cut-out tests

* move tests outside package
* update Expt. cls
* fix Ray test
* update docs
* fix pandas conf.

.shippable.yml

@@ -40,7 +40,7 @@ install:
   - mkdir libs
 
   - pip install -r requirements.txt
-  - pip install -r requirements-dev.txt
+  - pip install -r ./tests/requirements.txt
   - pip list
 
 script:

.travis.yml

@@ -32,7 +32,7 @@ before_install:
 install:
   - mkdir libs
   - pip install -r requirements.txt
-  - pip install -r requirements-dev.txt
+  - pip install -r ./tests/requirements.txt
   - pip list
 
 before_script:
@@ -44,7 +44,7 @@ before_script:
 script:
   - python setup.py build_ext --inplace
   # - pytest imsegm -v --doctest-modules
-  - nosetests imsegm -v --exe --with-doctest --with-xunit --with-coverage --cover-package=imsegm
+  - 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
 

MANIFEST.in

@@ -23,6 +23,8 @@ include setup.*
 exclude *.yml
 # Exclude testing
 exclude pytest.*
+recursive-exclude tests *
+exclude tests
 
 # Exclude experiments
 exclude */*.sh

README.md

@@ -178,7 +178,7 @@ We utilise (un)supervised segmentation according to given training examples or s
     ```bash
     python experiments_segmentation/run_segm_slic_model_graphcut.py \
        -l ./data_images/langerhans_islets/list_lang-isl_imgs-annot.csv -i "" \
-       --cdf experiments_segmentation/sample_config.yml \
+       -cfg experiments_segmentation/sample_config.yml \
        -o ./results -n langIsl --nb_classes 3 --visual --nb_workers 2
     ```
     OR specified on particular path:

circle.yml

@@ -9,7 +9,7 @@ references:
         sudo apt-get update
         sudo apt-get install tk-dev pkg-config python-dev python-tk
         sudo pip install --upgrade pip setuptools
-        sudo pip install -r requirements-dev.txt
+        sudo pip install -r ./tests/requirements.txt
         sudo pip install -U backports.functools_lru_cache  # required for matplotlib @py2
         pip install -r requirements.txt --user
         pip list
@@ -19,7 +19,7 @@ references:
       name: Testing and Formating
       command: |
         unset DISPLAY
-        coverage run --source imsegm -m py.test imsegm -v --doctest-modules --junitxml=test-reports/pytest_junit.xml
+        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
 
@@ -38,7 +38,7 @@ references:
      command: |
        sudo apt-get install pandoc
        sudo pip install -r docs/requirements.txt
-       # sphinx-apidoc -o ./docs/source ./imsegm **/tests/* --force --follow-links
+       # sphinx-apidoc -o ./docs/source ./imsegm ./tests/* --force --follow-links
        cd docs; make html
 
 jobs:

docs/requirements.txt

@@ -2,4 +2,5 @@ sphinx>=1.4
 recommonmark  # fails with badges
 m2r  # fails with multi-line text
 nbsphinx
-pandoc
+pandoc
+docutils<0.15  # higher version breaks py2

docs/source/conf.py

@@ -238,7 +238,7 @@
 def run_apidoc(_):
     for pkg in PACKAGES:
         argv = ['-e', '-o', PATH_HERE, os.path.join(PATH_HERE, PATH_ROOT, pkg),
-                '**/tests/*', '--force']
+                'tests/*', '--force']
         try:
             # Sphinx 1.7+
             from sphinx.ext import apidoc

experiments_ovary_centres/run_center_candidate_training.py

@@ -73,7 +73,7 @@
 NAME_YAML_PARAMS = 'configuration.yaml'
 NAME_DUMP_TRAIN_DATA = 'dump_training_data.npz'
 
