drop GCO dependency

* absolute imports
* split py2 py3 reqs
* CI: min-max build
* fix skimage region prop.
* general flake8 config
* CI: nose -> pytest
* fix numpy loads
* fix docs links

.travis.yml

@@ -5,48 +5,64 @@
 
 # See doc/travis_notes.txt for some guidelines
 
-dist: xenial  # Ubuntu 16.04
-
 env:
   global:
     - CODACY_PROJECT_TOKEN=ea6533a96dfb4b6a9b6b45fc134fabdf
     - DISPLAY=""
 
 language: python
 
-sudo: false
-
-python:
-  - 2.7
-  # - 3.4  # will be deprecated for pandas
-  - 3.5
-  - 3.6
-  - 3.7
+matrix:
+  include:
+    - dist: xenial  # Ubuntu 16.04
+      python: 2.7
+    - dist: xenial  # Ubuntu 16.04
+      python: 3.5
+    - dist: xenial  # Ubuntu 16.04
+      python: 3.6
+      env: MIN_REQUIREMENTS=1
+    #- dist: xenial  # Ubuntu 16.04
+    #  python: 3.7
+    #  env: MIN_REQUIREMENTS=1
+    - dist: bionic  # Ubuntu 18.04
+      python: 3.6
+    - dist: bionic  # Ubuntu 18.04
+      python: 3.7
 
 # See http://docs.travis-ci.com/user/caching/#pip-cache
 cache: pip
 
 before_install:
   - pip install --upgrade pip
+  # rewrite all minimal requirements as strict
+  - if [ "$TRAVIS_PYTHON_VERSION" == "2.7" ]; then
+      cp requirements-py27.txt requirements-ci.txt ;
+    else if [[ "${MIN_REQUIREMENTS}" == "1" ]]; then
+        python -c "req = open('requirements.txt').read().replace('>', '=') ; open('requirements-ci.txt', 'w').write(req)" ;
+      else
+        cp requirements.txt requirements-ci.txt ;
+      fi
+    fi
 
 install:
   - mkdir libs
-  - pip install -r requirements.txt
+  - pip install -r requirements-ci.txt -U
   - pip install -r ./tests/requirements.txt
   - pip list
 
 before_script:
+  - rm requirements-ci.txt
   - mkdir output && mkdir results
   - gcc --version ; python --version ; pip --version ; pwd ; ls -l
   - check-manifest
-  - python setup.py check -m -s
+  - python setup.py check --metadata --strict
 
 script:
   - python setup.py build_ext --inplace
   # - pytest imsegm -v --doctest-modules
-  - nosetests imsegm tests -v --exe --with-doctest --with-xunit --with-coverage --cover-package=imsegm
-  - flake8 . --ignore=E402,E731 --max-line-length=100
-  - python setup.py install
+  - coverage run -m py.test imsegm tests --doctest-modules --flake8
+  # - nosetests imsegm tests -v --exe --with-doctest --with-xunit --with-coverage --cover-package=imsegm
+  - python setup.py install --dry-run
 
 after_success:
   - codecov  # public repository on Travis CI

MANIFEST.in

@@ -11,6 +11,7 @@ include LICENSE
 
 # Include the Requirements
 include requirements.txt
+include requirements-py27.txt
 
 # Include package
 recursive-include imsegm *.py *.pyx

circle.yml

@@ -22,8 +22,8 @@ references:
         unset DISPLAY
         mkdir output && mkdir results && mkdir test-reports
         coverage run --source imsegm -m py.test imsegm tests -v --doctest-modules --junitxml=test-reports/pytest_junit.xml
-        python setup.py check -m -s
-        flake8 . --ignore=E402,E731 --max-line-length=100
+        python setup.py check --metadata --strict
+        flake8 .
 
