < min_version:
-        # since ensure_python_version is in the critical import path,
-        # we lazy import it.
-        from platform import python_version
-
-        raise ImportError("""
-
-You are running cucim on an unsupported version of Python.
-
-Unfortunately, cucim no longer supports your installed version of Python (%s).
-You therefore have two options: either upgrade to
-Python %s, or install an older version of cucim.
-
-Please also consider updating `pip` and `setuptools`:
-
- $ pip install pip setuptools --upgrade
-
-Newer versions of these tools avoid installing packages incompatible
-with your version of Python.
-""" % (python_version(), '.'.join([str(v) for v in min_version])))
-
-
 def _check_version(actver, version, cmp_op):
     """
     Check version string of an active module against a required version.

@@ -1258,7 +1258,7 @@ def lab2xyz(lab, illuminant="D65", observer="2", *, channel_axis=-1):
 
     nwarn = int(cp.count_nonzero(warnings))
     if nwarn > 0:  # synchronize!
-        warn('Color data out of range: Z < 0 in %s pixels' % nwarn,
+        warn(f'Color data out of range: Z < 0 in {nwarn} pixels',
              stacklevel=2)
     return xyz
 

@@ -210,16 +210,16 @@ def deltaE_ciede2000(lab1, lab2, kL=1, kC=1, kH=1, *, channel_axis=-1):
     L2, a2, b2 = cp.moveaxis(lab2, source=channel_axis, destination=0)[:3]
 
     # distort `a` based on average chroma
-    # then convert to lch coordines from distorted `a`
-    # all subsequence calculations are in the new coordiantes
+    # then convert to lch coordinates from distorted `a`
+    # all subsequence calculations are in the new coordinates
     # (often denoted "prime" in the literature)
     Cbar = 0.5 * (cp.hypot(a1, b1) + cp.hypot(a2, b2))
     c7 = Cbar ** 7
     G = 0.5 * (1 - cp.sqrt(c7 / (c7 + 25 ** 7)))
     scale = 1 + G
     C1, h1 = _cart2polar_2pi(a1 * scale, b1)
     C2, h2 = _cart2polar_2pi(a2 * scale, b2)
-    # recall that c, h are polar coordiantes.  c==r, h==theta
+    # recall that c, h are polar coordinates.  c==r, h==theta
 
     # cide2000 has four terms to delta_e:
     # 1) Luminance term
@@ -235,7 +235,7 @@ def deltaE_ciede2000(lab1, lab2, kL=1, kC=1, kH=1, *, channel_axis=-1):
     L_term = (L2 - L1) / (kL * SL)
 
     # chroma term
-    Cbar = 0.5 * (C1 + C2)  # new coordiantes
+    Cbar = 0.5 * (C1 + C2)  # new coordinates
     SC = 1 + 0.045 * Cbar
     C_term = (C2 - C1) / (kC * SC)
 

@@ -243,7 +243,7 @@ def test_label2rgb_nd(image_type):
     labels[2:-2, 1:3] = 1
     labels[3:-3, 6:9] = 2
 
-    # label in the 2D case (correct 2D output is tested in other funcitons)
+    # label in the 2D case (correct 2D output is tested in other functions)
     labeled_2d = label2rgb(labels, image=img, bg_label=0)
 
     # labeling a single line gives an equivalent result

@@ -169,11 +169,12 @@ def _clahe(image, kernel_size, clip_limit, nbins):
     hist_blocks = hist_blocks.reshape((_misc.prod(ns_hist), -1))
 
     # Calculate actual clip limit
+    kernel_elements = _misc.prod(kernel_size)
     if clip_limit > 0.0:
-        clim = int(max(clip_limit * _misc.prod(kernel_size), 1))
+        clim = int(max(clip_limit * kernel_elements, 1))
     else:
         # largest possible value, i.e., do not clip (AHE)
-        clim = np.product(kernel_size)
+        clim = kernel_elements
 