-NB_THREADS = max(1, int(mproc.cpu_count() * 0.9))
+NB_WORKERS = tl_expt.nb_workers(0.9)
 # position is label in loaded segm and nb are out labels
 LUT_ANNOT_CENTER_RELABEL = [0, 0, -1, 1]
 CROSS_VAL_LEAVE_OUT_SEARCH = 0.2
@@ -149,7 +149,7 @@ def arg_parse_params(params):
                         help='name of the experiment', default='ovary')
     parser.add_argument('-cfg', '--path_config', type=str, required=False,
                         help='path to the configuration', default=None)
-    parser.add_argument('--nb_workers', type=int, required=False, default=NB_THREADS,
+    parser.add_argument('--nb_workers', type=int, required=False, default=NB_WORKERS,
                         help='number of processes in parallel')
     params.update(vars(parser.parse_args()))
     paths = {}
@@ -445,7 +445,7 @@ def wrapper_draw_export_slic_centers(args):
     return export_show_image_points_labels(*args)
 
 
-def dataset_load_images_segms_compute_features(params, df_paths, nb_workers=NB_THREADS):
+def dataset_load_images_segms_compute_features(params, df_paths, nb_workers=NB_WORKERS):
     """ create whole dataset composed from loading input data, computing features
     and label points by label whether its positive or negative center candidate
 
@@ -502,7 +502,7 @@ def dataset_load_images_segms_compute_features(params, df_paths, nb_workers=NB_T
 
 
 def export_dataset_visual(path_output, dict_imgs, dict_segms, dict_slics,
-                          dict_points, dict_labels, nb_workers=NB_THREADS):
+                          dict_points, dict_labels, nb_workers=NB_WORKERS):
     """ visualise complete training dataset by marking labeld points
     over image and input segmentation
 

experiments_ovary_centres/run_center_evaluation.py

@@ -83,7 +83,7 @@ def estimate_eggs_from_info(row_slice, mask_shape):
     pos_ant, pos_lat, pos_post = tl_visu.parse_annot_rectangles(row_slice)
     list_masks = tl_visu.draw_eggs_rectangle(mask_shape, pos_ant, pos_lat,
                                              pos_post)
-    mask_eggs = tl_visu.merge_object_masks(list_masks, thr_overlap=0.5)
+    mask_eggs = tl_visu.merge_object_masks(list_masks, overlap_thr=0.5)
 
     return mask_eggs
 

experiments_ovary_detect/run_cut_segmented_objects.py

@@ -23,7 +23,7 @@
 import imsegm.utilities.data_io as tl_data
 import imsegm.utilities.experiments as tl_expt
 
-NB_THREADS = max(1, int(mproc.cpu_count() * 0.9))
+NB_WORKERS = tl_expt.nb_workers(0.9)
 PATH_IMAGES = tl_data.update_path(os.path.join('data_images', 'drosophila_ovary_slice'))
 PATH_RESULTS = tl_data.update_path('results', absolute=True)
 PATHS = {
@@ -54,7 +54,7 @@ def arg_parse_params(dict_paths):
                         help='mask by the segmentation', default=1)
     parser.add_argument('-bg', '--background', type=int, required=False,
                         help='using background color', default=None, nargs='+')
-    parser.add_argument('--nb_workers', type=int, required=False, default=NB_THREADS,
+    parser.add_argument('--nb_workers', type=int, required=False, default=NB_WORKERS,
                         help='number of processes in parallel')
     args = vars(parser.parse_args())
     logging.info('ARG PARAMETERS: \n %r', args)
@@ -92,7 +92,7 @@ def export_cut_objects(df_row, path_out, padding, use_mask=True, bg_color=None):
 
 
 def main(dict_paths, padding=0, use_mask=False, bg_color=None,
-         nb_workers=NB_THREADS):
+         nb_workers=NB_WORKERS):
     """ the main executable
 
     :param dict_paths:

experiments_ovary_detect/run_egg_swap_orientation.py

@@ -26,7 +26,7 @@
 
 IMAGE_CHANNEL = 0  # image channel for mass extraction
 
-NB_THREADS = max(1, int(mproc.cpu_count() * 0.8))
+NB_WORKERS = tl_expt.nb_workers(0.8)
 PATH_IMAGES = os.path.join(tl_data.update_path('data_images'),
                            'drosophila_ovary_slice')
 PATH_RESULTS = tl_data.update_path('results', absolute=True)

experiments_ovary_detect/run_ellipse_annot_match.py

@@ -33,7 +33,7 @@
 NAME_CSV_RESULTS = 'info_ovary_images_ellipses.csv'
 OVERLAP_THRESHOLD = 0.
 