   build_project: &build_project
    run:

docs/source/conf.py

@@ -295,7 +295,7 @@ def find_source():
             obj = getattr(obj, part)
         fname = inspect.getsourcefile(obj)
         # https://github.com/rtfd/readthedocs.org/issues/5735
-        if any([s in fname for s in ('readthedocs', 'checkouts')]):
+        if any([s in fname for s in ('readthedocs', 'rtfd', 'checkouts')]):
             path_top = os.path.abspath(os.path.join('..', '..', '..'))
             fname = os.path.relpath(fname, start=path_top)
         else:
@@ -313,6 +313,10 @@ def find_source():
     # import subprocess
     # tag = subprocess.Popen(['git', 'rev-parse', 'HEAD'], stdout=subprocess.PIPE,
     #                        universal_newlines=True).communicate()[0][:-1]
+    branch = filename.split('/')[0]
+    # do mapping from latest tags to master
+    branch = {'latest': 'master', 'stable': 'master'}.get(branch, branch)
+    filename = '/'.join([branch] + filename.split('/')[1:])
     return "https://github.com/%s/%s/blob/%s" \
            % (github_user, github_repo, filename)
 

experiments_ovary_centres/run_center_candidate_training.py

@@ -249,7 +249,7 @@ def load_image_segm_center(idx_row, path_out=None, dict_relabel=None):
 
     seg_ext = os.path.splitext(os.path.basename(row_path['path_segm']))[-1]
     if seg_ext == '.npz':
-        with np.load(row_path['path_segm']) as npzfile:
+        with np.load(row_path['path_segm'], allow_pickle=True) as npzfile:
             segm = npzfile[npzfile.files[0]]
         if dict_relabel is not None:
             segm = seg_lbs.merge_probab_labeling_2d(segm, dict_relabel)
@@ -620,7 +620,7 @@ def load_dump_data(path_dump_data):
     logging.info('loading dumped data "%s"', path_dump_data)
     # with open(os.path.join(path_out, NAME_DUMP_TRAIN_DATA), 'r') as f:
     #     dict_data = pickle.load(f)
-    npz_file = np.load(path_dump_data, encoding='bytes')
+    npz_file = np.load(path_dump_data, encoding='bytes', allow_pickle=True)
     dict_imgs = dict(npz_file['dict_images'].tolist())
     dict_segms = dict(npz_file['dict_segms'].tolist())
     dict_slics = dict(npz_file['dict_slics'].tolist())

experiments_ovary_detect/run_ovary_egg-segmentation.py

@@ -521,7 +521,7 @@ def segment_rg2sp_greedy(slic, seg, centers, labels_fg_prob, path_model,
                          dict_thresholds=RG2SP_THRESHOLDS, debug_export=''):
     """ wrapper for region growing method with some debug exporting """
     if os.path.splitext(path_model)[-1] == '.npz':
-        shape_model = np.load(path_model)
+        shape_model = np.load(path_model, allow_pickle=True)
     else:
         shape_model = pickle.load(open(path_model, 'rb'))
     dict_debug = dict() if os.path.isdir(debug_export) else None
@@ -550,7 +550,7 @@ def segment_rg2sp_graphcut(slic, seg, centers, labels_fg_prob, path_model,
                            dict_thresholds=RG2SP_THRESHOLDS, debug_export=''):
     """ wrapper for region growing method with some debug exporting """
     if os.path.splitext(path_model)[-1] == '.npz':
-        shape_model = np.load(path_model)
+        shape_model = np.load(path_model, allow_pickle=True)
     else:
         shape_model = pickle.load(open(path_model, 'rb'))
     dict_debug = dict() if os.path.isdir(debug_export) else None

experiments_segmentation/run_segm_slic_classif_graphcut.py

@@ -270,7 +270,7 @@ def load_dump_data(path_dump_data):
     logging.info('loading dumped data "%s"', path_dump_data)
     # with open(os.path.join(path_out, NAME_DUMP_TRAIN_DATA), 'r') as f:
     #     dict_data = pickle.load(f)
-    npz_file = np.load(path_dump_data)
+    npz_file = np.load(path_dump_data, allow_pickle=True)
     dict_imgs = dict(npz_file['dict_images'].tolist())
     dict_annot = dict(npz_file['dict_annot'].tolist())
     dict_slics = dict(npz_file['dict_slics'].tolist())

imsegm/__init__.py

@@ -11,7 +11,7 @@
     import traceback
     traceback.print_exc()
 