     # Note: for 4096, 4096 input and default args, shapes are:
     #    hist_blocks.shape = (64, 262144)
@@ -189,7 +190,7 @@ def _clahe(image, kernel_size, clip_limit, nbins):
         ))
     else:
         hist = cp.apply_along_axis(clip_histogram, -1, hist, clip_limit=clim)
-    hist = map_histogram(hist, 0, NR_OF_GRAY - 1, _misc.prod(kernel_size))
+    hist = map_histogram(hist, 0, NR_OF_GRAY - 1, kernel_elements)
     hist = hist.reshape(hist_block_assembled_shape[:ndim] + (-1,))
 
     # duplicate leading mappings in each dim

@@ -522,7 +522,7 @@ def rescale_intensity(image, in_range="image", out_range="dtype"):
     -----
     .. versionchanged:: 0.17
         The dtype of the output array has changed to match the output dtype, or
-        float if the output range is specified by a pair of floats.
+        float if the output range is specified by a pair of values.
 
     See Also
     --------
@@ -615,8 +615,8 @@ def _assert_non_negative(image):
 
 def _adjust_gamma_u8(image, gamma, gain):
     """LUT based implmentation of gamma adjustement."""
-    lut = (255 * gain * (np.linspace(0, 1, 256) ** gamma))
-    lut = np.minimum(lut, 255).astype('uint8')
+    lut = 255 * gain * (np.linspace(0, 1, 256) ** gamma)
+    lut = np.minimum(np.rint(lut), 255).astype('uint8')
     lut = cp.asarray(lut)
     return lut[image]
 

@@ -8,17 +8,27 @@ def _match_cumulative_cdf(source, template):
     Return modified source array so that the cumulative density function of
     its values matches the cumulative density function of the template.
     """
-    src_values, src_unique_indices, src_counts = cp.unique(source.ravel(),
-                                                           return_inverse=True,
-                                                           return_counts=True)
-    tmpl_values, tmpl_counts = cp.unique(template.ravel(), return_counts=True)
+    if source.dtype.kind == 'u':
+        src_lookup = source.reshape(-1)
+        src_counts = cp.bincount(src_lookup)
+        tmpl_counts = cp.bincount(template.reshape(-1))
+
+        # omit values where the count was 0
+        tmpl_values = cp.nonzero(tmpl_counts)[0]
+        tmpl_counts = tmpl_counts[tmpl_values]
+    else:
+        src_values, src_lookup, src_counts = cp.unique(source.reshape(-1),
+                                                       return_inverse=True,
+                                                       return_counts=True)
+        tmpl_values, tmpl_counts = cp.unique(template.reshape(-1),
+                                             return_counts=True)
 
     # calculate normalized quantiles for each array
     src_quantiles = cp.cumsum(src_counts) / source.size
     tmpl_quantiles = cp.cumsum(tmpl_counts) / template.size
 
     interp_a_values = cp.interp(src_quantiles, tmpl_quantiles, tmpl_values)
-    return interp_a_values[src_unique_indices].reshape(source.shape)
+    return interp_a_values[src_lookup].reshape(source.shape)
 
 
 @utils.channel_as_last_axis(channel_arg_positions=(0, 1))

@@ -440,7 +440,6 @@ def test_adapthist_color():
     assert_almost_equal(float(peak_snr(full_scale, adapted)), 109.393, 1)
     assert_almost_equal(
         float(norm_brightness_err(full_scale, adapted)), 0.02, 2)
-    return data, adapted
 
 
 def test_adapthist_alpha():
@@ -597,7 +596,7 @@ def test_adjust_gamma_1x1_shape():
 
 def test_adjust_gamma_one():
     """Same image should be returned for gamma equal to one"""
-    image = cp.random.uniform(0, 255, (8, 8))
+    image = cp.arange(0, 256, dtype=np.uint8).reshape((16, 16))
     result = exposure.adjust_gamma(image, 1)
     assert_array_almost_equal(result, image)
 