-NB_THREADS = max(1, int(mproc.cpu_count() * 0.8))
+NB_WORKERS = tl_expt.nb_workers(0.8)
 PATH_IMAGES = tl_data.update_path(os.path.join('data_images', 'drosophila_ovary_slice'))
 
 DEFAULT_PARAMS = {
@@ -61,7 +61,7 @@ def arg_parse_params(params):
     parser.add_argument('-out', '--path_output', type=str, required=False,
                         help='path to the output directory',
                         default=params.get('path_output', None))
-    parser.add_argument('--nb_workers', type=int, required=False, default=NB_THREADS,
+    parser.add_argument('--nb_workers', type=int, required=False, default=NB_WORKERS,
                         help='number of processes in parallel')
     arg_params = vars(parser.parse_args())
     params.update(arg_params)

experiments_ovary_detect/run_ellipse_cut_scale.py

@@ -34,7 +34,7 @@
 OVERLAP_THRESHOLD = 0.45
 NORM_FUNC = np.median  # other options - mean, max, ...
 
-NB_THREADS = max(1, int(mproc.cpu_count() * 0.8))
+NB_WORKERS = tl_expt.nb_workers(0.8)
 PATH_IMAGES = tl_data.update_path(os.path.join('data_images', 'drosophila_ovary_slice'))
 PATH_RESULTS = tl_data.update_path('results', absolute=True)
 

experiments_ovary_detect/run_export_user-annot-segm.py

@@ -38,7 +38,7 @@
 import imsegm.utilities.drawing as tl_visu
 import imsegm.annotation as seg_annot
 
-NB_THREADS = max(1, int(mproc.cpu_count() * 0.8))
+NB_WORKERS = tl_expt.nb_workers(0.8)
 PATH_IMAGES = tl_data.update_path(os.path.join('data_images', 'drosophila_ovary_slice'))
 PATH_RESULTS = tl_data.update_path('results', absolute=True)
 DEFAULT_PARAMS = {
@@ -77,7 +77,7 @@ def arg_parse_params(params):
     parser.add_argument('-out', '--path_output', type=str, required=False,
                         help='path to the output directory',
                         default=params['path_output'])
-    parser.add_argument('--nb_workers', type=int, required=False, default=NB_THREADS,
+    parser.add_argument('--nb_workers', type=int, required=False, default=NB_WORKERS,
                         help='number of processes in parallel')
     arg_params = vars(parser.parse_args())
     params.update(arg_params)

experiments_ovary_detect/run_ovary_egg-segmentation.py

@@ -57,7 +57,7 @@
 from morphsnakes import morphsnakes, multi_snakes
 # from libs import chanvese
 
-NB_THREADS = max(1, int(mproc.cpu_count() * 0.9))
+NB_WORKERS = tl_expt.nb_workers(0.8)
 NAME_EXPERIMENT = 'experiment_egg-segment'
 TYPE_LOAD_IMAGE = '2d_struct'
 DIR_VISUAL_POSIX = '___visu'
@@ -141,7 +141,7 @@ def arg_parse_params(params):
                         help='name of the experiment', default='ovary')
     parser.add_argument('-cfg', '--path_config', type=str, required=False,
                         help='path to the configuration', default=None)
-    parser.add_argument('--nb_workers', type=int, required=False, default=NB_THREADS,
+    parser.add_argument('--nb_workers', type=int, required=False, default=NB_WORKERS,
                         help='number of processes in parallel')
     parser.add_argument('-m', '--methods', type=str, required=False, nargs='+',
                         help='list of segment. methods', default=None)

experiments_ovary_detect/run_ovary_segm_evaluation.py

@@ -38,7 +38,7 @@
 import imsegm.labeling as seg_lbs
 
 EXPORT_VUSIALISATION = False
-NB_THREADS = max(1, int(mproc.cpu_count() * 0.9))
+NB_WORKERS = tl_expt.nb_workers(0.9)
 