-__version__ = '0.1.7'
+__version__ = '0.1.8'
 __author__ = 'Jiri Borovec'
 __author_email__ = 'jiri.borovec@fel.cvut.cz'
 __license__ = 'BSD 3-clause'

imsegm/annotation.py

@@ -15,7 +15,7 @@
 from scipy import interpolate
 
 # sys.path += [os.path.abspath('.'), os.path.abspath('..')]  # Add path to root
-from .utilities.data_io import io_imread
+from imsegm.utilities.data_io import io_imread
 
 #: names of annotated columns
 COLUMNS_POSITION = ('ant_x', 'ant_y', 'post_x', 'post_y', 'lat_x', 'lat_y')

imsegm/classification.py

@@ -31,8 +31,8 @@
 except Exception:
     from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
 
-from .labeling import relabel_max_overlap_unique
-from .utilities.experiments import WrapExecuteSequence, nb_workers
+from imsegm.labeling import relabel_max_overlap_unique
+from imsegm.utilities.experiments import WrapExecuteSequence, nb_workers
 
 # NAME_FILE_RESULTS = 'results.csv'
 #: name template forexporting trained classifier (adding classifier name and version)

imsegm/ellipse_fitting.py

@@ -10,10 +10,10 @@
 
 from skimage.measure import fit as sk_fit
 # from skimage.measure.fit import EllipseModel  # fix in future skimage>0.13.0
-from .utilities.drawing import ellipse
-from .descriptors import (
+from imsegm.utilities.drawing import ellipse
+from imsegm.descriptors import (
     reduce_close_points, compute_ray_features_segm_2d, reconstruct_ray_features_2d)
-from .superpixels import (
+from imsegm.superpixels import (
     segment_slic_img2d, superpixel_centers, make_graph_segm_connect_grid2d_conn4)
 
 # INIT_MASK_BORDER = 50.

imsegm/graph_cuts.py

@@ -7,16 +7,20 @@
 import logging
 
 import numpy as np
-from gco import cut_general_graph
+try:
+    from gco import cut_general_graph
+except Exception:
+    print('WARNING: Missing Grah-Cut (GCO) library,'
+          ' please install it from https://github.com/Borda/pyGCO.')
 from skimage import filters
 from sklearn import metrics, preprocessing
 from sklearn import pipeline, cluster, mixture, decomposition
 
-from .utilities.drawing import (
+from imsegm.utilities.drawing import (
     draw_graphcut_unary_cost_segments, draw_graphcut_weighted_edges, draw_color_labeling)
-from .superpixels import (
+from imsegm.superpixels import (
     make_graph_segm_connect_grid2d_conn4, make_graph_segm_connect_grid3d_conn6, superpixel_centers)
-from .descriptors import compute_selected_features_img2d
+from imsegm.descriptors import compute_selected_features_img2d
 
 #: define munber of iteration in Grap-Cut optimization
 DEFAULT_GC_ITERATIONS = 25

imsegm/labeling.py

@@ -10,7 +10,7 @@
 from scipy import ndimage
 import skimage.segmentation as sk_segm
 
-from .utilities.data_io import get_image2d_boundary_color
+from imsegm.utilities.data_io import get_image2d_boundary_color
 
 
 def neighbour_connect4(seg, label, pos):

imsegm/pipelines.py

@@ -11,14 +11,14 @@
 import skimage.color as sk_color
 # from sklearn import mixture
 