@@ -615,37 +614,74 @@ def test_adjust_gamma_zero(dtype):
 def test_adjust_gamma_less_one():
     """Verifying the output with expected results for gamma
     correction with gamma equal to half"""
-    image = cp.arange(0, 255, 4, cp.uint8).reshape((8, 8))
+    image = cp.arange(0, 256, dtype=np.uint8).reshape((16, 16))
+
     # fmt: off
-    expected = cp.array([
-        [  0,  31,  45,  55,  63,  71,  78,  84],  # noqa
-        [ 90,  95, 100, 105, 110, 115, 119, 123],  # noqa
-        [127, 131, 135, 139, 142, 146, 149, 153],
-        [156, 159, 162, 165, 168, 171, 174, 177],
-        [180, 183, 186, 188, 191, 194, 196, 199],
-        [201, 204, 206, 209, 211, 214, 216, 218],
-        [221, 223, 225, 228, 230, 232, 234, 236],
-        [238, 241, 243, 245, 247, 249, 251, 253]], dtype=cp.uint8)
+    expected = cp.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=cp.uint8).reshape((16, 16))
     # fmt: on
-
     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 = cp.arange(0, 255, 4, cp.uint8).reshape((8, 8))
+    image = np.arange(0, 256, dtype=np.uint8).reshape((16, 16))
+
     # fmt: off
-    expected = cp.array([
-        [  0,   0,   0,   0,   1,   1,   2,   3],  # noqa
-        [  4,   5,   6,   7,   9,  10,  12,  14],  # noqa
-        [ 16,  18,  20,  22,  25,  27,  30,  33],  # noqa
-        [ 36,  39,  42,  45,  49,  52,  56,  60],  # noqa
-        [ 64,  68,  72,  76,  81,  85,  90,  95],  # noqa
-        [100, 105, 110, 116, 121, 127, 132, 138],
-        [144, 150, 156, 163, 169, 176, 182, 189],
-        [196, 203, 211, 218, 225, 233, 241, 249]], dtype=cp.uint8)
+    expected = cp.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=cp.uint8).reshape((16, 16))
     # fmt: on
 
     result = exposure.adjust_gamma(image, 2)
@@ -838,7 +874,7 @@ def test_is_low_contrast_boolean():
                                            exposure.adjust_log,
                                            exposure.adjust_sigmoid])
 def test_negative_input(exposure_func):
-    image = cp.arange(-10, 245, 4).reshape((8, 8)).astype(cp.double)
+    image = cp.arange(-10, 245, 4).reshape((8, 8)).astype(cp.float64)
     with pytest.raises(ValueError):
         exposure_func(image)
 

@@ -121,3 +121,15 @@ def _calculate_image_empirical_pdf(cls, image):
             channels_pdf.append((channel_values, channel_quantiles))
 
         return np.asarray(channels_pdf, dtype=object)
+
+    def test_match_histograms_consistency(self):
+        """ensure equivalent results for float and integer-based code paths"""
+        image_u8 = self.image_rgb
+        reference_u8 = self.template_rgb
+        image_f64 = self.image_rgb.astype(np.float64)
+        reference_f64 = self.template_rgb.astype(np.float64, copy=False)
+        matched_u8 = exposure.match_histograms(image_u8, reference_u8)
+        matched_f64 = exposure.match_histograms(image_f64, reference_f64)
+        assert_array_almost_equal(
+            matched_u8.astype(np.float64), matched_f64, decimal=5
+        )
@@ -63,10 +63,10 @@ def _mutiscale_basic_features_singlechannel(
         at different scales are added to the feature set.
     sigma_min : float, optional
         Smallest value of the Gaussian kernel used to average local
-        neighbourhoods before extracting features.
+        neighborhoods before extracting features.
     sigma_max : float, optional
         Largest value of the Gaussian kernel used to average local
-        neighbourhoods before extracting features.
+        neighborhoods before extracting features.
     num_sigma : int, optional
         Number of values of the Gaussian kernel between sigma_min and sigma_max.
         If None, sigma_min multiplied by powers of 2 are used.
@@ -136,10 +136,10 @@ def multiscale_basic_features(
         at different scales are added to the feature set.
     sigma_min : float, optional
         Smallest value of the Gaussian kernel used to average local
-        neighbourhoods before extracting features.
+        neighborhoods before extracting features.
     sigma_max : float, optional
         Largest value of the Gaussian kernel used to average local
-        neighbourhoods before extracting features.
+        neighborhoods before extracting features.
     num_sigma : int, optional
         Number of values of the Gaussian kernel between sigma_min and sigma_max.
         If None, sigma_min multiplied by powers of 2 are used.