 NAME_DIR_VISUAL_1 = 'ALL_visualisation-1'
 NAME_DIR_VISUAL_2 = 'ALL_visualisation-2'
@@ -85,7 +85,7 @@ def arg_parse_params(paths):
                         help='path to the result directory',
                         default=paths['results'])
     parser.add_argument('--nb_workers', type=int, required=False,
-                        default=NB_THREADS, help='number of processes in parallel')
+                        default=NB_WORKERS, help='number of processes in parallel')
     parser.add_argument('--visual', required=False, action='store_true',
                         default=False, help='export visualisations')
     arg_params = vars(parser.parse_args())
@@ -251,7 +251,7 @@ def evaluate_folder(path_dir, dict_paths, export_visual=EXPORT_VUSIALISATION):
     return dict_eval
 
 
-def main(dict_paths, export_visual=EXPORT_VUSIALISATION, nb_workers=NB_THREADS):
+def main(dict_paths, export_visual=EXPORT_VUSIALISATION, nb_workers=NB_WORKERS):
     """ evaluate all segmentations in experiment folder
 
     :param {str: str} paths: path to all required directories

experiments_segmentation/run_compute_stat_annot_segm.py

@@ -36,7 +36,7 @@
 import imsegm.utilities.drawing as tl_visu
 import imsegm.classification as seg_clf
 
-NB_THREADS = max(1, int(mproc.cpu_count() * 0.9))
+NB_WORKERS = tl_expt.nb_workers(0.9)
 NAME_CVS_OVERALL = 'STATISTIC__%s___Overall.csv'
 NAME_CVS_PER_IMAGE = 'STATISTIC__%s___per-Image.csv'
 PATH_IMAGES = os.path.join(tl_data.update_path('data_images'),
@@ -73,7 +73,7 @@ def aparse_params(dict_paths):
                         help='list of skipped labels from statistic')
     parser.add_argument('--nb_workers', type=int, required=False,
                         help='number of processes in parallel',
-                        default=NB_THREADS)
+                        default=NB_WORKERS)
     parser.add_argument('--overlap', type=float, required=False,
                         help='alpha for segmentation', default=0.2)
     parser.add_argument('--relabel', required=False, action='store_true',
@@ -166,7 +166,7 @@ def stat_single_set(idx_row, drop_labels=None, relabel=False, path_visu='',
 
 
 def main(dict_paths, visual=True, drop_labels=None, relabel=True,
-         segm_alpha=1., nb_workers=NB_THREADS):
+         segm_alpha=1., nb_workers=NB_WORKERS):
     """ main evaluation
 
     :param {str: str} dict_paths:

experiments_segmentation/run_eval_superpixels.py

@@ -40,7 +40,7 @@
 from run_segm_slic_model_graphcut import TYPES_LOAD_IMAGE
 
 
-NB_THREADS = max(1, int(mproc.cpu_count() * 0.9))
+NB_WORKERS = tl_expt.nb_workers(0.9)
 PATH_IMAGES = os.path.join(tl_data.update_path('data_images'),
                            'drosophila_ovary_slice')
 PATH_RESULTS = tl_data.update_path('results', absolute=True)
@@ -78,7 +78,7 @@ def arg_parse_params(params):
                         default=0.25, help='superpixel regularization')
     parser.add_argument('--slico', action='store_true', required=False,
                         default=False, help='using SLICO (ASLIC)')
-    parser.add_argument('--nb_workers', type=int, required=False, default=NB_THREADS,
+    parser.add_argument('--nb_workers', type=int, required=False, default=NB_WORKERS,
                         help='number of processes in parallel')
     params = vars(parser.parse_args())
     logging.info('ARG PARAMETERS: \n %r', params)

experiments_segmentation/run_segm_slic_classif_graphcut.py

@@ -66,7 +66,7 @@
                                           write_skip_file)
 
 NAME_EXPERIMENT = 'experiment_segm-Supervised'
-NB_THREADS = max(1, int(mproc.cpu_count() * 0.9))
+NB_WORKERS = tl_expt.nb_workers(0.9)
 