-from .utilities.experiments import WrapExecuteSequence, nb_workers
-from .graph_cuts import segment_graph_cut_general, estim_class_model
-from .superpixels import segment_slic_img2d, segment_slic_img3d_gray
-from .descriptors import (
+from imsegm.utilities.experiments import WrapExecuteSequence, nb_workers
+from imsegm.graph_cuts import segment_graph_cut_general, estim_class_model
+from imsegm.superpixels import segment_slic_img2d, segment_slic_img3d_gray
+from imsegm.descriptors import (
     FEATURES_SET_COLOR, norm_features, compute_selected_features_img2d,
     compute_selected_features_gray3d)
-from .labeling import histogram_regions_labels_norm
-from .classification import (
+from imsegm.labeling import histogram_regions_labels_norm
+from imsegm.classification import (
     DEFAULT_CLASSIF_NAME, DEFAULT_CLUSTERING, convert_set_features_labels_2_dataset,
     CrossValidateGroups, create_classif_search_train_export)
 

imsegm/region_growing.py

@@ -12,13 +12,17 @@
 from scipy import stats, ndimage, interpolate
 from sklearn import cluster, mixture
 from skimage import morphology
-from gco import cut_general_graph, cut_grid_graph
-
-from .graph_cuts import MAX_PAIRWISE_COST, get_vertexes_edges, compute_spatial_dist
-from .labeling import histogram_regions_labels_norm
-from .descriptors import (
+try:
+    from gco import cut_general_graph, cut_grid_graph
+except Exception:
+    print('WARNING: Missing Grah-Cut (GCO) library,'
+          ' please install it from https://github.com/Borda/pyGCO.')
+
+from imsegm.graph_cuts import MAX_PAIRWISE_COST, get_vertexes_edges, compute_spatial_dist
+from imsegm.labeling import histogram_regions_labels_norm
+from imsegm.descriptors import (
     compute_ray_features_segm_2d, interpolate_ray_dist, shift_ray_features)
-from .superpixels import (
+from imsegm.superpixels import (
     superpixel_centers, get_neighboring_segments, make_graph_segm_connect_grid2d_conn4)
 
 #: all infinty values in Grah-Cut terms replace by this value
@@ -364,10 +368,11 @@ def transform_rays_model_cdf_mixture(list_rays, coef_components=1):
     >>> list_rays = [[9, 4, 9], [4, 9, 7], [9, 7, 11], [10, 8, 10],
     ...              [9, 11, 8], [4, 8, 5], [8, 10, 6], [9, 7, 11]]
     >>> mm, cdist = transform_rays_model_cdf_mixture(list_rays)
-    >>> np.round(cdist, 1).tolist()  # doctest: +NORMALIZE_WHITESPACE
-    [[1.0, 1.0, 1.0, 1.0, 1.0, 0.9, 0.8, 0.8, 0.6, 0.2, 0.0],
-     [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9, 0.8, 0.5, 0.2, 0.0],
-     [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9, 0.7, 0.5, 0.2, 0.0]]
+    >>> # the rounding variate a bit according GMM estimated model
+    >>> np.round(np.array(cdist) * 4) / 4.  # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
+    array([[ 1. , 1. , 1. , 1. , 1. , 1. , 0.75, 0.75, 0.5 , 0.25, 0. ],
+           [ 1. , 1. , 1. , 1. , 1. , 1. , 1.  , 0.75, 0.5 , 0.25, 0. ],
+           [ 1. , 1. , 1. , 1. , 1. , 1. , ...,  0.75, 0.5 , 0.25, 0. ]])
     """
     rays = np.array(list_rays)
     ms = cluster.MeanShift()
@@ -481,7 +486,7 @@ def transform_rays_model_cdf_spectral(list_rays, nb_components=5):
     >>> np.round(cdist, 1).tolist()  # doctest: +NORMALIZE_WHITESPACE
     [[1.0, 1.0, 1.0, 1.0, 1.0, 0.9, 0.8, 0.6, 0.5, 0.2, 0.0],
      [1.0, 1.0, 1.0, 1.0, 1.0, 0.9, 0.9, 0.7, 0.5, 0.2, 0.0],
-      [1.0, 1.0, 1.0, 1.0, 1.0, 0.9, 0.8, 0.7, 0.5, 0.3, 0.0]]
+     [1.0, 1.0, 1.0, 1.0, 1.0, 0.9, 0.8, 0.7, 0.5, 0.3, 0.0]]
     """
     rays = np.array(list_rays)
     sc = cluster.SpectralClustering(nb_components)

imsegm/utilities/data_io.py

@@ -3,7 +3,6 @@
 
 Copyright (C) 2015-2018 Jiri Borovec <jiri.borovec@fel.cvut.cz>
 """
-from __future__ import absolute_import
 
 import os
 import re
@@ -20,7 +19,7 @@
 from skimage import exposure, io, color, measure
 import nibabel
 
