Merge pull request #1324 from ahojnnes/rel-import

Use relative imports in skimage files

skimage/feature/_canny.py

@@ -16,8 +16,8 @@ import numpy as np
 import scipy.ndimage as ndi
 from scipy.ndimage import (gaussian_filter,
                            generate_binary_structure, binary_erosion, label)
-from skimage import dtype_limits
-from skimage._shared.utils import assert_nD
+from .. import dtype_limits
+from .._shared.utils import assert_nD
 
 
 def smooth_with_function_and_mask(image, function, mask):

skimage/feature/_daisy.py

@@ -1,9 +1,9 @@
 import numpy as np
 from scipy import sqrt, pi, arctan2, cos, sin, exp
 from scipy.ndimage import gaussian_filter
-import skimage.color
-from skimage import img_as_float, draw
-from skimage._shared.utils import assert_nD
+from .. import img_as_float, draw
+from ..color import gray2rgb
+from .._shared.utils import assert_nD
 
 
 def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8,
@@ -177,7 +177,7 @@ def daisy(img, step=4, radius=15, rings=3, histograms=8, orientations=8,
                 descs[:, :, i:i + orientations] /= norms[:, :, np.newaxis]
 
     if visualize:
-        descs_img = skimage.color.gray2rgb(img)
+        descs_img = gray2rgb(img)
         for i in range(descs.shape[0]):
             for j in range(descs.shape[1]):
                 # Draw center histogram sigma

skimage/feature/_hog.py

@@ -1,7 +1,7 @@
 import numpy as np
 from scipy import sqrt, pi, arctan2, cos, sin
 from scipy.ndimage import uniform_filter
-from skimage._shared.utils import assert_nD
+from .._shared.utils import assert_nD
 
 
 def hog(image, orientations=9, pixels_per_cell=(8, 8),
@@ -137,7 +137,7 @@ def hog(image, orientations=9, pixels_per_cell=(8, 8),
     hog_image = None
 
     if visualise:
-        from skimage import draw
+        from .. import draw
 
         radius = min(cx, cy) // 2 - 1
         hog_image = np.zeros((sy, sx), dtype=float)

skimage/feature/_texture.pyx

@@ -5,7 +5,7 @@
 import numpy as np
 cimport numpy as cnp
 from libc.math cimport sin, cos, abs
-from skimage._shared.interpolation cimport bilinear_interpolation, round
+from .._shared.interpolation cimport bilinear_interpolation, round
 
 
 def _glcm_loop(cnp.uint8_t[:, ::1] image, double[:] distances,

skimage/feature/blob.py

@@ -5,11 +5,11 @@ import itertools as itt
 import math
 from math import sqrt, hypot, log
 from numpy import arccos
-from skimage.util import img_as_float
+from ..util import img_as_float
 from .peak import peak_local_max
 from ._hessian_det_appx import _hessian_matrix_det
-from skimage.transform import integral_image
-from skimage._shared.utils import assert_nD
+from ..transform import integral_image
+from .._shared.utils import assert_nD
 
 
 # This basic blob detection algorithm is based on:

skimage/feature/brief.py

@@ -5,7 +5,7 @@ from .util import (DescriptorExtractor, _mask_border_keypoints,
                    _prepare_grayscale_input_2D)
 
 from .brief_cy import _brief_loop
-from skimage._shared.utils import assert_nD
+from .._shared.utils import assert_nD
 
 
 class BRIEF(DescriptorExtractor):

skimage/feature/censure.py

@@ -1,15 +1,13 @@
 import numpy as np
 from scipy.ndimage.filters import maximum_filter, minimum_filter, convolve
 
-from skimage.feature.util import FeatureDetector, _prepare_grayscale_input_2D
-
-from skimage.transform import integral_image
-from skimage.feature import structure_tensor
-from skimage.morphology import octagon, star
-from skimage.feature.util import _mask_border_keypoints
-
-from skimage.feature.censure_cy import _censure_dob_loop
-from skimage._shared.utils import assert_nD
+from ..transform import integral_image
+from ..feature import structure_tensor
+from ..morphology import octagon, star
+from ..feature.censure_cy import _censure_dob_loop
+from ..feature.util import (FeatureDetector, _prepare_grayscale_input_2D,
+                            _mask_border_keypoints)
+from .._shared.utils import assert_nD
 
 # The paper(Reference [1]) mentions the sizes of the Octagon shaped filter
 # kernel for the first seven scales only. The sizes of the later scales

skimage/feature/corner.py

@@ -2,10 +2,10 @@ import numpy as np
 from scipy import ndimage
 from scipy import stats
 
-from skimage.util import img_as_float, pad
-from skimage.feature import peak_local_max
-from skimage.feature.util import _prepare_grayscale_input_2D
-from skimage.feature.corner_cy import _corner_fast
+from ..util import img_as_float, pad
+from ..feature import peak_local_max
+from ..feature.util import _prepare_grayscale_input_2D
+from ..feature.corner_cy import _corner_fast
 from ._hessian_det_appx import _hessian_matrix_det
 from ..transform import integral_image
 from .._shared.utils import safe_as_int

skimage/feature/corner_cy.pyx

@@ -7,8 +7,8 @@ cimport numpy as cnp
 from libc.float cimport DBL_MAX
 from libc.math cimport atan2
 
-from skimage.util import img_as_float, pad
-from skimage.color import rgb2grey
+from ..util import img_as_float, pad
+from ..color import rgb2grey
 
 from .util import _prepare_grayscale_input_2D
 

skimage/feature/orb.py

@@ -1,13 +1,13 @@
 import numpy as np
 
-from skimage.feature.util import (FeatureDetector, DescriptorExtractor,
-                                  _mask_border_keypoints,
-                                  _prepare_grayscale_input_2D)
+from ..feature.util import (FeatureDetector, DescriptorExtractor,
+                            _mask_border_keypoints,
+                            _prepare_grayscale_input_2D)
 
-from skimage.feature import (corner_fast, corner_orientations, corner_peaks,
+from ..feature import (corner_fast, corner_orientations, corner_peaks,
                              corner_harris)
-from skimage.transform import pyramid_gaussian
-from skimage._shared.utils import assert_nD
+from ..transform import pyramid_gaussian
+from .._shared.utils import assert_nD
 
 from .orb_cy import _orb_loop
 

skimage/feature/orb_cy.pyx

@@ -6,12 +6,12 @@
 import os
 import numpy as np
 
-from skimage import data_dir
+from .. import data_dir
 
 cimport numpy as cnp
 from libc.math cimport sin, cos
 
-from skimage._shared.interpolation cimport round
+from .._shared.interpolation cimport round
 
 POS = np.loadtxt(os.path.join(data_dir, "orb_descriptor_positions.txt"),
                  dtype=np.int8)

skimage/feature/template.py

@@ -1,8 +1,8 @@
 import numpy as np
 from scipy.signal import fftconvolve
 
-from skimage.util import pad
-from skimage._shared.utils import assert_nD
+from ..util import pad
+from .._shared.utils import assert_nD
 
 
 def _window_sum_2d(image, window_shape):

skimage/feature/texture.py

@@ -3,7 +3,7 @@ Methods to characterize image textures.
 """
 
 import numpy as np
-from skimage._shared.utils import assert_nD
+from .._shared.utils import assert_nD
 from ._texture import _glcm_loop, _local_binary_pattern
 
 

skimage/feature/util.py

@@ -1,7 +1,7 @@
 import numpy as np
 
-from skimage.util import img_as_float
-from skimage._shared.utils import assert_nD
+from ..util import img_as_float
+from .._shared.utils import assert_nD
 
 
 class FeatureDetector(object):