 TYPES_LOAD_IMAGE = ['2d_rgb', '2d_split']
 NAME_FIG_LABEL_HISTO = 'fig_histogram_annot_segments.png'
@@ -604,7 +604,7 @@ def wrapper_filter_labels(name_img_labels_slic_label_hist, label_purity,
 
 def filter_train_with_purity(dict_imgs, dict_labels, dict_label_hist,
                              label_purity, dict_slics, drop_labels=None,
-                             path_visu=None, nb_workers=NB_THREADS):
+                             path_visu=None, nb_workers=NB_WORKERS):
     _w_filter = partial(wrapper_filter_labels, label_purity=label_purity,
                         drop_labels=drop_labels, path_visu=path_visu)
     iter_vals = ((n, dict_imgs[n], dict_labels[n], dict_slics[n],

experiments_segmentation/run_segm_slic_model_graphcut.py

@@ -55,7 +55,7 @@
 # sometimes it freeze in "Cython: computing Colour means for image"
 seg_fts.USE_CYTHON = False
 
-NB_THREADS = max(1, int(mproc.cpu_count() * 0.9))
+NB_WORKERS = tl_expt.nb_workers(0.9)
 TYPES_LOAD_IMAGE = ['2d_rgb', '2d_split']
 NAME_DUMP_MODEL = 'estimated_model.npz'
 NAME_CSV_ARS_CORES = 'metric_ARS.csv'
@@ -144,7 +144,7 @@ def arg_parse_params(params):
                         default=params.get('nb_classes', 2))
     parser.add_argument('--nb_workers', type=int, required=False,
                         help='number of processes in parallel',
-                        default=NB_THREADS)
+                        default=NB_WORKERS)
     parser.add_argument('--visual', required=False, action='store_true',
                         help='export debug visualisations', default=False)
     parser.add_argument('--unique', required=False, action='store_true',

handling_annotations/run_image_color_quantization.py

@@ -32,7 +32,7 @@
 import imsegm.annotation as seg_annot
 
 PATH_IMAGES = os.path.join('data_images', 'drosophila_ovary_slice', 'segm_rgb', '*.png')
-NB_THREADS = max(1, int(mproc.cpu_count() * 0.9))
+NB_WORKERS = tl_expt.nb_workers(0.9)
 THRESHOLD_INVALID_PIXELS = 5e-3
 
 
@@ -51,7 +51,7 @@ def parse_arg_params():
                         help='percentage of pixels of a color to be removed',
                         default=THRESHOLD_INVALID_PIXELS)
     parser.add_argument('--nb_workers', type=int, required=False,
-                        help='number of jobs in parallel', default=NB_THREADS)
+                        help='number of jobs in parallel', default=NB_WORKERS)
     args = vars(parser.parse_args())
     p_dir = tl_data.update_path(os.path.dirname(args['path_images']))
     assert os.path.isdir(p_dir), 'missing folder: %s' % args['path_images']

handling_annotations/run_image_convert_label_color.py

@@ -31,7 +31,7 @@
 PATH_INPUT = os.path.join('data_images', 'drosophila_ovary_slice', 'segm', '*.png')
 PATH_OUTPUT = os.path.join('data_images', 'drosophila_ovary_slice', 'segm_rgb')
 NAME_JSON_DICT = 'dictionary_label-color.json'
-NB_THREADS = max(1, int(mproc.cpu_count() * 0.9))
+NB_WORKERS = tl_expt.nb_workers(0.9)
 
 
 def parse_arg_params():
@@ -47,7 +47,7 @@ def parse_arg_params():
     parser.add_argument('-clrs', '--path_colors', type=str, required=False,
                         help='json with colour-label dict', default=None)
     parser.add_argument('--nb_workers', type=int, required=False,
-                        help='number of jobs in parallel', default=NB_THREADS)
+                        help='number of jobs in parallel', default=NB_WORKERS)
     args = vars(parser.parse_args())
     for n in ['path_images', 'path_out']:
         p_dir = tl_data.update_path(os.path.dirname(args[n]))