-from . import read_zvi
+from imsegm.utilities.read_zvi import load_image as load_zvi
 
 #: position columns
 COLUMNS_COORDS = ['X', 'Y']
@@ -733,7 +732,7 @@ def load_zvi_volume_double_band_split(path_img):
     (2, 488, 648)
     """
     assert os.path.isfile(path_img), 'missing: %s' % path_img
-    img = read_zvi.load_image(path_img)
+    img = load_zvi(path_img)
     nb_half = img.shape[0] / 2
     img_b1 = img[:int(nb_half)]
     img_b2 = img[int(nb_half):]
@@ -1058,14 +1057,21 @@ def add_padding(img_size, padding, min_row, min_col, max_row, max_col):
     return min_row, min_col, max_row, max_col
 
 
-def cut_object(img, mask, padding, use_mask=False, bg_color=None):
-    """ cut an object fro image according binary object segmentation
+# prepare a simple mask with one horizontal segment
+prop = measure.regionprops(np.array([[0] * 20, [1] * 20, [0] * 20], dtype=int))[0]
+#: according to skimage version the major axis are swapped
+PROP_ROTATION_OFFSET = prop.orientation
 
-    :param ndarray img:
-    :param ndarray mask:
+
+def cut_object(img, mask, padding, use_mask=False, bg_color=None, allow_rotate=True):
+    """ cut an object from image according binary object segmentation
+
+    :param ndarray img: inout image
+    :param ndarray mask: segmentation
     :param int padding: set padding around segmented object
-    :param use_mask: fill BG values also outside the mask
+    :param bool use_mask: fill BG values also outside the mask
     :param bg_color: set as default values outside bounding box
+    :param bool allow_rotate: allowing rotate object to get minimal bbox
     :return:
 
     >>> img = np.ones((10, 20), dtype=int)
@@ -1079,7 +1085,7 @@ def cut_object(img, mask, padding, use_mask=False, bg_color=None):
            [1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1],
            [1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1],
            [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
-    >>> cut_object(img, mask, 2, use_mask=True)
+    >>> cut_object(img, mask, 2, use_mask=True, allow_rotate=False)
     array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
            [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
            [1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1],
@@ -1096,15 +1102,18 @@ def cut_object(img, mask, padding, use_mask=False, bg_color=None):
     if not bg_color:
         bg_color = get_image2d_boundary_color(img)
 
-    rotate = np.rad2deg(prop.orientation)
-    shift = prop.centroid - (np.array(mask.shape) / 2.)
-    shift = np.append(shift, np.zeros(img.ndim - mask.ndim))
+    if allow_rotate:
+        rotate = np.rad2deg(prop.orientation - PROP_ROTATION_OFFSET)
+        shift = prop.centroid - (np.array(mask.shape) / 2.)
+        shift = np.append(shift, np.zeros(img.ndim - mask.ndim))
+
+        mask = ndimage.interpolation.shift(mask, -shift[:mask.ndim], order=0)
+        mask = ndimage.rotate(mask, -rotate, order=0, mode='constant', cval=np.nan)
 
-    mask = ndimage.interpolation.shift(mask, -shift[:mask.ndim], order=0)
-    mask = ndimage.rotate(mask, -rotate, order=0, mode='constant', cval=np.nan)
+        img = ndimage.interpolation.shift(img, -shift[:img.ndim], order=0)
+        img = ndimage.rotate(img, -rotate, order=0, mode='constant', cval=np.nan)
 
-    img_cut = ndimage.interpolation.shift(img, -shift[:img.ndim], order=0)
-    img_cut = ndimage.rotate(img_cut, -rotate, order=0, mode='constant', cval=np.nan)
+    img_cut = img.copy()
     img_cut[np.isnan(mask), ...] = bg_color
     mask[np.isnan(mask)] = bg_mask